multivariate regression experiment script added

fedot_ind/api/multi_ts_example.py

@@ -0,0 +1,84 @@
+import sys
+
+import numpy as np
+from fedot.api.main import Fedot
+from fedot.core.data.data import InputData
+from fedot.core.repository.dataset_types import DataTypesEnum
+from fedot.core.repository.tasks import Task, TaskTypesEnum
+
+from fedot_ind.core.architecture.preprocessing.DatasetLoader import DataLoader
+from fedot_ind.core.models.statistical.StatsExtractor import StatsExtractor
+from fedot_ind.core.operation.transformation.basis.data_driven import DataDrivenBasisImplementation
+from fedot_ind.core.operation.transformation.basis.fourier import FourierBasisImplementation
+from sklearn.decomposition import PCA
+from sklearn.metrics import explained_variance_score, f1_score, max_error, mean_absolute_error, mean_gamma_deviance, \
+    mean_poisson_deviance, \
+    mean_squared_error, \
+    mean_squared_log_error, \
+    mean_tweedie_deviance, median_absolute_error, r2_score, \
+    roc_auc_score
+
+
+def init_input(X, y):
+    input_data = InputData(idx=np.arange(len(X)),
+                           features=np.array(X.values.tolist()),
+                           target=y.reshape(-1, 1),
+                           task=Task(TaskTypesEnum.classification),
+                           data_type=DataTypesEnum.image)
+    return input_data
+
+
+if __name__ == "__main__":
+    # dataset_name = 'BenzeneConcentration'
+    # ddb_features_train = DataDrivenBasisImplementation({'window_size': 30,
+    #                                                     'sv_selector': 'median'}).transform(train_input_data)
+    # fourier_features_train = FourierBasisImplementation({"spectrum_type": "smoothed",
+    #                                                      "threshold": 20000}).transform(train_input_data)
+    # fourier_features_test = FourierBasisImplementation({"spectrum_type": "smoothed",
+    #                                                     "threshold": 20000}).transform(test_input_data)
+
+    dataset_name = 'LiveFuelMoistureContent'
+    pca_n_components = 300
+
+    train_data, test_data = DataLoader(dataset_name=dataset_name).load_data()
+    train_target = np.array([float(i) for i in train_data[1]])
+    test_target = np.array([float(i) for i in test_data[1]])
+    train_input_data = init_input(train_data[0], train_target)
+    test_input_data = init_input(test_data[0], test_target)
+
+    extractor = StatsExtractor({'window_mode': False,
+                                'window_size': None,
+                                'var_threshold': 0})
+    pca = PCA(n_components=pca_n_components,
+              svd_solver='full')
+
+    extracted_features_train = extractor.transform(train_input_data)
+    train_size = extracted_features_train.features.shape
+    train_features = extracted_features_train.features.reshape(train_size[0], train_size[1] * train_size[2])
+    train_features = pca.fit_transform(train_features)
+
+    extracted_features_test = extractor.transform(test_input_data)
+    test_size = extracted_features_test.features.shape
+    test_features = extracted_features_test.features.reshape(test_size[0], test_size[1] * test_size[2])
+    test_features = pca.transform(test_features)
+
+    predictor = Fedot(problem='regression', timeout=10, logging_level=20, metric='rmse')
+    model = predictor.fit(features=train_features, target=train_target)
+    labels = predictor.predict(features=test_features)
+
+    print('r2_score:', r2_score(test_target, labels))
+    print('mean_squared_error:', mean_squared_error(test_target, labels))
+    print('root_mean_squared_error:', np.sqrt(mean_squared_error(test_target, labels)))
+    print('mean_absolute_error', mean_absolute_error(test_target, labels))
+    print('median_absolute_error', median_absolute_error(test_target, labels))
+    print('mean_squared_log_error', mean_squared_log_error(test_target, labels))
+    print('explained_variance_score', explained_variance_score(test_target, labels))
+    print('max_error', max_error(test_target, labels))
+    print('mean_poisson_deviance', mean_poisson_deviance(test_target, labels))
+    print('mean_gamma_deviance', mean_gamma_deviance(test_target, labels))
+    print('mean_tweedie_deviance', mean_tweedie_deviance(test_target, labels))
+    print('mean_tweedie_deviance_power_1.5:', mean_tweedie_deviance(test_target, labels, power=1.5))
+    print('mean_tweedie_deviance_power_2:', mean_tweedie_deviance(test_target, labels, power=2))
+    print('mean_tweedie_deviance_power_3;', mean_tweedie_deviance(test_target, labels, power=3))
+
+    _ = 1