def test_timegapsplit_using_splits():
cv = TimeGapSplit(date_serie=df["date"],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=1),
n_splits=3)
assert len(list(cv.split(X_train, y_train))) == 3
def test_timegapsplit():
cv = TimeGapSplit(
date_serie=df["date"],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=0),
)
for i, indices in enumerate(cv.split(X_train, y_train)):
train_mindate = df.loc[X_train.iloc[indices[0]].index]["date"].min()
train_maxdate = df.loc[X_train.iloc[indices[0]].index]["date"].max()
valid_mindate = df.loc[X_train.iloc[indices[1]].index]["date"].min()
valid_maxdate = df.loc[X_train.iloc[indices[1]].index]["date"].max()
assert train_mindate <= train_maxdate <= valid_mindate <= valid_maxdate
# regression testing, check if output changes of the last fold
assert train_mindate == datetime.datetime.strptime(
"2018-01-16", "%Y-%m-%d")
assert train_maxdate == datetime.datetime.strptime(
"2018-01-20", "%Y-%m-%d")
assert valid_mindate == datetime.datetime.strptime(
"2018-01-21", "%Y-%m-%d")
assert valid_maxdate == datetime.datetime.strptime(
"2018-01-23", "%Y-%m-%d")
def test_timegapsplit_too_many_splits():
cv = TimeGapSplit(date_serie=df["date"],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=1),
n_splits=7)
with pytest.raises(ValueError):
list(cv.split(X_train, y_train))
def test_timegapsplit_without_train_duration():
cv = TimeGapSplit(date_serie=df["date"],
train_duration=None,
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=5),
n_splits=3)
csv = list(cv.split(X_train, y_train))
assert len(csv) == 3
assert cv.train_duration == timedelta(days=10)
def test_timegapsplit_summary():
cv = TimeGapSplit(date_serie=df['date'],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=0))
summary = cv.summary(X_train)
assert summary.shape == (12, 5)
def test_timegapsplit_with_a_gap():
gap_duration = timedelta(days=2)
cv_gap = TimeGapSplit(date_serie=df['date'],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=gap_duration)
for i, indices in enumerate(cv_gap.split(X_train, y_train)):
train_mindate = df.loc[X_train.iloc[indices[0]].index]['date'].min()
train_maxdate = df.loc[X_train.iloc[indices[0]].index]['date'].max()
valid_mindate = df.loc[X_train.iloc[indices[1]].index]['date'].min()
valid_maxdate = df.loc[X_train.iloc[indices[1]].index]['date'].max()
assert train_mindate <= train_maxdate <= valid_mindate <= valid_maxdate
assert valid_mindate - train_maxdate >= gap_duration
def test_timegapsplit_train_or_nsplit():
with pytest.raises(ValueError):
cv = TimeGapSplit(date_serie=df["date"],
train_duration=None,
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=5),
n_splits=None)
def test_timegapsplit_too_big_gap():
try:
TimeGapSplit(date_serie=df['date'],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=5))
except ValueError:
print("Successfully failed")
def test_timegapsplit_with_gridsearch():
cv = TimeGapSplit(
date_serie=df["date"],
train_duration=timedelta(days=5),
valid_duration=timedelta(days=3),
gap_duration=timedelta(days=0),
)
Lasso(random_state=0, tol=0.1, alpha=0.8).fit(X_train, y_train)
pipe = Pipeline([("reg", Lasso(random_state=0, tol=0.1))])
alphas = [0.1, 0.5, 0.8]
grid = GridSearchCV(pipe, {"reg__alpha": alphas}, cv=cv)
grid.fit(X_train, y_train)
best_C = grid.best_estimator_.get_params()["reg__alpha"]
assert best_C