add ks statistic & fix prng memory bug

core/psi/prng.h

@@ -63,7 +63,7 @@ public:
 private:
 
     // buffer num for aes cipher
-    static const size_t _s_buffer_size = 0x100000;
+    static const size_t _s_buffer_size = 0x10000;
 
     static const size_t _s_byte_capacity = _s_buffer_size * sizeof(block);
 

python/paddle_fl/mpc/metrics.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+"""
+MPC Metrics
+"""
+
+from paddle.fluid.metrics import MetricBase
+
+import numpy as np
+import scipy
+
+
+__all__ = [
+    'KSstatistic',
+]
+
+
+def _is_numpy_(var):
+    return isinstance(var, (np.ndarray, np.generic))
+
+
+class KSstatistic(MetricBase):
+    """
+    The is for binary classification.
+    Refer to https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Kolmogorov%E2%80%93Smirnov_statistic
+    Please notice that the KS statistic is implemented with scipy.
+
+    The `KSstatistic` function creates 2 local variables, `data1`, `data2`
+    which is predictions of positive samples and negative samples respectively
+    that are used to compute the KS statistic.
+
+    Args:
+        name (str, optional): Metric name. For details, please refer to :ref:`api_guide_Name`. Default is None.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle_fl.mpc
+            import numpy as np
+            # init the KSstatistic
+            ks = paddle_fl.mpc.metrics.KSstatistic('ks')
+
+            # suppose that batch_size is 128
+            batch_num = 100
+            batch_size = 128
+
+            for batch_id in range(batch_num):
+
+                class0_preds = np.random.random(size = (batch_size, 1))
+                class1_preds = 1 - class0_preds
+
+                preds = np.concatenate((class0_preds, class1_preds), axis=1)
+
+                labels = np.random.randint(2, size = (batch_size, 1))
+                ks.update(preds = preds, labels = labels)
+
+                # shall be some score closing to 0.1 as the preds are randomly assigned
+                print("ks statistic for iteration %d is %.2f" % (batch_id, ks.eval()))
+
+    """
+
+    def __init__(self, name=None):
+        super(KSstatistic, self).__init__(name=name)
+        self._data1 = []
+        self._data2 = []
+
+    def update(self, preds, labels):
+        """
+        Update the auc curve with the given predictions and labels.
+
+        Args:
+             preds (numpy.array): an numpy array in the shape of
+             (batch_size, 2), preds[i][j] denotes the probability of
+             classifying the instance i into the class j.
+
+             labels (numpy.array): an numpy array in the shape of
+             (batch_size, 1), labels[i] is either o or 1, representing
+             the label of the instance i.
+        """
+        if not _is_numpy_(labels):
+            raise ValueError("The 'labels' must be a numpy ndarray.")
+        if not _is_numpy_(preds):
+            raise ValueError("The 'predictions' must be a numpy ndarray.")
+
+        data1 = [preds[i, 1] for i, lbl in enumerate(labels) if lbl]
+        data2 = [preds[i, 1] for i, lbl in enumerate(labels) if not lbl]
+
+        self._data1 += data1
+        self._data2 += data2
+
+    def eval(self):
+        """
+        Return the area (a float score) under auc curve
+
+        Return:
+            float: the area under auc curve
+        """
+
+        return scipy.stats.ks_2samp(self._data1, self._data2).statistic