#Prepare ORD data for training a machine learning model

import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from rdkit.Chem import AllChem
from sklearn import model_selection, metrics
from glob import glob
from rdkit import Chem
from molvs import Standardizer

# Create new dataframe containing only columns to be used in modeling
model_cols = ['inputs["catalyst"].components[0].identifiers[1].value', #Pd catalyst
              'inputs["aryl halide"].components[0].identifiers[0].value', #Aryl Halide
              'inputs["base"].components[0].identifiers[1].value', #Base
              'inputs["additive"].components[0].identifiers[0].value', #Amine or solvent (in controls)
              'outcomes[0].products[0].measurements[0].percentage.value' #% yield
]

#Read data from sanitized data .csv file
file_path = 'data/Sanitized_Ahneman_ORD_Data.csv'  #Sanitized data
sanitized_df = pd.read_csv(file_path)
df = sanitized_df[model_cols] #Use sanitized data in the model with correct columns to use in the ML model

# Check for NaN values
print(f"number of NaN values: {df.isnull().sum().sum()}")

# Show column counts
print("Column Info")
df.info()

# Show dataset statistics for numerical fields
print("Dataset Statistics for Numerical Fields:")
df.describe()

#One-Hot Encoding (OHE)
# Convert reaction input labels to one-hot encoding
input_cols = model_cols[:-1]

# Assign names for each input
prefix = ["Catalyst", "Aryl Halide", "Base", "Additives"]

# Create one-hot encoded input dataset
ohe_df = pd.get_dummies(df[input_cols], prefix=prefix)

# Add yield column to ohe dataset
ohe_df["yield"] = df[model_cols[-1]] / 100 #yield is the target variable the ML model will learn to optimize

# View dataset
print(ohe_df.shape)

ohe_df.to_csv('data/Prepared_Data.csv', index=False)