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'],
}