Automated Latest Dependency Updates (#4459)

* Update latest dependencies

* Lint fixes and exclusions for docs with ruff

* Workaround for nltk issue

* Pin pytest version

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: eccabay <[email protected]>

Author: 3 people

docs/source/user_guide/automl.ipynb

@@ -266,9 +266,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from evalml.pipelines import MulticlassClassificationPipeline\n",
-    "\n",
-    "\n",
     "automl_custom = evalml.automl.AutoMLSearch(\n",
     "    X_train=X_train,\n",
     "    y_train=y_train,\n",
@@ -368,6 +365,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "\n",
     "# error_callback example; this is implemented in the evalml library\n",
     "def raise_error_callback(exception, traceback, automl, **kwargs):\n",
     "    \"\"\"Raises the exception thrown by the AutoMLSearch object. Also logs the exception as an error.\"\"\"\n",

docs/source/user_guide/data_check_actions.ipynb

@@ -32,7 +32,6 @@
    "outputs": [],
    "source": [
     "import woodwork as ww\n",
-    "import pandas as pd\n",
     "from evalml import AutoMLSearch\n",
     "from evalml.demos import load_fraud\n",
     "from evalml.preprocessing import split_data"

docs/source/user_guide/data_checks.ipynb

@@ -688,7 +688,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from evalml.data_checks import NoVarianceDataCheck, DataCheckWarning\n",
+    "from evalml.data_checks import NoVarianceDataCheck, DataCheckError\n",
     "\n",
     "X = pd.DataFrame(\n",
     "    {\n",

docs/source/user_guide/model_understanding.ipynb

@@ -928,8 +928,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import shap\n",
-    "\n",
     "from evalml.model_understanding.force_plots import graph_force_plot\n",
     "\n",
     "rows_to_explain = [0]  # Should be a list of integer indices of the rows to explain.\n",

docs/source/user_guide/pipelines.ipynb

@@ -586,9 +586,7 @@
    "source": [
     "from evalml.pipelines.utils import generate_pipeline_code\n",
     "from evalml.pipelines import BinaryClassificationPipeline\n",
-    "import pandas as pd\n",
     "from evalml.utils import infer_feature_types\n",
-    "from skopt.space import Integer\n",
     "\n",
     "\n",
     "class MyDropNullColumns(Transformer):\n",

docs/source/user_guide/timeseries.ipynb

@@ -496,7 +496,7 @@
    "outputs": [],
    "source": [
     "stl = STLDecomposer()\n",
-    "assert stl.period == None\n",
+    "assert stl.period is None\n",
     "stl.fit(X_stl, y_stl)\n",
     "print(stl.period)"
    ]

evalml/tests/dependency_update_check/latest_dependency_versions.txt

@@ -1,30 +1,30 @@
-black==24.4.2
+black==24.8.0
 catboost==1.2.5
 category-encoders==2.5.1.post0
 click==8.1.7
 cloudpickle==3.0.0
 colorama==0.4.6
-dask==2024.6.2
-dask-expr==1.1.6
-distributed==2024.6.2
+dask==2024.8.0
+dask-expr==1.1.10
+distributed==2024.8.0
 featuretools==1.31.0
 graphviz==0.20.3
-holidays==0.52
+holidays==0.55
 imbalanced-learn==0.12.3
-ipywidgets==8.1.3
+ipywidgets==8.1.5
 kaleido==0.2.1
-lightgbm==4.4.0
+lightgbm==4.5.0
 lime==0.2.0.1
-matplotlib==3.9.1
+matplotlib==3.9.2
 matplotlib-inline==0.1.7
 networkx==3.2.1
 nlp-primitives==2.13.0
 numpy==1.26.4
 packaging==24.1
 pandas==2.0.3
-plotly==5.22.0
+plotly==5.23.0
 pmdarima==2.0.4
-pyzmq==26.0.3
+pyzmq==26.2.0
 scikit-learn==1.4.2
 scikit-optimize==0.10.2
 scipy==1.13.1
@@ -35,4 +35,4 @@ statsmodels==0.14.2
 texttable==1.7.0
 tomli==2.0.1
 woodwork==0.31.0
-xgboost==2.1.0
+xgboost==2.1.1

evalml/tests/model_understanding_tests/test_partial_dependence.py

@@ -2881,11 +2881,14 @@ def test_partial_dependence_dfs_transformer_handling_with_multi_output_primitive
         index="index",
         make_index=True,
     )
-    X_fm, features = ft.dfs(
-        entityset=es,
-        target_dataframe_name="X",
-        trans_primitives=["LSA"],
-    )
+    try:
+        X_fm, features = ft.dfs(
+            entityset=es,
+            target_dataframe_name="X",
+            trans_primitives=["LSA"],
+        )
+    except ValueError:
+        pytest.xfail("NLTK bug within nlp-primitives")
 
     dfs_transformer = DFSTransformer(features=features)
     pipeline = RegressionPipeline(

pyproject.toml

@@ -77,7 +77,7 @@ dependencies = [
 
 [project.optional-dependencies]
 test = [
-    "pytest >= 7.1.2",
+    "pytest >= 7.1.2, < 8.3.0",
     "pytest-xdist >= 2.1.0",
     "pytest-timeout >= 1.4.2",
     "pytest-cov >= 2.10.1",
@@ -171,7 +171,7 @@ src = ["evalml"]
 "evalml/tests/**" = ["D"]
 "evalml/utils/**" = ["D"]
 ".github/**" = ["D"]
-"docs/**" = ["D"]
+"docs/**" = ["D", "I001", "E402", "F811", "F401"]
 
 [tool.ruff.lint.isort]
 known-first-party = ["evalml"]