def test_datetime_featurizer_numpy_array_input():
datetime_transformer = DateTimeFeaturizer()
X = np.array([['2007-02-03'], ['2016-06-07'], ['2020-05-19']], dtype='datetime64')
datetime_transformer.fit(X)
assert list(datetime_transformer.transform(X).columns) == ["0_year", "0_month", "0_day_of_week", "0_hour"]
assert datetime_transformer.get_feature_names() == {'0_month': {'February': 1, 'June': 5, 'May': 4},
'0_day_of_week': {'Saturday': 6, 'Tuesday': 2}}
def test_datetime_featurizer_custom_features_to_extract():
datetime_transformer = DateTimeFeaturizer(features_to_extract=["month", "year"])
rng = pd.date_range('2020-02-24', periods=20, freq='D')
X = pd.DataFrame({"date col": rng, "numerical": [0] * len(rng)})
datetime_transformer.fit(X)
assert list(datetime_transformer.transform(X).columns) == ["numerical", "date col_month", "date col_year"]
assert datetime_transformer.get_feature_names() == {"date col_month": {"February": 1, "March": 2}}
def test_datetime_featurizer_no_features_to_extract():
datetime_transformer = DateTimeFeaturizer(features_to_extract=[])
rng = pd.date_range('2020-02-24', periods=20, freq='D')
X = pd.DataFrame({"date col": rng, "numerical": [0] * len(rng)})
datetime_transformer.fit(X)
assert datetime_transformer.transform(X).equals(X)
assert datetime_transformer.get_feature_names() == {}
def test_datetime_featurizer_transform():
datetime_transformer = DateTimeFeaturizer(features_to_extract=["year"])
X = pd.DataFrame({
'Numerical 1':
range(20),
'Date Col 1':
pd.date_range('2000-05-19', periods=20, freq='D'),
'Date Col 2':
pd.date_range('2000-02-03', periods=20, freq='W'),
'Numerical 2': [0] * 20
})
X_test = pd.DataFrame({
'Numerical 1':
range(20),
'Date Col 1':
pd.date_range('2020-05-19', periods=20, freq='D'),
'Date Col 2':
pd.date_range('2020-02-03', periods=20, freq='W'),
'Numerical 2': [0] * 20
})
datetime_transformer.fit(X)
transformed_df = datetime_transformer.transform(X_test).to_dataframe()
assert list(transformed_df.columns) == [
'Numerical 1', 'Numerical 2', 'Date Col 1_year', 'Date Col 2_year'
]
assert transformed_df["Date Col 1_year"].equals(
pd.Series([2020] * 20, dtype="Int64"))
assert transformed_df["Date Col 2_year"].equals(
pd.Series([2020] * 20, dtype="Int64"))
assert datetime_transformer.get_feature_names() == {}
def test_datetime_featurizer_no_datetime_cols():
datetime_transformer = DateTimeFeaturizer(features_to_extract=["year", "month"])
X = pd.DataFrame([[1, 3, 4], [2, 5, 2]])
expected = X.astype("Int64")
datetime_transformer.fit(X)
transformed = datetime_transformer.transform(X).to_dataframe()
assert_frame_equal(expected, transformed)
assert datetime_transformer.get_feature_names() == {}
def test_datetime_featurizer_no_col_names():
datetime_transformer = DateTimeFeaturizer()
X = pd.DataFrame(pd.Series(pd.date_range('2020-02-24', periods=10, freq='D')))
datetime_transformer.fit(X)
assert list(datetime_transformer.transform(X).columns) == ['0_year', '0_month', '0_day_of_week', '0_hour']
assert datetime_transformer.get_feature_names() == {'0_month': {'February': 1, 'March': 2},
'0_day_of_week': {'Monday': 1, 'Tuesday': 2,
'Wednesday': 3, 'Thursday': 4, 'Friday': 5,
'Saturday': 6, 'Sunday': 0}}
def test_datetime_featurizer_no_features_to_extract():
datetime_transformer = DateTimeFeaturizer(features_to_extract=[])
rng = pd.date_range('2020-02-24', periods=20, freq='D')
X = pd.DataFrame({"date col": rng, "numerical": [0] * len(rng)})
expected = X.copy()
expected["numerical"] = expected["numerical"].astype("Int64")
datetime_transformer.fit(X)
transformed = datetime_transformer.transform(X).to_dataframe()
assert_frame_equal(expected, transformed)
assert datetime_transformer.get_feature_names() == {}
def test_datetime_featurizer_encodes_as_ints():
X = pd.DataFrame({"date": ["2016-04-10 16:10:09", "2017-03-15 13:32:05", "2018-07-10 07:15:10",
"2019-08-19 20:20:20", "2020-01-03 06:45:12"]})
dt = DateTimeFeaturizer()
X_transformed_df = dt.fit_transform(X)
expected = pd.DataFrame({"date_year": pd.Series([2016, 2017, 2018, 2019, 2020], dtype="Int64"),
"date_month": pd.Series([3, 2, 6, 7, 0], dtype="Int64"),
"date_day_of_week": pd.Series([0, 3, 2, 1, 5], dtype="Int64"),
"date_hour": pd.Series([16, 13, 7, 20, 6], dtype="Int64")})
feature_names = {'date_month': {'April': 3, 'March': 2, 'July': 6, 'August': 7, 'January': 0},
'date_day_of_week': {'Sunday': 0, 'Wednesday': 3, 'Tuesday': 2, 'Monday': 1, 'Friday': 5}
}
assert_frame_equal(expected, X_transformed_df.to_dataframe())
assert dt.get_feature_names() == feature_names
# Test that changing encode_as_categories to True only changes the dtypes but not the values
dt_with_cats = DateTimeFeaturizer(encode_as_categories=True)
X_transformed_df = dt_with_cats.fit_transform(X)
expected["date_month"] = pd.Categorical([3, 2, 6, 7, 0])
expected["date_day_of_week"] = pd.Categorical([0, 3, 2, 1, 5])
assert_frame_equal(expected, X_transformed_df.to_dataframe())
assert dt_with_cats.get_feature_names() == feature_names
# Test that sequential calls to the same DateTimeFeaturizer work as expected by using the first dt we defined
X = pd.DataFrame({"date": ["2020-04-10", "2017-03-15", "2019-08-19"]})
X_transformed_df = dt.fit_transform(X)
expected = pd.DataFrame({"date_year": pd.Series([2020, 2017, 2019], dtype="Int64"),
"date_month": pd.Series([3, 2, 7], dtype="Int64"),
"date_day_of_week": pd.Series([5, 3, 1], dtype="Int64"),
"date_hour": pd.Series([0, 0, 0], dtype="Int64")})
assert_frame_equal(expected, X_transformed_df.to_dataframe())
assert dt.get_feature_names() == {'date_month': {'April': 3, 'March': 2, 'August': 7},
'date_day_of_week': {'Friday': 5, 'Wednesday': 3, 'Monday': 1}}
dt = DateTimeFeaturizer(features_to_extract=["year", "hour"])
dt.fit_transform(X)
assert dt.get_feature_names() == {}
def test_datetime_featurizer_no_datetime_cols():
datetime_transformer = DateTimeFeaturizer(features_to_extract=["year", "month"])
X = pd.DataFrame([[1, 3, 4], [2, 5, 2]])
datetime_transformer.fit(X)
assert datetime_transformer.transform(X).equals(X)
assert datetime_transformer.get_feature_names() == {}