from pathlib import Path from datetime import datetime, timezone, timedelta import click from tqdm import tqdm import numpy as np import pandas as pd from service.App import * from common.gen_features import * from common.classifiers import * from common.model_store import * from common.generators import train_feature_set """ Train models for all target labels and all algorithms declared in the configuration using the specified features. """ # # Parameters # class P: in_nrows = 100_000_000 # For debugging tail_rows = 0 # How many last rows to select (for debugging) # Whether to store file with predictions store_predictions = True @click.command() @click.option('--config_file', '-c', type=click.Path(), default='', help='Configuration file name') def main(config_file): load_config(config_file) time_column = App.config["time_column"] now = datetime.now() # # Load feature matrix # symbol = App.config["symbol"] data_path = Path(App.config["data_folder"]) / symbol file_path = data_path / App.config.get("matrix_file_name") if not file_path.is_file(): print(f"ERROR: Input file does not exist: {file_path}") return print(f"Loading data from source data file {file_path}...") if file_path.suffix == ".parquet": df = pd.read_parquet(file_path) elif file_path.suffix == ".csv": df = pd.read_csv(file_path, parse_dates=[time_column], date_format="ISO8601", nrows=P.in_nrows) else: print(f"ERROR: Unknown extension of the input file '{file_path.suffix}'. Only 'csv' and 'parquet' are supported") return print(f"Finished loading {len(df)} records with {len(df.columns)} columns.") df = df.iloc[-P.tail_rows:] df = df.reset_index(drop=True) print(f"Input data size {len(df)} records. Range: [{df.iloc[0][time_column]}, {df.iloc[-1][time_column]}]") # # Prepare data by selecting columns and rows # # Default (common) values for all trained features label_horizon = App.config["label_horizon"] # Labels are generated from future data and hence we might want to explicitly remove some tail rows train_length = App.config.get("train_length") train_features = App.config.get("train_features") labels = App.config["labels"] algorithms = App.config.get("algorithms") # Select necessary features and labels out_columns = [time_column, 'open', 'high', 'low', 'close', 'volume', 'close_time'] out_columns = [x for x in out_columns if x in df.columns] all_features = train_features + labels df = df[out_columns + [x for x in all_features if x not in out_columns]] for label in labels: if np.issubdtype(df[label].dtype, bool): df[label] = df[label].astype(int) # For classification tasks we want to use integers # Remove the tail data for which no (correct) labels are available # The reason is that these labels are computed from future values which are not available and hence labels might be wrong if label_horizon: df = df.head(-label_horizon) df.replace([np.inf, -np.inf], np.nan, inplace=True) if len(df) == 0: print(f"ERROR: Empty data set after removing NULLs in feature columns. Some features might have all NULL values.") #print(df.isnull().sum().sort_values(ascending=False)) return # Limit maximum length for all algorithms (algorithms can further limit their train size) if train_length: df = df.tail(train_length) df = df.reset_index(drop=True) # To remove gaps in index before use # # Train feature models # train_feature_sets = App.config.get("train_feature_sets", []) if not train_feature_sets: print(f"ERROR: no train feature sets defined. Nothing to process.") return print(f"Start training models for {len(df)} input records.") out_df = pd.DataFrame() # Collect predictions models = dict() scores = dict() for i, fs in enumerate(train_feature_sets): fs_now = datetime.now() print(f"Start train feature set {i}/{len(train_feature_sets)}. Generator {fs.get('generator')}...") fs_out_df, fs_models, fs_scores = train_feature_set(df, fs, App.config) out_df = pd.concat([out_df, fs_out_df], axis=1) models.update(fs_models) scores.update(fs_scores) fs_elapsed = datetime.now() - fs_now print(f"Finished train feature set {i}/{len(train_feature_sets)}. Generator {fs.get('generator')}. Time: {str(fs_elapsed).split('.')[0]}") print(f"Finished training models.") # # Store all collected models in files # model_path = Path(App.config["model_folder"]) if not model_path.is_absolute(): model_path = data_path / model_path model_path = model_path.resolve() model_path.mkdir(parents=True, exist_ok=True) # Ensure that folder exists for score_column_name, model_pair in models.items(): save_model_pair(model_path, score_column_name, model_pair) print(f"Models stored in path: {model_path.absolute()}") # # Store scores # lines = list() for score_column_name, score in scores.items(): line = score_column_name + ", " + str(score) lines.append(line) metrics_file_name = f"prediction-metrics.txt" metrics_path = (data_path / metrics_file_name).resolve() with open(metrics_path, 'a+') as f: f.write("\n".join(lines) + "\n\n") print(f"Metrics stored in path: {metrics_path.absolute()}") # # Store predictions if necessary # if P.store_predictions: # Store only selected original data, labels, and their predictions out_df = out_df.join(df[out_columns + labels]) out_path = data_path / App.config.get("predict_file_name") print(f"Storing predictions with {len(out_df)} records and {len(out_df.columns)} columns in output file {out_path}...") if out_path.suffix == ".parquet": out_df.to_parquet(out_path, index=False) elif out_path.suffix == ".csv": out_df.to_csv(out_path, index=False, float_format='%.6f') else: print(f"ERROR: Unknown extension of the output file '{out_path.suffix}'. Only 'csv' and 'parquet' are supported") return print(f"Predictions stored in file: {out_path}. Length: {len(out_df)}. Columns: {len(out_df.columns)}") # # End # elapsed = datetime.now() - now print(f"Finished training models in {str(elapsed).split('.')[0]}") if __name__ == '__main__': main()