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_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 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_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}")