Unpin dependencies from [project.optional-dependencies]

.github/meta.yaml

@@ -52,7 +52,7 @@ outputs:
       imports:
         - evalml
       requires:
-        - pytest ==6.2.5
+        - pytest >=6.2.5
         - nbval >=0.9.3
       source_files:
         - evalml/*
@@ -85,7 +85,7 @@ outputs:
       imports:
         - evalml
       requires:
-        - pytest ==6.2.5
+        - pytest >=6.2.5
         - nbval >=0.9.3
         - python-graphviz >=0.8.4
         - category_encoders >=2.0.0, <=2.5.1.post0

docs/source/disable-warnings.py

@@ -1,6 +1,5 @@
-# flake8: noqa 401 imported to force console mode for tqdm in jupyter notebooks
-from tqdm.auto import tqdm
-
 import warnings
 
+from tqdm.auto import tqdm  # noqa: F401
+
 warnings.filterwarnings("ignore")

docs/source/release_notes.rst

@@ -9,6 +9,7 @@ Release Notes
         * Removed vowpalwabbit :pr:`4427`
         * Uncapped holidays :pr:`4428`
         * Unpinned kaleido :pr:`4423`
+        * Unpinned pinned dependencies under project.optional-dependencies :pr:`4431`
     * Documentation Changes
     * Testing Changes
         * Run airflow tests in Python 3.9 :pr:`4391`

evalml/tests/automl_tests/test_automl.py

@@ -1530,7 +1530,7 @@
 
     assert automl.results["pipeline_results"][0]["mean_cv_score"] == 1.0
 
-    with pytest.raises(AttributeError, match="set attribute"):
+    with pytest.raises(AttributeError):
         automl.results = 2.0
 
     automl.results["pipeline_results"][0]["mean_cv_score"] = 2.0
@@ -3846,13 +3846,13 @@
 def test_high_cv_check_no_warning_for_divide_by_zero(X_y_binary, dummy_binary_pipeline):
     X, y = X_y_binary
     automl = AutoMLSearch(X_train=X, y_train=y, problem_type="binary")
-    with pytest.warns(None) as warnings:
+    with warnings.catch_warnings(record=True) as automl_warnings:
         # mean is 0 but std is not
         automl._check_for_high_variance(
             dummy_binary_pipeline,
             cv_scores=[0.0, 1.0, -1.0],
         )
-    assert len(warnings) == 0
+    assert len(automl_warnings) == 0
 
 
 @pytest.mark.parametrize(
@@ -4367,7 +4367,7 @@
     mock_get_preprocessing_components.side_effect = (
         dummy_mock_get_preprocessing_components
     )
-    with pytest.warns(None) as warnings_logged:
+    with warnings.catch_warnings(record=True) as warnings_logged:
         automl = AutoMLSearch(
             X_train=X,
             y_train=y,
@@ -4850,7 +4850,7 @@
         max_batches=batches,
     )
     aml.search()
-    estimator_args = inspect.getargspec(RandomForestClassifier)
+    estimator_args = inspect.getfullargspec(RandomForestClassifier)
     # estimator_args[0] gives the parameter names, while [3] gives the associated values
     # estimator_args[0][i + 1] to skip 'self' in the estimator
     # we do len - 1 in order to skip the random seed, which isn't present in the row['parameters']

evalml/tests/automl_tests/test_pipeline_search_plots.py

@@ -1,7 +1,7 @@
+import warnings
 from unittest.mock import MagicMock, patch
 
 import pandas as pd
-import pytest
 
 from evalml.automl.pipeline_search_plots import SearchIterationPlot
 
@@ -53,12 +53,12 @@
     mock_data = MagicMock()
 
     jupyter_check.return_value = True
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         SearchIterationPlot(mock_data.results, mock_data.objective)
         assert len(graph_valid) == 0
         import_check.assert_called_with("ipywidgets", warning=True)
 
     jupyter_check.return_value = False
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         SearchIterationPlot(mock_data.results, mock_data.objective)
         assert len(graph_valid) == 0

evalml/tests/model_understanding_tests/prediction_explanations_tests/test_force_plots.py

@@ -1,3 +1,4 @@
+import warnings
 from itertools import product
 from unittest.mock import patch
 
@@ -79,7 +80,7 @@ def test_force_plot_binary(
     else:
         # Code chunk to test where initjs is called if jupyter is recognized
         jupyter_check.return_value = False
-        with pytest.warns(None) as graph_valid:
+        with warnings.catch_warnings(record=True) as graph_valid:
             results = graph_force_plot(
                 pipeline,
                 rows_to_explain=rows_to_explain,
@@ -88,11 +89,11 @@ def test_force_plot_binary(
                 matplotlib=False,
             )
             assert not initjs.called
-            warnings = set([str(gv) for gv in graph_valid.list])
-            assert all(["DeprecationWarning" in w for w in warnings])
+            warnings_deprecated = set([str(gv) for gv in graph_valid])
+            assert all(["DeprecationWarning" in w for w in warnings_deprecated])
 
         jupyter_check.return_value = True
-        with pytest.warns(None) as graph_valid:
+        with warnings.catch_warnings(record=True) as graph_valid:
             results = graph_force_plot(
                 pipeline,
                 rows_to_explain=rows_to_explain,
@@ -101,8 +102,8 @@ def test_force_plot_binary(
                 matplotlib=False,
             )
             assert initjs.called
-            warnings = set([str(gv) for gv in graph_valid.list])
-            assert all(["DeprecationWarning" in w for w in warnings])
+            warnings_deprecated = set([str(gv) for gv in graph_valid])
+            assert all(["DeprecationWarning" in w for w in warnings_deprecated])
 
     # Should have a result per row to explain.
     assert len(results) == len(rows_to_explain)

evalml/tests/model_understanding_tests/test_metrics.py

@@ -611,22 +611,22 @@ def test_jupyter_graph_check(
     y = y.ww.iloc[:20]
     logistic_regression_binary_pipeline.fit(X, y)
     jupyter_check.return_value = False
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         graph_confusion_matrix(y, y)
         assert len(graph_valid) == 0
 
     jupyter_check.return_value = True
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         rs = get_random_state(42)
         y_pred_proba = y * rs.random(y.shape)
         graph_precision_recall_curve(y, y_pred_proba)
         assert len(graph_valid) == 0
         import_check.assert_called_with("ipywidgets", warning=True)
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         graph_confusion_matrix(y, y)
         assert len(graph_valid) == 0
         import_check.assert_called_with("ipywidgets", warning=True)
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         rs = get_random_state(42)
         y_pred_proba = y * rs.random(y.shape)
         graph_roc_curve(y, y_pred_proba)

evalml/tests/model_understanding_tests/test_partial_dependence.py

@@ -1,5 +1,6 @@
 import collections
 import re
+import warnings
 from unittest.mock import patch
 
 import featuretools as ft
@@ -2465,7 +2466,7 @@ def test_partial_dependence_jupyter_graph_check(
     logistic_regression_binary_pipeline.fit(X, y)
 
     jupyter_check.return_value = True
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         graph_partial_dependence(
             logistic_regression_binary_pipeline,
             X,

evalml/tests/model_understanding_tests/test_permutation_importance.py

@@ -1,3 +1,4 @@
+import warnings
 from unittest.mock import PropertyMock, patch
 
 import numpy as np
@@ -923,7 +924,7 @@ def test_jupyter_graph_check(
     y = y.ww.iloc[:20]
     logistic_regression_binary_pipeline.fit(X, y)
     jupyter_check.return_value = False
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         graph_permutation_importance(
             logistic_regression_binary_pipeline,
             X,
@@ -933,7 +934,7 @@ def test_jupyter_graph_check(
         assert len(graph_valid) == 0
 
     jupyter_check.return_value = True
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         graph_permutation_importance(
             logistic_regression_binary_pipeline,
             X,

evalml/tests/model_understanding_tests/test_visualizations.py

@@ -1,4 +1,5 @@
 import os
+import warnings
 from collections import OrderedDict
 from unittest.mock import patch
 
@@ -236,7 +237,7 @@ def test_jupyter_graph_check(
         false_negative=-2,
     )
     jupyter_check.return_value = True
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         graph_binary_objective_vs_threshold(
             logistic_regression_binary_pipeline,
             X,
@@ -248,7 +249,7 @@ def test_jupyter_graph_check(
         import_check.assert_called_with("ipywidgets", warning=True)
 
     Xr, yr = X_y_regression
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         rs = get_random_state(42)
         y_preds = yr * rs.random(yr.shape)
         graph_prediction_vs_actual(yr, y_preds)

evalml/tests/objective_tests/test_standard_metrics.py

@@ -1,3 +1,4 @@
+import warnings
 from itertools import product
 
 import numpy as np
@@ -708,7 +709,7 @@ def test_mse_linear_model():
 def test_mcc_catches_warnings():
     y_true = [1, 0, 1, 1]
     y_predicted = [0, 0, 0, 0]
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings(record=True) as record:
         MCCBinary().objective_function(y_true, y_predicted)
         MCCMulticlass().objective_function(y_true, y_predicted)
         assert len(record) == 0

evalml/tests/pipeline_tests/test_graphs.py

@@ -1,4 +1,5 @@
 import os
+import warnings
 from unittest.mock import patch
 
 import numpy as np
@@ -157,12 +158,12 @@ def test_jupyter_graph_check(import_check, jupyter_check, X_y_binary, test_pipel
     clf = test_pipeline
     clf.fit(X, y)
     jupyter_check.return_value = False
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         clf.graph_feature_importance()
         assert len(graph_valid) == 0
 
     jupyter_check.return_value = True
-    with pytest.warns(None) as graph_valid:
+    with warnings.catch_warnings(record=True) as graph_valid:
         clf.graph_feature_importance()
         import_check.assert_called_with("ipywidgets", warning=True)
 

pyproject.toml

@@ -77,35 +77,35 @@ dependencies = [
 
 [project.optional-dependencies]
 test = [
-    "pytest == 7.1.2",
-    "pytest-xdist == 2.1.0",
-    "pytest-timeout == 1.4.2",
-    "pytest-cov == 2.10.1",
-    "nbval == 0.9.3",
-    "IPython >= 8.10.0, <8.12.1",
-    "PyYAML == 6.0.1",
+    "pytest >= 7.1.2",
+    "pytest-xdist >= 2.1.0",
+    "pytest-timeout >= 1.4.2",
+    "pytest-cov >= 2.10.1",
+    "nbval >= 0.9.3",
+    "IPython >= 8.10.0",
+    "PyYAML >= 6.0.1",
     "coverage[toml] >= 6.4",
 ]
 dev = [
-    "ruff == 0.0.228",
-    "darglint == 1.8.0",
+    "ruff >= 0.0.228",
+    "darglint >= 1.8.0",
     "pre-commit >= 2.20.0",
     "evalml[docs,test]",
 ]
 updater = [
     "alteryx-open-src-update-checker >= 2.1.0"
 ]
 docs = [
-    "docutils >=0.15.2, < 0.17",
+    "docutils >= 0.15.2, < 0.17",
     "pydata-sphinx-theme >= 0.3.1",
     "astroid <= 2.6.6",
     "Sphinx >= 5.0.0",
     "nbconvert >= 6.5.0",
     "nbsphinx >= 0.8.5, < 0.9.0",
     "sphinx-autoapi",
-    "sphinx-inline-tabs == 2022.1.2b11",
-    "sphinx-copybutton == 0.4.0",
-    "myst-parser == 0.18.0",
+    "sphinx-inline-tabs >= 2022.1.2b11",
+    "sphinx-copybutton >= 0.4.0",
+    "myst-parser >= 0.18.0",
 ]
 prophet = [
     "prophet >= 1.1.2",