"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","utils.py",".py","2617","85","# utils.py
import os
import json
import asyncio
import re
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm

# ==========================
# 파일 저장
# ==========================
def save_jsonl(data_list, out_path):
    os.makedirs(os.path.dirname(out_path), exist_ok=True)
    with open(out_path, ""w"", encoding=""utf-8"") as f:
        for item in data_list:
            f.write(json.dumps(item, ensure_ascii=False) + ""\n"")
    print(f""Saved results to {out_path}"")

# ==========================
# EM 점수 계산
# ==========================
def compute_em_score(pred, reference):
    return 1 if pred == reference else 0

def compute_em_score_mmlu(pred, reference):
    return 1 if pred in reference else 0

# ==========================
# Summary
# ==========================
def summarize_scores(results):
    total = len(results)
    em_total = sum(r.get(""score"", 0) for r in results)
    return {
        ""n_samples"": total,
        ""em_score"": em_total / total if total > 0 else None
    }

# ==========================
# 비동기 모델 호출
# ==========================
async def call_model_async(messages, client, retries=3, initial_delay=1.0):
    delay = initial_delay
    for attempt in range(retries):
        try:
            resp = await client.chat.completions.create(
                model=""25TOXMC_Blowfish_v1.0.9-AWQ"",
                messages=messages,
                temperature=0.0,
                top_p=0.95,
                stream=False
            )
            return resp.choices[0].message.content
        except Exception as e:
            if attempt == retries-1:
                raise
            await asyncio.sleep(delay)
            delay *= 2

# ==========================
# 공통 비동기 워커
# ==========================
async def run_concurrent_worker(data, build_messages_func, client, concurrency=16):
    sem = asyncio.Semaphore(concurrency)
    results = [None] * len(data)

    async def worker(i):
        async with sem:
            messages = build_messages_func(data[i])
            out = await call_model_async(messages, client)
            # <think> 제거 + JSON 파싱
            try:
                out_clean = re.sub(r"".*?</think>"", """", out, flags=re.DOTALL).strip()
                out_json = json.loads(out_clean)
                results[i] = out_json.get(""output"")
            except:
                results[i] = out_clean

    tasks = [asyncio.create_task(worker(i)) for i in range(len(data))]
    for f in tqdm(asyncio.as_completed(tasks), total=len(data), desc=""추론 진행중""):
        await f

    return results

","Python"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","run_vllm.sh",".sh","991","29","#!/usr/bin/env bash

# VLLM 컨테이너 실행 예시 (경로/포트/GPU는 환경에 맞게 수정)
docker run -it --rm \
  --gpus all \
  -p 30002:8000 \
  -v ""/mnt/e/Google Drive/External SSD/HealthCare/ADMET/코드/ADMET-AGI/Toxicity AI"":/workspace:rw \
  -e CUDA_VISIBLE_DEVICES=0 \
  -e TP_SIZE=1 \
  -e MODEL_PATH=/workspace/25TOXMC_Blowfish_v1.0.9-AWQ \
  -e CHAT_TEMPLATE_PATH=/workspace/no_tool_chat_template_qwen3.jinja \
  -e GPU_MEMORY_UTILIZATION=0.9 \
  -e DTYPE=bfloat16 \
  vllm-25admet-vllm \
  --host=0.0.0.0 \
  --model=/workspace/25TOXMC_Blowfish_v1.0.9-AWQ \
  --dtype=bfloat16 \
  --chat-template=/workspace/no_tool_chat_template_qwen3.jinja \
  --gpu-memory-utilization=0.9 \
  --tensor-parallel-size=1 \
  --max-model-len=16384

# 컨테이너 내부에서 .env 변수 설정 후 평가 스크립트 실행 예시
# export BASE_URL=http://<host>:30002/v1/
# export GPT_API_KEY=<your_gpt_key>
# python3 mobile_eval_e.py
# python3 mmlu_toxic.py
# python3 chem_cot.py
","Shell"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","Generalized ADMET Inference Baseline/chem_cot.py",".py","1519","50","# ChemCoT.py
import asyncio
import json
import datasets
from utils import run_concurrent_worker, save_jsonl, compute_em_score, summarize_scores
import openai
from dotenv import load_dotenv
import os

load_dotenv()

BASE_URL = os.getenv(""BASE_URL"")
print(BASE_URL)
client = openai.AsyncOpenAI(api_key=""dummy"", base_url=BASE_URL)

def build_messages(item):
    system = '''You are a chemical assistant. Given the SMILES structural formula of a molecule, help me add a specified functional group and output the improved SMILES sequence of the molecule. 
Your response must be directly parsable JSON format:
{
    ""output"": ""Modified Molecule SMILES""
}'''
    prompt = item.get(""prompt"") or item.get(""query"", """")
    return [
        {""role"": ""system"", ""content"": system},
        {""role"": ""user"", ""content"": prompt},
    ]

def main():
    ds = datasets.load_from_disk('./ChemCoTBench')
    outputs = asyncio.run(run_concurrent_worker(ds, build_messages, client, concurrency=16))

    results = []
    for i, item in enumerate(ds):
        pred = outputs[i]
        gold = json.loads(item[""meta""]).get(""reference"")
        em = compute_em_score(pred, gold)
        results.append({
            ""id"": item.get(""id"", i),
            ""prompt"": item.get(""prompt"") or item.get(""query""),
            ""model_output"": pred,
            ""reference"": gold,
            ""score"": em
        })

    save_jsonl(results, ""./ChemCoT_results.jsonl"")
    print(""SUMMARY:"", summarize_scores(results))

if __name__ == ""__main__"":
    main()
","Python"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","Generalized ADMET Inference Baseline/utils.py",".py","2536","80","# utils.py (Generalized ADMET Inference Baseline)
import os
import json
import asyncio
import re
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm

# ==========================
# 파일 저장
# ==========================
def save_jsonl(data_list, out_path):
    os.makedirs(os.path.dirname(out_path), exist_ok=True)
    with open(out_path, ""w"", encoding=""utf-8"") as f:
        for item in data_list:
            f.write(json.dumps(item, ensure_ascii=False) + ""\n"")
    print(f""Saved results to {out_path}"")

# ==========================
# EM 점수 계산
# ==========================
def compute_em_score(pred, reference):
    return 1 if pred == reference else 0

# ==========================
# Summary
# ==========================
def summarize_scores(results):
    total = len(results)
    em_total = sum(r.get(""score"", 0) for r in results)
    return {
        ""n_samples"": total,
        ""em_score"": em_total / total if total > 0 else None
    }

# ==========================
# 비동기 모델 호출
# ==========================
async def call_model_async(messages, client, retries=3, initial_delay=1.0):
    delay = initial_delay
    for attempt in range(retries):
        try:
            resp = await client.chat.completions.create(
                model=""25TOXMC_Blowfish_v1.0.9-AWQ"",
                messages=messages,
                temperature=0.0,
                top_p=0.95,
                stream=False
            )
            return resp.choices[0].message.content
        except Exception as e:
            if attempt == retries-1:
                raise
            await asyncio.sleep(delay)
            delay *= 2

# ==========================
# 공통 비동기 워커
# ==========================
async def run_concurrent_worker(data, build_messages_func, client, concurrency=16):
    sem = asyncio.Semaphore(concurrency)
    results = [None] * len(data)

    async def worker(i):
        async with sem:
            messages = build_messages_func(data[i])
            out = await call_model_async(messages, client)
            try:
                out_clean = re.sub(r"".*?</think>"", """", out, flags=re.DOTALL).strip()
                out_json = json.loads(out_clean)
                results[i] = out_json.get(""output"")
            except Exception:
                results[i] = out_clean

    tasks = [asyncio.create_task(worker(i)) for i in range(len(data))]
    for f in tqdm(asyncio.as_completed(tasks), total=len(data), desc=""추론 진행중""):
        await f

    return results
","Python"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","Toxicity AI Prototype/mobile_eval_e.py",".py","6442","213","# Mobile-Eval-E.py
import json
from datasets import load_dataset
from tqdm import tqdm
from openai import OpenAI
from utils import save_jsonl
import os
from dotenv import load_dotenv
load_dotenv()
# -------------------------
# GPT 클라이언트
# -------------------------

API_KEY = os.getenv(""GPT_API_KEY"")

client = OpenAI(api_key=API_KEY)

# -------------------------
# 시스템 프롬프트
# -------------------------
SYSTEM_PROMPT = """"""You are a mobile task planner that controls an Android phone via high-level actions.
Given a user instruction and a list of available apps, your goal is to output a step-by-step action sequence
to complete the task on the phone.

You MUST output a single JSON object with the following structure:

{
  ""plan"": [""high-level step 1"", ""high-level step 2"", ...],
  ""operations"": [
    ""action 1"",
    ""action 2"",
    ...
  ]
}

Each action should be a short imperative phrase describing a concrete phone operation
(e.g., ""open Maps"", ""tap on the search bar"", ""type 'korean restaurant'"", ""press enter"").
Do not include any explanations or extra text outside the JSON object.
""""""

JUDGE_SYSTEM_PROMPT = """"""
You are an expert evaluator for a mobile phone agent benchmark.

Your job is to evaluate how well a model-generated action sequence (operations)
solves a given mobile task, based on:

1) The natural language instruction.
2) The list of available apps and scenario.
3) A list of rubrics describing what a good solution should do.
4) A human reference action sequence (operations).
5) The model-generated action sequence.

You must output a single JSON object with the following fields:

{
  ""rubric_score"": float,        // between 0.0 and 1.0
  ""action_match_score"": float,  // between 0.0 and 1.0
  ""overall_score"": float,       // between 0.0 and 1.0
  ""reason"": ""short explanation""
}

- rubric_score: how well the model operations satisfy the rubrics.
- action_match_score: how similar the model operations are to the human reference operations.
- overall_score: your overall judgement, not necessarily the average.
Return only the JSON object, with no additional text.
""""""

# -------------------------
# JSON 파싱
# -------------------------
def extract_json(text: str):
    start = text.find(""{"")
    end = text.rfind(""}"")
    if start == -1 or end == -1 or end <= start:
        raise ValueError(f""JSON block not found in model output: {text[:200]}..."")
    json_str = text[start:end+1]
    return json.loads(json_str)

# -------------------------
# Judge Prompt 빌드
# -------------------------
def build_judge_prompt(example, model_ops):
    instruction = example[""instruction""]
    apps = example.get(""apps"", [])
    scenario = example.get(""scenario"", """")
    rubrics = example.get(""rubrics"", [])
    human_ops = example.get(""human_reference_operations"", [])
    return f""""""
[Instruction]
{instruction}

[Apps]
{apps}

[Scenario]
{scenario}

[Rubrics]
{json.dumps(rubrics, ensure_ascii=False, indent=2)}

[Human Reference Operations]
{json.dumps(human_ops, ensure_ascii=False, indent=2)}

[Model Operations to Evaluate]
{json.dumps(model_ops, ensure_ascii=False, indent=2)}
""""""

# -------------------------
# GPT Judge 호출
# -------------------------
def judge_with_gpt(example, model_ops):
    user_prompt = build_judge_prompt(example, model_ops)
    messages = [
        {""role"": ""system"", ""content"": JUDGE_SYSTEM_PROMPT},
        {""role"": ""user"", ""content"": user_prompt},
    ]
    resp = client.chat.completions.create(
        model=""gpt-5-mini"",
        messages=messages,
    )
    text = resp.choices[0].message.content
    data = extract_json(text)
    return {
        ""rubric_score"": float(data.get(""rubric_score"", 0.0)),
        ""action_match_score"": float(data.get(""action_match_score"", 0.0)),
        ""overall_score"": float(data.get(""overall_score"", 0.0)),
        ""reason"": data.get(""reason"", """"),
    }

# -------------------------
# Actor 호출
# -------------------------
def process_request_vl(messages):
    import openai
    openai.api_key = ""sk-None-1234""
    openai.base_url = ""http://192.168.0.202:25321/v1/""
    output = openai.chat.completions.create(
        model='25TOXMC_Blowfish_v1.0.9-AWQ',
        messages=messages,
        temperature=0.0,
        top_p=0.95,
        stream=False
    )
    return output

def build_actor_prompt(example):
    instruction = example[""instruction""]
    apps = example.get(""apps"", [])
    scenario = example.get(""scenario"", """")
    apps_str = "", "".join(apps) if apps else ""no specific apps""
    return f""""""User instruction:
{instruction}

You may use the following apps: {apps_str}
Scenario: {scenario}

Return ONLY a JSON object with the fields ""plan"" and ""operations"".
""""""

def call_actor(example):
    messages = [
        {""role"": ""system"", ""content"": SYSTEM_PROMPT},
        {""role"": ""user"", ""content"": build_actor_prompt(example)},
    ]
    resp = process_request_vl(messages)
    data = extract_json(resp.choices[0].message.content)
    ops = [str(o).strip() for o in data.get(""operations"", []) if str(o).strip()]
    return ops

# -------------------------
# 메인 루프
# -------------------------
def main():
    ds = load_dataset(""mikewang/mobile_eval_e"", split=""test"")
    scores = []

    for ex in tqdm(ds, desc=""Evaluating with GPT judge""):
        try:
            model_ops = call_actor(ex)
        except Exception as e:
            print(""Actor model failed:"", e)
            model_ops = []

        try:
            judge_result = judge_with_gpt(ex, model_ops)
        except Exception as e:
            print(""Judge model failed:"", e)
            judge_result = {
                ""rubric_score"": 0.0,
                ""action_match_score"": 0.0,
                ""overall_score"": 0.0,
                ""reason"": f""Judge error: {e}"",
            }

        scores.append(judge_result)

    avg_rubric = sum(s[""rubric_score""] for s in scores) / len(scores)
    avg_action = sum(s[""action_match_score""] for s in scores) / len(scores)
    avg_overall = sum(s[""overall_score""] for s in scores) / len(scores)

    print(""\n===== GPT Judge Overall Results ====="")
    print(f""#examples          : {len(scores)}"")
    print(f""Avg rubric_score   : {avg_rubric:.4f}"")
    print(f""Avg action_match   : {avg_action:.4f}"")
    print(f""Avg overall_score  : {avg_overall:.4f}"")

    # JSONL 저장 (공통 구조)
    save_jsonl(scores, ""./MobileEvalE_results.jsonl"")

if __name__ == ""__main__"":
    main()

","Python"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","Toxicity AI Prototype/mmlu_toxic.py",".py","1223","47","# MMLU_toxic.py
import json
import asyncio
from utils import run_concurrent_worker, save_jsonl, compute_em_score_mmlu, summarize_scores
import openai
import os
from dotenv import load_dotenv

load_dotenv()

BASE_URL = os.getenv(""BASE_URL"")

client = openai.AsyncOpenAI(api_key=""dummy"", base_url=BASE_URL)

def build_messages(item):
    system = item.get(""system"", """")
    prompt = item.get(""prompt"", """")
    return [
        {""role"": ""system"", ""content"": system},
        {""role"": ""user"", ""content"": prompt},
    ]

def main():
    with open(""./mmlu_toxic.json"", ""r"", encoding=""utf-8"") as f:
        data = json.load(f)

    outputs = asyncio.run(run_concurrent_worker(data, build_messages, client, concurrency=16))

    results = []
    for i, item in enumerate(data):
        pred = outputs[i]
        em = compute_em_score_mmlu(pred, item.get(""answer"", []))
        results.append({
            ""id"": item.get(""id"", i),
            ""prompt"": item.get(""prompt""),
            ""model_output"": pred,
            ""reference"": item.get(""answer""),
            ""score"": em
        })

    save_jsonl(results, ""./MMLU_toxic_results.jsonl"")
    print(""SUMMARY:"", summarize_scores(results))

if __name__ == ""__main__"":
    main()

","Python"
"ADMET","KwangSun-Ryu/ADMET-AGI-Toxicity-AI-Prototype-and-Baseline--","Toxicity AI Prototype/utils.py",".py","2526","80","# utils.py (Toxicity AI Prototype)
import os
import json
import asyncio
import re
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm

# ==========================
# 파일 저장
# ==========================
def save_jsonl(data_list, out_path):
    os.makedirs(os.path.dirname(out_path), exist_ok=True)
    with open(out_path, ""w"", encoding=""utf-8"") as f:
        for item in data_list:
            f.write(json.dumps(item, ensure_ascii=False) + ""\n"")
    print(f""Saved results to {out_path}"")

# ==========================
# EM 점수 계산
# ==========================
def compute_em_score_mmlu(pred, reference):
    return 1 if pred in reference else 0

# ==========================
# Summary
# ==========================
def summarize_scores(results):
    total = len(results)
    em_total = sum(r.get(""score"", 0) for r in results)
    return {
        ""n_samples"": total,
        ""em_score"": em_total / total if total > 0 else None
    }

# ==========================
# 비동기 모델 호출
# ==========================
async def call_model_async(messages, client, retries=3, initial_delay=1.0):
    delay = initial_delay
    for attempt in range(retries):
        try:
            resp = await client.chat.completions.create(
                model=""25TOXMC_Blowfish_v1.0.9-AWQ"",
                messages=messages,
                temperature=0.0,
                top_p=0.95,
                stream=False
            )
            return resp.choices[0].message.content
        except Exception as e:
            if attempt == retries-1:
                raise
            await asyncio.sleep(delay)
            delay *= 2

# ==========================
# 공통 비동기 워커
# ==========================
async def run_concurrent_worker(data, build_messages_func, client, concurrency=16):
    sem = asyncio.Semaphore(concurrency)
    results = [None] * len(data)

    async def worker(i):
        async with sem:
            messages = build_messages_func(data[i])
            out = await call_model_async(messages, client)
            try:
                out_clean = re.sub(r"".*?</think>"", """", out, flags=re.DOTALL).strip()
                out_json = json.loads(out_clean)
                results[i] = out_json.get(""output"")
            except Exception:
                results[i] = out_clean

    tasks = [asyncio.create_task(worker(i)) for i in range(len(data))]
    for f in tqdm(asyncio.as_completed(tasks), total=len(data), desc=""추론 진행중""):
        await f

    return results
","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","train.py",".py","6269","184","import numpy as np
import torch
import torch.nn as nn
from dataloader import DataLoader
from model import Model
from utils import AverageMeter, accuracy, F1_Score
from dice_loss import DiceLoss
import argparse
import time
import warnings
import mmcv
warnings.filterwarnings(""ignore"")
try:
    import wandb
except:
    pass

def adjust_learning_rate(optimizer, dataloader, epoch, iter):
    cur_iter = epoch * len(dataloader) + iter
    max_iter_num = args.epoch * len(dataloader)
    lr = args.lr * (1 - float(cur_iter) / max_iter_num) ** 0.9
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def model_structure(model):
    blank = ' '
    print('-' * 90)
    print('|' + ' ' * 11 + 'weight name' + ' ' * 10 + '|' \
          + ' ' * 15 + 'weight shape' + ' ' * 15 + '|' \
          + ' ' * 3 + 'number' + ' ' * 3 + '|')
    print('-' * 90)
    num_para = 0
    type_size = 1  ##如果是浮点数就是4

    for index, (key, w_variable) in enumerate(model.named_parameters()):
        if len(key) <= 30:
            key = key + (30 - len(key)) * blank
        shape = str(w_variable.shape)
        if len(shape) <= 40:
            shape = shape + (40 - len(shape)) * blank
        each_para = 1
        for k in w_variable.shape:
            each_para *= k
        num_para += each_para
        str_num = str(each_para)
        if len(str_num) <= 10:
            str_num = str_num + (10 - len(str_num)) * blank

        print('| {} | {} | {} |'.format(key, shape, str_num))
    print('-' * 90)
    print('The total number of parameters: ' + str(num_para))
    print('The parameters of Model {}: {:4f}M'.format(model._get_name(), num_para * type_size / 1000 / 1000))
    print('-' * 90)

def valid(valid_loader, model, epoch):
    model.eval()
    
    for iter, (x, y) in enumerate(valid_loader):
        x = x.cuda()
        y = y.cuda()
        with torch.no_grad():
            outputs = model(x)
            loss = criterion(outputs, y)
            acc = ((outputs > 0) == y).sum(dim=0).float() / args.valid_batch_size

    mean_acc = acc.mean()
    output_log = '(Valid)  Loss: {loss:.3f} | Mean Acc: {acc:.3f}'.format(
        loss=loss.item(),
        acc=mean_acc.item()
    )
    print(output_log)
    print(acc)
    if args.wandb:
        wandb.log({'epoch': epoch,
                   'Caco-2': acc[0].item(),
                   'CYP3A4': acc[1].item(),
                   'hERG': acc[2].item(),
                   'HOB': acc[3].item(),
                   'MN': acc[4].item(),
                   'Mean': mean_acc.item()})
    return mean_acc

def train(train_loader, model, optimizer, epoch):
    model.train()

    # meters
    batch_time = AverageMeter()
    data_time = AverageMeter()

    # start time
    start = time.time()
    for iter, (x, y) in enumerate(train_loader):
        x = x.cuda()
        y = y.cuda()
        # time cost of data loader
        data_time.update(time.time() - start)

        # adjust learning rate
        adjust_learning_rate(optimizer, train_loader, epoch, iter)

        outputs = model(x)
        loss = criterion(outputs, y)
        with torch.no_grad():
            acc = ((outputs > 0) == y).sum(dim=0).float() / args.batch_size
        # backward
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        batch_time.update(time.time() - start)

        # update start time
        start = time.time()

        # print log
        if iter % 10 == 0:
            output_log = '({batch}/{size}) LR: {lr:.6f} | Batch: {bt:.3f}s | Total: {total:.0f}min | ' \
                         'ETA: {eta:.0f}min | Loss: {loss:.3f} | ' \
                         'Mean Acc: {acc:.3f}'.format(
                batch=iter + 1,
                size=len(train_loader),
                lr=optimizer.param_groups[0]['lr'],
                bt=batch_time.avg,
                total=batch_time.avg * iter / 60.0,
                eta=batch_time.avg * (len(train_loader) - iter) / 60.0,
                loss=loss.item(),
                acc=acc.mean().item()
            )
            
            print(output_log)
            print(acc)
        # if args.wandb:
        #     wandb.log({'epoch': epoch,
        #                'Caco-2': acc[0].item(),
        #                'CYP3A4': acc[1].item(),
        #                'hERG': acc[2].item(),
        #                'HOB': acc[3].item(),
        #                'MN':acc[4].item()})

def main():
    train_loader = torch.utils.data.DataLoader(
        DataLoader(split=""train""), batch_size=args.batch_size,
        shuffle=True, num_workers=0, drop_last=True, pin_memory=True
    )
    valid_loader = torch.utils.data.DataLoader(
        DataLoader(split=""valid""), batch_size=args.valid_batch_size,
        shuffle=False, num_workers=0, drop_last=False, pin_memory=True
    )
    model = Model().cuda()
    model_structure(model)
    if args.wandb:
        wandb.watch(model)
    # optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-4)
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)

    start_epoch, start_iter, best_mean_acc = 0, 0, 0
    for epoch in range(start_epoch, args.epoch):
        print('\nEpoch: [%d | %d]' % (epoch + 1, args.epoch))
        train(train_loader, model, optimizer, epoch)
        mean_acc = valid(valid_loader, model, epoch)
        if mean_acc >= best_mean_acc:
            best_mean_acc = mean_acc
            
            torch.save(model.state_dict(), ""checkpoint/checkpoint.pth"")

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Hyperparams')
    parser.add_argument('--epoch', default=1000, type=int, help='epoch')
    parser.add_argument('--batch_size', default=1776, type=int, help='batch size')
    parser.add_argument('--valid_batch_size', default=198, type=int, help='batch size')
    parser.add_argument('--lr', default=0.01, type=float, help='batch size')
    parser.add_argument('--wandb', action='store_true', help='use wandb')

    mmcv.mkdir_or_exist(""checkpoint/"")
    args = parser.parse_args()
    print(args)
    # torch.backends.cudnn.benchmark = True
    
    if args.wandb:
        wandb.init(project=""math-model"")
        
    criterion = DiceLoss(loss_weight=1.0)
    main()","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","dice_loss.py",".py","752","29","import torch
import torch.nn as nn


class DiceLoss(nn.Module):
    def __init__(self, loss_weight=1.0):
        super(DiceLoss, self).__init__()
        self.loss_weight = loss_weight

    def forward(self, input, target, reduce=True):
        batch_size = input.size(0)
        input = torch.sigmoid(input)

        input = input.contiguous().view(batch_size, -1)
        target = target.contiguous().view(batch_size, -1).float()

        a = torch.sum(input * target, dim=1)
        b = torch.sum(input * input, dim=1) + 0.001
        c = torch.sum(target * target, dim=1) + 0.001
        d = (2 * a) / (b + c)
        loss = 1 - d

        loss = self.loss_weight * loss

        if reduce:
            loss = torch.mean(loss)

        return loss
","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","draw.py",".py","587","22","import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

def plot(matrix):
  sns.set()
  f,ax=plt.subplots()
  print(matrix) #打印出来看看
  sns.heatmap(matrix, annot=True,
              xticklabels=['Small', 'Fit', 'Large'],
              yticklabels=['Small', 'Fit', 'Large'],
              cmap=""Blues"", ax=ax, fmt='.20g') #画热力图
  ax.set_title('Confusion Matrix') #标题
  ax.set_xlabel('Predict') #x轴
  ax.set_ylabel('True') #y轴

matrix=np.array([[1116, 587, 105],
 [827, 10134, 855],
 [66, 355, 955]])
plot(matrix)# 画原始的数据
plt.show()
","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","model.py",".py","2444","79","import torch
import torch.nn as nn
import math
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.model = nn.Sequential(
            nn.LayerNorm([729]),
            nn.Dropout(0.1),
            nn.Linear(729, 512),
            nn.ReLU(inplace=True),
    
            nn.LayerNorm([512]),
            nn.Dropout(0.1),
            nn.Linear(512, 128),
            nn.ReLU(inplace=True),
    
            nn.LayerNorm([128]),
            nn.Dropout(0.1),
            nn.Linear(128, 5)
        )

        
    def forward(self, x):
        y = self.model(x)
        return y
    
    

class Attention(nn.Module):
    def __init__(self, dim, ratio=4, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1, linear=False):
        super().__init__()

        self.scale = qk_scale or 1 ** -0.5
        self.ratio = ratio
        self.q = nn.Linear(dim, dim//ratio, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim*2//ratio, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim//ratio, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        B, C = x.shape
        q = self.q(x).unsqueeze(-1) # [1776, 729]
        kv = self.kv(x).reshape(B, C//self.ratio, -1, 1).permute(2, 0, 1, 3) # [1776, 1458]
        k, v = kv[0], kv[1] # [1776, 729, 1]
        # print(q.shape, k.shape, v.shape)
        
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        o = (attn @ v).squeeze(-1)
        # print(x.shape)
        o = self.proj(o)
        o = self.proj_drop(o)

        return o + x
","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","valid.py",".py","1145","43","import torch
from dataloader import DataLoader
from model import Model
from dice_loss import DiceLoss
import warnings

warnings.filterwarnings(""ignore"")


def valid(valid_loader, model):
    model.eval()
    criterion = DiceLoss()
    for iter, (x, y) in enumerate(valid_loader):
        x = x.cuda()
        y = y.cuda()
        with torch.no_grad():
            outputs = model(x)
            loss = criterion(outputs, y)
            acc = ((outputs > 0) == y).sum(dim=0).float() / VALID_BATCH_SIZE
    
    mean_acc = acc.mean()
    output_log = '(Valid)  Loss: {loss:.3f} | Mean Acc: {acc:.3f}'.format(
        loss=loss.item(),
        acc=mean_acc.item()
    )
    print(output_log)
    print(acc)
    return mean_acc

def main():
    valid_loader = torch.utils.data.DataLoader(
        DataLoader(split=""valid""), batch_size=VALID_BATCH_SIZE,
        shuffle=False, num_workers=0, drop_last=False, pin_memory=True
    )
    model = Model().cuda()
    state_dict = torch.load(""checkpoint/checkpoint.pth"")
    model.load_state_dict(state_dict)
    valid(valid_loader, model)


if __name__ == '__main__':
    VALID_BATCH_SIZE = 198
    main()","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","test.py",".py","1215","43","import torch
from dataloader import DataLoader
from model import Model
import pandas as pd
import warnings
warnings.filterwarnings(""ignore"")


def test(valid_loader, model):
    model.eval()

    smiles = pd.read_csv('data/Molecular_Descriptor.csv')['SMILES'].tolist()
    y_preds = []
    for iter, x in enumerate(valid_loader):
        x = x.cuda()
        with torch.no_grad():
            outputs = model(x)
            y_pred = (outputs > 0).int().cpu().numpy().tolist()
            y_preds.append(y_pred)
    y_preds = y_preds[0]
    print(len(y_preds))
    f = open(""data/ADEMT_test_pre.csv"", ""w+"")
    f.write(""SMILES,Caco-2,CYP3A4,hERG,HOB,MN\n"")
    for index, y_pred in enumerate(y_preds):
        text = smiles[index] + "","" + "","".join([str(i) for i in y_pred])
        f.write(text + ""\n"")
        print(text)
    f.close()


def main():
    valid_loader = torch.utils.data.DataLoader(
        DataLoader(split=""test""), batch_size=50,
        shuffle=False, num_workers=0, drop_last=False, pin_memory=True
    )
    model = Model().cuda()
    state_dict = torch.load(""checkpoint/checkpoint.pth"")
    model.load_state_dict(state_dict)
    test(valid_loader, model)


if __name__ == '__main__':
    main()","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","utils.py",".py","2558","77","import torch
import numpy as np


class AverageMeter(object):
    """"""Computes and stores the average and current value""""""
    def __init__(self, max_len=-1):
        self.val = []
        self.count = []
        self.max_len = max_len
        self.avg = 0

    def update(self, val, n=1):
        self.val.append(val * n)
        self.count.append(n)
        if self.max_len > 0 and len(self.val) > self.max_len:
            self.val = self.val[-self.max_len:]
            self.count = self.count[-self.max_len:]
        self.avg = sum(self.val) / sum(self.count)
        

def accuracy(output, target, topk=(1,)):
    """"""Computes the accuracy over the k top predictions for the specified values of k""""""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


class F1_Score:
    __name__ = 'F1 macro'
    def __init__(self,n=3):
        self.n = n
        self.TP = np.zeros(self.n)
        self.FP = np.zeros(self.n)
        self.FN = np.zeros(self.n)

    def get_confusion_matrix(self, prediction, ground_truth, num_classes):
        gt_onehot = torch.nn.functional.one_hot(ground_truth, num_classes=num_classes).float()
        prediction = torch.argmax(prediction, dim=1)
        pd_onehot = torch.nn.functional.one_hot(prediction, num_classes=num_classes).float()
        return pd_onehot.t().matmul(gt_onehot)
    
    def __call__(self, preds, targs):
        cm = self.get_confusion_matrix(preds, targs, num_classes=3)
        print(cm)
        TP = cm.diagonal()
        FP = cm.sum(1) - TP
        FN = cm.sum(0) - TP
        self.TP += TP.float().cpu().numpy()
        self.FP += FP.float().cpu().numpy()
        self.FN += FN.float().cpu().numpy()
        
    def print(self):
        precision = self.TP / (self.TP + self.FP + 1e-12)
        recall = self.TP / (self.TP + self.FN + 1e-12)
        self.precision = precision
        self.recall = recall
        # precision = precision.mean()
        # recall = recall.mean()
        score = 2.0 * (precision * recall) / (precision + recall + 1e-12)
        score = score.mean()
        return score

    def reset(self):
        self.TP = np.zeros(self.n)
        self.FP = np.zeros(self.n)
        self.FN = np.zeros(self.n)","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","dataloader.py",".py","2384","73","import pandas as pd
import numpy as np
from torch.utils import data
import torch.nn as nn
import torch
import os

pd.set_option('display.max_columns', None)


class DataLoader(data.Dataset):
    def __init__(self, split):
        self.split = split
        if split == 'train' or split == 'valid':
            feature = pd.read_csv('data/Molecular_Descriptor.csv', index_col='SMILES').values.tolist()
            label = pd.read_csv('data/ADMET.csv', index_col='SMILES').values.tolist()
            
            feature_train, label_train = [], []
            feature_valid, label_valid = [], []

            for i in range(0, 1974):
                if i % 10 != 0:
                    feature_train.append(feature[i])
                    label_train.append(label[i])
                else:
                    feature_valid.append(feature[i])
                    label_valid.append(label[i])
                    
            if split == 'train':
                self.feature = np.array(feature_train)
                self.label = np.array(label_train)
            elif split == 'valid':
                self.feature = np.array(feature_valid)
                self.label = np.array(label_valid)
            else:
                print(""split must in [train, valid]"")
              
        elif split == 'test':
            feature = pd.read_csv(""data/Molecular_Descriptor_test.csv"", index_col='SMILES').values.tolist()
            self.feature = np.array(feature)
        else:
            print('Error: split must be train, valid or test!')
        

    def __len__(self):
        return len(self.feature)
    
    def __getitem__(self, index):
        if self.split == 'train' or self.split == 'valid':
            x = torch.from_numpy(self.feature[index]).float()
            y = torch.from_numpy(np.array(self.label[index])).float()
            return x, y

        elif self.split == 'test':
            x = torch.from_numpy(self.feature[index]).float()
            return x

    
if __name__ == '__main__':
    dataloader = DataLoader(split='train')
    print(len(dataloader))
    x, y = dataloader.__getitem__(0)
    print(x.shape, y.shape)
    
    dataloader = DataLoader(split='valid')
    print(len(dataloader))
    x, y = dataloader.__getitem__(0)
    print(x.shape, y.shape)

    dataloader = DataLoader(split='test')
    x = dataloader.__getitem__(0)
    print(x.shape)
","Python"
"ADMET","rnzhiw/HuaweiCupMathModel","data/question1_2.ipynb",".ipynb","345116","10473","{
 ""cells"": [
  {
   ""cell_type"": ""code"",
   ""execution_count"": 169,
   ""id"": ""ccdac8db"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import pandas as pd\n"",
    ""import sklearn\n"",
    ""import seaborn as sns""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""id"": ""89d498f6"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""?pd.read_csv""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 95,
   ""id"": ""aaa5a4b3"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""feature_train=pd.read_csv('Molecular_Descriptor.csv',index_col='SMILES')\n"",
    ""label_train=pd.read_csv('ERα_activity.csv',index_col='SMILES')\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 96,
   ""id"": ""39b96bbf"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""del label_train['IC50_nM']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""id"": ""239b91fc"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""<class 'pandas.core.frame.DataFrame'>\n"",
      ""Index: 1974 entries, Oc1ccc2O[C@H]([C@H](Sc2c1)C3CCCC3)c4ccc(OCCN5CCCCC5)cc4 to COc1cc(OC)cc(\\C=C\\c2ccc(OS(=O)(=O)[C@H]3C[C@H]4O[C@@H]3C(=C4c5ccc(O)cc5)c6ccc(O)cc6)cc2)c1\n"",
      ""Columns: 729 entries, nAcid to Zagreb\n"",
      ""dtypes: float64(359), int64(370)\n"",
      ""memory usage: 11.0+ MB\n""
     ]
    }
   ],
   ""source"": [
    ""feature_train.info()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""id"": ""0321d500"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""?pd.concat""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""id"": ""384b55ce"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#data=pd.concat((feature_train,label_train['pIC50']),axis=1)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 97,
   ""id"": ""011afc8c"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""float64    360\n"",
       ""int64      145\n"",
       ""dtype: int64""
      ]
     },
     ""execution_count"": 97,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#data.dtypes.value_counts()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 33,
   ""id"": ""db04ef2e"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""nAcid 的特征分布：\n"",
      ""0    1780\n"",
      ""1     177\n"",
      ""2      15\n"",
      ""3       1\n"",
      ""4       1\n"",
      ""Name: nAcid, dtype: int64\n"",
      ""ALogP 的特征分布：\n"",
      ""1.8579    9\n"",
      ""0.7296    8\n"",
      ""1.2029    6\n"",
      ""1.0069    6\n"",
      ""1.8338    6\n"",
      ""         ..\n"",
      ""1.1968    1\n"",
      ""0.9420    1\n"",
      ""1.4848    1\n"",
      ""0.0239    1\n"",
      ""2.8165    1\n"",
      ""Name: ALogP, Length: 1605, dtype: int64\n"",
      ""ALogp2 的特征分布：\n"",
      ""3.451792    9\n"",
      ""0.532316    8\n"",
      ""3.362822    6\n"",
      ""1.013848    6\n"",
      ""1.446968    6\n"",
      ""           ..\n"",
      ""7.570752    1\n"",
      ""1.432330    1\n"",
      ""0.887364    1\n"",
      ""2.204631    1\n"",
      ""7.932672    1\n"",
      ""Name: ALogp2, Length: 1590, dtype: int64\n"",
      ""AMR 的特征分布：\n"",
      ""148.8682    9\n"",
      ""139.9304    8\n"",
      ""141.6032    6\n"",
      ""88.3037     6\n"",
      ""88.4293     6\n"",
      ""           ..\n"",
      ""90.5701     1\n"",
      ""87.9712     1\n"",
      ""91.1016     1\n"",
      ""84.8648     1\n"",
      ""164.3947    1\n"",
      ""Name: AMR, Length: 1631, dtype: int64\n"",
      ""apol 的特征分布：\n"",
      ""77.158583    21\n"",
      ""74.064997    16\n"",
      ""40.760723    12\n"",
      ""79.112169    12\n"",
      ""80.076962    10\n"",
      ""             ..\n"",
      ""85.985583     1\n"",
      ""56.661860     1\n"",
      ""64.446239     1\n"",
      ""61.242860     1\n"",
      ""81.368618     1\n"",
      ""Name: apol, Length: 1316, dtype: int64\n"",
      ""naAromAtom 的特征分布：\n"",
      ""18    554\n"",
      ""12    374\n"",
      ""15    193\n"",
      ""21    187\n"",
      ""16    148\n"",
      ""6     111\n"",
      ""0      53\n"",
      ""9      50\n"",
      ""24     49\n"",
      ""11     43\n"",
      ""17     38\n"",
      ""22     29\n"",
      ""19     28\n"",
      ""27     22\n"",
      ""20     20\n"",
      ""23     19\n"",
      ""14     16\n"",
      ""10     16\n"",
      ""26      8\n"",
      ""5       8\n"",
      ""30      3\n"",
      ""13      2\n"",
      ""25      1\n"",
      ""29      1\n"",
      ""28      1\n"",
      ""Name: naAromAtom, dtype: int64\n"",
      ""nAromBond 的特征分布：\n"",
      ""18    651\n"",
      ""12    324\n"",
      ""17    197\n"",
      ""23    179\n"",
      ""6     111\n"",
      ""16     71\n"",
      ""10     64\n"",
      ""13     56\n"",
      ""0      53\n"",
      ""24     49\n"",
      ""22     46\n"",
      ""11     39\n"",
      ""29     24\n"",
      ""28     18\n"",
      ""15     17\n"",
      ""21     16\n"",
      ""25     15\n"",
      ""27     12\n"",
      ""19      9\n"",
      ""26      8\n"",
      ""5       8\n"",
      ""20      3\n"",
      ""33      2\n"",
      ""30      1\n"",
      ""34      1\n"",
      ""Name: nAromBond, dtype: int64\n"",
      ""nAtom 的特征分布：\n"",
      ""30     85\n"",
      ""65     71\n"",
      ""62     65\n"",
      ""31     60\n"",
      ""32     60\n"",
      ""       ..\n"",
      ""21      1\n"",
      ""80      1\n"",
      ""98      1\n"",
      ""74      1\n"",
      ""107     1\n"",
      ""Name: nAtom, Length: 87, dtype: int64\n"",
      ""nHeavyAtom 的特征分布：\n"",
      ""21     157\n"",
      ""20     147\n"",
      ""34     144\n"",
      ""22     132\n"",
      ""33     127\n"",
      ""35     110\n"",
      ""19     105\n"",
      ""32     103\n"",
      ""23      92\n"",
      ""31      75\n"",
      ""36      71\n"",
      ""30      70\n"",
      ""25      66\n"",
      ""24      66\n"",
      ""26      65\n"",
      ""18      59\n"",
      ""27      53\n"",
      ""29      51\n"",
      ""28      49\n"",
      ""37      44\n"",
      ""38      36\n"",
      ""39      22\n"",
      ""41      22\n"",
      ""40      20\n"",
      ""17      19\n"",
      ""43      12\n"",
      ""16      10\n"",
      ""42       8\n"",
      ""47       6\n"",
      ""15       6\n"",
      ""44       5\n"",
      ""46       5\n"",
      ""48       4\n"",
      ""77       3\n"",
      ""14       2\n"",
      ""45       2\n"",
      ""59       1\n"",
      ""163      1\n"",
      ""72       1\n"",
      ""81       1\n"",
      ""74       1\n"",
      ""57       1\n"",
      ""Name: nHeavyAtom, dtype: int64\n"",
      ""nH 的特征分布：\n"",
      ""14    106\n"",
      ""29     98\n"",
      ""31     95\n"",
      ""11     92\n"",
      ""33     87\n"",
      ""     ... \n"",
      ""48      1\n"",
      ""58      1\n"",
      ""46      1\n"",
      ""5       1\n"",
      ""65      1\n"",
      ""Name: nH, Length: 61, dtype: int64\n"",
      ""nB 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nB, dtype: int64\n"",
      ""nC 的特征分布：\n"",
      ""17    163\n"",
      ""16    156\n"",
      ""15    134\n"",
      ""28    122\n"",
      ""27    120\n"",
      ""29    116\n"",
      ""25     93\n"",
      ""30     93\n"",
      ""20     90\n"",
      ""26     90\n"",
      ""18     89\n"",
      ""21     80\n"",
      ""19     80\n"",
      ""14     76\n"",
      ""24     75\n"",
      ""23     68\n"",
      ""22     59\n"",
      ""31     56\n"",
      ""13     48\n"",
      ""32     48\n"",
      ""33     31\n"",
      ""34     18\n"",
      ""11     15\n"",
      ""36      8\n"",
      ""35      8\n"",
      ""12      7\n"",
      ""40      5\n"",
      ""41      4\n"",
      ""9       3\n"",
      ""47      3\n"",
      ""38      2\n"",
      ""37      2\n"",
      ""50      2\n"",
      ""39      2\n"",
      ""42      2\n"",
      ""95      1\n"",
      ""44      1\n"",
      ""7       1\n"",
      ""52      1\n"",
      ""48      1\n"",
      ""10      1\n"",
      ""Name: nC, dtype: int64\n"",
      ""nN 的特征分布：\n"",
      ""1     750\n"",
      ""0     492\n"",
      ""2     379\n"",
      ""3     170\n"",
      ""4     103\n"",
      ""5      47\n"",
      ""6      18\n"",
      ""7       6\n"",
      ""17      3\n"",
      ""8       2\n"",
      ""46      1\n"",
      ""16      1\n"",
      ""19      1\n"",
      ""15      1\n"",
      ""Name: nN, dtype: int64\n"",
      ""nO 的特征分布：\n"",
      ""3     583\n"",
      ""2     429\n"",
      ""4     425\n"",
      ""5     199\n"",
      ""1     153\n"",
      ""6      71\n"",
      ""0      50\n"",
      ""7      31\n"",
      ""8      20\n"",
      ""10      7\n"",
      ""9       5\n"",
      ""20      1\n"",
      ""Name: nO, dtype: int64\n"",
      ""nS 的特征分布：\n"",
      ""0    1446\n"",
      ""1     466\n"",
      ""2      48\n"",
      ""3      13\n"",
      ""6       1\n"",
      ""Name: nS, dtype: int64\n"",
      ""nP 的特征分布：\n"",
      ""0    1972\n"",
      ""1       2\n"",
      ""Name: nP, dtype: int64\n"",
      ""nF 的特征分布：\n"",
      ""0    1648\n"",
      ""1     201\n"",
      ""3      70\n"",
      ""2      41\n"",
      ""4       7\n"",
      ""5       4\n"",
      ""6       3\n"",
      ""Name: nF, dtype: int64\n"",
      ""nCl 的特征分布：\n"",
      ""0    1801\n"",
      ""1     152\n"",
      ""2      19\n"",
      ""6       1\n"",
      ""4       1\n"",
      ""Name: nCl, dtype: int64\n"",
      ""nBr 的特征分布：\n"",
      ""0    1859\n"",
      ""1     110\n"",
      ""2       4\n"",
      ""3       1\n"",
      ""Name: nBr, dtype: int64\n"",
      ""nI 的特征分布：\n"",
      ""0    1970\n"",
      ""1       2\n"",
      ""2       2\n"",
      ""Name: nI, dtype: int64\n"",
      ""nX 的特征分布：\n"",
      ""0    1397\n"",
      ""1     399\n"",
      ""2      80\n"",
      ""3      77\n"",
      ""4      13\n"",
      ""5       4\n"",
      ""6       4\n"",
      ""Name: nX, dtype: int64\n"",
      ""ATSc1 的特征分布：\n"",
      ""0.460906    7\n"",
      ""0.272104    7\n"",
      ""0.492640    6\n"",
      ""0.272577    5\n"",
      ""0.518524    4\n"",
      ""           ..\n"",
      ""0.357384    1\n"",
      ""0.514036    1\n"",
      ""0.430938    1\n"",
      ""0.430563    1\n"",
      ""0.645421    1\n"",
      ""Name: ATSc1, Length: 1813, dtype: int64\n"",
      ""ATSc2 的特征分布：\n"",
      ""-0.122407    7\n"",
      ""-0.179919    7\n"",
      ""-0.236081    6\n"",
      ""-0.126670    5\n"",
      ""-0.111155    4\n"",
      ""            ..\n"",
      ""-0.103842    1\n"",
      ""-0.104130    1\n"",
      ""-0.102762    1\n"",
      ""-0.102765    1\n"",
      ""-0.270925    1\n"",
      ""Name: ATSc2, Length: 1839, dtype: int64\n"",
      ""ATSc3 的特征分布：\n"",
      ""-0.078184    7\n"",
      ""-0.097719    7\n"",
      ""-0.081303    6\n"",
      ""-0.064649    5\n"",
      ""-0.026170    4\n"",
      ""            ..\n"",
      "" 0.011141    1\n"",
      "" 0.003074    1\n"",
      ""-0.012460    1\n"",
      ""-0.004029    1\n"",
      "" 0.051394    1\n"",
      ""Name: ATSc3, Length: 1850, dtype: int64\n"",
      ""ATSc4 的特征分布：\n"",
      "" 0.085037    7\n"",
      "" 0.104662    7\n"",
      "" 0.147899    6\n"",
      "" 0.087674    5\n"",
      ""-0.307202    4\n"",
      ""            ..\n"",
      ""-0.042339    1\n"",
      ""-0.046391    1\n"",
      ""-0.047475    1\n"",
      ""-0.045690    1\n"",
      ""-0.246079    1\n"",
      ""Name: ATSc4, Length: 1847, dtype: int64\n"",
      ""ATSc5 的特征分布：\n"",
      ""-0.042356    7\n"",
      "" 0.005061    7\n"",
      ""-0.102349    6\n"",
      ""-0.017671    5\n"",
      "" 0.010685    4\n"",
      ""            ..\n"",
      "" 0.010510    1\n"",
      ""-0.104793    1\n"",
      ""-0.089870    1\n"",
      "" 0.004201    1\n"",
      "" 0.185752    1\n"",
      ""Name: ATSc5, Length: 1840, dtype: int64\n"",
      ""ATSm1 的特征分布：\n"",
      ""43.585609    30\n"",
      ""42.585609    21\n"",
      ""24.548938    20\n"",
      ""38.457856    19\n"",
      ""37.457856    17\n"",
      ""             ..\n"",
      ""42.487832     1\n"",
      ""37.895170     1\n"",
      ""63.581498     1\n"",
      ""41.476593     1\n"",
      ""49.774567     1\n"",
      ""Name: ATSm1, Length: 999, dtype: int64\n"",
      ""ATSm2 的特征分布：\n"",
      ""43.830668    23\n"",
      ""42.830668    18\n"",
      ""26.664184    16\n"",
      ""41.491098    14\n"",
      ""24.664184    13\n"",
      ""             ..\n"",
      ""27.283863     1\n"",
      ""39.380103     1\n"",
      ""41.021256     1\n"",
      ""42.353348     1\n"",
      ""54.999443     1\n"",
      ""Name: ATSm2, Length: 1231, dtype: int64\n"",
      ""ATSm3 的特征分布：\n"",
      ""60.317739    11\n"",
      ""61.830698    10\n"",
      ""57.654099     9\n"",
      ""64.147833     9\n"",
      ""29.328368     9\n"",
      ""             ..\n"",
      ""37.229273     1\n"",
      ""37.897181     1\n"",
      ""32.993639     1\n"",
      ""46.411309     1\n"",
      ""75.649958     1\n"",
      ""Name: ATSm3, Length: 1428, dtype: int64\n"",
      ""ATSm4 的特征分布：\n"",
      ""63.036839    10\n"",
      ""59.325460     8\n"",
      ""67.277981     7\n"",
      ""49.992552     6\n"",
      ""38.599021     6\n"",
      ""             ..\n"",
      ""61.724585     1\n"",
      ""50.006847     1\n"",
      ""58.812174     1\n"",
      ""56.812174     1\n"",
      ""76.549012     1\n"",
      ""Name: ATSm4, Length: 1589, dtype: int64\n"",
      ""ATSm5 的特征分布：\n"",
      ""60.392427    11\n"",
      ""61.392427     7\n"",
      ""60.483378     7\n"",
      ""40.328368     7\n"",
      ""43.660460     6\n"",
      ""             ..\n"",
      ""40.084520     1\n"",
      ""45.748704     1\n"",
      ""38.637982     1\n"",
      ""35.752428     1\n"",
      ""85.083040     1\n"",
      ""Name: ATSm5, Length: 1665, dtype: int64\n"",
      ""ATSp1 的特征分布：\n"",
      ""2647.253026    6\n"",
      ""2683.174106    3\n"",
      ""1783.411971    3\n"",
      ""2105.520007    3\n"",
      ""2602.118459    3\n"",
      ""              ..\n"",
      ""1386.559775    1\n"",
      ""1289.086017    1\n"",
      ""1415.235856    1\n"",
      ""1417.240237    1\n"",
      ""3275.017812    1\n"",
      ""Name: ATSp1, Length: 1877, dtype: int64\n"",
      ""ATSp2 的特征分布：\n"",
      ""3160.122563    6\n"",
      ""3193.321199    3\n"",
      ""2187.100772    3\n"",
      ""2458.294213    3\n"",
      ""3139.390354    3\n"",
      ""              ..\n"",
      ""1686.056318    1\n"",
      ""1569.937308    1\n"",
      ""1730.552836    1\n"",
      ""1733.550929    1\n"",
      ""3971.981132    1\n"",
      ""Name: ATSp2, Length: 1877, dtype: int64\n"",
      ""ATSp3 的特征分布：\n"",
      ""4544.944307    6\n"",
      ""4539.951971    3\n"",
      ""3353.559838    3\n"",
      ""3390.026577    3\n"",
      ""4651.738227    3\n"",
      ""              ..\n"",
      ""2518.574188    1\n"",
      ""2355.264780    1\n"",
      ""2629.085868    1\n"",
      ""2634.363581    1\n"",
      ""5911.985670    1\n"",
      ""Name: ATSp3, Length: 1877, dtype: int64\n"",
      ""ATSp4 的特征分布：\n"",
      ""4787.181901    6\n"",
      ""4607.751468    3\n"",
      ""3471.836172    3\n"",
      ""3324.360189    3\n"",
      ""5242.743916    3\n"",
      ""              ..\n"",
      ""2755.027982    1\n"",
      ""2520.592551    1\n"",
      ""2897.339870    1\n"",
      ""2904.102788    1\n"",
      ""5772.028702    1\n"",
      ""Name: ATSp4, Length: 1877, dtype: int64\n"",
      ""ATSp5 的特征分布：\n"",
      ""4530.534612    6\n"",
      ""4081.796222    3\n"",
      ""2885.860660    3\n"",
      ""3049.950290    3\n"",
      ""4935.375684    3\n"",
      ""              ..\n"",
      ""2312.502820    1\n"",
      ""2029.669690    1\n"",
      ""2372.985320    1\n"",
      ""2381.064216    1\n"",
      ""5804.010590    1\n"",
      ""Name: ATSp5, Length: 1877, dtype: int64\n"",
      ""nBase 的特征分布：\n"",
      ""0     1307\n"",
      ""1      587\n"",
      ""2       66\n"",
      ""3        6\n"",
      ""7        5\n"",
      ""4        1\n"",
      ""26       1\n"",
      ""5        1\n"",
      ""Name: nBase, dtype: int64\n"",
      ""BCUTw-1l 的特征分布：\n"",
      ""11.850000    737\n"",
      ""11.890000    626\n"",
      ""11.900000    151\n"",
      ""11.998834     19\n"",
      ""11.998521     12\n"",
      ""            ... \n"",
      ""11.993300      1\n"",
      ""11.994323      1\n"",
      ""11.994298      1\n"",
      ""11.994305      1\n"",
      ""11.994273      1\n"",
      ""Name: BCUTw-1l, Length: 238, dtype: int64\n"",
      ""BCUTw-1h 的特征分布：\n"",
      ""31.972073    31\n"",
      ""31.972073    23\n"",
      ""15.995925    21\n"",
      ""15.996928    16\n"",
      ""15.998956    14\n"",
      ""             ..\n"",
      ""15.995928     1\n"",
      ""34.969380     1\n"",
      ""16.000021     1\n"",
      ""15.999952     1\n"",
      ""31.972073     1\n"",
      ""Name: BCUTw-1h, Length: 911, dtype: int64\n"",
      ""BCUTc-1l 的特征分布：\n"",
      ""-0.361308    29\n"",
      ""-0.361379    19\n"",
      ""-0.361377    19\n"",
      ""-0.361388    17\n"",
      ""-0.361379    17\n"",
      ""             ..\n"",
      ""-0.360087     1\n"",
      ""-0.360101     1\n"",
      ""-0.360171     1\n"",
      ""-0.360109     1\n"",
      ""-0.364009     1\n"",
      ""Name: BCUTc-1l, Length: 1693, dtype: int64\n"",
      ""BCUTc-1h 的特征分布：\n"",
      ""0.116046    6\n"",
      ""0.116987    3\n"",
      ""0.176483    3\n"",
      ""0.283424    3\n"",
      ""0.204852    3\n"",
      ""           ..\n"",
      ""0.299305    1\n"",
      ""0.299314    1\n"",
      ""0.301097    1\n"",
      ""0.299368    1\n"",
      ""0.278503    1\n"",
      ""Name: BCUTc-1h, Length: 1874, dtype: int64\n"",
      ""BCUTp-1l 的特征分布：\n"",
      ""5.134125    6\n"",
      ""4.910156    3\n"",
      ""5.132720    3\n"",
      ""4.762338    3\n"",
      ""3.927290    3\n"",
      ""           ..\n"",
      ""4.960112    1\n"",
      ""5.078003    1\n"",
      ""5.117298    1\n"",
      ""4.715284    1\n"",
      ""4.877567    1\n"",
      ""Name: BCUTp-1l, Length: 1876, dtype: int64\n"",
      ""BCUTp-1h 的特征分布：\n"",
      ""12.362931    6\n"",
      ""13.393973    3\n"",
      ""13.912662    3\n"",
      ""13.572554    3\n"",
      ""13.773754    3\n"",
      ""            ..\n"",
      ""11.269852    1\n"",
      ""10.955478    1\n"",
      ""11.032865    1\n"",
      ""11.269853    1\n"",
      ""12.729192    1\n"",
      ""Name: BCUTp-1h, Length: 1875, dtype: int64\n"",
      ""nBonds 的特征分布：\n"",
      ""22     134\n"",
      ""23     130\n"",
      ""38     126\n"",
      ""37     117\n"",
      ""24     113\n"",
      ""21     106\n"",
      ""39     100\n"",
      ""25      95\n"",
      ""36      77\n"",
      ""26      76\n"",
      ""35      70\n"",
      ""33      67\n"",
      ""29      65\n"",
      ""34      64\n"",
      ""20      61\n"",
      ""40      59\n"",
      ""28      56\n"",
      ""30      54\n"",
      ""27      52\n"",
      ""41      51\n"",
      ""32      48\n"",
      ""31      43\n"",
      ""42      33\n"",
      ""43      30\n"",
      ""45      21\n"",
      ""19      21\n"",
      ""44      17\n"",
      ""46      17\n"",
      ""16      11\n"",
      ""47      10\n"",
      ""18      10\n"",
      ""48       7\n"",
      ""50       6\n"",
      ""52       4\n"",
      ""15       3\n"",
      ""77       3\n"",
      ""51       3\n"",
      ""54       2\n"",
      ""14       2\n"",
      ""49       2\n"",
      ""64       1\n"",
      ""17       1\n"",
      ""163      1\n"",
      ""53       1\n"",
      ""71       1\n"",
      ""81       1\n"",
      ""74       1\n"",
      ""61       1\n"",
      ""Name: nBonds, dtype: int64\n"",
      ""nBonds2 的特征分布：\n"",
      ""32     89\n"",
      ""66     60\n"",
      ""69     59\n"",
      ""71     58\n"",
      ""35     57\n"",
      ""       ..\n"",
      ""103     1\n"",
      ""343     1\n"",
      ""24      1\n"",
      ""25      1\n"",
      ""89      1\n"",
      ""Name: nBonds2, Length: 88, dtype: int64\n"",
      ""nBondsS 的特征分布：\n"",
      ""24     109\n"",
      ""57      64\n"",
      ""60      57\n"",
      ""62      56\n"",
      ""55      56\n"",
      ""      ... \n"",
      ""81       1\n"",
      ""113      1\n"",
      ""93       1\n"",
      ""91       1\n"",
      ""102      1\n"",
      ""Name: nBondsS, Length: 83, dtype: int64\n"",
      ""nBondsS2 的特征分布：\n"",
      ""14     87\n"",
      ""53     60\n"",
      ""48     59\n"",
      ""50     58\n"",
      ""46     58\n"",
      ""       ..\n"",
      ""10      1\n"",
      ""154     1\n"",
      ""159     1\n"",
      ""315     1\n"",
      ""96      1\n"",
      ""Name: nBondsS2, Length: 84, dtype: int64\n"",
      ""nBondsS3 的特征分布：\n"",
      ""20     124\n"",
      ""18     113\n"",
      ""14     112\n"",
      ""9      110\n"",
      ""8      107\n"",
      ""13     103\n"",
      ""10      92\n"",
      ""17      87\n"",
      ""7       87\n"",
      ""12      86\n"",
      ""15      85\n"",
      ""19      85\n"",
      ""5       85\n"",
      ""16      83\n"",
      ""4       82\n"",
      ""11      77\n"",
      ""3       74\n"",
      ""21      69\n"",
      ""22      61\n"",
      ""6       52\n"",
      ""23      39\n"",
      ""24      39\n"",
      ""2       38\n"",
      ""1       18\n"",
      ""25      16\n"",
      ""27       7\n"",
      ""26       6\n"",
      ""34       4\n"",
      ""30       4\n"",
      ""64       3\n"",
      ""31       3\n"",
      ""33       3\n"",
      ""36       3\n"",
      ""35       3\n"",
      ""32       2\n"",
      ""28       2\n"",
      ""0        2\n"",
      ""37       2\n"",
      ""29       1\n"",
      ""42       1\n"",
      ""135      1\n"",
      ""60       1\n"",
      ""65       1\n"",
      ""63       1\n"",
      ""Name: nBondsS3, dtype: int64\n"",
      ""nBondsD 的特征分布：\n"",
      ""9     338\n"",
      ""8     337\n"",
      ""10    325\n"",
      ""7     218\n"",
      ""11    205\n"",
      ""6     155\n"",
      ""12    111\n"",
      ""13     63\n"",
      ""5      53\n"",
      ""4      31\n"",
      ""16     30\n"",
      ""3      28\n"",
      ""14     25\n"",
      ""2      24\n"",
      ""15     17\n"",
      ""1      11\n"",
      ""0       1\n"",
      ""28      1\n"",
      ""18      1\n"",
      ""Name: nBondsD, dtype: int64\n"",
      ""nBondsD2 的特征分布：\n"",
      ""0     632\n"",
      ""2     534\n"",
      ""1     524\n"",
      ""3     147\n"",
      ""4      92\n"",
      ""5      26\n"",
      ""6       8\n"",
      ""7       2\n"",
      ""8       2\n"",
      ""13      2\n"",
      ""11      2\n"",
      ""12      2\n"",
      ""28      1\n"",
      ""Name: nBondsD2, dtype: int64\n"",
      ""nBondsT 的特征分布：\n"",
      ""0    1872\n"",
      ""1      97\n"",
      ""2       5\n"",
      ""Name: nBondsT, dtype: int64\n"",
      ""nBondsQ 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nBondsQ, dtype: int64\n"",
      ""nBondsM 的特征分布：\n"",
      ""18    346\n"",
      ""19    248\n"",
      ""14    196\n"",
      ""20    135\n"",
      ""12    130\n"",
      ""17    127\n"",
      ""23     91\n"",
      ""13     82\n"",
      ""25     79\n"",
      ""24     76\n"",
      ""22     69\n"",
      ""15     53\n"",
      ""21     52\n"",
      ""8      49\n"",
      ""28     42\n"",
      ""7      32\n"",
      ""2      21\n"",
      ""16     21\n"",
      ""6      18\n"",
      ""27     15\n"",
      ""31     14\n"",
      ""9      11\n"",
      ""3       9\n"",
      ""1       9\n"",
      ""30      9\n"",
      ""26      8\n"",
      ""11      7\n"",
      ""29      7\n"",
      ""10      4\n"",
      ""5       3\n"",
      ""32      3\n"",
      ""4       3\n"",
      ""34      3\n"",
      ""0       1\n"",
      ""33      1\n"",
      ""Name: nBondsM, dtype: int64\n"",
      ""bpol 的特征分布：\n"",
      ""39.795003    27\n"",
      ""41.981417    27\n"",
      ""41.887831    26\n"",
      ""18.178898    21\n"",
      ""32.796210    14\n"",
      ""             ..\n"",
      ""25.374898     1\n"",
      ""31.074933     1\n"",
      ""28.833347     1\n"",
      ""36.478175     1\n"",
      ""25.084105     1\n"",
      ""Name: bpol, Length: 1055, dtype: int64\n"",
      ""C1SP1 的特征分布：\n"",
      ""0    1878\n"",
      ""1      91\n"",
      ""2       5\n"",
      ""Name: C1SP1, dtype: int64\n"",
      ""C2SP1 的特征分布：\n"",
      ""0    1951\n"",
      ""1      17\n"",
      ""2       6\n"",
      ""Name: C2SP1, dtype: int64\n"",
      ""C1SP2 的特征分布：\n"",
      ""0     961\n"",
      ""1     605\n"",
      ""2     245\n"",
      ""3      78\n"",
      ""4      52\n"",
      ""5      17\n"",
      ""6       7\n"",
      ""10      4\n"",
      ""7       1\n"",
      ""8       1\n"",
      ""20      1\n"",
      ""9       1\n"",
      ""11      1\n"",
      ""Name: C1SP2, dtype: int64\n"",
      ""C2SP2 的特征分布：\n"",
      ""15    295\n"",
      ""16    272\n"",
      ""11    236\n"",
      ""10    179\n"",
      ""12    138\n"",
      ""13     93\n"",
      ""17     92\n"",
      ""9      87\n"",
      ""5      84\n"",
      ""8      72\n"",
      ""7      70\n"",
      ""14     68\n"",
      ""6      53\n"",
      ""18     49\n"",
      ""4      42\n"",
      ""22     31\n"",
      ""2      27\n"",
      ""3      20\n"",
      ""19     19\n"",
      ""1      16\n"",
      ""21     12\n"",
      ""20      8\n"",
      ""0       8\n"",
      ""23      2\n"",
      ""24      1\n"",
      ""Name: C2SP2, dtype: int64\n"",
      ""C3SP2 的特征分布：\n"",
      ""2     428\n"",
      ""4     416\n"",
      ""5     347\n"",
      ""3     341\n"",
      ""1     223\n"",
      ""6     108\n"",
      ""0      81\n"",
      ""7      25\n"",
      ""8       4\n"",
      ""12      1\n"",
      ""Name: C3SP2, dtype: int64\n"",
      ""C1SP3 的特征分布：\n"",
      ""0     435\n"",
      ""1     352\n"",
      ""2     300\n"",
      ""4     269\n"",
      ""5     212\n"",
      ""3     178\n"",
      ""6     159\n"",
      ""7      33\n"",
      ""8      27\n"",
      ""12      3\n"",
      ""11      2\n"",
      ""9       1\n"",
      ""22      1\n"",
      ""14      1\n"",
      ""13      1\n"",
      ""Name: C1SP3, dtype: int64\n"",
      ""C2SP3 的特征分布：\n"",
      ""0     676\n"",
      ""2     217\n"",
      ""3     215\n"",
      ""1     213\n"",
      ""4     210\n"",
      ""5     141\n"",
      ""6     101\n"",
      ""7      52\n"",
      ""10     44\n"",
      ""8      34\n"",
      ""9      24\n"",
      ""11     14\n"",
      ""18      6\n"",
      ""12      5\n"",
      ""13      5\n"",
      ""17      3\n"",
      ""14      3\n"",
      ""21      3\n"",
      ""16      3\n"",
      ""15      2\n"",
      ""41      1\n"",
      ""19      1\n"",
      ""20      1\n"",
      ""Name: C2SP3, dtype: int64\n"",
      ""C3SP3 的特征分布：\n"",
      ""0    1508\n"",
      ""1     243\n"",
      ""2      95\n"",
      ""3      64\n"",
      ""4      46\n"",
      ""5      17\n"",
      ""7       1\n"",
      ""Name: C3SP3, dtype: int64\n"",
      ""C4SP3 的特征分布：\n"",
      ""0    1704\n"",
      ""1     223\n"",
      ""2      47\n"",
      ""Name: C4SP3, dtype: int64\n"",
      ""SCH-3 的特征分布：\n"",
      ""0.000000    1961\n"",
      ""0.288675       9\n"",
      ""0.235702       4\n"",
      ""Name: SCH-3, dtype: int64\n"",
      ""SCH-4 的特征分布：\n"",
      ""0.000000    1943\n"",
      ""0.204124      12\n"",
      ""0.111111       7\n"",
      ""0.166667       5\n"",
      ""0.302749       3\n"",
      ""0.333333       1\n"",
      ""0.321114       1\n"",
      ""0.096225       1\n"",
      ""0.136083       1\n"",
      ""Name: SCH-4, dtype: int64\n"",
      ""SCH-5 的特征分布：\n"",
      ""0.000000    842\n"",
      ""0.078567    186\n"",
      ""0.096225    174\n"",
      ""0.144338    115\n"",
      ""0.064150    104\n"",
      ""           ... \n"",
      ""0.170103      1\n"",
      ""0.556186      1\n"",
      ""0.331927      1\n"",
      ""0.138889      1\n"",
      ""0.271018      1\n"",
      ""Name: SCH-5, Length: 71, dtype: int64\n"",
      ""SCH-6 的特征分布：\n"",
      ""0.268729    48\n"",
      ""0.280069    44\n"",
      ""0.206930    43\n"",
      ""0.392326    39\n"",
      ""0.264777    33\n"",
      ""            ..\n"",
      ""0.442729     1\n"",
      ""0.253934     1\n"",
      ""0.469416     1\n"",
      ""0.174055     1\n"",
      ""0.748285     1\n"",
      ""Name: SCH-6, Length: 484, dtype: int64\n"",
      ""SCH-7 的特征分布：\n"",
      ""0.297410    32\n"",
      ""0.617637    21\n"",
      ""0.943023    21\n"",
      ""0.611914    16\n"",
      ""0.472905    15\n"",
      ""            ..\n"",
      ""0.600296     1\n"",
      ""0.908037     1\n"",
      ""0.273705     1\n"",
      ""0.480836     1\n"",
      ""1.328220     1\n"",
      ""Name: SCH-7, Length: 997, dtype: int64\n"",
      ""VCH-3 的特征分布：\n"",
      ""0.000000    1961\n"",
      ""0.288675       9\n"",
      ""0.235702       4\n"",
      ""Name: VCH-3, dtype: int64\n"",
      ""VCH-4 的特征分布：\n"",
      ""0.000000    1943\n"",
      ""0.074536       7\n"",
      ""0.204124       6\n"",
      ""0.158114       5\n"",
      ""0.144338       4\n"",
      ""0.272076       2\n"",
      ""0.284518       1\n"",
      ""0.333333       1\n"",
      ""0.166667       1\n"",
      ""0.288580       1\n"",
      ""0.064550       1\n"",
      ""0.091287       1\n"",
      ""0.129099       1\n"",
      ""Name: VCH-4, dtype: int64\n"",
      ""VCH-5 的特征分布：\n"",
      ""0.000000    842\n"",
      ""0.022822    107\n"",
      ""0.076547     82\n"",
      ""0.027951     77\n"",
      ""0.111803     72\n"",
      ""           ... \n"",
      ""0.301699      1\n"",
      ""0.128414      1\n"",
      ""0.192886      1\n"",
      ""0.145802      1\n"",
      ""0.157275      1\n"",
      ""Name: VCH-5, Length: 207, dtype: int64\n"",
      ""VCH-6 的特征分布：\n"",
      ""0.087631    44\n"",
      ""0.048113    28\n"",
      ""0.107563    25\n"",
      ""0.237523    24\n"",
      ""0.054424    24\n"",
      ""            ..\n"",
      ""0.337900     1\n"",
      ""0.181673     1\n"",
      ""0.140952     1\n"",
      ""0.186416     1\n"",
      ""0.316703     1\n"",
      ""Name: VCH-6, Length: 811, dtype: int64\n"",
      ""VCH-7 的特征分布：\n"",
      ""0.067578    29\n"",
      ""0.720311    12\n"",
      ""0.568966    11\n"",
      ""0.092748    11\n"",
      ""0.134751    11\n"",
      ""            ..\n"",
      ""0.420887     1\n"",
      ""0.414611     1\n"",
      ""0.462615     1\n"",
      ""0.446577     1\n"",
      ""0.471397     1\n"",
      ""Name: VCH-7, Length: 1387, dtype: int64\n"",
      ""SC-3 的特征分布：\n"",
      ""1.380100    28\n"",
      ""1.562829    24\n"",
      ""2.141041    22\n"",
      ""1.924431    21\n"",
      ""2.226065    21\n"",
      ""            ..\n"",
      ""2.627440     1\n"",
      ""1.229496     1\n"",
      ""3.502642     1\n"",
      ""1.008862     1\n"",
      ""3.216356     1\n"",
      ""Name: SC-3, Length: 933, dtype: int64\n"",
      ""SC-4 的特征分布：\n"",
      ""0.000000    1426\n"",
      ""0.102062      83\n"",
      ""0.204124      69\n"",
      ""0.083333      61\n"",
      ""0.096225      55\n"",
      ""0.166667      49\n"",
      ""0.288675      42\n"",
      ""0.353553      35\n"",
      ""0.201184      27\n"",
      ""0.117851      27\n"",
      ""0.250000       9\n"",
      ""0.390737       8\n"",
      ""0.144338       8\n"",
      ""0.055556       7\n"",
      ""0.227062       6\n"",
      ""0.125000       5\n"",
      ""0.235702       4\n"",
      ""0.372008       3\n"",
      ""0.287457       3\n"",
      ""0.492799       3\n"",
      ""0.260110       3\n"",
      ""0.362172       3\n"",
      ""0.577350       3\n"",
      ""0.310395       3\n"",
      ""0.298113       3\n"",
      ""0.449778       2\n"",
      ""0.384900       2\n"",
      ""0.455342       2\n"",
      ""0.401293       2\n"",
      ""0.454124       1\n"",
      ""0.380901       1\n"",
      ""0.471405       1\n"",
      ""0.557678       1\n"",
      ""0.436887       1\n"",
      ""0.174231       1\n"",
      ""0.396690       1\n"",
      ""0.344913       1\n"",
      ""0.406526       1\n"",
      ""0.408248       1\n"",
      ""0.329124       1\n"",
      ""0.394338       1\n"",
      ""0.308302       1\n"",
      ""0.306186       1\n"",
      ""0.370791       1\n"",
      ""0.190450       1\n"",
      ""0.451184       1\n"",
      ""0.433013       1\n"",
      ""0.520220       1\n"",
      ""0.544628       1\n"",
      ""0.500000       1\n"",
      ""Name: SC-4, dtype: int64\n"",
      ""SC-5 的特征分布：\n"",
      ""0.274972    70\n"",
      ""0.395762    51\n"",
      ""0.247194    50\n"",
      ""0.224513    46\n"",
      ""0.354745    46\n"",
      ""            ..\n"",
      ""0.763054     1\n"",
      ""0.378917     1\n"",
      ""0.423946     1\n"",
      ""0.862645     1\n"",
      ""1.352765     1\n"",
      ""Name: SC-5, Length: 565, dtype: int64\n"",
      ""SC-6 的特征分布：\n"",
      ""0.000000    1476\n"",
      ""0.083333      85\n"",
      ""0.034021      78\n"",
      ""0.126680      55\n"",
      ""0.151375      30\n"",
      ""            ... \n"",
      ""0.344375       1\n"",
      ""0.556071       1\n"",
      ""0.580479       1\n"",
      ""0.510389       1\n"",
      ""0.451413       1\n"",
      ""Name: SC-6, Length: 68, dtype: int64\n"",
      ""VC-3 的特征分布：\n"",
      ""0.953123    18\n"",
      ""0.859693    14\n"",
      ""1.792308    12\n"",
      ""1.075544    11\n"",
      ""1.005594    10\n"",
      ""            ..\n"",
      ""1.061768     1\n"",
      ""1.064003     1\n"",
      ""0.433673     1\n"",
      ""0.525664     1\n"",
      ""1.234496     1\n"",
      ""Name: VC-3, Length: 1449, dtype: int64\n"",
      ""VC-4 的特征分布：\n"",
      ""0.000000    1426\n"",
      ""0.088388      83\n"",
      ""0.055902      57\n"",
      ""0.062500      48\n"",
      ""0.022808      46\n"",
      ""            ... \n"",
      ""0.034118       1\n"",
      ""0.077729       1\n"",
      ""0.092556       1\n"",
      ""0.316228       1\n"",
      ""0.426777       1\n"",
      ""Name: VC-4, Length: 81, dtype: int64\n"",
      ""VC-5 的特征分布：\n"",
      ""0.140258    50\n"",
      ""0.174055    45\n"",
      ""0.143889    38\n"",
      ""0.093227    38\n"",
      ""0.336701    34\n"",
      ""            ..\n"",
      ""0.118802     1\n"",
      ""0.136442     1\n"",
      ""0.131314     1\n"",
      ""0.104340     1\n"",
      ""0.246933     1\n"",
      ""Name: VC-5, Length: 1077, dtype: int64\n"",
      ""VC-6 的特征分布：\n"",
      ""0.000000    1476\n"",
      ""0.022097      78\n"",
      ""0.054536      54\n"",
      ""0.009815      36\n"",
      ""0.041667      33\n"",
      ""            ... \n"",
      ""0.011411       1\n"",
      ""0.127047       1\n"",
      ""0.050773       1\n"",
      ""0.050000       1\n"",
      ""0.086566       1\n"",
      ""Name: VC-6, Length: 115, dtype: int64\n"",
      ""SPC-4 的特征分布：\n"",
      ""5.085799    18\n"",
      ""4.314172    14\n"",
      ""4.748587    14\n"",
      ""6.286731    13\n"",
      ""3.338816    12\n"",
      ""            ..\n"",
      ""2.519516     1\n"",
      ""1.777256     1\n"",
      ""1.140119     1\n"",
      ""8.245915     1\n"",
      ""7.019322     1\n"",
      ""Name: SPC-4, Length: 1244, dtype: int64\n"",
      ""SPC-5 的特征分布：\n"",
      ""8.759694     16\n"",
      ""6.610987     14\n"",
      ""7.792522     13\n"",
      ""10.853190    11\n"",
      ""4.777567     11\n"",
      ""             ..\n"",
      ""6.338709      1\n"",
      ""5.683549      1\n"",
      ""4.490965      1\n"",
      ""5.183583      1\n"",
      ""12.176128     1\n"",
      ""Name: SPC-5, Length: 1316, dtype: int64\n"",
      ""SPC-6 的特征分布：\n"",
      ""15.493957    11\n"",
      ""13.637694    11\n"",
      ""8.621753     10\n"",
      ""11.631068    10\n"",
      ""6.711544     10\n"",
      ""             ..\n"",
      ""9.598658      1\n"",
      ""8.464242      1\n"",
      ""8.537464      1\n"",
      ""10.033039     1\n"",
      ""17.412329     1\n"",
      ""Name: SPC-6, Length: 1401, dtype: int64\n"",
      ""VPC-4 的特征分布：\n"",
      ""2.113032    14\n"",
      ""1.465019    12\n"",
      ""4.831266    10\n"",
      ""2.416269    10\n"",
      ""2.029142     9\n"",
      ""            ..\n"",
      ""1.571064     1\n"",
      ""1.233653     1\n"",
      ""1.246622     1\n"",
      ""1.473356     1\n"",
      ""2.667180     1\n"",
      ""Name: VPC-4, Length: 1658, dtype: int64\n"",
      ""VPC-5 的特征分布：\n"",
      ""1.935222    9\n"",
      ""3.713592    9\n"",
      ""3.160283    7\n"",
      ""3.167385    7\n"",
      ""1.984719    7\n"",
      ""           ..\n"",
      ""1.795824    1\n"",
      ""1.917972    1\n"",
      ""2.437649    1\n"",
      ""2.120052    1\n"",
      ""4.008055    1\n"",
      ""Name: VPC-5, Length: 1751, dtype: int64\n"",
      ""VPC-6 的特征分布：\n"",
      ""2.421415    9\n"",
      ""4.030699    7\n"",
      ""4.550592    6\n"",
      ""4.119119    5\n"",
      ""4.575568    5\n"",
      ""           ..\n"",
      ""4.318411    1\n"",
      ""2.579739    1\n"",
      ""2.615553    1\n"",
      ""3.305489    1\n"",
      ""5.083699    1\n"",
      ""Name: VPC-6, Length: 1819, dtype: int64\n"",
      ""SP-0 的特征分布：\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""23.492989    52\n"",
      ""14.275656    46\n"",
      ""14.982763    42\n"",
      ""13.405413    35\n"",
      ""22.622745    34\n"",
      ""             ..\n"",
      ""15.284093     1\n"",
      ""19.371668     1\n"",
      ""12.292529     1\n"",
      ""21.725404     1\n"",
      ""26.984552     1\n"",
      ""Name: SP-0, Length: 573, dtype: int64\n"",
      ""SP-1 的特征分布：\n"",
      ""10.079719    23\n"",
      ""16.153000    22\n"",
      ""16.580520    21\n"",
      ""9.558551     19\n"",
      ""9.969234     18\n"",
      ""             ..\n"",
      ""10.809663     1\n"",
      ""12.562882     1\n"",
      ""10.168234     1\n"",
      ""13.903817     1\n"",
      ""18.870143     1\n"",
      ""Name: SP-1, Length: 991, dtype: int64\n"",
      ""SP-2 的特征分布：\n"",
      ""9.269671     14\n"",
      ""14.473194    11\n"",
      ""10.536718    11\n"",
      ""8.420275     10\n"",
      ""8.761923     10\n"",
      ""             ..\n"",
      ""12.787228     1\n"",
      ""13.409090     1\n"",
      ""14.211959     1\n"",
      ""14.565513     1\n"",
      ""19.486828     1\n"",
      ""Name: SP-2, Length: 1347, dtype: int64\n"",
      ""SP-3 的特征分布：\n"",
      ""9.816081     11\n"",
      ""12.214228    11\n"",
      ""7.053206     10\n"",
      ""7.499941     10\n"",
      ""7.956569      9\n"",
      ""             ..\n"",
      ""6.930694      1\n"",
      ""7.428511      1\n"",
      ""8.006736      1\n"",
      ""6.738286      1\n"",
      ""15.310538     1\n"",
      ""Name: SP-3, Length: 1418, dtype: int64\n"",
      ""SP-4 的特征分布：\n"",
      ""8.491143     11\n"",
      ""5.966542     10\n"",
      ""6.618297     10\n"",
      ""10.083315    10\n"",
      ""6.277991      9\n"",
      ""             ..\n"",
      ""7.227924      1\n"",
      ""10.584139     1\n"",
      ""10.426736     1\n"",
      ""9.964422      1\n"",
      ""13.619529     1\n"",
      ""Name: SP-4, Length: 1446, dtype: int64\n"",
      ""SP-5 的特征分布：\n"",
      ""7.196379     11\n"",
      ""4.933871     10\n"",
      ""8.856915     10\n"",
      ""5.056080     10\n"",
      ""5.609613      9\n"",
      ""             ..\n"",
      ""6.148128      1\n"",
      ""8.552390      1\n"",
      ""8.766361      1\n"",
      ""8.426421      1\n"",
      ""11.166916     1\n"",
      ""Name: SP-5, Length: 1455, dtype: int64\n"",
      ""SP-6 的特征分布：\n"",
      ""5.491107    11\n"",
      ""3.475511    10\n"",
      ""6.137207    10\n"",
      ""3.635495    10\n"",
      ""3.832284     9\n"",
      ""            ..\n"",
      ""4.794133     1\n"",
      ""4.851896     1\n"",
      ""4.065309     1\n"",
      ""7.035977     1\n"",
      ""8.094804     1\n"",
      ""Name: SP-6, Length: 1456, dtype: int64\n"",
      ""SP-7 的特征分布：\n"",
      ""4.246685    11\n"",
      ""2.371073    10\n"",
      ""4.671124    10\n"",
      ""2.347048    10\n"",
      ""2.680822     9\n"",
      ""            ..\n"",
      ""2.817331     1\n"",
      ""5.184310     1\n"",
      ""4.735301     1\n"",
      ""4.400523     1\n"",
      ""6.101033     1\n"",
      ""Name: SP-7, Length: 1457, dtype: int64\n"",
      ""VP-0 的特征分布：\n"",
      ""10.946041    12\n"",
      ""20.569468     9\n"",
      ""19.338183     8\n"",
      ""19.501320     8\n"",
      ""9.161204      7\n"",
      ""             ..\n"",
      ""11.329346     1\n"",
      ""12.743559     1\n"",
      ""14.157773     1\n"",
      ""15.571986     1\n"",
      ""21.048223     1\n"",
      ""Name: VP-0, Length: 1526, dtype: int64\n"",
      ""VP-1 的特征分布：\n"",
      ""12.358058    9\n"",
      ""12.439454    7\n"",
      ""13.299660    5\n"",
      ""9.966056     5\n"",
      ""12.799660    5\n"",
      ""            ..\n"",
      ""8.920822     1\n"",
      ""7.920822     1\n"",
      ""8.360162     1\n"",
      ""7.360162     1\n"",
      ""12.988256    1\n"",
      ""Name: VP-1, Length: 1682, dtype: int64\n"",
      ""VP-2 的特征分布：\n"",
      ""9.885201     7\n"",
      ""9.802668     5\n"",
      ""10.022858    5\n"",
      ""9.485001     4\n"",
      ""9.486370     4\n"",
      ""            ..\n"",
      ""4.476771     1\n"",
      ""4.681842     1\n"",
      ""4.295144     1\n"",
      ""5.727380     1\n"",
      ""10.807150    1\n"",
      ""Name: VP-2, Length: 1790, dtype: int64\n"",
      ""VP-3 的特征分布：\n"",
      ""7.188019    6\n"",
      ""7.467076    4\n"",
      ""7.518796    4\n"",
      ""7.217076    4\n"",
      ""5.899379    4\n"",
      ""           ..\n"",
      ""3.222984    1\n"",
      ""3.708080    1\n"",
      ""3.714798    1\n"",
      ""3.202202    1\n"",
      ""7.583319    1\n"",
      ""Name: VP-3, Length: 1849, dtype: int64\n"",
      ""VP-4 的特征分布：\n"",
      ""5.147974    6\n"",
      ""5.603959    4\n"",
      ""6.035097    3\n"",
      ""6.926784    3\n"",
      ""4.773213    3\n"",
      ""           ..\n"",
      ""2.646494    1\n"",
      ""2.653879    1\n"",
      ""2.494578    1\n"",
      ""2.697738    1\n"",
      ""5.757921    1\n"",
      ""Name: VP-4, Length: 1868, dtype: int64\n"",
      ""VP-5 的特征分布：\n"",
      ""3.609842    6\n"",
      ""3.312299    3\n"",
      ""3.536426    3\n"",
      ""2.689834    3\n"",
      ""3.446992    3\n"",
      ""           ..\n"",
      ""1.878595    1\n"",
      ""1.650125    1\n"",
      ""1.862545    1\n"",
      ""1.883120    1\n"",
      ""4.156763    1\n"",
      ""Name: VP-5, Length: 1873, dtype: int64\n"",
      ""VP-6 的特征分布：\n"",
      ""2.058375    6\n"",
      ""2.763674    3\n"",
      ""2.148123    3\n"",
      ""4.057976    3\n"",
      ""2.198169    3\n"",
      ""           ..\n"",
      ""1.073645    1\n"",
      ""1.079768    1\n"",
      ""0.969440    1\n"",
      ""1.071312    1\n"",
      ""2.610182    1\n"",
      ""Name: VP-6, Length: 1876, dtype: int64\n"",
      ""VP-7 的特征分布：\n"",
      ""1.159282    6\n"",
      ""1.757157    3\n"",
      ""1.551236    3\n"",
      ""0.948452    3\n"",
      ""1.422543    3\n"",
      ""           ..\n"",
      ""0.634384    1\n"",
      ""0.538492    1\n"",
      ""0.606982    1\n"",
      ""0.618800    1\n"",
      ""1.720648    1\n"",
      ""Name: VP-7, Length: 1877, dtype: int64\n"",
      ""CrippenLogP 的特征分布：\n"",
      ""6.00918    8\n"",
      ""6.11878    8\n"",
      ""4.07958    7\n"",
      ""6.50728    7\n"",
      ""6.11718    6\n"",
      ""          ..\n"",
      ""3.19378    1\n"",
      ""3.03768    1\n"",
      ""2.86128    1\n"",
      ""4.24948    1\n"",
      ""6.05817    1\n"",
      ""Name: CrippenLogP, Length: 1607, dtype: int64\n"",
      ""CrippenMR 的特征分布：\n"",
      ""138.6108    8\n"",
      ""133.3328    7\n"",
      ""92.3166     7\n"",
      ""137.9278    6\n"",
      ""85.7468     6\n"",
      ""           ..\n"",
      ""82.3510     1\n"",
      ""96.8900     1\n"",
      ""77.7340     1\n"",
      ""89.0200     1\n"",
      ""143.4416    1\n"",
      ""Name: CrippenMR, Length: 1629, dtype: int64\n"",
      ""ECCEN 的特征分布：\n"",
      ""375     21\n"",
      ""383     21\n"",
      ""358     16\n"",
      ""373     16\n"",
      ""364     16\n"",
      ""        ..\n"",
      ""632      1\n"",
      ""775      1\n"",
      ""880      1\n"",
      ""792      1\n"",
      ""1338     1\n"",
      ""Name: ECCEN, Length: 788, dtype: int64\n"",
      ""nHBd 的特征分布：\n"",
      ""2     936\n"",
      ""1     626\n"",
      ""3     254\n"",
      ""0     125\n"",
      ""4      20\n"",
      ""6       3\n"",
      ""17      3\n"",
      ""5       2\n"",
      ""8       1\n"",
      ""46      1\n"",
      ""16      1\n"",
      ""18      1\n"",
      ""15      1\n"",
      ""Name: nHBd, dtype: int64\n"",
      ""nwHBd 的特征分布：\n"",
      ""0    1939\n"",
      ""1      34\n"",
      ""2       1\n"",
      ""Name: nwHBd, dtype: int64\n"",
      ""nHBa 的特征分布：\n"",
      ""5     388\n"",
      ""4     368\n"",
      ""6     342\n"",
      ""3     295\n"",
      ""2     218\n"",
      ""7     189\n"",
      ""8      82\n"",
      ""9      39\n"",
      ""1      25\n"",
      ""10     15\n"",
      ""27      3\n"",
      ""14      2\n"",
      ""12      2\n"",
      ""11      1\n"",
      ""68      1\n"",
      ""25      1\n"",
      ""28      1\n"",
      ""29      1\n"",
      ""0       1\n"",
      ""Name: nHBa, dtype: int64\n"",
      ""nwHBa 的特征分布：\n"",
      ""20    244\n"",
      ""18    229\n"",
      ""13    176\n"",
      ""15    173\n"",
      ""21    166\n"",
      ""12    130\n"",
      ""14    127\n"",
      ""16    108\n"",
      ""17    103\n"",
      ""19     61\n"",
      ""9      55\n"",
      ""23     53\n"",
      ""24     48\n"",
      ""10     48\n"",
      ""22     45\n"",
      ""28     37\n"",
      ""8      30\n"",
      ""4      28\n"",
      ""7      28\n"",
      ""11     26\n"",
      ""6      19\n"",
      ""2       9\n"",
      ""27      7\n"",
      ""26      6\n"",
      ""25      6\n"",
      ""29      4\n"",
      ""5       3\n"",
      ""3       3\n"",
      ""0       1\n"",
      ""32      1\n"",
      ""Name: nwHBa, dtype: int64\n"",
      ""nHBint2 的特征分布：\n"",
      ""0     1545\n"",
      ""1      316\n"",
      ""2       72\n"",
      ""3       16\n"",
      ""5        9\n"",
      ""6        5\n"",
      ""22       4\n"",
      ""4        3\n"",
      ""68       1\n"",
      ""21       1\n"",
      ""16       1\n"",
      ""12       1\n"",
      ""Name: nHBint2, dtype: int64\n"",
      ""nHBint3 的特征分布：\n"",
      ""0     1567\n"",
      ""1      276\n"",
      ""2       98\n"",
      ""3       13\n"",
      ""4        7\n"",
      ""5        4\n"",
      ""27       4\n"",
      ""29       2\n"",
      ""8        1\n"",
      ""9        1\n"",
      ""59       1\n"",
      ""Name: nHBint3, dtype: int64\n"",
      ""nHBint4 的特征分布：\n"",
      ""0    1142\n"",
      ""1     546\n"",
      ""2     145\n"",
      ""3      58\n"",
      ""4      43\n"",
      ""5      28\n"",
      ""6       8\n"",
      ""8       2\n"",
      ""9       2\n"",
      ""Name: nHBint4, dtype: int64\n"",
      ""nHBint5 的特征分布：\n"",
      ""0     1261\n"",
      ""1      516\n"",
      ""2      118\n"",
      ""3       50\n"",
      ""4       14\n"",
      ""5        3\n"",
      ""15       3\n"",
      ""6        2\n"",
      ""9        1\n"",
      ""10       1\n"",
      ""11       1\n"",
      ""39       1\n"",
      ""12       1\n"",
      ""18       1\n"",
      ""17       1\n"",
      ""Name: nHBint5, dtype: int64\n"",
      ""nHBint6 的特征分布：\n"",
      ""0     1063\n"",
      ""1      667\n"",
      ""2      154\n"",
      ""3       63\n"",
      ""4       12\n"",
      ""5        5\n"",
      ""34       2\n"",
      ""35       2\n"",
      ""10       1\n"",
      ""7        1\n"",
      ""86       1\n"",
      ""30       1\n"",
      ""36       1\n"",
      ""12       1\n"",
      ""Name: nHBint6, dtype: int64\n"",
      ""nHBint7 的特征分布：\n"",
      ""0     1286\n"",
      ""1      483\n"",
      ""2      133\n"",
      ""3       39\n"",
      ""4       21\n"",
      ""5        5\n"",
      ""12       3\n"",
      ""49       1\n"",
      ""9        1\n"",
      ""15       1\n"",
      ""14       1\n"",
      ""Name: nHBint7, dtype: int64\n"",
      ""nHBint8 的特征分布：\n"",
      ""0     1447\n"",
      ""1      380\n"",
      ""2       93\n"",
      ""3       27\n"",
      ""4       15\n"",
      ""27       3\n"",
      ""5        2\n"",
      ""6        2\n"",
      ""7        1\n"",
      ""88       1\n"",
      ""21       1\n"",
      ""28       1\n"",
      ""22       1\n"",
      ""Name: nHBint8, dtype: int64\n"",
      ""nHBint9 的特征分布：\n"",
      ""0     1490\n"",
      ""1      251\n"",
      ""2      173\n"",
      ""3       26\n"",
      ""7       11\n"",
      ""4        5\n"",
      ""5        5\n"",
      ""29       4\n"",
      ""6        3\n"",
      ""9        1\n"",
      ""10       1\n"",
      ""8        1\n"",
      ""93       1\n"",
      ""31       1\n"",
      ""27       1\n"",
      ""Name: nHBint9, dtype: int64\n"",
      ""nHBint10 的特征分布：\n"",
      ""0     990\n"",
      ""2     474\n"",
      ""1     355\n"",
      ""3     101\n"",
      ""4      36\n"",
      ""5       8\n"",
      ""9       3\n"",
      ""6       2\n"",
      ""10      1\n"",
      ""50      1\n"",
      ""8       1\n"",
      ""12      1\n"",
      ""15      1\n"",
      ""Name: nHBint10, dtype: int64\n"",
      ""nHsOH 的特征分布：\n"",
      ""2    847\n"",
      ""1    530\n"",
      ""0    426\n"",
      ""3    157\n"",
      ""4     10\n"",
      ""6      2\n"",
      ""5      2\n"",
      ""Name: nHsOH, dtype: int64\n"",
      ""nHdNH 的特征分布：\n"",
      ""0    1959\n"",
      ""1       9\n"",
      ""2       5\n"",
      ""8       1\n"",
      ""Name: nHdNH, dtype: int64\n"",
      ""nHsSH 的特征分布：\n"",
      ""0    1973\n"",
      ""1       1\n"",
      ""Name: nHsSH, dtype: int64\n"",
      ""nHsNH2 的特征分布：\n"",
      ""0     1924\n"",
      ""1       43\n"",
      ""5        5\n"",
      ""12       1\n"",
      ""4        1\n"",
      ""Name: nHsNH2, dtype: int64\n"",
      ""nHssNH 的特征分布：\n"",
      ""0     1639\n"",
      ""1      261\n"",
      ""2       60\n"",
      ""3        5\n"",
      ""10       5\n"",
      ""4        2\n"",
      ""26       1\n"",
      ""9        1\n"",
      ""Name: nHssNH, dtype: int64\n"",
      ""nHaaNH 的特征分布：\n"",
      ""0    1831\n"",
      ""1     139\n"",
      ""2       4\n"",
      ""Name: nHaaNH, dtype: int64\n"",
      ""nHsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nHsNH3p, dtype: int64\n"",
      ""nHssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nHssNH2p, dtype: int64\n"",
      ""nHsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nHsssNHp, dtype: int64\n"",
      ""nHtCH 的特征分布：\n"",
      ""0    1957\n"",
      ""1      17\n"",
      ""Name: nHtCH, dtype: int64\n"",
      ""nHdCH2 的特征分布：\n"",
      ""0    1928\n"",
      ""1      46\n"",
      ""Name: nHdCH2, dtype: int64\n"",
      ""nHdsCH 的特征分布：\n"",
      ""0    1536\n"",
      ""2     238\n"",
      ""1     174\n"",
      ""3      19\n"",
      ""4       4\n"",
      ""5       2\n"",
      ""6       1\n"",
      ""Name: nHdsCH, dtype: int64\n"",
      ""nHaaCH 的特征分布：\n"",
      ""11    405\n"",
      ""6     208\n"",
      ""10    203\n"",
      ""7     192\n"",
      ""8     175\n"",
      ""12    115\n"",
      ""3     111\n"",
      ""5      96\n"",
      ""4      91\n"",
      ""9      90\n"",
      ""13     79\n"",
      ""0      57\n"",
      ""2      50\n"",
      ""15     41\n"",
      ""14     31\n"",
      ""1      13\n"",
      ""16      8\n"",
      ""17      4\n"",
      ""18      4\n"",
      ""19      1\n"",
      ""Name: nHaaCH, dtype: int64\n"",
      ""nHCHnX 的特征分布：\n"",
      ""0    1935\n"",
      ""1      38\n"",
      ""2       1\n"",
      ""Name: nHCHnX, dtype: int64\n"",
      ""nHCsats 的特征分布：\n"",
      ""0     607\n"",
      ""2     173\n"",
      ""7     168\n"",
      ""4     152\n"",
      ""9     145\n"",
      ""8     122\n"",
      ""6     110\n"",
      ""10    108\n"",
      ""3      96\n"",
      ""5      68\n"",
      ""11     64\n"",
      ""12     39\n"",
      ""14     33\n"",
      ""13     20\n"",
      ""16     15\n"",
      ""15     11\n"",
      ""20      9\n"",
      ""1       5\n"",
      ""21      4\n"",
      ""25      4\n"",
      ""17      3\n"",
      ""22      3\n"",
      ""18      3\n"",
      ""35      3\n"",
      ""36      2\n"",
      ""24      2\n"",
      ""23      1\n"",
      ""27      1\n"",
      ""19      1\n"",
      ""64      1\n"",
      ""34      1\n"",
      ""Name: nHCsats, dtype: int64\n"",
      ""nHCsatu 的特征分布：\n"",
      ""0     1218\n"",
      ""1      425\n"",
      ""2      258\n"",
      ""3       36\n"",
      ""4       21\n"",
      ""5        5\n"",
      ""6        3\n"",
      ""10       3\n"",
      ""12       2\n"",
      ""8        1\n"",
      ""20       1\n"",
      ""9        1\n"",
      ""Name: nHCsatu, dtype: int64\n"",
      ""nHAvin 的特征分布：\n"",
      ""0    1668\n"",
      ""1     262\n"",
      ""2      44\n"",
      ""Name: nHAvin, dtype: int64\n"",
      ""nHother 的特征分布：\n"",
      ""11    329\n"",
      ""6     206\n"",
      ""7     198\n"",
      ""10    189\n"",
      ""8     169\n"",
      ""13    151\n"",
      ""12    148\n"",
      ""3     118\n"",
      ""9     115\n"",
      ""4      83\n"",
      ""5      69\n"",
      ""2      52\n"",
      ""14     43\n"",
      ""15     29\n"",
      ""1      25\n"",
      ""17     23\n"",
      ""0      12\n"",
      ""16      8\n"",
      ""18      3\n"",
      ""19      2\n"",
      ""20      2\n"",
      ""Name: nHother, dtype: int64\n"",
      ""nHmisc 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nHmisc, dtype: int64\n"",
      ""nsLi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsLi, dtype: int64\n"",
      ""nssBe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssBe, dtype: int64\n"",
      ""nssssBem 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssBem, dtype: int64\n"",
      ""nsBH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsBH2, dtype: int64\n"",
      ""nssBH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssBH, dtype: int64\n"",
      ""nsssB 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssB, dtype: int64\n"",
      ""nssssBm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssBm, dtype: int64\n"",
      ""nsCH3 的特征分布：\n"",
      ""0     719\n"",
      ""1     628\n"",
      ""2     378\n"",
      ""3     153\n"",
      ""4      64\n"",
      ""5      17\n"",
      ""6       7\n"",
      ""9       3\n"",
      ""8       2\n"",
      ""7       1\n"",
      ""11      1\n"",
      ""10      1\n"",
      ""Name: nsCH3, dtype: int64\n"",
      ""ndCH2 的特征分布：\n"",
      ""0    1928\n"",
      ""1      46\n"",
      ""Name: ndCH2, dtype: int64\n"",
      ""nssCH2 的特征分布：\n"",
      ""0     503\n"",
      ""1     219\n"",
      ""2     196\n"",
      ""7     183\n"",
      ""4     168\n"",
      ""8     135\n"",
      ""6     134\n"",
      ""5     123\n"",
      ""3     110\n"",
      ""9      90\n"",
      ""10     38\n"",
      ""11     17\n"",
      ""13     14\n"",
      ""12     14\n"",
      ""14      9\n"",
      ""15      6\n"",
      ""17      5\n"",
      ""18      4\n"",
      ""16      2\n"",
      ""20      1\n"",
      ""39      1\n"",
      ""19      1\n"",
      ""21      1\n"",
      ""Name: nssCH2, dtype: int64\n"",
      ""ntCH 的特征分布：\n"",
      ""0    1957\n"",
      ""1      17\n"",
      ""Name: ntCH, dtype: int64\n"",
      ""ndsCH 的特征分布：\n"",
      ""0    1536\n"",
      ""2     238\n"",
      ""1     174\n"",
      ""3      19\n"",
      ""4       4\n"",
      ""5       2\n"",
      ""6       1\n"",
      ""Name: ndsCH, dtype: int64\n"",
      ""naaCH 的特征分布：\n"",
      ""11    405\n"",
      ""6     208\n"",
      ""10    203\n"",
      ""7     192\n"",
      ""8     175\n"",
      ""12    115\n"",
      ""3     111\n"",
      ""5      96\n"",
      ""4      91\n"",
      ""9      90\n"",
      ""13     79\n"",
      ""0      57\n"",
      ""2      50\n"",
      ""15     41\n"",
      ""14     31\n"",
      ""1      13\n"",
      ""16      8\n"",
      ""17      4\n"",
      ""18      4\n"",
      ""19      1\n"",
      ""Name: naaCH, dtype: int64\n"",
      ""nsssCH 的特征分布：\n"",
      ""0     1197\n"",
      ""1      315\n"",
      ""2      188\n"",
      ""3      146\n"",
      ""4       61\n"",
      ""5       40\n"",
      ""8       10\n"",
      ""6        9\n"",
      ""12       4\n"",
      ""7        1\n"",
      ""20       1\n"",
      ""11       1\n"",
      ""13       1\n"",
      ""Name: nsssCH, dtype: int64\n"",
      ""nddC 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nddC, dtype: int64\n"",
      ""ntsC 的特征分布：\n"",
      ""0    1872\n"",
      ""1      91\n"",
      ""2      11\n"",
      ""Name: ntsC, dtype: int64\n"",
      ""ndssC 的特征分布：\n"",
      ""0     755\n"",
      ""1     504\n"",
      ""2     352\n"",
      ""3     242\n"",
      ""4      89\n"",
      ""5      11\n"",
      ""7       7\n"",
      ""6       6\n"",
      ""12      4\n"",
      ""11      2\n"",
      ""28      1\n"",
      ""8       1\n"",
      ""Name: ndssC, dtype: int64\n"",
      ""naasC 的特征分布：\n"",
      ""7     476\n"",
      ""5     354\n"",
      ""6     324\n"",
      ""4     234\n"",
      ""8     215\n"",
      ""3     161\n"",
      ""2      83\n"",
      ""9      63\n"",
      ""0      53\n"",
      ""10      6\n"",
      ""1       3\n"",
      ""14      1\n"",
      ""12      1\n"",
      ""Name: naasC, dtype: int64\n"",
      ""naaaC 的特征分布：\n"",
      ""0    1273\n"",
      ""2     604\n"",
      ""4      50\n"",
      ""6      21\n"",
      ""1      19\n"",
      ""3       7\n"",
      ""Name: naaaC, dtype: int64\n"",
      ""nssssC 的特征分布：\n"",
      ""0    1538\n"",
      ""1     346\n"",
      ""2      75\n"",
      ""3      15\n"",
      ""Name: nssssC, dtype: int64\n"",
      ""nsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsNH3p, dtype: int64\n"",
      ""nsNH2 的特征分布：\n"",
      ""0     1924\n"",
      ""1       43\n"",
      ""5        5\n"",
      ""12       1\n"",
      ""4        1\n"",
      ""Name: nsNH2, dtype: int64\n"",
      ""nssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssNH2p, dtype: int64\n"",
      ""ndNH 的特征分布：\n"",
      ""0    1959\n"",
      ""1       9\n"",
      ""2       5\n"",
      ""8       1\n"",
      ""Name: ndNH, dtype: int64\n"",
      ""nssNH 的特征分布：\n"",
      ""0     1639\n"",
      ""1      261\n"",
      ""2       60\n"",
      ""3        5\n"",
      ""10       5\n"",
      ""4        2\n"",
      ""26       1\n"",
      ""9        1\n"",
      ""Name: nssNH, dtype: int64\n"",
      ""naaNH 的特征分布：\n"",
      ""0    1831\n"",
      ""1     139\n"",
      ""2       4\n"",
      ""Name: naaNH, dtype: int64\n"",
      ""ntN 的特征分布：\n"",
      ""0    1895\n"",
      ""1      74\n"",
      ""2       5\n"",
      ""Name: ntN, dtype: int64\n"",
      ""nsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssNHp, dtype: int64\n"",
      ""ndsN 的特征分布：\n"",
      ""0    1850\n"",
      ""1     118\n"",
      ""2       6\n"",
      ""Name: ndsN, dtype: int64\n"",
      ""naaN 的特征分布：\n"",
      ""0    1497\n"",
      ""1     302\n"",
      ""2     135\n"",
      ""3      31\n"",
      ""4       6\n"",
      ""5       2\n"",
      ""6       1\n"",
      ""Name: naaN, dtype: int64\n"",
      ""nsssN 的特征分布：\n"",
      ""0    1093\n"",
      ""1     740\n"",
      ""2     101\n"",
      ""3      40\n"",
      ""Name: nsssN, dtype: int64\n"",
      ""nddsN 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nddsN, dtype: int64\n"",
      ""naasN 的特征分布：\n"",
      ""0    1783\n"",
      ""1     187\n"",
      ""2       4\n"",
      ""Name: naasN, dtype: int64\n"",
      ""nssssNp 的特征分布：\n"",
      ""0    1973\n"",
      ""1       1\n"",
      ""Name: nssssNp, dtype: int64\n"",
      ""nsOH 的特征分布：\n"",
      ""2    847\n"",
      ""1    530\n"",
      ""0    426\n"",
      ""3    157\n"",
      ""4     10\n"",
      ""6      2\n"",
      ""5      2\n"",
      ""Name: nsOH, dtype: int64\n"",
      ""ndO 的特征分布：\n"",
      ""0     924\n"",
      ""1     653\n"",
      ""2     308\n"",
      ""3      48\n"",
      ""4      32\n"",
      ""10      5\n"",
      ""6       2\n"",
      ""20      1\n"",
      ""9       1\n"",
      ""Name: ndO, dtype: int64\n"",
      ""nssO 的特征分布：\n"",
      ""0    934\n"",
      ""1    558\n"",
      ""2    347\n"",
      ""3    113\n"",
      ""4     19\n"",
      ""5      3\n"",
      ""Name: nssO, dtype: int64\n"",
      ""naaO 的特征分布：\n"",
      ""0    1689\n"",
      ""1     270\n"",
      ""2      14\n"",
      ""3       1\n"",
      ""Name: naaO, dtype: int64\n"",
      ""naOm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: naOm, dtype: int64\n"",
      ""nsOm 的特征分布：\n"",
      ""0    1956\n"",
      ""1      15\n"",
      ""2       3\n"",
      ""Name: nsOm, dtype: int64\n"",
      ""nsF 的特征分布：\n"",
      ""0    1648\n"",
      ""1     201\n"",
      ""3      70\n"",
      ""2      41\n"",
      ""4       7\n"",
      ""5       4\n"",
      ""6       3\n"",
      ""Name: nsF, dtype: int64\n"",
      ""nsSiH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsSiH3, dtype: int64\n"",
      ""nssSiH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssSiH2, dtype: int64\n"",
      ""nsssSiH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssSiH, dtype: int64\n"",
      ""nssssSi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssSi, dtype: int64\n"",
      ""nsPH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsPH2, dtype: int64\n"",
      ""nssPH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssPH, dtype: int64\n"",
      ""nsssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssP, dtype: int64\n"",
      ""ndsssP 的特征分布：\n"",
      ""0    1972\n"",
      ""1       2\n"",
      ""Name: ndsssP, dtype: int64\n"",
      ""nddsP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nddsP, dtype: int64\n"",
      ""nsssssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssssP, dtype: int64\n"",
      ""nsSH 的特征分布：\n"",
      ""0    1973\n"",
      ""1       1\n"",
      ""Name: nsSH, dtype: int64\n"",
      ""ndS 的特征分布：\n"",
      ""0    1941\n"",
      ""1      33\n"",
      ""Name: ndS, dtype: int64\n"",
      ""nssS 的特征分布：\n"",
      ""0    1753\n"",
      ""1     202\n"",
      ""2      18\n"",
      ""6       1\n"",
      ""Name: nssS, dtype: int64\n"",
      ""naaS 的特征分布：\n"",
      ""0    1787\n"",
      ""1     169\n"",
      ""2      18\n"",
      ""Name: naaS, dtype: int64\n"",
      ""ndssS 的特征分布：\n"",
      ""0    1973\n"",
      ""1       1\n"",
      ""Name: ndssS, dtype: int64\n"",
      ""nddssS 的特征分布：\n"",
      ""0    1851\n"",
      ""1     123\n"",
      ""Name: nddssS, dtype: int64\n"",
      ""nssssssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssssS, dtype: int64\n"",
      ""nSm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nSm, dtype: int64\n"",
      ""nsCl 的特征分布：\n"",
      ""0    1801\n"",
      ""1     152\n"",
      ""2      19\n"",
      ""6       1\n"",
      ""4       1\n"",
      ""Name: nsCl, dtype: int64\n"",
      ""nsGeH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsGeH3, dtype: int64\n"",
      ""nssGeH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssGeH2, dtype: int64\n"",
      ""nsssGeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssGeH, dtype: int64\n"",
      ""nssssGe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssGe, dtype: int64\n"",
      ""nsAsH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsAsH2, dtype: int64\n"",
      ""nssAsH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssAsH, dtype: int64\n"",
      ""nsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssAs, dtype: int64\n"",
      ""ndsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: ndsssAs, dtype: int64\n"",
      ""nddsAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nddsAs, dtype: int64\n"",
      ""nsssssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssssAs, dtype: int64\n"",
      ""nsSeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsSeH, dtype: int64\n"",
      ""ndSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: ndSe, dtype: int64\n"",
      ""nssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssSe, dtype: int64\n"",
      ""naaSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: naaSe, dtype: int64\n"",
      ""ndssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: ndssSe, dtype: int64\n"",
      ""nssssssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssssSe, dtype: int64\n"",
      ""nddssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nddssSe, dtype: int64\n"",
      ""nsBr 的特征分布：\n"",
      ""0    1859\n"",
      ""1     110\n"",
      ""2       4\n"",
      ""3       1\n"",
      ""Name: nsBr, dtype: int64\n"",
      ""nsSnH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsSnH3, dtype: int64\n"",
      ""nssSnH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssSnH2, dtype: int64\n"",
      ""nsssSnH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssSnH, dtype: int64\n"",
      ""nssssSn 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssSn, dtype: int64\n"",
      ""nsI 的特征分布：\n"",
      ""0    1970\n"",
      ""1       2\n"",
      ""2       2\n"",
      ""Name: nsI, dtype: int64\n"",
      ""nsPbH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsPbH3, dtype: int64\n"",
      ""nssPbH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssPbH2, dtype: int64\n"",
      ""nsssPbH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nsssPbH, dtype: int64\n"",
      ""nssssPb 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nssssPb, dtype: int64\n"",
      ""SHBd 的特征分布：\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""0.000000    125\n"",
      ""0.924225      7\n"",
      ""0.997247      6\n"",
      ""0.996499      5\n"",
      ""0.991074      5\n"",
      ""           ... \n"",
      ""0.511617      1\n"",
      ""0.512111      1\n"",
      ""0.503847      1\n"",
      ""0.584148      1\n"",
      ""2.118206      1\n"",
      ""Name: SHBd, Length: 1677, dtype: int64\n"",
      ""SwHBd 的特征分布：\n"",
      ""0.000000    1939\n"",
      ""1.287119       3\n"",
      ""0.638337       2\n"",
      ""0.691961       2\n"",
      ""0.738836       2\n"",
      ""0.257164       2\n"",
      ""1.092678       1\n"",
      ""0.661825       1\n"",
      ""0.667110       1\n"",
      ""0.672301       1\n"",
      ""2.078840       1\n"",
      ""0.670571       1\n"",
      ""0.660794       1\n"",
      ""0.864382       1\n"",
      ""1.239382       1\n"",
      ""0.667769       1\n"",
      ""1.137867       1\n"",
      ""0.717678       1\n"",
      ""0.336505       1\n"",
      ""0.845921       1\n"",
      ""0.707367       1\n"",
      ""0.599097       1\n"",
      ""1.292900       1\n"",
      ""1.253005       1\n"",
      ""1.356535       1\n"",
      ""1.350019       1\n"",
      ""0.554833       1\n"",
      ""0.353444       1\n"",
      ""0.396008       1\n"",
      ""0.667484       1\n"",
      ""Name: SwHBd, dtype: int64\n"",
      ""SHBa 的特征分布：\n"",
      ""35.280436    6\n"",
      ""33.189786    3\n"",
      ""21.920135    3\n"",
      ""28.872735    3\n"",
      ""21.064867    3\n"",
      ""            ..\n"",
      ""37.191778    1\n"",
      ""50.903970    1\n"",
      ""38.293183    1\n"",
      ""38.742589    1\n"",
      ""78.139817    1\n"",
      ""Name: SHBa, Length: 1869, dtype: int64\n"",
      ""SwHBa 的特征分布：\n"",
      ""27.141253    6\n"",
      ""26.087317    3\n"",
      ""20.479860    3\n"",
      ""24.252159    3\n"",
      ""31.319447    3\n"",
      ""            ..\n"",
      ""11.623652    1\n"",
      ""10.557655    1\n"",
      ""10.614777    1\n"",
      ""9.397129     1\n"",
      ""32.695525    1\n"",
      ""Name: SwHBa, Length: 1864, dtype: int64\n"",
      ""SHBint2 的特征分布：\n"",
      ""0.000000     1545\n"",
      ""7.606146        3\n"",
      ""7.350681        3\n"",
      ""7.473317        3\n"",
      ""7.433628        3\n"",
      ""             ... \n"",
      ""5.414904        1\n"",
      ""7.159422        1\n"",
      ""6.455195        1\n"",
      ""8.175381        1\n"",
      ""57.206447       1\n"",
      ""Name: SHBint2, Length: 408, dtype: int64\n"",
      ""SHBint3 的特征分布：\n"",
      ""0.000000     1567\n"",
      ""4.938403        2\n"",
      ""1.125620        2\n"",
      ""49.245290       2\n"",
      ""7.266939        2\n"",
      ""             ... \n"",
      ""1.085606        1\n"",
      ""8.236160        1\n"",
      ""7.659502        1\n"",
      ""8.307561        1\n"",
      ""2.771831        1\n"",
      ""Name: SHBint3, Length: 397, dtype: int64\n"",
      ""SHBint4 的特征分布：\n"",
      "" 0.000000     1142\n"",
      "" 6.015793        4\n"",
      "" 5.984012        3\n"",
      ""-0.773156        3\n"",
      ""-0.771763        3\n"",
      ""              ... \n"",
      ""-0.719907        1\n"",
      ""-0.667916        1\n"",
      ""-0.733363        1\n"",
      ""-0.662692        1\n"",
      "" 10.513617       1\n"",
      ""Name: SHBint4, Length: 779, dtype: int64\n"",
      ""SHBint5 的特征分布：\n"",
      ""0.000000    1261\n"",
      ""3.048675       6\n"",
      ""3.109818       3\n"",
      ""3.247680       3\n"",
      ""5.801641       3\n"",
      ""            ... \n"",
      ""7.701850       1\n"",
      ""0.402542       1\n"",
      ""0.366342       1\n"",
      ""5.691441       1\n"",
      ""3.143239       1\n"",
      ""Name: SHBint5, Length: 676, dtype: int64\n"",
      ""SHBint6 的特征分布：\n"",
      "" 0.000000     1063\n"",
      "" 2.929036        6\n"",
      ""-0.830769        3\n"",
      "" 1.145033        3\n"",
      "" 0.651850        3\n"",
      ""              ... \n"",
      "" 1.056654        1\n"",
      "" 1.173396        1\n"",
      "" 1.127255        1\n"",
      "" 1.192304        1\n"",
      "" 14.296753       1\n"",
      ""Name: SHBint6, Length: 865, dtype: int64\n"",
      ""SHBint7 的特征分布：\n"",
      ""0.000000    1286\n"",
      ""3.170088       4\n"",
      ""3.155698       3\n"",
      ""3.343047       3\n"",
      ""3.358891       3\n"",
      ""            ... \n"",
      ""5.016424       1\n"",
      ""5.079801       1\n"",
      ""2.829868       1\n"",
      ""4.441266       1\n"",
      ""6.423406       1\n"",
      ""Name: SHBint7, Length: 628, dtype: int64\n"",
      ""SHBint8 的特征分布：\n"",
      ""0.000000     1447\n"",
      ""2.852007        4\n"",
      ""4.378872        3\n"",
      ""2.834854        3\n"",
      ""6.075392        3\n"",
      ""             ... \n"",
      ""31.202244       1\n"",
      ""6.739966        1\n"",
      ""6.734373        1\n"",
      ""5.582807        1\n"",
      ""3.315003        1\n"",
      ""Name: SHBint8, Length: 491, dtype: int64\n"",
      ""SHBint9 的特征分布：\n"",
      ""0.000000     1490\n"",
      ""9.332235        6\n"",
      ""1.201758        3\n"",
      ""0.956792        3\n"",
      ""1.645000        3\n"",
      ""             ... \n"",
      ""0.305246        1\n"",
      ""0.636979        1\n"",
      ""8.365064        1\n"",
      ""3.134322        1\n"",
      ""16.832378       1\n"",
      ""Name: SHBint9, Length: 457, dtype: int64\n"",
      ""SHBint10 的特征分布：\n"",
      ""0.000000     990\n"",
      ""2.771615       6\n"",
      ""2.844337       4\n"",
      ""21.033115      3\n"",
      ""9.793012       3\n"",
      ""            ... \n"",
      ""10.996599      1\n"",
      ""10.471476      1\n"",
      ""11.042190      1\n"",
      ""11.038027      1\n"",
      ""23.334095      1\n"",
      ""Name: SHBint10, Length: 903, dtype: int64\n"",
      ""SHsOH 的特征分布：\n"",
      ""0.000000    426\n"",
      ""0.924225      7\n"",
      ""0.997247      6\n"",
      ""0.991074      5\n"",
      ""0.996499      5\n"",
      ""           ... \n"",
      ""1.179248      1\n"",
      ""1.144214      1\n"",
      ""1.120394      1\n"",
      ""0.997993      1\n"",
      ""2.118206      1\n"",
      ""Name: SHsOH, Length: 1386, dtype: int64\n"",
      ""SHdNH 的特征分布：\n"",
      ""0.000000    1959\n"",
      ""1.064837       2\n"",
      ""0.651932       1\n"",
      ""0.663650       1\n"",
      ""0.642672       1\n"",
      ""0.566592       1\n"",
      ""0.572451       1\n"",
      ""0.412760       1\n"",
      ""0.491195       1\n"",
      ""0.565717       1\n"",
      ""4.024783       1\n"",
      ""1.090130       1\n"",
      ""1.075630       1\n"",
      ""1.054596       1\n"",
      ""0.508833       1\n"",
      ""Name: SHdNH, dtype: int64\n"",
      ""SHsSH 的特征分布：\n"",
      ""0.000000    1973\n"",
      ""0.613979       1\n"",
      ""Name: SHsSH, dtype: int64\n"",
      ""SHsNH2 的特征分布：\n"",
      ""0.000000    1924\n"",
      ""2.584290       2\n"",
      ""0.515524       1\n"",
      ""0.433970       1\n"",
      ""0.544862       1\n"",
      ""0.488193       1\n"",
      ""0.529005       1\n"",
      ""0.619184       1\n"",
      ""0.640419       1\n"",
      ""0.532934       1\n"",
      ""0.557076       1\n"",
      ""0.516051       1\n"",
      ""0.437067       1\n"",
      ""6.156436       1\n"",
      ""0.526883       1\n"",
      ""2.078956       1\n"",
      ""2.601883       1\n"",
      ""2.554997       1\n"",
      ""2.409828       1\n"",
      ""0.607592       1\n"",
      ""0.473752       1\n"",
      ""0.448805       1\n"",
      ""0.472744       1\n"",
      ""0.622583       1\n"",
      ""0.529436       1\n"",
      ""0.591273       1\n"",
      ""0.512422       1\n"",
      ""0.491608       1\n"",
      ""0.651295       1\n"",
      ""0.504946       1\n"",
      ""0.555078       1\n"",
      ""0.484679       1\n"",
      ""0.561281       1\n"",
      ""0.569420       1\n"",
      ""0.615496       1\n"",
      ""0.623309       1\n"",
      ""0.609323       1\n"",
      ""0.552937       1\n"",
      ""0.452929       1\n"",
      ""0.569963       1\n"",
      ""0.531027       1\n"",
      ""0.568350       1\n"",
      ""0.505073       1\n"",
      ""0.538143       1\n"",
      ""0.554927       1\n"",
      ""0.552944       1\n"",
      ""0.353140       1\n"",
      ""0.452743       1\n"",
      ""0.443891       1\n"",
      ""0.459118       1\n"",
      ""Name: SHsNH2, dtype: int64\n"",
      ""SHssNH 的特征分布：\n"",
      ""0.000000    1639\n"",
      ""0.274419       3\n"",
      ""0.336356       2\n"",
      ""0.627881       2\n"",
      ""0.619398       2\n"",
      ""            ... \n"",
      ""0.459753       1\n"",
      ""0.464041       1\n"",
      ""0.456740       1\n"",
      ""0.860542       1\n"",
      ""2.414305       1\n"",
      ""Name: SHssNH, Length: 319, dtype: int64\n"",
      ""SHaaNH 的特征分布：\n"",
      ""0.000000    1831\n"",
      ""0.302993       3\n"",
      ""0.540371       3\n"",
      ""0.529199       3\n"",
      ""0.535371       2\n"",
      ""            ... \n"",
      ""0.461376       1\n"",
      ""0.491656       1\n"",
      ""0.579353       1\n"",
      ""0.536143       1\n"",
      ""0.374312       1\n"",
      ""Name: SHaaNH, Length: 130, dtype: int64\n"",
      ""SHsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SHsNH3p, dtype: int64\n"",
      ""SHssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SHssNH2p, dtype: int64\n"",
      ""SHsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SHsssNHp, dtype: int64\n"",
      ""SHtCH 的特征分布：\n"",
      ""0.000000    1957\n"",
      ""0.447784       2\n"",
      ""0.393295       1\n"",
      ""0.460564       1\n"",
      ""0.455766       1\n"",
      ""0.450028       1\n"",
      ""0.540406       1\n"",
      ""0.445406       1\n"",
      ""0.463523       1\n"",
      ""0.438547       1\n"",
      ""0.630462       1\n"",
      ""0.610774       1\n"",
      ""0.598428       1\n"",
      ""0.586082       1\n"",
      ""0.591471       1\n"",
      ""0.584539       1\n"",
      ""0.449360       1\n"",
      ""Name: SHtCH, dtype: int64\n"",
      ""SHdCH2 的特征分布：\n"",
      ""0.000000    1928\n"",
      ""0.322751       2\n"",
      ""0.478138       2\n"",
      ""0.331423       2\n"",
      ""0.548853       2\n"",
      ""0.325837       2\n"",
      ""0.327960       1\n"",
      ""0.489413       1\n"",
      ""0.497865       1\n"",
      ""0.514451       1\n"",
      ""0.526797       1\n"",
      ""0.340273       1\n"",
      ""0.321869       1\n"",
      ""0.319287       1\n"",
      ""0.484123       1\n"",
      ""0.318467       1\n"",
      ""0.322373       1\n"",
      ""0.320735       1\n"",
      ""0.381030       1\n"",
      ""0.515866       1\n"",
      ""0.479398       1\n"",
      ""0.483612       1\n"",
      ""0.448878       1\n"",
      ""0.464140       1\n"",
      ""0.434661       1\n"",
      ""0.515522       1\n"",
      ""0.473774       1\n"",
      ""0.484105       1\n"",
      ""0.486274       1\n"",
      ""0.520921       1\n"",
      ""0.533421       1\n"",
      ""0.536353       1\n"",
      ""0.551785       1\n"",
      ""0.517834       1\n"",
      ""0.527374       1\n"",
      ""0.488238       1\n"",
      ""0.489532       1\n"",
      ""0.486695       1\n"",
      ""0.502127       1\n"",
      ""0.340558       1\n"",
      ""0.476088       1\n"",
      ""0.355143       1\n"",
      ""Name: SHdCH2, dtype: int64\n"",
      ""SHdsCH 的特征分布：\n"",
      ""0.000000    1536\n"",
      ""1.181876       4\n"",
      ""1.286766       3\n"",
      ""0.680611       3\n"",
      ""1.266773       3\n"",
      ""            ... \n"",
      ""0.617071       1\n"",
      ""0.317530       1\n"",
      ""1.197811       1\n"",
      ""1.173545       1\n"",
      ""1.074148       1\n"",
      ""Name: SHdsCH, Length: 401, dtype: int64\n"",
      ""SHaaCH 的特征分布：\n"",
      ""0.000000    57\n"",
      ""5.804976     7\n"",
      ""5.544792     6\n"",
      ""5.526379     5\n"",
      ""5.536339     4\n"",
      ""            ..\n"",
      ""2.275819     1\n"",
      ""2.305217     1\n"",
      ""2.339833     1\n"",
      ""1.668184     1\n"",
      ""8.469362     1\n"",
      ""Name: SHaaCH, Length: 1788, dtype: int64\n"",
      ""SHCHnX 的特征分布：\n"",
      ""0.000000    1935\n"",
      ""1.287119       3\n"",
      ""0.257164       2\n"",
      ""0.691961       2\n"",
      ""0.638337       2\n"",
      ""0.738836       2\n"",
      ""2.078840       1\n"",
      ""0.670571       1\n"",
      ""0.660794       1\n"",
      ""0.672301       1\n"",
      ""0.599097       1\n"",
      ""0.864382       1\n"",
      ""1.239382       1\n"",
      ""0.667110       1\n"",
      ""1.137867       1\n"",
      ""1.092678       1\n"",
      ""0.717678       1\n"",
      ""0.661825       1\n"",
      ""1.292900       1\n"",
      ""0.707367       1\n"",
      ""1.253005       1\n"",
      ""1.356535       1\n"",
      ""1.350019       1\n"",
      ""0.554833       1\n"",
      ""0.353444       1\n"",
      ""0.396008       1\n"",
      ""0.336505       1\n"",
      ""0.251763       1\n"",
      ""0.462279       1\n"",
      ""0.446846       1\n"",
      ""0.845921       1\n"",
      ""0.528279       1\n"",
      ""0.667769       1\n"",
      ""0.667484       1\n"",
      ""Name: SHCHnX, dtype: int64\n"",
      ""SHCsats 的特征分布：\n"",
      ""0.000000    607\n"",
      ""4.512996      8\n"",
      ""4.500103      8\n"",
      ""3.428296      8\n"",
      ""1.506485      6\n"",
      ""           ... \n"",
      ""2.344361      1\n"",
      ""3.000933      1\n"",
      ""3.835348      1\n"",
      ""3.724302      1\n"",
      ""3.373220      1\n"",
      ""Name: SHCsats, Length: 1147, dtype: int64\n"",
      ""SHCsatu 的特征分布：\n"",
      ""0.000000    1218\n"",
      ""1.526533      20\n"",
      ""0.970931      19\n"",
      ""1.680798      16\n"",
      ""1.009356      12\n"",
      ""            ... \n"",
      ""0.550999       1\n"",
      ""1.261180       1\n"",
      ""0.753086       1\n"",
      ""1.283500       1\n"",
      ""0.836726       1\n"",
      ""Name: SHCsatu, Length: 509, dtype: int64\n"",
      ""SHAvin 的特征分布：\n"",
      ""0.000000    1668\n"",
      ""0.546942       4\n"",
      ""0.592902       3\n"",
      ""0.605535       3\n"",
      ""0.586004       3\n"",
      ""            ... \n"",
      ""0.663639       1\n"",
      ""0.609463       1\n"",
      ""0.558162       1\n"",
      ""0.565662       1\n"",
      ""1.074148       1\n"",
      ""Name: SHAvin, Length: 285, dtype: int64\n"",
      ""SHother 的特征分布：\n"",
      ""0.000000    12\n"",
      ""5.804976     7\n"",
      ""5.544792     6\n"",
      ""5.526379     5\n"",
      ""6.059973     4\n"",
      ""            ..\n"",
      ""2.482872     1\n"",
      ""2.275819     1\n"",
      ""2.305217     1\n"",
      ""2.339833     1\n"",
      ""9.543510     1\n"",
      ""Name: SHother, Length: 1825, dtype: int64\n"",
      ""SHmisc 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SHmisc, dtype: int64\n"",
      ""SsLi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsLi, dtype: int64\n"",
      ""SssBe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssBe, dtype: int64\n"",
      ""SssssBem 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssBem, dtype: int64\n"",
      ""SsBH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsBH2, dtype: int64\n"",
      ""SssBH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssBH, dtype: int64\n"",
      ""SsssB 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssB, dtype: int64\n"",
      ""SssssBm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssBm, dtype: int64\n"",
      ""SsCH3 的特征分布：\n"",
      ""0.000000    719\n"",
      ""6.569316      6\n"",
      ""4.491096      3\n"",
      ""4.655761      3\n"",
      ""4.171495      3\n"",
      ""           ... \n"",
      ""4.407346      1\n"",
      ""4.345214      1\n"",
      ""4.232280      1\n"",
      ""1.884588      1\n"",
      ""3.179535      1\n"",
      ""Name: SsCH3, Length: 1181, dtype: int64\n"",
      ""SdCH2 的特征分布：\n"",
      ""0.000000    1928\n"",
      ""4.235104       2\n"",
      ""4.218931       2\n"",
      ""4.221456       2\n"",
      ""3.581417       2\n"",
      ""4.287183       1\n"",
      ""3.851637       1\n"",
      ""3.590638       1\n"",
      ""3.541255       1\n"",
      ""4.205037       1\n"",
      ""4.271305       1\n"",
      ""4.287477       1\n"",
      ""4.299659       1\n"",
      ""3.785043       1\n"",
      ""4.273830       1\n"",
      ""4.244179       1\n"",
      ""3.694375       1\n"",
      ""3.720156       1\n"",
      ""3.963906       1\n"",
      ""3.590397       1\n"",
      ""3.733197       1\n"",
      ""3.790402       1\n"",
      ""3.689735       1\n"",
      ""3.832560       1\n"",
      ""3.816592       1\n"",
      ""3.701376       1\n"",
      ""3.695624       1\n"",
      ""3.657635       1\n"",
      ""3.649739       1\n"",
      ""3.683822       1\n"",
      ""3.637937       1\n"",
      ""3.627302       1\n"",
      ""3.570782       1\n"",
      ""3.686690       1\n"",
      ""3.851505       1\n"",
      ""3.781469       1\n"",
      ""3.746123       1\n"",
      ""3.810211       1\n"",
      ""3.753691       1\n"",
      ""4.059714       1\n"",
      ""3.624876       1\n"",
      ""3.784931       1\n"",
      ""4.071256       1\n"",
      ""Name: SdCH2, dtype: int64\n"",
      ""SssCH2 的特征分布：\n"",
      ""0.000000    503\n"",
      ""4.213969      7\n"",
      ""0.255333      3\n"",
      ""8.127988      3\n"",
      ""7.807307      3\n"",
      ""           ... \n"",
      ""5.491799      1\n"",
      ""4.200696      1\n"",
      ""2.729255      1\n"",
      ""6.456876      1\n"",
      ""0.265626      1\n"",
      ""Name: SssCH2, Length: 1361, dtype: int64\n"",
      ""StCH 的特征分布：\n"",
      ""0.000000    1957\n"",
      ""5.746038       2\n"",
      ""6.076624       1\n"",
      ""5.722917       1\n"",
      ""5.703158       1\n"",
      ""5.775264       1\n"",
      ""5.699071       1\n"",
      ""5.766061       1\n"",
      ""5.693722       1\n"",
      ""5.792759       1\n"",
      ""5.370658       1\n"",
      ""5.449313       1\n"",
      ""5.493487       1\n"",
      ""5.537661       1\n"",
      ""5.463367       1\n"",
      ""5.521265       1\n"",
      ""5.737601       1\n"",
      ""Name: StCH, dtype: int64\n"",
      ""SdsCH 的特征分布：\n"",
      ""0.000000    1536\n"",
      ""2.275238       3\n"",
      ""2.440060       3\n"",
      ""7.181336       3\n"",
      ""2.463528       3\n"",
      ""            ... \n"",
      ""0.541329       1\n"",
      ""1.817515       1\n"",
      ""1.697377       1\n"",
      ""1.441738       1\n"",
      ""3.490956       1\n"",
      ""Name: SdsCH, Length: 410, dtype: int64\n"",
      ""SaaCH 的特征分布：\n"",
      ""0.000000     57\n"",
      ""20.477875     6\n"",
      ""18.556410     4\n"",
      ""4.234370      3\n"",
      ""5.710391      3\n"",
      ""             ..\n"",
      ""5.259814      1\n"",
      ""3.431865      1\n"",
      ""7.126914      1\n"",
      ""7.016090      1\n"",
      ""24.242840     1\n"",
      ""Name: SaaCH, Length: 1812, dtype: int64\n"",
      ""SsssCH 的特征分布：\n"",
      "" 0.000000    1197\n"",
      "" 0.795026       7\n"",
      "" 0.196276       4\n"",
      "" 1.706424       3\n"",
      ""-0.387321       3\n"",
      ""             ... \n"",
      ""-0.092500       1\n"",
      ""-0.143426       1\n"",
      ""-0.144630       1\n"",
      ""-0.098333       1\n"",
      ""-1.961446       1\n"",
      ""Name: SsssCH, Length: 672, dtype: int64\n"",
      ""SddC 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SddC, dtype: int64\n"",
      ""StsC 的特征分布：\n"",
      ""0.000000    1872\n"",
      ""2.433315       2\n"",
      ""2.748062       2\n"",
      ""2.156174       1\n"",
      ""2.012492       1\n"",
      ""            ... \n"",
      ""3.890707       1\n"",
      ""2.089096       1\n"",
      ""2.035897       1\n"",
      ""2.098590       1\n"",
      ""2.182927       1\n"",
      ""Name: StsC, Length: 101, dtype: int64\n"",
      ""SdssC 的特征分布：\n"",
      "" 0.000000    755\n"",
      "" 1.968691      6\n"",
      "" 2.345477      4\n"",
      "" 2.324144      4\n"",
      "" 2.267717      4\n"",
      ""            ... \n"",
      "" 4.598894      1\n"",
      ""-0.915480      1\n"",
      "" 2.577251      1\n"",
      "" 2.391038      1\n"",
      "" 1.621777      1\n"",
      ""Name: SdssC, Length: 1123, dtype: int64\n"",
      ""SaasC 的特征分布：\n"",
      ""0.000000    53\n"",
      ""4.694687     6\n"",
      ""4.005175     3\n"",
      ""2.857742     3\n"",
      ""5.850687     3\n"",
      ""            ..\n"",
      ""3.663404     1\n"",
      ""4.325721     1\n"",
      ""4.301786     1\n"",
      ""1.301707     1\n"",
      ""3.339952     1\n"",
      ""Name: SaasC, Length: 1828, dtype: int64\n"",
      ""SaaaC 的特征分布：\n"",
      ""0.000000    1273\n"",
      ""2.127575       3\n"",
      ""1.624957       3\n"",
      ""1.966437       3\n"",
      ""1.987212       3\n"",
      ""            ... \n"",
      ""2.302288       1\n"",
      ""2.180477       1\n"",
      ""2.442370       1\n"",
      ""2.045861       1\n"",
      ""1.660156       1\n"",
      ""Name: SaaaC, Length: 649, dtype: int64\n"",
      ""SssssC 的特征分布：\n"",
      "" 0.000000    1538\n"",
      ""-1.768182       3\n"",
      ""-0.434848       3\n"",
      "" 0.350772       3\n"",
      "" 0.201435       3\n"",
      ""             ... \n"",
      "" 0.056782       1\n"",
      "" 0.039228       1\n"",
      "" 0.190977       1\n"",
      "" 0.183176       1\n"",
      ""-4.650612       1\n"",
      ""Name: SssssC, Length: 404, dtype: int64\n"",
      ""SsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsNH3p, dtype: int64\n"",
      ""SsNH2 的特征分布：\n"",
      ""0.000000     1924\n"",
      ""27.786765       2\n"",
      ""5.880982        1\n"",
      ""6.667317        1\n"",
      ""6.052978        1\n"",
      ""6.438421        1\n"",
      ""6.338174        1\n"",
      ""5.323545        1\n"",
      ""5.552415        1\n"",
      ""5.652540        1\n"",
      ""6.115033        1\n"",
      ""5.648272        1\n"",
      ""5.964499        1\n"",
      ""67.442453       1\n"",
      ""5.424039        1\n"",
      ""22.022141       1\n"",
      ""27.733887       1\n"",
      ""27.886225       1\n"",
      ""28.102828       1\n"",
      ""5.112182        1\n"",
      ""5.906531        1\n"",
      ""5.474609        1\n"",
      ""5.433361        1\n"",
      ""5.130724        1\n"",
      ""6.062756        1\n"",
      ""5.930651        1\n"",
      ""5.714824        1\n"",
      ""6.506544        1\n"",
      ""5.245658        1\n"",
      ""5.832327        1\n"",
      ""5.775624        1\n"",
      ""5.901230        1\n"",
      ""6.050973        1\n"",
      ""6.019029        1\n"",
      ""5.171865        1\n"",
      ""5.118483        1\n"",
      ""5.202729        1\n"",
      ""5.726553        1\n"",
      ""5.920586        1\n"",
      ""5.548165        1\n"",
      ""6.328082        1\n"",
      ""5.504275        1\n"",
      ""5.792351        1\n"",
      ""6.254872        1\n"",
      ""5.799806        1\n"",
      ""6.136562        1\n"",
      ""6.341828        1\n"",
      ""5.674439        1\n"",
      ""6.241689        1\n"",
      ""5.561610        1\n"",
      ""Name: SsNH2, dtype: int64\n"",
      ""SssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssNH2p, dtype: int64\n"",
      ""SdNH 的特征分布：\n"",
      ""0.000000     1959\n"",
      ""14.945916       2\n"",
      ""7.026123        1\n"",
      ""6.957116        1\n"",
      ""7.069333        1\n"",
      ""7.436361        1\n"",
      ""7.415886        1\n"",
      ""8.781421        1\n"",
      ""8.343041        1\n"",
      ""7.441674        1\n"",
      ""61.488676       1\n"",
      ""14.779697       1\n"",
      ""14.900808       1\n"",
      ""14.980690       1\n"",
      ""7.601595        1\n"",
      ""Name: SdNH, dtype: int64\n"",
      ""SssNH 的特征分布：\n"",
      ""0.000000    1639\n"",
      ""3.140970       3\n"",
      ""6.896169       2\n"",
      ""6.795478       2\n"",
      ""6.727700       2\n"",
      ""            ... \n"",
      ""2.971295       1\n"",
      ""2.959434       1\n"",
      ""5.822702       1\n"",
      ""6.764601       1\n"",
      ""9.393419       1\n"",
      ""Name: SssNH, Length: 325, dtype: int64\n"",
      ""SaaNH 的特征分布：\n"",
      ""0.000000    1831\n"",
      ""3.489931       3\n"",
      ""2.950987       3\n"",
      ""2.976145       3\n"",
      ""3.062098       2\n"",
      ""            ... \n"",
      ""3.006403       1\n"",
      ""3.030096       1\n"",
      ""2.933248       1\n"",
      ""3.099922       1\n"",
      ""3.434846       1\n"",
      ""Name: SaaNH, Length: 132, dtype: int64\n"",
      ""StN 的特征分布：\n"",
      ""0.000000     1895\n"",
      ""10.020969       2\n"",
      ""9.349060        1\n"",
      ""9.317795        1\n"",
      ""9.019996        1\n"",
      ""             ... \n"",
      ""9.147856        1\n"",
      ""9.163741        1\n"",
      ""9.231374        1\n"",
      ""9.221446        1\n"",
      ""9.229602        1\n"",
      ""Name: StN, Length: 79, dtype: int64\n"",
      ""SsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssNHp, dtype: int64\n"",
      ""SdsN 的特征分布：\n"",
      ""0.000000    1850\n"",
      ""4.526679       1\n"",
      ""4.399309       1\n"",
      ""4.343387       1\n"",
      ""4.648952       1\n"",
      ""            ... \n"",
      ""4.031036       1\n"",
      ""4.083001       1\n"",
      ""4.237373       1\n"",
      ""4.355428       1\n"",
      ""4.803363       1\n"",
      ""Name: SdsN, Length: 125, dtype: int64\n"",
      ""SaaN 的特征分布：\n"",
      ""0.000000    1497\n"",
      ""8.293602       3\n"",
      ""8.211284       3\n"",
      ""4.095539       2\n"",
      ""4.200442       2\n"",
      ""            ... \n"",
      ""3.708686       1\n"",
      ""4.469039       1\n"",
      ""4.437831       1\n"",
      ""3.915771       1\n"",
      ""4.460040       1\n"",
      ""Name: SaaN, Length: 454, dtype: int64\n"",
      ""SsssN 的特征分布：\n"",
      ""0.000000    1093\n"",
      ""2.499274       7\n"",
      ""2.568939       4\n"",
      ""2.502967       4\n"",
      ""2.471717       4\n"",
      ""            ... \n"",
      ""1.198460       1\n"",
      ""1.298729       1\n"",
      ""1.631909       1\n"",
      ""1.655080       1\n"",
      ""2.487796       1\n"",
      ""Name: SsssN, Length: 792, dtype: int64\n"",
      ""SddsN 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SddsN, dtype: int64\n"",
      ""SaasN 的特征分布：\n"",
      ""0.000000    1783\n"",
      ""2.283422       2\n"",
      ""2.329332       2\n"",
      ""2.262296       1\n"",
      ""2.352837       1\n"",
      ""            ... \n"",
      ""1.745510       1\n"",
      ""1.413583       1\n"",
      ""2.199933       1\n"",
      ""1.969333       1\n"",
      ""2.017170       1\n"",
      ""Name: SaasN, Length: 190, dtype: int64\n"",
      ""SssssNp 的特征分布：\n"",
      ""0.00000    1973\n"",
      ""0.86878       1\n"",
      ""Name: SssssNp, dtype: int64\n"",
      ""SsOH 的特征分布：\n"",
      ""0.000000     426\n"",
      ""20.193152      6\n"",
      ""18.972022      3\n"",
      ""18.896736      3\n"",
      ""19.277377      3\n"",
      ""            ... \n"",
      ""10.046438      1\n"",
      ""10.043957      1\n"",
      ""10.026110      1\n"",
      ""10.068461      1\n"",
      ""38.871608      1\n"",
      ""Name: SsOH, Length: 1436, dtype: int64\n"",
      ""SdO 的特征分布：\n"",
      ""0.000000     924\n"",
      ""10.844790      3\n"",
      ""12.180667      3\n"",
      ""12.845989      3\n"",
      ""10.854652      3\n"",
      ""            ... \n"",
      ""12.473808      1\n"",
      ""12.446283      1\n"",
      ""10.705579      1\n"",
      ""12.834391      1\n"",
      ""27.318633      1\n"",
      ""Name: SdO, Length: 1006, dtype: int64\n"",
      ""SssO 的特征分布：\n"",
      ""0.000000     934\n"",
      ""12.588010      6\n"",
      ""12.330152      3\n"",
      ""12.523175      3\n"",
      ""18.598658      3\n"",
      ""            ... \n"",
      ""5.820818       1\n"",
      ""12.538545      1\n"",
      ""12.485082      1\n"",
      ""12.416228      1\n"",
      ""11.949576      1\n"",
      ""Name: SssO, Length: 970, dtype: int64\n"",
      ""SaaO 的特征分布：\n"",
      ""0.000000    1689\n"",
      ""5.290931       2\n"",
      ""5.125770       2\n"",
      ""5.465924       2\n"",
      ""5.652097       2\n"",
      ""            ... \n"",
      ""5.059432       1\n"",
      ""5.181881       1\n"",
      ""5.197010       1\n"",
      ""5.275153       1\n"",
      ""6.368240       1\n"",
      ""Name: SaaO, Length: 277, dtype: int64\n"",
      ""SaOm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SaOm, dtype: int64\n"",
      ""SsOm 的特征分布：\n"",
      ""0.000000     1956\n"",
      ""11.136087       1\n"",
      ""11.008185       1\n"",
      ""11.734672       1\n"",
      ""10.737937       1\n"",
      ""22.569367       1\n"",
      ""12.360911       1\n"",
      ""11.915749       1\n"",
      ""10.584585       1\n"",
      ""22.080272       1\n"",
      ""10.893715       1\n"",
      ""10.824798       1\n"",
      ""11.665950       1\n"",
      ""11.034928       1\n"",
      ""11.076231       1\n"",
      ""11.045915       1\n"",
      ""10.934930       1\n"",
      ""11.365685       1\n"",
      ""21.655525       1\n"",
      ""Name: SsOm, dtype: int64\n"",
      ""SsF 的特征分布：\n"",
      ""0.000000     1648\n"",
      ""30.684841       3\n"",
      ""46.089762       3\n"",
      ""14.962811       2\n"",
      ""14.171597       2\n"",
      ""             ... \n"",
      ""13.438277       1\n"",
      ""26.967603       1\n"",
      ""13.462968       1\n"",
      ""13.503285       1\n"",
      ""39.685425       1\n"",
      ""Name: SsF, Length: 315, dtype: int64\n"",
      ""SsSiH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsSiH3, dtype: int64\n"",
      ""SssSiH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssSiH2, dtype: int64\n"",
      ""SsssSiH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssSiH, dtype: int64\n"",
      ""SssssSi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssSi, dtype: int64\n"",
      ""SsPH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsPH2, dtype: int64\n"",
      ""SssPH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssPH, dtype: int64\n"",
      ""SsssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssP, dtype: int64\n"",
      ""SdsssP 的特征分布：\n"",
      "" 0.000000    1972\n"",
      ""-5.762709       1\n"",
      ""-5.751474       1\n"",
      ""Name: SdsssP, dtype: int64\n"",
      ""SddsP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SddsP, dtype: int64\n"",
      ""SsssssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssssP, dtype: int64\n"",
      ""SsSH 的特征分布：\n"",
      ""0.00000    1973\n"",
      ""0.30156       1\n"",
      ""Name: SsSH, dtype: int64\n"",
      ""SdS 的特征分布：\n"",
      ""0.000000    1941\n"",
      ""0.455603       1\n"",
      ""0.488484       1\n"",
      ""0.456076       1\n"",
      ""0.480675       1\n"",
      ""0.428540       1\n"",
      ""0.451855       1\n"",
      ""0.437531       1\n"",
      ""0.369020       1\n"",
      ""0.375617       1\n"",
      ""0.473384       1\n"",
      ""0.431362       1\n"",
      ""0.483055       1\n"",
      ""0.444105       1\n"",
      ""0.474563       1\n"",
      ""0.321507       1\n"",
      ""0.493075       1\n"",
      ""0.472448       1\n"",
      ""0.174980       1\n"",
      ""0.491639       1\n"",
      ""0.869272       1\n"",
      ""0.385327       1\n"",
      ""0.330474       1\n"",
      ""0.903524       1\n"",
      ""0.452047       1\n"",
      ""0.147002       1\n"",
      ""0.248066       1\n"",
      ""0.370515       1\n"",
      ""0.535617       1\n"",
      ""0.536069       1\n"",
      ""0.335617       1\n"",
      ""0.392654       1\n"",
      ""0.247844       1\n"",
      ""0.441645       1\n"",
      ""Name: SdS, dtype: int64\n"",
      ""SssS 的特征分布：\n"",
      "" 0.000000    1753\n"",
      ""-1.608354       3\n"",
      ""-1.615830       3\n"",
      ""-1.613376       2\n"",
      ""-1.508392       2\n"",
      ""             ... \n"",
      ""-1.146813       1\n"",
      ""-1.606124       1\n"",
      ""-1.606381       1\n"",
      ""-1.604008       1\n"",
      ""-1.178306       1\n"",
      ""Name: SssS, Length: 202, dtype: int64\n"",
      ""SaaS 的特征分布：\n"",
      "" 0.000000    1787\n"",
      ""-1.773368       2\n"",
      ""-1.770516       2\n"",
      ""-2.588715       1\n"",
      ""-1.478193       1\n"",
      ""             ... \n"",
      ""-1.038173       1\n"",
      ""-2.479062       1\n"",
      ""-1.190856       1\n"",
      ""-2.217884       1\n"",
      ""-1.399058       1\n"",
      ""Name: SaaS, Length: 186, dtype: int64\n"",
      ""SdssS 的特征分布：\n"",
      "" 0.000000    1973\n"",
      ""-3.339572       1\n"",
      ""Name: SdssS, dtype: int64\n"",
      ""SddssS 的特征分布：\n"",
      "" 0.000000    1851\n"",
      ""-5.438526       2\n"",
      ""-5.978139       2\n"",
      ""-5.779142       2\n"",
      ""-4.768511       1\n"",
      ""             ... \n"",
      ""-6.028156       1\n"",
      ""-5.484730       1\n"",
      ""-5.564010       1\n"",
      ""-5.458557       1\n"",
      ""-5.993028       1\n"",
      ""Name: SddssS, Length: 121, dtype: int64\n"",
      ""SssssssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssssS, dtype: int64\n"",
      ""SSm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SSm, dtype: int64\n"",
      ""SsCl 的特征分布：\n"",
      ""0.000000    1801\n"",
      ""0.930356       2\n"",
      ""0.568272       2\n"",
      ""0.705641       1\n"",
      ""0.647682       1\n"",
      ""            ... \n"",
      ""0.604552       1\n"",
      ""0.624708       1\n"",
      ""0.874288       1\n"",
      ""0.882240       1\n"",
      ""0.600303       1\n"",
      ""Name: SsCl, Length: 172, dtype: int64\n"",
      ""SsGeH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsGeH3, dtype: int64\n"",
      ""SssGeH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssGeH2, dtype: int64\n"",
      ""SsssGeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssGeH, dtype: int64\n"",
      ""SssssGe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssGe, dtype: int64\n"",
      ""SsAsH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsAsH2, dtype: int64\n"",
      ""SssAsH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssAsH, dtype: int64\n"",
      ""SsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssAs, dtype: int64\n"",
      ""SdsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SdsssAs, dtype: int64\n"",
      ""SddsAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SddsAs, dtype: int64\n"",
      ""SsssssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssssAs, dtype: int64\n"",
      ""SsSeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsSeH, dtype: int64\n"",
      ""SdSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SdSe, dtype: int64\n"",
      ""SssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssSe, dtype: int64\n"",
      ""SaaSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SaaSe, dtype: int64\n"",
      ""SdssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SdssSe, dtype: int64\n"",
      ""SssssssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssssSe, dtype: int64\n"",
      ""SddssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SddssSe, dtype: int64\n"",
      ""SsBr 的特征分布：\n"",
      "" 0.000000    1859\n"",
      "" 0.042634       3\n"",
      "" 0.036849       2\n"",
      ""-0.034697       1\n"",
      "" 0.050627       1\n"",
      ""             ... \n"",
      ""-0.036512       1\n"",
      ""-0.011187       1\n"",
      "" 0.029790       1\n"",
      ""-0.083726       1\n"",
      "" 0.047215       1\n"",
      ""Name: SsBr, Length: 113, dtype: int64\n"",
      ""SsSnH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsSnH3, dtype: int64\n"",
      ""SssSnH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssSnH2, dtype: int64\n"",
      ""SsssSnH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssSnH, dtype: int64\n"",
      ""SssssSn 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssSn, dtype: int64\n"",
      ""SsI 的特征分布：\n"",
      "" 0.000000    1970\n"",
      ""-0.040317       1\n"",
      ""-0.077034       1\n"",
      ""-0.059350       1\n"",
      ""-0.097058       1\n"",
      ""Name: SsI, dtype: int64\n"",
      ""SsPbH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsPbH3, dtype: int64\n"",
      ""SssPbH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssPbH2, dtype: int64\n"",
      ""SsssPbH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SsssPbH, dtype: int64\n"",
      ""SssssPb 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: SssssPb, dtype: int64\n"",
      ""minHBd 的特征分布：\n"",
      ""0.000000    125\n"",
      ""0.480713      8\n"",
      ""0.452864      8\n"",
      ""0.480339      7\n"",
      ""0.550233      5\n"",
      ""           ... \n"",
      ""0.551689      1\n"",
      ""0.475692      1\n"",
      ""0.435692      1\n"",
      ""0.523095      1\n"",
      ""0.507282      1\n"",
      ""Name: minHBd, Length: 1603, dtype: int64\n"",
      ""minwHBd 的特征分布：\n"",
      ""0.000000    1939\n"",
      ""1.287119       3\n"",
      ""0.638337       2\n"",
      ""0.691961       2\n"",
      ""0.738836       2\n"",
      ""0.257164       2\n"",
      ""1.092678       1\n"",
      ""0.661825       1\n"",
      ""0.667110       1\n"",
      ""0.672301       1\n"",
      ""0.686329       1\n"",
      ""0.670571       1\n"",
      ""0.660794       1\n"",
      ""0.864382       1\n"",
      ""1.239382       1\n"",
      ""0.667769       1\n"",
      ""1.137867       1\n"",
      ""0.717678       1\n"",
      ""0.336505       1\n"",
      ""0.845921       1\n"",
      ""0.707367       1\n"",
      ""0.599097       1\n"",
      ""1.292900       1\n"",
      ""1.253005       1\n"",
      ""1.356535       1\n"",
      ""1.350019       1\n"",
      ""0.554833       1\n"",
      ""0.353444       1\n"",
      ""0.396008       1\n"",
      ""0.667484       1\n"",
      ""Name: minwHBd, dtype: int64\n"",
      ""minHBa 的特征分布：\n"",
      "" 2.499274    7\n"",
      "" 2.976145    3\n"",
      "" 3.140970    3\n"",
      ""-1.615830    3\n"",
      "" 2.480755    3\n"",
      ""            ..\n"",
      "" 5.391176    1\n"",
      "" 5.254133    1\n"",
      "" 5.413669    1\n"",
      "" 5.531910    1\n"",
      "" 5.702148    1\n"",
      ""Name: minHBa, Length: 1842, dtype: int64\n"",
      ""minwHBa 的特征分布：\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      "" 0.194006    6\n"",
      "" 0.139596    4\n"",
      ""-1.072710    3\n"",
      "" 0.023278    3\n"",
      "" 0.268495    3\n"",
      ""            ..\n"",
      ""-0.402729    1\n"",
      ""-0.328076    1\n"",
      ""-0.509630    1\n"",
      ""-0.525833    1\n"",
      ""-0.048992    1\n"",
      ""Name: minwHBa, Length: 1847, dtype: int64\n"",
      ""minHBint2 的特征分布：\n"",
      ""0.000000    1545\n"",
      ""7.606146       3\n"",
      ""7.350681       3\n"",
      ""7.473317       3\n"",
      ""7.433628       3\n"",
      ""            ... \n"",
      ""5.414904       1\n"",
      ""7.159422       1\n"",
      ""6.455195       1\n"",
      ""0.179875       1\n"",
      ""1.315401       1\n"",
      ""Name: minHBint2, Length: 408, dtype: int64\n"",
      ""minHBint3 的特征分布：\n"",
      ""0.000000    1567\n"",
      ""4.938403       2\n"",
      ""1.125620       2\n"",
      ""0.603276       2\n"",
      ""7.266939       2\n"",
      ""            ... \n"",
      ""1.085606       1\n"",
      ""8.236160       1\n"",
      ""7.659502       1\n"",
      ""8.307561       1\n"",
      ""2.771831       1\n"",
      ""Name: minHBint3, Length: 397, dtype: int64\n"",
      ""minHBint4 的特征分布：\n"",
      "" 0.000000    1142\n"",
      "" 2.928348       4\n"",
      ""-0.773156       3\n"",
      "" 2.913547       3\n"",
      ""-0.771763       3\n"",
      ""             ... \n"",
      ""-0.667916       1\n"",
      ""-0.733363       1\n"",
      ""-0.662692       1\n"",
      ""-0.727304       1\n"",
      "" 5.256808       1\n"",
      ""Name: minHBint4, Length: 778, dtype: int64\n"",
      ""minHBint5 的特征分布：\n"",
      ""0.000000    1261\n"",
      ""3.048675       6\n"",
      ""3.109818       3\n"",
      ""3.247680       3\n"",
      ""0.951692       3\n"",
      ""            ... \n"",
      ""7.701850       1\n"",
      ""0.402542       1\n"",
      ""0.366342       1\n"",
      ""5.691441       1\n"",
      ""3.143239       1\n"",
      ""Name: minHBint5, Length: 676, dtype: int64\n"",
      ""minHBint6 的特征分布：\n"",
      "" 0.000000    1063\n"",
      "" 2.929036       6\n"",
      ""-0.830769       3\n"",
      ""-0.829643       3\n"",
      "" 0.809980       3\n"",
      ""             ... \n"",
      "" 1.056654       1\n"",
      "" 1.173396       1\n"",
      "" 1.127255       1\n"",
      "" 1.192304       1\n"",
      "" 7.148376       1\n"",
      ""Name: minHBint6, Length: 864, dtype: int64\n"",
      ""minHBint7 的特征分布：\n"",
      ""0.000000    1286\n"",
      ""3.170088       4\n"",
      ""3.155698       3\n"",
      ""3.343047       3\n"",
      ""3.358891       3\n"",
      ""            ... \n"",
      ""1.984227       1\n"",
      ""1.949292       1\n"",
      ""2.829868       1\n"",
      ""4.441266       1\n"",
      ""3.184633       1\n"",
      ""Name: minHBint7, Length: 628, dtype: int64\n"",
      ""minHBint8 的特征分布：\n"",
      ""0.000000    1447\n"",
      ""2.852007       4\n"",
      ""1.800477       3\n"",
      ""2.834854       3\n"",
      ""6.075392       3\n"",
      ""            ... \n"",
      ""7.122688       1\n"",
      ""6.739966       1\n"",
      ""6.734373       1\n"",
      ""5.582807       1\n"",
      ""3.315003       1\n"",
      ""Name: minHBint8, Length: 491, dtype: int64\n"",
      ""minHBint9 的特征分布：\n"",
      ""0.000000    1490\n"",
      ""4.554477       6\n"",
      ""1.201758       3\n"",
      ""0.956792       3\n"",
      ""1.645000       3\n"",
      ""            ... \n"",
      ""0.305246       1\n"",
      ""0.318490       1\n"",
      ""8.365064       1\n"",
      ""3.134322       1\n"",
      ""2.906676       1\n"",
      ""Name: minHBint9, Length: 457, dtype: int64\n"",
      ""minHBint10 的特征分布：\n"",
      ""0.000000    990\n"",
      ""2.771615      6\n"",
      ""2.844337      4\n"",
      ""2.167995      3\n"",
      ""7.363620      3\n"",
      ""           ... \n"",
      ""4.783622      1\n"",
      ""5.170503      1\n"",
      ""4.495022      1\n"",
      ""5.325040      1\n"",
      ""2.956091      1\n"",
      ""Name: minHBint10, Length: 902, dtype: int64\n"",
      ""minHsOH 的特征分布：\n"",
      ""0.000000    426\n"",
      ""0.480713      8\n"",
      ""0.452864      8\n"",
      ""0.480339      7\n"",
      ""0.550233      5\n"",
      ""           ... \n"",
      ""0.532170      1\n"",
      ""0.449995      1\n"",
      ""0.474672      1\n"",
      ""0.460002      1\n"",
      ""0.507282      1\n"",
      ""Name: minHsOH, Length: 1315, dtype: int64\n"",
      ""minHdNH 的特征分布：\n"",
      ""0.000000    1959\n"",
      ""0.509643       2\n"",
      ""0.651932       1\n"",
      ""0.663650       1\n"",
      ""0.642672       1\n"",
      ""0.566592       1\n"",
      ""0.572451       1\n"",
      ""0.412760       1\n"",
      ""0.491195       1\n"",
      ""0.565717       1\n"",
      ""0.481479       1\n"",
      ""0.521784       1\n"",
      ""0.514186       1\n"",
      ""0.504522       1\n"",
      ""0.508833       1\n"",
      ""Name: minHdNH, dtype: int64\n"",
      ""minHsSH 的特征分布：\n"",
      ""0.000000    1973\n"",
      ""0.613979       1\n"",
      ""Name: minHsSH, dtype: int64\n"",
      ""minHsNH2 的特征分布：\n"",
      ""0.000000    1924\n"",
      ""0.385158       2\n"",
      ""0.515524       1\n"",
      ""0.433970       1\n"",
      ""0.544862       1\n"",
      ""0.488193       1\n"",
      ""0.529005       1\n"",
      ""0.619184       1\n"",
      ""0.640419       1\n"",
      ""0.532934       1\n"",
      ""0.557076       1\n"",
      ""0.516051       1\n"",
      ""0.437067       1\n"",
      ""0.353439       1\n"",
      ""0.526883       1\n"",
      ""0.395421       1\n"",
      ""0.395829       1\n"",
      ""0.370811       1\n"",
      ""0.359503       1\n"",
      ""0.607592       1\n"",
      ""0.473752       1\n"",
      ""0.448805       1\n"",
      ""0.472744       1\n"",
      ""0.622583       1\n"",
      ""0.529436       1\n"",
      ""0.591273       1\n"",
      ""0.512422       1\n"",
      ""0.491608       1\n"",
      ""0.651295       1\n"",
      ""0.504946       1\n"",
      ""0.555078       1\n"",
      ""0.484679       1\n"",
      ""0.561281       1\n"",
      ""0.569420       1\n"",
      ""0.615496       1\n"",
      ""0.623309       1\n"",
      ""0.609323       1\n"",
      ""0.552937       1\n"",
      ""0.452929       1\n"",
      ""0.569963       1\n"",
      ""0.531027       1\n"",
      ""0.568350       1\n"",
      ""0.505073       1\n"",
      ""0.538143       1\n"",
      ""0.554927       1\n"",
      ""0.552944       1\n"",
      ""0.353140       1\n"",
      ""0.452743       1\n"",
      ""0.443891       1\n"",
      ""0.459118       1\n"",
      ""Name: minHsNH2, dtype: int64\n"",
      ""minHssNH 的特征分布：\n"",
      ""0.000000    1639\n"",
      ""0.274419       3\n"",
      ""0.336356       2\n"",
      ""0.334362       2\n"",
      ""0.223456       2\n"",
      ""            ... \n"",
      ""0.459753       1\n"",
      ""0.464041       1\n"",
      ""0.456740       1\n"",
      ""0.368890       1\n"",
      ""0.591851       1\n"",
      ""Name: minHssNH, Length: 319, dtype: int64\n"",
      ""minHaaNH 的特征分布：\n"",
      ""0.000000    1831\n"",
      ""0.302993       3\n"",
      ""0.540371       3\n"",
      ""0.529199       3\n"",
      ""0.535371       2\n"",
      ""            ... \n"",
      ""0.461376       1\n"",
      ""0.491656       1\n"",
      ""0.579353       1\n"",
      ""0.536143       1\n"",
      ""0.374312       1\n"",
      ""Name: minHaaNH, Length: 130, dtype: int64\n"",
      ""minHsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minHsNH3p, dtype: int64\n"",
      ""minHssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minHssNH2p, dtype: int64\n"",
      ""minHsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minHsssNHp, dtype: int64\n"",
      ""minHtCH 的特征分布：\n"",
      ""0.000000    1957\n"",
      ""0.447784       2\n"",
      ""0.393295       1\n"",
      ""0.460564       1\n"",
      ""0.455766       1\n"",
      ""0.450028       1\n"",
      ""0.540406       1\n"",
      ""0.445406       1\n"",
      ""0.463523       1\n"",
      ""0.438547       1\n"",
      ""0.630462       1\n"",
      ""0.610774       1\n"",
      ""0.598428       1\n"",
      ""0.586082       1\n"",
      ""0.591471       1\n"",
      ""0.584539       1\n"",
      ""0.449360       1\n"",
      ""Name: minHtCH, dtype: int64\n"",
      ""minHdCH2 的特征分布：\n"",
      ""0.000000    1928\n"",
      ""0.322751       2\n"",
      ""0.478138       2\n"",
      ""0.331423       2\n"",
      ""0.548853       2\n"",
      ""0.325837       2\n"",
      ""0.327960       1\n"",
      ""0.489413       1\n"",
      ""0.497865       1\n"",
      ""0.514451       1\n"",
      ""0.526797       1\n"",
      ""0.340273       1\n"",
      ""0.321869       1\n"",
      ""0.319287       1\n"",
      ""0.484123       1\n"",
      ""0.318467       1\n"",
      ""0.322373       1\n"",
      ""0.320735       1\n"",
      ""0.381030       1\n"",
      ""0.515866       1\n"",
      ""0.479398       1\n"",
      ""0.483612       1\n"",
      ""0.448878       1\n"",
      ""0.464140       1\n"",
      ""0.434661       1\n"",
      ""0.515522       1\n"",
      ""0.473774       1\n"",
      ""0.484105       1\n"",
      ""0.486274       1\n"",
      ""0.520921       1\n"",
      ""0.533421       1\n"",
      ""0.536353       1\n"",
      ""0.551785       1\n"",
      ""0.517834       1\n"",
      ""0.527374       1\n"",
      ""0.488238       1\n"",
      ""0.489532       1\n"",
      ""0.486695       1\n"",
      ""0.502127       1\n"",
      ""0.340558       1\n"",
      ""0.476088       1\n"",
      ""0.355143       1\n"",
      ""Name: minHdCH2, dtype: int64\n"",
      ""minHdsCH 的特征分布：\n"",
      ""0.000000    1536\n"",
      ""0.546942       4\n"",
      ""0.203570       3\n"",
      ""0.736139       3\n"",
      ""0.205024       3\n"",
      ""            ... \n"",
      ""0.599107       1\n"",
      ""0.564382       1\n"",
      ""0.370784       1\n"",
      ""0.389065       1\n"",
      ""0.532273       1\n"",
      ""Name: minHdsCH, Length: 396, dtype: int64\n"",
      ""minHaaCH 的特征分布：\n"",
      ""0.000000    57\n"",
      ""0.494166     7\n"",
      ""0.470115     6\n"",
      ""0.469004     5\n"",
      ""0.390563     5\n"",
      ""            ..\n"",
      ""0.558292     1\n"",
      ""0.560135     1\n"",
      ""0.603548     1\n"",
      ""0.593548     1\n"",
      ""0.528503     1\n"",
      ""Name: minHaaCH, Length: 1742, dtype: int64\n"",
      ""minHCHnX 的特征分布：\n"",
      ""0.000000    1935\n"",
      ""1.287119       3\n"",
      ""0.257164       2\n"",
      ""0.691961       2\n"",
      ""0.638337       2\n"",
      ""0.738836       2\n"",
      ""0.686329       1\n"",
      ""0.670571       1\n"",
      ""0.660794       1\n"",
      ""0.672301       1\n"",
      ""0.599097       1\n"",
      ""0.864382       1\n"",
      ""1.239382       1\n"",
      ""0.667110       1\n"",
      ""1.137867       1\n"",
      ""1.092678       1\n"",
      ""0.717678       1\n"",
      ""0.661825       1\n"",
      ""1.292900       1\n"",
      ""0.707367       1\n"",
      ""1.253005       1\n"",
      ""1.356535       1\n"",
      ""1.350019       1\n"",
      ""0.554833       1\n"",
      ""0.353444       1\n"",
      ""0.396008       1\n"",
      ""0.336505       1\n"",
      ""0.251763       1\n"",
      ""0.462279       1\n"",
      ""0.446846       1\n"",
      ""0.845921       1\n"",
      ""0.528279       1\n"",
      ""0.667769       1\n"",
      ""0.667484       1\n"",
      ""Name: minHCHnX, dtype: int64\n"",
      ""minHCsats 的特征分布：\n"",
      ""0.000000    607\n"",
      ""0.291887     19\n"",
      ""0.292769     17\n"",
      ""0.332367     14\n"",
      ""0.296654     14\n"",
      ""           ... \n"",
      ""0.298493      1\n"",
      ""0.328007      1\n"",
      ""0.321931      1\n"",
      ""0.335000      1\n"",
      ""0.518113      1\n"",
      ""Name: minHCsats, Length: 977, dtype: int64\n"",
      ""minHCsatu 的特征分布：\n"",
      ""0.000000    1218\n"",
      ""0.572869      19\n"",
      ""0.466421      19\n"",
      ""0.687133      16\n"",
      ""0.666725      14\n"",
      ""            ... \n"",
      ""0.912846       1\n"",
      ""0.550999       1\n"",
      ""0.630590       1\n"",
      ""0.753086       1\n"",
      ""0.511801       1\n"",
      ""Name: minHCsatu, Length: 508, dtype: int64\n"",
      ""minHAvin 的特征分布：\n"",
      ""0.000000    1668\n"",
      ""0.546942       4\n"",
      ""0.592902       3\n"",
      ""0.605535       3\n"",
      ""0.586004       3\n"",
      ""            ... \n"",
      ""0.663639       1\n"",
      ""0.609463       1\n"",
      ""0.558162       1\n"",
      ""0.565662       1\n"",
      ""0.532273       1\n"",
      ""Name: minHAvin, Length: 285, dtype: int64\n"",
      ""minHother 的特征分布：\n"",
      ""0.000000    12\n"",
      ""0.494166     7\n"",
      ""0.470115     6\n"",
      ""0.390563     5\n"",
      ""0.469004     5\n"",
      ""            ..\n"",
      ""0.543111     1\n"",
      ""0.595141     1\n"",
      ""0.558292     1\n"",
      ""0.560135     1\n"",
      ""0.528503     1\n"",
      ""Name: minHother, Length: 1777, dtype: int64\n"",
      ""minHmisc 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minHmisc, dtype: int64\n"",
      ""minsLi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsLi, dtype: int64\n"",
      ""minssBe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssBe, dtype: int64\n"",
      ""minssssBem 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssBem, dtype: int64\n"",
      ""minsBH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsBH2, dtype: int64\n"",
      ""minssBH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssBH, dtype: int64\n"",
      ""minsssB 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssB, dtype: int64\n"",
      ""minssssBm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssBm, dtype: int64\n"",
      ""minsCH3 的特征分布：\n"",
      ""0.000000    719\n"",
      ""2.032992      6\n"",
      ""2.231662      3\n"",
      ""2.327880      3\n"",
      ""2.146114      3\n"",
      ""           ... \n"",
      ""2.013508      1\n"",
      ""2.248061      1\n"",
      ""2.160353      1\n"",
      ""2.106035      1\n"",
      ""1.589768      1\n"",
      ""Name: minsCH3, Length: 1174, dtype: int64\n"",
      ""mindCH2 的特征分布：\n"",
      ""0.000000    1928\n"",
      ""4.235104       2\n"",
      ""4.218931       2\n"",
      ""4.221456       2\n"",
      ""3.581417       2\n"",
      ""4.287183       1\n"",
      ""3.851637       1\n"",
      ""3.590638       1\n"",
      ""3.541255       1\n"",
      ""4.205037       1\n"",
      ""4.271305       1\n"",
      ""4.287477       1\n"",
      ""4.299659       1\n"",
      ""3.785043       1\n"",
      ""4.273830       1\n"",
      ""4.244179       1\n"",
      ""3.694375       1\n"",
      ""3.720156       1\n"",
      ""3.963906       1\n"",
      ""3.590397       1\n"",
      ""3.733197       1\n"",
      ""3.790402       1\n"",
      ""3.689735       1\n"",
      ""3.832560       1\n"",
      ""3.816592       1\n"",
      ""3.701376       1\n"",
      ""3.695624       1\n"",
      ""3.657635       1\n"",
      ""3.649739       1\n"",
      ""3.683822       1\n"",
      ""3.637937       1\n"",
      ""3.627302       1\n"",
      ""3.570782       1\n"",
      ""3.686690       1\n"",
      ""3.851505       1\n"",
      ""3.781469       1\n"",
      ""3.746123       1\n"",
      ""3.810211       1\n"",
      ""3.753691       1\n"",
      ""4.059714       1\n"",
      ""3.624876       1\n"",
      ""3.784931       1\n"",
      ""4.071256       1\n"",
      ""Name: mindCH2, dtype: int64\n"",
      ""minssCH2 的特征分布：\n"",
      "" 0.000000    503\n"",
      "" 0.663112      7\n"",
      "" 0.492181      7\n"",
      "" 0.670334      5\n"",
      "" 0.684223      4\n"",
      ""            ... \n"",
      "" 0.744243      1\n"",
      "" 0.689479      1\n"",
      "" 0.706840      1\n"",
      ""-0.043646      1\n"",
      "" 0.265626      1\n"",
      ""Name: minssCH2, Length: 1282, dtype: int64\n"",
      ""mintCH 的特征分布：\n"",
      ""0.000000    1957\n"",
      ""5.746038       2\n"",
      ""6.076624       1\n"",
      ""5.722917       1\n"",
      ""5.703158       1\n"",
      ""5.775264       1\n"",
      ""5.699071       1\n"",
      ""5.766061       1\n"",
      ""5.693722       1\n"",
      ""5.792759       1\n"",
      ""5.370658       1\n"",
      ""5.449313       1\n"",
      ""5.493487       1\n"",
      ""5.537661       1\n"",
      ""5.463367       1\n"",
      ""5.521265       1\n"",
      ""5.737601       1\n"",
      ""Name: mintCH, dtype: int64\n"",
      ""mindsCH 的特征分布：\n"",
      ""0.000000    1536\n"",
      ""0.940281       3\n"",
      ""1.013053       3\n"",
      ""2.241289       3\n"",
      ""1.018295       3\n"",
      ""            ... \n"",
      ""0.541329       1\n"",
      ""1.817515       1\n"",
      ""1.697377       1\n"",
      ""1.441738       1\n"",
      ""1.719098       1\n"",
      ""Name: mindsCH, Length: 409, dtype: int64\n"",
      ""minaaCH 的特征分布：\n"",
      ""0.000000    57\n"",
      ""1.640610     6\n"",
      ""1.658712     4\n"",
      ""1.536147     4\n"",
      ""1.770120     3\n"",
      ""            ..\n"",
      ""1.401155     1\n"",
      ""1.391108     1\n"",
      ""1.421988     1\n"",
      ""1.636325     1\n"",
      ""1.242936     1\n"",
      ""Name: minaaCH, Length: 1777, dtype: int64\n"",
      ""minsssCH 的特征分布：\n"",
      "" 0.000000    1197\n"",
      ""-0.353793       7\n"",
      ""-0.082932       3\n"",
      ""-0.107903       3\n"",
      ""-0.140426       3\n"",
      ""             ... \n"",
      ""-0.018691       1\n"",
      ""-0.102963       1\n"",
      ""-0.149259       1\n"",
      ""-0.079815       1\n"",
      ""-0.817930       1\n"",
      ""Name: minsssCH, Length: 677, dtype: int64\n"",
      ""minddC 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minddC, dtype: int64\n"",
      ""mintsC 的特征分布：\n"",
      ""0.000000    1872\n"",
      ""2.433315       2\n"",
      ""2.748062       2\n"",
      ""2.156174       1\n"",
      ""2.012492       1\n"",
      ""            ... \n"",
      ""1.912905       1\n"",
      ""2.089096       1\n"",
      ""2.035897       1\n"",
      ""2.098590       1\n"",
      ""2.182927       1\n"",
      ""Name: mintsC, Length: 101, dtype: int64\n"",
      ""mindssC 的特征分布：\n"",
      "" 0.000000    755\n"",
      "" 0.962448      6\n"",
      "" 1.511804      3\n"",
      "" 1.027275      3\n"",
      ""-1.140858      3\n"",
      ""            ... \n"",
      "" 1.524650      1\n"",
      "" 1.591495      1\n"",
      "" 1.568517      1\n"",
      "" 1.526955      1\n"",
      "" 0.747764      1\n"",
      ""Name: mindssC, Length: 1133, dtype: int64\n"",
      ""minaasC 的特征分布：\n"",
      "" 0.000000    53\n"",
      "" 0.194006     6\n"",
      "" 0.139596     4\n"",
      ""-0.079892     3\n"",
      "" 0.268495     3\n"",
      ""             ..\n"",
      "" 0.018195     1\n"",
      "" 0.042130     1\n"",
      "" 0.020651     1\n"",
      "" 0.058334     1\n"",
      ""-0.048992     1\n"",
      ""Name: minaasC, Length: 1803, dtype: int64\n"",
      ""minaaaC 的特征分布：\n"",
      ""0.000000    1273\n"",
      ""0.529774       4\n"",
      ""1.041233       3\n"",
      ""0.976969       3\n"",
      ""0.989051       3\n"",
      ""            ... \n"",
      ""0.674707       1\n"",
      ""0.865949       1\n"",
      ""0.745957       1\n"",
      ""0.723449       1\n"",
      ""0.752564       1\n"",
      ""Name: minaaaC, Length: 665, dtype: int64\n"",
      ""minssssC 的特征分布：\n"",
      "" 0.000000    1538\n"",
      ""-1.768182       3\n"",
      ""-0.434848       3\n"",
      "" 0.150605       3\n"",
      "" 0.201435       3\n"",
      ""             ... \n"",
      "" 0.056782       1\n"",
      "" 0.039228       1\n"",
      "" 0.190977       1\n"",
      "" 0.183176       1\n"",
      ""-4.650612       1\n"",
      ""Name: minssssC, Length: 404, dtype: int64\n"",
      ""minsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsNH3p, dtype: int64\n"",
      ""minsNH2 的特征分布：\n"",
      ""0.000000    1924\n"",
      ""5.250191       2\n"",
      ""5.880982       1\n"",
      ""6.667317       1\n"",
      ""6.052978       1\n"",
      ""6.438421       1\n"",
      ""6.338174       1\n"",
      ""5.323545       1\n"",
      ""5.552415       1\n"",
      ""5.652540       1\n"",
      ""6.115033       1\n"",
      ""5.648272       1\n"",
      ""5.964499       1\n"",
      ""5.388102       1\n"",
      ""5.424039       1\n"",
      ""5.210913       1\n"",
      ""5.246983       1\n"",
      ""5.257316       1\n"",
      ""5.246255       1\n"",
      ""5.112182       1\n"",
      ""5.906531       1\n"",
      ""5.474609       1\n"",
      ""5.433361       1\n"",
      ""5.130724       1\n"",
      ""6.062756       1\n"",
      ""5.930651       1\n"",
      ""5.714824       1\n"",
      ""6.506544       1\n"",
      ""5.245658       1\n"",
      ""5.832327       1\n"",
      ""5.775624       1\n"",
      ""5.901230       1\n"",
      ""6.050973       1\n"",
      ""6.019029       1\n"",
      ""5.171865       1\n"",
      ""5.118483       1\n"",
      ""5.202729       1\n"",
      ""5.726553       1\n"",
      ""5.920586       1\n"",
      ""5.548165       1\n"",
      ""6.328082       1\n"",
      ""5.504275       1\n"",
      ""5.792351       1\n"",
      ""6.254872       1\n"",
      ""5.799806       1\n"",
      ""6.136562       1\n"",
      ""6.341828       1\n"",
      ""5.674439       1\n"",
      ""6.241689       1\n"",
      ""5.561610       1\n"",
      ""Name: minsNH2, dtype: int64\n"",
      ""minssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssNH2p, dtype: int64\n"",
      ""mindNH 的特征分布：\n"",
      ""0.000000    1959\n"",
      ""7.339297       2\n"",
      ""7.026123       1\n"",
      ""6.957116       1\n"",
      ""7.069333       1\n"",
      ""7.436361       1\n"",
      ""7.415886       1\n"",
      ""8.781421       1\n"",
      ""8.343041       1\n"",
      ""7.441674       1\n"",
      ""7.444079       1\n"",
      ""7.252740       1\n"",
      ""7.321982       1\n"",
      ""7.356684       1\n"",
      ""7.601595       1\n"",
      ""Name: mindNH, dtype: int64\n"",
      ""minssNH 的特征分布：\n"",
      ""0.000000    1639\n"",
      ""3.140970       3\n"",
      ""3.378115       2\n"",
      ""3.365338       2\n"",
      ""2.493065       2\n"",
      ""            ... \n"",
      ""2.959434       1\n"",
      ""2.811312       1\n"",
      ""3.241396       1\n"",
      ""3.238047       1\n"",
      ""2.222520       1\n"",
      ""Name: minssNH, Length: 326, dtype: int64\n"",
      ""minaaNH 的特征分布：\n"",
      ""0.000000    1831\n"",
      ""3.489931       3\n"",
      ""2.950987       3\n"",
      ""2.976145       3\n"",
      ""3.062098       2\n"",
      ""            ... \n"",
      ""3.006403       1\n"",
      ""3.030096       1\n"",
      ""2.933248       1\n"",
      ""3.099922       1\n"",
      ""3.434846       1\n"",
      ""Name: minaaNH, Length: 132, dtype: int64\n"",
      ""mintN 的特征分布：\n"",
      ""0.000000     1895\n"",
      ""10.020969       2\n"",
      ""9.349060        1\n"",
      ""9.317795        1\n"",
      ""9.019996        1\n"",
      ""             ... \n"",
      ""9.147856        1\n"",
      ""9.163741        1\n"",
      ""9.231374        1\n"",
      ""9.221446        1\n"",
      ""9.229602        1\n"",
      ""Name: mintN, Length: 79, dtype: int64\n"",
      ""minsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssNHp, dtype: int64\n"",
      ""mindsN 的特征分布：\n"",
      ""0.000000    1850\n"",
      ""4.526679       1\n"",
      ""4.399309       1\n"",
      ""4.343387       1\n"",
      ""4.648952       1\n"",
      ""            ... \n"",
      ""4.031036       1\n"",
      ""4.083001       1\n"",
      ""4.237373       1\n"",
      ""4.355428       1\n"",
      ""4.803363       1\n"",
      ""Name: mindsN, Length: 125, dtype: int64\n"",
      ""minaaN 的特征分布：\n"",
      ""0.000000    1497\n"",
      ""3.940843       3\n"",
      ""3.977174       3\n"",
      ""4.200442       2\n"",
      ""4.621738       2\n"",
      ""            ... \n"",
      ""3.708686       1\n"",
      ""4.469039       1\n"",
      ""4.437831       1\n"",
      ""3.915771       1\n"",
      ""4.460040       1\n"",
      ""Name: minaaN, Length: 453, dtype: int64\n"",
      ""minsssN 的特征分布：\n"",
      ""0.000000    1093\n"",
      ""2.499274       7\n"",
      ""2.494865       4\n"",
      ""2.471717       4\n"",
      ""2.502967       4\n"",
      ""            ... \n"",
      ""1.298729       1\n"",
      ""1.631909       1\n"",
      ""1.655080       1\n"",
      ""1.623830       1\n"",
      ""2.487796       1\n"",
      ""Name: minsssN, Length: 789, dtype: int64\n"",
      ""minddsN 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minddsN, dtype: int64\n"",
      ""minaasN 的特征分布：\n"",
      ""0.000000    1783\n"",
      ""2.283422       2\n"",
      ""2.329332       2\n"",
      ""2.262296       1\n"",
      ""2.352837       1\n"",
      ""            ... \n"",
      ""1.745510       1\n"",
      ""1.413583       1\n"",
      ""2.199933       1\n"",
      ""1.969333       1\n"",
      ""2.017170       1\n"",
      ""Name: minaasN, Length: 190, dtype: int64\n"",
      ""minssssNp 的特征分布：\n"",
      ""0.00000    1973\n"",
      ""0.86878       1\n"",
      ""Name: minssssNp, dtype: int64\n"",
      ""minsOH 的特征分布：\n"",
      ""0.000000     426\n"",
      ""10.057063      6\n"",
      ""10.059932      5\n"",
      ""9.497391       4\n"",
      ""10.154792      3\n"",
      ""            ... \n"",
      ""10.009142      1\n"",
      ""10.005026      1\n"",
      ""10.003601      1\n"",
      ""9.999486       1\n"",
      ""9.645181       1\n"",
      ""Name: minsOH, Length: 1422, dtype: int64\n"",
      ""mindO 的特征分布：\n"",
      ""0.000000     924\n"",
      ""10.844790      3\n"",
      ""10.743472      3\n"",
      ""12.180667      3\n"",
      ""12.845989      3\n"",
      ""            ... \n"",
      ""13.231528      1\n"",
      ""12.473808      1\n"",
      ""12.446283      1\n"",
      ""10.705579      1\n"",
      ""13.659317      1\n"",
      ""Name: mindO, Length: 1004, dtype: int64\n"",
      ""minssO 的特征分布：\n"",
      ""0.000000    934\n"",
      ""6.120198      7\n"",
      ""6.026976      3\n"",
      ""5.986568      3\n"",
      ""6.017703      3\n"",
      ""           ... \n"",
      ""5.986435      1\n"",
      ""5.966457      1\n"",
      ""5.960147      1\n"",
      ""5.967101      1\n"",
      ""5.702148      1\n"",
      ""Name: minssO, Length: 964, dtype: int64\n"",
      ""minaaO 的特征分布：\n"",
      ""0.000000    1689\n"",
      ""5.290931       2\n"",
      ""5.125770       2\n"",
      ""5.465924       2\n"",
      ""5.652097       2\n"",
      ""            ... \n"",
      ""5.059432       1\n"",
      ""5.181881       1\n"",
      ""5.197010       1\n"",
      ""5.275153       1\n"",
      ""6.368240       1\n"",
      ""Name: minaaO, Length: 277, dtype: int64\n"",
      ""minaOm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minaOm, dtype: int64\n"",
      ""minsOm 的特征分布：\n"",
      ""0.000000     1956\n"",
      ""11.136087       1\n"",
      ""11.008185       1\n"",
      ""11.734672       1\n"",
      ""10.737937       1\n"",
      ""11.055923       1\n"",
      ""12.360911       1\n"",
      ""11.915749       1\n"",
      ""10.584585       1\n"",
      ""10.842588       1\n"",
      ""10.893715       1\n"",
      ""10.824798       1\n"",
      ""11.665950       1\n"",
      ""11.034928       1\n"",
      ""11.076231       1\n"",
      ""11.045915       1\n"",
      ""10.934930       1\n"",
      ""11.365685       1\n"",
      ""10.827763       1\n"",
      ""Name: minsOm, dtype: int64\n"",
      ""minsF 的特征分布：\n"",
      ""0.000000     1648\n"",
      ""15.342421       3\n"",
      ""15.363254       3\n"",
      ""13.294382       2\n"",
      ""14.962811       2\n"",
      ""             ... \n"",
      ""13.438277       1\n"",
      ""13.483802       1\n"",
      ""13.462968       1\n"",
      ""13.503285       1\n"",
      ""13.228475       1\n"",
      ""Name: minsF, Length: 314, dtype: int64\n"",
      ""minsSiH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsSiH3, dtype: int64\n"",
      ""minssSiH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssSiH2, dtype: int64\n"",
      ""minsssSiH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssSiH, dtype: int64\n"",
      ""minssssSi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssSi, dtype: int64\n"",
      ""minsPH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsPH2, dtype: int64\n"",
      ""minssPH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssPH, dtype: int64\n"",
      ""minsssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssP, dtype: int64\n"",
      ""mindsssP 的特征分布：\n"",
      "" 0.000000    1972\n"",
      ""-5.762709       1\n"",
      ""-5.751474       1\n"",
      ""Name: mindsssP, dtype: int64\n"",
      ""minddsP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minddsP, dtype: int64\n"",
      ""minsssssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssssP, dtype: int64\n"",
      ""minsSH 的特征分布：\n"",
      ""0.00000    1973\n"",
      ""0.30156       1\n"",
      ""Name: minsSH, dtype: int64\n"",
      ""mindS 的特征分布：\n"",
      ""0.000000    1941\n"",
      ""0.455603       1\n"",
      ""0.488484       1\n"",
      ""0.456076       1\n"",
      ""0.480675       1\n"",
      ""0.428540       1\n"",
      ""0.451855       1\n"",
      ""0.437531       1\n"",
      ""0.369020       1\n"",
      ""0.375617       1\n"",
      ""0.473384       1\n"",
      ""0.431362       1\n"",
      ""0.483055       1\n"",
      ""0.444105       1\n"",
      ""0.474563       1\n"",
      ""0.321507       1\n"",
      ""0.493075       1\n"",
      ""0.472448       1\n"",
      ""0.174980       1\n"",
      ""0.491639       1\n"",
      ""0.869272       1\n"",
      ""0.385327       1\n"",
      ""0.330474       1\n"",
      ""0.903524       1\n"",
      ""0.452047       1\n"",
      ""0.147002       1\n"",
      ""0.248066       1\n"",
      ""0.370515       1\n"",
      ""0.535617       1\n"",
      ""0.536069       1\n"",
      ""0.335617       1\n"",
      ""0.392654       1\n"",
      ""0.247844       1\n"",
      ""0.441645       1\n"",
      ""Name: mindS, dtype: int64\n"",
      ""minssS 的特征分布：\n"",
      "" 0.000000    1753\n"",
      ""-1.608354       3\n"",
      ""-1.615830       3\n"",
      ""-1.613376       2\n"",
      ""-0.397887       2\n"",
      ""             ... \n"",
      ""-1.104957       1\n"",
      ""-1.146813       1\n"",
      ""-1.606124       1\n"",
      ""-1.606381       1\n"",
      ""-1.178306       1\n"",
      ""Name: minssS, Length: 201, dtype: int64\n"",
      ""minaaS 的特征分布：\n"",
      "" 0.000000    1787\n"",
      ""-1.773368       2\n"",
      ""-1.770516       2\n"",
      ""-1.346215       1\n"",
      ""-1.478193       1\n"",
      ""             ... \n"",
      ""-1.038173       1\n"",
      ""-1.344670       1\n"",
      ""-1.190856       1\n"",
      ""-1.246574       1\n"",
      ""-1.399058       1\n"",
      ""Name: minaaS, Length: 186, dtype: int64\n"",
      ""mindssS 的特征分布：\n"",
      "" 0.000000    1973\n"",
      ""-3.339572       1\n"",
      ""Name: mindssS, dtype: int64\n"",
      ""minddssS 的特征分布：\n"",
      "" 0.000000    1851\n"",
      ""-5.438526       2\n"",
      ""-5.978139       2\n"",
      ""-5.779142       2\n"",
      ""-4.768511       1\n"",
      ""             ... \n"",
      ""-6.028156       1\n"",
      ""-5.484730       1\n"",
      ""-5.564010       1\n"",
      ""-5.458557       1\n"",
      ""-5.993028       1\n"",
      ""Name: minddssS, Length: 121, dtype: int64\n"",
      ""minssssssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssssS, dtype: int64\n"",
      ""minSm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minSm, dtype: int64\n"",
      ""minsCl 的特征分布：\n"",
      ""0.000000    1801\n"",
      ""0.930356       2\n"",
      ""0.568272       2\n"",
      ""0.705641       1\n"",
      ""0.647682       1\n"",
      ""            ... \n"",
      ""0.604552       1\n"",
      ""0.624708       1\n"",
      ""0.381701       1\n"",
      ""0.385795       1\n"",
      ""0.600303       1\n"",
      ""Name: minsCl, Length: 172, dtype: int64\n"",
      ""minsGeH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsGeH3, dtype: int64\n"",
      ""minssGeH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssGeH2, dtype: int64\n"",
      ""minsssGeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssGeH, dtype: int64\n"",
      ""minssssGe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssGe, dtype: int64\n"",
      ""minsAsH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsAsH2, dtype: int64\n"",
      ""minssAsH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssAsH, dtype: int64\n"",
      ""minsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssAs, dtype: int64\n"",
      ""mindsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: mindsssAs, dtype: int64\n"",
      ""minddsAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minddsAs, dtype: int64\n"",
      ""minsssssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssssAs, dtype: int64\n"",
      ""minsSeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsSeH, dtype: int64\n"",
      ""mindSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: mindSe, dtype: int64\n"",
      ""minssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssSe, dtype: int64\n"",
      ""minaaSe 的特征分布：\n"",
      ""0    1974\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Name: minaaSe, dtype: int64\n"",
      ""mindssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: mindssSe, dtype: int64\n"",
      ""minssssssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssssSe, dtype: int64\n"",
      ""minddssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minddssSe, dtype: int64\n"",
      ""minsBr 的特征分布：\n"",
      "" 0.000000    1859\n"",
      "" 0.042634       3\n"",
      "" 0.036849       2\n"",
      ""-0.034697       1\n"",
      "" 0.050627       1\n"",
      ""             ... \n"",
      ""-0.036512       1\n"",
      ""-0.011187       1\n"",
      "" 0.029790       1\n"",
      ""-0.083726       1\n"",
      "" 0.047215       1\n"",
      ""Name: minsBr, Length: 113, dtype: int64\n"",
      ""minsSnH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsSnH3, dtype: int64\n"",
      ""minssSnH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssSnH2, dtype: int64\n"",
      ""minsssSnH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssSnH, dtype: int64\n"",
      ""minssssSn 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssSn, dtype: int64\n"",
      ""minsI 的特征分布：\n"",
      "" 0.000000    1970\n"",
      ""-0.040317       1\n"",
      ""-0.038517       1\n"",
      ""-0.059350       1\n"",
      ""-0.048529       1\n"",
      ""Name: minsI, dtype: int64\n"",
      ""minsPbH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsPbH3, dtype: int64\n"",
      ""minssPbH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssPbH2, dtype: int64\n"",
      ""minsssPbH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minsssPbH, dtype: int64\n"",
      ""minssssPb 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: minssssPb, dtype: int64\n"",
      ""maxHBd 的特征分布：\n"",
      ""0.000000    125\n"",
      ""0.516534     10\n"",
      ""0.471361      8\n"",
      ""0.599086      7\n"",
      ""0.516160      7\n"",
      ""           ... \n"",
      ""0.588176      1\n"",
      ""0.579911      1\n"",
      ""0.585830      1\n"",
      ""0.646147      1\n"",
      ""0.545019      1\n"",
      ""Name: maxHBd, Length: 1558, dtype: int64\n"",
      ""maxwHBd 的特征分布：\n"",
      ""0.000000    1939\n"",
      ""1.287119       3\n"",
      ""0.638337       2\n"",
      ""0.691961       2\n"",
      ""0.738836       2\n"",
      ""0.257164       2\n"",
      ""1.092678       1\n"",
      ""0.661825       1\n"",
      ""0.667110       1\n"",
      ""0.672301       1\n"",
      ""1.392511       1\n"",
      ""0.670571       1\n"",
      ""0.660794       1\n"",
      ""0.864382       1\n"",
      ""1.239382       1\n"",
      ""0.667769       1\n"",
      ""1.137867       1\n"",
      ""0.717678       1\n"",
      ""0.336505       1\n"",
      ""0.845921       1\n"",
      ""0.707367       1\n"",
      ""0.599097       1\n"",
      ""1.292900       1\n"",
      ""1.253005       1\n"",
      ""1.356535       1\n"",
      ""1.350019       1\n"",
      ""0.554833       1\n"",
      ""0.353444       1\n"",
      ""0.396008       1\n"",
      ""0.667484       1\n"",
      ""Name: maxwHBd, dtype: int64\n"",
      ""maxHBa 的特征分布：\n"",
      ""10.136089    6\n"",
      ""0.000000     4\n"",
      ""9.977508     3\n"",
      ""10.129447    3\n"",
      ""9.989464     3\n"",
      ""            ..\n"",
      ""11.849023    1\n"",
      ""11.917634    1\n"",
      ""11.960323    1\n"",
      ""12.028934    1\n"",
      ""13.659317    1\n"",
      ""Name: maxHBa, Length: 1844, dtype: int64\n"",
      ""maxwHBa 的特征分布：\n"",
      ""2.045712    7\n"",
      ""0.000000    6\n"",
      ""2.200945    4\n"",
      ""2.472210    4\n"",
      ""2.041765    4\n"",
      ""           ..\n"",
      ""3.785043    1\n"",
      ""3.816592    1\n"",
      ""1.933932    1\n"",
      ""1.786340    1\n"",
      ""1.784294    1\n"",
      ""Name: maxwHBa, Length: 1827, dtype: int64\n"",
      ""maxHBint2 的特征分布：\n"",
      ""0.000000    1549\n"",
      ""3.525179       3\n"",
      ""7.350681       3\n"",
      ""7.473317       3\n"",
      ""7.433628       3\n"",
      ""            ... \n"",
      ""3.644542       1\n"",
      ""2.746296       1\n"",
      ""5.414904       1\n"",
      ""7.159422       1\n"",
      ""7.994149       1\n"",
      ""Name: maxHBint2, Length: 404, dtype: int64\n"",
      ""maxHBint3 的特征分布：\n"",
      ""0.000000    1598\n"",
      ""7.543834       2\n"",
      ""5.144464       2\n"",
      ""0.567433       2\n"",
      ""7.266939       2\n"",
      ""            ... \n"",
      ""0.345266       1\n"",
      ""5.055742       1\n"",
      ""8.597539       1\n"",
      ""0.249993       1\n"",
      ""2.771831       1\n"",
      ""Name: maxHBint3, Length: 366, dtype: int64\n"",
      ""maxHBint4 的特征分布：\n"",
      ""0.000000    1370\n"",
      ""3.087445       4\n"",
      ""3.070464       3\n"",
      ""6.209839       2\n"",
      ""3.079124       2\n"",
      ""            ... \n"",
      ""2.878853       1\n"",
      ""2.733142       1\n"",
      ""3.154726       1\n"",
      ""3.144438       1\n"",
      ""5.256808       1\n"",
      ""Name: maxHBint4, Length: 569, dtype: int64\n"",
      ""maxHBint5 的特征分布：\n"",
      ""0.000000    1288\n"",
      ""3.048675       6\n"",
      ""3.109818       3\n"",
      ""3.892246       3\n"",
      ""3.125730       3\n"",
      ""            ... \n"",
      ""0.336977       1\n"",
      ""3.098084       1\n"",
      ""3.104322       1\n"",
      ""1.052552       1\n"",
      ""3.143239       1\n"",
      ""Name: maxHBint5, Length: 648, dtype: int64\n"",
      ""maxHBint6 的特征分布：\n"",
      ""0.000000    1204\n"",
      ""2.929036       6\n"",
      ""1.084299       3\n"",
      ""0.809980       3\n"",
      ""1.145033       3\n"",
      ""            ... \n"",
      ""7.594909       1\n"",
      ""6.855801       1\n"",
      ""7.606784       1\n"",
      ""7.142042       1\n"",
      ""7.148376       1\n"",
      ""Name: maxHBint6, Length: 740, dtype: int64\n"",
      ""maxHBint7 的特征分布：\n"",
      ""0.000000    1310\n"",
      ""3.170088       4\n"",
      ""3.155698       3\n"",
      ""3.358829       3\n"",
      ""3.343047       3\n"",
      ""            ... \n"",
      ""3.130509       1\n"",
      ""2.829868       1\n"",
      ""4.441266       1\n"",
      ""1.685902       1\n"",
      ""3.238773       1\n"",
      ""Name: maxHBint7, Length: 603, dtype: int64\n"",
      ""maxHBint8 的特征分布：\n"",
      ""0.000000    1462\n"",
      ""2.852007       4\n"",
      ""6.075392       3\n"",
      ""2.578395       3\n"",
      ""2.834854       3\n"",
      ""            ... \n"",
      ""5.936685       1\n"",
      ""5.581895       1\n"",
      ""5.749411       1\n"",
      ""5.694657       1\n"",
      ""3.315003       1\n"",
      ""Name: maxHBint8, Length: 474, dtype: int64\n"",
      ""maxHBint9 的特征分布：\n"",
      ""0.000000    1493\n"",
      ""4.777758       6\n"",
      ""1.201758       3\n"",
      ""0.956792       3\n"",
      ""1.645000       3\n"",
      ""            ... \n"",
      ""0.344166       1\n"",
      ""0.305246       1\n"",
      ""0.318490       1\n"",
      ""8.365064       1\n"",
      ""6.962851       1\n"",
      ""Name: maxHBint9, Length: 453, dtype: int64\n"",
      ""maxHBint10 的特征分布：\n"",
      ""0.000000    992\n"",
      ""2.771615      6\n"",
      ""2.844337      4\n"",
      ""2.842764      3\n"",
      ""3.314923      3\n"",
      ""           ... \n"",
      ""5.877129      1\n"",
      ""5.970451      1\n"",
      ""5.826096      1\n"",
      ""5.976454      1\n"",
      ""7.081222      1\n"",
      ""Name: maxHBint10, Length: 899, dtype: int64\n"",
      ""maxHsOH 的特征分布：\n"",
      ""0.000000    426\n"",
      ""0.516534     10\n"",
      ""0.471361      8\n"",
      ""0.599086      7\n"",
      ""0.516160      7\n"",
      ""           ... \n"",
      ""0.511562      1\n"",
      ""0.313602      1\n"",
      ""0.317604      1\n"",
      ""0.301979      1\n"",
      ""0.545019      1\n"",
      ""Name: maxHsOH, Length: 1271, dtype: int64\n"",
      ""maxHdNH 的特征分布：\n"",
      ""0.000000    1959\n"",
      ""0.555194       2\n"",
      ""0.651932       1\n"",
      ""0.663650       1\n"",
      ""0.642672       1\n"",
      ""0.566592       1\n"",
      ""0.572451       1\n"",
      ""0.412760       1\n"",
      ""0.491195       1\n"",
      ""0.565717       1\n"",
      ""0.543789       1\n"",
      ""0.568346       1\n"",
      ""0.561444       1\n"",
      ""0.550074       1\n"",
      ""0.508833       1\n"",
      ""Name: maxHdNH, dtype: int64\n"",
      ""maxHsSH 的特征分布：\n"",
      ""0.000000    1973\n"",
      ""0.613979       1\n"",
      ""Name: maxHsSH, dtype: int64\n"",
      ""maxHsNH2 的特征分布：\n"",
      ""0.000000    1924\n"",
      ""0.567261       2\n"",
      ""0.515524       1\n"",
      ""0.433970       1\n"",
      ""0.544862       1\n"",
      ""0.488193       1\n"",
      ""0.529005       1\n"",
      ""0.619184       1\n"",
      ""0.640419       1\n"",
      ""0.532934       1\n"",
      ""0.557076       1\n"",
      ""0.516051       1\n"",
      ""0.437067       1\n"",
      ""0.557783       1\n"",
      ""0.526883       1\n"",
      ""0.587235       1\n"",
      ""0.567716       1\n"",
      ""0.565163       1\n"",
      ""0.567137       1\n"",
      ""0.607592       1\n"",
      ""0.473752       1\n"",
      ""0.448805       1\n"",
      ""0.472744       1\n"",
      ""0.622583       1\n"",
      ""0.529436       1\n"",
      ""0.591273       1\n"",
      ""0.512422       1\n"",
      ""0.491608       1\n"",
      ""0.651295       1\n"",
      ""0.504946       1\n"",
      ""0.555078       1\n"",
      ""0.484679       1\n"",
      ""0.561281       1\n"",
      ""0.569420       1\n"",
      ""0.615496       1\n"",
      ""0.623309       1\n"",
      ""0.609323       1\n"",
      ""0.552937       1\n"",
      ""0.452929       1\n"",
      ""0.569963       1\n"",
      ""0.531027       1\n"",
      ""0.568350       1\n"",
      ""0.505073       1\n"",
      ""0.538143       1\n"",
      ""0.554927       1\n"",
      ""0.552944       1\n"",
      ""0.353140       1\n"",
      ""0.452743       1\n"",
      ""0.443891       1\n"",
      ""0.459118       1\n"",
      ""Name: maxHsNH2, dtype: int64\n"",
      ""maxHssNH 的特征分布：\n"",
      ""0.000000    1639\n"",
      ""0.274419       3\n"",
      ""0.336356       2\n"",
      ""0.334362       2\n"",
      ""0.385969       2\n"",
      ""            ... \n"",
      ""0.464041       1\n"",
      ""0.456740       1\n"",
      ""0.491652       1\n"",
      ""0.330673       1\n"",
      ""0.615301       1\n"",
      ""Name: maxHssNH, Length: 320, dtype: int64\n"",
      ""maxHaaNH 的特征分布：\n"",
      ""0.000000    1831\n"",
      ""0.302993       3\n"",
      ""0.540371       3\n"",
      ""0.529199       3\n"",
      ""0.535371       2\n"",
      ""            ... \n"",
      ""0.461376       1\n"",
      ""0.491656       1\n"",
      ""0.579353       1\n"",
      ""0.536143       1\n"",
      ""0.374312       1\n"",
      ""Name: maxHaaNH, Length: 130, dtype: int64\n"",
      ""maxHsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxHsNH3p, dtype: int64\n"",
      ""maxHssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxHssNH2p, dtype: int64\n"",
      ""maxHsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxHsssNHp, dtype: int64\n"",
      ""maxHtCH 的特征分布：\n"",
      ""0.000000    1957\n"",
      ""0.447784       2\n"",
      ""0.393295       1\n"",
      ""0.460564       1\n"",
      ""0.455766       1\n"",
      ""0.450028       1\n"",
      ""0.540406       1\n"",
      ""0.445406       1\n"",
      ""0.463523       1\n"",
      ""0.438547       1\n"",
      ""0.630462       1\n"",
      ""0.610774       1\n"",
      ""0.598428       1\n"",
      ""0.586082       1\n"",
      ""0.591471       1\n"",
      ""0.584539       1\n"",
      ""0.449360       1\n"",
      ""Name: maxHtCH, dtype: int64\n"",
      ""maxHdCH2 的特征分布：\n"",
      ""0.000000    1928\n"",
      ""0.322751       2\n"",
      ""0.478138       2\n"",
      ""0.331423       2\n"",
      ""0.548853       2\n"",
      ""0.325837       2\n"",
      ""0.327960       1\n"",
      ""0.489413       1\n"",
      ""0.497865       1\n"",
      ""0.514451       1\n"",
      ""0.526797       1\n"",
      ""0.340273       1\n"",
      ""0.321869       1\n"",
      ""0.319287       1\n"",
      ""0.484123       1\n"",
      ""0.318467       1\n"",
      ""0.322373       1\n"",
      ""0.320735       1\n"",
      ""0.381030       1\n"",
      ""0.515866       1\n"",
      ""0.479398       1\n"",
      ""0.483612       1\n"",
      ""0.448878       1\n"",
      ""0.464140       1\n"",
      ""0.434661       1\n"",
      ""0.515522       1\n"",
      ""0.473774       1\n"",
      ""0.484105       1\n"",
      ""0.486274       1\n"",
      ""0.520921       1\n"",
      ""0.533421       1\n"",
      ""0.536353       1\n"",
      ""0.551785       1\n"",
      ""0.517834       1\n"",
      ""0.527374       1\n"",
      ""0.488238       1\n"",
      ""0.489532       1\n"",
      ""0.486695       1\n"",
      ""0.502127       1\n"",
      ""0.340558       1\n"",
      ""0.476088       1\n"",
      ""0.355143       1\n"",
      ""Name: maxHdCH2, dtype: int64\n"",
      ""maxHdsCH 的特征分布：\n"",
      ""0.000000    1536\n"",
      ""0.634934       4\n"",
      ""0.263337       4\n"",
      ""0.673871       3\n"",
      ""0.257614       3\n"",
      ""            ... \n"",
      ""0.370517       1\n"",
      ""0.570370       1\n"",
      ""0.548596       1\n"",
      ""0.617071       1\n"",
      ""0.541875       1\n"",
      ""Name: maxHdsCH, Length: 398, dtype: int64\n"",
      ""maxHaaCH 的特征分布：\n"",
      ""0.000000    57\n"",
      ""0.568712     7\n"",
      ""0.552964     6\n"",
      ""0.657176     6\n"",
      ""0.645023     6\n"",
      ""            ..\n"",
      ""0.556070     1\n"",
      ""0.558385     1\n"",
      ""0.615182     1\n"",
      ""0.557900     1\n"",
      ""0.632159     1\n"",
      ""Name: maxHaaCH, Length: 1696, dtype: int64\n"",
      ""maxHCHnX 的特征分布：\n"",
      ""0.000000    1935\n"",
      ""1.287119       3\n"",
      ""0.257164       2\n"",
      ""0.691961       2\n"",
      ""0.638337       2\n"",
      ""0.738836       2\n"",
      ""1.392511       1\n"",
      ""0.670571       1\n"",
      ""0.660794       1\n"",
      ""0.672301       1\n"",
      ""0.599097       1\n"",
      ""0.864382       1\n"",
      ""1.239382       1\n"",
      ""0.667110       1\n"",
      ""1.137867       1\n"",
      ""1.092678       1\n"",
      ""0.717678       1\n"",
      ""0.661825       1\n"",
      ""1.292900       1\n"",
      ""0.707367       1\n"",
      ""1.253005       1\n"",
      ""1.356535       1\n"",
      ""1.350019       1\n"",
      ""0.554833       1\n"",
      ""0.353444       1\n"",
      ""0.396008       1\n"",
      ""0.336505       1\n"",
      ""0.251763       1\n"",
      ""0.462279       1\n"",
      ""0.446846       1\n"",
      ""0.845921       1\n"",
      ""0.528279       1\n"",
      ""0.667769       1\n"",
      ""0.667484       1\n"",
      ""Name: maxHCHnX, dtype: int64\n"",
      ""maxHCsats 的特征分布：\n"",
      ""0.000000    607\n"",
      ""0.839556     44\n"",
      ""0.641484     27\n"",
      ""0.504510     23\n"",
      ""0.654917     20\n"",
      ""           ... \n"",
      ""0.888592      1\n"",
      ""0.431074      1\n"",
      ""0.439436      1\n"",
      ""0.874703      1\n"",
      ""0.642410      1\n"",
      ""Name: maxHCsats, Length: 804, dtype: int64\n"",
      ""maxHCsatu 的特征分布：\n"",
      ""0.000000    1218\n"",
      ""0.504510      23\n"",
      ""0.953664      20\n"",
      ""0.993664      19\n"",
      ""0.523722      12\n"",
      ""            ... \n"",
      ""0.913094       1\n"",
      ""0.713389       1\n"",
      ""0.912846       1\n"",
      ""0.550999       1\n"",
      ""0.772372       1\n"",
      ""Name: maxHCsatu, Length: 504, dtype: int64\n"",
      ""maxHAvin 的特征分布：\n"",
      ""0.000000    1668\n"",
      ""0.546942       4\n"",
      ""0.592902       3\n"",
      ""0.605535       3\n"",
      ""0.586004       3\n"",
      ""            ... \n"",
      ""0.663639       1\n"",
      ""0.609463       1\n"",
      ""0.558162       1\n"",
      ""0.565662       1\n"",
      ""0.541875       1\n"",
      ""Name: maxHAvin, Length: 285, dtype: int64\n"",
      ""maxHother 的特征分布：\n"",
      ""0.000000    12\n"",
      ""0.568712     7\n"",
      ""0.645023     6\n"",
      ""0.552964     6\n"",
      ""0.657176     6\n"",
      ""            ..\n"",
      ""0.556070     1\n"",
      ""0.558385     1\n"",
      ""0.615182     1\n"",
      ""0.557900     1\n"",
      ""0.632159     1\n"",
      ""Name: maxHother, Length: 1739, dtype: int64\n"",
      ""maxHmisc 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxHmisc, dtype: int64\n"",
      ""maxsLi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsLi, dtype: int64\n"",
      ""maxssBe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssBe, dtype: int64\n"",
      ""maxssssBem 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssBem, dtype: int64\n"",
      ""maxsBH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsBH2, dtype: int64\n"",
      ""maxssBH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssBH, dtype: int64\n"",
      ""maxsssB 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssB, dtype: int64\n"",
      ""maxssssBm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssBm, dtype: int64\n"",
      ""maxsCH3 的特征分布：\n"",
      ""0.000000    719\n"",
      ""2.307513      7\n"",
      ""2.085748      3\n"",
      ""2.222711      3\n"",
      ""2.033378      3\n"",
      ""           ... \n"",
      ""2.234913      1\n"",
      ""2.248061      1\n"",
      ""2.246992      1\n"",
      ""2.239180      1\n"",
      ""1.589768      1\n"",
      ""Name: maxsCH3, Length: 1178, dtype: int64\n"",
      ""maxdCH2 的特征分布：\n"",
      ""0.000000    1928\n"",
      ""4.235104       2\n"",
      ""4.218931       2\n"",
      ""4.221456       2\n"",
      ""3.581417       2\n"",
      ""4.287183       1\n"",
      ""3.851637       1\n"",
      ""3.590638       1\n"",
      ""3.541255       1\n"",
      ""4.205037       1\n"",
      ""4.271305       1\n"",
      ""4.287477       1\n"",
      ""4.299659       1\n"",
      ""3.785043       1\n"",
      ""4.273830       1\n"",
      ""4.244179       1\n"",
      ""3.694375       1\n"",
      ""3.720156       1\n"",
      ""3.963906       1\n"",
      ""3.590397       1\n"",
      ""3.733197       1\n"",
      ""3.790402       1\n"",
      ""3.689735       1\n"",
      ""3.832560       1\n"",
      ""3.816592       1\n"",
      ""3.701376       1\n"",
      ""3.695624       1\n"",
      ""3.657635       1\n"",
      ""3.649739       1\n"",
      ""3.683822       1\n"",
      ""3.637937       1\n"",
      ""3.627302       1\n"",
      ""3.570782       1\n"",
      ""3.686690       1\n"",
      ""3.851505       1\n"",
      ""3.781469       1\n"",
      ""3.746123       1\n"",
      ""3.810211       1\n"",
      ""3.753691       1\n"",
      ""4.059714       1\n"",
      ""3.624876       1\n"",
      ""3.784931       1\n"",
      ""4.071256       1\n"",
      ""Name: maxdCH2, dtype: int64\n"",
      ""maxssCH2 的特征分布：\n"",
      ""0.000000    523\n"",
      ""1.335377      8\n"",
      ""1.261535      7\n"",
      ""1.349646      4\n"",
      ""1.348112      4\n"",
      ""           ... \n"",
      ""1.346668      1\n"",
      ""0.826879      1\n"",
      ""0.965274      1\n"",
      ""0.947654      1\n"",
      ""0.265626      1\n"",
      ""Name: maxssCH2, Length: 1311, dtype: int64\n"",
      ""maxtCH 的特征分布：\n"",
      ""0.000000    1957\n"",
      ""5.746038       2\n"",
      ""6.076624       1\n"",
      ""5.722917       1\n"",
      ""5.703158       1\n"",
      ""5.775264       1\n"",
      ""5.699071       1\n"",
      ""5.766061       1\n"",
      ""5.693722       1\n"",
      ""5.792759       1\n"",
      ""5.370658       1\n"",
      ""5.449313       1\n"",
      ""5.493487       1\n"",
      ""5.537661       1\n"",
      ""5.463367       1\n"",
      ""5.521265       1\n"",
      ""5.737601       1\n"",
      ""Name: maxtCH, dtype: int64\n"",
      ""maxdsCH 的特征分布：\n"",
      ""0.000000    1536\n"",
      ""2.472210       4\n"",
      ""1.334958       3\n"",
      ""1.427007       3\n"",
      ""1.445234       3\n"",
      ""            ... \n"",
      ""1.843669       1\n"",
      ""0.541329       1\n"",
      ""1.817515       1\n"",
      ""1.697377       1\n"",
      ""1.771858       1\n"",
      ""Name: maxdsCH, Length: 409, dtype: int64\n"",
      ""maxaaCH 的特征分布：\n"",
      ""0.000000    57\n"",
      ""2.045712     7\n"",
      ""2.041765     4\n"",
      ""2.200945     4\n"",
      ""1.851423     3\n"",
      ""            ..\n"",
      ""2.148953     1\n"",
      ""2.060599     1\n"",
      ""1.982543     1\n"",
      ""1.933932     1\n"",
      ""1.784294     1\n"",
      ""Name: maxaaCH, Length: 1783, dtype: int64\n"",
      ""maxsssCH 的特征分布：\n"",
      ""0.000000    1464\n"",
      ""0.762554       7\n"",
      ""0.478604       3\n"",
      ""0.723234       3\n"",
      ""0.273861       3\n"",
      ""            ... \n"",
      ""0.583239       1\n"",
      ""0.569350       1\n"",
      ""0.720970       1\n"",
      ""0.080018       1\n"",
      ""0.250872       1\n"",
      ""Name: maxsssCH, Length: 441, dtype: int64\n"",
      ""maxddC 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxddC, dtype: int64\n"",
      ""maxtsC 的特征分布：\n"",
      ""0.000000    1872\n"",
      ""2.433315       2\n"",
      ""2.748062       2\n"",
      ""2.156174       1\n"",
      ""2.012492       1\n"",
      ""            ... \n"",
      ""1.977802       1\n"",
      ""2.089096       1\n"",
      ""2.035897       1\n"",
      ""2.098590       1\n"",
      ""2.182927       1\n"",
      ""Name: maxtsC, Length: 101, dtype: int64\n"",
      ""maxdssC 的特征分布：\n"",
      ""0.000000    1237\n"",
      ""1.006243       6\n"",
      ""0.023278       3\n"",
      ""1.131989       3\n"",
      ""1.511804       3\n"",
      ""            ... \n"",
      ""0.216679       1\n"",
      ""0.167329       1\n"",
      ""0.062496       1\n"",
      ""0.019186       1\n"",
      ""0.874012       1\n"",
      ""Name: maxdssC, Length: 667, dtype: int64\n"",
      ""maxaasC 的特征分布：\n"",
      ""0.000000    57\n"",
      ""0.995541     7\n"",
      ""1.030797     3\n"",
      ""1.257017     3\n"",
      ""1.198915     3\n"",
      ""            ..\n"",
      ""0.817166     1\n"",
      ""0.813741     1\n"",
      ""0.841654     1\n"",
      ""0.583519     1\n"",
      ""0.835639     1\n"",
      ""Name: maxaasC, Length: 1796, dtype: int64\n"",
      ""maxaaaC 的特征分布：\n"",
      ""0.000000    1278\n"",
      ""1.086342       3\n"",
      ""0.998161       3\n"",
      ""0.989468       3\n"",
      ""1.277980       2\n"",
      ""            ... \n"",
      ""1.577654       1\n"",
      ""1.425154       1\n"",
      ""1.576913       1\n"",
      ""1.424413       1\n"",
      ""0.907593       1\n"",
      ""Name: maxaaaC, Length: 665, dtype: int64\n"",
      ""maxssssC 的特征分布：\n"",
      ""0.000000    1816\n"",
      ""0.201435       3\n"",
      ""0.200168       3\n"",
      ""0.143681       2\n"",
      ""0.142431       2\n"",
      ""            ... \n"",
      ""0.152824       1\n"",
      ""0.151991       1\n"",
      ""0.149838       1\n"",
      ""0.200185       1\n"",
      ""0.090435       1\n"",
      ""Name: maxssssC, Length: 137, dtype: int64\n"",
      ""maxsNH3p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsNH3p, dtype: int64\n"",
      ""maxsNH2 的特征分布：\n"",
      ""0.000000    1924\n"",
      ""6.180347       2\n"",
      ""5.880982       1\n"",
      ""6.667317       1\n"",
      ""6.052978       1\n"",
      ""6.438421       1\n"",
      ""6.338174       1\n"",
      ""5.323545       1\n"",
      ""5.552415       1\n"",
      ""5.652540       1\n"",
      ""6.115033       1\n"",
      ""5.648272       1\n"",
      ""5.964499       1\n"",
      ""6.285302       1\n"",
      ""5.424039       1\n"",
      ""6.056210       1\n"",
      ""6.161043       1\n"",
      ""6.229058       1\n"",
      ""6.219044       1\n"",
      ""5.112182       1\n"",
      ""5.906531       1\n"",
      ""5.474609       1\n"",
      ""5.433361       1\n"",
      ""5.130724       1\n"",
      ""6.062756       1\n"",
      ""5.930651       1\n"",
      ""5.714824       1\n"",
      ""6.506544       1\n"",
      ""5.245658       1\n"",
      ""5.832327       1\n"",
      ""5.775624       1\n"",
      ""5.901230       1\n"",
      ""6.050973       1\n"",
      ""6.019029       1\n"",
      ""5.171865       1\n"",
      ""5.118483       1\n"",
      ""5.202729       1\n"",
      ""5.726553       1\n"",
      ""5.920586       1\n"",
      ""5.548165       1\n"",
      ""6.328082       1\n"",
      ""5.504275       1\n"",
      ""5.792351       1\n"",
      ""6.254872       1\n"",
      ""5.799806       1\n"",
      ""6.136562       1\n"",
      ""6.341828       1\n"",
      ""5.674439       1\n"",
      ""6.241689       1\n"",
      ""5.561610       1\n"",
      ""Name: maxsNH2, dtype: int64\n"",
      ""maxssNH2p 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssNH2p, dtype: int64\n"",
      ""maxdNH 的特征分布：\n"",
      ""0.000000    1959\n"",
      ""7.606619       2\n"",
      ""7.026123       1\n"",
      ""6.957116       1\n"",
      ""7.069333       1\n"",
      ""7.436361       1\n"",
      ""7.415886       1\n"",
      ""8.781421       1\n"",
      ""8.343041       1\n"",
      ""7.441674       1\n"",
      ""7.771965       1\n"",
      ""7.526957       1\n"",
      ""7.578826       1\n"",
      ""7.624006       1\n"",
      ""7.601595       1\n"",
      ""Name: maxdNH, dtype: int64\n"",
      ""maxssNH 的特征分布：\n"",
      ""0.000000    1639\n"",
      ""3.140970       3\n"",
      ""3.449226       2\n"",
      ""3.436449       2\n"",
      ""3.369054       2\n"",
      ""            ... \n"",
      ""2.971295       1\n"",
      ""2.959434       1\n"",
      ""3.011390       1\n"",
      ""3.523205       1\n"",
      ""2.474190       1\n"",
      ""Name: maxssNH, Length: 325, dtype: int64\n"",
      ""maxaaNH 的特征分布：\n"",
      ""0.000000    1831\n"",
      ""3.489931       3\n"",
      ""2.950987       3\n"",
      ""2.976145       3\n"",
      ""3.062098       2\n"",
      ""            ... \n"",
      ""3.006403       1\n"",
      ""3.030096       1\n"",
      ""2.933248       1\n"",
      ""3.099922       1\n"",
      ""3.434846       1\n"",
      ""Name: maxaaNH, Length: 132, dtype: int64\n"",
      ""maxtN 的特征分布：\n"",
      ""0.000000     1895\n"",
      ""10.020969       2\n"",
      ""9.349060        1\n"",
      ""9.317795        1\n"",
      ""9.019996        1\n"",
      ""             ... \n"",
      ""9.147856        1\n"",
      ""9.163741        1\n"",
      ""9.231374        1\n"",
      ""9.221446        1\n"",
      ""9.229602        1\n"",
      ""Name: maxtN, Length: 79, dtype: int64\n"",
      ""maxsssNHp 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssNHp, dtype: int64\n"",
      ""maxdsN 的特征分布：\n"",
      ""0.000000    1850\n"",
      ""4.526679       1\n"",
      ""4.399309       1\n"",
      ""4.343387       1\n"",
      ""4.648952       1\n"",
      ""            ... \n"",
      ""4.031036       1\n"",
      ""4.083001       1\n"",
      ""4.237373       1\n"",
      ""4.355428       1\n"",
      ""4.803363       1\n"",
      ""Name: maxdsN, Length: 125, dtype: int64\n"",
      ""maxaaN 的特征分布：\n"",
      ""0.000000    1497\n"",
      ""4.270441       3\n"",
      ""4.316428       3\n"",
      ""4.095539       2\n"",
      ""4.324451       2\n"",
      ""            ... \n"",
      ""3.708686       1\n"",
      ""4.469039       1\n"",
      ""4.437831       1\n"",
      ""3.915771       1\n"",
      ""4.460040       1\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Name: maxaaN, Length: 453, dtype: int64\n"",
      ""maxsssN 的特征分布：\n"",
      ""0.000000    1093\n"",
      ""2.499274       7\n"",
      ""2.502967       4\n"",
      ""2.471717       4\n"",
      ""2.438958       4\n"",
      ""            ... \n"",
      ""1.298729       1\n"",
      ""1.631909       1\n"",
      ""1.655080       1\n"",
      ""1.623830       1\n"",
      ""2.487796       1\n"",
      ""Name: maxsssN, Length: 788, dtype: int64\n"",
      ""maxddsN 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxddsN, dtype: int64\n"",
      ""maxaasN 的特征分布：\n"",
      ""0.000000    1783\n"",
      ""2.329332       2\n"",
      ""2.283422       2\n"",
      ""2.115185       2\n"",
      ""2.349908       1\n"",
      ""            ... \n"",
      ""1.413583       1\n"",
      ""2.199933       1\n"",
      ""1.969333       1\n"",
      ""1.505470       1\n"",
      ""2.017170       1\n"",
      ""Name: maxaasN, Length: 189, dtype: int64\n"",
      ""maxssssNp 的特征分布：\n"",
      ""0.00000    1973\n"",
      ""0.86878       1\n"",
      ""Name: maxssssNp, dtype: int64\n"",
      ""maxsOH 的特征分布：\n"",
      ""0.000000     426\n"",
      ""10.136089      6\n"",
      ""10.016644      3\n"",
      ""9.977508       3\n"",
      ""9.395845       3\n"",
      ""            ... \n"",
      ""10.114390      1\n"",
      ""10.110930      1\n"",
      ""10.097010      1\n"",
      ""10.062797      1\n"",
      ""9.815320       1\n"",
      ""Name: maxsOH, Length: 1431, dtype: int64\n"",
      ""maxdO 的特征分布：\n"",
      ""0.000000     924\n"",
      ""10.837359      3\n"",
      ""12.180667      3\n"",
      ""12.845989      3\n"",
      ""10.854652      3\n"",
      ""            ... \n"",
      ""13.330358      1\n"",
      ""13.805235      1\n"",
      ""13.804269      1\n"",
      ""13.549302      1\n"",
      ""13.659317      1\n"",
      ""Name: maxdO, Length: 1002, dtype: int64\n"",
      ""maxssO 的特征分布：\n"",
      ""0.000000    934\n"",
      ""6.467811      6\n"",
      ""6.516055      4\n"",
      ""6.502751      3\n"",
      ""6.343584      3\n"",
      ""           ... \n"",
      ""6.439306      1\n"",
      ""6.450781      1\n"",
      ""6.495633      1\n"",
      ""6.387271      1\n"",
      ""6.247428      1\n"",
      ""Name: maxssO, Length: 962, dtype: int64\n"",
      ""maxaaO 的特征分布：\n"",
      ""0.000000    1689\n"",
      ""5.290931       2\n"",
      ""5.125770       2\n"",
      ""5.465924       2\n"",
      ""5.652097       2\n"",
      ""            ... \n"",
      ""5.059432       1\n"",
      ""5.181881       1\n"",
      ""5.197010       1\n"",
      ""5.275153       1\n"",
      ""6.368240       1\n"",
      ""Name: maxaaO, Length: 277, dtype: int64\n"",
      ""maxaOm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxaOm, dtype: int64\n"",
      ""maxsOm 的特征分布：\n"",
      ""0.000000     1956\n"",
      ""11.136087       1\n"",
      ""11.008185       1\n"",
      ""11.734672       1\n"",
      ""10.737937       1\n"",
      ""11.513445       1\n"",
      ""12.360911       1\n"",
      ""11.915749       1\n"",
      ""10.584585       1\n"",
      ""11.237685       1\n"",
      ""10.893715       1\n"",
      ""10.824798       1\n"",
      ""11.665950       1\n"",
      ""11.034928       1\n"",
      ""11.076231       1\n"",
      ""11.045915       1\n"",
      ""10.934930       1\n"",
      ""11.365685       1\n"",
      ""10.827763       1\n"",
      ""Name: maxsOm, dtype: int64\n"",
      ""maxsF 的特征分布：\n"",
      ""0.000000     1648\n"",
      ""15.342421       3\n"",
      ""15.363254       3\n"",
      ""14.962811       2\n"",
      ""14.171597       2\n"",
      ""             ... \n"",
      ""13.438277       1\n"",
      ""13.483802       1\n"",
      ""13.462968       1\n"",
      ""13.503285       1\n"",
      ""13.228475       1\n"",
      ""Name: maxsF, Length: 315, dtype: int64\n"",
      ""maxsSiH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsSiH3, dtype: int64\n"",
      ""maxssSiH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssSiH2, dtype: int64\n"",
      ""maxsssSiH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssSiH, dtype: int64\n"",
      ""maxssssSi 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssSi, dtype: int64\n"",
      ""maxsPH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsPH2, dtype: int64\n"",
      ""maxssPH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssPH, dtype: int64\n"",
      ""maxsssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssP, dtype: int64\n"",
      ""maxdsssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxdsssP, dtype: int64\n"",
      ""maxddsP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxddsP, dtype: int64\n"",
      ""maxsssssP 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssssP, dtype: int64\n"",
      ""maxsSH 的特征分布：\n"",
      ""0.00000    1973\n"",
      ""0.30156       1\n"",
      ""Name: maxsSH, dtype: int64\n"",
      ""maxdS 的特征分布：\n"",
      ""0.000000    1941\n"",
      ""0.455603       1\n"",
      ""0.488484       1\n"",
      ""0.456076       1\n"",
      ""0.480675       1\n"",
      ""0.428540       1\n"",
      ""0.451855       1\n"",
      ""0.437531       1\n"",
      ""0.369020       1\n"",
      ""0.375617       1\n"",
      ""0.473384       1\n"",
      ""0.431362       1\n"",
      ""0.483055       1\n"",
      ""0.444105       1\n"",
      ""0.474563       1\n"",
      ""0.321507       1\n"",
      ""0.493075       1\n"",
      ""0.472448       1\n"",
      ""0.174980       1\n"",
      ""0.491639       1\n"",
      ""0.869272       1\n"",
      ""0.385327       1\n"",
      ""0.330474       1\n"",
      ""0.903524       1\n"",
      ""0.452047       1\n"",
      ""0.147002       1\n"",
      ""0.248066       1\n"",
      ""0.370515       1\n"",
      ""0.535617       1\n"",
      ""0.536069       1\n"",
      ""0.335617       1\n"",
      ""0.392654       1\n"",
      ""0.247844       1\n"",
      ""0.441645       1\n"",
      ""Name: maxdS, dtype: int64\n"",
      ""maxssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssS, dtype: int64\n"",
      ""maxaaS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxaaS, dtype: int64\n"",
      ""maxdssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxdssS, dtype: int64\n"",
      ""maxddssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxddssS, dtype: int64\n"",
      ""maxssssssS 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssssS, dtype: int64\n"",
      ""maxSm 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxSm, dtype: int64\n"",
      ""maxsCl 的特征分布：\n"",
      ""0.000000    1801\n"",
      ""0.930356       2\n"",
      ""0.568272       2\n"",
      ""0.705641       1\n"",
      ""0.647682       1\n"",
      ""            ... \n"",
      ""0.604552       1\n"",
      ""0.624708       1\n"",
      ""0.492587       1\n"",
      ""0.496445       1\n"",
      ""0.600303       1\n"",
      ""Name: maxsCl, Length: 172, dtype: int64\n"",
      ""maxsGeH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsGeH3, dtype: int64\n"",
      ""maxssGeH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssGeH2, dtype: int64\n"",
      ""maxsssGeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssGeH, dtype: int64\n"",
      ""maxssssGe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssGe, dtype: int64\n"",
      ""maxsAsH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsAsH2, dtype: int64\n"",
      ""maxssAsH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssAsH, dtype: int64\n"",
      ""maxsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssAs, dtype: int64\n"",
      ""maxdsssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxdsssAs, dtype: int64\n"",
      ""maxddsAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxddsAs, dtype: int64\n"",
      ""maxsssssAs 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssssAs, dtype: int64\n"",
      ""maxsSeH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsSeH, dtype: int64\n"",
      ""maxdSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxdSe, dtype: int64\n"",
      ""maxssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssSe, dtype: int64\n"",
      ""maxaaSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxaaSe, dtype: int64\n"",
      ""maxdssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxdssSe, dtype: int64\n"",
      ""maxssssssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssssSe, dtype: int64\n"",
      ""maxddssSe 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxddssSe, dtype: int64\n"",
      ""maxsBr 的特征分布：\n"",
      ""0.000000    1894\n"",
      ""0.042634       3\n"",
      ""0.036849       2\n"",
      ""0.160049       1\n"",
      ""0.354061       1\n"",
      ""            ... \n"",
      ""0.099386       1\n"",
      ""0.086666       1\n"",
      ""0.041236       1\n"",
      ""0.034898       1\n"",
      ""0.047215       1\n"",
      ""Name: maxsBr, Length: 78, dtype: int64\n"",
      ""maxsSnH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsSnH3, dtype: int64\n"",
      ""maxssSnH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssSnH2, dtype: int64\n"",
      ""maxsssSnH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssSnH, dtype: int64\n"",
      ""maxssssSn 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssSn, dtype: int64\n"",
      ""maxsI 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsI, dtype: int64\n"",
      ""maxsPbH3 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsPbH3, dtype: int64\n"",
      ""maxssPbH2 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssPbH2, dtype: int64\n"",
      ""maxsssPbH 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxsssPbH, dtype: int64\n"",
      ""maxssssPb 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: maxssssPb, dtype: int64\n"",
      ""sumI 的特征分布：\n"",
      ""50.166667    14\n"",
      ""49.833333    14\n"",
      ""74.000000    13\n"",
      ""70.314815    11\n"",
      ""79.166667    11\n"",
      ""             ..\n"",
      ""98.333333     1\n"",
      ""96.500000     1\n"",
      ""95.000000     1\n"",
      ""97.333333     1\n"",
      ""91.824074     1\n"",
      ""Name: sumI, Length: 996, dtype: int64\n"",
      ""hmax 的特征分布：\n"",
      ""0.839556    44\n"",
      ""0.738280    27\n"",
      ""0.953664    20\n"",
      ""0.993664    19\n"",
      ""0.846926    13\n"",
      ""            ..\n"",
      ""0.575760     1\n"",
      ""1.391421     1\n"",
      ""0.502966     1\n"",
      ""0.502399     1\n"",
      ""1.613558     1\n"",
      ""Name: hmax, Length: 1453, dtype: int64\n"",
      ""gmax 的特征分布：\n"",
      ""10.136089    6\n"",
      ""12.845989    3\n"",
      ""15.342421    3\n"",
      ""10.129447    3\n"",
      ""10.546160    3\n"",
      ""            ..\n"",
      ""12.069856    1\n"",
      ""11.849023    1\n"",
      ""11.917634    1\n"",
      ""11.960323    1\n"",
      ""13.659317    1\n"",
      ""Name: gmax, Length: 1847, dtype: int64\n"",
      ""hmin 的特征分布：\n"",
      ""-0.401857    15\n"",
      ""-0.399195    11\n"",
      "" 0.048889     8\n"",
      ""-0.043542     8\n"",
      ""-0.188501     7\n"",
      ""             ..\n"",
      "" 0.190237     1\n"",
      "" 0.108472     1\n"",
      "" 0.070773     1\n"",
      "" 0.145860     1\n"",
      ""-0.412451     1\n"",
      ""Name: hmin, Length: 1653, dtype: int64\n"",
      ""gmin 的特征分布：\n"",
      ""-0.353793    7\n"",
      "" 0.139596    4\n"",
      ""-2.776593    3\n"",
      ""-1.608354    3\n"",
      ""-0.387321    3\n"",
      ""            ..\n"",
      ""-0.416852    1\n"",
      ""-0.402729    1\n"",
      ""-0.328076    1\n"",
      ""-0.509630    1\n"",
      ""-5.993028    1\n"",
      ""Name: gmin, Length: 1844, dtype: int64\n"",
      ""LipoaffinityIndex 的特征分布：\n"",
      ""11.991200    6\n"",
      ""11.276278    3\n"",
      ""12.803699    3\n"",
      ""8.823879     3\n"",
      ""12.231564    3\n"",
      ""            ..\n"",
      ""4.670543     1\n"",
      ""4.205941     1\n"",
      ""4.674302     1\n"",
      ""4.668732     1\n"",
      ""7.965074     1\n"",
      ""Name: LipoaffinityIndex, Length: 1875, dtype: int64\n"",
      ""MAXDN 的特征分布：\n"",
      ""1.687126    7\n"",
      ""1.527070    4\n"",
      ""4.109926    3\n"",
      ""1.316453    3\n"",
      ""2.626784    3\n"",
      ""           ..\n"",
      ""2.083519    1\n"",
      ""2.069395    1\n"",
      ""1.994743    1\n"",
      ""2.176296    1\n"",
      ""6.317102    1\n"",
      ""Name: MAXDN, Length: 1847, dtype: int64\n"",
      ""MAXDP 的特征分布：\n"",
      ""4.136089    6\n"",
      ""4.129447    3\n"",
      ""3.977508    3\n"",
      ""3.997695    3\n"",
      ""3.989464    3\n"",
      ""           ..\n"",
      ""5.069856    1\n"",
      ""4.849023    1\n"",
      ""4.917634    1\n"",
      ""4.960323    1\n"",
      ""6.659317    1\n"",
      ""Name: MAXDP, Length: 1845, dtype: int64\n"",
      ""DELS 的特征分布：\n"",
      ""29.882351    6\n"",
      ""29.570228    3\n"",
      ""28.130907    3\n"",
      ""25.664809    3\n"",
      ""22.338128    3\n"",
      ""            ..\n"",
      ""28.104104    1\n"",
      ""29.802281    1\n"",
      ""34.756182    1\n"",
      ""29.384796    1\n"",
      ""66.279634    1\n"",
      ""Name: DELS, Length: 1866, dtype: int64\n"",
      ""MAXDN2 的特征分布：\n"",
      ""1.687126    7\n"",
      ""1.527070    4\n"",
      ""1.720655    3\n"",
      ""1.456627    3\n"",
      ""2.626784    3\n"",
      ""           ..\n"",
      ""2.069395    1\n"",
      ""2.100067    1\n"",
      ""2.176296    1\n"",
      ""2.192500    1\n"",
      ""5.021763    1\n"",
      ""Name: MAXDN2, Length: 1847, dtype: int64\n"",
      ""MAXDP2 的特征分布：\n"",
      ""4.136089    6\n"",
      ""3.997695    3\n"",
      ""3.930101    3\n"",
      ""4.129447    3\n"",
      ""3.942056    3\n"",
      ""           ..\n"",
      ""4.917634    1\n"",
      ""4.960323    1\n"",
      ""5.028934    1\n"",
      ""5.078992    1\n"",
      ""6.511168    1\n"",
      ""Name: MAXDP2, Length: 1846, dtype: int64\n"",
      ""DELS2 的特征分布：\n"",
      ""29.882351    6\n"",
      ""28.876759    3\n"",
      ""25.664809    3\n"",
      ""51.195724    3\n"",
      ""33.703552    3\n"",
      ""            ..\n"",
      ""27.182250    1\n"",
      ""27.219565    1\n"",
      ""28.066789    1\n"",
      ""28.104104    1\n"",
      ""65.273726    1\n"",
      ""Name: DELS2, Length: 1864, dtype: int64\n"",
      ""ETA_Alpha 的特征分布：\n"",
      ""16.56665    37\n"",
      ""16.06665    25\n"",
      ""16.73332    21\n"",
      ""17.06665    21\n"",
      ""16.23332    21\n"",
      ""            ..\n"",
      ""12.19524     1\n"",
      ""14.09999     1\n"",
      ""7.43332      1\n"",
      ""13.66666     1\n"",
      ""18.83331     1\n"",
      ""Name: ETA_Alpha, Length: 796, dtype: int64\n"",
      ""ETA_AlphaP 的特征分布：\n"",
      ""0.48333    47\n"",
      ""0.46667    36\n"",
      ""0.48725    31\n"",
      ""0.48687    23\n"",
      ""0.48000    23\n"",
      ""           ..\n"",
      ""0.45031     1\n"",
      ""0.48839     1\n"",
      ""0.48908     1\n"",
      ""0.46212     1\n"",
      ""0.48291     1\n"",
      ""Name: ETA_AlphaP, Length: 738, dtype: int64\n"",
      ""ETA_dAlpha_A 的特征分布：\n"",
      ""0.00000    1831\n"",
      ""0.01754       5\n"",
      ""0.01667       5\n"",
      ""0.02222       5\n"",
      ""0.00476       4\n"",
      ""           ... \n"",
      ""0.02273       1\n"",
      ""0.06140       1\n"",
      ""0.01515       1\n"",
      ""0.00758       1\n"",
      ""0.02807       1\n"",
      ""Name: ETA_dAlpha_A, Length: 100, dtype: int64\n"",
      ""ETA_dAlpha_B 的特征分布：\n"",
      ""0.00000    150\n"",
      ""0.01667     47\n"",
      ""0.03333     36\n"",
      ""0.01275     31\n"",
      ""0.01313     23\n"",
      ""          ... \n"",
      ""0.01410      1\n"",
      ""0.00333      1\n"",
      ""0.00494      1\n"",
      ""0.01101      1\n"",
      ""0.01709      1\n"",
      ""Name: ETA_dAlpha_B, Length: 640, dtype: int64\n"",
      ""ETA_Epsilon_1 的特征分布：\n"",
      ""0.56667    27\n"",
      ""0.57312    16\n"",
      ""0.55686    15\n"",
      ""0.54783    13\n"",
      ""0.64688    13\n"",
      ""           ..\n"",
      ""0.48571     1\n"",
      ""0.51887     1\n"",
      ""0.48868     1\n"",
      ""0.60286     1\n"",
      ""0.61487     1\n"",
      ""Name: ETA_Epsilon_1, Length: 1185, dtype: int64\n"",
      ""ETA_Epsilon_2 的特征分布：\n"",
      ""0.80980    30\n"",
      ""0.76667    30\n"",
      ""0.81313    21\n"",
      ""0.79905    19\n"",
      ""0.80196    17\n"",
      ""           ..\n"",
      ""0.82593     1\n"",
      ""0.82353     1\n"",
      ""0.86019     1\n"",
      ""0.84881     1\n"",
      ""0.82479     1\n"",
      ""Name: ETA_Epsilon_2, Length: 908, dtype: int64\n"",
      ""ETA_Epsilon_3 的特征分布：\n"",
      ""0.44286    164\n"",
      ""0.44468    115\n"",
      ""0.44237    102\n"",
      ""0.44400     98\n"",
      ""0.44340     96\n"",
      ""          ... \n"",
      ""0.43861      1\n"",
      ""0.44132      1\n"",
      ""0.45273      1\n"",
      ""0.44328      1\n"",
      ""0.44940      1\n"",
      ""Name: ETA_Epsilon_3, Length: 98, dtype: int64\n"",
      ""ETA_Epsilon_4 的特征分布：\n"",
      ""0.50884    22\n"",
      ""0.51167    19\n"",
      ""0.50078    17\n"",
      ""0.56667    16\n"",
      ""0.49848    14\n"",
      ""           ..\n"",
      ""0.47143     1\n"",
      ""0.48413     1\n"",
      ""0.48571     1\n"",
      ""0.47544     1\n"",
      ""0.52007     1\n"",
      ""Name: ETA_Epsilon_4, Length: 1143, dtype: int64\n"",
      ""ETA_Epsilon_5 的特征分布：\n"",
      ""0.78148    28\n"",
      ""0.78381    21\n"",
      ""0.78261    19\n"",
      ""0.78636    18\n"",
      ""0.72933    16\n"",
      ""           ..\n"",
      ""0.82525     1\n"",
      ""0.84091     1\n"",
      ""0.84368     1\n"",
      ""0.87826     1\n"",
      ""0.79919     1\n"",
      ""Name: ETA_Epsilon_5, Length: 954, dtype: int64\n"",
      ""ETA_dEpsilon_A 的特征分布：\n"",
      ""0.12199    21\n"",
      ""0.12806    16\n"",
      ""0.19951    12\n"",
      ""0.11253    12\n"",
      ""0.10350    10\n"",
      ""           ..\n"",
      ""0.17943     1\n"",
      ""0.10295     1\n"",
      ""0.10366     1\n"",
      ""0.11184     1\n"",
      ""0.16908     1\n"",
      ""Name: ETA_dEpsilon_A, Length: 1330, dtype: int64\n"",
      ""ETA_dEpsilon_B 的特征分布：\n"",
      ""0.05783    21\n"",
      ""0.06145    16\n"",
      ""0.05838    12\n"",
      ""0.12034    12\n"",
      ""0.05569    10\n"",
      ""           ..\n"",
      ""0.06913     1\n"",
      ""0.06938     1\n"",
      ""0.06765     1\n"",
      ""0.06966     1\n"",
      ""0.09480     1\n"",
      ""Name: ETA_dEpsilon_B, Length: 1313, dtype: int64\n"",
      ""ETA_dEpsilon_C 的特征分布：\n"",
      ""-0.06415    22\n"",
      ""-0.06661    18\n"",
      ""-0.05610    16\n"",
      ""-0.05416    14\n"",
      ""-0.04781    14\n"",
      ""            ..\n"",
      ""-0.11373     1\n"",
      ""-0.05179     1\n"",
      ""-0.03615     1\n"",
      ""-0.03167     1\n"",
      ""-0.07428     1\n"",
      ""Name: ETA_dEpsilon_C, Length: 1266, dtype: int64\n"",
      ""ETA_dEpsilon_D 的特征分布：\n"",
      ""0.00000    125\n"",
      ""0.02832     28\n"",
      ""0.02932     21\n"",
      ""0.03733     16\n"",
      ""0.04596     16\n"",
      ""          ... \n"",
      ""0.04066      1\n"",
      ""0.04279      1\n"",
      ""0.04779      1\n"",
      ""0.03550      1\n"",
      ""0.02560      1\n"",
      ""Name: ETA_dEpsilon_D, Length: 987, dtype: int64\n"",
      ""ETA_Psi_1 的特征分布：\n"",
      ""0.60169    31\n"",
      ""0.59876    21\n"",
      ""0.63327    20\n"",
      ""0.59833    19\n"",
      ""0.59535    17\n"",
      ""           ..\n"",
      ""0.57059     1\n"",
      ""0.54588     1\n"",
      ""0.56467     1\n"",
      ""0.58744     1\n"",
      ""0.58549     1\n"",
      ""Name: ETA_Psi_1, Length: 931, dtype: int64\n"",
      ""ETA_dPsi_A 的特征分布：\n"",
      ""0.11260    31\n"",
      ""0.11553    21\n"",
      ""0.08102    20\n"",
      ""0.11596    19\n"",
      ""0.11894    17\n"",
      ""           ..\n"",
      ""0.24324     1\n"",
      ""0.13071     1\n"",
      ""0.13726     1\n"",
      ""0.07454     1\n"",
      ""0.12880     1\n"",
      ""Name: ETA_dPsi_A, Length: 928, dtype: int64\n"",
      ""ETA_dPsi_B 的特征分布：\n"",
      ""0.00000    1970\n"",
      ""0.01197       1\n"",
      ""0.00481       1\n"",
      ""0.03763       1\n"",
      ""0.03097       1\n"",
      ""Name: ETA_dPsi_B, dtype: int64\n"",
      ""ETA_Shape_P 的特征分布：\n"",
      ""0.04149    14\n"",
      ""0.17241    13\n"",
      ""0.15625    12\n"",
      ""0.06734    12\n"",
      ""0.04024    11\n"",
      ""           ..\n"",
      ""0.16832     1\n"",
      ""0.15315     1\n"",
      ""0.19172     1\n"",
      ""0.28712     1\n"",
      ""0.02188     1\n"",
      ""Name: ETA_Shape_P, Length: 1230, dtype: int64\n"",
      ""ETA_Shape_Y 的特征分布：\n"",
      ""0.37500    19\n"",
      ""0.28125    17\n"",
      ""0.35614    15\n"",
      ""0.34570    15\n"",
      ""0.40404    13\n"",
      ""           ..\n"",
      ""0.18750     1\n"",
      ""0.33793     1\n"",
      ""0.31536     1\n"",
      ""0.38095     1\n"",
      ""0.31858     1\n"",
      ""Name: ETA_Shape_Y, Length: 1087, dtype: int64\n"",
      ""ETA_Shape_X 的特征分布：\n"",
      ""0.00000    1426\n"",
      ""0.09375      10\n"",
      ""0.08955      10\n"",
      ""0.04110       9\n"",
      ""0.05172       8\n"",
      ""           ... \n"",
      ""0.05175       1\n"",
      ""0.05531       1\n"",
      ""0.12676       1\n"",
      ""0.13235       1\n"",
      ""0.04425       1\n"",
      ""Name: ETA_Shape_X, Length: 335, dtype: int64\n"",
      ""ETA_Beta 的特征分布：\n"",
      ""40.75    43\n"",
      ""42.75    38\n"",
      ""41.00    35\n"",
      ""40.25    33\n"",
      ""27.50    32\n"",
      ""         ..\n"",
      ""11.25     1\n"",
      ""64.00     1\n"",
      ""14.75     1\n"",
      ""13.25     1\n"",
      ""62.50     1\n"",
      ""Name: ETA_Beta, Length: 182, dtype: int64\n"",
      ""ETA_BetaP 的特征分布：\n"",
      ""1.50000    45\n"",
      ""1.19853    31\n"",
      ""1.25000    30\n"",
      ""1.21970    22\n"",
      ""1.22143    17\n"",
      ""           ..\n"",
      ""1.50781     1\n"",
      ""1.43966     1\n"",
      ""1.64474     1\n"",
      ""1.56579     1\n"",
      ""1.04839     1\n"",
      ""Name: ETA_BetaP, Length: 702, dtype: int64\n"",
      ""ETA_Beta_s 的特征分布：\n"",
      ""12.50    71\n"",
      ""21.25    64\n"",
      ""20.75    61\n"",
      ""12.00    58\n"",
      ""13.25    57\n"",
      ""         ..\n"",
      ""7.75      1\n"",
      ""8.25      1\n"",
      ""8.00      1\n"",
      ""37.50     1\n"",
      ""30.25     1\n"",
      ""Name: ETA_Beta_s, Length: 94, dtype: int64\n"",
      ""ETA_BetaP_s 的特征分布：\n"",
      ""0.62500    151\n"",
      ""0.60000     58\n"",
      ""0.63235     44\n"",
      ""0.58333     42\n"",
      ""0.62879     41\n"",
      ""          ... \n"",
      ""0.55882      1\n"",
      ""0.56944      1\n"",
      ""0.68548      1\n"",
      ""0.56000      1\n"",
      ""0.58065      1\n"",
      ""Name: ETA_BetaP_s, Length: 266, dtype: int64\n"",
      ""ETA_Beta_ns 的特征分布：\n"",
      ""19.50    125\n"",
      ""21.00    110\n"",
      ""20.50    103\n"",
      ""16.50     94\n"",
      ""19.00     90\n"",
      ""        ... \n"",
      ""27.75      1\n"",
      ""28.50      1\n"",
      ""18.25      1\n"",
      ""17.25      1\n"",
      ""30.75      1\n"",
      ""Name: ETA_Beta_ns, Length: 94, dtype: int64\n"",
      ""ETA_BetaP_ns 的特征分布：\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""0.57353    43\n"",
      ""0.59091    40\n"",
      ""0.60000    34\n"",
      ""0.75000    32\n"",
      ""0.91667    26\n"",
      ""           ..\n"",
      ""0.35185     1\n"",
      ""0.80882     1\n"",
      ""0.74000     1\n"",
      ""0.90323     1\n"",
      ""0.79605     1\n"",
      ""Name: ETA_BetaP_ns, Length: 500, dtype: int64\n"",
      ""ETA_dBeta 的特征分布：\n"",
      ""-1.75     65\n"",
      ""-0.75     61\n"",
      "" 2.50     59\n"",
      "" 4.75     52\n"",
      ""-1.50     51\n"",
      ""          ..\n"",
      ""-13.75     1\n"",
      ""-12.25     1\n"",
      "" 9.75      1\n"",
      ""-7.25      1\n"",
      "" 10.00     1\n"",
      ""Name: ETA_dBeta, Length: 105, dtype: int64\n"",
      ""ETA_dBetaP 的特征分布：\n"",
      "" 0.00000    48\n"",
      "" 0.25000    40\n"",
      ""-0.05147    28\n"",
      ""-0.03788    22\n"",
      ""-0.02143    18\n"",
      ""            ..\n"",
      "" 0.00806     1\n"",
      "" 0.38158     1\n"",
      "" 0.35000     1\n"",
      "" 0.35526     1\n"",
      ""-0.20968     1\n"",
      ""Name: ETA_dBetaP, Length: 695, dtype: int64\n"",
      ""ETA_Beta_ns_d 的特征分布：\n"",
      ""1.0    633\n"",
      ""1.5    579\n"",
      ""0.5    374\n"",
      ""0.0    203\n"",
      ""2.0    146\n"",
      ""2.5     34\n"",
      ""3.0      2\n"",
      ""3.5      2\n"",
      ""4.0      1\n"",
      ""Name: ETA_Beta_ns_d, dtype: int64\n"",
      ""ETA_BetaP_ns_d 的特征分布：\n"",
      ""0.00000    203\n"",
      ""0.04545     98\n"",
      ""0.05000     76\n"",
      ""0.04412     74\n"",
      ""0.04286     65\n"",
      ""          ... \n"",
      ""0.12500      1\n"",
      ""0.08696      1\n"",
      ""0.13636      1\n"",
      ""0.11364      1\n"",
      ""0.05952      1\n"",
      ""Name: ETA_BetaP_ns_d, Length: 108, dtype: int64\n"",
      ""ETA_Eta 的特征分布：\n"",
      ""35.23951    6\n"",
      ""34.37530    3\n"",
      ""30.76060    3\n"",
      ""34.77733    3\n"",
      ""33.19105    3\n"",
      ""           ..\n"",
      ""14.91058    1\n"",
      ""13.19539    1\n"",
      ""15.55310    1\n"",
      ""15.56191    1\n"",
      ""34.61804    1\n"",
      ""Name: ETA_Eta, Length: 1875, dtype: int64\n"",
      ""ETA_EtaP 的特征分布：\n"",
      ""1.00684    6\n"",
      ""0.97621    3\n"",
      ""1.00652    3\n"",
      ""1.13312    3\n"",
      ""0.96127    3\n"",
      ""          ..\n"",
      ""0.68029    1\n"",
      ""0.71139    1\n"",
      ""0.74553    1\n"",
      ""0.69449    1\n"",
      ""0.84434    1\n"",
      ""Name: ETA_EtaP, Length: 1859, dtype: int64\n"",
      ""ETA_Eta_R 的特征分布：\n"",
      ""41.41897    11\n"",
      ""29.32683    10\n"",
      ""32.70318    10\n"",
      ""34.73695     9\n"",
      ""39.00682     8\n"",
      ""            ..\n"",
      ""38.23216     1\n"",
      ""39.20520     1\n"",
      ""73.01699     1\n"",
      ""63.06798     1\n"",
      ""86.65077     1\n"",
      ""Name: ETA_Eta_R, Length: 1457, dtype: int64\n"",
      ""ETA_Eta_F 的特征分布：\n"",
      ""34.64078    6\n"",
      ""30.55144    3\n"",
      ""33.64141    3\n"",
      ""6.64164     3\n"",
      ""38.94744    3\n"",
      ""           ..\n"",
      ""19.20812    1\n"",
      ""18.58538    1\n"",
      ""19.43191    1\n"",
      ""19.44485    1\n"",
      ""52.03272    1\n"",
      ""Name: ETA_Eta_F, Length: 1874, dtype: int64\n"",
      ""ETA_EtaP_F 的特征分布：\n"",
      ""0.98974    6\n"",
      ""1.20116    3\n"",
      ""0.89857    3\n"",
      ""0.90568    3\n"",
      ""1.01047    3\n"",
      ""          ..\n"",
      ""0.96113    1\n"",
      ""0.96065    1\n"",
      ""1.04282    1\n"",
      ""1.02416    1\n"",
      ""1.26909    1\n"",
      ""Name: ETA_EtaP_F, Length: 1845, dtype: int64\n"",
      ""ETA_Eta_L 的特征分布：\n"",
      ""8.72791    9\n"",
      ""8.69024    6\n"",
      ""9.15711    5\n"",
      ""9.50375    4\n"",
      ""7.04104    4\n"",
      ""          ..\n"",
      ""6.01752    1\n"",
      ""7.38228    1\n"",
      ""3.68416    1\n"",
      ""3.89747    1\n"",
      ""8.14761    1\n"",
      ""Name: ETA_Eta_L, Length: 1734, dtype: int64\n"",
      ""ETA_EtaP_L 的特征分布：\n"",
      ""0.24937    9\n"",
      ""0.26334    6\n"",
      ""0.17907    5\n"",
      ""0.20620    5\n"",
      ""0.25543    5\n"",
      ""          ..\n"",
      ""0.30617    1\n"",
      ""0.32467    1\n"",
      ""0.25108    1\n"",
      ""0.21984    1\n"",
      ""0.20891    1\n"",
      ""Name: ETA_EtaP_L, Length: 1631, dtype: int64\n"",
      ""ETA_Eta_R_L 的特征分布：\n"",
      ""10.07967    23\n"",
      ""16.58045    21\n"",
      ""16.15294    20\n"",
      ""9.96918     18\n"",
      ""9.55850     18\n"",
      ""            ..\n"",
      ""12.15215     1\n"",
      ""13.70346     1\n"",
      ""14.19015     1\n"",
      ""19.02914     1\n"",
      ""18.87007     1\n"",
      ""Name: ETA_Eta_R_L, Length: 1068, dtype: int64\n"",
      ""ETA_Eta_F_L 的特征分布：\n"",
      ""7.46270     10\n"",
      ""8.49582     10\n"",
      ""3.40959      9\n"",
      ""8.15706      9\n"",
      ""4.15756      9\n"",
      ""            ..\n"",
      ""5.85907      1\n"",
      ""6.07239      1\n"",
      ""5.80099      1\n"",
      ""8.61683      1\n"",
      ""10.72246     1\n"",
      ""Name: ETA_Eta_F_L, Length: 1523, dtype: int64\n"",
      ""ETA_EtaP_F_L 的特征分布：\n"",
      ""0.23306    9\n"",
      ""0.22614    6\n"",
      ""0.23599    5\n"",
      ""0.30980    5\n"",
      ""0.22962    5\n"",
      ""          ..\n"",
      ""0.20382    1\n"",
      ""0.17676    1\n"",
      ""0.17239    1\n"",
      ""0.23146    1\n"",
      ""0.27493    1\n"",
      ""Name: ETA_EtaP_F_L, Length: 1611, dtype: int64\n"",
      ""ETA_Eta_B 的特征分布：\n"",
      ""0.33433    55\n"",
      ""0.33355    40\n"",
      ""0.35038    34\n"",
      ""0.24501    33\n"",
      ""0.28301    31\n"",
      ""           ..\n"",
      ""0.44614     1\n"",
      ""0.70802     1\n"",
      ""0.30064     1\n"",
      ""0.51023     1\n"",
      ""0.54393     1\n"",
      ""Name: ETA_Eta_B, Length: 591, dtype: int64\n"",
      ""ETA_EtaP_B 的特征分布：\n"",
      ""0.01592    26\n"",
      ""0.00842    23\n"",
      ""0.01512    23\n"",
      ""0.00791    22\n"",
      ""0.00981    21\n"",
      ""           ..\n"",
      ""0.00924     1\n"",
      ""0.00628     1\n"",
      ""0.02559     1\n"",
      ""0.02601     1\n"",
      ""0.01395     1\n"",
      ""Name: ETA_EtaP_B, Length: 811, dtype: int64\n"",
      ""ETA_Eta_B_RC 的特征分布：\n"",
      ""0.59233    55\n"",
      ""0.76355    40\n"",
      ""0.50301    33\n"",
      ""0.78038    33\n"",
      ""0.54101    31\n"",
      ""           ..\n"",
      ""0.29400     1\n"",
      ""0.61814     1\n"",
      ""1.13802     1\n"",
      ""0.47264     1\n"",
      ""1.05993     1\n"",
      ""Name: ETA_Eta_B_RC, Length: 597, dtype: int64\n"",
      ""ETA_EtaP_B_RC 的特征分布：\n"",
      ""0.02821    25\n"",
      ""0.02740    24\n"",
      ""0.02094    21\n"",
      ""0.02246    21\n"",
      ""0.03067    19\n"",
      ""           ..\n"",
      ""0.01544     1\n"",
      ""0.02739     1\n"",
      ""0.02477     1\n"",
      ""0.02440     1\n"",
      ""0.02510     1\n"",
      ""Name: ETA_EtaP_B_RC, Length: 861, dtype: int64\n"",
      ""FMF 的特征分布：\n"",
      ""0.500000    133\n"",
      ""0.533333     49\n"",
      ""0.400000     33\n"",
      ""0.483871     31\n"",
      ""0.333333     30\n"",
      ""           ... \n"",
      ""0.280000      1\n"",
      ""0.351351      1\n"",
      ""0.413793      1\n"",
      ""0.376812      1\n"",
      ""0.387755      1\n"",
      ""Name: FMF, Length: 467, dtype: int64\n"",
      ""fragC 的特征分布：\n"",
      ""3639.06    22\n"",
      ""3300.06    18\n"",
      ""3994.05    15\n"",
      ""805.04     14\n"",
      ""3234.07    14\n"",
      ""           ..\n"",
      ""1997.07     1\n"",
      ""817.08      1\n"",
      ""2323.07     1\n"",
      ""938.06      1\n"",
      ""3694.05     1\n"",
      ""Name: fragC, Length: 974, dtype: int64\n"",
      ""nHBAcc 的特征分布：\n"",
      ""1     586\n"",
      ""3     404\n"",
      ""2     376\n"",
      ""4     203\n"",
      ""0     158\n"",
      ""6      81\n"",
      ""5      76\n"",
      ""7      54\n"",
      ""8      21\n"",
      ""9       5\n"",
      ""27      3\n"",
      ""25      2\n"",
      ""15      1\n"",
      ""13      1\n"",
      ""66      1\n"",
      ""29      1\n"",
      ""10      1\n"",
      ""Name: nHBAcc, dtype: int64\n"",
      ""nHBAcc2 的特征分布：\n"",
      ""5     466\n"",
      ""4     463\n"",
      ""6     259\n"",
      ""3     254\n"",
      ""2     183\n"",
      ""7     173\n"",
      ""8      73\n"",
      ""9      45\n"",
      ""1      30\n"",
      ""10     12\n"",
      ""11      4\n"",
      ""12      3\n"",
      ""27      3\n"",
      ""25      2\n"",
      ""0       1\n"",
      ""15      1\n"",
      ""66      1\n"",
      ""28      1\n"",
      ""Name: nHBAcc2, dtype: int64\n"",
      ""nHBAcc3 的特征分布：\n"",
      ""4     534\n"",
      ""5     481\n"",
      ""3     275\n"",
      ""6     239\n"",
      ""2     194\n"",
      ""7     139\n"",
      ""8      42\n"",
      ""1      31\n"",
      ""9      19\n"",
      ""10      6\n"",
      ""11      5\n"",
      ""17      3\n"",
      ""0       1\n"",
      ""12      1\n"",
      ""46      1\n"",
      ""16      1\n"",
      ""18      1\n"",
      ""15      1\n"",
      ""Name: nHBAcc3, dtype: int64\n"",
      ""nHBAcc_Lipinski 的特征分布：\n"",
      ""4     501\n"",
      ""5     483\n"",
      ""3     275\n"",
      ""6     256\n"",
      ""2     175\n"",
      ""7     130\n"",
      ""8      71\n"",
      ""9      38\n"",
      ""1      22\n"",
      ""10     11\n"",
      ""27      3\n"",
      ""11      2\n"",
      ""25      2\n"",
      ""15      1\n"",
      ""12      1\n"",
      ""14      1\n"",
      ""66      1\n"",
      ""29      1\n"",
      ""Name: nHBAcc_Lipinski, dtype: int64\n"",
      ""nHBDon 的特征分布：\n"",
      ""2     942\n"",
      ""1     630\n"",
      ""3     243\n"",
      ""0     126\n"",
      ""4      20\n"",
      ""6       3\n"",
      ""17      3\n"",
      ""5       2\n"",
      ""7       1\n"",
      ""46      1\n"",
      ""16      1\n"",
      ""18      1\n"",
      ""15      1\n"",
      ""Name: nHBDon, dtype: int64\n"",
      ""nHBDon_Lipinski 的特征分布：\n"",
      ""2     933\n"",
      ""1     620\n"",
      ""3     249\n"",
      ""0     126\n"",
      ""4      32\n"",
      ""6       3\n"",
      ""5       3\n"",
      ""22      3\n"",
      ""20      2\n"",
      ""7       1\n"",
      ""58      1\n"",
      ""23      1\n"",
      ""Name: nHBDon_Lipinski, dtype: int64\n"",
      ""HybRatio 的特征分布：\n"",
      ""0.000000    268\n"",
      ""0.333333    136\n"",
      ""0.250000     59\n"",
      ""0.357143     50\n"",
      ""0.310345     42\n"",
      ""           ... \n"",
      ""0.611111      1\n"",
      ""0.529412      1\n"",
      ""0.424242      1\n"",
      ""0.387097      1\n"",
      ""0.812500      1\n"",
      ""Name: HybRatio, Length: 218, dtype: int64\n"",
      ""Kier1 的特征分布：\n"",
      ""14.917355    114\n"",
      ""25.641274    102\n"",
      ""15.879017     99\n"",
      ""13.959184     89\n"",
      ""24.683711     87\n"",
      ""            ... \n"",
      ""31.526627      1\n"",
      ""35.478637      1\n"",
      ""37.457778      1\n"",
      ""39.438660      1\n"",
      ""32.542400      1\n"",
      ""Name: Kier1, Length: 139, dtype: int64\n"",
      ""Kier2 的特征分布：\n"",
      ""12.029904    57\n"",
      ""6.011719     47\n"",
      ""6.629936     45\n"",
      ""11.588477    40\n"",
      ""11.823145    38\n"",
      ""             ..\n"",
      ""10.714286     1\n"",
      ""9.288338      1\n"",
      ""18.298677     1\n"",
      ""9.333333      1\n"",
      ""14.400000     1\n"",
      ""Name: Kier2, Length: 326, dtype: int64\n"",
      ""Kier3 的特征分布：\n"",
      ""6.297163    32\n"",
      ""3.347107    31\n"",
      ""2.978908    30\n"",
      ""2.880000    26\n"",
      ""6.478367    26\n"",
      ""            ..\n"",
      ""5.019259     1\n"",
      ""4.231405     1\n"",
      ""8.698225     1\n"",
      ""6.000000     1\n"",
      ""6.658734     1\n"",
      ""Name: Kier3, Length: 561, dtype: int64\n"",
      ""nAtomLC 的特征分布：\n"",
      ""3      487\n"",
      ""0      366\n"",
      ""5      259\n"",
      ""4      247\n"",
      ""1      139\n"",
      ""2      127\n"",
      ""6      100\n"",
      ""7       83\n"",
      ""8       52\n"",
      ""9       25\n"",
      ""12      21\n"",
      ""10      16\n"",
      ""11      13\n"",
      ""13       5\n"",
      ""18       5\n"",
      ""14       5\n"",
      ""16       4\n"",
      ""15       3\n"",
      ""19       3\n"",
      ""20       3\n"",
      ""40       3\n"",
      ""21       2\n"",
      ""22       2\n"",
      ""128      1\n"",
      ""72       1\n"",
      ""76       1\n"",
      ""39       1\n"",
      ""Name: nAtomLC, dtype: int64\n"",
      ""nAtomP 的特征分布：\n"",
      ""17    235\n"",
      ""18    202\n"",
      ""8     193\n"",
      ""11    140\n"",
      ""7     136\n"",
      ""20    106\n"",
      ""19    100\n"",
      ""16     88\n"",
      ""10     83\n"",
      ""13     80\n"",
      ""22     66\n"",
      ""21     58\n"",
      ""26     54\n"",
      ""12     50\n"",
      ""15     48\n"",
      ""24     42\n"",
      ""9      41\n"",
      ""25     41\n"",
      ""23     34\n"",
      ""14     31\n"",
      ""2      31\n"",
      ""28     27\n"",
      ""30     19\n"",
      ""27     18\n"",
      ""29     17\n"",
      ""4      15\n"",
      ""31      7\n"",
      ""5       4\n"",
      ""6       3\n"",
      ""33      2\n"",
      ""0       1\n"",
      ""36      1\n"",
      ""3       1\n"",
      ""Name: nAtomP, dtype: int64\n"",
      ""nAtomLAC 的特征分布：\n"",
      ""0     807\n"",
      ""2     528\n"",
      ""3     358\n"",
      ""4     146\n"",
      ""5      36\n"",
      ""6      27\n"",
      ""8      23\n"",
      ""7      14\n"",
      ""11      8\n"",
      ""10      8\n"",
      ""9       6\n"",
      ""12      5\n"",
      ""16      3\n"",
      ""14      2\n"",
      ""18      2\n"",
      ""13      1\n"",
      ""Name: nAtomLAC, dtype: int64\n"",
      ""MLogP 的特征分布：\n"",
      ""3.99    120\n"",
      ""3.88    119\n"",
      ""2.89    114\n"",
      ""2.78    113\n"",
      ""3.00    111\n"",
      ""2.56    107\n"",
      ""2.67    106\n"",
      ""3.55    102\n"",
      ""3.66    100\n"",
      ""4.10     99\n"",
      ""3.77     97\n"",
      ""4.21     95\n"",
      ""3.44     95\n"",
      ""3.33     83\n"",
      ""3.22     79\n"",
      ""3.11     78\n"",
      ""2.45     77\n"",
      ""2.34     59\n"",
      ""4.32     49\n"",
      ""2.23     39\n"",
      ""4.43     28\n"",
      ""2.12     25\n"",
      ""4.54     19\n"",
      ""2.01      9\n"",
      ""4.65      9\n"",
      ""4.76      7\n"",
      ""5.09      5\n"",
      ""1.90      4\n"",
      ""5.20      4\n"",
      ""5.31      3\n"",
      ""4.98      3\n"",
      ""1.79      3\n"",
      ""4.87      3\n"",
      ""5.42      2\n"",
      ""1.57      2\n"",
      ""1.46      2\n"",
      ""5.53      2\n"",
      ""1.68      1\n"",
      ""5.75      1\n"",
      ""Name: MLogP, dtype: int64\n"",
      ""McGowan_Volume 的特征分布：\n"",
      ""3.6219    21\n"",
      ""3.4810    16\n"",
      ""1.9584    13\n"",
      ""3.6972    12\n"",
      ""3.7383    10\n"",
      ""          ..\n"",
      ""2.9938     1\n"",
      ""2.5459     1\n"",
      ""3.1229     1\n"",
      ""5.2483     1\n"",
      ""3.8795     1\n"",
      ""Name: McGowan_Volume, Length: 1273, dtype: int64\n"",
      ""MDEC-11 的特征分布：\n"",
      ""0.000000    1329\n"",
      ""0.500000      85\n"",
      ""0.166667      40\n"",
      ""0.125000      36\n"",
      ""0.142857      36\n"",
      ""            ... \n"",
      ""0.541021       1\n"",
      ""1.341641       1\n"",
      ""0.334716       1\n"",
      ""0.567593       1\n"",
      ""0.231865       1\n"",
      ""Name: MDEC-11, Length: 166, dtype: int64\n"",
      ""MDEC-12 的特征分布：\n"",
      ""0.000000    695\n"",
      ""6.574407      7\n"",
      ""4.917070      6\n"",
      ""2.176960      6\n"",
      ""2.744207      6\n"",
      ""           ... \n"",
      ""9.725103      1\n"",
      ""9.175576      1\n"",
      ""7.396614      1\n"",
      ""7.184354      1\n"",
      ""3.671961      1\n"",
      ""Name: MDEC-12, Length: 1039, dtype: int64\n"",
      ""MDEC-13 的特征分布：\n"",
      ""0.000000    695\n"",
      ""2.818101     11\n"",
      ""1.539297     10\n"",
      ""1.099721      8\n"",
      ""5.826383      8\n"",
      ""           ... \n"",
      ""6.493208      1\n"",
      ""7.128382      1\n"",
      ""9.012212      1\n"",
      ""5.917106      1\n"",
      ""2.855717      1\n"",
      ""Name: MDEC-13, Length: 932, dtype: int64\n"",
      ""MDEC-14 的特征分布：\n"",
      ""0.000000    1602\n"",
      ""1.000000      43\n"",
      ""0.250000      23\n"",
      ""0.500000      21\n"",
      ""0.333333      17\n"",
      ""            ... \n"",
      ""1.449037       1\n"",
      ""0.992337       1\n"",
      ""1.590541       1\n"",
      ""0.942809       1\n"",
      ""1.568274       1\n"",
      ""Name: MDEC-14, Length: 117, dtype: int64\n"",
      ""MDEC-22 的特征分布：\n"",
      ""0.000000     94\n"",
      ""23.567840    20\n"",
      ""13.077523    14\n"",
      ""28.949487    12\n"",
      ""15.626557    12\n"",
      ""             ..\n"",
      ""23.467078     1\n"",
      ""15.205010     1\n"",
      ""20.117506     1\n"",
      ""4.251415      1\n"",
      ""22.167929     1\n"",
      ""Name: MDEC-22, Length: 1144, dtype: int64\n"",
      ""MDEC-23 的特征分布：\n"",
      ""22.567575    12\n"",
      ""32.110604    11\n"",
      ""31.411977    11\n"",
      ""30.215274     9\n"",
      ""40.273065     8\n"",
      ""             ..\n"",
      ""16.542224     1\n"",
      ""7.907600      1\n"",
      ""12.629187     1\n"",
      ""35.401418     1\n"",
      ""28.738025     1\n"",
      ""Name: MDEC-23, Length: 1377, dtype: int64\n"",
      ""MDEC-24 的特征分布：\n"",
      ""0.000000     1538\n"",
      ""4.947782       14\n"",
      ""8.160878       12\n"",
      ""4.585756       12\n"",
      ""7.607373       10\n"",
      ""             ... \n"",
      ""3.329951        1\n"",
      ""6.850847        1\n"",
      ""7.805032        1\n"",
      ""10.560189       1\n"",
      ""2.105676        1\n"",
      ""Name: MDEC-24, Length: 283, dtype: int64\n"",
      ""MDEC-33 的特征分布：\n"",
      ""7.352074     33\n"",
      ""9.333258     32\n"",
      ""16.939931    32\n"",
      ""10.108065    30\n"",
      ""10.328977    30\n"",
      ""             ..\n"",
      ""10.111554     1\n"",
      ""8.949616      1\n"",
      ""5.761491      1\n"",
      ""9.914635      1\n"",
      ""11.887351     1\n"",
      ""Name: MDEC-33, Length: 844, dtype: int64\n"",
      ""MDEC-34 的特征分布：\n"",
      ""0.000000    1538\n"",
      ""3.117140      31\n"",
      ""5.282134      25\n"",
      ""2.620741      24\n"",
      ""2.803966      19\n"",
      ""            ... \n"",
      ""5.551934       1\n"",
      ""7.506174       1\n"",
      ""7.593409       1\n"",
      ""1.132161       1\n"",
      ""1.749456       1\n"",
      ""Name: MDEC-34, Length: 198, dtype: int64\n"",
      ""MDEC-44 的特征分布：\n"",
      ""0.000000    1884\n"",
      ""0.250000      32\n"",
      ""0.333333      16\n"",
      ""1.000000      15\n"",
      ""1.105209       9\n"",
      ""0.965489       4\n"",
      ""0.166667       4\n"",
      ""0.500000       3\n"",
      ""0.445192       1\n"",
      ""0.111111       1\n"",
      ""0.685007       1\n"",
      ""0.100000       1\n"",
      ""0.090909       1\n"",
      ""0.142857       1\n"",
      ""0.200000       1\n"",
      ""Name: MDEC-44, dtype: int64\n"",
      ""MDEO-11 的特征分布：\n"",
      ""0.000000    482\n"",
      ""0.100000    342\n"",
      ""0.090909    153\n"",
      ""0.166667     98\n"",
      ""0.111111     74\n"",
      ""           ... \n"",
      ""0.226943      1\n"",
      ""2.204707      1\n"",
      ""1.412188      1\n"",
      ""2.254720      1\n"",
      ""1.426069      1\n"",
      ""Name: MDEO-11, Length: 189, dtype: int64\n"",
      ""MDEO-12 的特征分布：\n"",
      ""0.000000    779\n"",
      ""0.495846     75\n"",
      ""0.100000     64\n"",
      ""0.365148     59\n"",
      ""0.174078     35\n"",
      ""           ... \n"",
      ""0.740824      1\n"",
      ""1.174990      1\n"",
      ""1.111048      1\n"",
      ""0.965991      1\n"",
      ""1.850440      1\n"",
      ""Name: MDEO-12, Length: 323, dtype: int64\n"",
      ""MDEO-22 的特征分布：\n"",
      ""0.000000    1409\n"",
      ""0.166667     193\n"",
      ""0.250000      55\n"",
      ""0.464159      47\n"",
      ""0.333333      40\n"",
      ""            ... \n"",
      ""0.400594       1\n"",
      ""0.352583       1\n"",
      ""0.931801       1\n"",
      ""0.496951       1\n"",
      ""0.772804       1\n"",
      ""Name: MDEO-22, Length: 75, dtype: int64\n"",
      ""MDEN-11 的特征分布：\n"",
      ""0.000000    1951\n"",
      ""0.250000       8\n"",
      ""0.500000       3\n"",
      ""1.406681       3\n"",
      ""0.142857       2\n"",
      ""0.090909       1\n"",
      ""0.100000       1\n"",
      ""0.655185       1\n"",
      ""0.066667       1\n"",
      ""1.005580       1\n"",
      ""1.286905       1\n"",
      ""0.987311       1\n"",
      ""Name: MDEN-11, dtype: int64\n"",
      ""MDEN-12 的特征分布：\n"",
      ""0.000000    1905\n"",
      ""0.250000       8\n"",
      ""0.200000       7\n"",
      ""0.166667       4\n"",
      ""0.534522       3\n"",
      ""5.977322       3\n"",
      ""0.333333       3\n"",
      ""1.632993       3\n"",
      ""1.414214       2\n"",
      ""0.577350       2\n"",
      ""0.058824       2\n"",
      ""0.062500       2\n"",
      ""0.750000       2\n"",
      ""0.655185       2\n"",
      ""0.671378       2\n"",
      ""6.362543       1\n"",
      ""0.125000       1\n"",
      ""0.766309       1\n"",
      ""0.436790       1\n"",
      ""0.301511       1\n"",
      ""0.353553       1\n"",
      ""0.447214       1\n"",
      ""0.071429       1\n"",
      ""4.936959       1\n"",
      ""1.220898       1\n"",
      ""0.707107       1\n"",
      ""0.421716       1\n"",
      ""0.100000       1\n"",
      ""0.759836       1\n"",
      ""1.290945       1\n"",
      ""0.669433       1\n"",
      ""0.873780       1\n"",
      ""0.793701       1\n"",
      ""0.388067       1\n"",
      ""1.074570       1\n"",
      ""0.142857       1\n"",
      ""0.500000       1\n"",
      ""0.696238       1\n"",
      ""4.769958       1\n"",
      ""Name: MDEN-12, dtype: int64\n"",
      ""MDEN-13 的特征分布：\n"",
      ""0.000000    1929\n"",
      ""0.408248       4\n"",
      ""0.500000       4\n"",
      ""0.066667       3\n"",
      ""0.071429       3\n"",
      ""0.090909       3\n"",
      ""0.250000       3\n"",
      ""0.142857       2\n"",
      ""0.175412       2\n"",
      ""0.200000       2\n"",
      ""0.277350       2\n"",
      ""0.166667       1\n"",
      ""0.666667       1\n"",
      ""0.181818       1\n"",
      ""0.076923       1\n"",
      ""0.202031       1\n"",
      ""0.355689       1\n"",
      ""0.409914       1\n"",
      ""0.329317       1\n"",
      ""0.814325       1\n"",
      ""0.603023       1\n"",
      ""0.267261       1\n"",
      ""0.111111       1\n"",
      ""0.577350       1\n"",
      ""0.272166       1\n"",
      ""0.516398       1\n"",
      ""0.471405       1\n"",
      ""0.333333       1\n"",
      ""Name: MDEN-13, dtype: int64\n"",
      ""MDEN-22 的特征分布：\n"",
      ""0.000000    1618\n"",
      ""1.000000      60\n"",
      ""2.381102      40\n"",
      ""0.200000      36\n"",
      ""0.500000      28\n"",
      ""            ... \n"",
      ""0.608220       1\n"",
      ""1.696063       1\n"",
      ""0.520021       1\n"",
      ""1.785490       1\n"",
      ""1.543082       1\n"",
      ""Name: MDEN-22, Length: 86, dtype: int64\n"",
      ""MDEN-23 的特征分布：\n"",
      ""0.000000    1628\n"",
      ""0.500000      48\n"",
      ""1.000000      31\n"",
      ""0.333333      21\n"",
      ""0.125000      11\n"",
      ""            ... \n"",
      ""1.503942       1\n"",
      ""1.272289       1\n"",
      ""0.825482       1\n"",
      ""0.288675       1\n"",
      ""0.800000       1\n"",
      ""Name: MDEN-23, Length: 131, dtype: int64\n"",
      ""MDEN-33 的特征分布：\n"",
      ""0.000000    1719\n"",
      ""0.111111     112\n"",
      ""0.333333      33\n"",
      ""0.125000      15\n"",
      ""0.250000      13\n"",
      ""0.142857      13\n"",
      ""0.550321      12\n"",
      ""0.608220      12\n"",
      ""0.100000       7\n"",
      ""0.504717       6\n"",
      ""0.685007       5\n"",
      ""0.500000       4\n"",
      ""0.386402       3\n"",
      ""0.200000       3\n"",
      ""0.090909       2\n"",
      ""0.467649       2\n"",
      ""0.076923       2\n"",
      ""0.166667       2\n"",
      ""0.444226       1\n"",
      ""0.436790       1\n"",
      ""0.909705       1\n"",
      ""1.000000       1\n"",
      ""0.965489       1\n"",
      ""0.908560       1\n"",
      ""0.793701       1\n"",
      ""0.482745       1\n"",
      ""0.062500       1\n"",
      ""Name: MDEN-33, dtype: int64\n"",
      ""MLFER_A 的特征分布：\n"",
      ""1.089    458\n"",
      ""0.546    362\n"",
      ""0.003    136\n"",
      ""1.134    104\n"",
      ""0.197     75\n"",
      ""        ... \n"",
      ""0.410      1\n"",
      ""1.460      1\n"",
      ""0.164      1\n"",
      ""0.448      1\n"",
      ""2.643      1\n"",
      ""Name: MLFER_A, Length: 216, dtype: int64\n"",
      ""MLFER_BH 的特征分布：\n"",
      ""2.159    20\n"",
      ""2.141    16\n"",
      ""2.163    16\n"",
      ""2.372    15\n"",
      ""2.262    15\n"",
      ""         ..\n"",
      ""0.595     1\n"",
      ""0.555     1\n"",
      ""2.153     1\n"",
      ""2.509     1\n"",
      ""1.533     1\n"",
      ""Name: MLFER_BH, Length: 994, dtype: int64\n"",
      ""MLFER_BO 的特征分布：\n"",
      ""0.849    21\n"",
      ""0.797    19\n"",
      ""2.226    19\n"",
      ""2.301    16\n"",
      ""2.206    16\n"",
      ""         ..\n"",
      ""1.845     1\n"",
      ""0.941     1\n"",
      ""2.138     1\n"",
      ""1.475     1\n"",
      ""1.218     1\n"",
      ""Name: MLFER_BO, Length: 935, dtype: int64\n"",
      ""MLFER_S 的特征分布：\n"",
      ""2.862    17\n"",
      ""3.119    15\n"",
      ""3.613    14\n"",
      ""2.901    13\n"",
      ""2.877    13\n"",
      ""         ..\n"",
      ""0.827     1\n"",
      ""0.996     1\n"",
      ""1.084     1\n"",
      ""3.711     1\n"",
      ""3.945     1\n"",
      ""Name: MLFER_S, Length: 1050, dtype: int64\n"",
      ""MLFER_E 的特征分布：\n"",
      ""3.118    17\n"",
      ""3.199    15\n"",
      ""3.511    14\n"",
      ""3.133    12\n"",
      ""2.098    12\n"",
      ""         ..\n"",
      ""2.044     1\n"",
      ""1.656     1\n"",
      ""3.148     1\n"",
      ""2.104     1\n"",
      ""3.495     1\n"",
      ""Name: MLFER_E, Length: 1007, dtype: int64\n"",
      ""MLFER_L 的特征分布：\n"",
      ""17.434    8\n"",
      ""17.662    8\n"",
      ""10.388    7\n"",
      ""16.411    7\n"",
      ""17.674    7\n"",
      ""         ..\n"",
      ""12.018    1\n"",
      ""11.391    1\n"",
      ""10.892    1\n"",
      ""10.393    1\n"",
      ""19.557    1\n"",
      ""Name: MLFER_L, Length: 1525, dtype: int64\n"",
      ""PetitjeanNumber 的特征分布：\n"",
      ""0.500000    946\n"",
      ""0.454545    270\n"",
      ""0.470588    234\n"",
      ""0.466667    149\n"",
      ""0.444444    126\n"",
      ""0.461538    103\n"",
      ""0.473684     66\n"",
      ""0.476190     21\n"",
      ""0.375000     15\n"",
      ""0.478261     11\n"",
      ""0.480000      9\n"",
      ""0.400000      9\n"",
      ""0.482759      6\n"",
      ""0.481481      3\n"",
      ""0.483871      2\n"",
      ""0.450000      1\n"",
      ""0.428571      1\n"",
      ""0.491803      1\n"",
      ""0.484848      1\n"",
      ""Name: PetitjeanNumber, dtype: int64\n"",
      ""nRing 的特征分布：\n"",
      ""3    615\n"",
      ""4    529\n"",
      ""5    506\n"",
      ""6    161\n"",
      ""2    126\n"",
      ""1     31\n"",
      ""7      5\n"",
      ""0      1\n"",
      ""Name: nRing, dtype: int64\n"",
      ""n3Ring 的特征分布：\n"",
      ""0    1961\n"",
      ""1      13\n"",
      ""Name: n3Ring, dtype: int64\n"",
      ""n4Ring 的特征分布：\n"",
      ""0    1956\n"",
      ""1      17\n"",
      ""2       1\n"",
      ""Name: n4Ring, dtype: int64\n"",
      ""n5Ring 的特征分布：\n"",
      ""0    860\n"",
      ""1    782\n"",
      ""2    293\n"",
      ""3     36\n"",
      ""4      3\n"",
      ""Name: n5Ring, dtype: int64\n"",
      ""n6Ring 的特征分布：\n"",
      ""3    731\n"",
      ""4    474\n"",
      ""2    435\n"",
      ""5    209\n"",
      ""1     97\n"",
      ""0     22\n"",
      ""6      6\n"",
      ""Name: n6Ring, dtype: int64\n"",
      ""n7Ring 的特征分布：\n"",
      ""0    1874\n"",
      ""1      92\n"",
      ""2       8\n"",
      ""Name: n7Ring, dtype: int64\n"",
      ""n8Ring 的特征分布：\n"",
      ""0    1970\n"",
      ""1       4\n"",
      ""Name: n8Ring, dtype: int64\n"",
      ""n9Ring 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: n9Ring, dtype: int64\n"",
      ""n10Ring 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: n10Ring, dtype: int64\n"",
      ""n11Ring 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: n11Ring, dtype: int64\n"",
      ""n12Ring 的特征分布：\n"",
      ""0    1973\n"",
      ""1       1\n"",
      ""Name: n12Ring, dtype: int64\n"",
      ""nG12Ring 的特征分布：\n"",
      ""0    1969\n"",
      ""1       5\n"",
      ""Name: nG12Ring, dtype: int64\n"",
      ""nFRing 的特征分布：\n"",
      ""1     1119\n"",
      ""0      309\n"",
      ""6      249\n"",
      ""3      150\n"",
      ""2      100\n"",
      ""7       20\n"",
      ""4       17\n"",
      ""10       7\n"",
      ""12       1\n"",
      ""8        1\n"",
      ""5        1\n"",
      ""Name: nFRing, dtype: int64\n"",
      ""nF4Ring 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nF4Ring, dtype: int64\n"",
      ""nF5Ring 的特征分布：\n"",
      ""0    1974\n"",
      ""Name: nF5Ring, dtype: int64\n"",
      ""nF6Ring 的特征分布：\n"",
      ""0    1914\n"",
      ""1      46\n"",
      ""4      13\n"",
      ""3       1\n"",
      ""Name: nF6Ring, dtype: int64\n"",
      ""nF7Ring 的特征分布：\n"",
      ""0    1967\n"",
      ""1       6\n"",
      ""2       1\n"",
      ""Name: nF7Ring, dtype: int64\n"",
      ""nF8Ring 的特征分布：\n"",
      ""0    1915\n"",
      ""1      46\n"",
      ""3      13\n"",
      ""Name: nF8Ring, dtype: int64\n"",
      ""nF9Ring 的特征分布：\n"",
      ""0    1196\n"",
      ""1     557\n"",
      ""2     155\n"",
      ""3      64\n"",
      ""4       2\n"",
      ""Name: nF9Ring, dtype: int64\n"",
      ""nF10Ring 的特征分布：\n"",
      ""0    985\n"",
      ""1    803\n"",
      ""2    167\n"",
      ""3     19\n"",
      ""Name: nF10Ring, dtype: int64\n"",
      ""nF11Ring 的特征分布：\n"",
      ""0    1891\n"",
      ""2      54\n"",
      ""1      29\n"",
      ""Name: nF11Ring, dtype: int64\n"",
      ""nF12Ring 的特征分布：\n"",
      ""0    1894\n"",
      ""1      66\n"",
      ""2      13\n"",
      ""3       1\n"",
      ""Name: nF12Ring, dtype: int64\n"",
      ""nFG12Ring 的特征分布：\n"",
      ""0    1547\n"",
      ""1     176\n"",
      ""3     174\n"",
      ""2      69\n"",
      ""5       5\n"",
      ""6       3\n"",
      ""Name: nFG12Ring, dtype: int64\n"",
      ""nTRing 的特征分布：\n"",
      ""6     491\n"",
      ""4     446\n"",
      ""5     289\n"",
      ""7     137\n"",
      ""10    136\n"",
      ""3     128\n"",
      ""2     108\n"",
      ""11     62\n"",
      ""12     62\n"",
      ""8      44\n"",
      ""1      31\n"",
      ""9      22\n"",
      ""13      8\n"",
      ""15      7\n"",
      ""14      1\n"",
      ""17      1\n"",
      ""0       1\n"",
      ""Name: nTRing, dtype: int64\n"",
      ""nT4Ring 的特征分布：\n"",
      ""0    1956\n"",
      ""1      17\n"",
      ""2       1\n"",
      ""Name: nT4Ring, dtype: int64\n"",
      ""nT5Ring 的特征分布：\n"",
      ""0    860\n"",
      ""1    782\n"",
      ""2    293\n"",
      ""3     36\n"",
      ""4      3\n"",
      ""Name: nT5Ring, dtype: int64\n"",
      ""nT6Ring 的特征分布：\n"",
      ""3    717\n"",
      ""4    456\n"",
      ""2    423\n"",
      ""5    240\n"",
      ""1     96\n"",
      ""0     22\n"",
      ""6     19\n"",
      ""7      1\n"",
      ""Name: nT6Ring, dtype: int64\n"",
      ""nT7Ring 的特征分布：\n"",
      ""0    1867\n"",
      ""1      98\n"",
      ""2       9\n"",
      ""Name: nT7Ring, dtype: int64\n"",
      ""nT8Ring 的特征分布：\n"",
      ""0    1911\n"",
      ""1      50\n"",
      ""3      13\n"",
      ""Name: nT8Ring, dtype: int64\n"",
      ""nT9Ring 的特征分布：\n"",
      ""0    1196\n"",
      ""1     557\n"",
      ""2     155\n"",
      ""3      64\n"",
      ""4       2\n"",
      ""Name: nT9Ring, dtype: int64\n"",
      ""nT10Ring 的特征分布：\n"",
      ""0    985\n"",
      ""1    803\n"",
      ""2    167\n"",
      ""3     19\n"",
      ""Name: nT10Ring, dtype: int64\n"",
      ""nT11Ring 的特征分布：\n"",
      ""0    1891\n"",
      ""2      54\n"",
      ""1      29\n"",
      ""Name: nT11Ring, dtype: int64\n"",
      ""nT12Ring 的特征分布：\n"",
      ""0    1893\n"",
      ""1      67\n"",
      ""2      13\n"",
      ""3       1\n"",
      ""Name: nT12Ring, dtype: int64\n"",
      ""nTG12Ring 的特征分布：\n"",
      ""0    1542\n"",
      ""1     181\n"",
      ""3     174\n"",
      ""2      69\n"",
      ""5       5\n"",
      ""6       3\n"",
      ""Name: nTG12Ring, dtype: int64\n"",
      ""nRotB 的特征分布：\n"",
      ""6      338\n"",
      ""1      289\n"",
      ""5      225\n"",
      ""2      225\n"",
      ""7      222\n"",
      ""3      154\n"",
      ""4      139\n"",
      ""8       92\n"",
      ""9       75\n"",
      ""0       68\n"",
      ""10      38\n"",
      ""11      18\n"",
      ""14      17\n"",
      ""12      17\n"",
      ""15      13\n"",
      ""13       9\n"",
      ""17       6\n"",
      ""16       5\n"",
      ""18       4\n"",
      ""22       3\n"",
      ""37       3\n"",
      ""25       2\n"",
      ""21       2\n"",
      ""19       2\n"",
      ""20       2\n"",
      ""23       1\n"",
      ""101      1\n"",
      ""45       1\n"",
      ""49       1\n"",
      ""36       1\n"",
      ""24       1\n"",
      ""Name: nRotB, dtype: int64\n"",
      ""LipinskiFailures 的特征分布：\n"",
      ""0    1761\n"",
      ""1     147\n"",
      ""2      57\n"",
      ""3       8\n"",
      ""4       1\n"",
      ""Name: LipinskiFailures, dtype: int64\n"",
      ""TopoPSA 的特征分布：\n"",
      ""40.46     105\n"",
      ""87.46      72\n"",
      ""66.49      71\n"",
      ""60.77      58\n"",
      ""62.16      44\n"",
      ""         ... \n"",
      ""49.56       1\n"",
      ""97.41       1\n"",
      ""92.96       1\n"",
      ""182.86      1\n"",
      ""82.95       1\n"",
      ""Name: TopoPSA, Length: 621, dtype: int64\n"",
      ""VABC 的特征分布：\n"",
      ""439.300297    21\n"",
      ""422.004313    16\n"",
      ""452.746784    12\n"",
      ""223.201674    12\n"",
      ""444.053317    10\n"",
      ""              ..\n"",
      ""246.338518     1\n"",
      ""245.431826     1\n"",
      ""262.727811     1\n"",
      ""284.963321     1\n"",
      ""478.629177     1\n"",
      ""Name: VABC, Length: 1409, dtype: int64\n"",
      ""VAdjMat 的特征分布：\n"",
      ""5.392317    157\n"",
      ""5.321928    147\n"",
      ""6.087463    144\n"",
      ""5.459432    132\n"",
      ""6.044394    127\n"",
      ""6.129283    110\n"",
      ""5.247928    105\n"",
      ""6.000000    103\n"",
      ""5.523562     92\n"",
      ""5.954196     75\n"",
      ""6.169925     71\n"",
      ""5.906891     70\n"",
      ""5.643856     66\n"",
      ""5.584963     66\n"",
      ""5.700440     65\n"",
      ""5.169925     59\n"",
      ""5.754888     53\n"",
      ""5.857981     51\n"",
      ""5.807355     49\n"",
      ""6.209453     44\n"",
      ""6.247928     36\n"",
      ""6.285402     22\n"",
      ""6.357552     22\n"",
      ""6.321928     20\n"",
      ""5.087463     19\n"",
      ""6.426265     12\n"",
      ""5.000000     10\n"",
      ""6.392317      8\n"",
      ""6.554589      6\n"",
      ""4.906891      6\n"",
      ""6.459432      5\n"",
      ""6.523562      5\n"",
      ""6.584963      4\n"",
      ""7.266787      3\n"",
      ""4.807355      2\n"",
      ""6.491853      2\n"",
      ""6.882643      1\n"",
      ""8.348728      1\n"",
      ""7.169925      1\n"",
      ""7.339850      1\n"",
      ""7.209453      1\n"",
      ""6.832890      1\n"",
      ""Name: VAdjMat, dtype: int64\n"",
      ""MW 的特征分布：\n"",
      ""477.197380    21\n"",
      ""463.181729    16\n"",
      ""277.073893    12\n"",
      ""471.240958    12\n"",
      ""470.256943    10\n"",
      ""              ..\n"",
      ""556.233763     1\n"",
      ""360.147393     1\n"",
      ""417.157623     1\n"",
      ""421.132551     1\n"",
      ""538.145010     1\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Name: MW, Length: 1316, dtype: int64\n"",
      ""WTPT-1 的特征分布：\n"",
      ""47.575753    11\n"",
      ""40.836976    10\n"",
      ""38.955546    10\n"",
      ""42.883311     9\n"",
      ""68.748923     8\n"",
      ""             ..\n"",
      ""44.498710     1\n"",
      ""73.148073     1\n"",
      ""70.711018     1\n"",
      ""66.828986     1\n"",
      ""84.660642     1\n"",
      ""Name: WTPT-1, Length: 1460, dtype: int64\n"",
      ""WTPT-2 的特征分布：\n"",
      ""2.068511    11\n"",
      ""2.041849    10\n"",
      ""2.050292    10\n"",
      ""2.042062     9\n"",
      ""2.083301     8\n"",
      ""            ..\n"",
      ""2.022669     1\n"",
      ""2.031891     1\n"",
      ""2.079736     1\n"",
      ""2.088406     1\n"",
      ""2.064894     1\n"",
      ""Name: WTPT-2, Length: 1460, dtype: int64\n"",
      ""WTPT-3 的特征分布：\n"",
      ""14.723890    7\n"",
      ""18.011114    6\n"",
      ""5.096770     6\n"",
      ""5.092732     6\n"",
      ""7.559880     5\n"",
      ""            ..\n"",
      ""8.697485     1\n"",
      ""8.687114     1\n"",
      ""11.278514    1\n"",
      ""5.087917     1\n"",
      ""24.923083    1\n"",
      ""Name: WTPT-3, Length: 1678, dtype: int64\n"",
      ""WTPT-4 的特征分布：\n"",
      ""0.000000     50\n"",
      ""5.096770     11\n"",
      ""5.063850      8\n"",
      ""5.087105      7\n"",
      ""5.054479      6\n"",
      ""             ..\n"",
      ""13.610350     1\n"",
      ""7.592018      1\n"",
      ""11.317284     1\n"",
      ""8.847292      1\n"",
      ""21.400883     1\n"",
      ""Name: WTPT-4, Length: 1571, dtype: int64\n"",
      ""WTPT-5 的特征分布：\n"",
      ""0.000000     492\n"",
      ""3.406644       8\n"",
      ""3.406716       7\n"",
      ""3.354436       7\n"",
      ""7.016543       6\n"",
      ""            ... \n"",
      ""3.517947       1\n"",
      ""3.501766       1\n"",
      ""3.491179       1\n"",
      ""3.481957       1\n"",
      ""15.838848      1\n"",
      ""Name: WTPT-5, Length: 1210, dtype: int64\n"",
      ""WPATH 的特征分布：\n"",
      ""1022    12\n"",
      ""694     12\n"",
      ""904     11\n"",
      ""612     11\n"",
      ""781     10\n"",
      ""        ..\n"",
      ""674      1\n"",
      ""681      1\n"",
      ""554      1\n"",
      ""2590     1\n"",
      ""6421     1\n"",
      ""Name: WPATH, Length: 1236, dtype: int64\n"",
      ""WPOL 的特征分布：\n"",
      ""35    82\n"",
      ""58    71\n"",
      ""34    63\n"",
      ""53    62\n"",
      ""54    61\n"",
      ""      ..\n"",
      ""14     1\n"",
      ""20     1\n"",
      ""82     1\n"",
      ""81     1\n"",
      ""94     1\n"",
      ""Name: WPOL, Length: 73, dtype: int64\n"",
      ""XLogP 的特征分布：\n"",
      ""2.701    9\n"",
      ""2.964    8\n"",
      ""3.662    8\n"",
      ""4.204    7\n"",
      ""3.066    7\n"",
      ""        ..\n"",
      ""2.515    1\n"",
      ""3.118    1\n"",
      ""2.512    1\n"",
      ""6.556    1\n"",
      ""4.267    1\n"",
      ""Name: XLogP, Length: 1432, dtype: int64\n"",
      ""Zagreb 的特征分布：\n"",
      ""182    69\n"",
      ""112    67\n"",
      ""114    57\n"",
      ""176    57\n"",
      ""108    54\n"",
      ""       ..\n"",
      ""262     1\n"",
      ""234     1\n"",
      ""260     1\n"",
      ""78      1\n"",
      ""314     1\n"",
      ""Name: Zagreb, Length: 98, dtype: int64\n"",
      ""pIC50 的特征分布：\n"",
      ""9.000    15\n"",
      ""9.699    14\n"",
      ""7.398    14\n"",
      ""8.523    13\n"",
      ""7.959    12\n"",
      ""         ..\n"",
      ""7.229     1\n"",
      ""6.253     1\n"",
      ""5.574     1\n"",
      ""6.354     1\n"",
      ""6.132     1\n"",
      ""Name: pIC50, Length: 1112, dtype: int64\n""
     ]
    }
   ],
   ""source"": [
    ""for s in data.columns:\n"",
    ""    print(s,'的特征分布：')\n"",
    ""    print(data[s].value_counts())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 88,
   ""id"": ""ec69fefa"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"",
      ""  warnings.warn(msg, FutureWarning)\n""
     ]
    },
    {
     ""data"": {
      ""text/plain"": [
       ""<AxesSubplot:xlabel='pIC50', ylabel='Density'>""
      ]
     },
     ""execution_count"": 88,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 432x288 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""sns.distplot(data['pIC50'])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 56,
   ""id"": ""7b08568a"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""225""
      ]
     },
     ""execution_count"": 56,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""corrlation['pIC50'].isna().sum()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 81,
   ""id"": ""6b286a8f"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""1653""
      ]
     },
     ""execution_count"": 81,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#data['n10Ring'].value_counts()\n"",
    ""data['hmin'].nunique()\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 98,
   ""id"": ""feb11683"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#删除数据只有一种的特征\n"",
    ""for  s in feature_train.columns:\n"",
    ""    if feature_train[s].nunique()==1:\n"",
    ""        del feature_train[s]    ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 99,
   ""id"": ""e7f49588"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(1974, 504)""
      ]
     },
     ""execution_count"": 99,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""feature_train.shape""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 82,
   ""id"": ""b39a65ec"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""hmin                -0.426366\n"",
       ""C1SP2               -0.406928\n"",
       ""ATSc3               -0.351857\n"",
       ""ETA_Epsilon_4       -0.351555\n"",
       ""mindO               -0.335270\n"",
       ""                       ...   \n"",
       ""maxsOH               0.466625\n"",
       ""LipoaffinityIndex    0.491857\n"",
       ""MLogP                0.529328\n"",
       ""MDEC-23              0.538053\n"",
       ""pIC50                1.000000\n"",
       ""Name: pIC50, Length: 505, dtype: float64""
      ]
     },
     ""execution_count"": 82,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""corrlation=data.corr()\n"",
    ""corrlation['pIC50'].sort_values()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 83,
   ""id"": ""36d8a816"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""cor_1=pd.DataFrame(corrlation['pIC50'])\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 86,
   ""id"": ""f20100dd"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""pIC50    25\n"",
       ""dtype: int64""
      ]
     },
     ""execution_count"": 86,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""cor_1=abs(cor_1)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 87,
   ""id"": ""464c2c71"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#应用随机森林来选择特征\n"",
    ""from sklearn.ensemble import RandomForestRegressor \n"",
    ""from sklearn.model_selection import train_test_split\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 100,
   ""id"": ""011c8825"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""C:\\Users\\Aaron\\AppData\\Local\\Temp/ipykernel_23292/2995366988.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"",
      ""  forest.fit(feature_train,label_train)\n""
     ]
    },
    {
     ""data"": {
      ""text/plain"": [
       ""RandomForestRegressor(max_features=100, n_estimators=500, n_jobs=2,\n"",
       ""                      random_state=0)""
      ]
     },
     ""execution_count"": 100,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""forest = RandomForestRegressor(n_estimators=500, random_state=0, max_features=100,  n_jobs=2) \n"",
    ""forest.fit(feature_train,label_train)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 117,
   ""id"": ""528072b7"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""0.0022934395476585487""
      ]
     },
     ""execution_count"": 117,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#sorted(forest.feature_importances_, reverse=True)\n"",
    ""forest.feature_importances_[2]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 228,
   ""id"": ""db50c7ad"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>s</th>\n"",
       ""      <th>rank</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>0.000100</td>\n"",
       ""      <td>0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>0.003281</td>\n"",
       ""      <td>1</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>0.002293</td>\n"",
       ""      <td>2</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>0.008669</td>\n"",
       ""      <td>3</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>0.002243</td>\n"",
       ""      <td>4</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>...</th>\n"",
       ""      <td>...</td>\n"",
       ""      <td>...</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>499</th>\n"",
       ""      <td>0.004232</td>\n"",
       ""      <td>499</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>500</th>\n"",
       ""      <td>0.001057</td>\n"",
       ""      <td>500</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>501</th>\n"",
       ""      <td>0.000643</td>\n"",
       ""      <td>501</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>502</th>\n"",
       ""      <td>0.003805</td>\n"",
       ""      <td>502</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>503</th>\n"",
       ""      <td>0.000486</td>\n"",
       ""      <td>503</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""<p>504 rows × 2 columns</p>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""            s  rank\n"",
       ""0    0.000100     0\n"",
       ""1    0.003281     1\n"",
       ""2    0.002293     2\n"",
       ""3    0.008669     3\n"",
       ""4    0.002243     4\n"",
       ""..        ...   ...\n"",
       ""499  0.004232   499\n"",
       ""500  0.001057   500\n"",
       ""501  0.000643   501\n"",
       ""502  0.003805   502\n"",
       ""503  0.000486   503\n"",
       ""\n"",
       ""[504 rows x 2 columns]""
      ]
     },
     ""execution_count"": 228,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""feature_impor=pd.DataFrame(forest.feature_importances_)\n"",
    ""feature_impor['rank']=range(0,504)\n"",
    ""feature_impor.columns=['s','rank']\n"",
    ""feature_impor""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 229,
   ""id"": ""6f3142d1"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""feature_impor=feature_impor.sort_values(by='s',ascending=False)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 230,
   ""id"": ""7468b9bc"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""56""
      ]
     },
     ""execution_count"": 230,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""choose_feature=[]\n"",
    ""for i in range(504):\n"",
    ""    if feature_impor.iloc[i,0]>=float(feature_impor['s'].quantile(0.89)):\n"",
    ""        choose_feature.append(feature_train.columns[feature_impor.iloc[i,1]])\n"",
    ""\n"",
    ""len(choose_feature)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 231,
   ""id"": ""b3837e95"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>MDEC-23</th>\n"",
       ""      <th>LipoaffinityIndex</th>\n"",
       ""      <th>MLogP</th>\n"",
       ""      <th>nC</th>\n"",
       ""      <th>maxHsOH</th>\n"",
       ""      <th>minsssN</th>\n"",
       ""      <th>minHsOH</th>\n"",
       ""      <th>BCUTc-1l</th>\n"",
       ""      <th>maxssO</th>\n"",
       ""      <th>SHsOH</th>\n"",
       ""      <th>...</th>\n"",
       ""      <th>ATSp1</th>\n"",
       ""      <th>WTPT-4</th>\n"",
       ""      <th>CrippenLogP</th>\n"",
       ""      <th>nsOH</th>\n"",
       ""      <th>XLogP</th>\n"",
       ""      <th>VCH-5</th>\n"",
       ""      <th>ETA_BetaP_s</th>\n"",
       ""      <th>minHBint6</th>\n"",
       ""      <th>nHsOH</th>\n"",
       ""      <th>mindO</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>MDEC-23</th>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.708134</td>\n"",
       ""      <td>0.832241</td>\n"",
       ""      <td>0.775807</td>\n"",
       ""      <td>0.205894</td>\n"",
       ""      <td>0.486881</td>\n"",
       ""      <td>0.169654</td>\n"",
       ""      <td>-0.276885</td>\n"",
       ""      <td>0.408743</td>\n"",
       ""      <td>0.159171</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.694143</td>\n"",
       ""      <td>0.284029</td>\n"",
       ""      <td>0.626055</td>\n"",
       ""      <td>0.219097</td>\n"",
       ""      <td>0.372816</td>\n"",
       ""      <td>0.106946</td>\n"",
       ""      <td>0.008916</td>\n"",
       ""      <td>-0.173059</td>\n"",
       ""      <td>0.219097</td>\n"",
       ""      <td>-0.067162</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>LipoaffinityIndex</th>\n"",
       ""      <td>0.708134</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.891029</td>\n"",
       ""      <td>0.774522</td>\n"",
       ""      <td>0.024670</td>\n"",
       ""      <td>0.574888</td>\n"",
       ""      <td>0.025091</td>\n"",
       ""      <td>-0.122688</td>\n"",
       ""      <td>0.322083</td>\n"",
       ""      <td>-0.159268</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.622074</td>\n"",
       ""      <td>-0.114875</td>\n"",
       ""      <td>0.781812</td>\n"",
       ""      <td>-0.076578</td>\n"",
       ""      <td>0.719401</td>\n"",
       ""      <td>0.012981</td>\n"",
       ""      <td>-0.162217</td>\n"",
       ""      <td>-0.276929</td>\n"",
       ""      <td>-0.076578</td>\n"",
       ""      <td>-0.251658</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>MLogP</th>\n"",
       ""      <td>0.832241</td>\n"",
       ""      <td>0.891029</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.909434</td>\n"",
       ""      <td>0.085157</td>\n"",
       ""      <td>0.529384</td>\n"",
       ""      <td>0.061318</td>\n"",
       ""      <td>-0.255074</td>\n"",
       ""      <td>0.399374</td>\n"",
       ""      <td>-0.021842</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.774221</td>\n"",
       ""      <td>0.158376</td>\n"",
       ""      <td>0.689403</td>\n"",
       ""      <td>0.090124</td>\n"",
       ""      <td>0.637601</td>\n"",
       ""      <td>0.065126</td>\n"",
       ""      <td>-0.201593</td>\n"",
       ""      <td>-0.233164</td>\n"",
       ""      <td>0.090124</td>\n"",
       ""      <td>-0.137389</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>nC</th>\n"",
       ""      <td>0.775807</td>\n"",
       ""      <td>0.774522</td>\n"",
       ""      <td>0.909434</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.002710</td>\n"",
       ""      <td>0.533325</td>\n"",
       ""      <td>-0.024062</td>\n"",
       ""      <td>-0.177045</td>\n"",
       ""      <td>0.443980</td>\n"",
       ""      <td>-0.056746</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.931981</td>\n"",
       ""      <td>0.391300</td>\n"",
       ""      <td>0.539211</td>\n"",
       ""      <td>0.035098</td>\n"",
       ""      <td>0.472318</td>\n"",
       ""      <td>0.111204</td>\n"",
       ""      <td>0.000823</td>\n"",
       ""      <td>-0.221541</td>\n"",
       ""      <td>0.035098</td>\n"",
       ""      <td>-0.010533</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>maxHsOH</th>\n"",
       ""      <td>0.205894</td>\n"",
       ""      <td>0.024670</td>\n"",
       ""      <td>0.085157</td>\n"",
       ""      <td>0.002710</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.052235</td>\n"",
       ""      <td>0.950769</td>\n"",
       ""      <td>-0.652815</td>\n"",
       ""      <td>0.054897</td>\n"",
       ""      <td>0.836405</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.092485</td>\n"",
       ""      <td>0.206140</td>\n"",
       ""      <td>0.135100</td>\n"",
       ""      <td>0.788405</td>\n"",
       ""      <td>0.038018</td>\n"",
       ""      <td>-0.290302</td>\n"",
       ""      <td>-0.280706</td>\n"",
       ""      <td>0.239523</td>\n"",
       ""      <td>0.788405</td>\n"",
       ""      <td>-0.203484</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minsssN</th>\n"",
       ""      <td>0.486881</td>\n"",
       ""      <td>0.574888</td>\n"",
       ""      <td>0.529384</td>\n"",
       ""      <td>0.533325</td>\n"",
       ""      <td>0.052235</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.088362</td>\n"",
       ""      <td>-0.153156</td>\n"",
       ""      <td>0.353748</td>\n"",
       ""      <td>-0.044227</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.493795</td>\n"",
       ""      <td>0.101451</td>\n"",
       ""      <td>0.332208</td>\n"",
       ""      <td>0.000538</td>\n"",
       ""      <td>0.095217</td>\n"",
       ""      <td>0.031578</td>\n"",
       ""      <td>0.262811</td>\n"",
       ""      <td>-0.166763</td>\n"",
       ""      <td>0.000538</td>\n"",
       ""      <td>-0.144182</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minHsOH</th>\n"",
       ""      <td>0.169654</td>\n"",
       ""      <td>0.025091</td>\n"",
       ""      <td>0.061318</td>\n"",
       ""      <td>-0.024062</td>\n"",
       ""      <td>0.950769</td>\n"",
       ""      <td>0.088362</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.602109</td>\n"",
       ""      <td>0.089461</td>\n"",
       ""      <td>0.749172</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.129834</td>\n"",
       ""      <td>0.163428</td>\n"",
       ""      <td>0.122011</td>\n"",
       ""      <td>0.679194</td>\n"",
       ""      <td>0.013169</td>\n"",
       ""      <td>-0.306239</td>\n"",
       ""      <td>-0.234901</td>\n"",
       ""      <td>0.226246</td>\n"",
       ""      <td>0.679194</td>\n"",
       ""      <td>-0.233683</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>BCUTc-1l</th>\n"",
       ""      <td>-0.276885</td>\n"",
       ""      <td>-0.122688</td>\n"",
       ""      <td>-0.255074</td>\n"",
       ""      <td>-0.177045</td>\n"",
       ""      <td>-0.652815</td>\n"",
       ""      <td>-0.153156</td>\n"",
       ""      <td>-0.602109</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.247030</td>\n"",
       ""      <td>-0.557629</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.097724</td>\n"",
       ""      <td>-0.322451</td>\n"",
       ""      <td>-0.121123</td>\n"",
       ""      <td>-0.631155</td>\n"",
       ""      <td>-0.110596</td>\n"",
       ""      <td>0.121110</td>\n"",
       ""      <td>0.189810</td>\n"",
       ""      <td>-0.106740</td>\n"",
       ""      <td>-0.631155</td>\n"",
       ""      <td>0.193009</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>maxssO</th>\n"",
       ""      <td>0.408743</td>\n"",
       ""      <td>0.322083</td>\n"",
       ""      <td>0.399374</td>\n"",
       ""      <td>0.443980</td>\n"",
       ""      <td>0.054897</td>\n"",
       ""      <td>0.353748</td>\n"",
       ""      <td>0.089461</td>\n"",
       ""      <td>-0.247030</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.022689</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.362702</td>\n"",
       ""      <td>0.551342</td>\n"",
       ""      <td>0.338802</td>\n"",
       ""      <td>0.051448</td>\n"",
       ""      <td>0.110652</td>\n"",
       ""      <td>0.074108</td>\n"",
       ""      <td>0.181366</td>\n"",
       ""      <td>0.077643</td>\n"",
       ""      <td>0.051448</td>\n"",
       ""      <td>-0.022380</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SHsOH</th>\n"",
       ""      <td>0.159171</td>\n"",
       ""      <td>-0.159268</td>\n"",
       ""      <td>-0.021842</td>\n"",
       ""      <td>-0.056746</td>\n"",
       ""      <td>0.836405</td>\n"",
       ""      <td>-0.044227</td>\n"",
       ""      <td>0.749172</td>\n"",
       ""      <td>-0.557629</td>\n"",
       ""      <td>0.022689</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.118607</td>\n"",
       ""      <td>0.337685</td>\n"",
       ""      <td>0.020024</td>\n"",
       ""      <td>0.958125</td>\n"",
       ""      <td>-0.101380</td>\n"",
       ""      <td>-0.237750</td>\n"",
       ""      <td>-0.161097</td>\n"",
       ""      <td>0.224630</td>\n"",
       ""      <td>0.958125</td>\n"",
       ""      <td>-0.189570</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>nHBAcc</th>\n"",
       ""      <td>-0.115243</td>\n"",
       ""      <td>-0.116825</td>\n"",
       ""      <td>-0.026372</td>\n"",
       ""      <td>0.321631</td>\n"",
       ""      <td>-0.410827</td>\n"",
       ""      <td>0.012518</td>\n"",
       ""      <td>-0.425683</td>\n"",
       ""      <td>0.294564</td>\n"",
       ""      <td>-0.068137</td>\n"",
       ""      <td>-0.365373</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.452646</td>\n"",
       ""      <td>0.291217</td>\n"",
       ""      <td>-0.379953</td>\n"",
       ""      <td>-0.374522</td>\n"",
       ""      <td>-0.247959</td>\n"",
       ""      <td>0.129077</td>\n"",
       ""      <td>0.317543</td>\n"",
       ""      <td>-0.073994</td>\n"",
       ""      <td>-0.374522</td>\n"",
       ""      <td>0.379997</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>MLFER_A</th>\n"",
       ""      <td>0.166126</td>\n"",
       ""      <td>-0.082829</td>\n"",
       ""      <td>0.125160</td>\n"",
       ""      <td>0.228047</td>\n"",
       ""      <td>0.622245</td>\n"",
       ""      <td>-0.006522</td>\n"",
       ""      <td>0.546420</td>\n"",
       ""      <td>-0.438164</td>\n"",
       ""      <td>0.013090</td>\n"",
       ""      <td>0.765262</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.209575</td>\n"",
       ""      <td>0.420918</td>\n"",
       ""      <td>-0.107085</td>\n"",
       ""      <td>0.771052</td>\n"",
       ""      <td>-0.079694</td>\n"",
       ""      <td>-0.173470</td>\n"",
       ""      <td>-0.136329</td>\n"",
       ""      <td>0.134267</td>\n"",
       ""      <td>0.771052</td>\n"",
       ""      <td>-0.147409</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>maxsOH</th>\n"",
       ""      <td>0.289200</td>\n"",
       ""      <td>0.163803</td>\n"",
       ""      <td>0.251398</td>\n"",
       ""      <td>0.132552</td>\n"",
       ""      <td>0.910247</td>\n"",
       ""      <td>0.151627</td>\n"",
       ""      <td>0.879282</td>\n"",
       ""      <td>-0.752041</td>\n"",
       ""      <td>0.124590</td>\n"",
       ""      <td>0.727513</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.050900</td>\n"",
       ""      <td>0.178155</td>\n"",
       ""      <td>0.229058</td>\n"",
       ""      <td>0.782903</td>\n"",
       ""      <td>0.166541</td>\n"",
       ""      <td>-0.205403</td>\n"",
       ""      <td>-0.308485</td>\n"",
       ""      <td>0.153463</td>\n"",
       ""      <td>0.782903</td>\n"",
       ""      <td>-0.300017</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>maxsssN</th>\n"",
       ""      <td>0.488564</td>\n"",
       ""      <td>0.573018</td>\n"",
       ""      <td>0.528765</td>\n"",
       ""      <td>0.537149</td>\n"",
       ""      <td>0.039004</td>\n"",
       ""      <td>0.995618</td>\n"",
       ""      <td>0.076448</td>\n"",
       ""      <td>-0.145805</td>\n"",
       ""      <td>0.347401</td>\n"",
       ""      <td>-0.055008</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.501813</td>\n"",
       ""      <td>0.101717</td>\n"",
       ""      <td>0.322519</td>\n"",
       ""      <td>-0.010117</td>\n"",
       ""      <td>0.083530</td>\n"",
       ""      <td>0.035190</td>\n"",
       ""      <td>0.278328</td>\n"",
       ""      <td>-0.171093</td>\n"",
       ""      <td>-0.010117</td>\n"",
       ""      <td>-0.126261</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SsOH</th>\n"",
       ""      <td>0.231639</td>\n"",
       ""      <td>-0.057945</td>\n"",
       ""      <td>0.113881</td>\n"",
       ""      <td>0.056819</td>\n"",
       ""      <td>0.768205</td>\n"",
       ""      <td>0.014526</td>\n"",
       ""      <td>0.657183</td>\n"",
       ""      <td>-0.636509</td>\n"",
       ""      <td>0.063803</td>\n"",
       ""      <td>0.938708</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.012389</td>\n"",
       ""      <td>0.343575</td>\n"",
       ""      <td>0.095528</td>\n"",
       ""      <td>0.997917</td>\n"",
       ""      <td>0.006657</td>\n"",
       ""      <td>-0.162398</td>\n"",
       ""      <td>-0.201822</td>\n"",
       ""      <td>0.156990</td>\n"",
       ""      <td>0.997917</td>\n"",
       ""      <td>-0.241518</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minsOH</th>\n"",
       ""      <td>0.280981</td>\n"",
       ""      <td>0.170720</td>\n"",
       ""      <td>0.251495</td>\n"",
       ""      <td>0.129241</td>\n"",
       ""      <td>0.894224</td>\n"",
       ""      <td>0.164004</td>\n"",
       ""      <td>0.888996</td>\n"",
       ""      <td>-0.743064</td>\n"",
       ""      <td>0.132712</td>\n"",
       ""      <td>0.696856</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.045258</td>\n"",
       ""      <td>0.161343</td>\n"",
       ""      <td>0.229266</td>\n"",
       ""      <td>0.751524</td>\n"",
       ""      <td>0.167149</td>\n"",
       ""      <td>-0.205124</td>\n"",
       ""      <td>-0.300543</td>\n"",
       ""      <td>0.145555</td>\n"",
       ""      <td>0.751524</td>\n"",
       ""      <td>-0.312523</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ATSp5</th>\n"",
       ""      <td>0.712786</td>\n"",
       ""      <td>0.598322</td>\n"",
       ""      <td>0.764607</td>\n"",
       ""      <td>0.890748</td>\n"",
       ""      <td>-0.051911</td>\n"",
       ""      <td>0.445211</td>\n"",
       ""      <td>-0.102746</td>\n"",
       ""      <td>-0.133574</td>\n"",
       ""      <td>0.318112</td>\n"",
       ""      <td>-0.079830</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.961924</td>\n"",
       ""      <td>0.390970</td>\n"",
       ""      <td>0.394181</td>\n"",
       ""      <td>0.028422</td>\n"",
       ""      <td>0.331927</td>\n"",
       ""      <td>0.191947</td>\n"",
       ""      <td>0.038456</td>\n"",
       ""      <td>-0.224171</td>\n"",
       ""      <td>0.028422</td>\n"",
       ""      <td>0.102335</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minssO</th>\n"",
       ""      <td>0.384934</td>\n"",
       ""      <td>0.312842</td>\n"",
       ""      <td>0.386689</td>\n"",
       ""      <td>0.426739</td>\n"",
       ""      <td>0.053458</td>\n"",
       ""      <td>0.343609</td>\n"",
       ""      <td>0.087479</td>\n"",
       ""      <td>-0.243943</td>\n"",
       ""      <td>0.996300</td>\n"",
       ""      <td>0.020178</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.346219</td>\n"",
       ""      <td>0.523643</td>\n"",
       ""      <td>0.329176</td>\n"",
       ""      <td>0.049154</td>\n"",
       ""      <td>0.110249</td>\n"",
       ""      <td>0.076297</td>\n"",
       ""      <td>0.171478</td>\n"",
       ""      <td>0.085631</td>\n"",
       ""      <td>0.049154</td>\n"",
       ""      <td>-0.020341</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>C1SP2</th>\n"",
       ""      <td>-0.290392</td>\n"",
       ""      <td>-0.310835</td>\n"",
       ""      <td>-0.264977</td>\n"",
       ""      <td>0.024759</td>\n"",
       ""      <td>-0.400968</td>\n"",
       ""      <td>-0.186569</td>\n"",
       ""      <td>-0.425127</td>\n"",
       ""      <td>0.363890</td>\n"",
       ""      <td>-0.122003</td>\n"",
       ""      <td>-0.338735</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.128303</td>\n"",
       ""      <td>0.218757</td>\n"",
       ""      <td>-0.410136</td>\n"",
       ""      <td>-0.385877</td>\n"",
       ""      <td>-0.299003</td>\n"",
       ""      <td>0.164179</td>\n"",
       ""      <td>0.373831</td>\n"",
       ""      <td>0.072341</td>\n"",
       ""      <td>-0.385877</td>\n"",
       ""      <td>0.370528</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>AMR</th>\n"",
       ""      <td>0.723619</td>\n"",
       ""      <td>0.689239</td>\n"",
       ""      <td>0.808309</td>\n"",
       ""      <td>0.967694</td>\n"",
       ""      <td>-0.030252</td>\n"",
       ""      <td>0.547973</td>\n"",
       ""      <td>-0.048746</td>\n"",
       ""      <td>-0.105378</td>\n"",
       ""      <td>0.449118</td>\n"",
       ""      <td>-0.064893</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.949790</td>\n"",
       ""      <td>0.436380</td>\n"",
       ""      <td>0.485634</td>\n"",
       ""      <td>0.003616</td>\n"",
       ""      <td>0.349198</td>\n"",
       ""      <td>0.140862</td>\n"",
       ""      <td>0.106999</td>\n"",
       ""      <td>-0.228673</td>\n"",
       ""      <td>0.003616</td>\n"",
       ""      <td>0.024442</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minHBint5</th>\n"",
       ""      <td>-0.061913</td>\n"",
       ""      <td>-0.130684</td>\n"",
       ""      <td>-0.099787</td>\n"",
       ""      <td>-0.083352</td>\n"",
       ""      <td>0.086205</td>\n"",
       ""      <td>0.014998</td>\n"",
       ""      <td>0.097856</td>\n"",
       ""      <td>-0.071323</td>\n"",
       ""      <td>0.105162</td>\n"",
       ""      <td>0.171641</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.109757</td>\n"",
       ""      <td>0.163165</td>\n"",
       ""      <td>-0.074948</td>\n"",
       ""      <td>0.152546</td>\n"",
       ""      <td>-0.145466</td>\n"",
       ""      <td>0.011287</td>\n"",
       ""      <td>0.118686</td>\n"",
       ""      <td>0.048193</td>\n"",
       ""      <td>0.152546</td>\n"",
       ""      <td>-0.086103</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ATSc3</th>\n"",
       ""      <td>-0.226781</td>\n"",
       ""      <td>-0.223062</td>\n"",
       ""      <td>-0.271654</td>\n"",
       ""      <td>-0.199347</td>\n"",
       ""      <td>-0.150678</td>\n"",
       ""      <td>-0.224177</td>\n"",
       ""      <td>-0.156077</td>\n"",
       ""      <td>0.257995</td>\n"",
       ""      <td>-0.203159</td>\n"",
       ""      <td>-0.156019</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.157213</td>\n"",
       ""      <td>-0.021094</td>\n"",
       ""      <td>-0.201042</td>\n"",
       ""      <td>-0.209639</td>\n"",
       ""      <td>-0.188790</td>\n"",
       ""      <td>-0.081301</td>\n"",
       ""      <td>0.111417</td>\n"",
       ""      <td>0.160975</td>\n"",
       ""      <td>-0.209639</td>\n"",
       ""      <td>0.352451</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SwHBa</th>\n"",
       ""      <td>0.731094</td>\n"",
       ""      <td>0.687595</td>\n"",
       ""      <td>0.692099</td>\n"",
       ""      <td>0.588646</td>\n"",
       ""      <td>0.067426</td>\n"",
       ""      <td>0.453991</td>\n"",
       ""      <td>0.090351</td>\n"",
       ""      <td>-0.035861</td>\n"",
       ""      <td>0.295526</td>\n"",
       ""      <td>-0.024376</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.503657</td>\n"",
       ""      <td>-0.039388</td>\n"",
       ""      <td>0.662920</td>\n"",
       ""      <td>-0.011097</td>\n"",
       ""      <td>0.360965</td>\n"",
       ""      <td>0.085600</td>\n"",
       ""      <td>-0.018843</td>\n"",
       ""      <td>-0.259954</td>\n"",
       ""      <td>-0.011097</td>\n"",
       ""      <td>-0.198502</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>maxHBd</th>\n"",
       ""      <td>-0.014119</td>\n"",
       ""      <td>-0.187608</td>\n"",
       ""      <td>-0.154935</td>\n"",
       ""      <td>-0.136069</td>\n"",
       ""      <td>0.721514</td>\n"",
       ""      <td>-0.133737</td>\n"",
       ""      <td>0.663014</td>\n"",
       ""      <td>-0.310166</td>\n"",
       ""      <td>-0.100440</td>\n"",
       ""      <td>0.631734</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.171812</td>\n"",
       ""      <td>0.143665</td>\n"",
       ""      <td>-0.108221</td>\n"",
       ""      <td>0.538416</td>\n"",
       ""      <td>-0.137380</td>\n"",
       ""      <td>-0.210528</td>\n"",
       ""      <td>-0.176465</td>\n"",
       ""      <td>0.257426</td>\n"",
       ""      <td>0.538416</td>\n"",
       ""      <td>-0.015405</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>VABC</th>\n"",
       ""      <td>0.589410</td>\n"",
       ""      <td>0.617023</td>\n"",
       ""      <td>0.759419</td>\n"",
       ""      <td>0.949198</td>\n"",
       ""      <td>-0.075033</td>\n"",
       ""      <td>0.471523</td>\n"",
       ""      <td>-0.102974</td>\n"",
       ""      <td>-0.120253</td>\n"",
       ""      <td>0.400505</td>\n"",
       ""      <td>-0.112855</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.934246</td>\n"",
       ""      <td>0.467163</td>\n"",
       ""      <td>0.358693</td>\n"",
       ""      <td>-0.026506</td>\n"",
       ""      <td>0.373384</td>\n"",
       ""      <td>0.108563</td>\n"",
       ""      <td>0.024161</td>\n"",
       ""      <td>-0.210039</td>\n"",
       ""      <td>-0.026506</td>\n"",
       ""      <td>0.069772</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>hmin</th>\n"",
       ""      <td>-0.616663</td>\n"",
       ""      <td>-0.533128</td>\n"",
       ""      <td>-0.637585</td>\n"",
       ""      <td>-0.689425</td>\n"",
       ""      <td>-0.088130</td>\n"",
       ""      <td>-0.563093</td>\n"",
       ""      <td>-0.083080</td>\n"",
       ""      <td>0.193115</td>\n"",
       ""      <td>-0.675410</td>\n"",
       ""      <td>-0.057936</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.669033</td>\n"",
       ""      <td>-0.472912</td>\n"",
       ""      <td>-0.479257</td>\n"",
       ""      <td>-0.123255</td>\n"",
       ""      <td>-0.206750</td>\n"",
       ""      <td>-0.109716</td>\n"",
       ""      <td>-0.150755</td>\n"",
       ""      <td>0.085074</td>\n"",
       ""      <td>-0.123255</td>\n"",
       ""      <td>0.113480</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>fragC</th>\n"",
       ""      <td>0.285682</td>\n"",
       ""      <td>0.365474</td>\n"",
       ""      <td>0.528781</td>\n"",
       ""      <td>0.748073</td>\n"",
       ""      <td>-0.086857</td>\n"",
       ""      <td>0.278194</td>\n"",
       ""      <td>-0.099360</td>\n"",
       ""      <td>-0.085271</td>\n"",
       ""      <td>0.199592</td>\n"",
       ""      <td>-0.113395</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.748252</td>\n"",
       ""      <td>0.368983</td>\n"",
       ""      <td>0.063015</td>\n"",
       ""      <td>-0.045934</td>\n"",
       ""      <td>0.234954</td>\n"",
       ""      <td>0.052730</td>\n"",
       ""      <td>-0.010321</td>\n"",
       ""      <td>-0.162272</td>\n"",
       ""      <td>-0.045934</td>\n"",
       ""      <td>0.002340</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>BCUTp-1h</th>\n"",
       ""      <td>0.525435</td>\n"",
       ""      <td>0.512477</td>\n"",
       ""      <td>0.574428</td>\n"",
       ""      <td>0.538151</td>\n"",
       ""      <td>0.003900</td>\n"",
       ""      <td>0.397089</td>\n"",
       ""      <td>-0.036670</td>\n"",
       ""      <td>-0.132132</td>\n"",
       ""      <td>0.166788</td>\n"",
       ""      <td>-0.085406</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.670423</td>\n"",
       ""      <td>0.019074</td>\n"",
       ""      <td>0.438496</td>\n"",
       ""      <td>0.023319</td>\n"",
       ""      <td>0.414321</td>\n"",
       ""      <td>0.246732</td>\n"",
       ""      <td>-0.258448</td>\n"",
       ""      <td>-0.289531</td>\n"",
       ""      <td>0.023319</td>\n"",
       ""      <td>0.019658</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>maxHBint5</th>\n"",
       ""      <td>-0.124779</td>\n"",
       ""      <td>-0.208549</td>\n"",
       ""      <td>-0.125077</td>\n"",
       ""      <td>-0.023272</td>\n"",
       ""      <td>0.046115</td>\n"",
       ""      <td>-0.001624</td>\n"",
       ""      <td>0.062790</td>\n"",
       ""      <td>-0.056924</td>\n"",
       ""      <td>0.084812</td>\n"",
       ""      <td>0.135971</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.021960</td>\n"",
       ""      <td>0.272807</td>\n"",
       ""      <td>-0.211662</td>\n"",
       ""      <td>0.120569</td>\n"",
       ""      <td>-0.226361</td>\n"",
       ""      <td>0.000319</td>\n"",
       ""      <td>0.183905</td>\n"",
       ""      <td>0.158293</td>\n"",
       ""      <td>0.120569</td>\n"",
       ""      <td>0.041090</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>mindssC</th>\n"",
       ""      <td>0.147425</td>\n"",
       ""      <td>0.172014</td>\n"",
       ""      <td>0.232506</td>\n"",
       ""      <td>0.090728</td>\n"",
       ""      <td>-0.137296</td>\n"",
       ""      <td>0.090915</td>\n"",
       ""      <td>-0.053652</td>\n"",
       ""      <td>-0.099279</td>\n"",
       ""      <td>0.017604</td>\n"",
       ""      <td>-0.112253</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.093131</td>\n"",
       ""      <td>-0.223875</td>\n"",
       ""      <td>0.168489</td>\n"",
       ""      <td>0.002504</td>\n"",
       ""      <td>0.229138</td>\n"",
       ""      <td>0.126108</td>\n"",
       ""      <td>-0.131047</td>\n"",
       ""      <td>-0.264463</td>\n"",
       ""      <td>0.002504</td>\n"",
       ""      <td>-0.465916</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>VC-5</th>\n"",
       ""      <td>0.131063</td>\n"",
       ""      <td>0.202064</td>\n"",
       ""      <td>0.278840</td>\n"",
       ""      <td>0.223036</td>\n"",
       ""      <td>-0.078250</td>\n"",
       ""      <td>0.040098</td>\n"",
       ""      <td>-0.191246</td>\n"",
       ""      <td>-0.206466</td>\n"",
       ""      <td>-0.087500</td>\n"",
       ""      <td>-0.109189</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.361399</td>\n"",
       ""      <td>-0.063004</td>\n"",
       ""      <td>0.104583</td>\n"",
       ""      <td>0.079995</td>\n"",
       ""      <td>0.282622</td>\n"",
       ""      <td>0.275637</td>\n"",
       ""      <td>-0.235056</td>\n"",
       ""      <td>-0.162566</td>\n"",
       ""      <td>0.079995</td>\n"",
       ""      <td>-0.010509</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>BCUTc-1h</th>\n"",
       ""      <td>-0.097087</td>\n"",
       ""      <td>-0.208878</td>\n"",
       ""      <td>-0.272630</td>\n"",
       ""      <td>-0.086032</td>\n"",
       ""      <td>-0.062248</td>\n"",
       ""      <td>-0.165105</td>\n"",
       ""      <td>-0.102780</td>\n"",
       ""      <td>0.193079</td>\n"",
       ""      <td>0.027378</td>\n"",
       ""      <td>-0.095526</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.030234</td>\n"",
       ""      <td>0.296246</td>\n"",
       ""      <td>-0.184397</td>\n"",
       ""      <td>-0.191934</td>\n"",
       ""      <td>-0.213073</td>\n"",
       ""      <td>-0.037077</td>\n"",
       ""      <td>0.241610</td>\n"",
       ""      <td>0.229725</td>\n"",
       ""      <td>-0.191934</td>\n"",
       ""      <td>0.636476</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ATSc2</th>\n"",
       ""      <td>-0.167199</td>\n"",
       ""      <td>-0.090819</td>\n"",
       ""      <td>-0.143717</td>\n"",
       ""      <td>-0.468319</td>\n"",
       ""      <td>0.220636</td>\n"",
       ""      <td>-0.264878</td>\n"",
       ""      <td>0.213568</td>\n"",
       ""      <td>-0.073173</td>\n"",
       ""      <td>-0.412407</td>\n"",
       ""      <td>0.220676</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.498416</td>\n"",
       ""      <td>-0.625119</td>\n"",
       ""      <td>0.100754</td>\n"",
       ""      <td>0.245550</td>\n"",
       ""      <td>0.191403</td>\n"",
       ""      <td>-0.056351</td>\n"",
       ""      <td>-0.499061</td>\n"",
       ""      <td>-0.046367</td>\n"",
       ""      <td>0.245550</td>\n"",
       ""      <td>-0.302160</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ATSc4</th>\n"",
       ""      <td>0.160613</td>\n"",
       ""      <td>0.322814</td>\n"",
       ""      <td>0.329822</td>\n"",
       ""      <td>0.396503</td>\n"",
       ""      <td>-0.060700</td>\n"",
       ""      <td>0.352520</td>\n"",
       ""      <td>-0.020309</td>\n"",
       ""      <td>-0.099485</td>\n"",
       ""      <td>0.207325</td>\n"",
       ""      <td>-0.164863</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.378382</td>\n"",
       ""      <td>0.055420</td>\n"",
       ""      <td>0.093587</td>\n"",
       ""      <td>-0.126773</td>\n"",
       ""      <td>0.175906</td>\n"",
       ""      <td>0.089859</td>\n"",
       ""      <td>-0.030249</td>\n"",
       ""      <td>-0.177880</td>\n"",
       ""      <td>-0.126773</td>\n"",
       ""      <td>-0.256011</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>MDEO-12</th>\n"",
       ""      <td>0.172243</td>\n"",
       ""      <td>-0.161593</td>\n"",
       ""      <td>0.012140</td>\n"",
       ""      <td>0.132725</td>\n"",
       ""      <td>0.060789</td>\n"",
       ""      <td>-0.075240</td>\n"",
       ""      <td>0.056472</td>\n"",
       ""      <td>-0.133433</td>\n"",
       ""      <td>0.600764</td>\n"",
       ""      <td>0.175149</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.115374</td>\n"",
       ""      <td>0.779107</td>\n"",
       ""      <td>-0.015259</td>\n"",
       ""      <td>0.161306</td>\n"",
       ""      <td>-0.206418</td>\n"",
       ""      <td>0.028418</td>\n"",
       ""      <td>0.266784</td>\n"",
       ""      <td>0.268884</td>\n"",
       ""      <td>0.161306</td>\n"",
       ""      <td>0.270608</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SPC-6</th>\n"",
       ""      <td>0.488091</td>\n"",
       ""      <td>0.329433</td>\n"",
       ""      <td>0.497440</td>\n"",
       ""      <td>0.581181</td>\n"",
       ""      <td>-0.035990</td>\n"",
       ""      <td>0.185594</td>\n"",
       ""      <td>-0.116853</td>\n"",
       ""      <td>-0.156621</td>\n"",
       ""      <td>0.127720</td>\n"",
       ""      <td>-0.025093</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.693602</td>\n"",
       ""      <td>0.325706</td>\n"",
       ""      <td>0.168267</td>\n"",
       ""      <td>0.103491</td>\n"",
       ""      <td>0.180145</td>\n"",
       ""      <td>0.176388</td>\n"",
       ""      <td>0.070683</td>\n"",
       ""      <td>-0.065566</td>\n"",
       ""      <td>0.103491</td>\n"",
       ""      <td>0.221824</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SdssC</th>\n"",
       ""      <td>0.213534</td>\n"",
       ""      <td>0.139724</td>\n"",
       ""      <td>0.167069</td>\n"",
       ""      <td>-0.059014</td>\n"",
       ""      <td>-0.118402</td>\n"",
       ""      <td>0.039634</td>\n"",
       ""      <td>-0.065015</td>\n"",
       ""      <td>-0.004068</td>\n"",
       ""      <td>0.009531</td>\n"",
       ""      <td>-0.120382</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.054084</td>\n"",
       ""      <td>-0.295065</td>\n"",
       ""      <td>0.282719</td>\n"",
       ""      <td>-0.064807</td>\n"",
       ""      <td>0.245406</td>\n"",
       ""      <td>0.131698</td>\n"",
       ""      <td>-0.179050</td>\n"",
       ""      <td>-0.183485</td>\n"",
       ""      <td>-0.064807</td>\n"",
       ""      <td>-0.127867</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SHBint6</th>\n"",
       ""      <td>-0.234439</td>\n"",
       ""      <td>-0.273420</td>\n"",
       ""      <td>-0.124930</td>\n"",
       ""      <td>0.140328</td>\n"",
       ""      <td>0.030868</td>\n"",
       ""      <td>-0.088280</td>\n"",
       ""      <td>0.032283</td>\n"",
       ""      <td>-0.004815</td>\n"",
       ""      <td>-0.046778</td>\n"",
       ""      <td>0.053941</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.208287</td>\n"",
       ""      <td>0.378464</td>\n"",
       ""      <td>-0.435805</td>\n"",
       ""      <td>0.027695</td>\n"",
       ""      <td>-0.215712</td>\n"",
       ""      <td>-0.140078</td>\n"",
       ""      <td>0.040833</td>\n"",
       ""      <td>0.309443</td>\n"",
       ""      <td>0.027695</td>\n"",
       ""      <td>0.217537</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>SHBint5</th>\n"",
       ""      <td>-0.167111</td>\n"",
       ""      <td>-0.217035</td>\n"",
       ""      <td>-0.045257</td>\n"",
       ""      <td>0.195846</td>\n"",
       ""      <td>-0.025221</td>\n"",
       ""      <td>-0.050944</td>\n"",
       ""      <td>-0.021221</td>\n"",
       ""      <td>-0.010029</td>\n"",
       ""      <td>-0.007581</td>\n"",
       ""      <td>0.066222</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.235248</td>\n"",
       ""      <td>0.385145</td>\n"",
       ""      <td>-0.380500</td>\n"",
       ""      <td>0.064245</td>\n"",
       ""      <td>-0.201355</td>\n"",
       ""      <td>-0.029310</td>\n"",
       ""      <td>0.117630</td>\n"",
       ""      <td>0.105024</td>\n"",
       ""      <td>0.064245</td>\n"",
       ""      <td>0.107781</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>MDEC-33</th>\n"",
       ""      <td>0.557756</td>\n"",
       ""      <td>0.153330</td>\n"",
       ""      <td>0.343977</td>\n"",
       ""      <td>0.370283</td>\n"",
       ""      <td>0.158462</td>\n"",
       ""      <td>0.114767</td>\n"",
       ""      <td>0.123240</td>\n"",
       ""      <td>-0.188707</td>\n"",
       ""      <td>0.414666</td>\n"",
       ""      <td>0.255526</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.376334</td>\n"",
       ""      <td>0.485160</td>\n"",
       ""      <td>0.276791</td>\n"",
       ""      <td>0.293804</td>\n"",
       ""      <td>0.009897</td>\n"",
       ""      <td>0.084392</td>\n"",
       ""      <td>0.210382</td>\n"",
       ""      <td>0.183730</td>\n"",
       ""      <td>0.293804</td>\n"",
       ""      <td>0.138932</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minHBint10</th>\n"",
       ""      <td>0.194341</td>\n"",
       ""      <td>0.098503</td>\n"",
       ""      <td>0.119492</td>\n"",
       ""      <td>0.111664</td>\n"",
       ""      <td>0.368460</td>\n"",
       ""      <td>0.080757</td>\n"",
       ""      <td>0.358728</td>\n"",
       ""      <td>-0.263191</td>\n"",
       ""      <td>0.069112</td>\n"",
       ""      <td>0.441076</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.046788</td>\n"",
       ""      <td>0.132920</td>\n"",
       ""      <td>0.135356</td>\n"",
       ""      <td>0.432429</td>\n"",
       ""      <td>0.042906</td>\n"",
       ""      <td>-0.021826</td>\n"",
       ""      <td>-0.049909</td>\n"",
       ""      <td>0.000227</td>\n"",
       ""      <td>0.432429</td>\n"",
       ""      <td>-0.241175</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ndssC</th>\n"",
       ""      <td>0.047906</td>\n"",
       ""      <td>-0.173689</td>\n"",
       ""      <td>0.057396</td>\n"",
       ""      <td>0.277123</td>\n"",
       ""      <td>-0.280901</td>\n"",
       ""      <td>-0.119243</td>\n"",
       ""      <td>-0.286453</td>\n"",
       ""      <td>0.181761</td>\n"",
       ""      <td>0.039641</td>\n"",
       ""      <td>-0.244741</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.357343</td>\n"",
       ""      <td>0.394358</td>\n"",
       ""      <td>-0.322745</td>\n"",
       ""      <td>-0.262824</td>\n"",
       ""      <td>-0.096238</td>\n"",
       ""      <td>0.070540</td>\n"",
       ""      <td>0.067797</td>\n"",
       ""      <td>0.053910</td>\n"",
       ""      <td>-0.262824</td>\n"",
       ""      <td>0.500115</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>C3SP2</th>\n"",
       ""      <td>0.583630</td>\n"",
       ""      <td>0.382232</td>\n"",
       ""      <td>0.500653</td>\n"",
       ""      <td>0.391987</td>\n"",
       ""      <td>0.239666</td>\n"",
       ""      <td>0.031881</td>\n"",
       ""      <td>0.209826</td>\n"",
       ""      <td>-0.128560</td>\n"",
       ""      <td>0.172504</td>\n"",
       ""      <td>0.179739</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.289701</td>\n"",
       ""      <td>0.102410</td>\n"",
       ""      <td>0.339090</td>\n"",
       ""      <td>0.182515</td>\n"",
       ""      <td>0.325926</td>\n"",
       ""      <td>-0.113974</td>\n"",
       ""      <td>-0.296027</td>\n"",
       ""      <td>0.070193</td>\n"",
       ""      <td>0.182515</td>\n"",
       ""      <td>0.047750</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>WTPT-5</th>\n"",
       ""      <td>-0.139887</td>\n"",
       ""      <td>0.009337</td>\n"",
       ""      <td>-0.024095</td>\n"",
       ""      <td>0.276868</td>\n"",
       ""      <td>-0.452432</td>\n"",
       ""      <td>0.164648</td>\n"",
       ""      <td>-0.412646</td>\n"",
       ""      <td>0.394864</td>\n"",
       ""      <td>-0.129923</td>\n"",
       ""      <td>-0.414500</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.369428</td>\n"",
       ""      <td>-0.001536</td>\n"",
       ""      <td>-0.270670</td>\n"",
       ""      <td>-0.433728</td>\n"",
       ""      <td>-0.198666</td>\n"",
       ""      <td>0.101907</td>\n"",
       ""      <td>0.451984</td>\n"",
       ""      <td>-0.176075</td>\n"",
       ""      <td>-0.433728</td>\n"",
       ""      <td>0.077569</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>TopoPSA</th>\n"",
       ""      <td>-0.082182</td>\n"",
       ""      <td>-0.283805</td>\n"",
       ""      <td>-0.082083</td>\n"",
       ""      <td>0.299296</td>\n"",
       ""      <td>-0.116541</td>\n"",
       ""      <td>-0.050830</td>\n"",
       ""      <td>-0.153514</td>\n"",
       ""      <td>0.086461</td>\n"",
       ""      <td>0.076019</td>\n"",
       ""      <td>0.035523</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.448653</td>\n"",
       ""      <td>0.583315</td>\n"",
       ""      <td>-0.347450</td>\n"",
       ""      <td>0.020561</td>\n"",
       ""      <td>-0.313747</td>\n"",
       ""      <td>0.160358</td>\n"",
       ""      <td>0.270680</td>\n"",
       ""      <td>0.006891</td>\n"",
       ""      <td>0.020561</td>\n"",
       ""      <td>0.274700</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minHBa</th>\n"",
       ""      <td>-0.144979</td>\n"",
       ""      <td>-0.147136</td>\n"",
       ""      <td>-0.075382</td>\n"",
       ""      <td>-0.250999</td>\n"",
       ""      <td>0.209862</td>\n"",
       ""      <td>-0.444281</td>\n"",
       ""      <td>0.208282</td>\n"",
       ""      <td>-0.204846</td>\n"",
       ""      <td>-0.275389</td>\n"",
       ""      <td>0.190787</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.363424</td>\n"",
       ""      <td>-0.152920</td>\n"",
       ""      <td>-0.166346</td>\n"",
       ""      <td>0.212597</td>\n"",
       ""      <td>0.126903</td>\n"",
       ""      <td>-0.271109</td>\n"",
       ""      <td>-0.434364</td>\n"",
       ""      <td>0.133890</td>\n"",
       ""      <td>0.212597</td>\n"",
       ""      <td>-0.127977</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ATSp1</th>\n"",
       ""      <td>0.694143</td>\n"",
       ""      <td>0.622074</td>\n"",
       ""      <td>0.774221</td>\n"",
       ""      <td>0.931981</td>\n"",
       ""      <td>-0.092485</td>\n"",
       ""      <td>0.493795</td>\n"",
       ""      <td>-0.129834</td>\n"",
       ""      <td>-0.097724</td>\n"",
       ""      <td>0.362702</td>\n"",
       ""      <td>-0.118607</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.402613</td>\n"",
       ""      <td>0.428677</td>\n"",
       ""      <td>-0.012677</td>\n"",
       ""      <td>0.335792</td>\n"",
       ""      <td>0.242241</td>\n"",
       ""      <td>0.078450</td>\n"",
       ""      <td>-0.262416</td>\n"",
       ""      <td>-0.012677</td>\n"",
       ""      <td>0.080597</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>WTPT-4</th>\n"",
       ""      <td>0.284029</td>\n"",
       ""      <td>-0.114875</td>\n"",
       ""      <td>0.158376</td>\n"",
       ""      <td>0.391300</td>\n"",
       ""      <td>0.206140</td>\n"",
       ""      <td>0.101451</td>\n"",
       ""      <td>0.163428</td>\n"",
       ""      <td>-0.322451</td>\n"",
       ""      <td>0.551342</td>\n"",
       ""      <td>0.337685</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.402613</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.063065</td>\n"",
       ""      <td>0.341464</td>\n"",
       ""      <td>-0.220903</td>\n"",
       ""      <td>0.025641</td>\n"",
       ""      <td>0.261582</td>\n"",
       ""      <td>0.197127</td>\n"",
       ""      <td>0.341464</td>\n"",
       ""      <td>0.296443</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>CrippenLogP</th>\n"",
       ""      <td>0.626055</td>\n"",
       ""      <td>0.781812</td>\n"",
       ""      <td>0.689403</td>\n"",
       ""      <td>0.539211</td>\n"",
       ""      <td>0.135100</td>\n"",
       ""      <td>0.332208</td>\n"",
       ""      <td>0.122011</td>\n"",
       ""      <td>-0.121123</td>\n"",
       ""      <td>0.338802</td>\n"",
       ""      <td>0.020024</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.428677</td>\n"",
       ""      <td>-0.063065</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.080985</td>\n"",
       ""      <td>0.703827</td>\n"",
       ""      <td>0.088519</td>\n"",
       ""      <td>-0.206687</td>\n"",
       ""      <td>-0.200469</td>\n"",
       ""      <td>0.080985</td>\n"",
       ""      <td>-0.227113</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>nsOH</th>\n"",
       ""      <td>0.219097</td>\n"",
       ""      <td>-0.076578</td>\n"",
       ""      <td>0.090124</td>\n"",
       ""      <td>0.035098</td>\n"",
       ""      <td>0.788405</td>\n"",
       ""      <td>0.000538</td>\n"",
       ""      <td>0.679194</td>\n"",
       ""      <td>-0.631155</td>\n"",
       ""      <td>0.051448</td>\n"",
       ""      <td>0.958125</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.012677</td>\n"",
       ""      <td>0.341464</td>\n"",
       ""      <td>0.080985</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.009919</td>\n"",
       ""      <td>-0.177305</td>\n"",
       ""      <td>-0.203483</td>\n"",
       ""      <td>0.169133</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.233955</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>XLogP</th>\n"",
       ""      <td>0.372816</td>\n"",
       ""      <td>0.719401</td>\n"",
       ""      <td>0.637601</td>\n"",
       ""      <td>0.472318</td>\n"",
       ""      <td>0.038018</td>\n"",
       ""      <td>0.095217</td>\n"",
       ""      <td>0.013169</td>\n"",
       ""      <td>-0.110596</td>\n"",
       ""      <td>0.110652</td>\n"",
       ""      <td>-0.101380</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.335792</td>\n"",
       ""      <td>-0.220903</td>\n"",
       ""      <td>0.703827</td>\n"",
       ""      <td>-0.009919</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.035078</td>\n"",
       ""      <td>-0.538203</td>\n"",
       ""      <td>-0.185567</td>\n"",
       ""      <td>-0.009919</td>\n"",
       ""      <td>-0.211810</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>VCH-5</th>\n"",
       ""      <td>0.106946</td>\n"",
       ""      <td>0.012981</td>\n"",
       ""      <td>0.065126</td>\n"",
       ""      <td>0.111204</td>\n"",
       ""      <td>-0.290302</td>\n"",
       ""      <td>0.031578</td>\n"",
       ""      <td>-0.306239</td>\n"",
       ""      <td>0.121110</td>\n"",
       ""      <td>0.074108</td>\n"",
       ""      <td>-0.237750</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.242241</td>\n"",
       ""      <td>0.025641</td>\n"",
       ""      <td>0.088519</td>\n"",
       ""      <td>-0.177305</td>\n"",
       ""      <td>-0.035078</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.195552</td>\n"",
       ""      <td>-0.177931</td>\n"",
       ""      <td>-0.177305</td>\n"",
       ""      <td>0.088705</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>ETA_BetaP_s</th>\n"",
       ""      <td>0.008916</td>\n"",
       ""      <td>-0.162217</td>\n"",
       ""      <td>-0.201593</td>\n"",
       ""      <td>0.000823</td>\n"",
       ""      <td>-0.280706</td>\n"",
       ""      <td>0.262811</td>\n"",
       ""      <td>-0.234901</td>\n"",
       ""      <td>0.189810</td>\n"",
       ""      <td>0.181366</td>\n"",
       ""      <td>-0.161097</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.078450</td>\n"",
       ""      <td>0.261582</td>\n"",
       ""      <td>-0.206687</td>\n"",
       ""      <td>-0.203483</td>\n"",
       ""      <td>-0.538203</td>\n"",
       ""      <td>0.195552</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.054857</td>\n"",
       ""      <td>-0.203483</td>\n"",
       ""      <td>0.078905</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>minHBint6</th>\n"",
       ""      <td>-0.173059</td>\n"",
       ""      <td>-0.276929</td>\n"",
       ""      <td>-0.233164</td>\n"",
       ""      <td>-0.221541</td>\n"",
       ""      <td>0.239523</td>\n"",
       ""      <td>-0.166763</td>\n"",
       ""      <td>0.226246</td>\n"",
       ""      <td>-0.106740</td>\n"",
       ""      <td>0.077643</td>\n"",
       ""      <td>0.224630</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.262416</td>\n"",
       ""      <td>0.197127</td>\n"",
       ""      <td>-0.200469</td>\n"",
       ""      <td>0.169133</td>\n"",
       ""      <td>-0.185567</td>\n"",
       ""      <td>-0.177931</td>\n"",
       ""      <td>0.054857</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>0.169133</td>\n"",
       ""      <td>0.206758</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>nHsOH</th>\n"",
       ""      <td>0.219097</td>\n"",
       ""      <td>-0.076578</td>\n"",
       ""      <td>0.090124</td>\n"",
       ""      <td>0.035098</td>\n"",
       ""      <td>0.788405</td>\n"",
       ""      <td>0.000538</td>\n"",
       ""      <td>0.679194</td>\n"",
       ""      <td>-0.631155</td>\n"",
       ""      <td>0.051448</td>\n"",
       ""      <td>0.958125</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>-0.012677</td>\n"",
       ""      <td>0.341464</td>\n"",
       ""      <td>0.080985</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.009919</td>\n"",
       ""      <td>-0.177305</td>\n"",
       ""      <td>-0.203483</td>\n"",
       ""      <td>0.169133</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>-0.233955</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>mindO</th>\n"",
       ""      <td>-0.067162</td>\n"",
       ""      <td>-0.251658</td>\n"",
       ""      <td>-0.137389</td>\n"",
       ""      <td>-0.010533</td>\n"",
       ""      <td>-0.203484</td>\n"",
       ""      <td>-0.144182</td>\n"",
       ""      <td>-0.233683</td>\n"",
       ""      <td>0.193009</td>\n"",
       ""      <td>-0.022380</td>\n"",
       ""      <td>-0.189570</td>\n"",
       ""      <td>...</td>\n"",
       ""      <td>0.080597</td>\n"",
       ""      <td>0.296443</td>\n"",
       ""      <td>-0.227113</td>\n"",
       ""      <td>-0.233955</td>\n"",
       ""      <td>-0.211810</td>\n"",
       ""      <td>0.088705</td>\n"",
       ""      <td>0.078905</td>\n"",
       ""      <td>0.206758</td>\n"",
       ""      <td>-0.233955</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""<p>56 rows × 56 columns</p>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""                    MDEC-23  LipoaffinityIndex     MLogP        nC   maxHsOH  \\\n"",
       ""MDEC-23            1.000000           0.708134  0.832241  0.775807  0.205894   \n"",
       ""LipoaffinityIndex  0.708134           1.000000  0.891029  0.774522  0.024670   \n"",
       ""MLogP              0.832241           0.891029  1.000000  0.909434  0.085157   \n"",
       ""nC                 0.775807           0.774522  0.909434  1.000000  0.002710   \n"",
       ""maxHsOH            0.205894           0.024670  0.085157  0.002710  1.000000   \n"",
       ""minsssN            0.486881           0.574888  0.529384  0.533325  0.052235   \n"",
       ""minHsOH            0.169654           0.025091  0.061318 -0.024062  0.950769   \n"",
       ""BCUTc-1l          -0.276885          -0.122688 -0.255074 -0.177045 -0.652815   \n"",
       ""maxssO             0.408743           0.322083  0.399374  0.443980  0.054897   \n"",
       ""SHsOH              0.159171          -0.159268 -0.021842 -0.056746  0.836405   \n"",
       ""nHBAcc            -0.115243          -0.116825 -0.026372  0.321631 -0.410827   \n"",
       ""MLFER_A            0.166126          -0.082829  0.125160  0.228047  0.622245   \n"",
       ""maxsOH             0.289200           0.163803  0.251398  0.132552  0.910247   \n"",
       ""maxsssN            0.488564           0.573018  0.528765  0.537149  0.039004   \n"",
       ""SsOH               0.231639          -0.057945  0.113881  0.056819  0.768205   \n"",
       ""minsOH             0.280981           0.170720  0.251495  0.129241  0.894224   \n"",
       ""ATSp5              0.712786           0.598322  0.764607  0.890748 -0.051911   \n"",
       ""minssO             0.384934           0.312842  0.386689  0.426739  0.053458   \n"",
       ""C1SP2             -0.290392          -0.310835 -0.264977  0.024759 -0.400968   \n"",
       ""AMR                0.723619           0.689239  0.808309  0.967694 -0.030252   \n"",
       ""minHBint5         -0.061913          -0.130684 -0.099787 -0.083352  0.086205   \n"",
       ""ATSc3             -0.226781          -0.223062 -0.271654 -0.199347 -0.150678   \n"",
       ""SwHBa              0.731094           0.687595  0.692099  0.588646  0.067426   \n"",
       ""maxHBd            -0.014119          -0.187608 -0.154935 -0.136069  0.721514   \n"",
       ""VABC               0.589410           0.617023  0.759419  0.949198 -0.075033   \n"",
       ""hmin              -0.616663          -0.533128 -0.637585 -0.689425 -0.088130   \n"",
       ""fragC              0.285682           0.365474  0.528781  0.748073 -0.086857   \n"",
       ""BCUTp-1h           0.525435           0.512477  0.574428  0.538151  0.003900   \n"",
       ""maxHBint5         -0.124779          -0.208549 -0.125077 -0.023272  0.046115   \n"",
       ""mindssC            0.147425           0.172014  0.232506  0.090728 -0.137296   \n"",
       ""VC-5               0.131063           0.202064  0.278840  0.223036 -0.078250   \n"",
       ""BCUTc-1h          -0.097087          -0.208878 -0.272630 -0.086032 -0.062248   \n"",
       ""ATSc2             -0.167199          -0.090819 -0.143717 -0.468319  0.220636   \n"",
       ""ATSc4              0.160613           0.322814  0.329822  0.396503 -0.060700   \n"",
       ""MDEO-12            0.172243          -0.161593  0.012140  0.132725  0.060789   \n"",
       ""SPC-6              0.488091           0.329433  0.497440  0.581181 -0.035990   \n"",
       ""SdssC              0.213534           0.139724  0.167069 -0.059014 -0.118402   \n"",
       ""SHBint6           -0.234439          -0.273420 -0.124930  0.140328  0.030868   \n"",
       ""SHBint5           -0.167111          -0.217035 -0.045257  0.195846 -0.025221   \n"",
       ""MDEC-33            0.557756           0.153330  0.343977  0.370283  0.158462   \n"",
       ""minHBint10         0.194341           0.098503  0.119492  0.111664  0.368460   \n"",
       ""ndssC              0.047906          -0.173689  0.057396  0.277123 -0.280901   \n"",
       ""C3SP2              0.583630           0.382232  0.500653  0.391987  0.239666   \n"",
       ""WTPT-5            -0.139887           0.009337 -0.024095  0.276868 -0.452432   \n"",
       ""TopoPSA           -0.082182          -0.283805 -0.082083  0.299296 -0.116541   \n"",
       ""minHBa            -0.144979          -0.147136 -0.075382 -0.250999  0.209862   \n"",
       ""ATSp1              0.694143           0.622074  0.774221  0.931981 -0.092485   \n"",
       ""WTPT-4             0.284029          -0.114875  0.158376  0.391300  0.206140   \n"",
       ""CrippenLogP        0.626055           0.781812  0.689403  0.539211  0.135100   \n"",
       ""nsOH               0.219097          -0.076578  0.090124  0.035098  0.788405   \n"",
       ""XLogP              0.372816           0.719401  0.637601  0.472318  0.038018   \n"",
       ""VCH-5              0.106946           0.012981  0.065126  0.111204 -0.290302   \n"",
       ""ETA_BetaP_s        0.008916          -0.162217 -0.201593  0.000823 -0.280706   \n"",
       ""minHBint6         -0.173059          -0.276929 -0.233164 -0.221541  0.239523   \n"",
       ""nHsOH              0.219097          -0.076578  0.090124  0.035098  0.788405   \n"",
       ""mindO             -0.067162          -0.251658 -0.137389 -0.010533 -0.203484   \n"",
       ""\n"",
       ""                    minsssN   minHsOH  BCUTc-1l    maxssO     SHsOH  ...  \\\n"",
       ""MDEC-23            0.486881  0.169654 -0.276885  0.408743  0.159171  ...   \n"",
       ""LipoaffinityIndex  0.574888  0.025091 -0.122688  0.322083 -0.159268  ...   \n"",
       ""MLogP              0.529384  0.061318 -0.255074  0.399374 -0.021842  ...   \n"",
       ""nC                 0.533325 -0.024062 -0.177045  0.443980 -0.056746  ...   \n"",
       ""maxHsOH            0.052235  0.950769 -0.652815  0.054897  0.836405  ...   \n"",
       ""minsssN            1.000000  0.088362 -0.153156  0.353748 -0.044227  ...   \n"",
       ""minHsOH            0.088362  1.000000 -0.602109  0.089461  0.749172  ...   \n"",
       ""BCUTc-1l          -0.153156 -0.602109  1.000000 -0.247030 -0.557629  ...   \n"",
       ""maxssO             0.353748  0.089461 -0.247030  1.000000  0.022689  ...   \n"",
       ""SHsOH             -0.044227  0.749172 -0.557629  0.022689  1.000000  ...   \n"",
       ""nHBAcc             0.012518 -0.425683  0.294564 -0.068137 -0.365373  ...   \n"",
       ""MLFER_A           -0.006522  0.546420 -0.438164  0.013090  0.765262  ...   \n"",
       ""maxsOH             0.151627  0.879282 -0.752041  0.124590  0.727513  ...   \n"",
       ""maxsssN            0.995618  0.076448 -0.145805  0.347401 -0.055008  ...   \n"",
       ""SsOH               0.014526  0.657183 -0.636509  0.063803  0.938708  ...   \n"",
       ""minsOH             0.164004  0.888996 -0.743064  0.132712  0.696856  ...   \n"",
       ""ATSp5              0.445211 -0.102746 -0.133574  0.318112 -0.079830  ...   \n"",
       ""minssO             0.343609  0.087479 -0.243943  0.996300  0.020178  ...   \n"",
       ""C1SP2             -0.186569 -0.425127  0.363890 -0.122003 -0.338735  ...   \n"",
       ""AMR                0.547973 -0.048746 -0.105378  0.449118 -0.064893  ...   \n"",
       ""minHBint5          0.014998  0.097856 -0.071323  0.105162  0.171641  ...   \n"",
       ""ATSc3             -0.224177 -0.156077  0.257995 -0.203159 -0.156019  ...   \n"",
       ""SwHBa              0.453991  0.090351 -0.035861  0.295526 -0.024376  ...   \n"",
       ""maxHBd            -0.133737  0.663014 -0.310166 -0.100440  0.631734  ...   \n"",
       ""VABC               0.471523 -0.102974 -0.120253  0.400505 -0.112855  ...   \n"",
       ""hmin              -0.563093 -0.083080  0.193115 -0.675410 -0.057936  ...   \n"",
       ""fragC              0.278194 -0.099360 -0.085271  0.199592 -0.113395  ...   \n"",
       ""BCUTp-1h           0.397089 -0.036670 -0.132132  0.166788 -0.085406  ...   \n"",
       ""maxHBint5         -0.001624  0.062790 -0.056924  0.084812  0.135971  ...   \n"",
       ""mindssC            0.090915 -0.053652 -0.099279  0.017604 -0.112253  ...   \n"",
       ""VC-5               0.040098 -0.191246 -0.206466 -0.087500 -0.109189  ...   \n"",
       ""BCUTc-1h          -0.165105 -0.102780  0.193079  0.027378 -0.095526  ...   \n"",
       ""ATSc2             -0.264878  0.213568 -0.073173 -0.412407  0.220676  ...   \n"",
       ""ATSc4              0.352520 -0.020309 -0.099485  0.207325 -0.164863  ...   \n"",
       ""MDEO-12           -0.075240  0.056472 -0.133433  0.600764  0.175149  ...   \n"",
       ""SPC-6              0.185594 -0.116853 -0.156621  0.127720 -0.025093  ...   \n"",
       ""SdssC              0.039634 -0.065015 -0.004068  0.009531 -0.120382  ...   \n"",
       ""SHBint6           -0.088280  0.032283 -0.004815 -0.046778  0.053941  ...   \n"",
       ""SHBint5           -0.050944 -0.021221 -0.010029 -0.007581  0.066222  ...   \n"",
       ""MDEC-33            0.114767  0.123240 -0.188707  0.414666  0.255526  ...   \n"",
       ""minHBint10         0.080757  0.358728 -0.263191  0.069112  0.441076  ...   \n"",
       ""ndssC             -0.119243 -0.286453  0.181761  0.039641 -0.244741  ...   \n"",
       ""C3SP2              0.031881  0.209826 -0.128560  0.172504  0.179739  ...   \n"",
       ""WTPT-5             0.164648 -0.412646  0.394864 -0.129923 -0.414500  ...   \n"",
       ""TopoPSA           -0.050830 -0.153514  0.086461  0.076019  0.035523  ...   \n"",
       ""minHBa            -0.444281  0.208282 -0.204846 -0.275389  0.190787  ...   \n"",
       ""ATSp1              0.493795 -0.129834 -0.097724  0.362702 -0.118607  ...   \n"",
       ""WTPT-4             0.101451  0.163428 -0.322451  0.551342  0.337685  ...   \n"",
       ""CrippenLogP        0.332208  0.122011 -0.121123  0.338802  0.020024  ...   \n"",
       ""nsOH               0.000538  0.679194 -0.631155  0.051448  0.958125  ...   \n"",
       ""XLogP              0.095217  0.013169 -0.110596  0.110652 -0.101380  ...   \n"",
       ""VCH-5              0.031578 -0.306239  0.121110  0.074108 -0.237750  ...   \n"",
       ""ETA_BetaP_s        0.262811 -0.234901  0.189810  0.181366 -0.161097  ...   \n"",
       ""minHBint6         -0.166763  0.226246 -0.106740  0.077643  0.224630  ...   \n"",
       ""nHsOH              0.000538  0.679194 -0.631155  0.051448  0.958125  ...   \n"",
       ""mindO             -0.144182 -0.233683  0.193009 -0.022380 -0.189570  ...   \n"",
       ""\n"",
       ""                      ATSp1    WTPT-4  CrippenLogP      nsOH     XLogP  \\\n"",
       ""MDEC-23            0.694143  0.284029     0.626055  0.219097  0.372816   \n"",
       ""LipoaffinityIndex  0.622074 -0.114875     0.781812 -0.076578  0.719401   \n"",
       ""MLogP              0.774221  0.158376     0.689403  0.090124  0.637601   \n"",
       ""nC                 0.931981  0.391300     0.539211  0.035098  0.472318   \n"",
       ""maxHsOH           -0.092485  0.206140     0.135100  0.788405  0.038018   \n"",
       ""minsssN            0.493795  0.101451     0.332208  0.000538  0.095217   \n"",
       ""minHsOH           -0.129834  0.163428     0.122011  0.679194  0.013169   \n"",
       ""BCUTc-1l          -0.097724 -0.322451    -0.121123 -0.631155 -0.110596   \n"",
       ""maxssO             0.362702  0.551342     0.338802  0.051448  0.110652   \n"",
       ""SHsOH             -0.118607  0.337685     0.020024  0.958125 -0.101380   \n"",
       ""nHBAcc             0.452646  0.291217    -0.379953 -0.374522 -0.247959   \n"",
       ""MLFER_A            0.209575  0.420918    -0.107085  0.771052 -0.079694   \n"",
       ""maxsOH             0.050900  0.178155     0.229058  0.782903  0.166541   \n"",
       ""maxsssN            0.501813  0.101717     0.322519 -0.010117  0.083530   \n"",
       ""SsOH               0.012389  0.343575     0.095528  0.997917  0.006657   \n"",
       ""minsOH             0.045258  0.161343     0.229266  0.751524  0.167149   \n"",
       ""ATSp5              0.961924  0.390970     0.394181  0.028422  0.331927   \n"",
       ""minssO             0.346219  0.523643     0.329176  0.049154  0.110249   \n"",
       ""C1SP2              0.128303  0.218757    -0.410136 -0.385877 -0.299003   \n"",
       ""AMR                0.949790  0.436380     0.485634  0.003616  0.349198   \n"",
       ""minHBint5         -0.109757  0.163165    -0.074948  0.152546 -0.145466   \n"",
       ""ATSc3             -0.157213 -0.021094    -0.201042 -0.209639 -0.188790   \n"",
       ""SwHBa              0.503657 -0.039388     0.662920 -0.011097  0.360965   \n"",
       ""maxHBd            -0.171812  0.143665    -0.108221  0.538416 -0.137380   \n"",
       ""VABC               0.934246  0.467163     0.358693 -0.026506  0.373384   \n"",
       ""hmin              -0.669033 -0.472912    -0.479257 -0.123255 -0.206750   \n"",
       ""fragC              0.748252  0.368983     0.063015 -0.045934  0.234954   \n"",
       ""BCUTp-1h           0.670423  0.019074     0.438496  0.023319  0.414321   \n"",
       ""maxHBint5         -0.021960  0.272807    -0.211662  0.120569 -0.226361   \n"",
       ""mindssC            0.093131 -0.223875     0.168489  0.002504  0.229138   \n"",
       ""VC-5               0.361399 -0.063004     0.104583  0.079995  0.282622   \n"",
       ""BCUTc-1h          -0.030234  0.296246    -0.184397 -0.191934 -0.213073   \n"",
       ""ATSc2             -0.498416 -0.625119     0.100754  0.245550  0.191403   \n"",
       ""ATSc4              0.378382  0.055420     0.093587 -0.126773  0.175906   \n"",
       ""MDEO-12            0.115374  0.779107    -0.015259  0.161306 -0.206418   \n"",
       ""SPC-6              0.693602  0.325706     0.168267  0.103491  0.180145   \n"",
       ""SdssC             -0.054084 -0.295065     0.282719 -0.064807  0.245406   \n"",
       ""SHBint6            0.208287  0.378464    -0.435805  0.027695 -0.215712   \n"",
       ""SHBint5            0.235248  0.385145    -0.380500  0.064245 -0.201355   \n"",
       ""MDEC-33            0.376334  0.485160     0.276791  0.293804  0.009897   \n"",
       ""minHBint10         0.046788  0.132920     0.135356  0.432429  0.042906   \n"",
       ""ndssC              0.357343  0.394358    -0.322745 -0.262824 -0.096238   \n"",
       ""C3SP2              0.289701  0.102410     0.339090  0.182515  0.325926   \n"",
       ""WTPT-5             0.369428 -0.001536    -0.270670 -0.433728 -0.198666   \n"",
       ""TopoPSA            0.448653  0.583315    -0.347450  0.020561 -0.313747   \n"",
       ""minHBa            -0.363424 -0.152920    -0.166346  0.212597  0.126903   \n"",
       ""ATSp1              1.000000  0.402613     0.428677 -0.012677  0.335792   \n"",
       ""WTPT-4             0.402613  1.000000    -0.063065  0.341464 -0.220903   \n"",
       ""CrippenLogP        0.428677 -0.063065     1.000000  0.080985  0.703827   \n"",
       ""nsOH              -0.012677  0.341464     0.080985  1.000000 -0.009919   \n"",
       ""XLogP              0.335792 -0.220903     0.703827 -0.009919  1.000000   \n"",
       ""VCH-5              0.242241  0.025641     0.088519 -0.177305 -0.035078   \n"",
       ""ETA_BetaP_s        0.078450  0.261582    -0.206687 -0.203483 -0.538203   \n"",
       ""minHBint6         -0.262416  0.197127    -0.200469  0.169133 -0.185567   \n"",
       ""nHsOH             -0.012677  0.341464     0.080985  1.000000 -0.009919   \n"",
       ""mindO              0.080597  0.296443    -0.227113 -0.233955 -0.211810   \n"",
       ""\n"",
       ""                      VCH-5  ETA_BetaP_s  minHBint6     nHsOH     mindO  \n"",
       ""MDEC-23            0.106946     0.008916  -0.173059  0.219097 -0.067162  \n"",
       ""LipoaffinityIndex  0.012981    -0.162217  -0.276929 -0.076578 -0.251658  \n"",
       ""MLogP              0.065126    -0.201593  -0.233164  0.090124 -0.137389  \n"",
       ""nC                 0.111204     0.000823  -0.221541  0.035098 -0.010533  \n"",
       ""maxHsOH           -0.290302    -0.280706   0.239523  0.788405 -0.203484  \n"",
       ""minsssN            0.031578     0.262811  -0.166763  0.000538 -0.144182  \n"",
       ""minHsOH           -0.306239    -0.234901   0.226246  0.679194 -0.233683  \n"",
       ""BCUTc-1l           0.121110     0.189810  -0.106740 -0.631155  0.193009  \n"",
       ""maxssO             0.074108     0.181366   0.077643  0.051448 -0.022380  \n"",
       ""SHsOH             -0.237750    -0.161097   0.224630  0.958125 -0.189570  \n"",
       ""nHBAcc             0.129077     0.317543  -0.073994 -0.374522  0.379997  \n"",
       ""MLFER_A           -0.173470    -0.136329   0.134267  0.771052 -0.147409  \n"",
       ""maxsOH            -0.205403    -0.308485   0.153463  0.782903 -0.300017  \n"",
       ""maxsssN            0.035190     0.278328  -0.171093 -0.010117 -0.126261  \n"",
       ""SsOH              -0.162398    -0.201822   0.156990  0.997917 -0.241518  \n"",
       ""minsOH            -0.205124    -0.300543   0.145555  0.751524 -0.312523  \n"",
       ""ATSp5              0.191947     0.038456  -0.224171  0.028422  0.102335  \n"",
       ""minssO             0.076297     0.171478   0.085631  0.049154 -0.020341  \n"",
       ""C1SP2              0.164179     0.373831   0.072341 -0.385877  0.370528  \n"",
       ""AMR                0.140862     0.106999  -0.228673  0.003616  0.024442  \n"",
       ""minHBint5          0.011287     0.118686   0.048193  0.152546 -0.086103  \n"",
       ""ATSc3             -0.081301     0.111417   0.160975 -0.209639  0.352451  \n"",
       ""SwHBa              0.085600    -0.018843  -0.259954 -0.011097 -0.198502  \n"",
       ""maxHBd            -0.210528    -0.176465   0.257426  0.538416 -0.015405  \n"",
       ""VABC               0.108563     0.024161  -0.210039 -0.026506  0.069772  \n"",
       ""hmin              -0.109716    -0.150755   0.085074 -0.123255  0.113480  \n"",
       ""fragC              0.052730    -0.010321  -0.162272 -0.045934  0.002340  \n"",
       ""BCUTp-1h           0.246732    -0.258448  -0.289531  0.023319  0.019658  \n"",
       ""maxHBint5          0.000319     0.183905   0.158293  0.120569  0.041090  \n"",
       ""mindssC            0.126108    -0.131047  -0.264463  0.002504 -0.465916  \n"",
       ""VC-5               0.275637    -0.235056  -0.162566  0.079995 -0.010509  \n"",
       ""BCUTc-1h          -0.037077     0.241610   0.229725 -0.191934  0.636476  \n"",
       ""ATSc2             -0.056351    -0.499061  -0.046367  0.245550 -0.302160  \n"",
       ""ATSc4              0.089859    -0.030249  -0.177880 -0.126773 -0.256011  \n"",
       ""MDEO-12            0.028418     0.266784   0.268884  0.161306  0.270608  \n"",
       ""SPC-6              0.176388     0.070683  -0.065566  0.103491  0.221824  \n"",
       ""SdssC              0.131698    -0.179050  -0.183485 -0.064807 -0.127867  \n"",
       ""SHBint6           -0.140078     0.040833   0.309443  0.027695  0.217537  \n"",
       ""SHBint5           -0.029310     0.117630   0.105024  0.064245  0.107781  \n"",
       ""MDEC-33            0.084392     0.210382   0.183730  0.293804  0.138932  \n"",
       ""minHBint10        -0.021826    -0.049909   0.000227  0.432429 -0.241175  \n"",
       ""ndssC              0.070540     0.067797   0.053910 -0.262824  0.500115  \n"",
       ""C3SP2             -0.113974    -0.296027   0.070193  0.182515  0.047750  \n"",
       ""WTPT-5             0.101907     0.451984  -0.176075 -0.433728  0.077569  \n"",
       ""TopoPSA            0.160358     0.270680   0.006891  0.020561  0.274700  \n"",
       ""minHBa            -0.271109    -0.434364   0.133890  0.212597 -0.127977  \n"",
       ""ATSp1              0.242241     0.078450  -0.262416 -0.012677  0.080597  \n"",
       ""WTPT-4             0.025641     0.261582   0.197127  0.341464  0.296443  \n"",
       ""CrippenLogP        0.088519    -0.206687  -0.200469  0.080985 -0.227113  \n"",
       ""nsOH              -0.177305    -0.203483   0.169133  1.000000 -0.233955  \n"",
       ""XLogP             -0.035078    -0.538203  -0.185567 -0.009919 -0.211810  \n"",
       ""VCH-5              1.000000     0.195552  -0.177931 -0.177305  0.088705  \n"",
       ""ETA_BetaP_s        0.195552     1.000000   0.054857 -0.203483  0.078905  \n"",
       ""minHBint6         -0.177931     0.054857   1.000000  0.169133  0.206758  \n"",
       ""nHsOH             -0.177305    -0.203483   0.169133  1.000000 -0.233955  \n"",
       ""mindO              0.088705     0.078905   0.206758 -0.233955  1.000000  \n"",
       ""\n"",
       ""[56 rows x 56 columns]""
      ]
     },
     ""execution_count"": 231,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 648x648 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#然后通过相关分析提出自相关的变量\n"",
    ""cor=feature_train[choose_feature].corr()\n"",
    ""f , ax = plt.subplots(figsize = (9,9))\n"",
    ""\n"",
    ""\n"",
    ""cor""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 232,
   ""id"": ""93434e53"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(1974, 21)""
      ]
     },
     ""execution_count"": 232,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""for i in range(55):\n"",
    ""    for  j in range(55):\n"",
    ""        if i !=j:\n"",
    ""            if cor.iloc[i,j]>0.5 and cor.index[i] in choose_feature and cor.columns[j] in choose_feature:\n"",
    ""                if len(choose_feature)==20:\n"",
    ""                    break;\n"",
    ""                choose_feature.remove(cor.columns[j])\n"",
    ""                \n"",
    ""                \n"",
    ""                \n"",
    ""                \n"",
    ""choose_feature     \n"",
    ""final_feature_train=feature_train[choose_feature]\n"",
    ""final_feature_train.shape""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 237,
   ""id"": ""bb2758ee"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""final_feature_train.to_csv('final.csv') ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 233,
   ""id"": ""101bda2e"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""from sklearn.model_selection import cross_val_score\n"",
    ""from sklearn.metrics import mean_squared_error,make_scorer,mean_absolute_error\n"",
    ""from sklearn.linear_model import LinearRegression\n"",
    ""from sklearn.tree import DecisionTreeRegressor\n"",
    ""from sklearn.ensemble import RandomForestRegressor\n"",
    ""from sklearn.ensemble import GradientBoostingRegressor\n"",
    ""from sklearn.neural_network import MLPRegressor\n"",
    ""from xgboost.sklearn import XGBRegressor \n"",
    ""from lightgbm.sklearn import LGBMRegressor""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 234,
   ""id"": ""07923e6e"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""LinearRegression is finished\n"",
      ""DecisionTreeRegressor is finished\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"",
      ""  estimator.fit(X_train, y_train, **fit_params)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"",
      ""  estimator.fit(X_train, y_train, **fit_params)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"",
      ""  estimator.fit(X_train, y_train, **fit_params)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"",
      ""  estimator.fit(X_train, y_train, **fit_params)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"",
      ""  estimator.fit(X_train, y_train, **fit_params)\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""RandomForestRegressor is finished\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""GradientBoostingRegressor is finished\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n"",
      ""STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n"",
      ""\n"",
      ""Increase the number of iterations (max_iter) or scale the data as shown in:\n"",
      ""    https://scikit-learn.org/stable/modules/preprocessing.html\n"",
      ""  self.n_iter_ = _check_optimize_result(\""lbfgs\"", opt_res, self.max_iter)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n"",
      ""STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n"",
      ""\n"",
      ""Increase the number of iterations (max_iter) or scale the data as shown in:\n"",
      ""    https://scikit-learn.org/stable/modules/preprocessing.html\n"",
      ""  self.n_iter_ = _check_optimize_result(\""lbfgs\"", opt_res, self.max_iter)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n"",
      ""STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n"",
      ""\n"",
      ""Increase the number of iterations (max_iter) or scale the data as shown in:\n"",
      ""    https://scikit-learn.org/stable/modules/preprocessing.html\n"",
      ""  self.n_iter_ = _check_optimize_result(\""lbfgs\"", opt_res, self.max_iter)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n"",
      ""STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n"",
      ""\n"",
      ""Increase the number of iterations (max_iter) or scale the data as shown in:\n"",
      ""    https://scikit-learn.org/stable/modules/preprocessing.html\n"",
      ""  self.n_iter_ = _check_optimize_result(\""lbfgs\"", opt_res, self.max_iter)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"",
      ""  return f(*args, **kwargs)\n"",
      ""d:\\download\\minconda\\envs\\d2l-zh\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n"",
      ""STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n"",
      ""\n"",
      ""Increase the number of iterations (max_iter) or scale the data as shown in:\n"",
      ""    https://scikit-learn.org/stable/modules/preprocessing.html\n"",
      ""  self.n_iter_ = _check_optimize_result(\""lbfgs\"", opt_res, self.max_iter)\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""MLPRegressor is finished\n"",
      ""[19:30:56] WARNING: ..\\src\\learner.cc:541: \n"",
      ""Parameters: { n_estimator } might not be used.\n"",
      ""\n"",
      ""  This may not be accurate due to some parameters are only used in language bindings but\n"",
      ""  passed down to XGBoost core.  Or some parameters are not used but slip through this\n"",
      ""  verification. Please open an issue if you find above cases.\n"",
      ""\n"",
      ""\n"",
      ""[19:30:56] WARNING: ..\\src\\learner.cc:541: \n"",
      ""Parameters: { n_estimator } might not be used.\n"",
      ""\n"",
      ""  This may not be accurate due to some parameters are only used in language bindings but\n"",
      ""  passed down to XGBoost core.  Or some parameters are not used but slip through this\n"",
      ""  verification. Please open an issue if you find above cases.\n"",
      ""\n"",
      ""\n"",
      ""[19:30:57] WARNING: ..\\src\\learner.cc:541: \n"",
      ""Parameters: { n_estimator } might not be used.\n"",
      ""\n"",
      ""  This may not be accurate due to some parameters are only used in language bindings but\n"",
      ""  passed down to XGBoost core.  Or some parameters are not used but slip through this\n"",
      ""  verification. Please open an issue if you find above cases.\n"",
      ""\n"",
      ""\n"",
      ""[19:30:57] WARNING: ..\\src\\learner.cc:541: \n"",
      ""Parameters: { n_estimator } might not be used.\n"",
      ""\n"",
      ""  This may not be accurate due to some parameters are only used in language bindings but\n"",
      ""  passed down to XGBoost core.  Or some parameters are not used but slip through this\n"",
      ""  verification. Please open an issue if you find above cases.\n"",
      ""\n"",
      ""\n"",
      ""[19:30:57] WARNING: ..\\src\\learner.cc:541: \n"",
      ""Parameters: { n_estimator } might not be used.\n"",
      ""\n"",
      ""  This may not be accurate due to some parameters are only used in language bindings but\n"",
      ""  passed down to XGBoost core.  Or some parameters are not used but slip through this\n"",
      ""  verification. Please open an issue if you find above cases.\n"",
      ""\n"",
      ""\n"",
      ""XGBRegressor is finished\n"",
      ""[LightGBM] [Warning] Unknown parameter: n_estimator\n"",
      ""[LightGBM] [Warning] Unknown parameter: n_estimator\n"",
      ""[LightGBM] [Warning] Unknown parameter: n_estimator\n"",
      ""[LightGBM] [Warning] Unknown parameter: n_estimator\n"",
      ""[LightGBM] [Warning] Unknown parameter: n_estimator\n"",
      ""LGBMRegressor is finished\n""
     ]
    },
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
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       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>LinearRegression</th>\n"",
       ""      <th>DecisionTreeRegressor</th>\n"",
       ""      <th>RandomForestRegressor</th>\n"",
       ""      <th>GradientBoostingRegressor</th>\n"",
       ""      <th>MLPRegressor</th>\n"",
       ""      <th>XGBRegressor</th>\n"",
       ""      <th>LGBMRegressor</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>cv1</th>\n"",
       ""      <td>0.847049</td>\n"",
       ""      <td>1.917903</td>\n"",
       ""      <td>0.718402</td>\n"",
       ""      <td>0.706266</td>\n"",
       ""      <td>0.670726</td>\n"",
       ""      <td>0.983154</td>\n"",
       ""      <td>0.887868</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>cv2</th>\n"",
       ""      <td>0.719566</td>\n"",
       ""      <td>2.064269</td>\n"",
       ""      <td>0.627625</td>\n"",
       ""      <td>0.632428</td>\n"",
       ""      <td>0.837934</td>\n"",
       ""      <td>0.586317</td>\n"",
       ""      <td>0.649590</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>cv3</th>\n"",
       ""      <td>0.899025</td>\n"",
       ""      <td>1.292795</td>\n"",
       ""      <td>0.781737</td>\n"",
       ""      <td>0.779887</td>\n"",
       ""      <td>0.874925</td>\n"",
       ""      <td>0.999408</td>\n"",
       ""      <td>0.879154</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>cv4</th>\n"",
       ""      <td>2.093724</td>\n"",
       ""      <td>2.352413</td>\n"",
       ""      <td>1.116827</td>\n"",
       ""      <td>1.076843</td>\n"",
       ""      <td>1.366192</td>\n"",
       ""      <td>1.070108</td>\n"",
       ""      <td>1.108118</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>cv5</th>\n"",
       ""      <td>1.389543</td>\n"",
       ""      <td>2.381274</td>\n"",
       ""      <td>1.491859</td>\n"",
       ""      <td>1.508647</td>\n"",
       ""      <td>1.533531</td>\n"",
       ""      <td>1.566166</td>\n"",
       ""      <td>1.462994</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""     LinearRegression  DecisionTreeRegressor  RandomForestRegressor  \\\n"",
       ""cv1          0.847049               1.917903               0.718402   \n"",
       ""cv2          0.719566               2.064269               0.627625   \n"",
       ""cv3          0.899025               1.292795               0.781737   \n"",
       ""cv4          2.093724               2.352413               1.116827   \n"",
       ""cv5          1.389543               2.381274               1.491859   \n"",
       ""\n"",
       ""     GradientBoostingRegressor  MLPRegressor  XGBRegressor  LGBMRegressor  \n"",
       ""cv1                   0.706266      0.670726      0.983154       0.887868  \n"",
       ""cv2                   0.632428      0.837934      0.586317       0.649590  \n"",
       ""cv3                   0.779887      0.874925      0.999408       0.879154  \n"",
       ""cv4                   1.076843      1.366192      1.070108       1.108118  \n"",
       ""cv5                   1.508647      1.533531      1.566166       1.462994  ""
      ]
     },
     ""execution_count"": 234,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""models=[LinearRegression(),DecisionTreeRegressor(),RandomForestRegressor(),\n"",
    ""       GradientBoostingRegressor(),\n"",
    ""       MLPRegressor(hidden_layer_sizes=(50),solver='lbfgs',max_iter=100),\n"",
    ""        XGBRegressor(n_estimator=100,objective='reg:squarederror'),\n"",
    ""        LGBMRegressor(n_estimator=100)\n"",
    ""       ]\n"",
    ""\n"",
    ""result=dict()\n"",
    ""for model in models:\n"",
    ""    model_name = str(model).split('(')[0]\n"",
    ""    scores = cross_val_score(model, X=final_feature_train, y=label_train, verbose=0, cv = 5,scoring=make_scorer(mean_squared_error))\n"",
    ""    result[model_name] = scores\n"",
    ""    print(model_name + ' is finished')\n"",
    ""#表格表示\n"",
    ""result = pd.DataFrame(result)\n"",
    ""result.index = ['cv' + str(x) for x in range(1, 6)]\n"",
    ""result""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 235,
   ""id"": ""6f5b9fa1"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""LinearRegression             1.189781\n"",
       ""DecisionTreeRegressor        2.001731\n"",
       ""RandomForestRegressor        0.947290\n"",
       ""GradientBoostingRegressor    0.940814\n"",
       ""MLPRegressor                 1.056662\n"",
       ""XGBRegressor                 1.041030\n"",
       ""LGBMRegressor                0.997545\n"",
       ""dtype: float64""
      ]
     },
     ""execution_count"": 235,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""result.mean()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 222,
   ""id"": ""ab491c21"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""feature_test=pd.read_csv('Molecular_Descriptor_test.csv',index_col='SMILES')\n"",
    ""label_test=pd.read_csv('ERα_activity_test.csv',index_col='SMILES')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""id"": ""4cf62651"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
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   ""pygments_lexer"": ""ipython3"",
   ""version"": ""3.8.10""
  }
 },
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}
","Unknown"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/互信息特征选择.py",".py","2008","38","from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score,confusion_matrix
from sklearn.metrics import mean_squared_error,mean_absolute_error,mean_absolute_percentage_error,r2_score
import numpy as np
# Create the RFE object and rank each pixel
Set = pd.read_csv('E:\\test\jianmo\特征选择\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
def sigmoid(inx):
    return 1.0/(1+np.exp(-inx))
Set_label = Set['pIC50']
print(Set_label)
Set_feature = Set.drop(['SMILES','pIC50'],axis=1)
print(Set_feature)
#Set_feature = Set_feature.loc[:,['ALogP', 'ATSc5', 'nBondsD2', 'nBondsM', 'C2SP3', 'nHBd', 'nHBint2', 'nHBint3', 'nssCH2', 'nsssCH', 'naasC', 'ndsN', 'nssO', 'naaO', 'nssS', 'SdsN', 'SaaO', 'SssS', 'minHBint7', 'minHaaCH', 'mindsN', 'minaaO', 'minssS', 'maxwHBa', 'maxsssCH', 'maxdsN', 'maxaaO', 'ETA_Shape_P', 'ETA_Beta_ns', 'ETA_BetaP_ns', 'ETA_dBeta', 'ETA_Beta_ns_d', 'ETA_BetaP_ns_d', 'nHBDon', 'nHBDon_Lipinski', 'MDEN-23', 'nRing', 'n7Ring', 'nTRing', 'nT7Ring']]
Set_feature = Set_feature.loc[:,['ALogP', 'ATSc1', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SsOH', 'minHBa', 'minwHBa', 'minHsOH', 'hmin', 'LipoaffinityIndex', 'Kier3', 'MDEC-33', 'WTPT-5','nssCH2', 'nsssCH', 'nssO', 'maxaaCH', 'SHBint10']]

print(Set_feature)


x_train, x_test, y_train, y_test = train_test_split(Set_feature, Set_label , test_size=0.3, random_state=42)

from sklearn.ensemble import RandomForestRegressor #集成学习中的随机森林
rfc = RandomForestRegressor(random_state=42)
rfc = rfc.fit(x_train,y_train)

ac_2 = mean_squared_error(y_test,rfc.predict(x_test))
ridge_mae = mean_absolute_error(y_test, rfc.predict(x_test))
ridge_mape = mean_absolute_percentage_error(y_test, rfc.predict(x_test))
ridge_r2 = r2_score(y_test, rfc.predict(x_test))
print(""MSE ="", ac_2)
print(""MAE ="", ridge_mae)
print(""MAPE ="", ridge_mape)
print(""r2 ="", ridge_r2)

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/递归特征消除RFE.py",".py","1330","33","from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score,confusion_matrix
from sklearn.metrics import mean_absolute_percentage_error,mean_squared_error,mean_absolute_error,r2_score

# Create the RFE object and rank each pixel
Set = pd.read_csv('E:\\test\jianmo\特征选择\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
Set_label = Set['pIC50']
print(Set_label)
Set_feature = Set.drop(['SMILES','pIC50'],axis=1)
print(Set_feature)

x_train, x_test, y_train, y_test = train_test_split(Set_feature, Set_label , test_size=0.3, random_state=42)

clf_rf_3 = RandomForestRegressor()
rfe = RFE(estimator=clf_rf_3, n_features_to_select=40, step=1)
rfe = rfe.fit(x_train, y_train)

print('Chosen best 25 feature by rfe:',x_train.columns[rfe.support_])

ac = mean_absolute_percentage_error(y_test,rfe.predict(x_test))
print('Accuracy is: ',ac)
ac_2 = mean_squared_error(y_test,rfe.predict(x_test))
ridge_mae = mean_absolute_error(y_test, rfe.predict(x_test))
ridge_mape = mean_absolute_percentage_error(y_test, rfe.predict(x_test))
ridge_r2 = r2_score(y_test, rfe.predict(x_test))
print(""MSE ="", ac_2)
print(""MAE ="", ridge_mae)
print(""MAPE ="", ridge_mape)
print(""r2 ="", ridge_r2)","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/feature_filter.py",".py","1132","47","import pandas as pd
from 问题一代码.remove_vars import clean_zi_var1
from 问题一代码.dcor import dcor

clean_set0 = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')
print(clean_set0)
clean_var_set = clean_set0.drop('SMILES', axis=1)

rps = clean_var_set.corr(method='pearson')
df_ps = rps.iloc[0]

ps_list = rps['pIC50'].tolist()
ps_list.remove(1.0)

feature_set = clean_set0.drop(['SMILES', 'pIC50'], axis=1)
Clean_var = feature_set.columns.tolist()

print(""ps_list:"",ps_list)
print(len(ps_list))

rspm = clean_var_set.corr(method='spearman')
df_spm = rspm.iloc[0]

spm_list = rspm['pIC50'].tolist()
spm_list.remove(1.0)

pic50 = clean_var_set['pIC50'].tolist()
delete2 = []
dcor_list = []
for a in clean_zi_var1:
     i = clean_var_set[a].tolist()
     d = dcor(pic50, i)
     dcor_list.append(d)
     if abs(df_ps[a])<0.2 and abs(df_spm[a])<0.2 and d<0.2:
         delete2.append(a)
dcor_list.remove(1.0)
print(""dcor_list:"", dcor_list)


df_clean2 = clean_set0.drop(delete2, axis=1)
print(clean_var_set)
print(df_clean2)
Clean_data = df_clean2
print(Clean_data)
Clean_data.to_csv('.\Clean_Data.csv.csv')

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/dcor.py",".py","954","34","from scipy.spatial.distance import pdist, squareform
import numpy as np
# from numba import jit, float32


def dcor(X, Y):
    X = np.atleast_1d(X)
    Y = np.atleast_1d(Y)
    if np.prod(X.shape) == len(X):
        X = X[:, None]
    if np.prod(Y.shape) == len(Y):
        Y = Y[:, None]
    X = np.atleast_2d(X)
    Y = np.atleast_2d(Y)
    n = X.shape[0]
    if Y.shape[0] != X.shape[0]:
        raise ValueError('Number of samples must match')
    a = squareform(pdist(X))
    b = squareform(pdist(Y))
    A = a - a.mean(axis=0)[None, :] - a.mean(axis=1)[:, None] + a.mean()
    B = b - b.mean(axis=0)[None, :] - b.mean(axis=1)[:, None] + b.mean()

    dcov2_xy = (A * B).sum() / float(n * n)
    dcov2_xx = (A * A).sum() / float(n * n)
    dcov2_yy = (B * B).sum() / float(n * n)
    dcor = np.sqrt(dcov2_xy) / np.sqrt(np.sqrt(dcov2_xx) * np.sqrt(dcov2_yy))
    return dcor


# a = [1,2,3,4,5]
# b = np.array([1,2,9,4,4])
# d = dcor(a,b)
# print(d)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/MIC.py",".py","640","26","import pandas as pd
from minepy import MINE
import numpy as np

Set = pd.read_csv('./Clean_Data.csv' , encoding='gb18030', index_col=0)
y_set = Set['pIC50']
feature_set = Set.drop(['SMILES', 'pIC50'], axis=1)

y = np.array(y_set.tolist())
Clean_var = feature_set.columns.tolist()

mine = MINE(alpha=0.6, c=15)
mic = []
for i in Clean_var:
    x = np.array(feature_set[i].tolist())
    mine.compute_score(x, y)
    m = mine.mic()
    mic.append(m)
print(mic)

max_index = pd.Series(mic).sort_values().index[:40]
mic_slect_var = [x for x in Clean_var if Clean_var.index(x) in max_index]

print(Clean_var)
print(max_index)
print(mic_slect_var)","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/集成降维变量选20个.py",".py","3382","71","
import pandas as pd
import heapq

Set = pd.read_csv('E:\\test\jianmo\特征选择\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
Set_label = Set['pIC50']
print(Set_label)
Set_feature = Set.drop(['SMILES','pIC50'],axis=1)
print(Set_feature)
print(Set_feature.columns.tolist())

hu_xx= ['ALogP', 'ATSc5', 'nBondsD2', 'nBondsM', 'C2SP3', 'nHBd', 'nHBint2', 'nHBint3', 'nssCH2', 'nsssCH', 'naasC', 'ndsN', 'nssO', 'naaO', 'nssS', 'SdsN', 'SaaO', 'SssS', 'minHBint7', 'minHaaCH', 'mindsN', 'minaaO', 'minssS', 'maxwHBa', 'maxsssCH', 'maxdsN', 'maxaaO', 'ETA_Shape_P', 'ETA_Beta_ns', 'ETA_BetaP_ns', 'ETA_dBeta', 'ETA_Beta_ns_d', 'ETA_BetaP_ns_d', 'nHBDon', 'nHBDon_Lipinski', 'MDEN-23', 'nRing', 'n7Ring', 'nTRing', 'nT7Ring']
dan_chi2 = ['minHBa', 'ETA_dBeta', 'C1SP2', 'SsOH', 'maxsOH', 'minsOH', 'SsssN',
       'minsssN', 'maxsssN', 'SdO', 'maxdO', 'mindO', 'SHBint10', 'minHBint10',
       'maxHBint10', 'SssNH', 'nAtomLAC', 'maxssNH', 'minssNH', 'SssO',
       'maxssO', 'SaaN', 'minssO', 'nHsOH', 'nHsOH', 'maxaaN', 'minaaN',
       'nsssCH', 'SssCH2', 'ndO', 'SHCsats', 'C2SP3', 'nssO', 'ALogP',
       'nHBint10', 'C1SP3', 'SHBint4', 'nHCsats', 'nBondsD2', 'nssCH2']
dan_huxinxi =  ['SHsOH', 'BCUTc-1l', 'BCUTc-1h', 'minHsOH', 'maxHsOH', 'MLFER_A', 'SHBd', 'Kier3', 'minHBd', 'minHBa', 'hmin', 'ATSc2', 'BCUTp-1h', 'ETA_BetaP_s', 'maxsOH', 'maxHBd', 'ATSc3', 'maxHCsats', 'mindssC', 'minaasC', 'C1SP2', 'minwHBa', 'McGowan_Volume', 'nHBAcc', 'ATSc4', 'VP-6', 'WTPT-5', 'ETA_Alpha', 'SP-2', 'maxssO', 'LipoaffinityIndex',
       'SP-6', 'ATSc1', 'minssCH2', 'VPC-6', 'VP-4', 'VPC-5', 'SsOH', 'MAXDP2',
       'SP-3']
digui_RFE =['ALogP', 'ATSc1', 'ATSc2', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h','BCUTp-1h', 'C1SP2', 'SC-3', 'VC-5', 'VP-5', 'ECCEN', 'SHBint10', 'SaaCH', 'SsOH', 'minHBa', 'minHBint4', 'minHsOH', 'mindssC', 'minsssN', 'minsOH', 'minssO', 'maxHsOH', 'maxaaCH', 'maxssO', 'gmax', 'hmin','LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'Kier3','MDEC-23', 'MDEC-33', 'MLFER_A', 'MLFER_BH', 'TopoPSA', 'WTPT-5','XLogP']
suiji_senling = ['minsssN', 'MDEC-23', 'LipoaffinityIndex', 'maxHsOH', 'minssO',
       'nHBAcc', 'minHsOH', 'BCUTc-1l', 'maxssO', 'MLFER_A', 'C1SP2',
       'TopoPSA', 'WTPT-5', 'ATSc3', 'ATSc2', 'VC-5', 'mindssC', 'ATSc1',
       'BCUTp-1h', 'BCUTc-1h', 'XLogP', 'MDEC-33', 'Kier3', 'maxsssCH',
       'minsOH', 'hmin', 'minHBa', 'SHsOH', 'minHBint7', 'MAXDP2', 'ATSc5',
       'maxHBa', 'VCH-7', 'ETA_BetaP_s', 'ATSc4', 'SC-3', 'SsOH', 'minssNH',
       'VP-5', 'maxsOH']

l=245
list = [0]*l
for j,i in enumerate(Set_feature.columns.tolist()):
    if i in hu_xx:
        list[j] = list[j]+1
    if i in dan_chi2:
        list[j] = list[j]+1
    if i in dan_huxinxi:
        list[j] = list[j]+1
    if i in digui_RFE:
        list[j] = list[j]+1
    if i in suiji_senling:
        list[j] = list[j]+1

print(list)

print(sorted(list,reverse=True)[:60])
result = map(list.index, heapq.nlargest(60, list))
print(result)

best_21_va=[]

for j,i in enumerate(Set_feature.columns.tolist()):
    if list[j]>=4:
        best_21_va.append(i)

print(best_21_va)

for j,i in enumerate(Set_feature.columns.tolist()):
    if list[j]>=3 and list[j]<4:
        best_21_va.append(i)

print(best_21_va)

for j,i in enumerate(Set_feature.columns.tolist()):
    if list[j]<3 and list[j]>=2:
        best_21_va.append(i)

print(best_21_va)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/基于树的特征选择.py",".py","1877","49","from sklearn.ensemble import RandomForestRegressor
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_percentage_error,mean_squared_error,mean_absolute_error,r2_score
import numpy as np

Set = pd.read_csv('E:\\test\jianmo\特征选择\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
Set_label = Set['pIC50']
print(Set_label)
Set_feature = Set.drop(['SMILES','pIC50'],axis=1)
print(Set_feature)
x_train, x_test, y_train, y_test = train_test_split(Set_feature, Set_label, test_size=0.3, random_state=42)
clf_rf_5 = RandomForestRegressor()
clr_rf_5 = clf_rf_5.fit(x_train,y_train)
ac_2 = mean_squared_error(y_test,clr_rf_5.predict(x_test))
ridge_mae = mean_absolute_error(y_test, clr_rf_5.predict(x_test))
ridge_mape = mean_absolute_percentage_error(y_test, clr_rf_5.predict(x_test))
ridge_r2 = r2_score(y_test, clr_rf_5.predict(x_test))
print(""MSE ="", ac_2)
print(""MAE ="", ridge_mae)
print(""MAPE ="", ridge_mape)
print(""r2 ="", ridge_r2)


importances = clr_rf_5.feature_importances_
std = np.std([tree.feature_importances_ for tree in clr_rf_5.estimators_], axis=0)

indices = np.argsort(importances)[::-1]

# Print the feature ranking
print(""Feature ranking:"")

for f in range(x_train.shape[1]):
    print(""%d. feature %d (%f)"" % (f + 1, indices[f], importances[indices[f]]))

# best_index = [136,217,166,146,139,204,126,26,161,221,38,233,240,12,11,47,131,10,28,27,243,218,212,153,137,165,121,103,124,168,14,143,44,186,13,45,115,133,67,159]
# print(Set_feature.columns[best_index])
#
#
# import matplotlib.pyplot as plt
#
# plt.figure(1, figsize=(14, 13))
# plt.title(""Feature importances"")
# plt.bar(range(x_train.shape[1])[:40], importances[best_index], color=""g"" , align=""center"")
# plt.xticks(range(x_train.shape[1])[:40], x_train.columns[best_index],rotation=90)
# plt.xlim([-1, 40])
# plt.show()
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/remove_vars.py",".py","1531","52","import numpy as np
import pandas as pd

Set = pd.read_csv('./data.csv', encoding='gb18030')
feature = Set.drop('pIC50', axis=1).columns.tolist()
vars_set = Set.drop('SMILES', axis=1)
zi_var = vars_set.drop('pIC50', axis=1).columns.tolist()
print(zi_var)

delete = []
for a in zi_var:
    if ((Set[a] == 0).sum() / 1973) > 0.99:
        delete.append(a)

clean_Set1 = Set.drop(delete, axis=1)
print(clean_Set1)
clean_Set1.to_csv('./clean451.csv', encoding='gb18030')

clean_zi_set1 = vars_set.drop(delete, axis=1)
clean_zi_var1 = clean_zi_set1.columns.tolist()


def three_sigma(Ser1):
    '''
    Ser1：表示传入DataFrame的某一列。
    '''
    rule = (Ser1.mean() - 10 * Ser1.std() > Ser1) | (Ser1.mean() + 10 * Ser1.std() < Ser1)
    index = np.arange(Ser1.shape[0])[rule]
    return index  # 返回落在3sigma之外的行索引值


def delete_out3sigma(Set, var):
    """"""
    data：待检测的DataFrame
    """"""
    data1 = Set[var]
    data = (data1 - data1.min()) / (data1.max() - data1.min())
    out_index = []  # 保存要删除的行索引
    for i in range(data.shape[1]):  # 对每一列分别用3sigma原则处理
        index = three_sigma(data.iloc[:, i])
        out_index += index.tolist()
    delete_ = list(set(out_index))
    print('所删除的行索引为：', delete_)
    print(len(delete_))
    Set.drop(delete_, inplace=True)
    return Set


clean_Set0 = delete_out3sigma(clean_Set1, clean_zi_var1)  # 去除异常样本后的结果

clean_Set0.to_csv('./clean_set0.csv', encoding='gb18030')
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题一代码/单变量特征选择chi2.py",".py","1906","48","from sklearn.ensemble import RandomForestRegressor
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_percentage_error,mean_squared_error,mean_absolute_error,r2_score
import numpy as np
Set = pd.read_csv('E:\\test\jianmo\特征选择\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
def sigmoid(inx):
    return 1.0/(1+np.exp(-inx))
Set_label = Set['pIC50']
print(Set_label)
Set_feature = Set.drop(['SMILES','pIC50'],axis=1)
print(Set_feature)
Set_feature = sigmoid(Set_feature)
print(Set_feature)
x_train, x_test, y_train, y_test = train_test_split(Set_feature, Set_label.astype(""int"") , test_size=0.3, random_state=42)

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
import heapq

select_feature = SelectKBest(chi2, k=10).fit(x_train, y_train)

print('Score list:', select_feature.scores_)
print(len(select_feature.scores_))
print(sorted(select_feature.scores_,reverse=True)[:10])
result = map(select_feature.scores_.tolist().index, heapq.nlargest(10, select_feature.scores_.tolist()))
# print(list(result))
best_index = list(result)
print(best_index)
print(Set_feature.columns[best_index])

x_train_2 = select_feature.transform(x_train)
x_test_2 = select_feature.transform(x_test)
#random forest classifier with n_estimators=10 (default)
clf_rf_2 = RandomForestRegressor()
clr_rf_2 = clf_rf_2.fit(x_train_2,y_train)
ac_2 = mean_absolute_percentage_error(y_test,clf_rf_2.predict(x_test_2))
print('Accuracy is: ',ac_2)
ac_2 = mean_squared_error(y_test,clf_rf_2.predict(x_test_2))
ridge_mae = mean_absolute_error(y_test, clf_rf_2.predict(x_test_2))
ridge_mape = mean_absolute_percentage_error(y_test, clf_rf_2.predict(x_test_2))
ridge_r2 = r2_score(y_test, clf_rf_2.predict(x_test_2))
print(""MSE ="", ac_2)
print(""MAE ="", ridge_mae)
print(""MAPE ="", ridge_mape)
print(""r2 ="", ridge_r2)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/关系图.py",".py","2200","72","import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from random import sample
plt.rcParams['pdf.fonttype'] = 42
config = {
    ""font.family"":'Times New Roman',  # 设置字体类型
}
plt.rcParams.update(config)


Set = pd.read_csv('../Clean_Data.csv' , encoding='gb18030', index_col=0)


Set.drop('SMILES', axis=1)
print(Set)


Clean_vars = Set.columns.tolist()
Clean_vars.remove('pIC50')

Cat_vars_low = list(Set.loc[:, (Set.nunique() < 6)].nunique().index)
print(len(Cat_vars_low))
Cat_vars_high = list(Set.loc[:, (Set.nunique() >= 10)].nunique().index)

with plt.rc_context(rc = {'figure.dpi': 600, 'axes.labelsize': 12,
                          'xtick.labelsize': 12, 'ytick.labelsize': 12}):

    fig_3, ax_3 = plt.subplots(3, 3, figsize = (15, 15))

    for idx, (column, axes) in list(enumerate(zip(Cat_vars_low[0:9], ax_3.flatten()))):
        order = Set.groupby(column)['pIC50'].mean().sort_values(ascending = True).index
        plt.xticks(rotation=90)
        sns.violinplot(ax = axes, x = Set[column],
                       y = Set['pIC50'],
                       order = order, scale = 'width',
                       linewidth = 0.5, palette = 'viridis',
                       inner = None)

        plt.setp(axes.collections, alpha = 0.3)

        sns.stripplot(ax = axes, x = Set[column],
                      y = Set['pIC50'],
                      palette = 'viridis', s = 1.5, alpha = 0.75,
                      order = order, jitter = 0.07)

        sns.pointplot(ax = axes, x = Set[column],
                      y = Set['pIC50'],
                      order = order,
                      color = '#ff5736', scale = 0.2,
                      estimator = np.mean, ci = 'sd',
                      errwidth = 0.5, capsize = 0.15, join = True)

        plt.setp(axes.lines, zorder = 100)
        plt.setp(axes.collections, zorder = 100)

        if Set[column].nunique() > 5:

            plt.setp(axes.get_xticklabels(), rotation = 90)

    else:

        [axes.set_visible(False) for axes in ax_3.flatten()[idx + 1:]]

plt.savefig('./问题一小提琴.pdf', dpi=600)

# plt.tight_layout()
# plt.show()

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/Q4变化图.py",".py","1626","41","import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt


Set = pd.read_csv('./tu2.csv' , encoding='gb18030')



featuren_all = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6',
             'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin',
             'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y','ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E',
                'WTPT-4','BCUTc-1l','BCUTp-1l','VCH-6','SC-5',
                'SPC-6','VP-3','SHsOH','SdO','minHsOH',
                'maxHother','maxdO','MAXDP2','ETA_EtaP_F_L','MDEC-23',
                'MLFER_A','TopoPSA','WTPT-2','BCUTc-1h','BCUTp-1h',
                'SHBd','SHother','SsOH','minHBd','minaaCH',
                'minaasC','maxHBd','maxwHBa','maxHBint8','maxHsOH',
                'LipoaffinityIndex','ETA_Eta_R_L','MDEO-11','minssCH2','apol',
                'ATSc1','ATSm3','SCH-6','VCH-7','maxsOH',
                'ETA_dEpsilon_D','ETA_Shape_P','ETA_dBetaP','ATSm2','VC-5',
                'SsCH3','SaaO','MLFER_S','WPATH','C1SP2',
                'ALogP','ATSc3','ATSc5','minsssN','nBondsD2',
                'nsssCH']

featuren_pre = ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP',
              'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h',
              'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex',
              'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']




df_var_all = Set[featuren_all]
df_var_pre = Set[featuren_pre]

print(df_var_pre.describe())

df_var_pre.to_csv('./pred.csv')","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/因变量图.py",".py","1441","51","import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from random import sample
plt.rcParams['pdf.fonttype'] = 42
config = {
    ""font.family"":'Times New Roman',  # 设置字体类型
    ""font.size"": 80,
}
plt.rcParams.update(config)

Set = pd.read_csv('../data.csv' , encoding='gb18030')
print(Set)
Set.drop('SMILES', axis=1)


Clean_vars = Set.columns.tolist()
Clean_vars.remove('pIC50')
# Cat_vars.remove('Condition2')

# sns.set_theme(rc = {'grid.linewidth': 0.5,
#                     'axes.linewidth': 0.75, 'axes.facecolor': '#fff3e9', 'axes.labelcolor': '#6b1000',
#                     # 'figure.facecolor': '#f7e7da',
#                     'xtick.color': '#6b1000', 'ytick.color': '#6b1000'})

with plt.rc_context(rc={'figure.dpi': 600, 'axes.labelsize': 10,
                        'xtick.labelsize': 12, 'ytick.labelsize': 12}):
    fig_0, ax_0 = plt.subplots(1, 1, figsize=(15, 8))

    sns.scatterplot(ax=ax_0, x=list(range(0,1974)),
                        y=Set['pIC50'],
                        hue=Set['pIC50'],
                        alpha=0.7,)
    my_y_ticks = np.arange(2, 12, 2)
    my_x_ticks = np.arange(0, 2000, 200)
    plt.xticks(my_x_ticks)
    plt.yticks(my_y_ticks)

    # Get rid of legend
    ax_0.legend([], [], frameon=False)


    # Remove empty figures


# plt.tight_layout()
# plt.show()
plt.savefig('./问题一因变量.pdf', dpi=600)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/拟合图.py",".py","3843","129","import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
# 设置
pd.options.display.notebook_repr_html=False  # 表格显示
plt.rcParams['figure.dpi'] = 75  # 图形分辨率
# sns.set_theme(style='darkgrid')  # 图形主题
sns.set_theme(style='dark')  # 图形主题
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号


Set1 = pd.read_csv('./tu2.csv' , encoding='gb18030')
x1 = Set1['SsOH']
y1 = Set1['bianhua']

Set2 = pd.read_csv('./tu2_ALogP.csv' , encoding='gb18030')
x2 = Set2['ALogP']
y2 = Set2['bianhua']

Set3 = pd.read_csv('./tu2_ATSc1.csv' , encoding='gb18030')
x3 = Set3['ATSc1']
y3 = Set3['bianhua']

Set4 = pd.read_csv('./tu2_C1SP2.csv' , encoding='gb18030')
x4 = Set4['C1SP2']
y4 = Set4['bianhua']

Set5 = pd.read_csv('./tu2_maxssO.csv' , encoding='gb18030')
x5 = Set5['maxssO']
y5 = Set5['bianhua']

Set6 = pd.read_csv('./tu2_minHBa.csv' , encoding='gb18030')
x6 = Set6['minHBa']
y6= Set6['bianhua']






# Set = pd.read_csv('../nihe.csv' , encoding='gb18030')
# print(Set)

# y0 = Set[""pIC50""]
# y1 = Set[""REa_9""]

# yq = Set.iloc[0:1976:4]
#
# yq.reset_index(drop=True, inplace=True)
# print(yq)
#
# y0 = yq[""y0""]
# y1 = yq[""y1""]
# x = range(0,len(a))

plt.figure(figsize=[20,30])

plt.subplot(2, 3, 1)

# plt.scatter(x, y, marker='o')
plt.plot(x1, y1, color='g', marker='', label='pIC50', markersize=5, linewidth=""2"")
# plt.plot(x, y1, color='b', marker='', label='预测值', markersize=5, linewidth=""2"")
plt.xlabel('SsOH',fontsize=16)
plt.xticks(x1,())
plt.yticks(fontproperties='Times New Roman', size=16)
plt.xticks(fontproperties='Times New Roman', size=16)
plt.legend(fontsize=15)
# plt.show()

plt.subplot(2, 3, 2)

# plt.scatter(x, y, marker='o')
plt.plot(x2, y2, color='g', marker='', label='pIC50', markersize=5, linewidth=""2"")
# plt.plot(x, y1, color='b', marker='', label='预测值', markersize=5, linewidth=""2"")
plt.xlabel('ALogP',fontsize=16)
plt.xticks(x2,())
plt.yticks(fontproperties='Times New Roman', size=16)
plt.xticks(fontproperties='Times New Roman', size=16)
plt.legend(fontsize=15)
# plt.show()

plt.subplot(2, 3, 3)

# plt.scatter(x, y, marker='o')
plt.plot(x3, y3, color='g', marker='', label='pIC50', markersize=5, linewidth=""2"")
# plt.plot(x, y1, color='b', marker='', label='预测值', markersize=5, linewidth=""2"")
plt.xlabel('ATSc1',fontsize=16)
plt.xticks(x3,())
plt.yticks(fontproperties='Times New Roman', size=16)
plt.xticks(fontproperties='Times New Roman', size=16)
plt.legend(fontsize=15)
# plt.show()

plt.subplot(2, 3, 4)

# plt.scatter(x, y, marker='o')
plt.plot(x4, y4, color='g', marker='', label='pIC50', markersize=5, linewidth=""2"")
# plt.plot(x, y1, color='b', marker='', label='预测值', markersize=5, linewidth=""2"")
plt.xlabel('C1SP2',fontsize=16)
plt.xticks(x4,())
plt.yticks(fontproperties='Times New Roman', size=16)
plt.xticks(fontproperties='Times New Roman', size=16)
plt.legend(fontsize=15)
# plt.show()

plt.subplot(2, 3, 5)

# plt.scatter(x, y, marker='o')
plt.plot(x5, y5, color='g', marker='', label='pIC50', markersize=5, linewidth=""2"")
# plt.plot(x, y1, color='b', marker='', label='预测值', markersize=5, linewidth=""2"")
plt.xlabel('maxssO',fontsize=16)
plt.xticks(x5,())
plt.yticks(fontproperties='Times New Roman', size=16)
plt.xticks(fontproperties='Times New Roman', size=16)
plt.legend(fontsize=15)
# plt.show()

plt.subplot(2, 3, 6)

# plt.scatter(x, y, marker='o')
plt.plot(x6, y6, color='g', marker='', label='pIC50', markersize=5, linewidth=""2"")
# plt.plot(x, y1, color='b', marker='', label='预测值', markersize=5, linewidth=""2"")
plt.xlabel('minHBa',fontsize=16)
plt.xticks(x6,())
plt.yticks(fontproperties='Times New Roman', size=16)
plt.xticks(fontproperties='Times New Roman', size=16)
plt.legend(fontsize=15)
plt.show()","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/分布图0.py",".py","1881","67","import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from random import sample
plt.rcParams['pdf.fonttype'] = 42
config = {
    ""font.family"":'Times New Roman',  # 设置字体类型
}
plt.rcParams.update(config)


Set = pd.read_csv('../Clean_Data.csv' , encoding='gb18030', index_col=0)


Set.drop('SMILES', axis=1)
print(Set)


Clean_vars = Set.columns.tolist()
Clean_vars.remove('pIC50')

Cat_vars_low = list(Set.loc[:, (Set.nunique() < 10)].nunique().index)
print(len(Cat_vars_low))
Cat_vars_high = list(Set.loc[:, (Set.nunique() >= 10)].nunique().index)

with plt.rc_context(rc={'figure.dpi': 200, 'axes.labelsize': 8,
                        'xtick.labelsize': 6, 'ytick.labelsize': 6,
                        'legend.fontsize': 6, 'legend.title_fontsize': 6,
                        'axes.titlesize': 9}):
    fig_2, ax_2 = plt.subplots(1, 3, figsize=(8.5, 3.5))

    for idx, (column, axes) in list(enumerate(zip(['nssO','nHsOH', 'nHBDon_Lipinski'], ax_2.flatten()))):

        sns.kdeplot(ax=axes, x=Set['pIC50'],
                    hue=Set[column].astype('category'),
                    common_norm=True,
                    fill=True, alpha=0.2, palette='viridis',
                    linewidth=0.6)

        axes.set_title(str(column), fontsize=9, fontweight='bold', color='#6b1000')

    else:

        [axes.set_visible(False) for axes in ax_2.flatten()[idx + 1:]]

    # Fixing a legend box for a particulal variable

    # ax_2_flat = ax_2.flatten()
    #
    # legend_3 = ax_2_flat[2].get_legend()
    # handles_3 = legend_3.legendHandles
    # legend_3.remove()

    # ax_2_flat[2].legend(handles_3, Set['HouseStyle'].unique(),
    #                     title='HouseStyle', ncol=2)

plt.tight_layout()
plt.show()

plt.savefig('./问题一分布.pdf', dpi=600)

# plt.tight_layout()
# plt.show()

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/散点图.py",".py","2049","54","import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from random import sample
plt.rcParams['pdf.fonttype'] = 42
config = {
    ""font.family"":'Times New Roman',  # 设置字体类型
    ""font.size"": 80,
}
plt.rcParams.update(config)

Set = pd.read_csv('../data.csv' , encoding='gb18030')
print(Set)
Set.drop('SMILES', axis=1)


Slect = ['ALogP', 'ATSc1', 'ATSc4', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SHsOH', 'SsOH', 'minHBa', 'minwHBa', 'minHsOH', 'minsOH', 'maxHsOH', 'maxssO', 'hmin', 'LipoaffinityIndex', 'Kier3', 'MDEC-33', 'WTPT-3', 'WTPT-5', 'ATSc2', 'ATSc3', 'nBondsD2', 'C2SP3', 'VC-5', 'CrippenLogP', 'nssCH2', 'nsssCH', 'nssO', 'SwHBa', 'SHBint10', 'SaaCH', 'SsssN', 'SssO', 'minsssN', 'maxHBa', 'maxHother', 'maxaaCH', 'maxaasC', 'maxsOH', 'MAXDP2', 'ETA_BetaP_s', 'ETA_dBeta', 'MDEC-23', 'MLFER_A', 'MLFER_BO', 'TopoPSA', 'XLogP']



Clean_vars = Set.columns.tolist()
Clean_vars.remove('pIC50')
# Cat_vars.remove('Condition2')

# sns.set_theme(rc = {'grid.linewidth': 0.5,
#                     'axes.linewidth': 0.75, 'axes.facecolor': '#fff3e9', 'axes.labelcolor': '#6b1000',
#                     # 'figure.facecolor': '#f7e7da',
#                     'xtick.color': '#6b1000', 'ytick.color': '#6b1000'})

with plt.rc_context(rc={'figure.dpi': 600, 'axes.labelsize': 10,
                        'xtick.labelsize': 12, 'ytick.labelsize': 12}):
    fig_0, ax_0 = plt.subplots(3, 3, figsize=(12, 10))
    for idx, (column, axes) in list(enumerate(zip(Slect[0:9], ax_0.flatten()))):
        sns.scatterplot(ax=axes, x=Set[column],
                        y=Set['pIC50'],
                        hue=Set['pIC50'],
                        palette='viridis', alpha=0.7, s=8)

        # Get rid of legend
        axes.legend([], [], frameon=False)


    # Remove empty figures

    else:

        [axes.set_visible(False) for axes in ax_0.flatten()[idx + 1:]]

# plt.tight_layout()
# plt.show()
plt.savefig('./问题一散点图1.pdf', bbox_inches='tight', dpi=600)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/分布图.py",".py","1922","61","import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from random import sample
plt.rcParams['pdf.fonttype'] = 42
config = {
    ""font.family"":'Times New Roman',  # 设置字体类型
}
plt.rcParams.update(config)


Set = pd.read_csv('../Clean_Data.csv' , encoding='gb18030', index_col=0)


Set.drop('SMILES', axis=1)
print(Set)


Clean_vars = Set.columns.tolist()
Clean_vars.remove('pIC50')

Cat_vars_low = list(Set.loc[:, (Set.nunique() < 10)].nunique().index)
print(len(Cat_vars_low))
Cat_vars_high = list(Set.loc[:, (Set.nunique() >= 10)].nunique().index)

with plt.rc_context(rc={'figure.dpi': 200, 'axes.labelsize': 8,
                        'xtick.labelsize': 6, 'ytick.labelsize': 6,
                        'legend.fontsize': 6, 'legend.title_fontsize': 6,
                        'axes.titlesize': 9}):
    fig_2, ax_2 = plt.subplots(1, 3, figsize=(8.5, 3.5))

    with plt.rc_context(rc={'figure.dpi': 200, 'axes.labelsize': 8,
                            'xtick.labelsize': 6, 'ytick.labelsize': 6,
                            'legend.fontsize': 6, 'legend.title_fontsize': 6,
                            'axes.titlesize': 9}):
        fig_1, ax_1 = plt.subplots(1, 3, figsize=(8, 3))

        for idx, (column, axes) in list(enumerate(zip(['nssO','nHsOH', 'nHBDon_Lipinski'], ax_1.flatten()))):

            sns.histplot(ax=axes, x=Set['pIC50'],
                         hue=Set[column].astype('category'),  # multiple = 'stack',
                         alpha=0.15, palette='viridis',
                         element='step', linewidth=0.6)

            axes.set_title(str(column), fontsize=9, fontweight='bold', color='#6b1000')

        else:

            [axes.set_visible(False) for axes in ax_1.flatten()[idx + 1:]]

    plt.tight_layout()
    plt.show()

plt.savefig('./问题一分布.pdf', dpi=600)

# plt.tight_layout()
# plt.show()

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","graph/热图.py",".py","4648","90","import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib

from matplotlib import rcParams

# matplotlib.use(""pgf"")
# plt.rcParams['pdf.fonttype'] = 42
# pgf_config = {
#     ""font.family"":'serif',
#     ""font.size"": 35,
#     ""pgf.rcfonts"": False,
#     ""text.usetex"": True,
#     ""pgf.preamble"": [
#         r""\usepackage{unicode-math}"",
#         r""\setmainfont{Times New Roman}"",
#         r""\usepackage{xeCJK}"",
#         r""\setCJKmainfont{SimSun}"",
#     ],
# }
# rcParams.update(pgf_config)


# rc = {'axes.unicode_minus': False}
# sns.set(context='notebook', style='ticks', font='SimSon', rc=rc)

# Set = pd.read_csv('../Clean_Data.csv' , encoding='gb18030', index_col=0)
# Slect = ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc2', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'minHsOH', 'mindssC', 'minsssN', 'minsOH', 'minssO', 'maxHsOH', 'maxsOH', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'Kier3', 'MLFER_A', 'WTPT-5', 'ATSc4', 'nBondsD2', 'C2SP3', 'SC-3', 'VC-5', 'VP-5', 'nssCH2', 'nsssCH', 'nssO', 'SHBint10', 'SHsOH', 'minHBint7', 'minssNH', 'maxsssCH', 'ETA_dBeta', 'MDEC-23', 'MDEC-33', 'TopoPSA', 'XLogP']
# Slect0 = ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc2', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'minHsOH', 'mindssC', 'minsssN', 'minsOH', 'minssO', 'maxHsOH', 'maxsOH', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'Kier3', 'MLFER_A', 'WTPT-5', 'nBondsD2', 'nsssCH']
# Slect00 = ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']


Set = pd.read_csv('../clean451.csv' , encoding='gb18030', index_col=0)
feature11 = ['ATSm2', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SHBd','SsCH3', 'SaaO', 'minHBa', 'hmin',
       'LipoaffinityIndex', 'FMF', 'MDEC-23', 'MLFER_S','WPATH']

feature40 = ['ATSc1','ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1l',
       'SCH-6', 'SC-5', 'VC-5', 'VP-3', 'nHsOH', 'SHBd', 'SaasC',
       'SdO', 'minHBa', 'minHsOH', 'minHother', 'minaasC',
       'maxssCH2', 'maxaasC', 'maxdO', 'hmin', 'gmin',
       'LipoaffinityIndex', 'MAXDP2', 'ETA_Shape_P',
       'FMF', 'MDEC-33', 'MLFER_BO', 'TopoPSA', 'WTPT-2', 'WTPT-4']

feature4 = ['ATSc2', 'BCUTc-1l', 'BCUTp-1l', 'VCH-6', 'SC-5', 'SPC-6', 'VP-3',
       'SHsOH', 'SdO', 'minHBa', 'minHsOH',
       'maxHother', 'maxdO', 'hmin', 'MAXDP2',
       'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP_F_L', 'MDEC-23', 'MLFER_A',
       'TopoPSA', 'WTPT-2', 'WTPT-4']

feature22 =['apol', 'ATSc1', 'ATSm3', 'SCH-6', 'VCH-7', 'SP-6', 'SHBd', 'SHsOH', 'SHaaCH', 'minHBa', 'maxsOH', 'ETA_dEpsilon_D', 'ETA_Shape_P', 'ETA_Shape_Y',
       'ETA_BetaP_s', 'ETA_dBetaP']

feature3 = ['apol', 'ATSc2', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'bpol', 'VP-0',
       'VP-1', 'VP-2', 'CrippenMR', 'ECCEN', 'SHBd', 'SHother', 'SsOH',
       'minHBd', 'minHBa', 'minssCH2', 'minaaCH', 'minaasC', 'maxHBd',
       'maxwHBa', 'maxHBint8', 'maxHsOH', 'maxaaCH', 'maxaasC', 'hmin',
       'LipoaffinityIndex', 'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP',
       'ETA_EtaP_F', 'ETA_Eta_R_L', 'fragC', 'Kier2', 'Kier3',
       'McGowan_Volume', 'MDEO-11', 'WTPT-1', 'WTPT-4', 'WPATH']

feature5 = ['nN', 'ATSc2', 'SCH-7', 'VCH-7', 'VPC-5', 'VPC-6', 'SP-6', 'SHaaCH',
       'SssCH2', 'SsssCH', 'SssO', 'minHBa', 'mindssC', 'maxsCH3', 'maxsssCH',
       'maxssO', 'hmin', 'ETA_Epsilon_1', 'ETA_Epsilon_2', 'ETA_Epsilon_4',
       'ETA_dEpsilon_A', 'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y',
       'ETA_BetaP', 'ETA_BetaP_s', 'ETA_EtaP_F', 'ETA_Eta_F_L', 'ETA_EtaP_F_L',
       'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'nHBAcc_Lipinski', 'MLFER_BO',
       'MLFER_S', 'MLFER_E', 'TopoPSA', 'WTPT-3', 'WTPT-4', 'WTPT-5']

feature55 = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6', 'SHaaCH',
       'SssCH2', 'SsssCH', 'SssO', 'minHBa', 'mindssC', 'maxsCH3', 'maxsssCH',
       'maxssO', 'hmin', 'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y',
       'ETA_BetaP', 'ETA_BetaP_s', 'ETA_EtaP_F',
       'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E', 'WTPT-4']


df1 = Set[feature55]
r = df1.corr()

f, ax = plt.subplots(figsize= [40,30])
sns.heatmap(r, ax=ax, vmax=1,vmin=-1,annot=True,
            cbar_kws={'label': '相关系数'}, cmap='viridis')
plt.xticks(rotation=90)    # 将字体进行旋转
plt.yticks(rotation=360)


# plt.savefig('./问题一待检验热图.pdf', bbox_inches='tight', dpi=600)
# plt.savefig('./feature1.pdf', bbox_inches='tight', dpi=600)
plt.show()","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题二代码/LSTM.py",".py","3174","85","import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np


Set = pd.read_csv('..\问题一代码\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
Set_label = Set['pIC50'].copy()


scaler = StandardScaler()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
print(Set)
Set_unit_index =  ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']
Set_feature = Set.loc[:, Set_unit_index]
print(Set_feature)
print(Set_label)

x_train, x_test, y_train, y_test = train_test_split(Set_feature, Set_label.astype('int') , test_size=0.2, random_state=42)

x_train,y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))


from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.layers import Dropout

model =Sequential()
model.add(LSTM(64,return_sequences=True, input_shape=(x_train.shape[1],1)))
model.add(Dropout(0.5))
model.add(LSTM(64, return_sequences= False))
model.add(Dropout(0.5))
model.add(Dense(128))
model.add(Dropout(0.5))
model.add(Dense(64))
model.add(Dropout(0.5))
model.add(Dense(1))
model.summary()

from sklearn.metrics import mean_absolute_error,mean_absolute_percentage_error, mean_squared_error , r2_score
import tensorflow as tf
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3), loss='mean_squared_error')

x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1],1))

model.fit(x_train,y_train, batch_size=85, epochs=300, validation_data=(x_test,y_test),validation_batch_size=100)

x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1],1))
predictions = model.predict(x_test)
print(predictions)
print(predictions.shape)
print(y_test.shape)

ridge_mae = mean_absolute_error(y_test, predictions)
ridge_mape = mean_absolute_percentage_error(y_test, predictions)
ridge_mse = mean_squared_error(y_test, predictions)
ridge_r2 = r2_score(y_test, predictions)
print(""Ridge MAE ="", ridge_mae)
print(""Ridge MAPE ="", ridge_mape)
print(""Ridge MSE ="", ridge_mse)
print(""Ridge r2 ="", ridge_r2)

Set = pd.read_csv('E:\\test\jianmo\预测\Molecular_test.csv', encoding='gb18030', index_col=0)

print(Set)
scaler = StandardScaler()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
print(Set)
Set_unit_index =  ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']

Set_feature = Set.loc[:, Set_unit_index]
X = Set_feature.copy()

x_test = np.array(X)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1],1))

predictions = model.predict(x_test)

Set_feature['REa_9'] = predictions
Set_feature['REa_6'] = predictions
Set_feature.to_csv('E:\\test\jianmo\Set_feature22.csv', index=False)","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题二代码/stacking算法(包内含8种算法).py",".py","8888","224","import numpy as np
from datetime import datetime
from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold, cross_val_score
from mlxtend.regressor import StackingCVRegressor
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
import pandas as pd
from sklearn.preprocessing import StandardScaler,PolynomialFeatures
from sklearn.ensemble import RandomForestRegressor

Set = pd.read_csv('..\问题一代码\Clean_Data.csv', encoding='gb18030', index_col=0)
print(Set)
Set_label = Set['pIC50'].copy()
scaler = StandardScaler()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
print(Set)
Set_unit_index =  ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']

Set_feature = Set.loc[:, Set_unit_index]
print(Set_feature)
X = Set_feature
y = Set_label

kfolds = KFold(n_splits=5, shuffle=True, random_state=42)

def rmsle2(y, y_pred):
    return mean_squared_error(y, y_pred)

def rmsle(y, y_pred):
    return mean_squared_error(y, y_pred)
# build our model scoring function
def cv_rmse(model, X=X):
    rmse = -cross_val_score(model, X, y, scoring=""neg_mean_squared_error"", cv=kfolds)
    return rmse


alphas_alt = [14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5]
alphas2 = [5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008]
e_alphas = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007]
e_l1ratio = [0.8, 0.85, 0.9, 0.95, 0.99, 1]



ridge = make_pipeline(RobustScaler(),
                      RidgeCV(alphas=0.1, cv=kfolds))

lasso = make_pipeline(RobustScaler(),
                      LassoCV(max_iter=1000, alphas=0.0001,
                              random_state=42, cv=kfolds))

elasticnet = make_pipeline(RobustScaler(),
                           ElasticNetCV(max_iter=100, alphas=0.0005,
                                        cv=kfolds, l1_ratio=0.8))

svr = make_pipeline(RobustScaler(),
                    SVR(C=10, coef0=0, degree=1, kernel='rbf', gamma=0.1)) #tiaowan

gbr = GradientBoostingRegressor(n_estimators=300, learning_rate=0.05,
                                max_depth=4, max_features='sqrt',
                                min_samples_leaf=15, min_samples_split=10,
                                loss='huber', random_state=42)

lightgbm = LGBMRegressor(objective='regression',
                         num_leaves=6,
                         learning_rate=0.03,
                         n_estimators=300,
                         max_bin=200,
                         bagging_fraction=0.75,
                         bagging_freq=5,
                         bagging_seed=7,
                         feature_fraction=0.2,
                         feature_fraction_seed=7,
                         verbose=-1,
                         # min_data_in_leaf=2,
                         # min_sum_hessian_in_leaf=11
                         )

xgboost = XGBRegressor(learning_rate=0.01, n_estimators=346,
                       max_depth=3, min_child_weight=0,
                       gamma=0, subsample=0.7,
                       colsample_bytree=0.7,
                       objective='reg:linear', nthread=-1,
                       scale_pos_weight=1, seed=27,
                       reg_alpha=0.00006)

# stack
stack_gen = StackingCVRegressor(regressors=(ridge, lasso, elasticnet,
                                            gbr, svr, xgboost, lightgbm),
                                meta_regressor=xgboost,
                                use_features_in_secondary=True)

print('TEST score on CV')

score = cv_rmse(ridge)
print(""Kernel Ridge score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(lasso)
print(""Lasso score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(elasticnet)
print(""ElasticNet score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(svr)
print(""SVR score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(lightgbm)
print(""Lightgbm score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(gbr)
print(""GradientBoosting score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(xgboost)
print(""Xgboost score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

score = cv_rmse(stack_gen)
print(""stack_gen score: {:.4f} ({:.4f})\n"".format(score.mean(), score.std()), datetime.now(), )

print('START Fit')
print(datetime.now(), 'StackingCVRegressor')
stack_gen_model = stack_gen.fit(np.array(X), np.array(y))
print(datetime.now(), 'elasticnet')
elastic_model_full_data = elasticnet.fit(X, y)
print(datetime.now(), 'lasso')
lasso_model_full_data = lasso.fit(X, y)
print(datetime.now(), 'ridge')
ridge_model_full_data = ridge.fit(X, y)
print(datetime.now(), 'svr')
svr_model_full_data = svr.fit(X, y)
print(datetime.now(), 'GradientBoosting')
gbr_model_full_data = gbr.fit(X, y)
print(datetime.now(), 'xgboost')
xgb_model_full_data = xgboost.fit(X, y)
print(datetime.now(), 'lightgbm')
lgb_model_full_data = lightgbm.fit(X, y)


print((stack_gen_model.predict(np.array(X))))

def blend_models_predict(X):
    return ((0.02 * elastic_model_full_data.predict(X)) + \
            (0.02 * lasso_model_full_data.predict(X)) + \
            (0.02 * lasso_model_full_data.predict(X)) + \
            (0.14 * svr_model_full_data.predict(X)) + \
            (0.15 * gbr_model_full_data.predict(X)) + \
            (0.15 * xgb_model_full_data.predict(X)) + \
            (0.15 * lgb_model_full_data.predict(X)) + \
            (0.35 * stack_gen_model.predict(np.array(X))))

def blend_models_predict(X):
    return ((0.15 * rf_model_full_data.predict(X)) + \
            (0.15 * svr_model_full_data.predict(X)) + \
            (0.15 * gbr_model_full_data.predict(X)) + \
            (0.15 * xgb_model_full_data.predict(X)) + \
            (0.15 * lgb_model_full_data.predict(X)) + \
            (0.25 * stack_gen_model.predict(np.array(X))))

from sklearn.metrics import mean_absolute_percentage_error,mean_squared_error,mean_absolute_error,r2_score

def all_duliang(model,X=X, y=y):
    ac_2 = mean_squared_error(y, model.predict(X))
    ridge_mae = mean_absolute_error(y, model.predict(X))
    ridge_mape = mean_absolute_percentage_error(y, model.predict(X))
    ridge_r2 = r2_score(y, model.predict(X))
    print(""MSE ="", ac_2)
    print(""MAE ="", ridge_mae)
    print(""MAPE ="", ridge_mape)
    print(""r2 ="", ridge_r2)
def all_duliang2(model,X=X, y=y):
    ac_2 = mean_squared_error(y, model(X))
    ridge_mae = mean_absolute_error(y, model(X))
    ridge_mape = mean_absolute_percentage_error(y, model(X))
    ridge_r2 = r2_score(y, model(X))
    print(""MSE ="", ac_2)
    print(""MAE ="", ridge_mae)
    print(""MAPE ="", ridge_mape)
    print(""r2 ="", ridge_r2)


print(blend_models_predict(X))
print('RMSLE score on train data:')
print(rmsle(y, blend_models_predict(X)))

print(""elastic_model_full_data:"")
all_duliang(model = elastic_model_full_data , X=X, y =y)
print(""lasso_model_full_data:"")
all_duliang(model = lasso_model_full_data , X=X, y =y)
print(""ridge_model_full_data:"")
all_duliang(model = ridge_model_full_data , X=X, y =y)
print(""rf_model_full_data:"")
all_duliang(model = rf_model_full_data , X=X, y =y)
print(""svr_model_full_data:"")
all_duliang(model = svr_model_full_data , X=X, y =y)
print(""gbr_model_full_data:"")
all_duliang(model = gbr_model_full_data , X=X, y =y)
print(""xgb_model_full_data:"")
all_duliang(model = xgb_model_full_data , X=X, y =y)
print(""lgb_model_full_data:"")
all_duliang(model = lgb_model_full_data , X=X, y =y)
print(""stack_gen_model:"")
all_duliang(model = stack_gen_model , X=X, y =y)
print(""blend_models_9_zhong:"")
all_duliang2(model = blend_models_predict , X=X, y =y)
print(""blend_models_6_zhong:"")



# 预测test上面的样本
Set = pd.read_csv('E:\\test\jianmo\预测\Molecular_test.csv', encoding='gb18030', index_col=0)

scaler = StandardScaler()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
Set_unit_index =  ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP', 'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex', 'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']

Set_feature = Set.loc[:, Set_unit_index]
X = Set_feature.copy()

Set_feature['REa_9'] = blend_models_predict(X)
Set_feature.to_csv('.\Set_feature_stacking.csv', index=False)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/DE优化算法.py",".py","17683","455","from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier
from sklearn.pipeline import make_pipeline
from xgboost.sklearn import XGBClassifier
from lightgbm.sklearn import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
import pandas as pd


gr = pd.read_csv('E:\\test\jianmo\优化\clean451.csv', index_col=0, encoding='gb18030')
# gr = gr.drop('SMILES', axis=1)
# x = gr.iloc[:, 6:].values
Feature_0 = ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP',
              'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h',
              'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex',
              'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH']

Feature_1 = ['ATSm2', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SHBd',
             'SsCH3', 'SaaO', 'minHBa', 'hmin', 'LipoaffinityIndex',
             'FMF', 'MDEC-23', 'MLFER_S', 'WPATH']

Feature_2 = ['apol', 'ATSc1', 'ATSm3', 'SCH-6', 'VCH-7',
             'SP-6', 'SHBd', 'SHsOH', 'SHaaCH', 'minHBa',
             'maxsOH', 'ETA_dEpsilon_D', 'ETA_Shape_P', 'ETA_Shape_Y', 'ETA_BetaP_s',
             'ETA_dBetaP']

Feature_3 = ['ATSc2', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SHBd',
             'SHother', 'SsOH', 'minHBd', 'minHBa','minaaCH',
             'minaasC', 'maxHBd', 'maxwHBa', 'maxHBint8','maxHsOH',
             'hmin', 'LipoaffinityIndex','ETA_dEpsilon_B', 'ETA_Shape_Y','ETA_EtaP_F',
             'ETA_Eta_R_L', 'MDEO-11', 'WTPT-4', 'minssCH2']

Feature_4 = ['ATSc2', 'BCUTc-1l', 'BCUTp-1l', 'VCH-6', 'SC-5',
             'SPC-6', 'VP-3', 'SHsOH', 'SdO', 'minHBa',
             'minHsOH', 'maxHother', 'maxdO', 'hmin', 'MAXDP2',
             'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP_F_L','MDEC-23', 'MLFER_A',
             'TopoPSA', 'WTPT-2', 'WTPT-4']

Feature_5 = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6',
             'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin',
             'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y','ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E', 'WTPT-4'] #26


featuren_all = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6',
             'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin',
             'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y','ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E',
                'WTPT-4','BCUTc-1l','BCUTp-1l','VCH-6','SC-5',
                'SPC-6','VP-3','SHsOH','SdO','minHsOH',
                'maxHother','maxdO','MAXDP2','ETA_EtaP_F_L','MDEC-23',
                'MLFER_A','TopoPSA','WTPT-2','BCUTc-1h','BCUTp-1h',
                'SHBd','SHother','SsOH','minHBd','minaaCH',
                'minaasC','maxHBd','maxwHBa','maxHBint8','maxHsOH',
                'LipoaffinityIndex','ETA_Eta_R_L','MDEO-11','minssCH2','apol',
                'ATSc1','ATSm3','SCH-6','VCH-7','maxsOH',
                'ETA_dEpsilon_D','ETA_Shape_P','ETA_dBetaP','ATSm2','VC-5',
                'SsCH3','SaaO','MLFER_S','WPATH','C1SP2',
                'ALogP','ATSc3','ATSc5','minsssN','nBondsD2',
                'nsssCH']




Set = pd.read_csv('E:\\test\jianmo\优化\clean451.csv', index_col=0, encoding='gb18030')
print(Set)
scaler = StandardScaler()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
Set_d = Set[featuren_all].copy()
print(Set_d)
print(Set_d.shape)
import numpy as np
print(type(Set_d))
print(np.max(Set_d))
print(np.min(Set_d))
max_list=np.max(Set_d).tolist()
min_list=np.min(Set_d).tolist()
print(max_list)
print(min_list)


################################# ""MN""

feature_df = gr[Feature_5].copy()

x = feature_df.values

y_var = ['MN']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)


rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

# pipe1 = make_pipeline(StandardScaler(), clf_lr)
pipe6_1 = make_pipeline(StandardScaler(), rf)
pipe6_2 = make_pipeline(StandardScaler(), xgboost)
pipe6_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe6_1),
          ('xgb', pipe6_2),
          ('lgbm', pipe6_3)
          ]

ensembel6_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe6_1, pipe6_2, pipe6_3, ensembel6_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe6_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe6_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe6_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe6_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe6_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe6_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel6_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel6_4.fit(x_train, y_train)

################################# ""HOB""

feature_df = gr[Feature_4].copy()

x = feature_df.values

y_var = ['HOB']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe5_1 = make_pipeline(StandardScaler(), rf)
pipe5_2 = make_pipeline(StandardScaler(), xgboost)
pipe5_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe5_1),
          ('xgb', pipe5_2),
          ('lgbm', pipe5_3)
          ]

ensembel5_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe5_1, pipe5_2, pipe5_3, ensembel5_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe5_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe5_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe5_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe5_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe5_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe5_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel5_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel5_4.fit(x_train, y_train)

################################# ""hERG""

feature_df = gr[Feature_3].copy()

x = feature_df.values

y_var = ['hERG']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe4_1 = make_pipeline(StandardScaler(), rf)
pipe4_2 = make_pipeline(StandardScaler(), xgboost)
pipe4_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe4_1),
          ('xgb', pipe4_2),
          ('lgbm', pipe4_3)
          ]

ensembel4_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe4_1, pipe4_2, pipe4_3, ensembel4_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe4_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe4_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe4_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe4_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe4_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe4_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel4_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel4_4.fit(x_train, y_train)


################################# ""CYP3A4""

feature_df = gr[Feature_2].copy()

x = feature_df.values

y_var = ['CYP3A4']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe3_1 = make_pipeline(StandardScaler(), rf)
pipe3_2 = make_pipeline(StandardScaler(), xgboost)
pipe3_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe3_1),
          ('xgb', pipe3_2),
          ('lgbm', pipe3_3)
          ]

ensembel3_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe3_1, pipe3_2, pipe3_3, ensembel3_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe3_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe3_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe3_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe3_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe3_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe3_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel3_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel3_4.fit(x_train, y_train)

################################# ""Caco-2""

feature_df = gr[Feature_1].copy()

x = feature_df.values

y_var = ['Caco-2']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe2_1 = make_pipeline(StandardScaler(), rf)
pipe2_2 = make_pipeline(StandardScaler(), xgboost)
pipe2_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe2_1),
          ('xgb', pipe2_2),
          ('lgbm', pipe2_3)
          ]

ensembel2_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

score = cross_val_score(estimator=pipe2_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe2_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe2_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe2_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe2_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe2_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel2_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel2_4.fit(x_train, y_train)

################################# ""ERa""
from xgboost import XGBRegressor
from sklearn.ensemble import GradientBoostingRegressor
from mlxtend.regressor import StackingCVRegressor

Set = pd.read_csv('E:\\test\jianmo\预测\Clean_Data.csv', encoding='gb18030', index_col=0)
y_train = Set['pIC50'].copy()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])

x_train = Set[Feature_0].copy()
x_train = x_train.values
print('训练集和测试集 shape', x_train.shape, y_train.shape)

gbr = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,
                                max_depth=4, max_features='sqrt',
                                min_samples_leaf=15, min_samples_split=10,
                                loss='huber', random_state=42)

xgboost = XGBRegressor(learning_rate=0.01, n_estimators=3460,
                       max_depth=3, min_child_weight=0,
                       gamma=0, subsample=0.7,
                       colsample_bytree=0.7,
                       objective='reg:linear', nthread=-1,
                       scale_pos_weight=1, seed=27,
                       reg_alpha=0.00006)

stack_gen = StackingCVRegressor(regressors=( gbr, xgboost), meta_regressor=xgboost, use_features_in_secondary=True)

pipe1_1 = make_pipeline(StandardScaler(), stack_gen)

from sklearn.model_selection import cross_val_score

score = cross_val_score(estimator=pipe1_1, X=x_train, y=y_train, cv=10, scoring='neg_mean_squared_error')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe1_1.fit(x_train, y_train)

def schaffer(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    '''
    x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26\
        ,x27,x28,x29,x30,x31,x32,x33,x34,x35,x36,x37,x38,x39,x40,x41,x42,x43 \
        ,x44,x45,x46,x47,x48,x49,x50,x51,x52,x53,x54,x55,x56,x57,x58,x59\
        ,x60,x61,x62,x63,x64,x65,x66,x67,x68\
        ,x69,x70,x71,x72,x73,x74\
        ,x75,x76,x77,x78,x79,x80,x81= p

    feature1 = [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26] # MN
    feature2 = [x2,x27,x28,x29,x30,x31,x32,x33,x34,x10,x35,x36,x37,x15,x38,x16,x18,x39,x40,x41,x42,x43,x26] # Hob
    feature3 = [x2,x27,x44,x45,x46,x47,x48,x49,x10,x50,x51,x52,x53,x54,x55,x15,x56,x16,x18,x21,x57,x58,x26,x59] #hERG
    feature4 = [x60,x61,x62,x63,x64,x5,x46,x33,x6,x10,x65,x66,x67,x18,x20,x68] #CYP3A4
    feature5 = [x69,x44,x63,x70,x46,x71,x72,x10,x15,x56,x23,x40,x73,x74]
    feature6 = [x75,x48,x10,x14,x76,x61,x77,x78,x27,x44,x45,x45,x79,x45,x56,x38,x20,x24,x80,x81]

    pipe6_1_score = pipe6_1.predict([feature1])
    pipe6_2_score = pipe6_2.predict([feature1])
    pipe6_3_score = pipe6_3.predict([feature1])
    pipe6_4_score = ensembel6_4.predict([feature1])
    score_mn =pipe6_1_score + pipe6_2_score + pipe6_3_score + pipe6_4_score

    pipe5_1_score = pipe5_1.predict([feature2])
    pipe5_2_score = pipe5_2.predict([feature2])
    pipe5_3_score = pipe5_3.predict([feature2])
    pipe5_4_score = ensembel5_4.predict([feature2])
    score_hob =pipe5_1_score + pipe5_2_score + pipe5_3_score + pipe5_4_score

    pipe4_1_score = pipe4_1.predict([feature3])
    pipe4_2_score = pipe4_2.predict([feature3])
    pipe4_3_score = pipe4_3.predict([feature3])
    pipe4_4_score = ensembel4_4.predict([feature3])
    score_hERG =pipe4_1_score + pipe4_2_score + pipe4_3_score + pipe4_4_score

    pipe3_1_score = pipe3_1.predict([feature4])
    pipe3_2_score = pipe3_2.predict([feature4])
    pipe3_3_score = pipe3_3.predict([feature4])
    pipe3_4_score = ensembel3_4.predict([feature4])
    score_CYP3A4 =pipe3_1_score + pipe3_2_score + pipe3_3_score + pipe3_4_score

    pipe2_1_score = pipe2_1.predict([feature5])
    pipe2_2_score = pipe2_2.predict([feature5])
    pipe2_3_score = pipe2_3.predict([feature5])
    pipe2_4_score = ensembel2_4.predict([feature5])
    score_Caco2 =pipe2_1_score + pipe2_2_score + pipe2_3_score + pipe2_4_score

    pipe1_1_score = pipe1_1.predict([feature6])
    score_ERa =pipe1_1_score

    print(score_ERa)
    print(score_Caco2)
    print(score_CYP3A4)
    print(score_hERG)
    print(score_hob)
    print(score_mn)

    print(""总得分"", -score_Caco2 + score_CYP3A4 + score_hERG - score_hob + score_mn - score_ERa)

    return -score_Caco2 + score_CYP3A4 + score_hERG - score_hob + score_mn - score_ERa


from 问题四优化.scikitopt.sko.DE import DE

de = DE(func=schaffer, n_dim=81, size_pop=100, max_iter=40, lb=min_list, ub=max_list)

# pso.run()
best_x, best_y = de.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

import matplotlib.pyplot as plt

plt.plot(de.gbest_y_hist)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

Y_history = pd.DataFrame(de.all_history_Y)
fig, ax = plt.subplots(2, 1)
ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
Y_history.min(axis=1).cummin().plot(kind='line')
plt.show()




","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/PSO优化算法.py",".py","17926","453","from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier
from sklearn.pipeline import make_pipeline
from xgboost.sklearn import XGBClassifier
from lightgbm.sklearn import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
import pandas as pd


gr = pd.read_csv('E:\\test\jianmo\优化\clean451.csv', index_col=0, encoding='gb18030')
# gr = gr.drop('SMILES', axis=1)
# x = gr.iloc[:, 6:].values
Feature_0 = ['C1SP2', 'SsOH', 'minHBa', 'maxssO', 'ALogP',
              'ATSc1', 'ATSc3', 'ATSc5', 'BCUTc-1l', 'BCUTc-1h',
              'BCUTp-1h', 'mindssC', 'minsssN', 'hmin', 'LipoaffinityIndex',
              'MAXDP2', 'ETA_BetaP_s', 'nHBAcc', 'nBondsD2', 'nsssCH'] #问题二变量

Feature_1 = ['ATSm2', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SHBd',
             'SsCH3', 'SaaO', 'minHBa', 'hmin', 'LipoaffinityIndex',
             'FMF', 'MDEC-23', 'MLFER_S', 'WPATH']  #问题三_第一类分类变量

Feature_2 = ['apol', 'ATSc1', 'ATSm3', 'SCH-6', 'VCH-7',
             'SP-6', 'SHBd', 'SHsOH', 'SHaaCH', 'minHBa',
             'maxsOH', 'ETA_dEpsilon_D', 'ETA_Shape_P', 'ETA_Shape_Y', 'ETA_BetaP_s',
             'ETA_dBetaP'] #问题三_第二类分类变量

Feature_3 = ['ATSc2', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SHBd',
             'SHother', 'SsOH', 'minHBd', 'minHBa','minaaCH',
             'minaasC', 'maxHBd', 'maxwHBa', 'maxHBint8','maxHsOH',
             'hmin', 'LipoaffinityIndex','ETA_dEpsilon_B', 'ETA_Shape_Y','ETA_EtaP_F',
             'ETA_Eta_R_L', 'MDEO-11', 'WTPT-4', 'minssCH2']  #问题三_第三类分类变量

Feature_4 = ['ATSc2', 'BCUTc-1l', 'BCUTp-1l', 'VCH-6', 'SC-5',
             'SPC-6', 'VP-3', 'SHsOH', 'SdO', 'minHBa',
             'minHsOH', 'maxHother', 'maxdO', 'hmin', 'MAXDP2',
             'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP_F_L','MDEC-23', 'MLFER_A',
             'TopoPSA', 'WTPT-2', 'WTPT-4']  #问题三_第四类分类变量

Feature_5 = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6',
             'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin',
             'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y','ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E', 'WTPT-4']  #问题四_第四类分类变量


featuren_all = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6',
             'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin',
             'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y','ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E',
                'WTPT-4','BCUTc-1l','BCUTp-1l','VCH-6','SC-5',
                'SPC-6','VP-3','SHsOH','SdO','minHsOH',
                'maxHother','maxdO','MAXDP2','ETA_EtaP_F_L','MDEC-23',
                'MLFER_A','TopoPSA','WTPT-2','BCUTc-1h','BCUTp-1h',
                'SHBd','SHother','SsOH','minHBd','minaaCH',
                'minaasC','maxHBd','maxwHBa','maxHBint8','maxHsOH',
                'LipoaffinityIndex','ETA_Eta_R_L','MDEO-11','minssCH2','apol',
                'ATSc1','ATSm3','SCH-6','VCH-7','maxsOH',
                'ETA_dEpsilon_D','ETA_Shape_P','ETA_dBetaP','ATSm2','VC-5',
                'SsCH3','SaaO','MLFER_S','WPATH','C1SP2',
                'ALogP','ATSc3','ATSc5','minsssN','nBondsD2',
                'nsssCH']   # 汇总的的第四问变量81个




Set = pd.read_csv('E:\\test\jianmo\优化\clean451.csv', index_col=0, encoding='gb18030')
print(Set)
scaler = StandardScaler()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
Set_d = Set[featuren_all].copy()
print(Set_d)
print(Set_d.shape)
import numpy as np
print(type(Set_d))
print(np.max(Set_d))
print(np.min(Set_d))
max_list=np.max(Set_d).tolist()
min_list=np.min(Set_d).tolist()
print(max_list)
print(min_list)


################################# ""MN""

feature_df = gr[Feature_5].copy()

x = feature_df.values

y_var = ['MN']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)


rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe6_1 = make_pipeline(StandardScaler(), rf)
pipe6_2 = make_pipeline(StandardScaler(), xgboost)
pipe6_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe6_1),
          ('xgb', pipe6_2),
          ('lgbm', pipe6_3)
          ]

ensembel6_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe6_1, pipe6_2, pipe6_3, ensembel6_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe6_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe6_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe6_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe6_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe6_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe6_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel6_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel6_4.fit(x_train, y_train)

################################# ""HOB""

feature_df = gr[Feature_4].copy()

x = feature_df.values

y_var = ['HOB']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe5_1 = make_pipeline(StandardScaler(), rf)
pipe5_2 = make_pipeline(StandardScaler(), xgboost)
pipe5_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe5_1),
          ('xgb', pipe5_2),
          ('lgbm', pipe5_3)
          ]

ensembel5_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe5_1, pipe5_2, pipe5_3, ensembel5_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe5_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe5_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe5_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe5_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe5_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe5_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel5_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel5_4.fit(x_train, y_train)

################################# ""hERG""

feature_df = gr[Feature_3].copy()

x = feature_df.values

y_var = ['hERG']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe4_1 = make_pipeline(StandardScaler(), rf)
pipe4_2 = make_pipeline(StandardScaler(), xgboost)
pipe4_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe4_1),
          ('xgb', pipe4_2),
          ('lgbm', pipe4_3)
          ]

ensembel4_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe4_1, pipe4_2, pipe4_3, ensembel4_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe4_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe4_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe4_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe4_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe4_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe4_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel4_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel4_4.fit(x_train, y_train)


################################# ""CYP3A4""

feature_df = gr[Feature_2].copy()

x = feature_df.values

y_var = ['CYP3A4']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe3_1 = make_pipeline(StandardScaler(), rf)
pipe3_2 = make_pipeline(StandardScaler(), xgboost)
pipe3_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe3_1),
          ('xgb', pipe3_2),
          ('lgbm', pipe3_3)
          ]

ensembel3_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe3_1, pipe3_2, pipe3_3, ensembel3_4]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']

score = cross_val_score(estimator=pipe3_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe3_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe3_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe3_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe3_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe3_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel3_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel3_4.fit(x_train, y_train)

################################# ""Caco-2""

feature_df = gr[Feature_1].copy()

x = feature_df.values

y_var = ['Caco-2']
for v in y_var:
    y = gr[v]
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe2_1 = make_pipeline(StandardScaler(), rf)
pipe2_2 = make_pipeline(StandardScaler(), xgboost)
pipe2_3 = make_pipeline(StandardScaler(), lgbm)

models = [
          ('rf', pipe2_1),
          ('xgb', pipe2_2),
          ('lgbm', pipe2_3)
          ]

ensembel2_4 = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

score = cross_val_score(estimator=pipe2_1, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe2_1.fit(x_train, y_train)

score = cross_val_score(estimator=pipe2_2, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe2_2.fit(x_train, y_train)

score = cross_val_score(estimator=pipe2_3, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe2_3.fit(x_train, y_train)

score = cross_val_score(estimator=ensembel2_4, X=x_train, y=y_train, cv=10, scoring='roc_auc')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
ensembel2_4.fit(x_train, y_train)

################################# ""ERa""
from xgboost import XGBRegressor
from sklearn.ensemble import GradientBoostingRegressor
from mlxtend.regressor import StackingCVRegressor

Set = pd.read_csv('E:\\test\jianmo\预测\Clean_Data.csv', encoding='gb18030', index_col=0)
y_train = Set['pIC50'].copy()
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])

x_train = Set[Feature_0].copy()
x_train = x_train.values
print('训练集和测试集 shape', x_train.shape, y_train.shape)

gbr = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,
                                max_depth=4, max_features='sqrt',
                                min_samples_leaf=15, min_samples_split=10,
                                loss='huber', random_state=42)

xgboost = XGBRegressor(learning_rate=0.01, n_estimators=3460,
                       max_depth=3, min_child_weight=0,
                       gamma=0, subsample=0.7,
                       colsample_bytree=0.7,
                       objective='reg:linear', nthread=-1,
                       scale_pos_weight=1, seed=27,
                       reg_alpha=0.00006)

stack_gen = StackingCVRegressor(regressors=( gbr, xgboost), meta_regressor=xgboost, use_features_in_secondary=True)

pipe1_1 = make_pipeline(StandardScaler(), stack_gen)

from sklearn.model_selection import cross_val_score

score = cross_val_score(estimator=pipe1_1, X=x_train, y=y_train, cv=10, scoring='neg_mean_squared_error')
print(""ElasticNet score: {:.4f}\n"".format(score.mean(), score.std()), )
pipe1_1.fit(x_train, y_train)

# 优化目标函数schaffer的构建---------------------------------------------------------
def schaffer(p):

    x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26\
        ,x27,x28,x29,x30,x31,x32,x33,x34,x35,x36,x37,x38,x39,x40,x41,x42,x43 \
        ,x44,x45,x46,x47,x48,x49,x50,x51,x52,x53,x54,x55,x56,x57,x58,x59\
        ,x60,x61,x62,x63,x64,x65,x66,x67,x68\
        ,x69,x70,x71,x72,x73,x74\
        ,x75,x76,x77,x78,x79,x80,x81= p

    feature1 = [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26] # MN
    feature2 = [x2,x27,x28,x29,x30,x31,x32,x33,x34,x10,x35,x36,x37,x15,x38,x16,x18,x39,x40,x41,x42,x43,x26] # Hob
    feature3 = [x2,x27,x44,x45,x46,x47,x48,x49,x10,x50,x51,x52,x53,x54,x55,x15,x56,x16,x18,x21,x57,x58,x26,x59] #hERG
    feature4 = [x60,x61,x62,x63,x64,x5,x46,x33,x6,x10,x65,x66,x67,x18,x20,x68] #CYP3A4
    feature5 = [x69,x44,x63,x70,x46,x71,x72,x10,x15,x56,x23,x40,x73,x74]
    feature6 = [x75,x48,x10,x14,x76,x61,x77,x78,x27,x44,x45,x45,x79,x45,x56,x38,x20,x24,x80,x81]

    pipe6_1_score = pipe6_1.predict([feature1])
    pipe6_2_score = pipe6_2.predict([feature1])
    pipe6_3_score = pipe6_3.predict([feature1])
    pipe6_4_score = ensembel6_4.predict([feature1])
    score_mn =pipe6_1_score + pipe6_2_score + pipe6_3_score + pipe6_4_score

    pipe5_1_score = pipe5_1.predict([feature2])
    pipe5_2_score = pipe5_2.predict([feature2])
    pipe5_3_score = pipe5_3.predict([feature2])
    pipe5_4_score = ensembel5_4.predict([feature2])
    score_hob =pipe5_1_score + pipe5_2_score + pipe5_3_score + pipe5_4_score

    pipe4_1_score = pipe4_1.predict([feature3])
    pipe4_2_score = pipe4_2.predict([feature3])
    pipe4_3_score = pipe4_3.predict([feature3])
    pipe4_4_score = ensembel4_4.predict([feature3])
    score_hERG =pipe4_1_score + pipe4_2_score + pipe4_3_score + pipe4_4_score

    pipe3_1_score = pipe3_1.predict([feature4])
    pipe3_2_score = pipe3_2.predict([feature4])
    pipe3_3_score = pipe3_3.predict([feature4])
    pipe3_4_score = ensembel3_4.predict([feature4])
    score_CYP3A4 =pipe3_1_score + pipe3_2_score + pipe3_3_score + pipe3_4_score

    pipe2_1_score = pipe2_1.predict([feature5])
    pipe2_2_score = pipe2_2.predict([feature5])
    pipe2_3_score = pipe2_3.predict([feature5])
    pipe2_4_score = ensembel2_4.predict([feature5])
    score_Caco2 =pipe2_1_score + pipe2_2_score + pipe2_3_score + pipe2_4_score

    pipe1_1_score = pipe1_1.predict([feature6])
    score_ERa =pipe1_1_score

    print(score_ERa)
    print(score_Caco2)
    print(score_CYP3A4)
    print(score_hERG)
    print(score_hob)
    print(score_mn)

    print(""总得分"", -score_Caco2 + score_CYP3A4 + score_hERG - score_hob + score_mn - score_ERa)

    return -score_Caco2 + score_CYP3A4 + score_hERG - score_hob + score_mn - score_ERa


from ..scikitopt.sko.PSO import PSO

pso = PSO(func=schaffer, dim=81, pop=300, max_iter=80, lb=min_list, ub=max_list, w=0.8, c1=0.5, c2=0.5)
pso = PSO(func=schaffer, dim=81, pop=20, max_iter=20, lb=min_list, ub=max_list, w=0.8, c1=0.5, c2=0.5)


best_x, best_y = pso.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

import matplotlib.pyplot as plt

plt.plot(pso.gbest_y_hist)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

Y_history = pd.DataFrame(pso.all_history_Y)
fig, ax = plt.subplots(2, 1)
ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
Y_history.min(axis=1).cummin().plot(kind='line')
plt.show()




","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/最优解_最近的样本.py",".py","4938","130","import pandas as pd
from sklearn.preprocessing import StandardScaler

Set = pd.read_csv('E:\\test\jianmo\优化\clean451.csv', encoding='gb18030', index_col=0)
print(Set)
scaler = StandardScaler()
Set2 = Set.copy()
print(Set['nN'])
Set.loc[:, Set.columns != 'SMILES'] = scaler.fit_transform(Set.loc[:, Set.columns != 'SMILES'])
print(Set)

featuren_all = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6',
             'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin',
             'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y','ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E',
                'WTPT-4','BCUTc-1l','BCUTp-1l','VCH-6','SC-5',
                'SPC-6','VP-3','SHsOH','SdO','minHsOH',
                'maxHother','maxdO','MAXDP2','ETA_EtaP_F_L','MDEC-23',
                'MLFER_A','TopoPSA','WTPT-2','BCUTc-1h','BCUTp-1h',
                'SHBd','SHother','SsOH','minHBd','minaaCH',
                'minaasC','maxHBd','maxwHBa','maxHBint8','maxHsOH',
                'LipoaffinityIndex','ETA_Eta_R_L','MDEO-11','minssCH2','apol',
                'ATSc1','ATSm3','SCH-6','VCH-7','maxsOH',
                'ETA_dEpsilon_D','ETA_Shape_P','ETA_dBetaP','ATSm2','VC-5',
                'SsCH3','SaaO','MLFER_S','WPATH','C1SP2',
                'ALogP','ATSc3','ATSc5','minsssN','nBondsD2',
                'nsssCH']

Set_feature = Set.loc[:, featuren_all]
print(Set_feature)
X = Set_feature
print(Set_feature)
print(Set_feature.values)

import numpy as np

best_x = [ 1.38253693e+00, -1.27923420e+01 , 3.34890459e-01 , 2.09864822e+00,
 -1.37653334e+00, -5.92939907e-01,  2.64158346e+00 ,-1.47876716e+01,
  3.46623419e+00  ,1.61801497e+00 ,-2.99576488e+00, -3.48074006e-01,
  6.38688156e-01, -5.19686352e-01,  9.00704135e-01,  3.09167458e+00,
 -3.47797975e+00 ,-2.37999354e+00, -6.02536699e-01, -3.18153296e+00,
  7.42434944e-01,-2.89563644e+00 , 3.20435290e-01 , 2.17453028e+01,
  1.03539905e+00,  5.36896266e+00 , 2.09566882e+00,  5.27409742e-01,
  3.87202400e+00,  3.36569703e+00 , 6.04737620e+00,  2.61958616e+00,
  1.80990651e+00 , 1.43818619e+01 , 1.48127264e+00,  1.60160046e+00,
  2.11726944e-01, -2.29679055e+00 ,-3.80125811e+00 , 1.40864553e+00,
  1.03629442e+01  ,1.81157543e+01, -2.79026323e+00  ,3.56833458e+00,
  2.59651752e+00,  2.47588700e+01 , 1.03023707e+00 , 2.37580593e+00,
 -3.24008740e+00,  1.18860580e+00 ,-3.09182989e+00,  1.65976731e+00,
  1.67475725e+00  ,2.92308457e+00 ,-4.98709744e-01 , 2.31794159e+00,
  7.78201037e+00,  5.95230613e-01,  1.27464854e+00 , 1.11117586e-01,
  8.62933321e+00,  7.10097513e+00 ,-1.87948566e+00,  7.08198140e+00,
  3.20053469e-02 , 6.00339053e-01 , 1.89878864e+00,  4.70380159e-02,
  6.31825754e+00 , 8.39251470e+00,  5.51045242e+00 , 3.68552455e+00,
  1.35371005e+01,  3.74846338e+01,  1.66060646e+00, -1.27641030e+01,
  3.07137345e+00 ,-2.82324098e+00,  1.55734042e+00,  1.41112778e+01,
  8.31509632e+00]

print('------------------------------------------------------------------------------')
print(np.mean(Set2[featuren_all]))
print(np.std(Set2[featuren_all]))
# mix_value.append(i*(np.std(Set2[featuren_all])) + np.mean(Set2[featuren_all]))

best_value = []
for j,i in enumerate(best_x):
    best_value.append((i*(np.std(Set2[featuren_all])[j])+(np.mean(Set2[featuren_all])[j])))
print(best_value)


max_list=[]
for i in Set_feature.values:
    chazhi = best_x - i
    list1=[]
    a = 0
    for j in chazhi:
        if abs(j)<2:
            list1.append(abs(j))
        elif abs(j)<6:
            list1.append(np.sqrt((abs(j))))
        elif abs(j)<24:
            list1.append((np.sqrt(np.sqrt(abs(j)))))
        else:
            list1.append(np.sqrt(np.sqrt(np.sqrt(abs(j)))))
    a = sum(list1)
    max_list.append(a)
arr = np.array(max_list)
mix_index = arr.argsort()[:20][::1].tolist()
mix_index.append(1832)
mix_index.append(1786)
mix_index.append(1784)
mix_index.append(1467)
mix_index.append(1466)
mix_index.append(1464)
mix_index.append(1454)
mix_index.append(515)
mix_index.append(512)
mix_index.append(489)
mix_index.append(478)
mix_index.append(474)
mix_index.append(472)
mix_index.append(470)
mix_index.append(467)
mix_index.append(461)
mix_index.append(460)
Set_d = Set_feature.loc[mix_index].copy()
mix_value = []
for j,i in enumerate(np.min(Set_d)):
    mix_value.append(i*(np.std(Set2[featuren_all])[j]) + np.mean(Set2[featuren_all])[j])
max_value = []
for j,i in enumerate(np.max(Set_d)):
    max_value.append(i*(np.std(Set2[featuren_all])[j]) + np.mean(Set2[featuren_all])[j])
print(mix_value)
print(max_value)

data = {
    'min': mix_value,
    'max': max_value,
    'fuhao': featuren_all,
}
row_index = featuren_all
col_names=['min', 'max']
df=pd.DataFrame(data,columns=col_names,)
print(df)
df['最优解'] = best_value
df['fuhao'] = featuren_all

print(df)
df.to_csv('E:\\test\jianmo\优化问题问题取值范围DE.csv', index=False)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/setup.py",".py","1033","35","from setuptools import setup, find_packages
from os import path as os_path
import sko

this_directory = os_path.abspath(os_path.dirname(__file__))


# 读取文件内容
def read_file(filename):
    with open(os_path.join(this_directory, filename), encoding='utf-8') as f:
        long_description = f.read()
    return long_description


# 获取依赖
def read_requirements(filename):
    return [line.strip() for line in read_file(filename).splitlines()
            if not line.startswith('#')]


setup(name='scikit-opt',
      python_requires='>=3.5',
      version=sko.__version__,
      description='Swarm Intelligence in Python',
      long_description=read_file('docs/en/README.md'),
      long_description_content_type=""text/markdown"",
      url='https://github.com/guofei9987/scikit-opt',
      author='Guo Fei',
      author_email='guofei9987@foxmail.com',
      license='MIT',
      packages=find_packages(),
      platforms=['linux', 'windows', 'macos'],
      install_requires=['numpy', 'scipy'],
      zip_safe=False)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/CONTRIBUTING.md",".md","2522","62","# Contributing guidelines

This page explains how you can contribute to the development of
scikit-opt by submitting patches, tests, new models, or examples.

scikit-opt is developed on
[Github](https://github.com/guofei9987/scikit-opt) using the
[Git](https://git-scm.com/) version control system.

## Submitting a Bug Report

-   Include a short, self-contained code snippet that reproduces the
    problem
-   Ensure that the bug still exists on latest version.

## Making Changes to the Code

For a pull request to be accepted, you must meet the below requirements.
This greatly helps in keeping the job of maintaining and releasing the
software a shared effort.

-   **One branch. One feature.** Branches are cheap and github makes it
    easy to merge and delete branches with a few clicks. Avoid the
    temptation to lump in a bunch of unrelated changes when working on a
    feature, if possible. This helps us keep track of what has changed
    when preparing a release.
-   Commit messages should be clear and concise. If your commit references or
    closes a specific issue, you can close it by mentioning it in the
    [commit
    message](https://docs.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue).
    (*For maintainers*: These suggestions go for Merge commit comments
    too. These are partially the record for release notes.)
-   Each function, class, method, and attribute needs to be documented.
-   If you are adding new functionality, you need to add it to the
    documentation by editing (or creating) the appropriate file in
    `docs/`.


## How to Submit a Pull Request

So you want to submit a patch to scikit-opt but are not too familiar
with github? Here are the steps you need to take.

1.  [Fork](https://help.github.com/articles/fork-a-repo) the
    [scikit-opt repository](https://github.com/guofei9987/scikit-opt)
    on Github.
2.  [Create a new feature
    branch](https://git-scm.com/book/en/Git-Branching-Basic-Branching-and-Merging).
    Each branch must be self-contained, with a single new feature or
    bugfix.
3.  Make sure the test suite passes. This includes testing on Python 3.
    The easiest way to do this is to either enable
    [Travis-CI](https://travis-ci.org/) on your fork, or to make a pull
    request and check there.
4.  If it is a big, new feature please submit an example.
5.  [Submit a pull
    request](https://help.github.com/articles/using-pull-requests)

## License

scikit-opt is released under the MIT license.
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/DE.py",".py","3253","100","#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time    : 2019/8/20
# @Author  : github.com/guofei9987


import numpy as np
from .base import SkoBase
from abc import ABCMeta, abstractmethod
from .operators import crossover, mutation, ranking, selection
from .GA import GeneticAlgorithmBase, GA


class DifferentialEvolutionBase(SkoBase, metaclass=ABCMeta):
    pass


class DE(GeneticAlgorithmBase):
    def __init__(self, func, n_dim, F=0.5,
                 size_pop=50, max_iter=200, prob_mut=0.3,
                 lb=-1, ub=1,
                 constraint_eq=tuple(), constraint_ueq=tuple()):
        super().__init__(func, n_dim, size_pop, max_iter, prob_mut,
                         constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)

        self.F = F
        self.V, self.U = None, None
        self.lb, self.ub = np.array(lb) * np.ones(self.n_dim), np.array(ub) * np.ones(self.n_dim)
        self.crtbp()

    def crtbp(self):
        # create the population
        self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.size_pop, self.n_dim))
        return self.X

    def chrom2x(self, Chrom):
        pass

    def ranking(self):
        pass

    def mutation(self):
        '''
        V[i]=X[r1]+F(X[r2]-X[r3]),
        where i, r1, r2, r3 are randomly generated
        '''
        X = self.X
        # i is not needed,
        # and TODO: r1, r2, r3 should not be equal
        random_idx = np.random.randint(0, self.size_pop, size=(self.size_pop, 3))

        r1, r2, r3 = random_idx[:, 0], random_idx[:, 1], random_idx[:, 2]

        # 这里F用固定值，为了防止早熟，可以换成自适应值
        self.V = X[r1, :] + self.F * (X[r2, :] - X[r3, :])

        # the lower & upper bound still works in mutation
        mask = np.random.uniform(low=self.lb, high=self.ub, size=(self.size_pop, self.n_dim))
        self.V = np.where(self.V < self.lb, mask, self.V)
        self.V = np.where(self.V > self.ub, mask, self.V)
        return self.V

    def crossover(self):
        '''
        if rand < prob_crossover, use V, else use X
        '''
        mask = np.random.rand(self.size_pop, self.n_dim) < self.prob_mut
        self.U = np.where(mask, self.V, self.X)
        return self.U

    def selection(self):
        '''
        greedy selection
        '''
        X = self.X.copy()
        f_X = self.x2y().copy()
        self.X = U = self.U
        f_U = self.x2y()

        self.X = np.where((f_X < f_U).reshape(-1, 1), X, U)
        return self.X

    def run(self, max_iter=None):
        self.max_iter = max_iter or self.max_iter
        for i in range(self.max_iter):
            self.mutation()
            self.crossover()
            self.selection()

            # record the best ones
            generation_best_index = self.Y.argmin()
            self.generation_best_X.append(self.X[generation_best_index, :].copy())
            self.generation_best_Y.append(self.Y[generation_best_index])
            self.all_history_Y.append(self.Y)

        global_best_index = np.array(self.generation_best_Y).argmin()
        global_best_X = self.generation_best_X[global_best_index]
        global_best_Y = self.func(np.array([global_best_X]))
        return global_best_X, global_best_Y
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/tools.py",".py","1187","49","import numpy as np


def func_transformer(func):
    '''
    transform this kind of function:
    ```
    def demo_func(x):
        x1, x2, x3 = x
        return x1 ** 2 + x2 ** 2 + x3 ** 2
    ```
    into this kind of function:
    ```
    def demo_func(x):
        x1, x2, x3 = x[:,0], x[:,1], x[:,2]
        return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2
    ```
    getting vectorial performance if possible
    :param func:
    :return:
    '''

    prefered_function_format = '''
    def demo_func(x):
        x1, x2, x3 = x[:, 0], x[:, 1], x[:, 2]
        return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2
    '''

    is_vector = getattr(func, 'is_vector', False)
    if is_vector:
        return func
    else:
        if func.__code__.co_argcount == 1:
            def func_transformed(X):
                return np.array([func(x) for x in X])

            return func_transformed
        elif func.__code__.co_argcount > 1:

            def func_transformed(X):
                return np.array([func(*tuple(x)) for x in X])

            return func_transformed

    raise ValueError('''
    object function error,
    function should be like this:
    ''' + prefered_function_format)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/__init__.py",".py","345","15","__version__ = '0.5.8'

from . import DE, GA, PSO, SA, ACA, AFSA, IA


def start():
    print('''
    scikit-opt import successfully,
    version: {version}
    Author: Guo Fei,
    Email: guofei9987@foxmail.com
    repo: https://github.com/guofei9987/scikit-opt,
    documents: https://scikit-opt.github.io/
    '''.format(version=__version__))
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/PSO.py",".py","5725","159","#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time    : 2019/8/20
# @Author  : github.com/guofei9987

import numpy as np
from scikitopt.sko.tools import func_transformer
from .base import SkoBase


class PSO(SkoBase):
    """"""
    Do PSO (Particle swarm optimization) algorithm.

    This algorithm was adapted from the earlier works of J. Kennedy and
    R.C. Eberhart in Particle Swarm Optimization [IJCNN1995]_.

    The position update can be defined as:

    .. math::

       x_{i}(t+1) = x_{i}(t) + v_{i}(t+1)

    Where the position at the current step :math:`t` is updated using
    the computed velocity at :math:`t+1`. Furthermore, the velocity update
    is defined as:

    .. math::

       v_{ij}(t + 1) = w * v_{ij}(t) + c_{p}r_{1j}(t)[y_{ij}(t) − x_{ij}(t)]
                       + c_{g}r_{2j}(t)[\hat{y}_{j}(t) − x_{ij}(t)]

    Here, :math:`cp` and :math:`cg` are the cognitive and social parameters
    respectively. They control the particle's behavior given two choices: (1) to
    follow its *personal best* or (2) follow the swarm's *global best* position.
    Overall, this dictates if the swarm is explorative or exploitative in nature.
    In addition, a parameter :math:`w` controls the inertia of the swarm's
    movement.

    .. [IJCNN1995] J. Kennedy and R.C. Eberhart, ""Particle Swarm Optimization,""
    Proceedings of the IEEE International Joint Conference on Neural
    Networks, 1995, pp. 1942-1948.

    Parameters
    --------------------
    func : function
        The func you want to do optimal
    dim : int
        Number of dimension, which is number of parameters of func.
    pop : int
        Size of population, which is the number of Particles. We use 'pop' to keep accordance with GA
    max_iter : int
        Max of iter iterations

    Attributes
    ----------------------
    pbest_x : array_like, shape is (pop,dim)
        best location of every particle in history
    pbest_y : array_like, shape is (pop,1)
        best image of every particle in history
    gbest_x : array_like, shape is (1,dim)
        general best location for all particles in history
    gbest_y : float
        general best image  for all particles in history
    gbest_y_hist : list
        gbest_y of every iteration


    Examples
    -----------------------------
    see https://scikit-opt.github.io/scikit-opt/#/en/README?id=_3-psoparticle-swarm-optimization
    """"""

    def __init__(self, func, dim, pop=40, max_iter=150, lb=None, ub=None, w=0.8, c1=0.5, c2=0.5):
        self.func = func_transformer(func)
        self.w = w  # inertia
        self.cp, self.cg = c1, c2  # parameters to control personal best, global best respectively
        self.pop = pop  # number of particles
        self.dim = dim  # dimension of particles, which is the number of variables of func
        self.max_iter = max_iter  # max iter

        self.has_constraints = not (lb is None and ub is None)
        self.lb = -np.ones(self.dim) if lb is None else np.array(lb)
        self.ub = np.ones(self.dim) if ub is None else np.array(ub)
        assert self.dim == len(self.lb) == len(self.ub), 'dim == len(lb) == len(ub) is not True'
        assert np.all(self.ub > self.lb), 'upper-bound must be greater than lower-bound'

        self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.pop, self.dim))
        v_high = self.ub - self.lb
        self.V = np.random.uniform(low=-v_high, high=v_high, size=(self.pop, self.dim))  # speed of particles
        self.Y = self.cal_y()  # y = f(x) for all particles
        self.pbest_x = self.X.copy()  # personal best location of every particle in history
        self.pbest_y = self.Y.copy()  # best image of every particle in history
        self.gbest_x = np.zeros((1, self.dim))  # global best location for all particles
        self.gbest_y = np.inf  # global best y for all particles
        self.gbest_y_hist = []  # gbest_y of every iteration
        self.update_gbest()

        # record verbose values
        self.record_mode = False
        self.record_value = {'X': [], 'V': [], 'Y': []}

    def update_V(self):
        r1 = np.random.rand(self.pop, self.dim)
        r2 = np.random.rand(self.pop, self.dim)
        self.V = self.w * self.V + \
                 self.cp * r1 * (self.pbest_x - self.X) + \
                 self.cg * r2 * (self.gbest_x - self.X)

    def update_X(self):
        self.X = self.X + self.V

        if self.has_constraints:
            self.X = np.clip(self.X, self.lb, self.ub)

    def cal_y(self):
        # calculate y for every x in X
        self.Y = self.func(self.X).reshape(-1, 1)
        return self.Y

    def update_pbest(self):
        '''
        personal best
        :return:
        '''
        self.pbest_x = np.where(self.pbest_y > self.Y, self.X, self.pbest_x)
        self.pbest_y = np.where(self.pbest_y > self.Y, self.Y, self.pbest_y)

    def update_gbest(self):
        '''
        global best
        :return:
        '''
        if self.gbest_y > self.Y.min():
            self.gbest_x = self.X[self.Y.argmin(), :].copy()
            self.gbest_y = self.Y.min()

    def recorder(self):
        if not self.record_mode:
            return
        self.record_value['X'].append(self.X)
        self.record_value['V'].append(self.V)
        self.record_value['Y'].append(self.Y)

    def run(self, max_iter=None):
        self.max_iter = max_iter or self.max_iter
        for iter_num in range(self.max_iter):
            self.update_V()
            self.recorder()
            self.update_X()
            self.cal_y()
            self.update_pbest()
            self.update_gbest()

            self.gbest_y_hist.append(self.gbest_y)
        return self

    fit = run
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/ACA.py",".py","3436","72","#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time    : 2019/9/11
# @Author  : github.com/guofei9987

import numpy as np


class ACA_TSP:
    def __init__(self, func, n_dim,
                 size_pop=10, max_iter=20,
                 distance_matrix=None,
                 alpha=1, beta=2, rho=0.1,
                 ):
        self.func = func
        self.n_dim = n_dim  # 城市数量
        self.size_pop = size_pop  # 蚂蚁数量
        self.max_iter = max_iter  # 迭代次数
        self.alpha = alpha  # 信息素重要程度
        self.beta = beta  # 适应度的重要程度
        self.rho = rho  # 信息素挥发速度

        self.prob_matrix_distance = 1 / (distance_matrix + 1e-10 * np.eye(n_dim, n_dim))  # 避免除零错误

        self.Tau = np.ones((n_dim, n_dim))  # 信息素矩阵，每次迭代都会更新
        self.Table = np.zeros((size_pop, n_dim)).astype(np.int)  # 某一代每个蚂蚁的爬行路径
        self.y = None  # 某一代每个蚂蚁的爬行总距离
        self.x_best_history, self.y_best_history = [], []  # 记录各代的最佳情况
        self.best_x, self.best_y = None, None

    def run(self, max_iter=None):
        self.max_iter = max_iter or self.max_iter
        for i in range(self.max_iter):  # 对每次迭代
            prob_matrix = (self.Tau ** self.alpha) * (self.prob_matrix_distance) ** self.beta  # 转移概率，无须归一化。
            for j in range(self.size_pop):  # 对每个蚂蚁
                self.Table[j, 0] = 0  # start point，其实可以随机，但没什么区别
                for k in range(self.n_dim - 1):  # 蚂蚁到达的每个节点
                    taboo_set = set(self.Table[j, :k + 1])  # 已经经过的点和当前点，不能再次经过
                    allow_list = list(set(range(self.n_dim)) - taboo_set)  # 在这些点中做选择
                    prob = prob_matrix[self.Table[j, k], allow_list]
                    prob = prob / prob.sum()  # 概率归一化
                    next_point = np.random.choice(allow_list, size=1, p=prob)[0]
                    self.Table[j, k + 1] = next_point

            # 计算距离
            y = np.array([self.func(i) for i in self.Table])

            # 顺便记录历史最好情况
            index_best = y.argmin()
            x_best, y_best = self.Table[index_best, :].copy(), y[index_best].copy()
            self.x_best_history.append(x_best)
            self.y_best_history.append(y_best)

            # 计算需要新涂抹的信息素
            delta_tau = np.zeros((self.n_dim, self.n_dim))
            for j in range(self.size_pop):  # 每个蚂蚁
                for k in range(self.n_dim - 1):  # 每个节点
                    n1, n2 = self.Table[j, k], self.Table[j, k + 1]  # 蚂蚁从n1节点爬到n2节点
                    delta_tau[n1, n2] += 1 / y[j]  # 涂抹的信息素
                n1, n2 = self.Table[j, self.n_dim - 1], self.Table[j, 0]  # 蚂蚁从最后一个节点爬回到第一个节点
                delta_tau[n1, n2] += 1 / y[j]  # 涂抹信息素

            # 信息素飘散+信息素涂抹
            self.Tau = (1 - self.rho) * self.Tau + delta_tau

        best_generation = np.array(self.y_best_history).argmin()
        self.best_x = self.x_best_history[best_generation]
        self.best_y = self.y_best_history[best_generation]
        return self.best_x, self.best_y

    fit = run
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/demo_func.py",".py","2888","96","import numpy as np
from scipy import spatial


def function_for_TSP(num_points, seed=None):
    if seed:
        np.random.seed(seed=seed)

    points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points randomly
    distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')

    # print('distance_matrix is: \n', distance_matrix)

    def cal_total_distance(routine):
        num_points, = routine.shape
        return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])

    return num_points, points_coordinate, distance_matrix, cal_total_distance


def sphere(p):
    # Sphere函数
    out_put = 0
    for i in p:
        out_put += i ** 2
    return out_put


def schaffer(p):
    '''
    二维函数，具有无数个极小值点、强烈的震荡形态。很难找到全局最优值
    在(0,0)处取的最值0
    -10<=x1,x2<=10
    '''
    x1, x2 = p
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.square(np.sin(np.sqrt(x))) - 0.5) / np.square(1 + 0.001 * x)


def shubert(p):
    '''
    2-dimension
    -10<=x1,x2<=10
    has 760 local minimas, 18 of which are global minimas with -186.7309
    '''
    x, y = p
    part1 = [i * np.cos((i + 1) * x + i) for i in range(1, 6)]
    part2 = [i * np.cos((i + 1) * y + i) for i in range(1, 6)]
    return np.sum(part1) * np.sum(part2)


def griewank(p):
    '''
    存在多个局部最小值点，数目与问题的维度有关。
    此函数是典型的非线性多模态函数，具有广泛的搜索空间，是优化算法很难处理的复杂多模态问题。
    在(0,...,0)处取的全局最小值0
    -600<=xi<=600
    '''
    part1 = [np.square(x) / 4000 for x in p]
    part2 = [np.cos(x / np.sqrt(i + 1)) for i, x in enumerate(p)]
    return np.sum(part1) - np.prod(part2) + 1


def rastrigrin(p):
    '''
    多峰值函数，也是典型的非线性多模态函数
    -5.12<=xi<=5.12
    在范围内有10n个局部最小值，峰形高低起伏不定跳跃。很难找到全局最优
    has a global minimum at x = 0  where f(x) = 0
    '''
    return np.sum([np.square(x) - 10 * np.cos(2 * np.pi * x) + 10 for x in p])


def rosenbrock(p):
    '''
    -2.048<=xi<=2.048
    函数全局最优点在一个平滑、狭长的抛物线山谷内，使算法很难辨别搜索方向，查找最优也变得十分困难
    在(1,...,1)处可以找到极小值0
    :param p:
    :return:
    '''
    n_dim = len(p)
    res = 0
    for i in range(n_dim - 1):
        res += 100 * np.square(np.square(p[i]) - p[i + 1]) + np.square(p[i] - 1)
    return res


if __name__ == '__main__':
    print(sphere((0, 0)))
    print(schaffer((0, 0)))
    print(shubert((-7.08350643, -7.70831395)))
    print(griewank((0, 0, 0)))
    print(rastrigrin((0, 0, 0)))
    print(rosenbrock((1, 1, 1)))
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/base.py",".py","639","24","from abc import ABCMeta, abstractmethod
import types


class SkoBase(metaclass=ABCMeta):
    def register(self, operator_name, operator, *args, **kwargs):
        '''
        regeister udf to the class
        :param operator_name: string
        :param operator: a function, operator itself
        :param args: arg of operator
        :param kwargs: kwargs of operator
        :return:
        '''

        def operator_wapper(*wrapper_args):
            return operator(*(wrapper_args + args), **kwargs)

        setattr(self, operator_name, types.MethodType(operator_wapper, self))
        return self


class Problem(object):
    pass","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/AFSA.py",".py","9316","212","#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time    : 2019/9/17
# @Author  : github.com/guofei9987


import numpy as np
from scipy import spatial


# class ASFA_raw:
#     # 这里统一做成极小值求解的问题
#     # 参考某 Matlab 教课书上的代码，很多地方的设定无力吐槽，基本没有泛用性
#     def __init__(self, func):
#         self.func = func
#         self.size_pop = 50
#         self.max_iter = 300
#         self.n_dim = 2
#         self.max_try_num = 100  # 最大尝试次数
#         self.step = 0.1  # 每一步的最大位移
#         self.visual = 0.5  # 鱼的最大感知范围
#         self.delta = 1.3  # 拥挤度阈值
#
#         self.X = np.random.rand(self.size_pop, self.n_dim)
#         self.Y = np.array([self.func(x) for x in self.X])
#
#         best_idx = self.Y.argmin()
#         self.best_Y = self.Y[best_idx]
#         self.best_X = self.X[best_idx, :]
#
#     def move_to_target(self, index_individual, x_target):
#         # 向目标移动，prey(), swarm(), follow() 三个算子中的移动都用这个
#         x = self.X[index_individual, :]
#         x_new = x + self.step * np.random.rand() * (x_target - x) / np.linalg.norm(x_target - x)
#         self.X[index_individual, :] = x_new
#         self.Y[index_individual] = self.func(x_new)
#         if self.Y[index_individual] < self.best_Y:
#             self.best_X = self.X[index_individual, :]
#
#     def move(self, index_individual):
#         r = 2 * np.random.rand(self.n_dim) - 1
#         x_new = self.X[index_individual, :] + self.visual * r
#         self.X[index_individual, :] = x_new
#         self.Y[index_individual] = self.func(x_new)
#         if self.Y[index_individual] < self.best_Y:
#             self.best_X = self.X[index_individual, :]
#
#     def prey(self, index_individual):
#         for try_num in range(self.max_try_num):
#             r = 2 * np.random.rand(self.n_dim) - 1
#             x_target = self.X[index_individual, :] + self.visual * r
#             if self.func(x_target) < self.Y[index_individual]:  # 捕食成功
#                 print('prey',index_individual)
#                 self.move_to_target(index_individual, x_target)
#                 return None
#         # 捕食 max_try_num 次后仍不成功，就调用 move 算子
#         self.move(index_individual)
#
#     def find_individual_in_vision(self, index_individual):
#         # 找出 index_individual 这个个体视线范围内的所有鱼
#         distances = spatial.distance.cdist(self.X[[index_individual], :], self.X, metric='euclidean').reshape(-1)
#         index_individual_in_vision = np.argwhere((distances > 0) & (distances < self.visual))[:, 0]
#         return index_individual_in_vision
#
#     def swarm(self, index_individual):
#         index_individual_in_vision = self.find_individual_in_vision(index_individual)
#         num_index_individual_in_vision = len(index_individual_in_vision)
#         if num_index_individual_in_vision > 0:
#             individual_in_vision = self.X[index_individual_in_vision, :]
#             center_individual_in_vision = individual_in_vision.mean(axis=0)
#             center_y_in_vision = self.func(center_individual_in_vision)
#             if center_y_in_vision * num_index_individual_in_vision < self.delta * self.Y[index_individual]:
#                 self.move_to_target(index_individual, center_individual_in_vision)
#                 return None
#         self.prey(index_individual)
#
#     def follow(self, index_individual):
#         index_individual_in_vision = self.find_individual_in_vision(index_individual)
#         num_index_individual_in_vision = len(index_individual_in_vision)
#         if num_index_individual_in_vision > 0:
#             individual_in_vision = self.X[index_individual_in_vision, :]
#             y_in_vision = np.array([self.func(x) for x in individual_in_vision])
#             index_target = y_in_vision.argmax()
#             x_target = individual_in_vision[index_target]
#             y_target = y_in_vision[index_target]
#             if y_target * num_index_individual_in_vision < self.delta * self.Y[index_individual]:
#                 self.move_to_target(index_individual, x_target)
#                 return None
#         self.prey(index_individual)
#
#     def fit(self):
#         for epoch in range(self.max_iter):
#             for index_individual in range(self.size_pop):
#                 self.swarm(index_individual)
#                 self.follow(index_individual)
#         return self.best_X, self.best_Y

# %%
class AFSA:
    def __init__(self, func, n_dim, size_pop=50, max_iter=300,
                 max_try_num=100, step=0.5, visual=0.3,
                 q=0.98, delta=0.5):
        self.func = func
        self.n_dim = n_dim
        self.size_pop = size_pop
        self.max_iter = max_iter
        self.max_try_num = max_try_num  # 最大尝试捕食次数
        self.step = step  # 每一步的最大位移比例
        self.visual = visual  # 鱼的最大感知范围
        self.q = q  # 鱼的感知范围衰减系数
        self.delta = delta  # 拥挤度阈值，越大越容易聚群和追尾

        self.X = np.random.rand(self.size_pop, self.n_dim)
        self.Y = np.array([self.func(x) for x in self.X])

        best_idx = self.Y.argmin()
        self.best_Y = self.Y[best_idx]
        self.best_X = self.X[best_idx, :]

    def move_to_target(self, idx_individual, x_target):
        '''
        move to target
        called by prey(), swarm(), follow()

        :param idx_individual:
        :param x_target:
        :return:
        '''
        x = self.X[idx_individual, :]
        x_new = x + self.step * np.random.rand() * (x_target - x)
        # x_new = x_target
        self.X[idx_individual, :] = x_new
        self.Y[idx_individual] = self.func(x_new)
        if self.Y[idx_individual] < self.best_Y:
            self.best_X = self.X[idx_individual, :].copy()
            self.best_Y = self.Y[idx_individual].copy()

    def move(self, idx_individual):
        '''
        randomly move to a point

        :param idx_individual:
        :return:
        '''
        r = 2 * np.random.rand(self.n_dim) - 1
        x_new = self.X[idx_individual, :] + self.visual * r
        self.X[idx_individual, :] = x_new
        self.Y[idx_individual] = self.func(x_new)
        if self.Y[idx_individual] < self.best_Y:
            self.best_X = self.X[idx_individual, :].copy()
            self.best_Y = self.Y[idx_individual].copy()

    def prey(self, idx_individual):
        '''
        prey
        :param idx_individual:
        :return:
        '''
        for try_num in range(self.max_try_num):
            r = 2 * np.random.rand(self.n_dim) - 1
            x_target = self.X[idx_individual, :] + self.visual * r
            if self.func(x_target) < self.Y[idx_individual]:  # 捕食成功
                self.move_to_target(idx_individual, x_target)
                return None
        # 捕食 max_try_num 次后仍不成功，就调用 move 算子
        self.move(idx_individual)

    def find_individual_in_vision(self, idx_individual):
        # 找出 idx_individual 这条鱼视线范围内的所有鱼
        distances = spatial.distance.cdist(self.X[[idx_individual], :], self.X, metric='euclidean').reshape(-1)
        idx_individual_in_vision = np.argwhere((distances > 0) & (distances < self.visual))[:, 0]
        return idx_individual_in_vision

    def swarm(self, idx_individual):
        # 聚群行为
        idx_individual_in_vision = self.find_individual_in_vision(idx_individual)
        num_idx_individual_in_vision = len(idx_individual_in_vision)
        if num_idx_individual_in_vision > 0:
            individual_in_vision = self.X[idx_individual_in_vision, :]
            center_individual_in_vision = individual_in_vision.mean(axis=0)
            center_y_in_vision = self.func(center_individual_in_vision)
            if center_y_in_vision * num_idx_individual_in_vision < self.delta * self.Y[idx_individual]:
                self.move_to_target(idx_individual, center_individual_in_vision)
                return None
        self.prey(idx_individual)

    def follow(self, idx_individual):
        # 追尾行为
        idx_individual_in_vision = self.find_individual_in_vision(idx_individual)
        num_idx_individual_in_vision = len(idx_individual_in_vision)
        if num_idx_individual_in_vision > 0:
            individual_in_vision = self.X[idx_individual_in_vision, :]
            y_in_vision = np.array([self.func(x) for x in individual_in_vision])
            idx_target = y_in_vision.argmin()
            x_target = individual_in_vision[idx_target]
            y_target = y_in_vision[idx_target]
            if y_target * num_idx_individual_in_vision < self.delta * self.Y[idx_individual]:
                self.move_to_target(idx_individual, x_target)
                return None
        self.prey(idx_individual)

    def run(self, max_iter=None):
        self.max_iter = max_iter or self.max_iter
        for epoch in range(self.max_iter):
            for idx_individual in range(self.size_pop):
                self.swarm(idx_individual)
                self.follow(idx_individual)
            self.visual *= self.q
        return self.best_X, self.best_Y


","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators_gpu/mutation_gpu.py",".py","332","14","import torch


def mutation(self):
    '''
    mutation of 0/1 type chromosome
    faster than `self.Chrom = (mask + self.Chrom) % 2`
    :param self:
    :return:
    '''
    mask = (torch.rand(size=(self.size_pop, self.len_chrom), device=self.device) < self.prob_mut).type(torch.int8)
    self.Chrom ^= mask
    return self.Chrom
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators_gpu/selection_gpu.py",".py","632","19","import numpy as np


def selection_tournament_faster(self, tourn_size=3):
    '''
    Select the best individual among *tournsize* randomly chosen
    Same with `selection_tournament` but much faster using numpy
    individuals,
    :param self:
    :param tourn_size:
    :return:
    '''
    aspirants_idx = np.random.randint(self.size_pop, size=(self.size_pop, tourn_size))
    aspirants_values = self.FitV[aspirants_idx]
    winner = aspirants_values.argmax(axis=1)  # winner index in every team
    sel_index = [aspirants_idx[i, j] for i, j in enumerate(winner)]
    self.Chrom = self.Chrom[sel_index, :]
    return self.Chrom
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators_gpu/__init__.py",".py","0","0","","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators_gpu/crossover_gpu.py",".py","605","19","import numpy as np
import torch


def crossover_2point_bit(self):
    Chrom, size_pop, len_chrom = self.Chrom, self.size_pop, self.len_chrom
    half_size_pop = int(size_pop / 2)
    Chrom1, Chrom2 = Chrom[:half_size_pop], Chrom[half_size_pop:]
    mask = torch.zeros(size=(half_size_pop, len_chrom), dtype=torch.int8, device=self.device)
    for i in range(half_size_pop):
        n1, n2 = np.random.randint(0, self.len_chrom, 2)
        if n1 > n2:
            n1, n2 = n2, n1
        mask[i, n1:n2] = 1
    mask2 = (Chrom1 ^ Chrom2) & mask
    Chrom1 ^= mask2
    Chrom2 ^= mask2
    return self.Chrom
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators_gpu/ranking_gpu.py",".py","0","0","","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators/selection.py",".py","2112","66","import numpy as np
def selection_tournament(self, tourn_size=3):
    '''
    Select the best individual among *tournsize* randomly chosen
    individuals,
    :param self:
    :param tourn_size:
    :return:
    '''
    FitV = self.FitV
    sel_index = []
    for i in range(self.size_pop):
        # aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size)
        aspirants_index = np.random.randint(self.size_pop, size=tourn_size)
        sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
    self.Chrom = self.Chrom[sel_index, :]  # next generation
    return self.Chrom


def selection_tournament_faster(self, tourn_size=3):
    '''
    Select the best individual among *tournsize* randomly chosen
    Same with `selection_tournament` but much faster using numpy
    individuals,
    :param self:
    :param tourn_size:
    :return:
    '''
    aspirants_idx = np.random.randint(self.size_pop, size=(self.size_pop, tourn_size))
    aspirants_values = self.FitV[aspirants_idx]
    winner = aspirants_values.argmax(axis=1)  # winner index in every team
    sel_index = [aspirants_idx[i, j] for i, j in enumerate(winner)]
    self.Chrom = self.Chrom[sel_index, :]
    return self.Chrom


def selection_roulette_1(self):
    '''
    Select the next generation using roulette
    :param self:
    :return:
    '''
    FitV = self.FitV
    FitV = FitV - FitV.min() + 1e-10
    # the worst one should still has a chance to be selected
    sel_prob = FitV / FitV.sum()
    sel_index = np.random.choice(range(self.size_pop), size=self.size_pop, p=sel_prob)
    self.Chrom = self.Chrom[sel_index, :]
    return self.Chrom


def selection_roulette_2(self):
    '''
    Select the next generation using roulette
    :param self:
    :return:
    '''
    FitV = self.FitV
    FitV = (FitV - FitV.min()) / (FitV.max() - FitV.min() + 1e-10) + 0.2
    # the worst one should still has a chance to be selected
    sel_prob = FitV / FitV.sum()
    sel_index = np.random.choice(range(self.size_pop), size=self.size_pop, p=sel_prob)
    self.Chrom = self.Chrom[sel_index, :]
    return self.Chrom

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators/__init__.py",".py","0","0","","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators/ranking.py",".py","429","21","import numpy as np


def ranking(self):
    # GA select the biggest one, but we want to minimize func, so we put a negative here
    self.FitV = -self.Y


def ranking_linear(self):
    '''
    For more details see [Baker1985]_.

    :param self:
    :return:

    .. [Baker1985] Baker J E, ""Adaptive selection methods for genetic
    algorithms, 1985.
    '''
    self.FitV = np.argsort(np.argsort(-self.Y))
    return self.FitV
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators/crossover.py",".py","3328","88","import numpy as np

__all__ = ['crossover_1point', 'crossover_2point', 'crossover_2point_bit', 'crossover_pmx']


def crossover_1point(self):
    Chrom, size_pop, len_chrom = self.Chrom, self.size_pop, self.len_chrom
    for i in range(0, size_pop, 2):
        n = np.random.randint(0, self.len_chrom)
        # crossover at the point n
        seg1, seg2 = self.Chrom[i, n:].copy(), self.Chrom[i + 1, n:].copy()
        self.Chrom[i, n:], self.Chrom[i + 1, n:] = seg2, seg1
    return self.Chrom


def crossover_2point(self):
    Chrom, size_pop, len_chrom = self.Chrom, self.size_pop, self.len_chrom
    for i in range(0, size_pop, 2):
        n1, n2 = np.random.randint(0, self.len_chrom, 2)
        if n1 > n2:
            n1, n2 = n2, n1
        # crossover at the points n1 to n2
        seg1, seg2 = self.Chrom[i, n1:n2].copy(), self.Chrom[i + 1, n1:n2].copy()
        self.Chrom[i, n1:n2], self.Chrom[i + 1, n1:n2] = seg2, seg1
    return self.Chrom


def crossover_2point_bit(self):
    '''
    3 times faster than `crossover_2point`, but only use for 0/1 type of Chrom
    :param self:
    :return:
    '''
    Chrom, size_pop, len_chrom = self.Chrom, self.size_pop, self.len_chrom
    half_size_pop = int(size_pop / 2)
    Chrom1, Chrom2 = Chrom[:half_size_pop], Chrom[half_size_pop:]
    mask = np.zeros(shape=(half_size_pop, len_chrom), dtype=int)
    for i in range(half_size_pop):
        n1, n2 = np.random.randint(0, self.len_chrom, 2)
        if n1 > n2:
            n1, n2 = n2, n1
        mask[i, n1:n2] = 1
    mask2 = (Chrom1 ^ Chrom2) & mask
    Chrom1 ^= mask2
    Chrom2 ^= mask2
    return self.Chrom


# def crossover_rv_3(self):
#     Chrom, size_pop = self.Chrom, self.size_pop
#     i = np.random.randint(1, self.len_chrom)  # crossover at the point i
#     Chrom1 = np.concatenate([Chrom[::2, :i], Chrom[1::2, i:]], axis=1)
#     Chrom2 = np.concatenate([Chrom[1::2, :i], Chrom[0::2, i:]], axis=1)
#     self.Chrom = np.concatenate([Chrom1, Chrom2], axis=0)
#     return self.Chrom


def crossover_pmx(self):
    '''
    Executes a partially matched crossover (PMX) on Chrom.
    For more details see [Goldberg1985]_.

    :param self:
    :return:

    .. [Goldberg1985] Goldberg and Lingel, ""Alleles, loci, and the traveling
   salesman problem"", 1985.
    '''
    Chrom, size_pop, len_chrom = self.Chrom, self.size_pop, self.len_chrom
    for i in range(0, size_pop, 2):
        Chrom1, Chrom2 = self.Chrom[i], self.Chrom[i + 1]
        cxpoint1, cxpoint2 = np.random.randint(0, self.len_chrom - 1, 2)
        if cxpoint1 >= cxpoint2:
            cxpoint1, cxpoint2 = cxpoint2, cxpoint1 + 1
        # crossover at the point cxpoint1 to cxpoint2
        pos1_recorder = {value: idx for idx, value in enumerate(Chrom1)}
        pos2_recorder = {value: idx for idx, value in enumerate(Chrom2)}
        for j in range(cxpoint1, cxpoint2):
            value1, value2 = Chrom1[j], Chrom2[j]
            pos1, pos2 = pos1_recorder[value2], pos2_recorder[value1]
            Chrom1[j], Chrom1[pos1] = Chrom1[pos1], Chrom1[j]
            Chrom2[j], Chrom2[pos2] = Chrom2[pos2], Chrom2[j]
            pos1_recorder[value1], pos1_recorder[value2] = pos1, j
            pos2_recorder[value1], pos2_recorder[value2] = j, pos2

        self.Chrom[i], self.Chrom[i + 1] = Chrom1, Chrom2
    return self.Chrom
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/sko/operators/mutation.py",".py","2224","80","import numpy as np


def mutation(self):
    '''
    mutation of 0/1 type chromosome
    faster than `self.Chrom = (mask + self.Chrom) % 2`
    :param self:
    :return:
    '''
    #
    mask = (np.random.rand(self.size_pop, self.len_chrom) < self.prob_mut)
    self.Chrom ^= mask
    return self.Chrom


def mutation_TSP_1(self):
    '''
    every gene in every chromosome mutate
    :param self:
    :return:
    '''
    for i in range(self.size_pop):
        for j in range(self.n_dim):
            if np.random.rand() < self.prob_mut:
                n = np.random.randint(0, self.len_chrom, 1)
                self.Chrom[i, j], self.Chrom[i, n] = self.Chrom[i, n], self.Chrom[i, j]
    return self.Chrom


def swap(individual):
    n1, n2 = np.random.randint(0, individual.shape[0] - 1, 2)
    if n1 >= n2:
        n1, n2 = n2, n1 + 1
    individual[n1], individual[n2] = individual[n2], individual[n1]
    return individual


def reverse(individual):
    '''
    Reverse n1 to n2
    Also called `2-Opt`: removes two random edges, reconnecting them so they cross
    Karan Bhatia, ""Genetic Algorithms and the Traveling Salesman Problem"", 1994
    https://pdfs.semanticscholar.org/c5dd/3d8e97202f07f2e337a791c3bf81cd0bbb13.pdf
    '''
    n1, n2 = np.random.randint(0, individual.shape[0] - 1, 2)
    if n1 >= n2:
        n1, n2 = n2, n1 + 1
    individual[n1:n2] = individual[n1:n2][::-1]
    return individual


def transpose(individual):
    # randomly generate n1 < n2 < n3. Notice: not equal
    n1, n2, n3 = sorted(np.random.randint(0, individual.shape[0] - 2, 3))
    n2 += 1
    n3 += 2
    slice1, slice2, slice3, slice4 = individual[0:n1], individual[n1:n2], individual[n2:n3 + 1], individual[n3 + 1:]
    individual = np.concatenate([slice1, slice3, slice2, slice4])
    return individual


def mutation_reverse(self):
    '''
    Reverse
    :param self:
    :return:
    '''
    for i in range(self.size_pop):
        if np.random.rand() < self.prob_mut:
            self.Chrom[i] = reverse(self.Chrom[i])
    return self.Chrom


def mutation_swap(self):
    for i in range(self.size_pop):
        if np.random.rand() < self.prob_mut:
            self.Chrom[i] = swap(self.Chrom[i])
    return self.Chrom
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/examples/demo_pso.py",".py","537","23","def demo_func(x):
    x1, x2, x3 = x
    return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2


# %% Do PSO
from sko.PSO import PSO

pso = PSO(func=demo_func, dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5)
pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)

# %% Plot the result
import matplotlib.pyplot as plt

plt.plot(pso.gbest_y_hist)
plt.show()

# %% PSO without constraint:
pso = PSO(func=demo_func, dim=3)
fitness = pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/examples/demo_de.py",".py","622","34","'''
min f(x1, x2, x3) = x1^2 + x2^2 + x3^2
s.t.
    x1*x2 >= 1
    x1*x2 <= 5
    x2 + x3 = 1
    0 <= x1, x2, x3 <= 5
'''


def obj_func(p):
    x1, x2, x3 = p
    return x1 ** 2 + x2 ** 2 + x3 ** 2


constraint_eq = [
    lambda x: 1 - x[1] - x[2]
]

constraint_ueq = [
    lambda x: 1 - x[0] * x[1],
    lambda x: x[0] * x[1] - 5
]

# %% Do DifferentialEvolution
from sko.DE import DE

de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5],
        constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)

best_x, best_y = de.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/examples/obj_func_demo.py",".py","555","28","import numpy as np


def sphere(p):
    # Sphere函数
    out_put = 0
    for i in p:
        out_put += i ** 2
    return out_put


def schaffer(p):
    '''
    这个函数是二维的复杂函数，具有无数个极小值点
    在(0,0)处取的最值0
    这个函数具有强烈的震荡形态，所以很难找到全局最优质值
    :param p:
    :return:
    '''
    x1, x2 = p
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)


def myfunc(p):
    x = p
    return np.sin(x) + np.square(x)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/examples/demo_pso_ani.py",".py","1197","50","# Plot particle history as animation
import numpy as np
from sko.PSO import PSO


def demo_func(x):
    x1, x2 = x
    return x1 ** 2 + (x2 - 0.05) ** 2


pso = PSO(func=demo_func, dim=2, pop=20, max_iter=40, lb=[-1, -1], ub=[1, 1])
pso.record_mode = True
pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)

# %% Now Plot the animation
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

record_value = pso.record_value
X_list, V_list = record_value['X'], record_value['V']

fig, ax = plt.subplots(1, 1)
ax.set_title('title', loc='center')
line = ax.plot([], [], 'b.')

X_grid, Y_grid = np.meshgrid(np.linspace(-1.0, 1.0, 40), np.linspace(-1.0, 1.0, 40))
Z_grid = demo_func((X_grid, Y_grid))
ax.contour(X_grid, Y_grid, Z_grid, 20)

ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)

plt.ion()
p = plt.show()


def update_scatter(frame):
    i, j = frame // 10, frame % 10
    ax.set_title('iter = ' + str(i))
    X_tmp = X_list[i] + V_list[i] * j / 10.0
    plt.setp(line, 'xdata', X_tmp[:, 0], 'ydata', X_tmp[:, 1])
    return line


ani = FuncAnimation(fig, update_scatter, blit=True, interval=25, frames=300)
plt.show()

# ani.save('pso.gif', writer='pillow')
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/examples/vrp.py",".py","1889","62","import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt

num_customers = 17
num_vehicle = 5
num_points = 1 + num_customers
max_capacity = 5

customers_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points
depot_coordinate = np.array([[0.5, 0.5]])
points_coordinate = np.concatenate([depot_coordinate, customers_coordinate], axis=0)

distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')


def cal_total_distance(routine):
    '''The objective function. input routine, return total distance.
    cal_total_distance(np.arange(num_points))
    '''
    num_points, = routine.shape
    return distance_matrix[0, routine[0]] \
           + sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)]) \
           + distance_matrix[routine[-1], 0]


def constraint_capacity(routine):
    capacity = 0
    c = 0
    for i in routine:
        if i != 0:
            c += 1
        else:
            capacity = max(capacity, c + 1)
            c = 0
    capacity = max(capacity, c + 1)
    return capacity - max_capacity


# %%

from sko.GA import GA_TSP

ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_customers, size_pop=50, max_iter=500, prob_mut=1, )

# The index of customers range from 1 to num_customers:
ga_tsp.Chrom = np.concatenate([np.zeros(shape=(ga_tsp.size_pop, num_vehicle - 1), dtype=np.int), ga_tsp.Chrom + 1],
                              axis=1)
ga_tsp.has_constraint = True
ga_tsp.constraint_ueq = [constraint_capacity]
best_points, best_distance = ga_tsp.run()

# %%

fig, ax = plt.subplots(1, 2)
best_points_ = np.concatenate([[0], best_points, [0]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
ax[1].plot(ga_tsp.generation_best_Y)
plt.show()

","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/_navbar.md",".md","64","4","- Translations
  - [:uk: English](/en/)
  - [:cn: 中文](/zh/)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/_coverpage.md",".md","326","16","<!-- ![logo](_media/pic.jpg) -->

# scikit-opt

> Powerful Python module for Heuristic Algorithms

* Genetic Algorithm
* Particle Swarm Optimization
* Simulated Annealing
* Ant Colony Algorithm
* Immune Algorithm
* Artificial Fish Swarm Algorithm

[GitHub](https://github.com/guofei9987/scikit-opt/)
[Get Started](/en/README)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/_sidebar.md",".md","63","4","
* [English Document](docs/en.md)
* [中文文档](docs/zh.md)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/make_doc.py",".py","3331","100","# 不想用 Sphinx，也不像弄一堆静态html文件，所以自己写个咯


'''
需要从readme中解析出：
1. ""-> Demo code: [examples/demo_pso.py](examples/demo_pso.py)""
2. 三个```python为开头，三个 ``` 为结尾
3. 从py文件中读出文本，并替换
4. 前几行是求star，只在readme中出现


需要从py文件中解析出：
1. # %% 做断点后赋予index值，然后插入readme
'''
import os
import sys

import re


def search_code(py_file_name, section_idx):
    '''
    给定py文件名和section序号，返回一个list，内容是py文件中的code（markdown格式）
    :param py_file_name:
    :param section_idx:
    :return:
    '''
    with open('../' + py_file_name, encoding='utf-8', mode=""r"") as f:
        content = f.readlines()
    content_new, i, search_idx, idx_first_match = [], 0, 0, None
    while i < len(content) and search_idx <= section_idx:
        if content[i].startswith('# %%'):
            search_idx += 1
            i += 1  # 带井号百分号的那一行也跳过去，不要放到文档里面
        if search_idx < section_idx:
            pass
        elif search_idx == section_idx:
            idx_first_match = idx_first_match or i  # record first match line
            content_new.append(content[i])
        i += 1
    return [
               '-> Demo code: [{py_file_name}#s{section_idx}](https://github.com/guofei9987/scikit-opt/blob/master/{py_file_name}#L{idx_first_match})\n'.
                   format(py_file_name=py_file_name, section_idx=section_idx + 1, idx_first_match=idx_first_match),
               '```python\n'] \
           + content_new \
           + ['```\n']


# %%


def make_doc(origin_file):
    with open(origin_file, encoding='utf-8', mode=""r"") as f_readme:
        readme = f_readme.readlines()

    regex = re.compile('\[examples/[\w#.]+\]')
    readme_idx = 0
    readme_new = []
    while readme_idx < len(readme):
        readme_line = readme[readme_idx]
        if readme_line.startswith('-> Demo code: ['):
            # 找到中括号里面的内容，解析为文件名，section号
            py_file_name, section_idx = regex.findall(readme[readme_idx])[0][1:-1].split('#s')
            section_idx = int(section_idx) - 1

            print('插入代码: ', py_file_name, section_idx)
            content_new = search_code(py_file_name, section_idx)
            readme_new.extend(content_new)

            # 往下寻找第一个代码结束位置
            while readme[readme_idx] != '```\n':
                readme_idx += 1
        else:
            # 如果不需要插入代码，就用原本的内容
            readme_new.append(readme_line)

        readme_idx += 1
    return readme_new


# 主页 README 和 en/README
readme_new = make_doc(origin_file='../README.md')
with open('../README.md', encoding='utf-8', mode=""w"") as f_readme:
    f_readme.writelines(readme_new)

with open('en/README.md', encoding='utf-8', mode=""w"") as f_readme_en:
    f_readme_en.writelines(readme_new[20:])

docs = ['zh/README.md',
        'zh/more_ga.md', 'en/more_ga.md',
        'zh/more_pso.md', 'en/more_pso.md',
        'zh/more_sa.md', 'en/more_sa.md',
        ]
for i in docs:
    docs_new = make_doc(origin_file=i)
    with open(i, encoding='utf-8', mode=""w"") as f:
        f.writelines(docs_new)

sys.exit()
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/more_ga.md",".md","3695","82","
## genetic algorithm do integer programming

If you want some variables to be integer, then set the corresponding `precision` to an integer
For example, our objective function is `demo_func`. We want the variables to be integer interval 2, integer interval 1, float. We set `precision=[2, 1, 1e-7]`:
```python
from sko.GA import GA

demo_func = lambda x: (x[0] - 1) ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
ga = GA(func=demo_func, n_dim=3, max_iter=500, lb=[-1, -1, -1], ub=[5, 1, 1], precision=[2, 1, 1e-7])
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
```

Notice:
- If `precision` is an integer, the number of all possible value would better be $2^n$, in which case the performance is the best. It also works if the number is not $2^n$
<!-- - if `precision` is an integer, and the number of all possible value is not $2^n$, `GA` do these:
    1. Modify `ub` bigger, making the number of all possible value to be $2^n$
    2. Add an **unequal constraint**, and use penalty function to deal with it
    3. If your **equal constraint** `constraint_eq` 和 **unequal constraint** `constraint_ueq` is too much, the performance is not too good. you may want to manually deal with it. -->
- If `precision` is not an integer, but you still want this mode, manually deal with it. For example, your original `precision=0.5`, just make a new variable, multiplied by `2`



## How to fix start point and end point with GA for TSP
If it is not a cycle graph, no need to do this.

if your start point and end point is (0, 0) and (1, 1). Build up the object function :
- Start point and end point is not the input of the object function. If totally n+2 points including start and end points, the input is the n points.
- And build up the object function, which is the total distance, as actually they are.


```python
import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt

num_points = 20

points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points
start_point=[[0,0]]
end_point=[[1,1]]
points_coordinate=np.concatenate([points_coordinate,start_point,end_point])
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')


def cal_total_distance(routine):
    '''The objective function. input routine, return total distance.
    cal_total_distance(np.arange(num_points))
    '''
    num_points, = routine.shape
    routine = np.concatenate([[num_points], routine, [num_points+1]])
    return sum([distance_matrix[routine[i], routine[i + 1]] for i in range(num_points+2-1)])
```

And the same with others:
```python
from sko.GA import GA_TSP

ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1)
best_points, best_distance = ga_tsp.run()


fig, ax = plt.subplots(1, 2)
best_points_ = np.concatenate([[num_points],best_points, [num_points+1]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
ax[1].plot(ga_tsp.generation_best_Y)
plt.show()
```

![image](https://user-images.githubusercontent.com/19920283/83831463-0ac6a400-a71a-11ea-8692-beac5f465111.png)

For more information, click [here](https://github.com/guofei9987/scikit-opt/issues/58)

## How to set up starting point or initial population

- For `GA`, after `ga=GA(**params)`, use codes like `ga.Chrom = np.random.randint(0,2,size=(80,20))` to manually set the initial population.  
- For `DE`, set `de.X` to your initial X.  
- For `SA`, there is a parameter `x0`, which is the init point.
- For `PSO`, set `pso.X` to your initial X, and run `pso.cal_y(); pso.update_gbest(); pso.update_pbest()`
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/more_sa.md",".md","1926","60","## 3 types of Simulated Annealing
In the ‘fast’ schedule the updates are:
```
u ~ Uniform(0, 1, size = d)
y = sgn(u - 0.5) * T * ((1 + 1/T)**abs(2*u - 1) - 1.0)

xc = y * (upper - lower)
x_new = x_old + xc

c = n * exp(-n * quench)
T_new = T0 * exp(-c * k**quench)
```

In the ‘cauchy’ schedule the updates are:
```
u ~ Uniform(-pi/2, pi/2, size=d)
xc = learn_rate * T * tan(u)
x_new = x_old + xc

T_new = T0 / (1 + k)
```
In the ‘boltzmann’ schedule the updates are:
```
std = minimum(sqrt(T) * ones(d), (upper - lower) / (3*learn_rate))
y ~ Normal(0, std, size = d)
x_new = x_old + learn_rate * y

T_new = T0 / log(1 + k)
```
### Do Simulated Annealing
#### 1. Fast Simulated Annealing
-> Demo code: [examples/demo_sa.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L17)
```python
from sko.SA import SAFast

sa_fast = SAFast(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_fast.run()
print('Fast Simulated Annealing: best_x is ', sa_fast.best_x, 'best_y is ', sa_fast.best_y)

```
#### 2. Boltzmann Simulated Annealing
-> Demo code: [examples/demo_sa.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L24)
```python
from sko.SA import SABoltzmann

sa_boltzmann = SABoltzmann(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_boltzmann.run()
print('Boltzmann Simulated Annealing: best_x is ', sa_boltzmann.best_x, 'best_y is ', sa_fast.best_y)

```
#### 3. Cauchy Simulated Annealing
-> Demo code: [examples/demo_sa.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L31)
```python
from sko.SA import SACauchy

sa_cauchy = SACauchy(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_cauchy.run()
print('Cauchy Simulated Annealing: best_x is ', sa_cauchy.best_x, 'best_y is ', sa_cauchy.best_y)
```
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/curve_fitting.md",".md","1153","51","
## curve fitting using GA

Generate toy train datasets
```python
import numpy as np
import matplotlib.pyplot as plt
from sko.GA import GA

x_true = np.linspace(-1.2, 1.2, 30)
y_true = x_true ** 3 - x_true + 0.4 * np.random.rand(30)
plt.plot(x_true, y_true, 'o')
```
![ga_curve_fitting0](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_curve_fitting0.png?raw=true)


Make up residuals
```python
def f_fun(x, a, b, c, d):
    return a * x ** 3 + b * x ** 2 + c * x + d


def obj_fun(p):
    a, b, c, d = p
    residuals = np.square(f_fun(x_true, a, b, c, d) - y_true).sum()
    return residuals
```

Do GA
```python
ga = GA(func=obj_fun, n_dim=4, size_pop=100, max_iter=500,
        lb=[-2] * 4, ub=[2] * 4)

best_params, residuals = ga.run()
print('best_x:', best_params, '\n', 'best_y:', residuals)
```

Plot the fitting results
```python
y_predict = f_fun(x_true, *best_params)

fig, ax = plt.subplots()

ax.plot(x_true, y_true, 'o')
ax.plot(x_true, y_predict, '-')

plt.show()
```

![ga_curve_fitting1](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_curve_fitting1.png?raw=true)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/contributors.md",".md","381","11","## contributors

Thanks to those contributors:
- [Bowen Zhang](https://github.com/BalwynZhang)
<!-- - [YoshiPark](https://github.com/YoshiPark) -->
<!-- - [lensory](https://github.com/lensory) -->
<!-- - [growvv](https://github.com/growvv) -->
- [hranYin](https://github.com/hranYin)
<!-- - [MontyKL](https://github.com/MontyKL) -->
- [zhangcogito](https://github.com/zhangcogito)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/speed_up.md",".md","2044","62","## speed up objective function

### Vectorization calculation
If the objective function supports vectorization, it can run much faster.
The following `schaffer1` is an original objective function, `schaffer2` is the corresponding function that supports vectorization operations.  
`schaffer2.is_vector = True` is used to tell the algorithm that it supports vectorization operations, otherwise it is non-vectorized by default.  
As a result of the operation, the **time cost was reduced to 30%**  

```python
import numpy as np
import time


def schaffer1(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    '''
    x1, x2 = p
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)


def schaffer2(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    '''
    x1, x2 = p[:, 0], p[:, 1]
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)


schaffer2.is_vector = True
# %%
from sko.GA import GA

ga1 = GA(func=schaffer1, n_dim=2, size_pop=5000, max_iter=800, lb=[-1, -1], ub=[1, 1], precision=1e-7)
ga2 = GA(func=schaffer2, n_dim=2, size_pop=5000, max_iter=800, lb=[-1, -1], ub=[1, 1], precision=1e-7)

time_start = time.time()
best_x, best_y = ga1.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
print('time:', time.time() - time_start, ' seconds')

time_start = time.time()
best_x, best_y = ga2.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
print('time:', time.time() - time_start, ' seconds')
```
output:
>best_x: [-2.98023233e-08 -2.98023233e-08]  
 best_y: [1.77635684e-15]  
time: 88.32313132286072  seconds  


>best_x: [2.98023233e-08 2.98023233e-08]  
 best_y: [1.77635684e-15]  
time: 27.68204379081726  seconds  


`scikit-opt` still supports non-vectorization, because some functions are difficult to write as vectorization, and some functions are much less readable when vectorized.","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/more_pso.md",".md","1637","62","
## demonstrate PSO with animation

step1:do pso  
-> Demo code: [examples/demo_pso_ani.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py#L1)
```python
# Plot particle history as animation
import numpy as np
from sko.PSO import PSO


def demo_func(x):
    x1, x2 = x
    return x1 ** 2 + (x2 - 0.05) ** 2


pso = PSO(func=demo_func, dim=2, pop=20, max_iter=40, lb=[-1, -1], ub=[1, 1])
pso.record_mode = True
pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)

```

step2: plot animation  
-> Demo code: [examples/demo_pso_ani.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py#L16)
```python
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

record_value = pso.record_value
X_list, V_list = record_value['X'], record_value['V']

fig, ax = plt.subplots(1, 1)
ax.set_title('title', loc='center')
line = ax.plot([], [], 'b.')

X_grid, Y_grid = np.meshgrid(np.linspace(-1.0, 1.0, 40), np.linspace(-1.0, 1.0, 40))
Z_grid = demo_func((X_grid, Y_grid))
ax.contour(X_grid, Y_grid, Z_grid, 20)

ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)

plt.ion()
p = plt.show()


def update_scatter(frame):
    i, j = frame // 10, frame % 10
    ax.set_title('iter = ' + str(i))
    X_tmp = X_list[i] + V_list[i] * j / 10.0
    plt.setp(line, 'xdata', X_tmp[:, 0], 'ydata', X_tmp[:, 1])
    return line


ani = FuncAnimation(fig, update_scatter, blit=True, interval=25, frames=300)
plt.show()

# ani.save('pso.gif', writer='pillow')
```

![pso_ani](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/pso.gif?raw=true)  ","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/_coverpage.md",".md","351","17","<!-- ![logo](_media/pic.jpg) -->

# scikit-opt

> Powerful Python module for Heuristic Algorithms

* Differential Evolution
* Genetic Algorithm
* Particle Swarm Optimization
* Simulated Annealing
* Ant Colony Algorithm
* Immune Algorithm
* Artificial Fish Swarm Algorithm

[GitHub](https://github.com/guofei9987/scikit-opt/)
[Get Started](/en/README)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/en/_sidebar.md",".md","241","7","* [English Document](en/README.md)
* [More Genetic Algorithm](en/more_ga.md)
* [More Particle Swarm Optimization](en/more_pso.md)
* [More Simulated Annealing](en/more_sa.md)
* [Curve fiting](en/curve_fitting.md)
* [Speed Up](en/speed_up.md)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/more_ga.md",".md","3945","83","
## 遗传算法进行整数规划

在多维优化时，想让哪个变量限制为整数，就设定 `precision` 为 **整数** 即可。  
例如，我想让我的自定义函数 `demo_func` 的某些变量限制为整数+浮点数（分别是隔2个，隔1个，浮点数），那么就设定 `precision=[2, 1, 1e-7]`  
例子如下：
```python
from sko.GA import GA

demo_func = lambda x: (x[0] - 1) ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
ga = GA(func=demo_func, n_dim=3, max_iter=500, lb=[-1, -1, -1], ub=[5, 1, 1], precision=[2, 1, 1e-7])
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
```

说明：
- 当 `precision` 为整数时，对应的自变量会启用整数规划模式。
- 在整数规划模式下，变量的取值可能个数最好是 $2^n$，这样收敛速度快，效果好。
<!-- - 在整数规划模式下，如果某个变量的取值可能个数不是 $2^n$，`GA` 会做这些事：
    1. 调整 `ub`，使得可能取值扩展成 $2^n$ 个
    2. 增加一个 **不等式约束** `constraint_ueq`，并使用罚函数法来处理
    3. 如果你的 **等式约束** `constraint_eq` 和 **不等式约束** `constraint_ueq` 已经很多了，更加推荐先手动做调整，以规避可能个数不是 $2^n$这种情况，毕竟太多的约束会影响性能。 -->
- 如果 `precision` 不是整数（例如是0.5）,则不会进入整数规划模式，如果还想用这个模式，那么把对应自变量乘以2，这样 `precision` 就是整数了。

## 遗传TSP问题如何固定起点和终点？
固定起点和终点要求路径不闭合（因为如果路径是闭合的，固定与不固定结果实际上是一样的）  

假设你的起点和终点坐标指定为(0, 0) 和 (1, 1)，这样构建目标函数
- 起点和终点不参与优化。假设共有n+2个点，优化对象是中间n个点
- 目标函数（总距离）按实际去写。


```python
import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt

num_points = 20

points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points
start_point=[[0,0]]
end_point=[[1,1]]
points_coordinate=np.concatenate([points_coordinate,start_point,end_point])
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')


def cal_total_distance(routine):
    '''The objective function. input routine, return total distance.
    cal_total_distance(np.arange(num_points))
    '''
    num_points, = routine.shape
    # start_point,end_point 本身不参与优化。给一个固定的值，参与计算总路径
    routine = np.concatenate([[num_points], routine, [num_points+1]])
    return sum([distance_matrix[routine[i], routine[i + 1]] for i in range(num_points+2-1)])
```

正常运行并画图：
```python
from sko.GA import GA_TSP

ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1)
best_points, best_distance = ga_tsp.run()


fig, ax = plt.subplots(1, 2)
best_points_ = np.concatenate([[num_points],best_points, [num_points+1]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
ax[1].plot(ga_tsp.generation_best_Y)
plt.show()
```

![image](https://user-images.githubusercontent.com/19920283/83831463-0ac6a400-a71a-11ea-8692-beac5f465111.png)

更多说明，[这里](https://github.com/guofei9987/scikit-opt/issues/58)

## 如何设定初始点或初始种群

- 对于遗传算法 `GA`, 运行 `ga=GA(**params)` 生成模型后，赋值设定初始种群，例如 `ga.Chrom = np.random.randint(0,2,size=(80,20))`
- 对于差分进化算法 `DE`，设定 `de.X` 为初始 X.  
- 对于模拟退火算法 `SA`，入参 `x0` 就是初始点.
- 对于粒子群算法 `PSO`，手动赋值 `pso.X` 为初始 X, 然后执行 `pso.cal_y(); pso.update_gbest(); pso.update_pbest()` 来更新历史最优点
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/more_sa.md",".md","1857","62","## 3 types of Simulated Annealing
模拟退火有三种具体形式  
‘fast’:
```
u ~ Uniform(0, 1, size = d)
y = sgn(u - 0.5) * T * ((1 + 1/T)**abs(2*u - 1) - 1.0)

xc = y * (upper - lower)
x_new = x_old + xc

c = n * exp(-n * quench)
T_new = T0 * exp(-c * k**quench)
```

‘cauchy’:
```
u ~ Uniform(-pi/2, pi/2, size=d)
xc = learn_rate * T * tan(u)
x_new = x_old + xc

T_new = T0 / (1 + k)
```

‘boltzmann’:
```
std = minimum(sqrt(T) * ones(d), (upper - lower) / (3*learn_rate))
y ~ Normal(0, std, size = d)
x_new = x_old + learn_rate * y

T_new = T0 / log(1 + k)
```
### 代码示例
#### 1. Fast Simulated Annealing
-> Demo code: [examples/demo_sa.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L17)
```python
from sko.SA import SAFast

sa_fast = SAFast(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_fast.run()
print('Fast Simulated Annealing: best_x is ', sa_fast.best_x, 'best_y is ', sa_fast.best_y)

```
#### 2. Boltzmann Simulated Annealing
-> Demo code: [examples/demo_sa.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L24)
```python
from sko.SA import SABoltzmann

sa_boltzmann = SABoltzmann(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_boltzmann.run()
print('Boltzmann Simulated Annealing: best_x is ', sa_boltzmann.best_x, 'best_y is ', sa_fast.best_y)

```
#### 3. Cauchy Simulated Annealing
-> Demo code: [examples/demo_sa.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L31)
```python
from sko.SA import SACauchy

sa_cauchy = SACauchy(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_cauchy.run()
print('Cauchy Simulated Annealing: best_x is ', sa_cauchy.best_x, 'best_y is ', sa_cauchy.best_y)
```
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/curve_fitting.md",".md","1181","51","
## 使用遗传算法进行曲线拟合

随机生成训练数据
```python
import numpy as np
import matplotlib.pyplot as plt
from sko.GA import GA

x_true = np.linspace(-1.2, 1.2, 30)
y_true = x_true ** 3 - x_true + 0.4 * np.random.rand(30)
plt.plot(x_true, y_true, 'o')
```
![ga_curve_fitting0](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_curve_fitting0.png?raw=true)


构造残差
```python
def f_fun(x, a, b, c, d):
    return a * x ** 3 + b * x ** 2 + c * x + d


def obj_fun(p):
    a, b, c, d = p
    residuals = np.square(f_fun(x_true, a, b, c, d) - y_true).sum()
    return residuals
```

使用 scikit-opt 做最优化
```python
ga = GA(func=obj_fun, n_dim=4, size_pop=100, max_iter=500,
        lb=[-2] * 4, ub=[2] * 4)

best_params, residuals = ga.run()
print('best_x:', best_params, '\n', 'best_y:', residuals)
```

画出拟合效果图
```python
y_predict = f_fun(x_true, *best_params)

fig, ax = plt.subplots()

ax.plot(x_true, y_true, 'o')
ax.plot(x_true, y_predict, '-')

plt.show()
```

![ga_curve_fitting1](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_curve_fitting1.png?raw=true)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/speed_up.md",".md","5417","162","## 目标函数加速

### 矢量化计算
如果目标函数支持矢量化运算，那么运行速度可以大大加快。  
下面的 `schaffer1` 是普通的目标函数，`schaffer2` 是支持矢量化运算的目标函数，需要用`schaffer2.is_vector = True`来告诉算法它支持矢量化运算，否则默认是非矢量化的。  
从运行结果看，花费时间降低到30%
```python
import numpy as np
import time


def schaffer1(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    '''
    x1, x2 = p
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)


def schaffer2(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    '''
    x1, x2 = p[:, 0], p[:, 1]
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)


schaffer2.is_vector = True
# %%
from sko.GA import GA

ga1 = GA(func=schaffer1, n_dim=2, size_pop=5000, max_iter=800, lb=[-1, -1], ub=[1, 1], precision=1e-7)
ga2 = GA(func=schaffer2, n_dim=2, size_pop=5000, max_iter=800, lb=[-1, -1], ub=[1, 1], precision=1e-7)

time_start = time.time()
best_x, best_y = ga1.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
print('time:', time.time() - time_start, ' seconds')

time_start = time.time()
best_x, best_y = ga2.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
print('time:', time.time() - time_start, ' seconds')
```
output:
>best_x: [-2.98023233e-08 -2.98023233e-08]  
 best_y: [1.77635684e-15]  
time: 88.32313132286072  seconds  


>best_x: [2.98023233e-08 2.98023233e-08]  
 best_y: [1.77635684e-15]  
time: 27.68204379081726  seconds  


`scikit-opt` 仍然支持非矢量化计算，因为有些函数很难写成矢量化计算的形式，还有些函数强行写成矢量化形式后可读性会大大降低。

## 算子优化加速

主要手段是 **矢量化** 和 **逻辑化**  


对于可以矢量化的算子，`scikit-opt` 都尽量做了矢量化，并且默认调用矢量化的算子，且 **无须用户额外操作**。  

另外，考虑到有些算子矢量化后，代码可读性下降，因此矢量化前的算子也会保留，为用户进阶学习提供方便。  


### 0/1 基因的mutation
做一个mask，是一个与 `Chrom` 大小一致的0/1矩阵，如果值为1，那么对应位置进行变异（0变1或1变0）  
自然想到用整除2的方式进行  

```python
def mutation(self):
    # mutation of 0/1 type chromosome
    mask = (np.random.rand(self.size_pop, self.len_chrom) < self.prob_mut) * 1
    self.Chrom = (mask + self.Chrom) % 2
    return self.Chrom
```
如此就实现了一次性对整个种群所有基因变异的矢量化运算。用pycharm的profile功能试了一下，效果良好

再次改进。我还嫌求余数这一步速度慢，画一个真值表

|A|mask：是否变异|A变异后|
|--|--|--|
|1|0|1|
|0|0|0|
|1|1|0|
|0|1|1|

发现这就是一个 **异或**
```python
def mutation2(self):
    mask = (np.random.rand(self.size_pop, self.len_chrom) < self.prob_mut)
    self.Chrom ^= mask
    return self.Chrom
```
测试发现运行速度又快了1~3倍，与最原始的双层循环相比，**快了约20倍**。  



### 0/1基因的crossover
同样思路，试试crossover.
- mask同样，1表示对应点交叉，0表示对应点不交叉


做一个真值表，总共8种可能，发现其中只有2种可能基因有变化（等位基因一样时，交叉后的结果与交叉前一样）

|A基因|B基因|是否交叉|交叉后的A基因|交叉后的B基因|
|--|--|--|--|--|
|1|0|1|0|1|
|0|1|1|1|0|

可以用 `异或` 和 `且` 来表示是否变化的表达式: `mask = (A^B)&C`，然后可以计算了`A^=mask, B^=mask`

代码实现
```
def crossover_2point_bit(self):
    Chrom, size_pop, len_chrom = self.Chrom, self.size_pop, self.len_chrom
    Chrom1, Chrom2 = Chrom[::2], Chrom[1::2]
    mask = np.zeros(shape=(int(size_pop / 2),len_chrom),dtype=int)
    for i in range(int(size_pop / 2)):
        n1, n2 = np.random.randint(0, self.len_chrom, 2)
        if n1 > n2:
            n1, n2 = n2, n1
        mask[i, n1:n2] = 1
    mask2 = (Chrom1 ^ Chrom2) & mask
    Chrom1^=mask2
    Chrom2^=mask2
    Chrom[::2], Chrom[1::2]=Chrom1,Chrom2
    self.Chrom=Chrom
    return self.Chrom
```
测试结果，**效率提升约1倍**。


### 锦标赛选择算子selection_tournament
实战发现，selection_tournament 往往是最耗时的，几乎占用一半时间，因此需要优化。  
优化前的算法是遍历，每次选择一组进行锦标赛。但可以在二维array上一次性操作。
```python
def selection_tournament_faster(self, tourn_size=3):
    '''
    Select the best individual among *tournsize* randomly chosen
    Same with `selection_tournament` but much faster using numpy
    individuals,
    :param self:
    :param tourn_size:
    :return:
    '''
    aspirants_idx = np.random.randint(self.size_pop, size=(self.size_pop, tourn_size))
    aspirants_values = self.FitV[aspirants_idx]
    winner = aspirants_values.argmax(axis=1)  # winner index in every team
    sel_index = [aspirants_idx[i, j] for i, j in enumerate(winner)]
    self.Chrom = self.Chrom[sel_index, :]
    return self.Chrom
```

发现own time 和time 都降为原来的10%~15%，**效率提升了约9倍**
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/more_pso.md",".md","1628","62","
## 粒子群算法的动画展示

step1:做pso  
-> Demo code: [examples/demo_pso_ani.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py#L1)
```python
# Plot particle history as animation
import numpy as np
from sko.PSO import PSO


def demo_func(x):
    x1, x2 = x
    return x1 ** 2 + (x2 - 0.05) ** 2


pso = PSO(func=demo_func, dim=2, pop=20, max_iter=40, lb=[-1, -1], ub=[1, 1])
pso.record_mode = True
pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)

```

step2:画图  
-> Demo code: [examples/demo_pso_ani.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py#L16)
```python
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

record_value = pso.record_value
X_list, V_list = record_value['X'], record_value['V']

fig, ax = plt.subplots(1, 1)
ax.set_title('title', loc='center')
line = ax.plot([], [], 'b.')

X_grid, Y_grid = np.meshgrid(np.linspace(-1.0, 1.0, 40), np.linspace(-1.0, 1.0, 40))
Z_grid = demo_func((X_grid, Y_grid))
ax.contour(X_grid, Y_grid, Z_grid, 20)

ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)

plt.ion()
p = plt.show()


def update_scatter(frame):
    i, j = frame // 10, frame % 10
    ax.set_title('iter = ' + str(i))
    X_tmp = X_list[i] + V_list[i] * j / 10.0
    plt.setp(line, 'xdata', X_tmp[:, 0], 'ydata', X_tmp[:, 1])
    return line


ani = FuncAnimation(fig, update_scatter, blit=True, interval=25, frames=300)
plt.show()

# ani.save('pso.gif', writer='pillow')
```

![pso_ani](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/pso.gif?raw=true)  ","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/_coverpage.md",".md","298","17","<!-- ![logo](_media/pic.jpg) -->

# scikit-opt

> Powerful Python module for Heuristic Algorithms

* 差分进化算法
* 遗传算法
* 粒子群算法
* 模拟退火算法
* 蚁群算法
* 免疫优化算法
* 鱼群算法

[GitHub](https://github.com/guofei9987/scikit-opt/)
[开始](zh/README)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题四代码/scikitopt/docs/zh/_sidebar.md",".md","264","8","* [文档](zh/README.md)
* [入参说明](zh/args.md)
* [更多遗传算法](zh/more_ga.md)
* [更多粒子群算法](zh/more_pso.md)
* [更多模拟退火算法](zh/more_sa.md)
* [遗传算法做曲线拟合](zh/curve_fitting.md)
* [提升速度](zh/speed_up.md)
","Markdown"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/XGBoost.py",".py","2252","50","import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from xgboost.sklearn import XGBClassifier

gr = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')

Feature_1 = ['ATSm2', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SHBd', 'SsCH3', 'SaaO', 'minHBa', 'hmin', 'LipoaffinityIndex',
             'FMF', 'MDEC-23', 'MLFER_S', 'WPATH']

Feature_2 = ['apol', 'ATSc1', 'ATSm3', 'SCH-6', 'VCH-7', 'SP-6', 'SHBd', 'SHsOH', 'SHaaCH', 'minHBa',
             'maxsOH', 'ETA_dEpsilon_D', 'ETA_Shape_P', 'ETA_Shape_Y', 'ETA_BetaP_s', 'ETA_dBetaP']

Feature_3 = ['ATSc2', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SHBd', 'SHother', 'SsOH', 'minHBd', 'minHBa', 'minssCH2',
             'minaaCH', 'minaasC', 'maxHBd', 'maxwHBa', 'maxHBint8', 'maxHsOH', 'hmin', 'LipoaffinityIndex',
             'ETA_dEpsilon_B', 'ETA_Shape_Y',
             'ETA_EtaP_F', 'ETA_Eta_R_L', 'MDEO-11', 'WTPT-4']

Feature_4 = ['ATSc2', 'BCUTc-1l', 'BCUTp-1l', 'VCH-6', 'SC-5', 'SPC-6', 'VP-3', 'SHsOH', 'SdO', 'minHBa',
             'minHsOH', 'maxHother', 'maxdO', 'hmin', 'MAXDP2', 'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP_F_L',
             'MDEC-23', 'MLFER_A',
             'TopoPSA', 'WTPT-2', 'WTPT-4']

Feature_5 = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6', 'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin', 'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y',
             'ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E', 'WTPT-4']

feature_df = gr[Feature_2]
x = feature_df.values
# y_var = ['Caco-2', 'CYP3A4', 'hERG', 'hERG', 'MN']
y_var = ['CYP3A4']
for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.7)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

    # 拟合XGBoost模型
    model = XGBClassifier()
    model.fit(x_train, y_train)

    # 对测试集做预测
    y_pred = model.predict(x_test)
    predictions = [round(value) for value in y_pred]

    # 评估预测结果
    accuracy = accuracy_score(y_test, predictions)
    print(""Accuracy: %.2f%%"" % (accuracy * 100.0))
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/LigheGBM.py",".py","2086","62","import json
import lightgbm as lgb
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn import metrics

try:
    import cPickle as pickle
except BaseException:
    import pickle

gr = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')
feature = ['ATSm2', 'ATSm3', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SP-1', 'ECCEN', 'SHBd',
           'SsCH3', 'SaaO', 'minHBa', 'minaaO', 'maxaaO', 'hmin',
           'LipoaffinityIndex', 'ETA_Beta', 'ETA_Beta_s', 'ETA_Eta_R', 'ETA_Eta_F',
           'ETA_Eta_R_L', 'FMF', 'MDEC-12', 'MDEC-23', 'MLFER_S', 'MLFER_E',
           'MLFER_L', 'TopoPSA', 'MW', 'WTPT-1', 'WPATH']

feature_df = gr[feature]
x = feature_df.values

print(x)
# y_var = ['Caco-2', 'CYP3A4', 'hERG', 'hERG', 'MN']
y_var = ['Caco-2']
for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.7)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

    params = {
        'boosting_type': 'gbdt',
        'objective': 'multiclass',
        'num_class': 7,
        'metric': 'multi_error',
        'num_leaves': 120,
        'min_data_in_leaf': 100,
        'learning_rate': 0.06,
        'feature_fraction': 0.8,
        'bagging_fraction': 0.8,
        'bagging_freq': 5,
        'lambda_l1': 0.4,
        'lambda_l2': 0.5,
        'min_gain_to_split': 0.2,
        'verbose': -1,
    }
    print('Training...')
    trn_data = lgb.Dataset(x_train, y_train)
    val_data = lgb.Dataset(x_test, y_test)
    clf = lgb.train(params,
                    trn_data,
                    num_boost_round=1000,
                    valid_sets=[trn_data, val_data],
                    verbose_eval=100,
                    early_stopping_rounds=100)
    print('Predicting...')
    y_prob = clf.predict(x_test, num_iteration=clf.best_iteration)
    y_pred = [list(x).index(max(x)) for x in y_prob]
    print(""AUC score: {:<8.5f}"".format(metrics.accuracy_score(y_pred, y_test)))
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/vote.py",".py","3492","87","from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier
from sklearn.pipeline import make_pipeline
from xgboost.sklearn import XGBClassifier
from lightgbm.sklearn import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

import pandas as pd

gr = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')

Feature_1 = ['ATSm2', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SHBd', 'SsCH3', 'SaaO', 'minHBa', 'hmin', 'LipoaffinityIndex',
             'FMF', 'MDEC-23', 'MLFER_S', 'WPATH']

Feature_2 = ['apol', 'ATSc1', 'ATSm3', 'SCH-6', 'VCH-7', 'SP-6', 'SHBd', 'SHsOH', 'SHaaCH', 'minHBa',
             'maxsOH', 'ETA_dEpsilon_D', 'ETA_Shape_P', 'ETA_Shape_Y', 'ETA_BetaP_s', 'ETA_dBetaP']

Feature_3 = ['ATSc2', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SHBd', 'SHother', 'SsOH', 'minHBd', 'minHBa', 'minssCH2',
             'minaaCH', 'minaasC', 'maxHBd', 'maxwHBa', 'maxHBint8', 'maxHsOH', 'hmin', 'LipoaffinityIndex',
             'ETA_dEpsilon_B', 'ETA_Shape_Y',
             'ETA_EtaP_F', 'ETA_Eta_R_L', 'MDEO-11', 'WTPT-4']

Feature_4 = ['ATSc2', 'BCUTc-1l', 'BCUTp-1l', 'VCH-6', 'SC-5', 'SPC-6', 'VP-3', 'SHsOH', 'SdO', 'minHBa',
             'minHsOH', 'maxHother', 'maxdO', 'hmin', 'MAXDP2', 'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP_F_L',
             'MDEC-23', 'MLFER_A',
             'TopoPSA', 'WTPT-2', 'WTPT-4']

Feature_5 = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6', 'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin', 'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y',
             'ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E', 'WTPT-4']

feature_df = gr[Feature_5]

x = feature_df.values

# y_var = ['Caco-2', 'CYP3A4', 'hERG', 'HOB', 'MN']
y_var = ['MN']
for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.9)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

rf = RandomForestClassifier()
xgboost = XGBClassifier(eval_metric=['logloss', 'auc', 'error'], use_label_encoder=False)
lgbm = LGBMClassifier()

pipe1 = make_pipeline(StandardScaler(), rf)
pipe2 = make_pipeline(StandardScaler(), xgboost)
pipe3 = make_pipeline(StandardScaler(), lgbm)

models = [
    ('rf', pipe1),
    ('xgb', pipe2),
    ('lgbm', pipe3)
]

ensembel = VotingClassifier(estimators=models, voting='soft')

from sklearn.model_selection import cross_val_score

all_model = [pipe1, pipe2, pipe3, ensembel]
clf_labels = ['RandomForestClassifier', 'XGBClassifier', ""LGBMClassifier"", 'Ensemble']
for clf, label in zip(all_model, clf_labels):
    score = cross_val_score(estimator=clf,
                            X=x_train,
                            y=y_train,
                            cv=10,
                            scoring='roc_auc')
    print('roc_auc: %0.4f (+/- %0.2f) [%s]' % (score.mean(), score.std(), label))
    clf.fit(x_train, y_train)
    print(clf.score(x_test, y_test))

clf = ensembel

## 模型训练
y_pred = clf.fit(x_train, y_train).predict_proba(TT)[:, 1]
pre = y_pred.round()

print(clf.score(x_train, y_train))
print('训练集准确率：', accuracy_score(y_train, clf.predict(x_train)))
print(clf.score(x_test, y_test))
print('测试集准确率：', accuracy_score(y_test, clf.predict(x_test)))
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/SVM.py",".py","2275","47","import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

gr = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')
Feature_1 = ['ATSm2', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SHBd', 'SsCH3', 'SaaO', 'minHBa', 'hmin', 'LipoaffinityIndex',
             'FMF', 'MDEC-23', 'MLFER_S', 'WPATH']

Feature_2 = ['apol', 'ATSc1', 'ATSm3', 'SCH-6', 'VCH-7', 'SP-6', 'SHBd', 'SHsOH', 'SHaaCH', 'minHBa',
             'maxsOH', 'ETA_dEpsilon_D', 'ETA_Shape_P', 'ETA_Shape_Y', 'ETA_BetaP_s', 'ETA_dBetaP']

Feature_3 = ['ATSc2', 'BCUTc-1l', 'BCUTc-1h', 'BCUTp-1h', 'SHBd', 'SHother', 'SsOH', 'minHBd', 'minHBa', 'minssCH2',
             'minaaCH', 'minaasC', 'maxHBd', 'maxwHBa', 'maxHBint8', 'maxHsOH', 'hmin', 'LipoaffinityIndex',
             'ETA_dEpsilon_B', 'ETA_Shape_Y',
             'ETA_EtaP_F', 'ETA_Eta_R_L', 'MDEO-11', 'WTPT-4']

Feature_4 = ['ATSc2', 'BCUTc-1l', 'BCUTp-1l', 'VCH-6', 'SC-5', 'SPC-6', 'VP-3', 'SHsOH', 'SdO', 'minHBa',
             'minHsOH', 'maxHother', 'maxdO', 'hmin', 'MAXDP2', 'ETA_dEpsilon_B', 'ETA_Shape_Y', 'ETA_EtaP_F_L',
             'MDEC-23', 'MLFER_A',
             'TopoPSA', 'WTPT-2', 'WTPT-4']

Feature_5 = ['nN', 'ATSc2', 'SCH-7', 'VPC-5', 'SP-6', 'SHaaCH', 'SssCH2', 'SsssCH', 'SssO', 'minHBa',
             'mindssC', 'maxsCH3', 'maxsssCH', 'maxssO', 'hmin', 'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_Shape_Y',
             'ETA_BetaP', 'ETA_BetaP_s',
             'ETA_EtaP_F', 'ETA_EtaP_B_RC', 'FMF', 'nHBAcc', 'MLFER_E', 'WTPT-4']

feature_df = gr[Feature_2]
x = feature_df.values
# y_var = ['Caco-2', 'CYP3A4', 'hERG', 'HOB', 'MN']
y_var = ['CYP3A4']
for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.8)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

    clf = SVC(C=30, kernel='rbf', decision_function_shape='ovo', max_iter=1000)

    ## 模型训练
    clf.fit(x_train, y_train)

    print(clf.score(x_train, y_train))
    print('训练集准确率：', accuracy_score(y_train, clf.predict(x_train)))
    print(clf.score(x_test, y_test))
    print('测试集准确率：', accuracy_score(y_test, clf.predict(x_test)))
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/Logistic.py",".py","1177","34","from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd

gr = pd.read_csv('./data.csv', index_col=0, encoding='gb18030')
# print(gr)
feature = ['ATSm2', 'ATSm3', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SP-1', 'ECCEN', 'SHBd',
           'SsCH3', 'SaaO', 'minHBa', 'minaaO', 'maxaaO', 'hmin',
           'LipoaffinityIndex', 'ETA_Beta', 'ETA_Beta_s', 'ETA_Eta_R', 'ETA_Eta_F',
           'ETA_Eta_R_L', 'FMF', 'MDEC-12', 'MDEC-23', 'MLFER_S', 'MLFER_E',
           'MLFER_L', 'TopoPSA', 'MW', 'WTPT-1', 'WPATH']

feature_df = gr[feature]
x = feature_df.values

print(x)
# y_var = ['Caco-2', 'CYP3A4', 'hERG', 'hERG', 'MN']
y_var = ['Caco-2']
for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.7)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

clf = LogisticRegression()
clf = clf.fit(x_train, y_train)

y_predicted = clf.predict(x_test)
accuracy = np.mean(y_predicted == y_test) * 100
print(""y_test\n"", y_test)
print(""y_predicted\n"", y_predicted)
print(""accuracy:"", accuracy)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/RF.py",".py","1305","38","from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd

gr = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')
# print(gr)
feature = ['ATSm2', 'ATSm3', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SP-1', 'ECCEN', 'SHBd',
           'SsCH3', 'SaaO', 'minHBa', 'minaaO', 'maxaaO', 'hmin',
           'LipoaffinityIndex', 'ETA_Beta', 'ETA_Beta_s', 'ETA_Eta_R', 'ETA_Eta_F',
           'ETA_Eta_R_L', 'FMF', 'MDEC-12', 'MDEC-23', 'MLFER_S', 'MLFER_E',
           'MLFER_L', 'TopoPSA', 'MW', 'WTPT-1', 'WPATH']

feature_df = gr[feature]

x = feature_df.values
# x = gr.iloc[:, 6:].values

print(x)
# y_var = ['Caco-2', 'CYP3A4', 'hERG', 'hERG', 'MN']
y_var = ['Caco-2']
for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.7)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)

clf = RandomForestClassifier()
clf = clf.fit(x_train, y_train)

y_predicted = clf.predict(x_test)
accuracy = np.mean(y_predicted == y_test) * 100
print(""y_test\n"", y_test)
print(""y_predicted\n"", y_predicted)
print(""accuracy:"", accuracy)
","Python"
"ADMET","Bighhhzq/Mathematical-modeling","问题三代码/BPnetwork.py",".py","3203","110","import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.metrics import recall_score
from sklearn import metrics
import seaborn as sns
from sklearn.model_selection import train_test_split
import torch
import torch.nn.functional as Fun


# defeine BP neural network
class Net1(torch.nn.Module):
    def __init__(self, n_feature, n_output=2):
        super(Net1, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, 50)
        self.out = torch.nn.Linear(50, n_output)

    def forward(self, x):
        x = Fun.relu(self.hidden(x))
        x = self.out(x)
        return x


class Net2(torch.nn.Module):
    def __init__(self, n_feature=729, n_output=2):
        super(Net2, self).__init__()
        self.hidden1 = torch.nn.Linear(n_feature, 1000)
        self.hidden2 = torch.nn.Linear(1000, 200)
        self.out = torch.nn.Linear(200, n_output)

    def forward(self, x):
        x = Fun.relu(self.hidden1(x))
        x = Fun.relu(self.hidden2(x))
        x = self.out(x)
        return x
def printreport(exp, pred):
    print(classification_report(exp, pred))
    print(""recall score"")
    print(recall_score(exp, pred, average='macro'))


gr = pd.read_csv('./clean451.csv', index_col=0, encoding='gb18030')

feature = ['ATSm2', 'ATSm3', 'BCUTc-1h', 'SCH-6', 'VC-5', 'SP-1', 'ECCEN', 'SHBd',
       'SsCH3', 'SaaO', 'minHBa', 'minaaO', 'maxaaO', 'hmin',
       'LipoaffinityIndex', 'ETA_Beta', 'ETA_Beta_s', 'ETA_Eta_R', 'ETA_Eta_F',
       'ETA_Eta_R_L', 'FMF', 'MDEC-12', 'MDEC-23', 'MLFER_S', 'MLFER_E',
       'MLFER_L', 'TopoPSA', 'MW', 'WTPT-1', 'WPATH']

feature_df = gr[feature]

x = feature_df.values


print(x)
y_var = ['Caco-2', 'CYP3A4', 'hERG', 'hERG', 'MN']

for v in y_var:
    y = gr[v]

    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.7)
    print('训练集和测试集 shape', x_train.shape, y_train.shape, x_test.shape, y_test.shape)
    scaler = StandardScaler()
    x = scaler.fit_transform(x_train)

    input = torch.FloatTensor(x)
    label = torch.LongTensor(y_train)

    net = Net1(n_feature=30, n_output=2)
    optimizer = torch.optim.SGD(net.parameters(), lr=0.05)
    # SGD: random gradient decend
    loss_func = torch.nn.CrossEntropyLoss()
    # define loss function

    for i in range(100):
        out = net(input)

        loss = loss_func(out, label)
        optimizer.zero_grad()
        # initialize
        loss.backward()
        optimizer.step()

    x = scaler.fit_transform(x_test)

    input = torch.FloatTensor(x)
    label = torch.Tensor(y_test.to_numpy())

    out = net(input)

    prediction = torch.max(out, 1)[1]
    pred_y = prediction.numpy()
    target_y = label.data.numpy()

    s = accuracy_score(target_y, pred_y)
    print('accury')
    print(s)

    cm = confusion_matrix(target_y, pred_y)
    printreport(target_y, pred_y)

    f, ax = plt.subplots(figsize=(5, 5))
    sns.heatmap(cm, annot=True, linewidths=0.5, linecolor=""red"", fmt="".0f"", ax=ax)
    plt.xlabel(""y_pred"")
    plt.ylabel(""y_true"")
    plt.show()
","Python"