added combined model FS code and run script
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scripts/ml/untitled7_boruta.py
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scripts/ml/untitled7_boruta.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Sep 2 12:13:53 2022
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@author: tanu
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"""
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# https://analyticsindiamag.com/hands-on-guide-to-automated-feature-selection-using-boruta/
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import pandas as pd
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import numpy as np
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from sklearn.ensemble import RandomForestClassifier
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from boruta import BorutaPy
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import matthews_corrcoef
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URL = "https://raw.githubusercontent.com/Aditya1001001/English-Premier-League/master/pos_modelling_data.csv"
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data = pd.read_csv(URL)
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data.info()
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X = data.drop('Position', axis = 1)
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y = data['Position']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 1)
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rf_all_features = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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rf_all_features.fit(X_train, y_train)
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y_pred = rf_all_features.predict(X_test)
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accuracy_score(y_test, rf_all_features.predict(X_test))
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accuracy_score(y_test, y_pred)
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matthews_corrcoef(y_test, rf_all_features.predict(X_test))
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# BORUTA
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rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)
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boruta_selector.fit(np.array(X_train), np.array(y_train))
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# Tells you how many features: GOOD
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print("Ranking: ",boruta_selector.ranking_)
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print("No. of significant features: ", boruta_selector.n_features_)
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selected_rf_features = pd.DataFrame({'Feature':list(X_train.columns),
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'Ranking':boruta_selector.ranking_})
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# tells you the ranking: GOOD
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selected_rf_features.sort_values(by='Ranking')
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X_important_train = boruta_selector.transform(np.array(X_train))
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X_important_test = boruta_selector.transform(np.array(X_test))
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rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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rf_boruta.fit(X_important_train, y_train)
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accuracy_score(y_test, rf_boruta.predict(X_important_test))
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matthews_corrcoef(y_test, rf_boruta.predict(X_important_test))
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##############################################################################
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# my data : ONLY numerical values
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# from old scripts (cm_logo_skf_v2.py)
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fooD = combined_DF_OS(combined_df)
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allF = fooD['X']
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numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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# just numerical for X_train and X_test
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X_train_numF = fooD['X'][numerical_ix]
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X_test_numF = fooD['X_bts'][numerical_ix]
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#X_train = allF
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X_train = X_train_numF
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X_test = X_test_numF
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y_train = fooD['y']
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y_test = fooD['y_bts']
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# 1 model
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rf_all_features = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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rf_all_features.fit(X_train, y_train)
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accuracy_score(y_test, rf_all_features.predict(X_test))
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matthews_corrcoef(y_test, rf_all_features.predict(X_test))
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# BORUTA
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rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)
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boruta_selector.fit(np.array(X_train), np.array(y_train))
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# Tells you how many features: GOOD
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print("Ranking: ",boruta_selector.ranking_)
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print("No. of significant features: ", boruta_selector.n_features_)
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selected_rf_features = pd.DataFrame({'Feature':list(X_train.columns),
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'Ranking':boruta_selector.ranking_})
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# tells you the ranking: GOOD
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selected_rf_features.sort_values(by='Ranking')
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X_important_train = boruta_selector.transform(np.array(X_train))
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X_important_test = boruta_selector.transform(np.array(X_test))
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rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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rf_boruta.fit(X_important_train, y_train)
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accuracy_score(y_test, rf_boruta.predict(X_important_test))
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matthews_corrcoef(y_test, rf_boruta.predict(X_important_test))
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##############################################################################
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# my data : using both numerical and categorical
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# from old scripts (cm_logo_skf_v2.py)
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fooD = combined_DF_OS(combined_df)
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numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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print("\nNo. of numerical indices:", len(numerical_ix))
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categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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print("\nNo. of categorical indices:", len(categorical_ix))
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var_type = "mixeds"
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if var_type == 'mixed':
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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#--------------ALEX help
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# col_transform
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# col_transform.fit(X)
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# test = col_transform.transform(X)
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# print(col_transform.get_feature_names_out())
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# foo = col_transform.fit_transform(X)
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Xm_train = col_transform.fit_transform(fooD['X'])
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fooD['X'].shape
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Xm_train.shape
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Xm_test = col_transform.fit_transform(fooD['X_bts'])
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fooD['X_bts'].shape
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Xm_test.shape
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X_train = Xm_train.copy()
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X_test = Xm_test.copy()
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X_train.shape
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X_test.shape
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y_train = fooD['y']
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y_test = fooD['y_bts']
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y_train.shape
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y_test.shape
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# perhaps
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#col_transform.fit(fooD['X'])
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#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
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#======================
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# 1 model
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rf_all_features = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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rf_all_features.fit(X_train, y_train)
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accuracy_score(y_test, rf_all_features.predict(X_test))
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matthews_corrcoef(y_test, rf_all_features.predict(X_test))
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# BORUTA
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rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)
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boruta_selector.fit(np.array(X_train), np.array(y_train))
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# Tells you how many features: GOOD
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print("Ranking: ",boruta_selector.ranking_)
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print("No. of significant features: ", boruta_selector.n_features_)
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#selected_rf_features = pd.DataFrame({'Feature':list(X_train.columns),
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# 'Ranking':boruta_selector.ranking_})
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# tells you the ranking: GOOD
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foo2 = selected_rf_features.sort_values(by='Ranking')
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X_important_train = boruta_selector.transform(np.array(X_train))
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X_important_test = boruta_selector.transform(np.array(X_test))
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rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
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rf_boruta.fit(X_important_train, y_train)
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accuracy_score(y_test, rf_boruta.predict(X_important_test))
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matthews_corrcoef(y_test, rf_boruta.predict(X_important_test))
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##################################
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# trying to one hot encode at start
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# perhaps
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#col_transform.fit(fooD['X'])
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#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
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# def encode_and_bind(original_dataframe, feature_to_encode):
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# dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])
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# res = pd.concat([original_dataframe, dummies], axis=1)
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# res = res.drop([feature_to_encode], axis=1)
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# return(res)
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# features_to_encode = ['feature_1', 'feature_2', 'feature_3',
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# 'feature_4']
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# features_to_encode = list(categorical_ix.copy())
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# for feature in features_to_encode:
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# X_train_enc = encode_and_bind(fooD['X'], feature)
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# X_test_enc = encode_and_bind(fooD['X_bts'], feature)
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# c1 = X_train_enc.columns
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# c2 = X_test_enc.columns
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# X_train_enc.shape
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# X_test_enc.shape
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# This one is better!
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a = pd.get_dummies(combined_df, columns=features_to_encode)
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a1=a.columns
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a2 = a.drop(['gene_name', 'dst', 'dst_mode'])
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