fixed formatting with black==25.1.0

edwinnglabs · edwinnglabs · commit 208f235eb432 · 2025-06-04T22:17:24.000-07:00
diff --git a/orbit/template/dlt.py b/orbit/template/dlt.py
@@ -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 + (
diff --git a/orbit/template/ktrlite.py b/orbit/template/ktrlite.py
@@ -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):
@@ -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:
diff --git a/orbit/template/lgt.py b/orbit/template/lgt.py
@@ -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(
diff --git a/tests/orbit/diagnostics/test_backtest.py b/tests/orbit/diagnostics/test_backtest.py
@@ -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
diff --git a/tests/orbit/models/test_ktr.py b/tests/orbit/models/test_ktr.py
@@ -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
 
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
diff --git a/tests/orbit/models/test_ktrlite.py b/tests/orbit/models/test_ktrlite.py
@@ -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
@@ -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
@@ -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):
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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

Original file line number	Diff line number	Diff line change
`@@ -751,10 +751,9 @@ def predict(`
`751`	`751`	`global_trend_level + global_trend_slope * idx * self._time_delta`
`752`	`752`	`)`
`753`	`753`	`elif self.global_trend_option == GlobalTrendOption.loglinear.name:`
`754`		`- full_global_trend[`
`755`		`- :, idx`
`756`		`- ] = global_trend_level + global_trend_slope * np.log(`
`757`		`- 1 + idx * self._time_delta`
	`754`	`+ full_global_trend[:, idx] = (`
	`755`	`+ global_trend_level`
	`756`	`+ + global_trend_slope * np.log(1 + idx * self._time_delta)`
`758`	`757`	`)`
`759`	`758`	`elif self.global_trend_option == GlobalTrendOption.logistic.name:`
`760`	`759`	`full_global_trend[:, idx] = self.global_floor + (`
Original file line number	Diff line number	Diff line change
`@@ -102,7 +102,7 @@ def test_backtester_test_metrics(iclaims_training_data, metrics):`
`102`	`102`	`"missing_flag", [False, True], ids=["full-values", "missing-values"]`
`103`	`103`	`)`
`104`	`104`	`@pytest.mark.parametrize(`
`105`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`105`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`106`	`106`	`)`
`107`	`107`	`def test_backtester_ktr_and_missing_val(make_daily_data, missing_flag):`
`108`	`108`	`train_df, test_df, _ = make_daily_data`
Original file line number	Diff line number	Diff line change
`@@ -8,7 +8,7 @@`
`8`	`8`	`SMAPE_TOLERANCE = 0.2`
`9`	`9`
`10`	`10`
`11`		`-@pytest.mark.parametrize("make_daily_data", [({"seasonality": None})], indirect=True)`
	`11`	`+@pytest.mark.parametrize("make_daily_data", [{"seasonality": None}], indirect=True)`
`12`	`12`	`def test_ktr_basic(make_daily_data):`
`13`	`13`	`train_df, _, _ = make_daily_data`
`14`	`14`
`@@ -101,7 +101,7 @@ def test_ktr_seasonality(make_daily_data, seasonality, seas_segments):`
`101`	`101`
`102`	`102`	`@pytest.mark.parametrize("regressor_col", [None, ["a", "b", "c"]])`
`103`	`103`	`@pytest.mark.parametrize(`
`104`		`- "make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True`
	`104`	`+ "make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True`
`105`	`105`	`)`
`106`	`106`	`def test_ktr_regression(make_daily_data, regressor_col):`
`107`	`107`	`train_df, test_df, coef = make_daily_data`
`@@ -134,7 +134,7 @@ def test_ktr_regression(make_daily_data, regressor_col):`
`134`	`134`	`[pd.date_range(start="2016-03-01", end="2019-01-01", freq="3M")],`
`135`	`135`	`)`
`136`	`136`	`@pytest.mark.parametrize(`
`137`		`- "make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True`
	`137`	`+ "make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True`
`138`	`138`	`)`
`139`	`139`	`def test_ktrx_coef_knot_dates(make_daily_data, regression_knot_dates):`
`140`	`140`	`train_df, test_df, coef = make_daily_data`
`@@ -167,7 +167,7 @@ def test_ktrx_coef_knot_dates(make_daily_data, regression_knot_dates):`
`167`	`167`
`168`	`168`	`@pytest.mark.parametrize("regression_knot_distance", [90, 120])`
`169`	`169`	`@pytest.mark.parametrize(`
`170`		`- "make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True`
	`170`	`+ "make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True`
`171`	`171`	`)`
`172`	`172`	`def test_ktrx_coef_knot_distance(make_daily_data, regression_knot_distance):`
`173`	`173`	`train_df, test_df, coef = make_daily_data`
`@@ -203,7 +203,7 @@ def test_ktrx_coef_knot_distance(make_daily_data, regression_knot_distance):`
`203`	`203`	`ids=["positive_only", "negative_only", "regular_only", "mixed_signs"],`
`204`	`204`	`)`
`205`	`205`	`@pytest.mark.parametrize(`
`206`		`- "make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True`
	`206`	`+ "make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True`
`207`	`207`	`)`
`208`	`208`	`def test_ktrx_regressor_sign(make_daily_data, regressor_signs):`
`209`	`209`	`train_df, test_df, coef = make_daily_data`
`@@ -256,7 +256,7 @@ def test_ktrx_regressor_sign(make_daily_data, regressor_signs):`
`256`	`256`	`],`
`257`	`257`	`)`
`258`	`258`	`@pytest.mark.parametrize(`
`259`		`- "make_daily_data", [({"seasonality": "dual", "with_coef": True})], indirect=True`
	`259`	`+ "make_daily_data", [{"seasonality": "dual", "with_coef": True}], indirect=True`
`260`	`260`	`)`
`261`	`261`	`def test_ktrx_prior_ingestion(make_daily_data, coef_prior_list):`
`262`	`262`	`train_df, test_df, coef = make_daily_data`
Original file line number	Diff line number	Diff line change
`@@ -13,7 +13,7 @@`
`13`	`13`	`"seasonality_fs_order", [None, [5]], ids=["default_order", "manual_order"]`
`14`	`14`	`)`
`15`	`15`	`@pytest.mark.parametrize(`
`16`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`16`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`17`	`17`	`)`
`18`	`18`	`def test_ktrlite_single_seas(make_daily_data, seasonality_fs_order):`
`19`	`19`	`train_df, _, _ = make_daily_data`
`@@ -45,7 +45,7 @@ def test_ktrlite_single_seas(make_daily_data, seasonality_fs_order):`
`45`	`45`	`"seasonality_fs_order", [None, [2, 5]], ids=["default_order", "manual_order"]`
`46`	`46`	`)`
`47`	`47`	`@pytest.mark.parametrize(`
`48`		`- "make_daily_data", [({"with_dual_sea": True, "with_coef": False})], indirect=True`
	`48`	`+ "make_daily_data", [{"with_dual_sea": True, "with_coef": False}], indirect=True`
`49`	`49`	`)`
`50`	`50`	`def test_ktrlite_dual_seas(make_daily_data, seasonality_fs_order):`
`51`	`51`	`train_df, _, _ = make_daily_data`
`@@ -74,7 +74,7 @@ def test_ktrlite_dual_seas(make_daily_data, seasonality_fs_order):`
`74`	`74`
`75`	`75`
`76`	`76`	`@pytest.mark.parametrize(`
`77`		`- "make_daily_data", [({"with_dual_sea": True, "with_coef": False})], indirect=True`
	`77`	`+ "make_daily_data", [{"with_dual_sea": True, "with_coef": False}], indirect=True`
`78`	`78`	`)`
`79`	`79`	`@pytest.mark.parametrize("level_segments", [20, 10, 2])`
`80`	`80`	`def test_ktrlite_level_segments(make_daily_data, level_segments):`
`@@ -112,7 +112,7 @@ def test_ktrlite_level_segments(make_daily_data, level_segments):`
`112`	`112`	`],`
`113`	`113`	`)`
`114`	`114`	`@pytest.mark.parametrize(`
`115`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`115`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`116`	`116`	`)`
`117`	`117`	`def test_ktrlite_level_knot_dates(make_daily_data, level_knot_dates):`
`118`	`118`	`train_df, test_df, coef = make_daily_data`
`@@ -141,7 +141,7 @@ def test_ktrlite_level_knot_dates(make_daily_data, level_knot_dates):`
`141`	`141`
`142`	`142`	`@pytest.mark.parametrize("level_knot_distance", [90, 120])`
`143`	`143`	`@pytest.mark.parametrize(`
`144`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`144`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`145`	`145`	`)`
`146`	`146`	`def test_ktrlite_level_knot_distance(make_daily_data, level_knot_distance):`
`147`	`147`	`train_df, test_df, coef = make_daily_data`
`@@ -175,7 +175,7 @@ def test_ktrlite_level_knot_distance(make_daily_data, level_knot_distance):`
`175`	`175`	`],`
`176`	`176`	`)`
`177`	`177`	`@pytest.mark.parametrize(`
`178`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`178`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`179`	`179`	`)`
`180`	`180`	`def test_ktrlite_seas_segments(make_daily_data, seas_segments):`
`181`	`181`	`train_df, test_df, coef = make_daily_data`
`@@ -204,7 +204,7 @@ def test_ktrlite_seas_segments(make_daily_data, seas_segments):`
`204`	`204`
`205`	`205`
`206`	`206`	`@pytest.mark.parametrize(`
`207`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`207`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`208`	`208`	`)`
`209`	`209`	`def test_ktrlite_predict_decompose(make_daily_data):`
`210`	`210`	`train_df, test_df, coef = make_daily_data`
`@@ -245,7 +245,7 @@ def test_ktrlite_predict_decompose(make_daily_data):`
`245`	`245`
`246`	`246`
`247`	`247`	`@pytest.mark.parametrize(`
`248`		`- "make_daily_data", [({"seasonality": "single", "with_coef": False})], indirect=True`
	`248`	`+ "make_daily_data", [{"seasonality": "single", "with_coef": False}], indirect=True`
`249`	`249`	`)`
`250`	`250`	`def test_ktrlite_predict_decompose_point_estimate(make_daily_data):`
`251`	`251`	`train_df, test_df, coef = make_daily_data`