[python] save pandas_categorical to model string and JSON (#1766)

* add pandas_categorical to returned json

* save pandas_categorical to model file

* added regression test

* removed excess conversion to list

* removed deprecated line

* hotfix

python-package/lightgbm/basic.py

@@ -295,13 +295,18 @@ def _label_from_pandas(label):
     return label
 
 
-def _save_pandas_categorical(file_name, pandas_categorical):
+def _dump_pandas_categorical(pandas_categorical, file_name=None):
+    pandas_str = ('\npandas_categorical:'
+                  + json.dumps(pandas_categorical, default=json_default_with_numpy)
+                  + '\n')
+    if file_name is not None:
         with open(file_name, 'a') as f:
-        f.write('\npandas_categorical:'
-                + json.dumps(pandas_categorical, default=json_default_with_numpy) + '\n')
+            f.write(pandas_str)
+    return pandas_str
 
 
-def _load_pandas_categorical(file_name):
+def _load_pandas_categorical(file_name=None, model_str=None):
+    if file_name is not None:
         with open(file_name, 'r') as f:
             lines = f.readlines()
             last_line = lines[-1]
@@ -309,6 +314,13 @@ def _load_pandas_categorical(file_name):
                 last_line = lines[-2]
             if last_line.startswith('pandas_categorical:'):
                 return json.loads(last_line[len('pandas_categorical:'):])
+    elif model_str is not None:
+        lines = model_str.split('\n')
+        last_line = lines[-1]
+        if last_line.strip() == "":
+            last_line = lines[-2]
+        if last_line.startswith('pandas_categorical:'):
+            return json.loads(last_line[len('pandas_categorical:'):])
     return None
 
 
@@ -350,7 +362,7 @@ class _InnerPredictor(object):
                 ctypes.byref(out_num_class)))
             self.num_class = out_num_class.value
             self.num_total_iteration = out_num_iterations.value
-            self.pandas_categorical = _load_pandas_categorical(model_file)
+            self.pandas_categorical = _load_pandas_categorical(file_name=model_file)
         elif booster_handle is not None:
             self.__is_manage_handle = False
             self.handle = booster_handle
@@ -1531,9 +1543,9 @@ class Booster(object):
                 self.handle,
                 ctypes.byref(out_num_class)))
             self.__num_class = out_num_class.value
-            self.pandas_categorical = _load_pandas_categorical(model_file)
+            self.pandas_categorical = _load_pandas_categorical(file_name=model_file)
         elif 'model_str' in params:
-            self.model_from_string(params['model_str'])
+            self.model_from_string(params['model_str'], False)
         else:
             raise TypeError('Need at least one training dataset or model file to create booster instance')
         self.params = params
@@ -1556,7 +1568,6 @@ class Booster(object):
     def __deepcopy__(self, _):
         model_str = self.model_to_string(num_iteration=-1)
         booster = Booster({'model_str': model_str})
-        booster.pandas_categorical = self.pandas_categorical
         return booster
 
     def __getstate__(self):
@@ -1950,7 +1961,7 @@ class Booster(object):
             ctypes.c_int(start_iteration),
             ctypes.c_int(num_iteration),
             c_str(filename)))
-        _save_pandas_categorical(filename, self.pandas_categorical)
+        _dump_pandas_categorical(self.pandas_categorical, filename)
         return self
 
     def shuffle_models(self, start_iteration=0, end_iteration=-1):
@@ -2006,6 +2017,7 @@ class Booster(object):
         if verbose:
             print('Finished loading model, total used %d iterations' % int(out_num_iterations.value))
         self.__num_class = out_num_class.value
+        self.pandas_categorical = _load_pandas_categorical(model_str=model_str)
         return self
 
     def model_to_string(self, num_iteration=None, start_iteration=0):
@@ -2050,7 +2062,9 @@ class Booster(object):
                 ctypes.c_int64(actual_len),
                 ctypes.byref(tmp_out_len),
                 ptr_string_buffer))
-        return string_buffer.value.decode()
+        ret = string_buffer.value.decode()
+        ret += _dump_pandas_categorical(self.pandas_categorical)
+        return ret
 
     def dump_model(self, num_iteration=None, start_iteration=0):
         """Dump Booster to JSON format.
@@ -2094,7 +2108,10 @@ class Booster(object):
                 ctypes.c_int64(actual_len),
                 ctypes.byref(tmp_out_len),
                 ptr_string_buffer))
-        return json.loads(string_buffer.value.decode())
+        ret = json.loads(string_buffer.value.decode())
+        ret['pandas_categorical'] = json.loads(json.dumps(self.pandas_categorical,
+                                                          default=json_default_with_numpy))
+        return ret
 
     def predict(self, data, num_iteration=None,
                 raw_score=False, pred_leaf=False, pred_contrib=False,

tests/python_package_test/test_engine.py

@@ -560,26 +560,33 @@ class TestEngine(unittest.TestCase):
         }
         lgb_train = lgb.Dataset(X, y)
         gbm0 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False)
-        pred0 = list(gbm0.predict(X_test))
+        pred0 = gbm0.predict(X_test)
         lgb_train = lgb.Dataset(X, pd.DataFrame(y))  # also test that label can be one-column pd.DataFrame
         gbm1 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
                          categorical_feature=[0])
-        pred1 = list(gbm1.predict(X_test))
+        pred1 = gbm1.predict(X_test)
         lgb_train = lgb.Dataset(X, pd.Series(y))  # also test that label can be pd.Series
         gbm2 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
                          categorical_feature=['A'])
-        pred2 = list(gbm2.predict(X_test))
+        pred2 = gbm2.predict(X_test)
         lgb_train = lgb.Dataset(X, y)
         gbm3 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
                          categorical_feature=['A', 'B', 'C', 'D'])
-        pred3 = list(gbm3.predict(X_test))
+        pred3 = gbm3.predict(X_test)
         gbm3.save_model('categorical.model')
         gbm4 = lgb.Booster(model_file='categorical.model')
-        pred4 = list(gbm4.predict(X_test))
+        pred4 = gbm4.predict(X_test)
+        model_str = gbm4.model_to_string()
+        gbm4.model_from_string(model_str, False)
+        pred5 = gbm4.predict(X_test)
+        gbm5 = lgb.Booster({'model_str': model_str})
+        pred6 = gbm5.predict(X_test)
         np.testing.assert_almost_equal(pred0, pred1)
         np.testing.assert_almost_equal(pred0, pred2)
         np.testing.assert_almost_equal(pred0, pred3)
         np.testing.assert_almost_equal(pred0, pred4)
+        np.testing.assert_almost_equal(pred0, pred5)
+        np.testing.assert_almost_equal(pred0, pred6)
 
     def test_reference_chain(self):
         X = np.random.normal(size=(100, 2))