Merge branch 'dev-2.0.0-beta' of https://github.com/FederatedAI/FATE …

…into feature-2.0.0-beta-fate-test

python/fate/arch/dataframe/manager/block_manager.py

@@ -294,10 +294,13 @@ def __init__(self, *args, **kwargs):
     def convert_block(block):
         if isinstance(block, torch.Tensor):
             if block.dtype == torch.int32:
-                return block.clone().detach()
+                return block
             else:
                 return block.to(torch.int32)
-        return torch.tensor(np.array(block, dtype="int32"), dtype=torch.int32)
+        try:
+            return torch.tensor(block, dtype=torch.int32)
+        except ValueError:
+            return torch.tensor(np.array(block, dtype="int32"), dtype=torch.int32)
 
 
 class Int64Block(Block):
@@ -307,6 +310,11 @@ def __init__(self, *args, **kwargs):
 
     @staticmethod
     def convert_block(block):
+        if isinstance(block, torch.Tensor):
+            if block.dtype == torch.int64:
+                return block
+            else:
+                return block.to(torch.int64)
         try:
             return torch.tensor(block, dtype=torch.int64)
         except ValueError:
@@ -320,6 +328,11 @@ def __init__(self, *args, **kwargs):
 
     @staticmethod
     def convert_block(block):
+        if isinstance(block, torch.Tensor):
+            if block.dtype == torch.float32:
+                return block
+            else:
+                return block.to(torch.float32)
         try:
             return torch.tensor(block, dtype=torch.float32)
         except ValueError:
@@ -333,6 +346,11 @@ def __init__(self, *args, **kwargs):
 
     @staticmethod
     def convert_block(block):
+        if isinstance(block, torch.Tensor):
+            if block.dtype == torch.float64:
+                return block
+            else:
+                return block.to(torch.float64)
         try:
             return torch.tensor(block, dtype=torch.float64)
         except ValueError:
@@ -346,6 +364,11 @@ def __init__(self, *args, **kwargs):
 
     @staticmethod
     def convert_block(block):
+        if isinstance(block, torch.Tensor):
+            if block.dtype == torch.bool:
+                return block
+            else:
+                return block.to(torch.bool)
         try:
             return torch.tensor(block, dtype=torch.bool)
         except ValueError:

python/fate/arch/dataframe/ops/_encoder.py

@@ -221,7 +221,7 @@ def _mapper(
             if isinstance(blocks[src_bid], torch.Tensor):
                 ret = torch.bucketize(blocks[src_bid][:, [src_offset]], boundary, out_int32=False)
             else:
-                ret = torch.bucketize(blocks[src_bid][:, [src_offset]], boundary)
+                ret = np.digitize(blocks[src_bid][:, [src_offset]], boundary)
 
             ret_blocks[dst_bid] = dm.blocks[dst_bid].convert_block(ret)
 

python/fate/arch/dataframe/utils/_sample.py

@@ -115,7 +115,7 @@ def _sample_guest(
             regenerated_sample_id_prefix = generate_sample_id_prefix()
             choice_with_regenerated_ids = None
             for label, f in frac.items():
-                label_df = df[(df.label == label).as_tensor()]
+                label_df = df.iloc(df.label == label)
                 label_n = max(1, int(label_df.shape[0] * f))
                 choices = resample(list(range(label_df.shape[0])), replace=True,
                                    n_samples=label_n, random_state=random_state)
@@ -139,7 +139,7 @@ def _sample_guest(
         else:
             sample_df = None
             for label, f in frac.items():
-                label_df = df[(df.label == label).as_tensor()]
+                label_df = df.iloc(df.label == label)
                 label_n = max(1, int(label_df.shape[0] * f))
                 sample_label_df = label_df.sample(n=label_n, random_state=random_state)
 

python/fate/ml/model_selection/data_split.py

@@ -167,7 +167,7 @@ def sample_per_label(train_data, sample_count=None, random_state=None):
     sampled_n = 0
     data_n = train_data.shape[0]
     for i, label in enumerate(labels):
-        label_data = train_data[(train_data.label == int(label)).as_tensor()]
+        label_data = train_data.iloc(train_data.label == int(label))
         if i == len(labels) - 1:
             # last label:
             to_sample_n = sample_count - sampled_n