index_filter = args.index_filter
feature_filter = args.feature_filter
if index_filter is not None:
index_filter = pd.read_csv(index_filter, index_col=0).index
paths = [sets_path + catalog + '_sampled_' + str(i) + '.csv' for i in xrange(n_samples)]
if model == 'tree':
partial_fit = partial(parallel.fit_tree, feature_filter=feature_filter, folds=folds,
inverse=inverse, max_depth=max_depth,
min_samples_split=min_samples_split, lc_filter=lc_filter)
elif model == 'rf':
partial_fit = partial(parallel.fit_rf, feature_filter=feature_filter, folds=folds,
inverse=inverse, lc_filter=lc_filter)
elif model == 'sktree':
partial_fit = partial(parallel.fit_sktree, feature_filter=feature_filter, folds=folds,
inverse=inverse, max_depth=max_depth,
min_samples_split=min_samples_split, lc_filter=lc_filter)
pool = Pool(processes=n_processes, maxtasksperchild=2)
resultados = pool.map(partial_fit, paths)
pool.close()
pool.join()
result = metrics.aggregate_predictions(resultados)
result.to_csv(result_path)
print metrics.weighted_f_score(metrics.confusion_matrix(result))
train_X, train_y = data.iloc[train_index], y.iloc[train_index]
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)
aux = []
for path in paths:
test_data = pd.read_csv(path, index_col=0)
test_data = test_data.loc[data.index].sort_index()
test_data, test_y = utils.filter_data(
test_data, feature_filter=feature_filter)
test_X, test_y = test_data.iloc[train_index], test_y.iloc[
train_index]
aux.append(metrics.predict_table(clf, test_X, test_y))
results.append(metrics.aggregate_predictions(aux))
ids.extend(test_X.index.tolist())
result = pd.concat(results)
output = open(result_path + 'Arboles/Arbol_' + percentage + '.pkl', 'wb+')
pickle.dump(clf, output)
output.close()
result.to_csv(result_path + 'Predicciones/result_' + percentage + '.csv')
ids = []
for train_index, test_index in skf:
train_X, train_y = data.iloc[train_index], y.iloc[train_index]
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)
aux = []
for path in paths:
test_data = pd.read_csv(path, index_col=0)
test_data = test_data.loc[data.index].sort_index()
test_data, test_y = utils.filter_data(test_data, feature_filter=feature_filter)
test_X, test_y = test_data.iloc[train_index], test_y.iloc[train_index]
aux.append(metrics.predict_table(clf, test_X, test_y))
results.append(metrics.aggregate_predictions(aux))
ids.extend(test_X.index.tolist())
result = pd.concat(results)
output = open(result_path + 'Arboles/Arbol_' + percentage + '.pkl', 'wb+')
pickle.dump(clf, output)
output.close()
result.to_csv(result_path + 'Predicciones/result_' + percentage + '.csv')
