def test_fit(self): pipe = Pipeline(self.rf_pipe) tmp = pipe.fit(self.data.df_devices, self.data.df_activities)
""" Example: Cross Validation """ ts = TimeSeriesSplit(n_splits=5) scores = [] # cross validation on train set for train_int, val_int in ts.split(X_train): steps = [('enc', BinaryEncoder(encode='raw')), ('lbl', TrainOrEvalOnlyWrapper(LabelEncoder(idle=True))), ('drop_val', TrainOnlyWrapper(CVSubset(train_int))), ('drop_train', EvalOnlyWrapper(CVSubset(val_int))), ('drop_time_idx', DropTimeIndex()), ('classifier', RandomForestClassifier(random_state=42))] pipe = Pipeline(steps).train() pipe.fit(X_train, y_train) # evaluate pipe = pipe.eval() scores.append(pipe.score(X_train, y_train)) print('scores of the pipeline: {}'.format(str(scores))) print('mean score: {:.3f}'.format(np.array(scores).mean())) """ Simple Example Gridsearch """ from pyadlml.model_selection import GridSearchCV param_grid = { 'encode_devices__encode': ['changepoint', 'raw', 'lastfired'], }