Rewrites if len(..) == 0 into if .. (#125)

* Rewrites if len(..) == 0 into if ..

* improves code

* code coverage

* fix unit tests

* unittest

Author: xadupre

README.rst

@@ -1,5 +1,5 @@
 
-.. image:: https://github.com/sdpython/mlinsights/blob/main/_doc/sphinxdoc/source/_static/project_ico.png?raw=true
+.. image:: https://github.com/sdpython/mlinsights/raw/main/_doc/_static/project_ico.png
     :target: https://github.com/sdpython/mlinsights/
 
 mlinsights: extensions to scikit-learn

_doc/examples/plot_search_images_torch.py

@@ -74,7 +74,7 @@
 
 url = "https://github.com/sdpython/mlinsights/raw/ref/_doc/examples/data/dog-cat-pixabay.zip"
 files = unzip_files(url, where_to="simages/category")
-if len(files) == 0:
+if not files:
     raise FileNotFoundError(f"No images where unzipped from {url!r}.")
 len(files), files[0]
 

_unittests/ut_mlmodel/test_classification_kmeans.py

@@ -19,7 +19,7 @@
 
 
 class TestClassifierAfterKMeans(ExtTestCase):
-    @ignore_warnings(category=ConvergenceWarning)
+    @ignore_warnings(category=(ConvergenceWarning, FutureWarning))
     def test_classification_kmeans(self):
         iris = datasets.load_iris()
         X, y = iris.data, iris.target
@@ -32,7 +32,7 @@ def test_classification_kmeans(self):
         dec = clr.decision_function(X)
         self.assertEqual(prob.shape, dec.shape)
 
-    @ignore_warnings(category=ConvergenceWarning)
+    @ignore_warnings(category=(ConvergenceWarning, FutureWarning))
     def test_classification_kmeans_intercept_weights(self):
         iris = datasets.load_iris()
         X, y = iris.data, iris.target
@@ -41,7 +41,7 @@ def test_classification_kmeans_intercept_weights(self):
         acc = clr.score(X, y)
         self.assertGreater(acc, 0)
 
-    @ignore_warnings(category=ConvergenceWarning)
+    @ignore_warnings(category=(ConvergenceWarning, FutureWarning))
     def test_classification_kmeans_pickle(self):
         iris = datasets.load_iris()
         X, y = iris.data, iris.target
@@ -51,15 +51,15 @@ def test_classification_kmeans_clone(self):
         self.maxDiff = None
         run_test_sklearn_clone(lambda: ClassifierAfterKMeans())
 
-    @ignore_warnings(category=ConvergenceWarning)
+    @ignore_warnings(category=(ConvergenceWarning, FutureWarning))
     def test_classification_kmeans_grid_search(self):
         iris = datasets.load_iris()
         X, y = iris.data, iris.target
         self.assertRaise(
             lambda: run_test_sklearn_grid_search_cv(
                 lambda: ClassifierAfterKMeans(), X, y
             ),
-            ValueError,
+            AssertionError,
         )
         res = run_test_sklearn_grid_search_cv(
             lambda: ClassifierAfterKMeans(), X, y, c_n_clusters=[2, 3]
@@ -69,7 +69,7 @@ def test_classification_kmeans_grid_search(self):
         self.assertGreater(res["score"], 0)
         self.assertLesser(res["score"], 1)
 
-    @ignore_warnings(category=ConvergenceWarning)
+    @ignore_warnings(category=(ConvergenceWarning, FutureWarning))
     def test_classification_kmeans_relevance(self):
         state = RandomState(seed=0)
         Xs = []
@@ -89,7 +89,7 @@ def test_classification_kmeans_relevance(self):
         score = clk.score(X, Y)
         self.assertGreater(score, 0.95)
 
-    @ignore_warnings(category=ConvergenceWarning)
+    @ignore_warnings(category=(ConvergenceWarning, FutureWarning))
     def test_issue(self):
         X, labels_true = datasets.make_blobs(n_samples=750, centers=6, cluster_std=0.4)[
             :2

_unittests/ut_mlmodel/test_decision_tree_logistic_regression.py

@@ -80,7 +80,7 @@ def test_classifier_grid_search(self):
             lambda: run_test_sklearn_grid_search_cv(
                 lambda: DecisionTreeLogisticRegression(fit_improve_algo=None), X, Y
             ),
-            ValueError,
+            AssertionError,
         )
         res = run_test_sklearn_grid_search_cv(
             lambda: DecisionTreeLogisticRegression(fit_improve_algo=None),
@@ -98,7 +98,7 @@ def test_iris(self):
         X, y = data.data, data.target
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)
         dtlr = DecisionTreeLogisticRegression(fit_improve_algo=None)
-        self.assertRaise(lambda: dtlr.fit(X_train, y_train), RuntimeError)
+        self.assertRaise(lambda: dtlr.fit(X_train, y_train), AssertionError)
         y = y % 2
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)
         dtlr.fit(X_train, y_train)
@@ -128,7 +128,7 @@ def test_iris_fit_improve(self):
             ValueError,
         )
         dtlr = DecisionTreeLogisticRegression(fit_improve_algo="intercept_sort_always")
-        self.assertRaise(lambda: dtlr.fit(X_train, y_train), RuntimeError)
+        self.assertRaise(lambda: dtlr.fit(X_train, y_train), AssertionError)
         y = y % 2
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)
         dtlr.fit(X_train, y_train)

_unittests/ut_mlmodel/test_piecewise_classifier.py

@@ -179,7 +179,7 @@ def test_piecewise_classifier_grid_search(self):
             lambda: run_test_sklearn_grid_search_cv(
                 lambda: PiecewiseClassifier(), X, Y
             ),
-            ValueError,
+            AssertionError,
         )
         res = run_test_sklearn_grid_search_cv(
             lambda: PiecewiseClassifier(), X, Y, binner__max_depth=[2, 3]

_unittests/ut_mlmodel/test_piecewise_regressor.py

@@ -144,7 +144,7 @@ def test_piecewise_regressor_grid_search(self):
         Y = X.ravel() * 3.4 + 5.6 + eps
         self.assertRaise(
             lambda: run_test_sklearn_grid_search_cv(lambda: PiecewiseRegressor(), X, Y),
-            ValueError,
+            AssertionError,
         )
         res = run_test_sklearn_grid_search_cv(
             lambda: PiecewiseRegressor(), X, Y, binner__max_depth=[2, 3]

_unittests/ut_mlmodel/test_quantile_mlpregression.py

@@ -75,7 +75,7 @@ def test_quantile_regression_grid_search(self):
             lambda: run_test_sklearn_grid_search_cv(
                 lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)), X, Y
             ),
-            ValueError,
+            AssertionError,
         )
         res = run_test_sklearn_grid_search_cv(
             lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)),

_unittests/ut_mlmodel/test_quantile_regression.py

@@ -166,7 +166,7 @@ def test_quantile_regression_grid_search(self):
             lambda: run_test_sklearn_grid_search_cv(
                 lambda: QuantileLinearRegression(), X, Y
             ),
-            (ValueError, TypeError),
+            (AssertionError, ValueError, TypeError),
         )
         res = run_test_sklearn_grid_search_cv(
             lambda: QuantileLinearRegression(), X, Y, delta=[0.1, 0.001]

_unittests/ut_timeseries/test_dummy_timeseries.py

@@ -10,7 +10,7 @@ def test_dummy_timesieres_regressor_2(self):
         X = None
         y = numpy.arange(10)
         bs = DummyTimeSeriesRegressor(past=2)
-        self.assertRaise(lambda: bs.fit(X, y), TypeError)
+        self.assertRaise(lambda: bs.fit(X, y), AssertionError)
         y = y.astype(numpy.float64)
         np = bs.predict(X, y)
         self.assertEqual(np.ravel()[2:], numpy.arange(1, 9).astype(numpy.float64))

_unittests/ut_timeseries/test_plot_timeseries.py

@@ -2,7 +2,7 @@
 import datetime
 import warnings
 import sys
-from mlinsights.ext_test_case import ExtTestCase
+from mlinsights.ext_test_case import ExtTestCase, ignore_warnings
 from mlinsights.timeseries.datasets import artificial_data
 from mlinsights.timeseries.agg import aggregate_timeseries
 from mlinsights.timeseries.plotting import plot_week_timeseries
@@ -13,6 +13,7 @@ class TestPlotTimeSeries(ExtTestCase):
         sys.platform == "win32" and __name__ != "__main__",
         reason="issue with matplotlib",
     )
+    @ignore_warnings(UserWarning)
     def test_plot_data(self):
         try:
             import matplotlib.pyplot as plt