def test_pipe_classification():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [5]
pipe = Pype([('seg', SegmentX()), ('ftr', FeatureRep()),
('rf', RandomForestClassifier(n_estimators=10))])
pipe.fit(X, y)
pipe.predict(X)
pipe.transform_predict(X, y)
pipe.predict_proba(X)
pipe.predict_log_proba(X)
pipe.score(X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = TS_Data(Xt, Xc)
y = [5]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# multiple time series
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# univariate data
Xt = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
Xc = np.random.rand(3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# transform pipe
pipe = Pype([('seg', SegmentX()), ('ftr', FeatureRep()),
('scaler', StandardScaler())])
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform(X, y)
pipe.fit_transform(X, y)
kf = KFold(n_splits=5, random_state=42, shuffle=True)
model_v1_list = []
score_v1_list = []
for train_index, test_index in kf.split(X_train):
model_v1 = Pype([('segment', SegmentX(width=10)),
('features', FeatureRep()),
('scaler', StandardScaler()),
('rf', RandomForestClassifier(n_estimators=100, random_state=42))])
model_v1.fit(np.array(X_train)[train_index], y_train[train_index])
model_v1_list.append(model_v1)
y_pred = []
for test_sample in np.array(X_train)[test_index]:
result = model_v1.predict_proba([test_sample])
pred = np.argmax(np.sum(result, axis=0) / result.shape[0])
y_pred.append(pred)
score_v1_list.append(f1_score(y_train[test_index], y_pred, average='macro'))
print(score_v1_list)
print(np.mean(score_v1_list), np.std(score_v1_list))
result_list = []
for model in model_v1_list:
result = []
for test_sample in X_test:
sample_result = model_v1.predict_proba([test_sample])
proba = np.sum(sample_result, axis=0) / sample_result.shape[0]
result.append(proba)
result_list.append(result)