def run_upsample(json_file_path, fmt_file_path): json_manager = JsonManager(json_file_path) if json_manager.get_upsample_status() == True: print(f"Upsampling started using {json_file_path} and {fmt_file_path}") upsampled_path = json_manager.get_upsampled_path() constants.remove_folder_if_exists(\ constants.UPSAMPLED_CSV_FOLDER_NAME, upsampled_path) hot_encoded_folder = os.fsdecode(os.path.join(\ json_manager.get_hot_encoded_path(), \ constants.HOT_ENCODED_CSV_FOLDER_NAME)) hot_encoded_file = os.fsdecode(os.path.join(\ hot_encoded_folder, \ constants.HOT_ENCODED_CSV_FILENAME)) hotEncoded_data = pd.read_csv(hot_encoded_file) features_data = pd.read_csv(hot_encoded_file, \ usecols = list(hotEncoded_data.columns)[:-1]) # everything except label labels_data = pd.read_csv(hot_encoded_file, \ usecols = [list(hotEncoded_data.columns)[-1]]) # label sm = SVMSMOTE(random_state=json_manager.get_random_state()) X_res, y_res = sm.fit_resample(features_data, labels_data) csv_ready = np.append(X_res, y_res, axis=constants.COLUMN_AXIS) upsampled_folder = constants.add_folder_to_directory(\ constants.UPSAMPLED_CSV_FOLDER_NAME, upsampled_path) upsampled_file_path = os.fsdecode(os.path.join(\ upsampled_folder, constants.UPSAMPLED_CSV_FILENAME)) if os.path.exists(upsampled_file_path): os.remove(upsampled_file_path) f = open(fmt_file_path, "r") fmt = f.readline() f.close() header = ','.join(str(i) for i in hotEncoded_data.columns) np.savetxt(upsampled_file_path, csv_ready, \ fmt = fmt, \ delimiter = constants.CSV_DELIMITER, \ header = header, \ comments='') print(f"Upsampling finished, results in {upsampled_file_path}")
def main(): json_file_path = process_command_line_args() json_manager = JsonManager(json_file_path) csv_folder = json_manager.get_csv_path() normalized_folder = json_manager.get_normalized_path() feature_columns = json_manager.get_feature_columns() label_columns = json_manager.get_label_columns() lag_features = json_manager.get_lag_features() lag_window_length = json_manager.get_sliding_window_length() destination_path = constants.add_folder_to_directory(\ constants.NORMALIZED_CSV_FOLDER_NAME, normalized_folder) for file in os.listdir(csv_folder): complete_file_path = os.fsdecode(os.path.join(csv_folder, file)) if is_file_CSV(file): normalized_filename = make_modified_filename(\ file, CSV_NAME_EXTENSION) normalized_file_path = os.fsdecode(os.path.join(\ destination_path, normalized_filename)) current_csv_obj = open(complete_file_path) normalized_csv_obj = open(normalized_file_path, mode='w') csv_reader = csv.reader(current_csv_obj, \ delimiter = constants.CSV_DELIMITER) csv_writer = csv.writer(normalized_csv_obj, \ delimiter = constants.CSV_DELIMITER, \ quotechar = constants.CSV_QUOTECHAR, \ quoting=csv.QUOTE_MINIMAL) all_lag_queues = [[""] * lag_window_length for lag_feature in lag_features] header_row = list(feature_columns) header_row.append(constants.LABEL_COLUMN_NAME) csv_writer.writerow(header_row) label_indices = list(label_columns.values()) header_row_being_read = True for timeseries_row in csv_reader: if header_row_being_read: header_row_being_read = False continue label_values = [ timeseries_row[index] for index in label_indices ] label_value = next((label_value for label_value in label_values \ if label_value), None) if label_value: new_normalize_row = [] for column_name, column_index in feature_columns.items(): if column_name in lag_features: index = lag_features.index(column_name) lagged_feature = update_lag_feature_queue(\ all_lag_queues[index], timeseries_row[column_index]) new_normalize_row.append(lagged_feature) else: new_normalize_row.append(\ timeseries_row[feature_columns[column_name]]) new_normalize_row.append(label_value) csv_writer.writerow(new_normalize_row) else: for column_index, column_name in enumerate(lag_features): value = timeseries_row[feature_columns[column_name]] update_lag_feature_queue(all_lag_queues[column_index], value) current_csv_obj.close() normalized_csv_obj.close() combined_csv_file_path = os.path.join(destination_path, constants.COMBINED_CSV_FILENAME) if os.path.exists(combined_csv_file_path): os.remove(combined_csv_file_path) combined_csv = pd.concat([pd.read_csv(os.fsdecode(os.path.join(destination_path, f)))\ for f in os.listdir(destination_path)]) combined_csv.to_csv( os.fsdecode(combined_csv_file_path), \ index = False, encoding = 'utf-8-sig')
def run(self): """Reads in data, trains, and reports results """ kFold = self.json_manager.get_kfold() max_depth = self.json_manager.get_decision_tree_depth() constants.remove_folder_if_exists( self.get_output_folder(kFold, max_depth)) fmt, label_encoding = self.get_file_fmt_and_label_encoding() self.supervised_learning_dataframe = pd.read_csv( self.get_supervised_data_csv_filepath()) self.features_data = self.supervised_learning_dataframe.loc[:, self. supervised_learning_dataframe .columns != constants. LABEL_COLUMN_NAME] self.labels_data = self.supervised_learning_dataframe.loc[:, self. supervised_learning_dataframe .columns == constants. LABEL_COLUMN_NAME] output_full_path = self.create_output_folder(kFold, max_depth) self.k_fold_train_decision_tree_w_max_depth(kFold, max_depth, output_full_path) report_file = "{}_kFold_{}_maxDepth.txt".format(kFold, max_depth) dot_pdf_header = "{}_kFold_{}_maxDepth".format(kFold, max_depth) report_file_path = os.path.join(output_full_path, report_file) report_file_obj = open(report_file_path, "w") report_file_obj.write("Decision Tree with max_depth: {}, and kFold: {}\n".format(\ max_depth, kFold)) # report_file_obj.write(" Average train error with {} fold: {}\n".format(\ # kFold, sum(self.train_scores)/len(self.train_accu))) # report_file_obj.write(" Average test error with {} fold: {}\n".format(\ # kFold, sum(self.test_accu)/len(self.test_accu))) report_file_obj.write( " Decision Tree (DOT format) saved to: {}\n".format( dot_pdf_header)) report_file_obj.write( " Decision Tree (PDF format) saved to: {}.pdf\n".format( dot_pdf_header)) report_file_obj.write("Check {} for appropriate pruning.\n\n\n".format( PRUNING_GRAPH_FILENAME)) clf = tree.DecisionTreeClassifier(random_state = self.json_manager.get_random_state(), \ max_depth = max_depth) clf = clf.fit(self.features_data, self.labels_data) dot_pdf_full_path = os.fsdecode( os.path.join(output_full_path, dot_pdf_header)) plot_decision_tree(clf, dot_pdf_full_path, self.features_data.columns) prune_path = clf.cost_complexity_pruning_path(self.features_data, self.labels_data) ccp_alphas, impurities = prune_path.ccp_alphas, prune_path.impurities pruning_folder = constants.add_folder_to_directory(\ constants.PRUNE_FOLDER_NAME, output_full_path) clfs = [] train_scores = [] for i, ccp_alpha in enumerate(ccp_alphas): clf = tree.DecisionTreeClassifier(random_state = self.json_manager.get_random_state(), \ max_depth = max_depth, ccp_alpha=ccp_alpha) clf.fit(self.features_data, self.labels_data) score = clf.score(self.features_data, self.labels_data) clfs.append(clf) train_scores.append(score) newPrunePath = constants.add_folder_to_directory( "Pruning_{0}_{1:.6g}".format(i, ccp_alpha), pruning_folder) decision_tree_path = os.fsdecode(os.path.join(\ newPrunePath, "{0}_kFold_{1}_maxDepth_{2}_{3:.6g}_prune".format(kFold, max_depth,i, ccp_alpha))) plot_decision_tree(clf, decision_tree_path, self.features_data.columns) decision_tree_obj = clf.tree_ # theoretical split to dump decision trees out to files # TODO: should we include categorical features as binary? full_binary_set = self.generate_full_binary_set() behavior_tree_obj = btBuilder.bt_espresso_mod(\ decision_tree_obj, self.features_data.columns, label_encoding, self.json_manager.get_binary_features()) behaviot_tree_full_path = os.fsdecode(os.path.join(\ newPrunePath, constants.BEHAVIOR_TREE_XML_FILENAME)) # btBuilder.save_tree(behavior_tree_obj, behaviot_tree_full_path) btBuilder.save_tree(behavior_tree_obj, newPrunePath) report_file_obj.write("prune: {} \n".format(i)) report_file_obj.write(" ccp_alpha: {}, train score: {}\n".format( ccp_alpha, train_scores[i])) report_file_obj.write( " Decision Tree saved to {}\n".format(decision_tree_path)) report_file_obj.write(" Behavior Tree saved to {}\n\n".format( behaviot_tree_full_path)) report_file_obj.write("") fig, ax = plt.subplots() ax.set_xlabel("alpha") ax.set_ylabel("accuracy") ax.set_title( "Accuracy vs alpha for Final Tree Prunes (note: uses all data for final training)" ) ax.plot(ccp_alphas, self.train_scores, marker='o', label="train", drawstyle="steps-post") ax.plot(ccp_alphas, self.test_scores, marker='x', label="test", drawstyle="steps-post") ax.legend() graph_path = os.fsdecode( os.path.join(output_full_path, PRUNING_GRAPH_FILENAME)) plt.savefig(graph_path) results_txt_file = open( os.fsdecode(os.path.join(output_full_path, RESULTS_TEXT_FILENAME)), "w") alist = ccp_alphas.flatten().tolist() acc_diffs = [ a_i - b_i for a_i, b_i in zip(self.train_scores, self.test_scores) ] float_precision = 6 results_txt_file.write( f"alphas:\t\t{self.format_float_list_to_precision(alist, float_precision)}\n" ) results_txt_file.write( f"train acc:\t{self.format_float_list_to_precision(self.train_scores, float_precision)}\n" ) results_txt_file.write( f"test acc:\t{self.format_float_list_to_precision(self.test_scores, float_precision)}\n" ) results_txt_file.write( f"acc diff:\t{self.format_float_list_to_precision(acc_diffs, float_precision)}\n" ) results_txt_file.close() report_file_obj.close() print(f"BehaviorTree buidling finished, results in {output_full_path}")
def create_output_folder(self, kFold, max_depth): output_folder = constants.add_folder_to_directory(\ constants.PIPELINE_OUTPUT_FOLDER_NAME, self.json_manager.get_output_path()) folder_name = "{}_kFold_{}_maxDepth".format(kFold, max_depth) return constants.add_folder_to_directory(folder_name, output_folder)
def main(): json_file_path, log_file_path = process_command_line_args() json_manager = JsonManager(json_file_path) log_file = open(log_file_path, "r") fmt = log_file.readline() label_encoding = eval(log_file.readline()) log_file.close() supervised_learning_data = None if json_manager.get_upsample_status() == True: upsampled_folder = os.fsdecode(os.path.join(\ json_manager.get_upsampled_path(), constants.UPSAMPLED_CSV_FOLDER_NAME)) supervised_learning_data = os.fsdecode(os.path.join(\ upsampled_folder, constants.UPSAMPLED_CSV_FILENAME)) else: hot_encoded_folder = os.fsdecode(os.path.join(\ json_manager.get_hot_encoded_path(), constants.HOT_ENCODED_CSV_FOLDER_NAME)) supervised_learning_data = os.fsdecode(os.path.join(\ hot_encoded_folder, constants.HOT_ENCODED_CSV_FILENAME)) supervised_learning_dataframe = pd.read_csv(supervised_learning_data) features_data = pd.read_csv(supervised_learning_data, \ usecols = list(supervised_learning_dataframe.columns)[:-1]) labels_data = pd.read_csv(supervised_learning_data, \ usecols = [list(supervised_learning_dataframe.columns)[-1]]) kFold = json_manager.get_kfold() max_depth = json_manager.get_decision_tree_depth() output_folder = constants.add_folder_to_directory(\ constants.OUTPUT_FOLDER_NAME, json_manager.get_output_path()) folder_name = "{}_kFold_{}_maxDepth".format(kFold, max_depth) output_full_path = constants.add_folder_to_directory(folder_name, output_folder) clfs = [] trains_accu = [] test_accu = [] # for j in range(4): kf = KFold(shuffle = True, n_splits = kFold) for train_index, test_index in kf.split(features_data): X_train, X_test = features_data.iloc[train_index], features_data.iloc[test_index] y_train, y_test = labels_data.iloc[train_index], labels_data.iloc[test_index] clf = tree.DecisionTreeClassifier(random_state = json_manager.get_random_state(), \ max_depth = max_depth) clf = clf.fit(X_train, y_train) trains_accu.append(clf.score(X_train, y_train)) test_accu.append(clf.score(X_test, y_test)) clfs.append(clf) report_file = "{}_kFold_{}_maxDepth.txt".format(kFold, max_depth) dot_pdf_header = "{}_kFold_{}_maxDepth".format(kFold, max_depth) report_file_path = os.path.join(output_full_path, report_file) # if os.path.exists(decisionTreeFile_path): # os.remove(decisionTreeFile_path) report_file_obj = open(report_file_path, "w") report_file_obj.write("Decision Tree with max_depth: {}, and kFold: {}\n".format(\ max_depth, kFold)) report_file_obj.write(" Average train error with {} fold: {}\n".format(\ kFold, sum(trains_accu)/len(trains_accu))) report_file_obj.write(" Average test error with {} fold: {}\n".format(\ kFold, sum(test_accu)/len(test_accu))) report_file_obj.write(" Decision Tree (DOT format) saved to: {}\n".format(dot_pdf_header)) report_file_obj.write(" Decision Tree (PDF format) saved to: {}.pdf\n".format(dot_pdf_header)) report_file_obj.write("Check {} for appropriate pruning.\n\n\n".format(PRUNING_GRAPH_FILENAME)) clf = tree.DecisionTreeClassifier(random_state = json_manager.get_random_state(), \ max_depth = max_depth) clf = clf.fit(features_data, labels_data) dot_pdf_full_path = os.fsdecode(os.path.join(output_full_path, dot_pdf_header)) plot_decision_tree(clf, dot_pdf_full_path, features_data.columns) prune_path = clf.cost_complexity_pruning_path(features_data, labels_data) ccp_alphas, impurities = prune_path.ccp_alphas, prune_path.impurities pruning_folder = constants.add_folder_to_directory(\ constants.PRUNE_FOLDER_NAME, output_full_path) clfs = [] train_scores = [] for i, ccp_alpha in enumerate(ccp_alphas): clf = tree.DecisionTreeClassifier(random_state = json_manager.get_random_state(), \ max_depth = max_depth, ccp_alpha=ccp_alpha) clf.fit(features_data, labels_data) score = clf.score(features_data, labels_data) clfs.append(clf) train_scores.append(score) newPrunePath = constants.add_folder_to_directory("Pruning_{}".format(i), pruning_folder) decision_tree_path = os.fsdecode(os.path.join(\ newPrunePath, "{}_kFold_{}_maxDepth_{}_prune".format(kFold, max_depth, i))) plot_decision_tree(clf, decision_tree_path, features_data.columns) decision_tree_obj = clf.tree_ behavior_tree_obj = btBuilder.bt_espresso_mod(\ decision_tree_obj, features_data.columns, label_encoding) behaviot_tree_full_path = os.fsdecode(os.path.join(\ newPrunePath, constants.BEHAVIOR_TREE_XML_FILENAME)) # btBuilder.save_tree(behavior_tree_obj, behaviot_tree_full_path) btBuilder.save_tree(behavior_tree_obj, newPrunePath) report_file_obj.write("prune: {} \n".format(i)) report_file_obj.write(" ccp_alpha: {}, train score: {}\n".format(ccp_alpha, train_scores[i])) report_file_obj.write(" Decision Tree saved to {}\n".format(decision_tree_path)) report_file_obj.write(" Behavior Tree saved to {}\n\n".format(behaviot_tree_full_path)) report_file_obj.write("") fig, ax = plt.subplots() ax.set_xlabel("alpha") ax.set_ylabel("accuracy") ax.set_title("Accuracy vs alpha for training sets") ax.plot(ccp_alphas, train_scores, marker='o', label="train", drawstyle="steps-post") ax.legend() graph_path = os.fsdecode(os.path.join(output_full_path, PRUNING_GRAPH_FILENAME)) plt.savefig(graph_path) report_file_obj.close()
def main(): json_file_path = process_command_line_args() json_manager = JsonManager(json_file_path) feature_columns = json_manager.get_feature_columns() categorical_features = json_manager.get_categorical_features() binary_features = json_manager.get_binary_features() hot_encoded_path = json_manager.get_hot_encoded_path() normalized_folder = os.fsdecode(os.path.join(\ json_manager.get_normalized_path(), \ constants.NORMALIZED_CSV_FOLDER_NAME)) combined_csv_file = os.fsdecode(os.path.join(\ normalized_folder, \ constants.COMBINED_CSV_FILENAME)) features_data = pd.read_csv(combined_csv_file, usecols=feature_columns) for binary_variable in binary_features: features_data[binary_variable] = features_data[binary_variable].fillna( value=-1) features_data[binary_variable] = features_data[binary_variable] * 1 true_false_columns = features_data[binary_features] true_false_columns_array = true_false_columns.to_numpy() # true_false_features(features_data, true_false_features) # hot encoded features hot_encoded_array, hot_encoded_header = hot_encode_features(\ features_data, categorical_features) # remove hot encoded features from features_data dataframe features_data = features_data.drop(columns=categorical_features + binary_features) features_data_array = features_data.to_numpy() # encode labels labels_data = pd.read_csv(combined_csv_file, \ usecols = [constants.LABEL_COLUMN_NAME]) label_encoder, labels_column_array = encode_label_column(labels_data) # add hot_encoded columns, than numerical columns, then encoded labels to one array final_csv = np.concatenate(\ (hot_encoded_array, binary_columns_array, \ features_data_array, labels_column_array), \ axis = constants.COLUMN_AXIS) hot_encoded_folder = constants.add_folder_to_directory(\ constants.HOT_ENCODED_CSV_FOLDER_NAME, hot_encoded_path) hot_encoded_file_path = os.fsdecode(os.path.join(\ hot_encoded_folder, constants.HOT_ENCODED_CSV_FILENAME)) if os.path.exists(hot_encoded_file_path): os.remove(hot_encoded_file_path) # make_formatter_string(hot_encoded_header, numerical_columns, label_column) hot_encode_fmt = "%i," * len( hot_encoded_header + binary_features) # format hot encoded columns to ints feature_data_fmt = "%1.3f," * len( features_data.columns) # format numerical columns to doubles total_fmt = hot_encode_fmt + feature_data_fmt + "%i" # for label final_header = ','.join( str(i) for i in (hot_encoded_header + binary_features + list(features_data.columns))) final_header += "," + constants.LABEL_COLUMN_NAME # for label np.savetxt(hot_encoded_file_path, final_csv, \ fmt = total_fmt, \ header = final_header, \ delimiter = constants.CSV_DELIMITER, \ comments='') f = open(OUTPUT_LOG_FILE, "w") f.write("{}\n".format(total_fmt)) f.write(str((label_encoder.classes_).tolist())) f.close()
def run_normalize(json_file_path): global add_last_action_taken print(f"Normalizing started using {json_file_path}") json_manager = JsonManager(json_file_path) csv_folder = json_manager.get_csv_path() normalized_folder = json_manager.get_normalized_path() feature_list = json_manager.get_feature_columns() label_columns = json_manager.get_label_columns() lag_features = json_manager.get_lag_features() lag_window_length = json_manager.get_sliding_window_length() add_last_action_taken = json_manager.get_add_last_action_taken() constants.remove_folder_if_exists(\ constants.NORMALIZED_CSV_FOLDER_NAME, normalized_folder) destination_path = constants.add_folder_to_directory(\ constants.NORMALIZED_CSV_FOLDER_NAME, normalized_folder) for file in os.listdir(csv_folder): complete_file_path = os.fsdecode(os.path.join(csv_folder, file)) last_action_taken = None if is_file_CSV(file): print(f"Reading in csv: {complete_file_path}") normalized_filename = make_modified_filename(\ file, CSV_NAME_EXTENSION) normalized_file_path = os.fsdecode(os.path.join(\ destination_path, normalized_filename)) current_csv_obj = open(complete_file_path) normalized_csv_obj = open(normalized_file_path, mode='w') csv_reader = csv.reader(current_csv_obj, \ delimiter = constants.CSV_DELIMITER) csv_writer = csv.writer(normalized_csv_obj, \ delimiter = constants.CSV_DELIMITER, \ quotechar = constants.CSV_QUOTECHAR, \ quoting=csv.QUOTE_MINIMAL) all_lag_queues = [[""] * lag_window_length for lag_feature in lag_features] header_row = list(feature_list) if (add_last_action_taken): header_row.append(constants.LAST_ACTION_TAKEN_COLUMN_NAME) header_row.append(constants.LABEL_COLUMN_NAME) csv_writer.writerow(header_row) header_row_being_read = True for timeseries_row in csv_reader: if header_row_being_read: feature_columns = generate_feature_col_dictionary( timeseries_row, feature_list, False) label_indices = list( generate_feature_col_dictionary( timeseries_row, label_columns, True).values()) header_row_being_read = False continue label_values = [ timeseries_row[index] for index in label_indices ] label_value = next((label_value for label_value in label_values \ if label_value), None) if label_value: new_normalize_row = [] for column_name, column_index in feature_columns.items(): if column_name in lag_features: index = lag_features.index(column_name) lagged_feature = update_lag_feature_queue(\ all_lag_queues[index], timeseries_row[column_index]) new_normalize_row.append(lagged_feature) elif column_name == constants.LAST_ACTION_TAKEN_COLUMN_NAME: new_normalize_row.append(last_action_taken) else: new_normalize_row.append(\ timeseries_row[feature_columns[column_name]]) new_normalize_row.append(label_value) last_action_taken = label_value csv_writer.writerow(new_normalize_row) else: for column_index, column_name in enumerate(lag_features): value = timeseries_row[feature_columns[column_name]] update_lag_feature_queue(all_lag_queues[column_index], value) current_csv_obj.close() normalized_csv_obj.close() combined_csv_file_path = os.path.join(destination_path, constants.COMBINED_CSV_FILENAME) if os.path.exists(combined_csv_file_path): os.remove(combined_csv_file_path) combined_csv = pd.concat([pd.read_csv(os.fsdecode(os.path.join(destination_path, f)))\ for f in os.listdir(destination_path)]) combined_csv.to_csv( os.fsdecode(combined_csv_file_path), \ index = False, encoding = 'utf-8-sig') print(f"Normalizing finished, results in {normalized_file_path}")
def run_hotencode(json_file_path): global add_last_action_taken print(f"Hot encoding started using {json_file_path}") json_manager = JsonManager(json_file_path) feature_list = json_manager.get_feature_columns() categorical_features = json_manager.get_categorical_features() add_last_action_taken = json_manager.get_add_last_action_taken() if add_last_action_taken: categorical_features.append(constants.LAST_ACTION_TAKEN_COLUMN_NAME) binary_features = json_manager.get_binary_features() hot_encoded_path = json_manager.get_hot_encoded_path() constants.remove_folder_if_exists(\ constants.HOT_ENCODED_CSV_FOLDER_NAME, hot_encoded_path) hot_encoded_folder = constants.add_folder_to_directory(\ constants.HOT_ENCODED_CSV_FOLDER_NAME, hot_encoded_path) hot_encoded_file_path = os.fsdecode(os.path.join(\ hot_encoded_folder, constants.HOT_ENCODED_CSV_FILENAME)) normalized_folder = os.fsdecode(os.path.join(\ json_manager.get_normalized_path(), \ constants.NORMALIZED_CSV_FOLDER_NAME)) combined_csv_file = os.fsdecode(os.path.join(\ normalized_folder, \ constants.COMBINED_CSV_FILENAME)) feature_columns = generate_feature_col_dictionary( get_header_row(combined_csv_file), feature_list, False) features_data = pd.read_csv(combined_csv_file, usecols=feature_columns) features_data[binary_features] = features_data[binary_features].fillna(0) features_data[binary_features] = features_data[binary_features].astype( bool) binary_columns_array = features_data[binary_features].to_numpy() # hot encoded features hot_encoded_array, hot_encoded_header = hot_encode_features(\ features_data, categorical_features) # remove hot encoded features from features_data dataframe features_data = features_data.drop(columns=categorical_features + binary_features) features_data_array = features_data.to_numpy() # encode labels labels_data = pd.read_csv(combined_csv_file, \ usecols = [constants.LABEL_COLUMN_NAME]) label_encoder, labels_column_array = encode_label_column(labels_data) # add hot_encoded columns, than numerical columns, then encoded labels to one array final_csv = np.concatenate(\ (hot_encoded_array, binary_columns_array, \ features_data_array, labels_column_array), \ axis = constants.COLUMN_AXIS) # make_formatter_string(hot_encoded_header, numerical_columns, label_column) hot_encode_fmt = "%s," * len( hot_encoded_header + binary_features) # format hot encoded columns to binary features feature_data_fmt = "%1.3f," * len( features_data.columns) # format numerical columns to doubles total_fmt = hot_encode_fmt + feature_data_fmt + "%i" # for label final_header = ','.join( str(i) for i in (hot_encoded_header + binary_features + list(features_data.columns))) final_header += "," + constants.LABEL_COLUMN_NAME # for label np.savetxt(hot_encoded_file_path, final_csv, \ fmt = total_fmt, \ header = final_header, \ delimiter = constants.CSV_DELIMITER, \ comments='') f = open(OUTPUT_LOG_FILE, "w") f.write("{}\n".format(total_fmt)) f.write(str((label_encoder.classes_).tolist())) f.close() print(f"Hot Encoding finished, results in {hot_encoded_file_path}")