fixed formatting with black==25.1.0

Author: edwinnglabs

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 + (

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:

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(

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

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

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