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