rf_balanced.py

# coding=utf-8

# Entrena un random forest y guarda sus resultados
# -------------------------------------------------------------------------------------------------

import argparse
import pickle
import sys

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

import metrics
import utils

if __name__ == '__main__':

    print ' '.join(sys.argv)
    parser = argparse.ArgumentParser()
    parser.add_argument('--percentage', required=True, type=str)
    parser.add_argument('--n_processes', required=True, type=int)
    parser.add_argument('--catalog', default='MACHO', choices=['MACHO', 'EROS', 'OGLE'])

    parser.add_argument('--training_set_path', required=True, type=str)
    parser.add_argument('--test_set_path', required=True, type=str)
    parser.add_argument('--result_path', required=True, type=str)
    
    parser.add_argument('--n_estimators', required=False, type=int)
    parser.add_argument('--criterion', required=False, type=str)
    parser.add_argument('--max_depth', required=False, type=int)
    parser.add_argument('--min_samples_split', required=False, type=int)

    
    parser.add_argument('--feature_filter',  nargs='*', type=str)

    args = parser.parse_args(sys.argv[1:])
    
    percentage = args.percentage
    n_processes = args.n_processes
    catalog = args.catalog

    training_set_path = args.training_set_path
    test_set_path = args.test_set_path
    result_path = args.result_path

    n_estimators = args.n_estimators
    criterion = args.criterion
    max_depth = args.max_depth
    min_samples_split = args.min_samples_split

    feature_filter = args.feature_filter

    train_data = pd.read_csv(training_set_path, index_col=0)
    train_X, train_y = utils.filter_data(train_data, feature_filter=feature_filter)

    test_data = pd.read_csv(test_set_path, index_col=0)
    test_X, test_y = utils.filter_data(test_data, feature_filter=feature_filter)


    clf = None
    clf = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion,
                                 max_depth=max_depth, min_samples_split=min_samples_split,
                                 n_jobs=n_processes)

    clf.fit(train_X, train_y)
    result = metrics.predict_table(clf, test_X, test_y)

    result['indice'] = test_X.index.tolist()
    result.set_index('indice')
    result.index.name = catalog + '_id'
    result = result.drop('indice', axis=1)

    output = open(result_path + 'Arboles/Arbol_' + percentage + '.pkl', 'wb+')
    pickle.dump(clf, output)
    output.close()

    result.to_csv(result_path + 'Predicciones/result_' + percentage + '.csv')

    print metrics.weighted_f_score(metrics.confusion_matrix(result))