use of select from model

find_best_attributes_from.py

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+# main imports
+import os
+import sys
+import argparse
+import pandas as pd
+import numpy as np
+import logging
+import datetime
+
+# model imports
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier, VotingClassifier
+
+import joblib
+import sklearn.svm as svm
+from sklearn.utils import shuffle
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import cross_val_score
+from sklearn.feature_selection import SelectFromModel
+from sklearn.ensemble import ExtraTreesClassifier
+# modules and config imports
+sys.path.insert(0, '') # trick to enable import of main folder module
+
+import custom_config as cfg
+import models as mdl
+
+# variables and parameters
+models_list         = cfg.models_names_list
+number_of_values    = 30
+ils_iteration       = 4000
+ls_iteration        = 10
+
+# default validator
+def validator(solution):
+
+    if list(solution.data).count(1) < 5:
+        return False
+
+    return True
+
+def loadDataset(filename):
+
+    ########################
+    # 1. Get and prepare data
+    ########################
+    dataset_train = pd.read_csv(filename + '.train', header=None, sep=";")
+    dataset_test = pd.read_csv(filename + '.test', header=None, sep=";")
+
+    # default first shuffle of data
+    dataset_train = shuffle(dataset_train)
+    dataset_test = shuffle(dataset_test)
+
+    # get dataset with equal number of classes occurences
+    noisy_df_train = dataset_train[dataset_train.iloc[:, 0] == 1]
+    not_noisy_df_train = dataset_train[dataset_train.iloc[:, 0] == 0]
+    #nb_noisy_train = len(noisy_df_train.index)
+
+    noisy_df_test = dataset_test[dataset_test.iloc[:, 0] == 1]
+    not_noisy_df_test = dataset_test[dataset_test.iloc[:, 0] == 0]
+    #nb_noisy_test = len(noisy_df_test.index)
+
+    # use of all data
+    final_df_train = pd.concat([not_noisy_df_train, noisy_df_train])
+    final_df_test = pd.concat([not_noisy_df_test, noisy_df_test])
+
+    # shuffle data another time
+    final_df_train = shuffle(final_df_train)
+    final_df_test = shuffle(final_df_test)
+
+    # use of the whole data set for training
+    x_dataset_train = final_df_train.iloc[:,1:]
+    x_dataset_test = final_df_test.iloc[:,1:]
+
+    y_dataset_train = final_df_train.iloc[:,0]
+    y_dataset_test = final_df_test.iloc[:,0]
+
+    return x_dataset_train, y_dataset_train, x_dataset_test, y_dataset_test
+
+def main():
+
+    parser = argparse.ArgumentParser(description="Train and find best filters to use for model")
+
+    parser.add_argument('--data', type=str, help='dataset filename prefix (without .train and .test)', required=True)
+    parser.add_argument('--choice', type=str, help='model choice from list of choices', choices=models_list, required=True)
+    parser.add_argument('--length', type=str, help='max data length (need to be specify for evaluator)', required=True)
+
+    args = parser.parse_args()
+
+    p_data_file = args.data
+    p_choice    = args.choice
+    p_length    = args.length
+
+    print(p_data_file)
+
+    # load data from file
+    x_train, y_train, x_test, y_test = loadDataset(p_data_file)
+
+    
+    # clf = ExtraTreesClassifier(n_estimators=100)
+    # clf = clf.fit(x_train, y_train)
+    # print(clf.feature_importances_)
+
+
+    for i in (np.arange(11) + 5):
+
+
+        model = SelectFromModel(ExtraTreesClassifier(n_estimators=100), max_features=i)
+        selector = model.fit(x_train, y_train)
+
+        binary_selection = [ 0 if x < selector.threshold_ else 1 for x in selector.estimator_.feature_importances_ ]
+        X_train_new = selector.transform(x_train)
+        X_test_new = selector.transform(x_test)
+
+        print('Shape for {}, is now {}'.format(i, X_train_new.shape))
+
+        svm_model = mdl.get_trained_model(p_choice, X_train_new, y_train)
+
+        y_test_model = svm_model.predict(X_test_new)
+        test_roc_auc = roc_auc_score(y_test, y_test_model)
+        
+        with open('data/results/selectFromModel.csv', 'a') as f:
+            line += str(len(binary_selection)) + ';'
+            line += str(test_roc_auc) + ';'
+            
+            for index, b in enumerate(binary_selection):
+
+                line += str(b)
+                if index < len(binary_selection) - 1:
+                    line += ','
+
+            f.write(line + '\n')
+
+    # create `logs` folder if necessary
+    if not os.path.exists(cfg.output_logs_folder):
+        os.makedirs(cfg.output_logs_folder)
+
+    logging.basicConfig(format='%(asctime)s %(message)s', filename='data/logs/%s.log' % p_data_file.split('/')[-1], level=logging.DEBUG)
+
+    # init solution (`n` attributes)
+    
+
+
+if __name__ == "__main__":
+    main()