How to use the lightgbm.plot_metric function in lightgbm

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microsoft / LightGBM / tests / python_package_test / test_plotting.py View on Github

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(), '')

microsoft / LightGBM / tests / python_package_test / test_plotting.py View on Github

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(), '')

microsoft / LightGBM / tests / python_package_test / test_plotting.py View on Github

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)

microsoft / LightGBM / tests / python_package_test / test_plotting.py View on Github

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(), '')

microsoft / LightGBM / examples / python-guide / plot_example.py View on Github

evals_result = {}  # to record eval results for plotting

print('Starting training...')
# train
gbm = lgb.train(params,
                lgb_train,
                num_boost_round=100,
                valid_sets=[lgb_train, lgb_test],
                feature_name=['f' + str(i + 1) for i in range(X_train.shape[-1])],
                categorical_feature=[21],
                evals_result=evals_result,
                verbose_eval=10)

print('Plotting metrics recorded during training...')
ax = lgb.plot_metric(evals_result, metric='l1')
plt.show()

print('Plotting feature importances...')
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()

print('Plotting split value histogram...')
ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
plt.show()

print('Plotting 54th tree...')  # one tree use categorical feature to split
ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=['split_gain'])
plt.show()

print('Plotting 54th tree with graphviz...')
graph = lgb.create_tree_digraph(gbm, tree_index=53, name='Tree54')

How to use the lightgbm.plot_metric function in lightgbm

To help you get started, we’ve selected a few lightgbm examples, based on popular ways it is used in public projects.

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