[python-package] migrate test_plotting.py to pytest (#3811)

* TST Migrates test_plotting.py to pytest

* STY Fixes linting

tests/python_package_test/test_plotting.py

 # coding: utf-8
-import unittest
-
 import lightgbm as lgb
 from lightgbm.compat import MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED
 from sklearn.model_selection import train_test_split
+import pytest
 
 if MATPLOTLIB_INSTALLED:
     import matplotlib
@@ -14,164 +13,188 @@ if GRAPHVIZ_INSTALLED:
 from .utils import load_breast_cancer
 
 
-class TestBasic(unittest.TestCase):
+@pytest.fixture(scope="module")
+def breast_cancer_split():
+    return train_test_split(*load_breast_cancer(return_X_y=True),
+                            test_size=0.1, random_state=1)
+
+
+@pytest.fixture(scope="module")
+def train_data(breast_cancer_split):
+    X_train, _, y_train, _ = breast_cancer_split
+    return lgb.Dataset(X_train, y_train)
+
 
-    def setUp(self):
-        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(*load_breast_cancer(return_X_y=True),
-                                                                                test_size=0.1, random_state=1)
-        self.train_data = lgb.Dataset(self.X_train, self.y_train)
-        self.params = {
-            "objective": "binary",
+@pytest.fixture
+def params():
+    return {"objective": "binary",
             "verbose": -1,
-            "num_leaves": 3
-        }
-
-    @unittest.skipIf(not MATPLOTLIB_INSTALLED, 'matplotlib is not installed')
-    def test_plot_importance(self):
-        gbm0 = lgb.train(self.params, self.train_data, num_boost_round=10)
-        ax0 = lgb.plot_importance(gbm0)
-        self.assertIsInstance(ax0, matplotlib.axes.Axes)
-        self.assertEqual(ax0.get_title(), 'Feature importance')
-        self.assertEqual(ax0.get_xlabel(), 'Feature importance')
-        self.assertEqual(ax0.get_ylabel(), 'Features')
-        self.assertLessEqual(len(ax0.patches), 30)
-
-        gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
-        gbm1.fit(self.X_train, self.y_train)
-
-        ax1 = lgb.plot_importance(gbm1, color='r', title='t', xlabel='x', ylabel='y')
-        self.assertIsInstance(ax1, matplotlib.axes.Axes)
-        self.assertEqual(ax1.get_title(), 't')
-        self.assertEqual(ax1.get_xlabel(), 'x')
-        self.assertEqual(ax1.get_ylabel(), 'y')
-        self.assertLessEqual(len(ax1.patches), 30)
-        for patch in ax1.patches:
-            self.assertTupleEqual(patch.get_facecolor(), (1., 0, 0, 1.))  # red
-
-        ax2 = lgb.plot_importance(gbm0, color=['r', 'y', 'g', 'b'],
-                                  title=None, xlabel=None, ylabel=None)
-        self.assertIsInstance(ax2, matplotlib.axes.Axes)
-        self.assertEqual(ax2.get_title(), '')
-        self.assertEqual(ax2.get_xlabel(), '')
-        self.assertEqual(ax2.get_ylabel(), '')
-        self.assertLessEqual(len(ax2.patches), 30)
-        self.assertTupleEqual(ax2.patches[0].get_facecolor(), (1., 0, 0, 1.))  # r
-        self.assertTupleEqual(ax2.patches[1].get_facecolor(), (.75, .75, 0, 1.))  # y
-        self.assertTupleEqual(ax2.patches[2].get_facecolor(), (0, .5, 0, 1.))  # g
-        self.assertTupleEqual(ax2.patches[3].get_facecolor(), (0, 0, 1., 1.))  # b
-
-    @unittest.skipIf(not MATPLOTLIB_INSTALLED, 'matplotlib is not installed')
-    def test_plot_split_value_histogram(self):
-        gbm0 = lgb.train(self.params, self.train_data, num_boost_round=10)
-        ax0 = lgb.plot_split_value_histogram(gbm0, 27)
-        self.assertIsInstance(ax0, matplotlib.axes.Axes)
-        self.assertEqual(ax0.get_title(), 'Split value histogram for feature with index 27')
-        self.assertEqual(ax0.get_xlabel(), 'Feature split value')
-        self.assertEqual(ax0.get_ylabel(), 'Count')
-        self.assertLessEqual(len(ax0.patches), 2)
-
-        gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
-        gbm1.fit(self.X_train, self.y_train)
-
-        ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
-                                             title='Histogram for feature @index/name@ @feature@',
-                                             xlabel='x', ylabel='y', color='r')
-        self.assertIsInstance(ax1, matplotlib.axes.Axes)
-        self.assertEqual(ax1.get_title(),
-                         'Histogram for feature name {}'.format(gbm1.booster_.feature_name()[27]))
-        self.assertEqual(ax1.get_xlabel(), 'x')
-        self.assertEqual(ax1.get_ylabel(), 'y')
-        self.assertLessEqual(len(ax1.patches), 2)
-        for patch in ax1.patches:
-            self.assertTupleEqual(patch.get_facecolor(), (1., 0, 0, 1.))  # red
-
-        ax2 = lgb.plot_split_value_histogram(gbm0, 27, bins=10, color=['r', 'y', 'g', 'b'],
-                                             title=None, xlabel=None, ylabel=None)
-        self.assertIsInstance(ax2, matplotlib.axes.Axes)
-        self.assertEqual(ax2.get_title(), '')
-        self.assertEqual(ax2.get_xlabel(), '')
-        self.assertEqual(ax2.get_ylabel(), '')
-        self.assertEqual(len(ax2.patches), 10)
-        self.assertTupleEqual(ax2.patches[0].get_facecolor(), (1., 0, 0, 1.))  # r
-        self.assertTupleEqual(ax2.patches[1].get_facecolor(), (.75, .75, 0, 1.))  # y
-        self.assertTupleEqual(ax2.patches[2].get_facecolor(), (0, .5, 0, 1.))  # g
-        self.assertTupleEqual(ax2.patches[3].get_facecolor(), (0, 0, 1., 1.))  # b
-
-        self.assertRaises(ValueError, lgb.plot_split_value_histogram, gbm0, 0)  # was not used in splitting
-
-    @unittest.skipIf(not MATPLOTLIB_INSTALLED or not GRAPHVIZ_INSTALLED, 'matplotlib or graphviz is not installed')
-    def test_plot_tree(self):
-        gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
-        gbm.fit(self.X_train, self.y_train, verbose=False)
-
-        self.assertRaises(IndexError, lgb.plot_tree, gbm, tree_index=83)
-
-        ax = lgb.plot_tree(gbm, tree_index=3, figsize=(15, 8), show_info=['split_gain'])
-        self.assertIsInstance(ax, matplotlib.axes.Axes)
-        w, h = ax.axes.get_figure().get_size_inches()
-        self.assertEqual(int(w), 15)
-        self.assertEqual(int(h), 8)
-
-    @unittest.skipIf(not GRAPHVIZ_INSTALLED, 'graphviz is not installed')
-    def test_create_tree_digraph(self):
-        constraints = [-1, 1] * int(self.X_train.shape[1] / 2)
-        gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, monotone_constraints=constraints)
-        gbm.fit(self.X_train, self.y_train, verbose=False)
-
-        self.assertRaises(IndexError, lgb.create_tree_digraph, gbm, tree_index=83)
-
-        graph = lgb.create_tree_digraph(gbm, tree_index=3,
-                                        show_info=['split_gain', 'internal_value', 'internal_weight'],
-                                        name='Tree4', node_attr={'color': 'red'})
-        graph.render(view=False)
-        self.assertIsInstance(graph, graphviz.Digraph)
-        self.assertEqual(graph.name, 'Tree4')
-        self.assertEqual(graph.filename, 'Tree4.gv')
-        self.assertEqual(len(graph.node_attr), 1)
-        self.assertEqual(graph.node_attr['color'], 'red')
-        self.assertEqual(len(graph.graph_attr), 0)
-        self.assertEqual(len(graph.edge_attr), 0)
-        graph_body = ''.join(graph.body)
-        self.assertIn('leaf', graph_body)
-        self.assertIn('gain', graph_body)
-        self.assertIn('value', graph_body)
-        self.assertIn('weight', graph_body)
-        self.assertIn('#ffdddd', graph_body)
-        self.assertIn('#ddffdd', graph_body)
-        self.assertNotIn('data', graph_body)
-        self.assertNotIn('count', graph_body)
-
-    @unittest.skipIf(not MATPLOTLIB_INSTALLED, 'matplotlib is not installed')
-    def test_plot_metrics(self):
-        test_data = lgb.Dataset(self.X_test, self.y_test, reference=self.train_data)
-        self.params.update({"metric": {"binary_logloss", "binary_error"}})
-
-        evals_result0 = {}
-        gbm0 = lgb.train(self.params, self.train_data,
-                         valid_sets=[self.train_data, test_data],
-                         valid_names=['v1', 'v2'],
-                         num_boost_round=10,
-                         evals_result=evals_result0,
-                         verbose_eval=False)
-        ax0 = lgb.plot_metric(evals_result0)
-        self.assertIsInstance(ax0, matplotlib.axes.Axes)
-        self.assertEqual(ax0.get_title(), 'Metric during training')
-        self.assertEqual(ax0.get_xlabel(), 'Iterations')
-        self.assertIn(ax0.get_ylabel(), {'binary_logloss', 'binary_error'})
-        ax0 = lgb.plot_metric(evals_result0, metric='binary_error')
-        ax0 = lgb.plot_metric(evals_result0, metric='binary_logloss', dataset_names=['v2'])
-
-        evals_result1 = {}
-        gbm1 = lgb.train(self.params, self.train_data,
-                         num_boost_round=10,
-                         evals_result=evals_result1,
-                         verbose_eval=False)
-        self.assertRaises(ValueError, lgb.plot_metric, evals_result1)
-
-        gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
-        gbm2.fit(self.X_train, self.y_train, eval_set=[(self.X_test, self.y_test)], verbose=False)
-        ax2 = lgb.plot_metric(gbm2, title=None, xlabel=None, ylabel=None)
-        self.assertIsInstance(ax2, matplotlib.axes.Axes)
-        self.assertEqual(ax2.get_title(), '')
-        self.assertEqual(ax2.get_xlabel(), '')
-        self.assertEqual(ax2.get_ylabel(), '')
+            "num_leaves": 3}
+
+
+@pytest.mark.skipif(not MATPLOTLIB_INSTALLED, reason='matplotlib is not installed')
+def test_plot_importance(params, breast_cancer_split, train_data):
+    X_train, _, y_train, _ = breast_cancer_split
+
+    gbm0 = lgb.train(params, train_data, num_boost_round=10)
+    ax0 = lgb.plot_importance(gbm0)
+    assert isinstance(ax0, matplotlib.axes.Axes)
+    assert ax0.get_title() == 'Feature importance'
+    assert ax0.get_xlabel() == 'Feature importance'
+    assert ax0.get_ylabel() == 'Features'
+    assert len(ax0.patches) <= 30
+
+    gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm1.fit(X_train, y_train)
+
+    ax1 = lgb.plot_importance(gbm1, color='r', title='t', xlabel='x', ylabel='y')
+    assert isinstance(ax1, matplotlib.axes.Axes)
+    assert ax1.get_title() == 't'
+    assert ax1.get_xlabel() == 'x'
+    assert ax1.get_ylabel() == 'y'
+    assert len(ax1.patches) <= 30
+    for patch in ax1.patches:
+        assert patch.get_facecolor() == (1., 0, 0, 1.)  # red
+
+    ax2 = lgb.plot_importance(gbm0, color=['r', 'y', 'g', 'b'],
+                              title=None, xlabel=None, ylabel=None)
+    assert isinstance(ax2, matplotlib.axes.Axes)
+    assert ax2.get_title() == ''
+    assert ax2.get_xlabel() == ''
+    assert ax2.get_ylabel() == ''
+    assert len(ax2.patches) <= 30
+    assert ax2.patches[0].get_facecolor() == (1., 0, 0, 1.)  # r
+    assert ax2.patches[1].get_facecolor() == (.75, .75, 0, 1.)  # y
+    assert ax2.patches[2].get_facecolor() == (0, .5, 0, 1.)  # g
+    assert ax2.patches[3].get_facecolor() == (0, 0, 1., 1.)  # b
+
+
+@pytest.mark.skipif(not MATPLOTLIB_INSTALLED, reason='matplotlib is not installed')
+def test_plot_split_value_histogram(params, breast_cancer_split, train_data):
+    X_train, _, y_train, _ = breast_cancer_split
+
+    gbm0 = lgb.train(params, train_data, num_boost_round=10)
+    ax0 = lgb.plot_split_value_histogram(gbm0, 27)
+    assert isinstance(ax0, matplotlib.axes.Axes)
+    assert ax0.get_title() == 'Split value histogram for feature with index 27'
+    assert ax0.get_xlabel() == 'Feature split value'
+    assert ax0.get_ylabel() == 'Count'
+    assert len(ax0.patches) <= 2
+
+    gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm1.fit(X_train, y_train)
+
+    ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
+                                         title='Histogram for feature @index/name@ @feature@',
+                                         xlabel='x', ylabel='y', color='r')
+    assert isinstance(ax1, matplotlib.axes.Axes)
+    title = 'Histogram for feature name {}'.format(gbm1.booster_.feature_name()[27])
+    assert ax1.get_title() == title
+    assert ax1.get_xlabel() == 'x'
+    assert ax1.get_ylabel() == 'y'
+    assert len(ax1.patches) <= 2
+    for patch in ax1.patches:
+        assert patch.get_facecolor() == (1., 0, 0, 1.)  # red
+
+    ax2 = lgb.plot_split_value_histogram(gbm0, 27, bins=10, color=['r', 'y', 'g', 'b'],
+                                         title=None, xlabel=None, ylabel=None)
+    assert isinstance(ax2, matplotlib.axes.Axes)
+    assert ax2.get_title() == ''
+    assert ax2.get_xlabel() == ''
+    assert ax2.get_ylabel() == ''
+    assert len(ax2.patches) == 10
+    assert ax2.patches[0].get_facecolor() == (1., 0, 0, 1.)  # r
+    assert ax2.patches[1].get_facecolor() == (.75, .75, 0, 1.)  # y
+    assert ax2.patches[2].get_facecolor() == (0, .5, 0, 1.)  # g
+    assert ax2.patches[3].get_facecolor() == (0, 0, 1., 1.)  # b
+
+    with pytest.raises(ValueError):
+        lgb.plot_split_value_histogram(gbm0, 0)  # was not used in splitting
+
+
+@pytest.mark.skipif(not MATPLOTLIB_INSTALLED or not GRAPHVIZ_INSTALLED,
+                    reason='matplotlib or graphviz is not installed')
+def test_plot_tree(breast_cancer_split):
+    X_train, _, y_train, _ = breast_cancer_split
+    gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm.fit(X_train, y_train, verbose=False)
+
+    with pytest.raises(IndexError):
+        lgb.plot_tree(gbm, tree_index=83)
+
+    ax = lgb.plot_tree(gbm, tree_index=3, figsize=(15, 8), show_info=['split_gain'])
+    assert isinstance(ax, matplotlib.axes.Axes)
+    w, h = ax.axes.get_figure().get_size_inches()
+    assert int(w) == 15
+    assert int(h) == 8
+
+
+@pytest.mark.skipif(not GRAPHVIZ_INSTALLED, reason='graphviz is not installed')
+def test_create_tree_digraph(breast_cancer_split):
+    X_train, _, y_train, _ = breast_cancer_split
+
+    constraints = [-1, 1] * int(X_train.shape[1] / 2)
+    gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, monotone_constraints=constraints)
+    gbm.fit(X_train, y_train, verbose=False)
+
+    with pytest.raises(IndexError):
+        lgb.create_tree_digraph(gbm, tree_index=83)
+
+    graph = lgb.create_tree_digraph(gbm, tree_index=3,
+                                    show_info=['split_gain', 'internal_value', 'internal_weight'],
+                                    name='Tree4', node_attr={'color': 'red'})
+    graph.render(view=False)
+    assert isinstance(graph, graphviz.Digraph)
+    assert graph.name == 'Tree4'
+    assert graph.filename == 'Tree4.gv'
+    assert len(graph.node_attr) == 1
+    assert graph.node_attr['color'] == 'red'
+    assert len(graph.graph_attr) == 0
+    assert len(graph.edge_attr) == 0
+    graph_body = ''.join(graph.body)
+    assert 'leaf' in graph_body
+    assert 'gain' in graph_body
+    assert 'value' in graph_body
+    assert 'weight' in graph_body
+    assert '#ffdddd' in graph_body
+    assert '#ddffdd' in graph_body
+    assert 'data' not in graph_body
+    assert 'count' not in graph_body
+
+
+@pytest.mark.skipif(not MATPLOTLIB_INSTALLED, reason='matplotlib is not installed')
+def test_plot_metrics(params, breast_cancer_split, train_data):
+    X_train, X_test, y_train, y_test = breast_cancer_split
+    test_data = lgb.Dataset(X_test, y_test, reference=train_data)
+    params.update({"metric": {"binary_logloss", "binary_error"}})
+
+    evals_result0 = {}
+    lgb.train(params, train_data,
+              valid_sets=[train_data, test_data],
+              valid_names=['v1', 'v2'],
+              num_boost_round=10,
+              evals_result=evals_result0,
+              verbose_eval=False)
+    ax0 = lgb.plot_metric(evals_result0)
+    assert isinstance(ax0, matplotlib.axes.Axes)
+    assert ax0.get_title() == 'Metric during training'
+    assert ax0.get_xlabel() == 'Iterations'
+    assert ax0.get_ylabel() in {'binary_logloss', 'binary_error'}
+    ax0 = lgb.plot_metric(evals_result0, metric='binary_error')
+    ax0 = lgb.plot_metric(evals_result0, metric='binary_logloss', dataset_names=['v2'])
+
+    evals_result1 = {}
+    lgb.train(params, train_data,
+              num_boost_round=10,
+              evals_result=evals_result1,
+              verbose_eval=False)
+    with pytest.raises(ValueError):
+        lgb.plot_metric(evals_result1)
+
+    gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm2.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
+    ax2 = lgb.plot_metric(gbm2, title=None, xlabel=None, ylabel=None)
+    assert isinstance(ax2, matplotlib.axes.Axes)
+    assert ax2.get_title() == ''
+    assert ax2.get_xlabel() == ''
+    assert ax2.get_ylabel() == ''