def test_predict_proba():
clf = TimeSeriesForestClassifier(n_estimators=2)
clf.fit(X, y)
proba = clf.predict_proba(X)
assert proba.shape == (X.shape[0], n_classes)
np.testing.assert_array_equal(np.ones(X.shape[0]), np.sum(proba, axis=1))
# test single row input
y_proba = clf.predict_proba(X.iloc[[0], :])
assert y_proba.shape == (1, n_classes)
y_pred = clf.predict(X.iloc[[0], :])
assert y_pred.shape == (1,)
y_train = pd.Series(y_train.reshape(-1))
y_test = pd.Series(y_test.reshape(-1))
# Timeseries random foreset for every column
for i, col in enumerate(col_names[:2]):
print(col)
# Choose one feature
X_train_step = X_train.iloc[:, [i]]
X_test_step = X_test.iloc[:, [i]]
# Time series forest clf
classifier = TimeSeriesForestClassifier()
classifier.fit(X_train_step, y_train)
y_pred = classifier.predict(X_test_step)
# Metrics
print(f'accuracy_test: {accuracy_score(y_test, y_pred)}')
print(f"recall_test: {recall_score(y_test, y_pred)}")
print(f"precisoin_test: {precision_score(y_test, y_pred)}")
print(f"f1_test: {f1_score(y_test, y_pred)}")
# clf2 = pickle.loads(s)
# clf2.predict(X_test[0:1])
# # KNeighbors Classifier
# clf = KNeighborsTimeSeriesClassifier(n_neighbors=2,