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fix formatting with black==25.1.0 #883

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Merged
edwinnglabs merged 1 commit into from
Jun 5, 2025
Merged

fix formatting with black==25.1.0 #883

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7 changes: 3 additions & 4 deletions orbit/template/dlt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -751,10 +751,9 @@ def predict(
global_trend_level + global_trend_slope * idx * self._time_delta
)
elif self.global_trend_option == GlobalTrendOption.loglinear.name:
full_global_trend[
:, idx
] = global_trend_level + global_trend_slope * np.log(
1 + idx * self._time_delta
full_global_trend[:, idx] = (
global_trend_level
+ global_trend_slope * np.log(1 + idx * self._time_delta)
)
elif self.global_trend_option == GlobalTrendOption.logistic.name:
full_global_trend[:, idx] = self.global_floor + (
Expand Down
12 changes: 6 additions & 6 deletions orbit/template/ktrlite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -189,9 +189,9 @@ def set_init_values(self):
init_values = None
if len(self._seasonality) > 1 and self.num_of_regressors > 0:
init_values = dict()
init_values[
RegressionSamplingParameters.COEFFICIENTS_KNOT.value
] = np.zeros((self.num_of_regressors, self.num_knots_coefficients))
init_values[RegressionSamplingParameters.COEFFICIENTS_KNOT.value] = (
np.zeros((self.num_of_regressors, self.num_knots_coefficients))
)
self._init_values = init_values

def _set_default_args(self):
Expand Down Expand Up @@ -496,9 +496,9 @@ def predict(
seas_regression = np.sum(
seas_coef * seasonal_regressor_matrix.transpose(1, 0), axis=-2
)
seas_decomp[
"seasonality_{}".format(self._seasonality[idx])
] = seas_regression
seas_decomp["seasonality_{}".format(self._seasonality[idx])] = (
seas_regression
)
pos += len(cols)
total_seas_regression += seas_regression
if include_error:
Expand Down
6 changes: 3 additions & 3 deletions orbit/template/lgt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,9 +231,9 @@ def set_init_values(self):
-1.0,
1.0,
)
init_values[
LatentSamplingParameters.INITIAL_SEASONALITY.value
] = init_sea
init_values[LatentSamplingParameters.INITIAL_SEASONALITY.value] = (
init_sea
)
if self.num_of_positive_regressors > 0:
x = np.clip(
np.random.normal(
Expand Down
2 changes: 1 addition & 1 deletion tests/orbit/diagnostics/test_backtest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,7 +102,7 @@ def test_backtester_test_metrics(iclaims_training_data, metrics):
"missing_flag", [False, True], ids=["full-values", "missing-values"]
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_backtester_ktr_and_missing_val(make_daily_data, missing_flag):
train_df, test_df, _ = make_daily_data
Expand Down
12 changes: 6 additions & 6 deletions tests/orbit/models/test_ktr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
SMAPE_TOLERANCE = 0.2


@pytest.mark.parametrize("make_daily_data", [({"seasonality": None})], indirect=True)
@pytest.mark.parametrize("make_daily_data", [{"seasonality": None}], indirect=True)
def test_ktr_basic(make_daily_data):
train_df, _, _ = make_daily_data

Expand Down Expand Up @@ -101,7 +101,7 @@ def test_ktr_seasonality(make_daily_data, seasonality, seas_segments):

@pytest.mark.parametrize("regressor_col", [None, ["a", "b", "c"]])
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True
"make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True
)
def test_ktr_regression(make_daily_data, regressor_col):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -134,7 +134,7 @@ def test_ktr_regression(make_daily_data, regressor_col):
[pd.date_range(start="2016-03-01", end="2019-01-01", freq="3M")],
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True
"make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True
)
def test_ktrx_coef_knot_dates(make_daily_data, regression_knot_dates):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -167,7 +167,7 @@ def test_ktrx_coef_knot_dates(make_daily_data, regression_knot_dates):

@pytest.mark.parametrize("regression_knot_distance", [90, 120])
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True
"make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True
)
def test_ktrx_coef_knot_distance(make_daily_data, regression_knot_distance):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -203,7 +203,7 @@ def test_ktrx_coef_knot_distance(make_daily_data, regression_knot_distance):
ids=["positive_only", "negative_only", "regular_only", "mixed_signs"],
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True
"make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True
)
def test_ktrx_regressor_sign(make_daily_data, regressor_signs):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -256,7 +256,7 @@ def test_ktrx_regressor_sign(make_daily_data, regressor_signs):
],
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True
"make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True
)
def test_ktrx_prior_ingestion(make_daily_data, coef_prior_list):
train_df, test_df, coef = make_daily_data
Expand Down
16 changes: 8 additions & 8 deletions tests/orbit/models/test_ktrlite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
"seasonality_fs_order", [None, [5]], ids=["default_order", "manual_order"]
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_ktrlite_single_seas(make_daily_data, seasonality_fs_order):
train_df, _, _ = make_daily_data
Expand Down Expand Up @@ -45,7 +45,7 @@ def test_ktrlite_single_seas(make_daily_data, seasonality_fs_order):
"seasonality_fs_order", [None, [2, 5]], ids=["default_order", "manual_order"]
)
@pytest.mark.parametrize(
"make_daily_data", [({"with_dual_sea": True, "with_coef": False})], indirect=True
"make_daily_data", [{"with_dual_sea": True, "with_coef": False}], indirect=True
)
def test_ktrlite_dual_seas(make_daily_data, seasonality_fs_order):
train_df, _, _ = make_daily_data
Expand Down Expand Up @@ -74,7 +74,7 @@ def test_ktrlite_dual_seas(make_daily_data, seasonality_fs_order):


@pytest.mark.parametrize(
"make_daily_data", [({"with_dual_sea": True, "with_coef": False})], indirect=True
"make_daily_data", [{"with_dual_sea": True, "with_coef": False}], indirect=True
)
@pytest.mark.parametrize("level_segments", [20, 10, 2])
def test_ktrlite_level_segments(make_daily_data, level_segments):
Expand Down Expand Up @@ -112,7 +112,7 @@ def test_ktrlite_level_segments(make_daily_data, level_segments):
],
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_ktrlite_level_knot_dates(make_daily_data, level_knot_dates):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -141,7 +141,7 @@ def test_ktrlite_level_knot_dates(make_daily_data, level_knot_dates):

@pytest.mark.parametrize("level_knot_distance", [90, 120])
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_ktrlite_level_knot_distance(make_daily_data, level_knot_distance):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -175,7 +175,7 @@ def test_ktrlite_level_knot_distance(make_daily_data, level_knot_distance):
],
)
@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_ktrlite_seas_segments(make_daily_data, seas_segments):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -204,7 +204,7 @@ def test_ktrlite_seas_segments(make_daily_data, seas_segments):


@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_ktrlite_predict_decompose(make_daily_data):
train_df, test_df, coef = make_daily_data
Expand Down Expand Up @@ -245,7 +245,7 @@ def test_ktrlite_predict_decompose(make_daily_data):


@pytest.mark.parametrize(
"make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True
"make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True
)
def test_ktrlite_predict_decompose_point_estimate(make_daily_data):
train_df, test_df, coef = make_daily_data
Expand Down