[python] fix class_weight (#2199)

* fixed class_weight

* fixed lint

* added test

* hotfix

python-package/lightgbm/sklearn.py

@@ -10,7 +10,7 @@ from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase,
                      LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase,
                      _LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckConsistentLength,
                      _LGBMAssertAllFinite, _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
-                     argc_, range_, string_type, DataFrame, DataTable)
+                     argc_, range_, zip_, string_type, DataFrame, DataTable)
 from .engine import train
 
 
@@ -320,6 +320,8 @@ class LGBMModel(_LGBMModelBase):
         self._other_params = {}
         self._objective = objective
         self.class_weight = class_weight
+        self._class_weight = None
+        self._class_map = None
         self._n_features = None
         self._classes = None
         self._n_classes = None
@@ -529,8 +531,10 @@ class LGBMModel(_LGBMModelBase):
         else:
             _X, _y = X, y
 
-        if self.class_weight is not None:
-            class_sample_weight = _LGBMComputeSampleWeight(self.class_weight, y)
+        if self._class_weight is None:
+            self._class_weight = self.class_weight
+        if self._class_weight is not None:
+            class_sample_weight = _LGBMComputeSampleWeight(self._class_weight, y)
             if sample_weight is None or len(sample_weight) == 0:
                 sample_weight = class_sample_weight
             else:
@@ -547,7 +551,7 @@ class LGBMModel(_LGBMModelBase):
         valid_sets = []
         if eval_set is not None:
 
-            def _get_meta_data(collection, i):
+            def _get_meta_data(collection, name, i):
                 if collection is None:
                     return None
                 elif isinstance(collection, list):
@@ -555,8 +559,7 @@ class LGBMModel(_LGBMModelBase):
                 elif isinstance(collection, dict):
                     return collection.get(i, None)
                 else:
-                    raise TypeError('eval_sample_weight, eval_class_weight, eval_init_score, and eval_group '
-                                    'should be dict or list')
+                    raise TypeError('{} should be dict or list'.format(name))
 
             if isinstance(eval_set, tuple):
                 eval_set = [eval_set]
@@ -565,16 +568,18 @@ class LGBMModel(_LGBMModelBase):
                 if valid_data[0] is X and valid_data[1] is y:
                     valid_set = train_set
                 else:
-                    valid_weight = _get_meta_data(eval_sample_weight, i)
-                    if _get_meta_data(eval_class_weight, i) is not None:
-                        valid_class_sample_weight = _LGBMComputeSampleWeight(_get_meta_data(eval_class_weight, i),
-                                                                             valid_data[1])
+                    valid_weight = _get_meta_data(eval_sample_weight, 'eval_sample_weight', i)
+                    valid_class_weight = _get_meta_data(eval_class_weight, 'eval_class_weight', i)
+                    if valid_class_weight is not None:
+                        if isinstance(valid_class_weight, dict) and self._class_map is not None:
+                            valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()}
+                        valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1])
                         if valid_weight is None or len(valid_weight) == 0:
                             valid_weight = valid_class_sample_weight
                         else:
                             valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
-                    valid_init_score = _get_meta_data(eval_init_score, i)
-                    valid_group = _get_meta_data(eval_group, i)
+                    valid_init_score = _get_meta_data(eval_init_score, 'eval_init_score', i)
+                    valid_group = _get_meta_data(eval_group, 'eval_group', i)
                     valid_set = _construct_dataset(valid_data[0], valid_data[1],
                                                    valid_weight, valid_init_score, valid_group, params)
                 valid_sets.append(valid_set)
@@ -750,6 +755,9 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
         _LGBMCheckClassificationTargets(y)
         self._le = _LGBMLabelEncoder().fit(y)
         _y = self._le.transform(y)
+        self._class_map = dict(zip_(self._le.classes_, self._le.transform(self._le.classes_)))
+        if isinstance(self.class_weight, dict):
+            self._class_weight = {self._class_map[k]: v for k, v in self.class_weight.items()}
 
         self._classes = self._le.classes_
         self._n_classes = len(self._classes)

tests/python_package_test/test_sklearn.py

@@ -1,5 +1,6 @@
 # coding: utf-8
 # pylint: skip-file
+import itertools
 import math
 import os
 import unittest
@@ -615,3 +616,37 @@ class TestSklearn(unittest.TestCase):
                       'verbose': False, 'early_stopping_rounds': 5}
         gbm = lgb.LGBMRegressor(**params).fit(**params_fit)
         np.testing.assert_array_equal(gbm.evals_result_['training']['l2'], np.nan)
+
+    def test_class_weight(self):
+        X, y = load_digits(10, True)
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        y_train_str = y_train.astype('str')
+        y_test_str = y_test.astype('str')
+        gbm = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', silent=True)
+        gbm.fit(X_train, y_train,
+                eval_set=[(X_train, y_train), (X_test, y_test), (X_test, y_test),
+                          (X_test, y_test), (X_test, y_test)],
+                eval_class_weight=['balanced', None, 'balanced', {1: 10, 4: 20}, {5: 30, 2: 40}],
+                verbose=False)
+        for eval_set1, eval_set2 in itertools.combinations(gbm.evals_result_.keys(), 2):
+            for metric in gbm.evals_result_[eval_set1]:
+                np.testing.assert_raises(AssertionError,
+                                         np.testing.assert_allclose,
+                                         gbm.evals_result_[eval_set1][metric],
+                                         gbm.evals_result_[eval_set2][metric])
+        gbm_str = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', silent=True)
+        gbm_str.fit(X_train, y_train_str,
+                    eval_set=[(X_train, y_train_str), (X_test, y_test_str),
+                              (X_test, y_test_str), (X_test, y_test_str), (X_test, y_test_str)],
+                    eval_class_weight=['balanced', None, 'balanced', {'1': 10, '4': 20}, {'5': 30, '2': 40}],
+                    verbose=False)
+        for eval_set1, eval_set2 in itertools.combinations(gbm_str.evals_result_.keys(), 2):
+            for metric in gbm_str.evals_result_[eval_set1]:
+                np.testing.assert_raises(AssertionError,
+                                         np.testing.assert_allclose,
+                                         gbm_str.evals_result_[eval_set1][metric],
+                                         gbm_str.evals_result_[eval_set2][metric])
+        for eval_set in gbm.evals_result_:
+            for metric in gbm.evals_result_[eval_set]:
+                np.testing.assert_allclose(gbm.evals_result_[eval_set][metric],
+                                           gbm_str.evals_result_[eval_set][metric])