[Numpy] Random.choice implemented

python/mxnet/ndarray/numpy/random.py

@@ -24,7 +24,7 @@
 from ...base import numeric_types
 
 
-__all__ = ['randint', 'uniform', 'normal']
+__all__ = ['randint', 'uniform', 'normal', "choice"]
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -243,3 +243,79 @@ def multinomial(n, pvals, size=None):
         if any(isinstance(i, list) for i in pvals):
             raise ValueError('object too deep for desired array')
         return _npi.multinomial(n=n, pvals=pvals, size=size)
+
+
+def choice(a, size=None, replace=True, p=None, **kwargs):
+    """Generates a random sample from a given 1-D array
+
+    Parameters
+    -----------
+    a : 1-D array-like or int
+        If an ndarray, a random sample is generated from its elements.
+        If an int, the random sample is generated as if a were np.arange(a)
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    replace : boolean, optional
+        Whether the sample is with or without replacement
+    p : 1-D array-like, optional
+        The probabilities associated with each entry in a.
+        If not given the sample assumes a uniform distribution over all
+        entries in a.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    --------
+    samples : ndarray
+        The generated random samples
+
+    Examples
+    ---------
+    Generate a uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3)
+    array([0, 3, 4])
+    >>> #This is equivalent to np.random.randint(0,5,3)
+
+    Generate a non-uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+    array([3, 3, 0])
+
+    Generate a uniform random sample from np.arange(5) of size 3 without
+    replacement:
+
+    >>> np.random.choice(5, 3, replace=False)
+    array([3,1,0])
+    >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+    Generate a non-uniform random sample from np.arange(5) of size
+    3 without replacement:
+
+    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+    array([2, 3, 0])
+    """
+    from ...numpy import ndarray as np_ndarray
+    ctx = kwargs.pop('ctx', None)
+    if ctx is None:
+        ctx = current_context()
+    out = kwargs.pop('out', None)
+    if size == ():
+        size = None
+    if isinstance(a, np_ndarray):
+        ctx = None
+        if p is None:
+            indices = _npi.choice(a, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=False)
+            return _npi.take(a, indices)
+        else:
+            indices = _npi.choice(a, p, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=True)
+            return _npi.take(a, indices)
+    else:
+        if p is None:
+            return _npi.choice(a=a, size=size, replace=replace, ctx=ctx, weighted=False, out=out)
+        else:
+            return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)

python/mxnet/numpy/random.py

@@ -21,7 +21,7 @@
 from ..ndarray import numpy as _mx_nd_np
 
 
-__all__ = ["randint", "uniform", "normal"]
+__all__ = ["randint", "uniform", "normal", "choice"]
 
 
 def randint(low, high=None, size=None, dtype=None, **kwargs):
@@ -180,3 +180,58 @@ def multinomial(n, pvals, size=None, **kwargs):
     array([32, 68])
     """
     return _mx_nd_np.random.multinomial(n, pvals, size, **kwargs)
+
+
+def choice(a, size=None, replace=True, p=None, **kwargs):
+    """Generates a random sample from a given 1-D array
+
+    Parameters
+    -----------
+    a : 1-D array-like or int
+        If an ndarray, a random sample is generated from its elements.
+        If an int, the random sample is generated as if a were np.arange(a)
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    replace : boolean, optional
+        Whether the sample is with or without replacement
+    p : 1-D array-like, optional
+        The probabilities associated with each entry in a.
+        If not given the sample assumes a uniform distribution over all
+        entries in a.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    --------
+    samples : ndarray
+        The generated random samples
+
+    Examples
+    ---------
+    Generate a uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3)
+    array([0, 3, 4])
+    >>> #This is equivalent to np.random.randint(0,5,3)
+
+    Generate a non-uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+    array([3, 3, 0])
+
+    Generate a uniform random sample from np.arange(5) of size 3 without
+    replacement:
+
+    >>> np.random.choice(5, 3, replace=False)
+    array([3,1,0])
+    >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+    Generate a non-uniform random sample from np.arange(5) of size
+    3 without replacement:
+
+    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+    array([2, 3, 0])
+    """
+    return _mx_nd_np.random.choice(a, size, replace, p, **kwargs)

python/mxnet/symbol/numpy/random.py

@@ -190,3 +190,80 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         raise NotImplementedError('np.random.normal only supports loc and scale of '
                                   'numeric types for now')
     return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+
+
+def choice(a, size=None, replace=True, p=None, **kwargs):
+    """Generates a random sample from a given 1-D array
+
+    Parameters
+    -----------
+    a : 1-D array-like or int
+        If an ndarray, a random sample is generated from its elements.
+        If an int, the random sample is generated as if a were np.arange(a)
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    replace : boolean, optional
+        Whether the sample is with or without replacement
+    p : 1-D array-like, optional
+        The probabilities associated with each entry in a.
+        If not given the sample assumes a uniform distribution over all
+        entries in a.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    --------
+    samples : _Symbol
+        The generated random samples
+
+    Examples
+    ---------
+    Generate a uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3)
+    array([0, 3, 4])
+    >>> #This is equivalent to np.random.randint(0,5,3)
+
+    Generate a non-uniform random sample from np.arange(5) of size 3:
+
+    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+    array([3, 3, 0])
+
+    Generate a uniform random sample from np.arange(5) of size 3 without
+    replacement:
+
+    >>> np.random.choice(5, 3, replace=False)
+    array([3,1,0])
+    >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+    Generate a non-uniform random sample from np.arange(5) of size
+    3 without replacement:
+
+    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+    array([2, 3, 0])
+    """
+    from ._symbol import _Symbol as np_symbol
+    ctx = kwargs.pop('ctx', None)
+    if ctx is None:
+        ctx = current_context()
+    out = kwargs.pop('out', None)
+    if size == ():
+        size = None
+
+    if isinstance(a, np_symbol):
+        ctx = None
+        if p is None:
+            indices = _npi.choice(a, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=False)
+            return _npi.take(a, indices)
+        else:
+            indices = _npi.choice(a, p, a=None, size=size,
+                                  replace=replace, ctx=ctx, weighted=True)
+            return _npi.take(a, indices)
+    else:
+        if p is None:
+            return _npi.choice(a=a, size=size, replace=replace, ctx=ctx, weighted=False, out=out)
+        else:
+            return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)

src/operator/numpy/random/np_choice_op.cc

@@ -0,0 +1,81 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_choice_op.cc
+ * \brief Operator for random subset sampling
+ */
+
+#include "./np_choice_op.h"
+#include <algorithm>
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _sort<cpu>(float* key, int64_t* data, index_t length) {
+  std::sort(data, data + length,
+            [key](int64_t const& i, int64_t const& j) -> bool {
+              return key[i] > key[j];
+            });
+}
+
+DMLC_REGISTER_PARAMETER(NumpyChoiceParam);
+
+NNVM_REGISTER_OP(_npi_choice)
+.describe("random choice")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    int num_input = 0;
+    const NumpyChoiceParam& param = nnvm::get<NumpyChoiceParam>(attrs.parsed);
+    if (param.weighted) num_input += 1;
+    if (!param.a.has_value()) num_input += 1;
+    return num_input;
+})
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>(
+    "FListInputNames",
+    [](const NodeAttrs& attrs) {
+      int num_input = 0;
+      const NumpyChoiceParam& param =
+          nnvm::get<NumpyChoiceParam>(attrs.parsed);
+      if (param.weighted) num_input += 1;
+      if (!param.a.has_value()) num_input += 1;
+      if (num_input == 0) return std::vector<std::string>();
+      if (num_input == 1) return std::vector<std::string>{"input1"};
+      return std::vector<std::string>{"input1", "input2"};
+})
+.set_attr_parser(ParamParser<NumpyChoiceParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyChoiceOpShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyChoiceOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom,
+        ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyChoiceForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyChoiceParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet

src/operator/numpy/random/np_choice_op.cu

@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_choice_op.cu
+ * \brief Operator for random subset sampling
+ */
+
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+#include <thrust/swap.h>
+#include "./np_choice_op.h"
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _sort<gpu>(float* key, int64_t* data, index_t length) {
+  thrust::device_ptr<float> dev_key(key);
+  thrust::device_ptr<int64_t> dev_data(data);
+  thrust::sort_by_key(dev_key, dev_key + length, dev_data,
+                      thrust::greater<float>());
+}
+
+NNVM_REGISTER_OP(_npi_choice)
+.set_attr<FCompute>("FCompute<gpu>", NumpyChoiceForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet