[python] added get_data() method to Dataset class

docs/FAQ.rst

@@ -211,15 +211,15 @@ Python-package
    If you set ``free_raw_data=True`` (default), the raw data (with Python data struct) will be freed.
    So, if you want to:
 
-   -  get label (or weight/init\_score/group) before constructing a dataset, it's same as get ``self.label``
+   -  get label (or weight/init\_score/group/data) before constructing a dataset, it's same as get ``self.label``;
 
-   -  set label (or weight/init\_score/group) before constructing a dataset, it's same as ``self.label=some_label_array``
+   -  set label (or weight/init\_score/group) before constructing a dataset, it's same as ``self.label=some_label_array``;
 
    -  get num\_data (or num\_feature) before constructing a dataset, you can get data with ``self.data``.
-      Then, if your data is ``numpy.ndarray``, use some code like ``self.data.shape``
+      Then, if your data is ``numpy.ndarray``, use some code like ``self.data.shape``. But do not do this after subsetting the Dataset, because you'll get always ``None``;
 
    -  set predictor (or reference/categorical feature) after constructing a dataset,
-      you should set ``free_raw_data=False`` or init a Dataset object with the same raw data
+      you should set ``free_raw_data=False`` or init a Dataset object with the same raw data.
 
 --------------
 

python-package/lightgbm/basic.py

@@ -687,6 +687,7 @@ def __init__(self, data, label=None, reference=None,
         self.params = copy.deepcopy(params)
         self.free_raw_data = free_raw_data
         self.used_indices = None
+        self.need_slice = True
         self._predictor = None
         self.pandas_categorical = None
         self.params_back_up = None
@@ -974,6 +975,8 @@ def construct(self):
                         ctypes.c_int(used_indices.shape[0]),
                         c_str(params_str),
                         ctypes.byref(self.handle)))
+                    self.data = self.reference.data
+                    self.get_data()
                     if self.group is not None:
                         self.set_group(self.group)
                     if self.get_label() is None:
@@ -1041,7 +1044,8 @@ def subset(self, used_indices, params=None):
         if params is None:
             params = self.params
         ret = Dataset(None, reference=self, feature_name=self.feature_name,
-                      categorical_feature=self.categorical_feature, params=params)
+                      categorical_feature=self.categorical_feature, params=params,
+                      free_raw_data=self.free_raw_data)
         ret._predictor = self._predictor
         ret.pandas_categorical = self.pandas_categorical
         ret.used_indices = used_indices
@@ -1375,6 +1379,27 @@ def get_init_score(self):
             self.init_score = self.get_field('init_score')
         return self.init_score
 
+    def get_data(self):
+        """Get the raw data of the Dataset.
+
+        Returns
+        -------
+        data : string, numpy array, pandas DataFrame, scipy.sparse, list of numpy arrays or None
+            Raw data used in the Dataset construction.
+        """
+        if self.handle is None:
+            raise Exception("Cannot get data before construct Dataset")
+        if self.data is not None and self.used_indices is not None and self.need_slice:
+            if isinstance(self.data, np.ndarray) or scipy.sparse.issparse(self.data):
+                self.data = self.data[self.used_indices, :]
+            elif isinstance(self.data, DataFrame):
+                self.data = self.data.iloc[self.used_indices].copy()
+            else:
+                warnings.warn("Cannot subset {} type of raw data.\n"
+                              "Returning original raw data".format(type(self.data).__name__))
+            self.need_slice = False
+        return self.data
+
     def get_group(self):
         """Get the group of the Dataset.
 

tests/python_package_test/test_engine.py

@@ -808,3 +808,23 @@ def test_constant_features_multiclassova(self):
         }
         self.test_constant_features([0.0, 1.0, 2.0, 0.0], [0.5, 0.25, 0.25], params)
         self.test_constant_features([0.0, 1.0, 2.0, 1.0], [0.25, 0.5, 0.25], params)
+
+    def test_fpreproc(self):
+        def preprocess_data(dtrain, dtest, params):
+            train_data = dtrain.construct().get_data()
+            test_data = dtest.construct().get_data()
+            train_data[:, 0] += 1
+            test_data[:, 0] += 1
+            dtrain.label[-5:] = 3
+            dtest.label[-5:] = 3
+            dtrain = lgb.Dataset(train_data, dtrain.label)
+            dtest = lgb.Dataset(test_data, dtest.label, reference=dtrain)
+            params['num_class'] = 4
+            return dtrain, dtest, params
+
+        X, y = load_iris(True)
+        dataset = lgb.Dataset(X, y, free_raw_data=False)
+        params = {'objective': 'multiclass', 'num_class': 3, 'verbose': -1}
+        results = lgb.cv(params, dataset, num_boost_round=10, fpreproc=preprocess_data)
+        self.assertIn('multi_logloss-mean', results)
+        self.assertEqual(len(results['multi_logloss-mean']), 10)