DistrictDataLabs · lwgray · Feb 19, 2025 · Feb 19, 2025 · Feb 20, 2025 · Feb 20, 2025
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -38,11 +38,13 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install -r tests/requirements.txt
-        python -m nltk.downloader popular
+        python -m nltk.downloader popular punkt punkt_tab
 
     - name: Run Unit Tests
       run: |
         make test
+        env:
+          MPLBACKEND: Agg
 
     - name: Upload Coverage
       uses: codecov/codecov-action@v1

diff --git a/requirements.txt b/requirements.txt
@@ -1,14 +1,14 @@
 ## Dependencies
-matplotlib>=2.0.2,!=3.0.0
-scipy>=1.0.0
+matplotlib>=3.6.0
+scipy>=1.9.0
 scikit-learn>=1.0.2
-numpy>=1.16.0
-cycler>=0.10.0
+numpy>=1.21.0
+cycler>=0.11.0
 
 ## Optional Dependencies (uncomment to use)
-# nltk>=3.2
-# pandas>=1.0.4
-# umap-learn>=0.5
+# nltk>=3.6.0
+# pandas>=1.4.0
+# umap-learn>=0.5.3
 
 ## Testing Requirements (pip install -r tests/requirements.txt)
 # pytest>=6.1

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diff --git a/tests/baseline_images/test_target/test_class_balance/test_multiclass_compare.png b/tests/baseline_images/test_target/test_class_balance/test_multiclass_compare.png
diff --git a/...baseline_images/test_target/test_class_balance/test_numpy_occupancy_balance.png b/...baseline_images/test_target/test_class_balance/test_numpy_occupancy_balance.png
diff --git a/...baseline_images/test_target/test_class_balance/test_numpy_occupancy_compare.png b/...baseline_images/test_target/test_class_balance/test_numpy_occupancy_compare.png
diff --git a/...aseline_images/test_target/test_class_balance/test_pandas_occupancy_balance.png b/...aseline_images/test_target/test_class_balance/test_pandas_occupancy_balance.png
diff --git a/...aseline_images/test_target/test_class_balance/test_pandas_occupancy_compare.png b/...aseline_images/test_target/test_class_balance/test_pandas_occupancy_compare.png
diff --git a/tests/baseline_images/test_target/test_class_balance/test_quick_method.png b/tests/baseline_images/test_target/test_class_balance/test_quick_method.png
diff --git a/...aseline_images/test_target/test_class_balance/test_quick_method_with_splits.png b/...aseline_images/test_target/test_class_balance/test_quick_method_with_splits.png
diff --git a/..._correlation/test_feature_correlation_integrated_mutual_info_classification.png b/..._correlation/test_feature_correlation_integrated_mutual_info_classification.png
diff --git a/...ture_correlation/test_feature_correlation_integrated_mutual_info_regression.png b/...ture_correlation/test_feature_correlation_integrated_mutual_info_regression.png
diff --git a/...target/test_feature_correlation/test_feature_correlation_integrated_pearson.png b/...target/test_feature_correlation/test_feature_correlation_integrated_pearson.png
diff --git a/.../test_target/test_feature_correlation/test_feature_correlation_quick_method.png b/.../test_target/test_feature_correlation/test_feature_correlation_quick_method.png
diff --git a/tests/baseline_images/test_text/test_correlation/test_quick_method.png b/tests/baseline_images/test_text/test_correlation/test_quick_method.png
diff --git a/.../baseline_images/test_text/test_correlation/test_word_correlation_generator.png b/.../baseline_images/test_text/test_correlation/test_word_correlation_generator.png
diff --git a/...aseline_images/test_text/test_correlation/test_word_correlation_ignore_case.png b/...aseline_images/test_text/test_correlation/test_word_correlation_ignore_case.png
diff --git a/tests/baseline_images/test_text/test_correlation/test_word_correlation_ngrams.png b/tests/baseline_images/test_text/test_correlation/test_word_correlation_ngrams.png
diff --git a/tests/baseline_images/test_text/test_correlation/test_word_correlation_plot.png b/tests/baseline_images/test_text/test_correlation/test_word_correlation_plot.png
diff --git a/tests/baseline_images/test_text/test_tsne/test_integrated_tsne.png b/tests/baseline_images/test_text/test_tsne/test_integrated_tsne.png
diff --git a/tests/baseline_images/test_text/test_tsne/test_make_classification_tsne.png b/tests/baseline_images/test_text/test_tsne/test_make_classification_tsne.png
diff --git a/...eline_images/test_text/test_tsne/test_make_classification_tsne_class_labels.png b/...eline_images/test_text/test_tsne/test_make_classification_tsne_class_labels.png
diff --git a/tests/baseline_images/test_text/test_tsne/test_no_target_tsne.png b/tests/baseline_images/test_text/test_tsne/test_no_target_tsne.png
diff --git a/tests/baseline_images/test_text/test_tsne/test_quick_method.png b/tests/baseline_images/test_text/test_tsne/test_quick_method.png
diff --git a/tests/baseline_images/test_text/test_tsne/test_visualizer_with_pandas.png b/tests/baseline_images/test_text/test_tsne/test_visualizer_with_pandas.png
diff --git a/tests/requirements.txt b/tests/requirements.txt
@@ -4,25 +4,25 @@
 # are probably using. We recommend monitoring Libraries.io to alert for changes.
 
 # Library Dependencies
-matplotlib==3.4.2
-scipy==1.8.0
-scikit-learn==1.0.2
-numpy==1.22.0
-cycler==0.10.0
+matplotlib>=3.6.0
+scipy>=1.9.0
+scikit-learn>=1.1.0
+numpy>=1.21.0
+cycler>=0.11.0
 
 # Testing Requirements
-pytest==6.2.4
-pytest-cov==2.12.1
-pytest-flakes==4.0.3
+pytest>=7.1.0
+pytest-cov>=3.0.0
+pytest-flakes>=4.0.5
 pytest-spec>=2.0.0
-coverage==5.5
+coverage>=6.4.0
 
 # Optional Testing Dependencies
-nltk==3.6.7
+nltk>=3.7.0
 # spacy>=2.0.18
-pandas==1.3.0
-umap-learn==0.5.3
-numba==0.55.2
+pandas>=1.4.0
+umap-learn>=0.5.3
+numba>=0.56.0
 
 # Third-Party Estimator Tests
 # xgboost==1.2.0

diff --git a/tests/test_classifier/test_prcurve.py b/tests/test_classifier/test_prcurve.py
@@ -324,7 +324,7 @@ def test_multiclass_probability_with_class_labels(self):
         # Will not check for these as they appears okay in other test images.
         for child in oz.ax.get_children():
             if isinstance(child, matplotlib.text.Annotation):
-                oz.ax.texts.remove(child)
+                child.remove()
 
         # Compare the images
         tol = (
@@ -438,9 +438,11 @@ def test_quick_method_with_test_set(self):
 
         viz = precision_recall_curve(
             RandomForestClassifier(random_state=72),
-            X_train, y_train,
-            X_test, y_test,
-            show=False
+            X_train,
+            y_train,
+            X_test,
+            y_test,
+            show=False,
         )
         self.assert_images_similar(viz)
 
@@ -487,24 +489,31 @@ def test_within_pipeline(self):
         classes = ["unoccupied", "occupied"]
 
         X_train, X_test, y_train, y_test = tts(
-                    X, y, test_size=0.2, shuffle=True, random_state=42
-                )
-
-        model = Pipeline([
-            ('minmax', MinMaxScaler()), 
-            ('prc', PrecisionRecallCurve(SVC(random_state=42),
-                                            per_class=True,
-                                            micro=False,
-                                            fill_area=False,
-                                            iso_f1_curves=True,
-                                            ap_score=False,
-                                            classes=classes))
-        ])
+            X, y, test_size=0.2, shuffle=True, random_state=42
+        )
+
+        model = Pipeline(
+            [
+                ("minmax", MinMaxScaler()),
+                (
+                    "prc",
+                    PrecisionRecallCurve(
+                        SVC(random_state=42),
+                        per_class=True,
+                        micro=False,
+                        fill_area=False,
+                        iso_f1_curves=True,
+                        ap_score=False,
+                        classes=classes,
+                    ),
+                ),
+            ]
+        )
 
         model.fit(X_train, y_train)
         model.score(X_test, y_test)
-        model['prc'].finalize()
-        self.assert_images_similar(model['prc'], tol=5.5)
+        model["prc"].finalize()
+        self.assert_images_similar(model["prc"], tol=5.5)
 
     def test_within_pipeline_quickmethod(self):
         """
@@ -514,22 +523,32 @@ def test_within_pipeline_quickmethod(self):
         X, y = load_occupancy(return_dataset=True).to_pandas()
 
         X_train, X_test, y_train, y_test = tts(
-                    X, y, test_size=0.2, shuffle=True, random_state=42
-                )
-
-        model = Pipeline([
-            ('minmax', MinMaxScaler()), 
-            ('prc', precision_recall_curve(SVC(random_state=42),
-                                            X_train, y_train, X_test, y_test,
-                                            per_class=True,
-                                            micro=False,
-                                            fill_area=False,
-                                            iso_f1_curves=True,
-                                            ap_score=False,
-                                            classes=["unoccupied", "occupied"],
-                                            show=False))
-            ])
-        self.assert_images_similar(model['prc'], tol=5.5)
+            X, y, test_size=0.2, shuffle=True, random_state=42
+        )
+
+        model = Pipeline(
+            [
+                ("minmax", MinMaxScaler()),
+                (
+                    "prc",
+                    precision_recall_curve(
+                        SVC(random_state=42),
+                        X_train,
+                        y_train,
+                        X_test,
+                        y_test,
+                        per_class=True,
+                        micro=False,
+                        fill_area=False,
+                        iso_f1_curves=True,
+                        ap_score=False,
+                        classes=["unoccupied", "occupied"],
+                        show=False,
+                    ),
+                ),
+            ]
+        )
+        self.assert_images_similar(model["prc"], tol=5.5)
 
     def test_pipeline_as_model_input(self):
         """
@@ -539,21 +558,20 @@ def test_pipeline_as_model_input(self):
         classes = ["unoccupied", "occupied"]
 
         X_train, X_test, y_train, y_test = tts(
-                    X, y, test_size=0.2, shuffle=True, random_state=42
-                )
-
-        model = Pipeline([
-            ('minmax', MinMaxScaler()), 
-            ('svc', SVC(random_state=42))
-        ])
-
-        oz = PrecisionRecallCurve(model,
-                                  per_class=True,
-                                  micro=False,
-                                  fill_area=False,
-                                  iso_f1_curves=True,
-                                  ap_score=False,
-                                  classes=classes)
+            X, y, test_size=0.2, shuffle=True, random_state=42
+        )
+
+        model = Pipeline([("minmax", MinMaxScaler()), ("svc", SVC(random_state=42))])
+
+        oz = PrecisionRecallCurve(
+            model,
+            per_class=True,
+            micro=False,
+            fill_area=False,
+            iso_f1_curves=True,
+            ap_score=False,
+            classes=classes,
+        )
         oz.fit(X_train, y_train)
         oz.score(X_test, y_test)
         oz.finalize()
@@ -567,20 +585,23 @@ def test_pipeline_as_model_input_quickmethod(self):
         X, y = load_occupancy(return_dataset=True).to_pandas()
 
         X_train, X_test, y_train, y_test = tts(
-                    X, y, test_size=0.2, shuffle=True, random_state=42
-                )
-
-        model = Pipeline([
-            ('minmax', MinMaxScaler()), 
-            ('svc', SVC(random_state=42))
-        ])
-
-        oz = precision_recall_curve(model, X_train, y_train, X_test, y_test, 
-                     per_class=True,
-                     micro=False,
-                     fill_area=False,
-                     iso_f1_curves=True,
-                     ap_score=False,
-                     classes=["unoccupied", "occupied"],
-                     show=False)
-        self.assert_images_similar(oz, tol=5.5)
+            X, y, test_size=0.2, shuffle=True, random_state=42
+        )
+
+        model = Pipeline([("minmax", MinMaxScaler()), ("svc", SVC(random_state=42))])
+
+        oz = precision_recall_curve(
+            model,
+            X_train,
+            y_train,
+            X_test,
+            y_test,
+            per_class=True,
+            micro=False,
+            fill_area=False,
+            iso_f1_curves=True,
+            ap_score=False,
+            classes=["unoccupied", "occupied"],
+            show=False,
+        )
+        self.assert_images_similar(oz, tol=5.5)
diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py
@@ -120,9 +120,9 @@ def test_distortion_score_empty_clusters(self):
         """
         Ensure no ValueError is thrown when there are empty clusters #1185
         """
-        X = np.array([[1,2],[3,4],[5,6]])
-        valuea = distortion_score(X, np.array([1,3,3]))
-        valueb = distortion_score(X, np.array([0,1,1]))
+        X = np.array([[1, 2], [3, 4], [5, 6]])
+        valuea = distortion_score(X, np.array([1, 3, 3]))
+        valueb = distortion_score(X, np.array([0, 1, 1]))
         assert valuea == valueb
 
 
@@ -238,7 +238,9 @@ def test_valid_k(self):
         visualizer = KElbowVisualizer(KMeans(), k=np.arange(10, 100, 10)).fit(X)
         assert visualizer.k_values_ == list(np.arange(10, 100, 10))
 
-        visualizer = KElbowVisualizer(KMeans(), k=[10, 20, 30, 40, 50, 60, 70, 80, 90]).fit(X)
+        visualizer = KElbowVisualizer(
+            KMeans(), k=[10, 20, 30, 40, 50, 60, 70, 80, 90]
+        ).fit(X)
         assert visualizer.k_values_ == list(np.arange(10, 100, 10))
 
     @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows")
@@ -255,7 +257,7 @@ def test_distortion_metric(self):
         )
         visualizer.fit(self.clusters.X)
 
-        expected = np.array([69.100065, 54.081571, 43.146921, 34.978487])
+        expected = np.array([69.100065, 54.891057, 44.319888, 35.857462])
         assert len(visualizer.k_scores_) == 4
 
         visualizer.finalize()
@@ -276,7 +278,7 @@ def test_silhouette_metric(self):
         )
         visualizer.fit(self.clusters.X)
 
-        expected = np.array([0.691636, 0.456646, 0.255174, 0.239842])
+        expected = np.array([0.691636, 0.453478, 0.242102, 0.235422])
         assert len(visualizer.k_scores_) == 4
 
         visualizer.finalize()
@@ -299,7 +301,7 @@ def test_calinski_harabasz_metric(self):
         assert len(visualizer.k_scores_) == 4
         assert visualizer.elbow_value_ is None
 
-        expected = np.array([81.662726, 50.992378, 40.952179, 35.939494])
+        expected = np.array([81.662726, 50.129783, 39.744834, 34.978841])
 
         visualizer.finalize()
         self.assert_images_similar(visualizer)
@@ -308,21 +310,22 @@ def test_calinski_harabasz_metric(self):
     @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows")
     def test_distance_metric(self):
         """
-        Test the manhattan distance metric of the distortion metric of the k-elbow visualizer
+        Test the manhattan distance metric of the distortion metric of the k-elbow
+        visualizer
         """
         visualizer = KElbowVisualizer(
             KMeans(random_state=0),
             k=5,
             metric="distortion",
-            distance_metric='manhattan',
+            distance_metric="manhattan",
             timings=False,
             locate_elbow=False,
         )
         visualizer.fit(self.clusters.X)
         assert len(visualizer.k_scores_) == 4
         assert visualizer.elbow_value_ is None
 
-        expected = np.array([189.060129, 154.096223, 124.271208, 107.087566])
+        expected = np.array([189.06013, 152.276395, 132.668674, 110.741248])
 
         visualizer.finalize()
         self.assert_images_similar(visualizer)
@@ -350,7 +353,7 @@ def test_locate_elbow(self):
         visualizer.fit(X)
         assert len(visualizer.k_scores_) == 5
         assert visualizer.elbow_value_ == 3
-        expected = np.array([4286.5, 12463.4,  8766.8,  6950.1,  5863.6])
+        expected = np.array([4286.5, 12463.4, 8766.8, 6950.1, 5863.6])
 
         visualizer.finalize()
         self.assert_images_similar(visualizer, tol=0.5, windows_tol=2.2)
@@ -483,7 +486,8 @@ def test_set_colors_manually(self):
         Test the silhouette metric of the k-elbow visualizer
         """
         oz = KElbowVisualizer(
-            KMeans(random_state=0), k=5,
+            KMeans(random_state=0),
+            k=5,
         )
 
         oz.metric_color = "r"
@@ -493,7 +497,7 @@ def test_set_colors_manually(self):
         # Create artificial "fit" data for testing purposes
         oz.k_values_ = [1, 2, 3, 4, 5, 6, 7, 8]
         oz.k_timers_ = [6.2, 8.3, 10.1, 15.8, 21.2, 27.9, 38.2, 44.9]
-        oz.k_scores_ = [.8, .7, .55, .48, .40, .38, .35, .30]
+        oz.k_scores_ = [0.8, 0.7, 0.55, 0.48, 0.40, 0.38, 0.35, 0.30]
         oz.elbow_value_ = 5
         oz.elbow_score_ = 0.40
 
@@ -507,7 +511,8 @@ def test_get_params(self):
         Ensure the get params works for sklearn-compatibility
         """
         oz = KElbowVisualizer(
-            KMeans(random_state=0), k=5,
+            KMeans(random_state=0),
+            k=5,
         )
         params = oz.get_params()
-        assert len(params) > 0
+        assert len(params) > 0