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Automated Latest Dependency Updates (#4459)
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* 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 <becca.mcbrayer@alteryx.com>
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3 people authored Aug 23, 2024
1 parent d5d42a4 commit e20058e
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Showing 9 changed files with 28 additions and 28 deletions.
8 changes: 5 additions & 3 deletions docs/source/user_guide/automl.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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",
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1 change: 0 additions & 1 deletion docs/source/user_guide/data_check_actions.ipynb
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Expand Up @@ -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"
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2 changes: 1 addition & 1 deletion docs/source/user_guide/data_checks.ipynb
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Expand Up @@ -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",
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2 changes: 0 additions & 2 deletions docs/source/user_guide/model_understanding.ipynb
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Expand Up @@ -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",
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2 changes: 0 additions & 2 deletions docs/source/user_guide/pipelines.ipynb
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Expand Up @@ -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",
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2 changes: 1 addition & 1 deletion docs/source/user_guide/timeseries.ipynb
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Expand Up @@ -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)"
]
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22 changes: 11 additions & 11 deletions evalml/tests/dependency_update_check/latest_dependency_versions.txt
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@@ -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
Expand All @@ -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
Original file line number Diff line number Diff line change
Expand Up @@ -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(
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4 changes: 2 additions & 2 deletions pyproject.toml
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Expand Up @@ -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",
Expand Down Expand Up @@ -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"]
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