fix some bugs in basic.py

python-package/lightgbm/basic.py

@@ -78,10 +78,21 @@ def is_numpy_1d_array(data):
     else:
         return False
 
+def is_1d_list(data):
+    if not isinstance(data, list):
+        return False
+    if len(data) > 0:
+        if not isinstance(data[0], (int, str, bool) ):
+            return False
+    return True
+
 def list_to_1d_numpy(data, dtype):
     if is_numpy_1d_array(data):
-        return data
-    elif isinstance(data, list):
+        if data.dtype == dtype:
+            return data
+        else:
+            return data.astype(dtype=dtype, copy=False)
+    elif is_1d_list(data):
         return np.array(data, dtype=dtype, copy=False)
     else:
         raise TypeError("Unknow type({})".format(type(data).__name__))
@@ -140,7 +151,7 @@ FIELD_TYPE_MAPPER = {"label":C_API_DTYPE_FLOAT32,
 
 def c_float_array(data):
     """Convert numpy array / list to c float array."""
-    if isinstance(data, list):
+    if is_1d_list(data):
         data = np.array(data, copy=False)
     if is_numpy_1d_array(data):
         if data.dtype == np.float32:
@@ -157,7 +168,7 @@ def c_float_array(data):
 
 def c_int_array(data):
     """Convert numpy array to c int array."""
-    if isinstance(data, list):
+    if is_1d_list(data):
         data = np.array(data, copy=False)
     if is_numpy_1d_array(data):
         if data.dtype == np.int32:
@@ -256,7 +267,7 @@ class Predictor(object):
         else:
             try:
                 csr = scipy.sparse.csr_matrix(data)
-                res = self.__pred_for_csr(csr, num_iteration, predict_type)
+                preds, nrow = self.__pred_for_csr(csr, num_iteration, predict_type)
             except:
                 raise TypeError('can not predict data for type {}'.format(type(data).__name__))
         if pred_leaf:
@@ -417,7 +428,7 @@ class Dataset(object):
         else:
             try:
                 csr = scipy.sparse.csr_matrix(data)
-                self.__init_from_csr(csr)
+                self.__init_from_csr(csr, params_str, ref_dataset)
             except:
                 raise TypeError('can not initialize Dataset from {}'.format(type(data).__name__))
         self.__label = None
@@ -618,8 +629,6 @@ class Dataset(object):
             The label information to be set into Dataset
         """
         label = list_to_1d_numpy(label, np.float32)
-        if label.dtype != np.float32:
-            label = label.astype(np.float32, copy=False)
         self.__label = label
         self.set_field('label', label)
 
@@ -633,8 +642,6 @@ class Dataset(object):
         """
         if weight is not None:
             weight = list_to_1d_numpy(weight, np.float32)
-            if weight.dtype != np.float32:
-                weight = weight.astype(np.float32, copy=False)
         self.__weight = weight
         self.set_field('weight', weight)
 
@@ -647,8 +654,6 @@ class Dataset(object):
         """
         if score is not None:
             score = list_to_1d_numpy(score, np.float32)
-            if score.dtype != np.float32:
-                score = score.astype(np.float32, copy=False)
         self.__init_score = score
         self.set_field('init_score', score)
 
@@ -662,8 +667,6 @@ class Dataset(object):
         """
         if group is not None:
             group = list_to_1d_numpy(group, np.int32)
-            if group.dtype != np.int32:
-                group = group.astype(np.int32, copy=False)
         self.__group = group
         self.set_field('group', group)
 
@@ -678,8 +681,6 @@ class Dataset(object):
         """
         if group_id is not None:
             group_id = list_to_1d_numpy(group_id, np.int32)
-            if group_id.dtype != np.int32:
-                group_id = group_id.astype(np.int32, copy=False)
         self.set_field('group_id', group_id)
 
     def get_label(self):
@@ -890,26 +891,36 @@ class Booster(object):
               and you should group grad and hess in this way as well
         Parameters
         ----------
-        grad : 1d numpy with dtype=float32
+        grad : 1d numpy or list
             The first order of gradient.
-        hess : 1d numpy with dtype=float32
+        hess : 1d numpy or list
             The second order of gradient.
 
         Returns
         -------
         is_finished, bool
         """
-        if not is_numpy_1d_array(grad) and not is_numpy_1d_array(hess):
-            raise TypeError('type of grad / hess should be 1d numpy object')
-        if not grad.dtype == np.float32 and not hess.dtype == np.float32:
-            raise TypeError('type of grad / hess should be np.float32')
+        if not is_numpy_1d_array(grad):
+            if is_1d_list(grad):
+                grad = np.array(grad, dtype=np.float32, copy=False)
+            else:
+                raise TypeError("grad should be numpy 1d array or 1d list")
+        if not is_numpy_1d_array(hess):
+            if is_1d_list(hess):
+                hess = np.array(hess, dtype=np.float32, copy=False)
+            else:
+                raise TypeError("hess should be numpy 1d array or 1d list")
         if len(grad) != len(hess):
             raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
+        if grad.dtype != np.float32:
+            grad = grad.astype(np.float32, copy=False)
+        if hess.dtype != np.float32:
+            hess = hess.astype(np.float32, copy=False)
         is_finished = ctypes.c_int(0)
         _safe_call(_LIB.LGBM_BoosterUpdateOneIterCustom(
             self.handle,
-            grad.ctypes.data_as(ctypes.ctypes.POINTER(ctypes.c_float)),
-            hess.ctypes.data_as(ctypes.ctypes.POINTER(ctypes.c_float)),
+            grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
+            hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
             ctypes.byref(is_finished)))
         return is_finished.value == 1
 
@@ -950,7 +961,7 @@ class Booster(object):
                     break
         """need push new valid data"""
         if data_idx == -1:
-            self.add_valid_data(data, name)
+            self.add_valid(data, name)
             data_idx = self.__num_dataset - 1
 
         return self.__inner_eval(name, data_idx, feval)