From 17073c14b8ce00468ddd4f648b27a7a4fa276826 Mon Sep 17 00:00:00 2001 From: morrisnein Date: Sat, 2 Dec 2023 00:01:50 +0300 Subject: [PATCH] =?UTF-8?q?=D1=81=D1=80=D1=83=D1=81=D1=80=20=D0=B5=D1=83?= =?UTF-8?q?=D1=8B=D0=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- fedot/core/data/data.py | 2 +- test/data/expected_metric_values.json | 94 +++++++++---------- test/data/temp/binary_pred.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_pred_accuracy.pkl | Bin 0 -> 2305 bytes test/data/temp/binary_pred_f1.pkl | Bin 0 -> 2305 bytes test/data/temp/binary_pred_neg_log_loss.pkl | Bin 0 -> 2305 bytes test/data/temp/binary_pred_precision.pkl | Bin 0 -> 2305 bytes test/data/temp/binary_pred_roc_auc.pkl | Bin 0 -> 2305 bytes test/data/temp/binary_pred_roc_auc_pen.pkl | Bin 0 -> 2305 bytes test/data/temp/binary_test.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_test_accuracy.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_test_f1.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_test_neg_log_loss.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_test_precision.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_test_roc_auc.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_test_roc_auc_pen.pkl | Bin 0 -> 10857 bytes test/data/temp/binary_train.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_train_accuracy.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_train_f1.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_train_neg_log_loss.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_train_precision.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_train_roc_auc.pkl | Bin 0 -> 40623 bytes test/data/temp/binary_train_roc_auc_pen.pkl | Bin 0 -> 40623 bytes test/unit/composer/test_metrics.py | 94 +++++++++++++++---- test/unit/tasks/test_regression.py | 2 +- 25 files changed, 127 insertions(+), 65 deletions(-) create mode 100644 test/data/temp/binary_pred.pkl create mode 100644 test/data/temp/binary_pred_accuracy.pkl create mode 100644 test/data/temp/binary_pred_f1.pkl create mode 100644 test/data/temp/binary_pred_neg_log_loss.pkl create mode 100644 test/data/temp/binary_pred_precision.pkl create mode 100644 test/data/temp/binary_pred_roc_auc.pkl create mode 100644 test/data/temp/binary_pred_roc_auc_pen.pkl create mode 100644 test/data/temp/binary_test.pkl create mode 100644 test/data/temp/binary_test_accuracy.pkl create mode 100644 test/data/temp/binary_test_f1.pkl create mode 100644 test/data/temp/binary_test_neg_log_loss.pkl create mode 100644 test/data/temp/binary_test_precision.pkl create mode 100644 test/data/temp/binary_test_roc_auc.pkl create mode 100644 test/data/temp/binary_test_roc_auc_pen.pkl create mode 100644 test/data/temp/binary_train.pkl create mode 100644 test/data/temp/binary_train_accuracy.pkl create mode 100644 test/data/temp/binary_train_f1.pkl create mode 100644 test/data/temp/binary_train_neg_log_loss.pkl create mode 100644 test/data/temp/binary_train_precision.pkl create mode 100644 test/data/temp/binary_train_roc_auc.pkl create mode 100644 test/data/temp/binary_train_roc_auc_pen.pkl diff --git a/fedot/core/data/data.py b/fedot/core/data/data.py index fc071d5a99..fc03906cbd 100644 --- a/fedot/core/data/data.py +++ b/fedot/core/data/data.py @@ -4,7 +4,7 @@ import os from copy import copy, deepcopy from dataclasses import dataclass, field -from typing import Any, Iterable, List, Optional, Tuple, Union +from typing import Any, Iterable, List, Optional, Tuple, Union, overload import numpy as np import pandas as pd diff --git a/test/data/expected_metric_values.json b/test/data/expected_metric_values.json index 9c9c193e94..4403a3cba7 100644 --- a/test/data/expected_metric_values.json +++ b/test/data/expected_metric_values.json @@ -2,64 +2,64 @@ "complexity": { "node_number": 0.4, "structural": 0.43333333333333335, - "computation_time_in_seconds": 0.001 + "computation_time_in_seconds": 0.0 }, "binary": { - "roc_auc": -0.9967141292442498, - "precision": 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"r2": 0.5213672450946683, - "rmse_pen": 52.77158291572949 + "rmse": 52.5400204534369, + "mse": 2760.4537492475683, + "neg_mean_squared_log_error": 0.11968905129112402, + "mape": 0.32791432529967834, + "smape": 49.48675123994897, + "mae": 41.55089094096007, + "r2": 0.389822739375963, + "rmse_pen": 52.64510049434378 }, "multitarget": { - "rmse": 4.242740603698809, - "mse": 34.447243787575864, - "neg_mean_squared_log_error": 0.0016149396995723982, - "mape": 0.024811474274980972, - "smape": 2.460824128804083, - "mae": 2.792325756927689, - "r2": 0.8938302810224498, - "rmse_pen": 4.2512260849062065 + "rmse": 15.753366859480218, + "mse": 377.5025166058113, + "neg_mean_squared_log_error": 0.030627538521796293, + "mape": 0.15337090733886807, + "smape": 14.144394353302935, + "mae": 13.50645038033778, + "r2": -2.9713973901034954, + "rmse_pen": 15.784873593199178 }, "ts": { - "mase": 0.15114880404154094, - "rmse": 1.5387331777721858, - "mse": 2.367699792376889, - "neg_mean_squared_log_error": 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zuSUZbd!G4AeqyP6VwQWIJEUMpLW(KboM1JfA8agml3&LZn`B#KO0rnvobhhmtH96b zILYReMAlJ9*d3PD3AWS}lg(~%n4JkWEAP?)wy30pIJ1*=_9T~ywXr*F(Uz1HON@I{ p!6=)Zw=*X_ddSymvzijDPW02k(YB=2WcH?9cHT=7{tM6}0z?1+ literal 0 HcmV?d00001 diff --git a/test/unit/composer/test_metrics.py b/test/unit/composer/test_metrics.py index bc677e5d50..bf71f32ceb 100644 --- a/test/unit/composer/test_metrics.py +++ b/test/unit/composer/test_metrics.py @@ -1,4 +1,5 @@ import json +import pickle from itertools import product from typing import Callable, Dict, Tuple, Union @@ -23,6 +24,7 @@ @pytest.fixture(scope='session') def data_setup(request): task_type = request.param + validation_blocks = None if task_type in ('binary', 'complexity'): x, y = load_breast_cancer(return_X_y=True) task = Task(TaskTypesEnum.classification) @@ -43,8 +45,9 @@ def data_setup(request): file_path = fedot_project_root() / 'test/data/short_time_series.csv' df = pd.read_csv(file_path) x = y = df['sea_height'].to_numpy() - task = Task(TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=10)) + task = Task(TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=5)) data_type = DataTypesEnum.ts + validation_blocks = 2 elif task_type == 'multits': file_path = fedot_project_root() / 'test/data/short_time_series.csv' df = pd.read_csv(file_path) @@ -52,6 +55,7 @@ def data_setup(request): y = df['sea_height'].to_numpy() task = Task(TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=10)) data_type = DataTypesEnum.multi_ts + validation_blocks = 2 else: raise ValueError(f'Unsupported task type: {task_type}') @@ -64,8 +68,8 @@ def data_setup(request): task=task, data_type=data_type) # Train test split - train_data, test_data = train_test_data_setup(input_data) - return train_data, test_data, task_type + train_data, test_data = train_test_data_setup(input_data, validation_blocks=validation_blocks) + return train_data, test_data, task_type, validation_blocks def get_classification_pipeline(): @@ -105,34 +109,92 @@ def expected_values() -> Dict[str, Dict[str, float]]: @pytest.mark.parametrize( - 'metric, pipeline_func, data_setup, validation_blocks', + 'metric, pipeline_func, data_setup', [ - *product(ComplexityMetricsEnum, [get_classification_pipeline], ['complexity'], [None]), - *product(ClassificationMetricsEnum, [get_classification_pipeline], ['binary', 'multiclass'], [None]), - *product(RegressionMetricsEnum, [get_regression_pipeline], ['regression', 'multitarget'], [None]), - *product(TimeSeriesForecastingMetricsEnum, [get_ts_pipeline], ['ts', 'multits'], [2]) + *product(ComplexityMetricsEnum, [get_classification_pipeline], ['complexity']), + *product(ClassificationMetricsEnum, [get_classification_pipeline], ['multiclass']), + *product(RegressionMetricsEnum, [get_regression_pipeline], ['regression', 'multitarget']), + *product(TimeSeriesForecastingMetricsEnum, [get_ts_pipeline], ['ts', 'multits']) ], indirect=['data_setup'] ) def test_metrics(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], Pipeline], - validation_blocks: Union[int, None], data_setup: Tuple[InputData, InputData, str], - expected_values: Dict[str, Dict[str, float]], update_expected_values: bool = False): - train, _, task_type = data_setup + data_setup: Tuple[InputData, InputData, str, Union[int, None]], + expected_values: Dict[str, Dict[str, float]], update_expected_values: bool = True): + train, test, task_type, validation_blocks = data_setup + + pipeline = pipeline_func() + pipeline.fit(input_data=train) + metric_function = MetricsRepository.get_metric(metric) + metric_class = MetricsRepository.get_metric_class(metric) + metric_value = metric_function(pipeline=pipeline, reference_data=test, validation_blocks=validation_blocks) + + if update_expected_values: + with open(fedot_project_root() / 'test/data/expected_metric_values.json', 'w') as f: + expected_values[task_type] = expected_values.get(task_type) or {} + expected_values[task_type][str(metric)] = metric_value + json.dump(expected_values, f, indent=2) + + expected_value = expected_values[task_type][str(metric)] + + assert np.isclose(metric_value, expected_value, rtol=0.001, atol=0.001) + assert not np.isclose(metric_value, metric_class.default_value, rtol=0.01, atol=0.01) + + +@pytest.mark.parametrize( + 'metric, pipeline_func, data_setup', + [ + *product(ClassificationMetricsEnum, [get_classification_pipeline], ['binary']), + ], + indirect=['data_setup'] +) +def test_binary_classification(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], Pipeline], + data_setup: Tuple[InputData, InputData, str, Union[int, None]], + expected_values: Dict[str, Dict[str, float]], update_expected_values: bool = False): + train, test, task_type, validation_blocks = data_setup pipeline = pipeline_func() pipeline.fit(input_data=train) metric_function = MetricsRepository.get_metric(metric) metric_class = MetricsRepository.get_metric_class(metric) - metric_value = metric_function(pipeline=pipeline, reference_data=train, validation_blocks=validation_blocks) + metric_value = metric_function(pipeline=pipeline, reference_data=test, validation_blocks=validation_blocks) if update_expected_values: with open(fedot_project_root() / 'test/data/expected_metric_values.json', 'w') as f: - expected_values = dict(binary={}, multiclass={}, regression={}, multitarget={}, ts={}, multits={}) + expected_values[task_type] = expected_values.get(task_type) or {} expected_values[task_type][str(metric)] = metric_value - json.dump(expected_values, f) + json.dump(expected_values, f, indent=2) + + pred = pipeline.predict(test) + expected_data_path = fedot_project_root() / 'test' / 'data' / 'temp' + expected_train_path = expected_data_path / f'{task_type}_train_{metric}.pkl' + expected_test_path = expected_data_path / f'{task_type}_test_{metric}.pkl' + expected_pred_path = expected_data_path / f'{task_type}_pred_{metric}.pkl' + if update_expected_values: + with open(expected_train_path, 'wb') as f: + pickle.dump(train, f) + with open(expected_test_path, 'wb') as f: + pickle.dump(test, f) + with open(expected_pred_path, 'wb') as f: + pickle.dump(pred, f) + expected_train = train + expected_test = test + expected_pred = pred + else: + with open(expected_train_path, 'rb') as f: + expected_train = pickle.load(f) + with open(expected_test_path, 'rb') as f: + expected_test = pickle.load(f) + with open(expected_pred_path, 'rb') as f: + expected_pred = pickle.load(f) expected_value = expected_values[task_type][str(metric)] + assert (train.features == expected_train.features).all() + assert (np.ravel(train.target) == np.ravel(expected_train.target)).all() + assert (test.features == expected_test.features).all() + assert (np.ravel(test.target) == np.ravel(expected_test.target)).all() + assert (np.ravel(pred.predict) == np.ravel(expected_pred.predict)).all() assert np.isclose(metric_value, expected_value, rtol=0.001, atol=0.001) assert not np.isclose(metric_value, metric_class.default_value, rtol=0.01, atol=0.01) @@ -149,7 +211,7 @@ def test_metrics(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], def test_ideal_case_metrics(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], Pipeline], validation_blocks: Union[int, None], data_setup: Tuple[InputData, InputData, str], expected_values): - reference, _, task_type = data_setup + reference, _, task_type, _ = data_setup metric_class = MetricsRepository.get_metric_class(metric) predicted = OutputData(idx=reference.idx, task=reference.task, data_type=reference.data_type) if task_type == 'multiclass' and metric_class.output_mode != 'labels': @@ -167,7 +229,7 @@ def test_ideal_case_metrics(metric: ClassificationMetricsEnum, pipeline_func: Ca @pytest.mark.parametrize('data_setup', ['multitarget'], indirect=True) def test_predict_shape_multi_target(data_setup: Tuple[InputData, InputData, str]): - train, test, _ = data_setup + train, test, _, _ = data_setup simple_pipeline = Pipeline(PipelineNode('linear')) simple_pipeline.fit(input_data=train) diff --git a/test/unit/tasks/test_regression.py b/test/unit/tasks/test_regression.py index 8d90a5f9be..7489d1c5b6 100644 --- a/test/unit/tasks/test_regression.py +++ b/test/unit/tasks/test_regression.py @@ -103,7 +103,7 @@ def test_regression_pipeline_with_data_operation_fit_predict_correct(): @pytest.mark.parametrize('data_setup', ['multitarget'], indirect=True) def test_multi_target_regression_composing_correct(data_setup): # Load simple dataset for multi-target - train, test, _ = data_setup + train, test, _, _ = data_setup problem = 'regression' timeout = 0.1