def build_ar_models_for_traces(trace_lst, results_dict, train_prop, val_prop, max_mem): fixed_model_params = {'train_prop': train_prop, 'val_prop': val_prop, 'max_mem': max_mem} ar_params_lst = [{'p': p, **fixed_model_params} for p in AR_COMPS] analysis.get_best_model_results_for_traces( TraceAR, ar_params_lst, trace_lst, results_dict, specs.MODELS_COUNT, fixed_model_params)
def build_es_models_for_traces(trace_lst, results_dict, train_prop, val_prop, max_mem): fixed_model_params = {'initial_pred': 0.001, 'train_prop': train_prop, 'val_prop': val_prop, 'max_mem': max_mem} es_params_lst = [{'alpha': alpha_val, **fixed_model_params} for alpha_val in ALPHAS] analysis.get_best_model_results_for_traces( TraceExponentialSmoothing, es_params_lst, trace_lst, results_dict, specs.MODELS_COUNT, fixed_model_params)
def build_ma_models_for_traces(trace_lst, results_dict, train_prop, val_prop, max_mem): fixed_model_params = {'initial_pred': 0.0, 'train_prop': train_prop, 'val_prop': val_prop, 'max_mem': max_mem} ma_params_lst = [{'window_length': ma_win, **fixed_model_params} for ma_win in MA_WINDOWS] analysis.get_best_model_results_for_traces( TraceMovingAverage, ma_params_lst, trace_lst, results_dict, specs.MODELS_COUNT, fixed_model_params)
def build_arima_models_for_traces(traces_lst, results_dict, train_prop, val_prop, max_mem): fixed_model_params = {'train_prop': train_prop, 'val_prop': val_prop, 'max_mem': max_mem} arima_params_lst = [{'p': p, 'd': d, 'q': q, **fixed_model_params} for p, d, q in ARIMA_PARAMS] analysis.get_best_model_results_for_traces( TraceARIMA, arima_params_lst, traces_lst, results_dict, specs.MODELS_COUNT, fixed_model_params)
def build_arimax_models_for_traces(traces_lst, results_dict, train_prop, val_prop, max_mem): target_col = specs.get_target_variable(max_mem) data_handler = MLDataHandler( FEATURE_COLS, [target_col], train_prop, val_prop) arimax_params_lst = [{'data_handler': data_handler, 'lags': specs.LAGS, 'p': p, 'd': d, 'q': q} for p, d, q in product(ARIMA_p, ARIMA_d, ARIMA_q)] analysis.get_best_model_results_for_traces( TraceARIMAX, arimax_params_lst, traces_lst, results_dict, specs.MODELS_COUNT)
def build_reg_models_for_traces(trace_lst, results_dict, train_prop, val_prop, max_mem): target_col = specs.get_target_variable(max_mem) data_handler = MLDataHandler( FEATURE_COLS, [target_col], train_prop, val_prop) fixed_model_params = {'data_handler': data_handler, 'lags': specs.LAGS} reg_params_lst = [{'reg_val': reg_val, **fixed_model_params} for reg_val in REG_VALS] analysis.get_best_model_results_for_traces( TraceRegression, reg_params_lst, trace_lst, results_dict, specs.MODELS_COUNT, fixed_model_params)
def build_xgb_models_for_traces(trace_lst, results_dict, train_prop, val_prop, max_mem): target_col = specs.get_target_variable(max_mem) data_handler = MLDataHandler( FEATURE_COLS, [target_col], train_prop, val_prop) fixed_model_params = {'data_handler': data_handler, 'lags': specs.LAGS} xgb_params_lst = [{'learning_rate': learning_rate, 'estimators': n_estimators, 'depth': depth, **fixed_model_params} for learning_rate, n_estimators, depth in product(LEARNING_RATES, ESTIMATORS, DEPTHS)] analysis.get_best_model_results_for_traces( TraceXGB, xgb_params_lst, trace_lst, results_dict, specs.MODELS_COUNT, fixed_model_params)