Numpy Updates to Tests

docs-requirements.txt

@@ -1,5 +1,6 @@
 docutils>=0.15.2,<0.17
 pydata-sphinx-theme>=0.3.1
+astroid<=2.6.6
 Sphinx>=2.0.1,<4.0.0
 nbconvert>=5.5.0
 nbsphinx>=0.8.5

docs/source/release_notes.rst

@@ -13,13 +13,15 @@ Release Notes
         * Updated ``get_best_sampler_for_data`` to consider all non-numeric datatypes as categorical for SMOTE :pr:`2590`
         * Fixed inconsistent test results from `TargetDistributionDataCheck` :pr:`2608`
         * Adopted vectorized pd.NA checking for Woodwork 0.5.1 support :pr:`2626`
+        * Pinned upper version of astroid to 2.6.6 to keep ReadTheDocs working. :pr:`2638`
     * Changes
         * Renamed SMOTE samplers to SMOTE oversampler :pr:`2595`
         * Changed ``partial_dependence`` and ``graph_partial_dependence`` to raise a ``PartialDependenceError`` instead of ``ValueError``. This is not a breaking change because ``PartialDependenceError`` is a subclass of ``ValueError`` :pr:`2604`
         * Cleaned up code duplication in ``ComponentGraph`` :pr:`2612`
     * Documentation Changes
         * To avoid local docs build error, only add warning disable and download headers on ReadTheDocs builds, not locally :pr:`2617`
     * Testing Changes
+        * Updated partial_dependence tests to change the element-wise comparison per the Plotly 5.2.1 upgrade :pr:`2638`
         * Changed the lint CI job to only check against python 3.9 via the `-t` flag :pr:`2586`
         * Installed Prophet in linux nightlies test and fixed ``test_all_components`` :pr:`2598`
         * Refactored and fixed all ``make_pipeline`` tests to assert correct order and address new Woodwork Unknown type inference :pr:`2572`

evalml/tests/model_understanding_tests/test_partial_dependence.py

@@ -809,7 +809,7 @@ def test_graph_partial_dependence_regression_and_binary_categorical(
     )
     plot_data = fig.to_dict()["data"][0]
     assert plot_data["type"] == "bar"
-    assert plot_data["x"] == ["0", "1", "2"]
+    assert list(plot_data["x"]) == ["0", "1", "2"]
 
     fig = graph_partial_dependence(
         pipeline, X, features=("0", "categorical_column"), grid_resolution=5
@@ -875,7 +875,7 @@ def test_partial_dependence_multiclass_categorical(
 
     for i, plot_data in enumerate(fig.to_dict()["data"]):
         assert plot_data["type"] == "bar"
-        assert plot_data["x"] == ["0", "1", "2"]
+        assert list(plot_data["x"]) == ["0", "1", "2"]
         if class_label is None:
             assert plot_data["name"] == f"class_{i}"
         else:
@@ -1092,7 +1092,7 @@ def test_graph_partial_dependence_regression_and_binary_datetime(
     fig = graph_partial_dependence(pipeline, X, features="dt_column", grid_resolution=5)
     plot_data = fig.to_dict()["data"][0]
     assert plot_data["type"] == "scatter"
-    assert plot_data["x"] == list(pd.date_range("20200101", periods=5))
+    assert list(plot_data["x"]) == list(pd.date_range("20200101", periods=5))
 
 
 def test_graph_partial_dependence_regression_date_order(X_y_binary):
@@ -1126,7 +1126,7 @@ def test_graph_partial_dependence_regression_date_order(X_y_binary):
     fig = graph_partial_dependence(pipeline, X, features="dt_column", grid_resolution=5)
     plot_data = fig.to_dict()["data"][0]
     assert plot_data["type"] == "scatter"
-    assert plot_data["x"] == list(pd.date_range("20200101", periods=5))
+    assert list(plot_data["x"]) == list(pd.date_range("20200101", periods=5))
 
 
 def test_partial_dependence_respect_grid_resolution(fraud_100):