def test_evaluator_df_preds_with_scaling_not_equal_without_scaling(self): inference_params = { "datetime_start": datetime.datetime(2016, 5, 31, 0), "hours_to_forecast": 336, "dataset_params": self.data_base_params, "test_csv_path": os.path.join(self.test_path2, "keag_small.csv"), "num_prediction_samples": 10, } inference_params_with_scaling = { "datetime_start": datetime.datetime(2016, 5, 31, 0), "hours_to_forecast": 336, "dataset_params": self.data_base_params_with_scaling, "test_csv_path": os.path.join(self.test_path2, "keag_small.csv"), "num_prediction_samples": 10, } model_result_1 = evaluate_model( self.model, "PyTorch", ["cfs"], ["MSE", "L1"], inference_params, {} ) model_result_2 = evaluate_model( self.model, "PyTorch", ["cfs"], ["MSE", "L1"], inference_params_with_scaling, {}, ) self.assertFalse(model_result_1[3].equals(model_result_2[3]))
def train_function(model_type: str, params:Dict): """ Function to train a Model(TimeSeriesModel) or da_rnn. Will return the trained model model_type str: Type of the model (for now) must be da_rnn or :params dict: Dictionary containing all the parameters needed to run the model """ dataset_params = params["dataset_params"] if model_type == "da_rnn": from flood_forecast.da_rnn.train_da import da_rnn, train from flood_forecast.preprocessing.preprocess_da_rnn import make_data preprocessed_data = make_data(params["dataset_params"]["training_path"], params["dataset_params"]["target_col"], params["dataset_params"]["forecast_length"]) config, model = da_rnn(preprocessed_data, len(dataset_params["target_col"])) # All train functions return trained_model trained_model = train(model, preprocessed_data, config) elif model_type == "PyTorch": trained_model = PyTorchForecast( params["model_name"], dataset_params["training_path"], dataset_params["validation_path"], dataset_params["test_path"], params) train_transformer_style(trained_model, params["training_params"], params["forward_params"]) params["inference_params"]["dataset_params"]["scaling"] = scaler_dict[dataset_params["scaler"]] test_acc = evaluate_model( trained_model, model_type, params["dataset_params"]["target_col"], params["metrics"], params["inference_params"], {}) wandb.run.summary["test_accuracy"] = test_acc[0] df_train_and_test = test_acc[1] forecast_start_idx = test_acc[2] df_prediction_samples = test_acc[3] inverse_mae = 1 / ( df_train_and_test.loc[forecast_start_idx:, "preds"] - df_train_and_test.loc[forecast_start_idx:, params["dataset_params"]["target_col"][0]]).abs() pred_std = df_prediction_samples.std(axis=1) average_prediction_sharpe = (inverse_mae / pred_std).mean() wandb.log({'average_prediction_sharpe': average_prediction_sharpe}) # Log plots test_plot = plot_df_test_with_confidence_interval( df_train_and_test, df_prediction_samples, forecast_start_idx, params, ci=95, alpha=0.25) wandb.log({"test_plot": test_plot}) test_plot_all = go.Figure() for relevant_col in params["dataset_params"]["relevant_cols"]: test_plot_all.add_trace(go.Scatter(x=df_train_and_test.index, y=df_train_and_test[relevant_col], name=relevant_col)) wandb.log({"test_plot_all": test_plot_all}) else: raise Exception("Please supply valid model type for forecasting") return trained_model
def test_evaluator(self): inference_params = { "datetime_start": datetime.datetime(2016, 5, 31, 0), "hours_to_forecast": 336, "dataset_params": self.data_base_params, "test_csv_path": os.path.join(self.test_path2, "keag_small.csv") } model_result = evaluate_model(self.model, "PyTorch", ["cfs"], ["MSE", "L1"], inference_params, {}) self.assertGreater(model_result[0]["cfs_L1"], 0) self.assertGreater(model_result[0]["cfs_MSE"], 1)
def test_evaluator_generate_prediction_samples(self): inference_params = { "datetime_start": datetime.datetime(2016, 5, 31, 0), "hours_to_forecast": 336, "dataset_params": self.data_base_params, "test_csv_path": os.path.join(self.test_path2, "keag_small.csv"), "num_prediction_samples": 100, } model_result = evaluate_model( self.model, "PyTorch", ["cfs"], ["MSE", "L1"], inference_params, {} ) df_train_and_test = model_result[1] df_prediction_samples = model_result[3] self.assertTrue(df_train_and_test.index.equals(df_prediction_samples.index)) self.assertEqual(100, df_prediction_samples.shape[1])
def test_evaluator(self): inference_params = { "datetime_start": datetime.datetime(2016, 5, 31, 0), "hours_to_forecast": 336, "dataset_params": self.data_base_params, "test_csv_path": os.path.join(self.test_path2, "keag_small.csv"), } model_result = evaluate_model(self.model, "PyTorch", ["cfs"], ["MSE", "L1"], inference_params, {}) print(model_result) eval_dict = model_result[0] self.assertGreater(eval_dict["cfs_MAPELoss"], 0) self.assertGreater(eval_dict["cfs_MSELoss"], 420) # self.assertNotAlmostEqual(eval_dict["cfs_MAPELoss"], eval_dict["cfs_MSELoss"]) self.assertLessEqual(eval_dict["cfs_MAPELoss"].item(), 400)
def train_function(model_type: str, params: Dict): """Function to train a Model(TimeSeriesModel) or da_rnn. Will return the trained model :param model_type: Type of the model. In almost all cases this will be 'PyTorch' :type model_type: str :param params: Dictionary containing all the parameters needed to run the model :type Dict: """ dataset_params = params["dataset_params"] if model_type == "da_rnn": from flood_forecast.da_rnn.train_da import da_rnn, train from flood_forecast.preprocessing.preprocess_da_rnn import make_data preprocessed_data = make_data( params["dataset_params"]["training_path"], params["dataset_params"]["target_col"], params["dataset_params"]["forecast_length"]) config, model = da_rnn(preprocessed_data, len(dataset_params["target_col"])) # All train functions return trained_model trained_model = train(model, preprocessed_data, config) elif model_type == "PyTorch": trained_model = PyTorchForecast(params["model_name"], dataset_params["training_path"], dataset_params["validation_path"], dataset_params["test_path"], params) takes_target = False if "takes_target" in trained_model.params: takes_target = trained_model.params["takes_target"] train_transformer_style(model=trained_model, training_params=params["training_params"], takes_target=takes_target, forward_params=params["forward_params"]) # To do delete if "scaler" in dataset_params: if "scaler_params" in dataset_params: params["inference_params"]["dataset_params"][ "scaling"] = scaling_function({}, dataset_params)["scaling"] else: params["inference_params"]["dataset_params"][ "scaling"] = scaling_function({}, dataset_params)["scaling"] params["inference_params"]["dataset_params"].pop( 'scaler_params', None) test_acc = evaluate_model(trained_model, model_type, params["dataset_params"]["target_col"], params["metrics"], params["inference_params"], {}) wandb.run.summary["test_accuracy"] = test_acc[0] df_train_and_test = test_acc[1] forecast_start_idx = test_acc[2] df_prediction_samples = test_acc[3] mae = (df_train_and_test.loc[forecast_start_idx:, "preds"] - df_train_and_test.loc[forecast_start_idx:, params["dataset_params"]["target_col"][0]] ).abs() inverse_mae = 1 / mae i = 0 for df in df_prediction_samples: pred_std = df.std(axis=1) average_prediction_sharpe = (inverse_mae / pred_std).mean() wandb.log({ 'average_prediction_sharpe' + str(i): average_prediction_sharpe }) i += 1 df_train_and_test.to_csv("temp_preds.csv") # Log plots now if "probabilistic" in params["inference_params"]: test_plot = plot_df_test_with_probabilistic_confidence_interval( df_train_and_test, forecast_start_idx, params, ) elif len(df_prediction_samples) > 0: for thing in zip(df_prediction_samples, params["dataset_params"]["target_col"]): thing[0].to_csv(thing[1] + ".csv") test_plot = plot_df_test_with_confidence_interval( df_train_and_test, thing[0], forecast_start_idx, params, targ_col=thing[1], ci=95, alpha=0.25) wandb.log({"test_plot_" + thing[1]: test_plot}) else: pd.options.plotting.backend = "plotly" t = params["dataset_params"]["target_col"][0] test_plot = df_train_and_test[[t, "preds"]].plot() wandb.log({"test_plot_" + t: test_plot}) print("Now plotting final plots") test_plot_all = go.Figure() for relevant_col in params["dataset_params"]["relevant_cols"]: test_plot_all.add_trace( go.Scatter(x=df_train_and_test.index, y=df_train_and_test[relevant_col], name=relevant_col)) wandb.log({"test_plot_all": test_plot_all}) else: raise Exception("Please supply valid model type for forecasting") return trained_model
def handle_model_evaluation1(trained_model, params: Dict, model_type: str) -> None: """Utility function to help handle model evaluation. Primarily used at the moment for forcast :param trained_model: A PyTorchForecast model that has already been trained. :type trained_model: PyTorchForecast :param params: A dictionary of the trained model parameters. :type params: Dict :param model_type: The type of model. Almost always PyTorch in practice. :type model_type: str """ test_acc = evaluate_model(trained_model, model_type, params["dataset_params"]["target_col"], params["metrics"], params["inference_params"], {}) wandb.run.summary["test_accuracy"] = test_acc[0] df_train_and_test = test_acc[1] forecast_start_idx = test_acc[2] df_prediction_samples = test_acc[3] mae = (df_train_and_test.loc[forecast_start_idx:, "preds"] - df_train_and_test.loc[forecast_start_idx:, params["dataset_params"]["target_col"][0]] ).abs() inverse_mae = 1 / mae i = 0 for df in df_prediction_samples: pred_std = df.std(axis=1) average_prediction_sharpe = (inverse_mae / pred_std).mean() wandb.log( {'average_prediction_sharpe' + str(i): average_prediction_sharpe}) i += 1 df_train_and_test.to_csv("temp_preds.csv") # Log plots now if "probabilistic" in params["inference_params"]: test_plot = plot_df_test_with_probabilistic_confidence_interval( df_train_and_test, forecast_start_idx, params, ) elif len(df_prediction_samples) > 0: for thing in zip(df_prediction_samples, params["dataset_params"]["target_col"]): thing[0].to_csv(thing[1] + ".csv") test_plot = plot_df_test_with_confidence_interval( df_train_and_test, thing[0], forecast_start_idx, params, targ_col=thing[1], ci=95, alpha=0.25) wandb.log({"test_plot_" + thing[1]: test_plot}) else: pd.options.plotting.backend = "plotly" t = params["dataset_params"]["target_col"][0] test_plot = df_train_and_test[[t, "preds"]].plot() wandb.log({"test_plot_" + t: test_plot}) print("Now plotting final plots") test_plot_all = go.Figure() for relevant_col in params["dataset_params"]["relevant_cols"]: test_plot_all.add_trace( go.Scatter(x=df_train_and_test.index, y=df_train_and_test[relevant_col], name=relevant_col)) wandb.log({"test_plot_all": test_plot_all})