mustafa-qamaruddin

model = build_model(_alpha=1.0, _l1_ratio=0.3)
kfcv = KFold(n_splits=5)
scores = cross_val_score(model, X_train, y_train, cv=kfcv, scoring=r2)
print("Loss: {0:.3f} (+/- {1:.3f})".format(scores.mean(), scores.std()))

mustafa-qamaruddin

Loss: -103.076 (+/- 205.979)

mustafa-qamaruddin

model = build_model(_alpha=1.0, _l1_ratio=0.3)
tscv = TimeSeriesSplit(n_splits=5)
scores = cross_val_score(model, X_train, y_train, cv=tscv, scoring=r2)
print("Loss: {0:.3f} (+/- {1:.3f})".format(scores.mean(), scores.std()))

mustafa-qamaruddin

Loss: -9.799 (+/- 19.292)

mustafa-qamaruddin

class BlockingTimeSeriesSplit():
    def __init__(self, n_splits):
        self.n_splits = n_splits
    
    def get_n_splits(self, X, y, groups):
        return self.n_splits
    
    def split(self, X, y=None, groups=None):
        n_samples = len(X)
        k_fold_size = n_samples // self.n_splits

mustafa-qamaruddin

model = build_model(_alpha=1.0, _l1_ratio=0.3)
btscv = BlockingTimeSeriesSplit(n_splits=5)
scores = cross_val_score(model, X_train, y_train, cv=btscv, scoring=r2)
print("Loss: {0:.3f} (+/- {1:.3f})".format(scores.mean(), scores.std()))

mustafa-qamaruddin

Loss: -15.527 (+/- 27.488)

mustafa-qamaruddin

params = {
    'estimator__alpha':(0.1, 0.3, 0.5, 0.7, 0.9),
    'estimator__l1_ratio':(0.1, 0.3, 0.5, 0.7, 0.9)
}

for i in range(100):
    model = build_model(_alpha=1.0, _l1_ratio=0.3)

    finder = GridSearchCV(
        estimator=model,

mustafa-qamaruddin

# optimal model
model = build_model(_alpha=0.1, _l1_ratio=0.1)
# train model
model.fit(X_train, y_train)
# test score
y_predicted = model.predict(X_test)
score = r2_score(y_test, y_predicted, multioutput='uniform_average')
print("Test Loss: {0:.3f}".format(score))

mustafa-qamaruddin

Test Loss: 0.925