diff --git "a/data/dataset_ADMET.csv" "b/data/dataset_ADMET.csv" new file mode 100644--- /dev/null +++ "b/data/dataset_ADMET.csv" @@ -0,0 +1,16598 @@ +"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) + # 제거 + JSON 파싱 + try: + out_clean = re.sub(r"".*?"", """", 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://:30002/v1/ +# export GPT_API_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"".*?"", """", 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"".*?"", """", 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"": [ + ""\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���：\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"": [ + """" + ] + }, + ""execution_count"": 88, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": ""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\n"", + ""text/plain"": [ + ""
"" + ] + }, + ""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"": [ + ""
\n"", + ""\n"", + ""\n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + ""
srank
00.0001000
10.0032811
20.0022932
30.0086693
40.0022434
.........
4990.004232499
5000.001057500
5010.000643501
5020.003805502
5030.000486503
\n"", + ""

504 rows × 2 columns

\n"", + ""
"" + ], + ""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"": [ + ""
\n"", + ""\n"", + ""\n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + ""
MDEC-23LipoaffinityIndexMLogPnCmaxHsOHminsssNminHsOHBCUTc-1lmaxssOSHsOH...ATSp1WTPT-4CrippenLogPnsOHXLogPVCH-5ETA_BetaP_sminHBint6nHsOHmindO
MDEC-231.0000000.7081340.8322410.7758070.2058940.4868810.169654-0.2768850.4087430.159171...0.6941430.2840290.6260550.2190970.3728160.1069460.008916-0.1730590.219097-0.067162
LipoaffinityIndex0.7081341.0000000.8910290.7745220.0246700.5748880.025091-0.1226880.322083-0.159268...0.622074-0.1148750.781812-0.0765780.7194010.012981-0.162217-0.276929-0.076578-0.251658
MLogP0.8322410.8910291.0000000.9094340.0851570.5293840.061318-0.2550740.399374-0.021842...0.7742210.1583760.6894030.0901240.6376010.065126-0.201593-0.2331640.090124-0.137389
nC0.7758070.7745220.9094341.0000000.0027100.533325-0.024062-0.1770450.443980-0.056746...0.9319810.3913000.5392110.0350980.4723180.1112040.000823-0.2215410.035098-0.010533
maxHsOH0.2058940.0246700.0851570.0027101.0000000.0522350.950769-0.6528150.0548970.836405...-0.0924850.2061400.1351000.7884050.038018-0.290302-0.2807060.2395230.788405-0.203484
minsssN0.4868810.5748880.5293840.5333250.0522351.0000000.088362-0.1531560.353748-0.044227...0.4937950.1014510.3322080.0005380.0952170.0315780.262811-0.1667630.000538-0.144182
minHsOH0.1696540.0250910.061318-0.0240620.9507690.0883621.000000-0.6021090.0894610.749172...-0.1298340.1634280.1220110.6791940.013169-0.306239-0.2349010.2262460.679194-0.233683
BCUTc-1l-0.276885-0.122688-0.255074-0.177045-0.652815-0.153156-0.6021091.000000-0.247030-0.557629...-0.097724-0.322451-0.121123-0.631155-0.1105960.1211100.189810-0.106740-0.6311550.193009
maxssO0.4087430.3220830.3993740.4439800.0548970.3537480.089461-0.2470301.0000000.022689...0.3627020.5513420.3388020.0514480.1106520.0741080.1813660.0776430.051448-0.022380
SHsOH0.159171-0.159268-0.021842-0.0567460.836405-0.0442270.749172-0.5576290.0226891.000000...-0.1186070.3376850.0200240.958125-0.101380-0.237750-0.1610970.2246300.958125-0.189570
nHBAcc-0.115243-0.116825-0.0263720.321631-0.4108270.012518-0.4256830.294564-0.068137-0.365373...0.4526460.291217-0.379953-0.374522-0.2479590.1290770.317543-0.073994-0.3745220.379997
MLFER_A0.166126-0.0828290.1251600.2280470.622245-0.0065220.546420-0.4381640.0130900.765262...0.2095750.420918-0.1070850.771052-0.079694-0.173470-0.1363290.1342670.771052-0.147409
maxsOH0.2892000.1638030.2513980.1325520.9102470.1516270.879282-0.7520410.1245900.727513...0.0509000.1781550.2290580.7829030.166541-0.205403-0.3084850.1534630.782903-0.300017
maxsssN0.4885640.5730180.5287650.5371490.0390040.9956180.076448-0.1458050.347401-0.055008...0.5018130.1017170.322519-0.0101170.0835300.0351900.278328-0.171093-0.010117-0.126261
SsOH0.231639-0.0579450.1138810.0568190.7682050.0145260.657183-0.6365090.0638030.938708...0.0123890.3435750.0955280.9979170.006657-0.162398-0.2018220.1569900.997917-0.241518
minsOH0.2809810.1707200.2514950.1292410.8942240.1640040.888996-0.7430640.1327120.696856...0.0452580.1613430.2292660.7515240.167149-0.205124-0.3005430.1455550.751524-0.312523
ATSp50.7127860.5983220.7646070.890748-0.0519110.445211-0.102746-0.1335740.318112-0.079830...0.9619240.3909700.3941810.0284220.3319270.1919470.038456-0.2241710.0284220.102335
minssO0.3849340.3128420.3866890.4267390.0534580.3436090.087479-0.2439430.9963000.020178...0.3462190.5236430.3291760.0491540.1102490.0762970.1714780.0856310.049154-0.020341
C1SP2-0.290392-0.310835-0.2649770.024759-0.400968-0.186569-0.4251270.363890-0.122003-0.338735...0.1283030.218757-0.410136-0.385877-0.2990030.1641790.3738310.072341-0.3858770.370528
AMR0.7236190.6892390.8083090.967694-0.0302520.547973-0.048746-0.1053780.449118-0.064893...0.9497900.4363800.4856340.0036160.3491980.1408620.106999-0.2286730.0036160.024442
minHBint5-0.061913-0.130684-0.099787-0.0833520.0862050.0149980.097856-0.0713230.1051620.171641...-0.1097570.163165-0.0749480.152546-0.1454660.0112870.1186860.0481930.152546-0.086103
ATSc3-0.226781-0.223062-0.271654-0.199347-0.150678-0.224177-0.1560770.257995-0.203159-0.156019...-0.157213-0.021094-0.201042-0.209639-0.188790-0.0813010.1114170.160975-0.2096390.352451
SwHBa0.7310940.6875950.6920990.5886460.0674260.4539910.090351-0.0358610.295526-0.024376...0.503657-0.0393880.662920-0.0110970.3609650.085600-0.018843-0.259954-0.011097-0.198502
maxHBd-0.014119-0.187608-0.154935-0.1360690.721514-0.1337370.663014-0.310166-0.1004400.631734...-0.1718120.143665-0.1082210.538416-0.137380-0.210528-0.1764650.2574260.538416-0.015405
VABC0.5894100.6170230.7594190.949198-0.0750330.471523-0.102974-0.1202530.400505-0.112855...0.9342460.4671630.358693-0.0265060.3733840.1085630.024161-0.210039-0.0265060.069772
hmin-0.616663-0.533128-0.637585-0.689425-0.088130-0.563093-0.0830800.193115-0.675410-0.057936...-0.669033-0.472912-0.479257-0.123255-0.206750-0.109716-0.1507550.085074-0.1232550.113480
fragC0.2856820.3654740.5287810.748073-0.0868570.278194-0.099360-0.0852710.199592-0.113395...0.7482520.3689830.063015-0.0459340.2349540.052730-0.010321-0.162272-0.0459340.002340
BCUTp-1h0.5254350.5124770.5744280.5381510.0039000.397089-0.036670-0.1321320.166788-0.085406...0.6704230.0190740.4384960.0233190.4143210.246732-0.258448-0.2895310.0233190.019658
maxHBint5-0.124779-0.208549-0.125077-0.0232720.046115-0.0016240.062790-0.0569240.0848120.135971...-0.0219600.272807-0.2116620.120569-0.2263610.0003190.1839050.1582930.1205690.041090
mindssC0.1474250.1720140.2325060.090728-0.1372960.090915-0.053652-0.0992790.017604-0.112253...0.093131-0.2238750.1684890.0025040.2291380.126108-0.131047-0.2644630.002504-0.465916
VC-50.1310630.2020640.2788400.223036-0.0782500.040098-0.191246-0.206466-0.087500-0.109189...0.361399-0.0630040.1045830.0799950.2826220.275637-0.235056-0.1625660.079995-0.010509
BCUTc-1h-0.097087-0.208878-0.272630-0.086032-0.062248-0.165105-0.1027800.1930790.027378-0.095526...-0.0302340.296246-0.184397-0.191934-0.213073-0.0370770.2416100.229725-0.1919340.636476
ATSc2-0.167199-0.090819-0.143717-0.4683190.220636-0.2648780.213568-0.073173-0.4124070.220676...-0.498416-0.6251190.1007540.2455500.191403-0.056351-0.499061-0.0463670.245550-0.302160
ATSc40.1606130.3228140.3298220.396503-0.0607000.352520-0.020309-0.0994850.207325-0.164863...0.3783820.0554200.093587-0.1267730.1759060.089859-0.030249-0.177880-0.126773-0.256011
MDEO-120.172243-0.1615930.0121400.1327250.060789-0.0752400.056472-0.1334330.6007640.175149...0.1153740.779107-0.0152590.161306-0.2064180.0284180.2667840.2688840.1613060.270608
SPC-60.4880910.3294330.4974400.581181-0.0359900.185594-0.116853-0.1566210.127720-0.025093...0.6936020.3257060.1682670.1034910.1801450.1763880.070683-0.0655660.1034910.221824
SdssC0.2135340.1397240.167069-0.059014-0.1184020.039634-0.065015-0.0040680.009531-0.120382...-0.054084-0.2950650.282719-0.0648070.2454060.131698-0.179050-0.183485-0.064807-0.127867
SHBint6-0.234439-0.273420-0.1249300.1403280.030868-0.0882800.032283-0.004815-0.0467780.053941...0.2082870.378464-0.4358050.027695-0.215712-0.1400780.0408330.3094430.0276950.217537
SHBint5-0.167111-0.217035-0.0452570.195846-0.025221-0.050944-0.021221-0.010029-0.0075810.066222...0.2352480.385145-0.3805000.064245-0.201355-0.0293100.1176300.1050240.0642450.107781
MDEC-330.5577560.1533300.3439770.3702830.1584620.1147670.123240-0.1887070.4146660.255526...0.3763340.4851600.2767910.2938040.0098970.0843920.2103820.1837300.2938040.138932
minHBint100.1943410.0985030.1194920.1116640.3684600.0807570.358728-0.2631910.0691120.441076...0.0467880.1329200.1353560.4324290.042906-0.021826-0.0499090.0002270.432429-0.241175
ndssC0.047906-0.1736890.0573960.277123-0.280901-0.119243-0.2864530.1817610.039641-0.244741...0.3573430.394358-0.322745-0.262824-0.0962380.0705400.0677970.053910-0.2628240.500115
C3SP20.5836300.3822320.5006530.3919870.2396660.0318810.209826-0.1285600.1725040.179739...0.2897010.1024100.3390900.1825150.325926-0.113974-0.2960270.0701930.1825150.047750
WTPT-5-0.1398870.009337-0.0240950.276868-0.4524320.164648-0.4126460.394864-0.129923-0.414500...0.369428-0.001536-0.270670-0.433728-0.1986660.1019070.451984-0.176075-0.4337280.077569
TopoPSA-0.082182-0.283805-0.0820830.299296-0.116541-0.050830-0.1535140.0864610.0760190.035523...0.4486530.583315-0.3474500.020561-0.3137470.1603580.2706800.0068910.0205610.274700
minHBa-0.144979-0.147136-0.075382-0.2509990.209862-0.4442810.208282-0.204846-0.2753890.190787...-0.363424-0.152920-0.1663460.2125970.126903-0.271109-0.4343640.1338900.212597-0.127977
ATSp10.6941430.6220740.7742210.931981-0.0924850.493795-0.129834-0.0977240.362702-0.118607...1.0000000.4026130.428677-0.0126770.3357920.2422410.078450-0.262416-0.0126770.080597
WTPT-40.284029-0.1148750.1583760.3913000.2061400.1014510.163428-0.3224510.5513420.337685...0.4026131.000000-0.0630650.341464-0.2209030.0256410.2615820.1971270.3414640.296443
CrippenLogP0.6260550.7818120.6894030.5392110.1351000.3322080.122011-0.1211230.3388020.020024...0.428677-0.0630651.0000000.0809850.7038270.088519-0.206687-0.2004690.080985-0.227113
nsOH0.219097-0.0765780.0901240.0350980.7884050.0005380.679194-0.6311550.0514480.958125...-0.0126770.3414640.0809851.000000-0.009919-0.177305-0.2034830.1691331.000000-0.233955
XLogP0.3728160.7194010.6376010.4723180.0380180.0952170.013169-0.1105960.110652-0.101380...0.335792-0.2209030.703827-0.0099191.000000-0.035078-0.538203-0.185567-0.009919-0.211810
VCH-50.1069460.0129810.0651260.111204-0.2903020.031578-0.3062390.1211100.074108-0.237750...0.2422410.0256410.088519-0.177305-0.0350781.0000000.195552-0.177931-0.1773050.088705
ETA_BetaP_s0.008916-0.162217-0.2015930.000823-0.2807060.262811-0.2349010.1898100.181366-0.161097...0.0784500.261582-0.206687-0.203483-0.5382030.1955521.0000000.054857-0.2034830.078905
minHBint6-0.173059-0.276929-0.233164-0.2215410.239523-0.1667630.226246-0.1067400.0776430.224630...-0.2624160.197127-0.2004690.169133-0.185567-0.1779310.0548571.0000000.1691330.206758
nHsOH0.219097-0.0765780.0901240.0350980.7884050.0005380.679194-0.6311550.0514480.958125...-0.0126770.3414640.0809851.000000-0.009919-0.177305-0.2034830.1691331.000000-0.233955
mindO-0.067162-0.251658-0.137389-0.010533-0.203484-0.144182-0.2336830.193009-0.022380-0.189570...0.0805970.296443-0.227113-0.233955-0.2118100.0887050.0789050.206758-0.2339551.000000
\n"", + ""

56 rows × 56 columns

\n"", + ""
"" + ], + ""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"": [ + ""
"" + ] + }, + ""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"": [ + ""
\n"", + ""\n"", + ""\n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + ""
LinearRegressionDecisionTreeRegressorRandomForestRegressorGradientBoostingRegressorMLPRegressorXGBRegressorLGBMRegressor
cv10.8470491.9179030.7184020.7062660.6707260.9831540.887868
cv20.7195662.0642690.6276250.6324280.8379340.5863170.649590
cv30.8990251.2927950.7817370.7798870.8749250.9994080.879154
cv42.0937242.3524131.1168271.0768431.3661921.0701081.108118
cv51.3895432.3812741.4918591.5086471.5335311.5661661.462994
\n"", + ""
"" + ], + ""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"": { + ""kernelspec"": { + ""display_name"": ""Python 3 (ipykernel)"", + ""language"": ""python"", + ""name"": ""python3"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 3 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython3"", + ""version"": ""3.8.10"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 5 +} +","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���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"," + +# 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 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) + + + +- [hranYin](https://github.com/hranYin) + +- [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"," + +# 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��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。 + +- 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��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"," + +# 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"