Automated Latest Dependency Updates (#4403)

* Update latest dependencies * Pin shap and fix skopt test * lint fix with black updates --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: eccabay <becca.mcbrayer@alteryx.com>
alteryx · Mar 22, 2024 · 980b303 · 980b303
1 parent 83f482f
commit 980b303
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Showing 10 changed files with 41 additions and 41 deletions.
diff --git a/.github/meta.yaml b/.github/meta.yaml
@@ -34,7 +34,7 @@ outputs:
         - colorama >=0.4.4
         - cloudpickle >=1.5.0
         - click>=8.0.0
-        - shap >=0.42.0
+        - shap >=0.42.0, <0.45.0
         - texttable >=1.6.2
         - woodwork >=0.22.0
         - featuretools>=1.16.0

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -27,7 +27,7 @@ repos:
       - id: add-trailing-comma
         name: Add trailing comma
   - repo: https://github.com/python/black
-    rev: 22.12.0
+    rev: 24.3.0
     hooks:
       - id: black
         args:

diff --git a/evalml/automl/engine/engine_base.py b/evalml/automl/engine/engine_base.py
@@ -346,9 +346,9 @@ def _train_and_score(X_train, X_score, y_train, y_score, fold_num=None):
             and stored_pipeline is not None
             and stored_pipeline.threshold is not None
         ):
-            evaluation_entry[
-                "binary_classification_threshold"
-            ] = stored_pipeline.threshold
+            evaluation_entry["binary_classification_threshold"] = (
+                stored_pipeline.threshold
+            )
         cv_data.append(evaluation_entry)
     cv_scores = pd.Series([fold["mean_cv_score"] for fold in cv_data])
     cv_score_mean = cv_scores.mean()

diff --git a/evalml/data_checks/data_check_action_option.py b/evalml/data_checks/data_check_action_option.py
@@ -88,11 +88,11 @@ def to_dict(self):
 
         parameters_dict = self.parameters.copy()
         for parameter_dict in parameters_dict.values():
-            parameter_dict[
-                "parameter_type"
-            ] = DCAOParameterType.handle_dcao_parameter_type(
-                parameter_dict["parameter_type"],
-            ).value
+            parameter_dict["parameter_type"] = (
+                DCAOParameterType.handle_dcao_parameter_type(
+                    parameter_dict["parameter_type"],
+                ).value
+            )
 
         action_option_dict.update({"parameters": parameters_dict})
         return action_option_dict

diff --git a/evalml/model_understanding/prediction_explanations/_user_interface.py b/evalml/model_understanding/prediction_explanations/_user_interface.py
@@ -955,14 +955,14 @@ def make_dict(self, data):
             if self.heading_maker:
                 section["rank"] = self.heading_maker.make_dict(rank)
             if self.make_predicted_values_maker:
-                section[
-                    "predicted_values"
-                ] = self.make_predicted_values_maker.make_dict(
-                    index,
-                    data.y_pred,
-                    data.y_true,
-                    data.errors,
-                    pd.Series(data.pipeline_features.index),
+                section["predicted_values"] = (
+                    self.make_predicted_values_maker.make_dict(
+                        index,
+                        data.y_pred,
+                        data.y_true,
+                        data.errors,
+                        pd.Series(data.pipeline_features.index),
+                    )
                 )
             section["explanations"] = self.table_maker.make_dict(
                 index,
@@ -993,9 +993,9 @@ def make_dataframe(self, data):
                 for key, value in heading.items():
                     if key == "probabilities":
                         for class_name, probability in value.items():
-                            explanation_table[
-                                f"label_{class_name}_probability"
-                            ] = probability
+                            explanation_table[f"label_{class_name}_probability"] = (
+                                probability
+                            )
                     else:
                         explanation_table[key] = value
             if self.heading_maker:

diff --git a/evalml/pipelines/components/transformers/preprocessing/time_series_featurizer.py b/evalml/pipelines/components/transformers/preprocessing/time_series_featurizer.py
@@ -141,13 +141,13 @@ def fit(self, X, y=None):
 
             self.statistically_significant_lags = {}
             for column in y.columns:
-                self.statistically_significant_lags[
-                    column
-                ] = self._find_significant_lags(
-                    y[column],
-                    conf_level=self.conf_level,
-                    start_delay=self.start_delay,
-                    max_delay=self.max_delay,
+                self.statistically_significant_lags[column] = (
+                    self._find_significant_lags(
+                        y[column],
+                        conf_level=self.conf_level,
+                        start_delay=self.start_delay,
+                        max_delay=self.max_delay,
+                    )
                 )
                 if len(y.columns) == 1:
                     self.statistically_significant_lags = (

diff --git a/evalml/pipelines/components/transformers/preprocessing/time_series_regularizer.py b/evalml/pipelines/components/transformers/preprocessing/time_series_regularizer.py
@@ -203,9 +203,9 @@ def _identify_indices(
 
         for each_duplicate in duplicates:
             for each_range in range(each_duplicate["range"]):
-                error_dict["duplicate"][
-                    each_duplicate["idx"] + each_range
-                ] = pd.to_datetime(each_duplicate["dt"])
+                error_dict["duplicate"][each_duplicate["idx"] + each_range] = (
+                    pd.to_datetime(each_duplicate["dt"])
+                )
 
         for each_extra in extra:
             for each_range in range(each_extra["range"]):
@@ -310,9 +310,9 @@ def transform(self, X, y=None):
         for index, values in self.error_dict["misaligned"].items():
             to_replace = X.iloc[index]
             to_replace[self.time_index] = values["correct"]
-            cleaned_x.loc[
-                cleaned_x[self.time_index] == values["correct"]
-            ] = to_replace.values
+            cleaned_x.loc[cleaned_x[self.time_index] == values["correct"]] = (
+                to_replace.values
+            )
             if y is not None and isinstance(y, pd.Series):
                 cleaned_y.loc[cleaned_y[self.time_index] == values["correct"]] = y.iloc[
                     index

diff --git a/evalml/tests/dependency_update_check/latest_dependency_versions.txt b/evalml/tests/dependency_update_check/latest_dependency_versions.txt
@@ -1,4 +1,4 @@
-black==24.2.0
+black==24.3.0
 catboost==1.2.3
 category-encoders==2.5.1.post0
 click==8.1.7
@@ -7,7 +7,7 @@ colorama==0.4.6
 dask==2023.5.0
 distributed==2023.5.0
 featuretools==1.28.0
-graphviz==0.20.1
+graphviz==0.20.3
 holidays==0.20
 imbalanced-learn==0.12.0
 ipywidgets==8.1.2
@@ -19,13 +19,13 @@ matplotlib-inline==0.1.6
 networkx==3.1
 nlp-primitives==2.11.0
 numpy==1.24.4
-packaging==23.2
+packaging==24.0
 pandas==2.0.3
-plotly==5.19.0
+plotly==5.20.0
 pmdarima==2.0.4
 pyzmq==25.1.2
 scikit-learn==1.3.2
-scikit-optimize==0.9.0
+scikit-optimize==0.10.1
 scipy==1.10.1
 seaborn==0.13.2
 shap==0.44.1

diff --git a/evalml/tests/tuner_tests/test_skopt_tuner.py b/evalml/tests/tuner_tests/test_skopt_tuner.py
@@ -102,7 +102,7 @@ def test_skopt_tuner_basic():
 def test_skopt_tuner_invalid_ranges():
     with pytest.raises(
         ValueError,
-        match="Invalid dimension \\[\\]. Read the documentation for supported types.",
+        match="Invalid dimension",
     ):
         SKOptTuner(
             {

diff --git a/pyproject.toml b/pyproject.toml
@@ -38,7 +38,7 @@ dependencies = [
     "colorama >= 0.4.4",
     "cloudpickle >= 1.5.0",
     "click >= 8.0.0",
-    "shap >= 0.42.0",
+    "shap >= 0.42.0, < 0.45.0",
     "statsmodels >= 0.12.2",
     "texttable >= 1.6.2",
     "woodwork[dask] >= 0.22.0",