[geometric] fix english doc (#46485)

* fix geometric doc

[geometric] fix english doc (#46485)
* fix geometric doc
a3e8ca42 · Siming Dai · GitHub · b420d4dd · a3e8ca42 · a3e8ca42
4 changed file
--- a/python/paddle/geometric/math.py
+++ b/python/paddle/geometric/math.py
@@ -38,10 +38,9 @@ def segment_sum(data, segment_ids, name=None):
                            For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-       output (Tensor): the reduced result.
+        - output (Tensor), the reduced result.
    Examples:
        .. code-block:: python
            import paddle
@@ -85,7 +84,7 @@ def segment_mean(data, segment_ids, name=None):
    r"""
    Segment mean Operator.
-    Ihis operator calculate the mean value of input `data` which
+    This operator calculate the mean value of input `data` which
    with the same index in `segment_ids`.
    It computes a tensor such that $out_i = \\frac{1}{n_i}  \\sum_{j} data[j]$
    where sum is over j such that 'segment_ids[j] == i' and $n_i$ is the number
@@ -100,10 +99,9 @@ def segment_mean(data, segment_ids, name=None):
                            For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-       output (Tensor): the reduced result.
+        - output (Tensor), the reduced result.
    Examples:
        .. code-block:: python
            import paddle
@@ -162,10 +160,9 @@ def segment_min(data, segment_ids, name=None):
                            For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-       output (Tensor): the reduced result.
+        - output (Tensor), the reduced result.
    Examples:
        .. code-block:: python
            import paddle
@@ -224,10 +221,9 @@ def segment_max(data, segment_ids, name=None):
                            For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-       output (Tensor): the reduced result.
+        - output (Tensor), the reduced result.
    Examples:
        .. code-block:: python
            import paddle

--- a/python/paddle/geometric/message_passing/send_recv.py
+++ b/python/paddle/geometric/message_passing/send_recv.py
@@ -36,7 +36,6 @@ def send_u_recv(
    x, src_index, dst_index, reduce_op="sum", out_size=None, name=None
 ):
    """
    Graph Learning message passing api.
    This api is mainly used in Graph Learning domain, and the main purpose is to reduce intermediate memory
@@ -82,9 +81,8 @@ def send_u_recv(
                              For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-        out (Tensor): The output tensor, should have the same shape and same dtype as input tensor `x`.
+        - out (Tensor), the output tensor, should have the same shape and same dtype as input tensor `x`.
-                      If `out_size` is set correctly, then it should have the same shape as `x` except
+          If `out_size` is set correctly, then it should have the same shape as `x` except the 0th dimension.
-                      the 0th dimension.
    Examples:
        .. code-block:: python
@@ -229,6 +227,7 @@ def send_ue_recv(
           out = [[1, 3, 4],
                  [4, 10, 12],
                  [2, 5, 6]]
    Args:
        x (Tensor): The input node feature tensor, and the available data type is float32, float64, int32, int64.
                    And we support float16 in gpu version.
@@ -248,9 +247,8 @@ def send_ue_recv(
                              For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-        out (Tensor): The output tensor, should have the same shape and same dtype as input tensor `x`.
+        - out (Tensor), the output tensor, should have the same shape and same dtype as input tensor `x`.
-                      If `out_size` is set correctly, then it should have the same shape as `x` except
+          If `out_size` is set correctly, then it should have the same shape as `x` except the 0th dimension.
-                      the 0th dimension.
    Examples:
        .. code-block:: python
@@ -432,9 +430,10 @@ def send_uv(x, y, src_index, dst_index, message_op="add", name=None):
                              For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-        out (Tensor): The output tensor.
+        - out (Tensor), the output tensor.
    Examples:
        .. code-block:: python
            import paddle

--- a/python/paddle/geometric/reindex.py
+++ b/python/paddle/geometric/reindex.py
@@ -28,25 +28,19 @@ def reindex_graph(
    Reindex Graph API.
    This API is mainly used in Graph Learning domain, which should be used
-    in conjunction with `graph_sample_neighbors` API. And the main purpose
+    in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose
    is to reindex the ids information of the input nodes, and return the
    corresponding graph edges after reindex.
-    **Notes**:
+    Take input nodes x = [0, 1, 2] as an example. If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
-        The number in x should be unique, otherwise it would cause potential errors.
+    then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7].
-    We will reindex all the nodes from 0.
+    Then after graph_reindex, we will have 3 different outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6], reindex_dst: [0, 0, 1, 1, 1, 2, 2]
+    and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]. We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index
-    Take input nodes x = [0, 1, 2] as an example.
-    If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
-    then we know that the neighbors of 0 is [8, 9], the neighbors of 1
-    is [0, 4, 7], and the neighbors of 2 is [6, 7].
-    Then after graph_reindex, we will have 3 different outputs:
-        1. reindex_src: [3, 4, 0, 5, 6, 7, 6]
-        2. reindex_dst: [0, 0, 1, 1, 1, 2, 2]
-        3. out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]
-    We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index
    of nodes in `out_nodes`.
+    Note:
+        The number in x should be unique, otherwise it would cause potential errors. We will reindex all the nodes from 0.
    Args:
        x (Tensor): The input nodes which we sample neighbors for. The available
                    data type is int32, int64.
@@ -64,27 +58,23 @@ def reindex_graph(
                              For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-        reindex_src (Tensor): The source node index of graph edges after reindex.
+        - reindex_src (Tensor), the source node index of graph edges after reindex.
-        reindex_dst (Tensor): The destination node index of graph edges after reindex.
-        out_nodes (Tensor): The index of unique input nodes and neighbors before reindex,
-                            where we put the input nodes `x` in the front, and put neighbor
-                            nodes in the back.
-    Examples:
+        - reindex_dst (Tensor), the destination node index of graph edges after reindex.
+        - out_nodes (Tensor), the index of unique input nodes and neighbors before reindex, where we put the input nodes `x` in the front, and put neighbor nodes in the back.
+    Examples:
        .. code-block:: python
            import paddle
            x = [0, 1, 2]
            neighbors = [8, 9, 0, 4, 7, 6, 7]
            count = [2, 3, 2]
            x = paddle.to_tensor(x, dtype="int64")
            neighbors = paddle.to_tensor(neighbors, dtype="int64")
            count = paddle.to_tensor(count, dtype="int32")
+            reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_graph(x, neighbors, count)
-        reindex_src, reindex_dst, out_nodes = \
-             paddle.geometric.reindex_graph(x, neighbors, count)
            # reindex_src: [3, 4, 0, 5, 6, 7, 6]
            # reindex_dst: [0, 0, 1, 1, 1, 2, 2]
            # out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]
@@ -150,26 +140,18 @@ def reindex_heter_graph(
    Reindex HeterGraph API.
    This API is mainly used in Graph Learning domain, which should be used
-    in conjunction with `graph_sample_neighbors` API. And the main purpose
+    in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose
    is to reindex the ids information of the input nodes, and return the
    corresponding graph edges after reindex.
-    **Notes**:
+    Take input nodes x = [0, 1, 2] as an example. For graph A, suppose we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
-        The number in x should be unique, otherwise it would cause potential errors.
+    then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7]. For graph B,
-    We support multi-edge-types neighbors reindexing in reindex_heter_graph api.
+    suppose we have neighbors = [0, 2, 3, 5, 1], and count = [1, 3, 1], then we know that the neighbors of 0 is [0], the neighbors of 1 is [2, 3, 5],
-    We will reindex all the nodes from 0.
+    and the neighbors of 3 is [1]. We will get following outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1], reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
+    and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5].
-    Take input nodes x = [0, 1, 2] as an example.
-    For graph A, suppose we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
+    Note:
-    then we know that the neighbors of 0 is [8, 9], the neighbors of 1
+        The number in x should be unique, otherwise it would cause potential errors. We support multi-edge-types neighbors reindexing in reindex_heter_graph api. We will reindex all the nodes from 0.
-    is [0, 4, 7], and the neighbors of 2 is [6, 7].
-    For graph B, suppose we have neighbors = [0, 2, 3, 5, 1], and count = [1, 3, 1],
-    then we know that the neighbors of 0 is [0], the neighbors of 1 is [2, 3, 5],
-    and the neighbors of 3 is [1].
-    We will get following outputs:
-        1. reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1]
-        2. reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
-        3. out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5]
    Args:
        x (Tensor): The input nodes which we sample neighbors for. The available
@@ -188,34 +170,31 @@ def reindex_heter_graph(
                              For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-        reindex_src (Tensor): The source node index of graph edges after reindex.
+        - reindex_src (Tensor), the source node index of graph edges after reindex.
-        reindex_dst (Tensor): The destination node index of graph edges after reindex.
-        out_nodes (Tensor): The index of unique input nodes and neighbors before reindex,
+        - reindex_dst (Tensor), the destination node index of graph edges after reindex.
+        - out_nodes (Tensor), the index of unique input nodes and neighbors before reindex,
                              where we put the input nodes `x` in the front, and put neighbor
                              nodes in the back.
    Examples:
        .. code-block:: python
            import paddle
            x = [0, 1, 2]
            neighbors_a = [8, 9, 0, 4, 7, 6, 7]
            count_a = [2, 3, 2]
            x = paddle.to_tensor(x, dtype="int64")
            neighbors_a = paddle.to_tensor(neighbors_a, dtype="int64")
            count_a = paddle.to_tensor(count_a, dtype="int32")
            neighbors_b = [0, 2, 3, 5, 1]
            count_b = [1, 3, 1]
            neighbors_b = paddle.to_tensor(neighbors_b, dtype="int64")
            count_b = paddle.to_tensor(count_b, dtype="int32")
            neighbors = [neighbors_a, neighbors_b]
            count = [count_a, count_b]
-        reindex_src, reindex_dst, out_nodes = \
+            reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_heter_graph(x, neighbors, count)
-             paddle.geometric.reindex_heter_graph(x, neighbors, count)
            # reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1]
            # reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
            # out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5]

--- a/python/paddle/geometric/sampling/neighbors.py
+++ b/python/paddle/geometric/sampling/neighbors.py
@@ -65,14 +65,17 @@ def sample_neighbors(
                              For more information, please refer to :ref:`api_guide_Name`.
    Returns:
-        out_neighbors (Tensor): The sample neighbors of the input nodes.
+        - out_neighbors (Tensor), the sample neighbors of the input nodes.
-        out_count (Tensor): The number of sampling neighbors of each input node, and the shape
+        - out_count (Tensor), the number of sampling neighbors of each input node, and the shape
                              should be the same with `input_nodes`.
-        out_eids (Tensor): If `return_eids` is True, we will return the eid information of the
+        - out_eids (Tensor), if `return_eids` is True, we will return the eid information of the
                             sample edges.
    Examples:
        .. code-block:: python
            import paddle
            # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
            #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
@@ -83,9 +86,7 @@ def sample_neighbors(
            row = paddle.to_tensor(row, dtype="int64")
            colptr = paddle.to_tensor(colptr, dtype="int64")
            nodes = paddle.to_tensor(nodes, dtype="int64")
-        out_neighbors, out_count = \
+            out_neighbors, out_count = paddle.geometric.sample_neighbors(row, colptr, nodes, sample_size=sample_size)
-            paddle.geometric.sample_neighbors(row, colptr, nodes,
-                                              sample_size=sample_size)
    """