FeatureSelection.py

# from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.feature_selection import RFECV
import numpy as np
import pandas as pd
from sklearn import preprocessing
import math

import warnings
warnings.filterwarnings("ignore")

# dataset= load_boston()




model_dict={'gradient_boost': GradientBoostingRegressor()}

# def transform_to_df(X):
#     X_df=pd.DataFrame(data=X, columns= feature_names)
#     return X_df 
cv_list = np.arange(2, 5)
step_list= [0.2, 0.3, 0.5, 0.6, 0.7, 1]



class feature_selection:
    def __init__(self,
                 model_dict,
                 cv_list,
                 step_list):
        self.model_dict= model_dict
        self.cv_list= cv_list
        self.step_list= step_list

    def fit_rfecv(self,
                  X,
                  y):
        selected_features = []
        X_sel= []
        for model in self.model_dict.values():
            for cv in self.cv_list:
                for step in self.step_list:
                    try:
                        rfecv= RFECV(estimator= model, cv= cv, step= step)
                        _ = rfecv.fit(X, y)
                        X.drop(X.columns[np.where(rfecv.support_ == False)[0]], axis=1, inplace=True)
            # selected_features.append((X.columns[np.where(rfecv.support_)]))
                        # print(X.columns)
                        X_sel.append(X)
                        selected_features.append(X.columns)
                       
                    except:
                        continue
                    
        # print('This is the list:', X_sel) 
            # print(selected_features)
        # return selected_features, X_sel
        return selected_features
    
    



def feature_selection_function(X, y):
    X_sel_functions= []
    col_name= []
    for model in model_dict.values():
        for cv in cv_list:
            for step in step_list:
                try:
                    rfecv = RFECV(estimator=model, cv=cv, step=step)
                    rfecv.fit(X, y)
                    X.drop(X.columns[np.where(rfecv.support_ == False)[0]], axis=1, inplace=True)
                # selected_features.append((X.columns[np.where(rfecv.support_)]))
                    # print(X.columns)
                    X_sel_functions.append(X)
                    col_name.append(X.columns)
                except:
                    continue
                
    return X_sel_functions, col_name


def fs_scaled(X, y, is_scaling= 1):
    if is_scaling == 1:
        scaler = preprocessing.StandardScaler().fit(X)
        X_scaled = scaler.transform(X)
        X_scaled= pd.DataFrame(data= X_scaled, columns=(X.columns))
        X_sel_functions, col_name= feature_selection_function(X_scaled, y)
    else:
        X_sel_functions, col_name= feature_selection_function(X, y)
    return X_sel_functions, col_name


def sand_paper(X):
    for i in range(X.shape[0]):
        for j in range(X.shape[1]):
            X.iloc[i, j] = ((X.iloc[i, j] + 10**(-2))/(10**(-2)))

    return X

def fs_sanding(X, y, is_scaling= 1):
    if is_scaling== 1:
        X_sand= sand_paper(X)
        X_sel_functions, col_name = feature_selection_function(X_sand, y)
    else:
        X_sel_functions, col_name = feature_selection_function(X, y)
    return X_sel_functions, col_name