szha commented on a change in pull request #9747: Add contrib.rand_zipfian
URL: https://github.com/apache/incubator-mxnet/pull/9747#discussion_r169540698
##########
File path: python/mxnet/symbol/contrib.py
##########
@@ -18,9 +18,76 @@
# coding: utf-8
# pylint: disable=wildcard-import, unused-wildcard-import
"""Contrib Symbol API of MXNet."""
+import math
+from .random import uniform
+from .symbol import Symbol
try:
from .gen_contrib import *
except ImportError:
pass
-__all__ = []
+__all__ = ["rand_zipfian"]
+
+def rand_zipfian(true_classes, num_sampled, range_max):
+ """Draw random samples from an approximately log-uniform or Zipfian
distribution.
+
+ This operation randomly samples *num_sampled* candidates the range of
integers [0, range_max).
+ The elements of sampled_candidates are drawn with replacement from the
base distribution.
+
+ The base distribution for this operator is an approximately log-uniform or
Zipfian distribution:
+
+ P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)
+
+ This sampler is useful when the true classes approximately follow such a
distribution.
+ For example, if the classes represent words in a lexicon sorted in
decreasing order of \
+ frequency. If your classes are not ordered by decreasing frequency, do not
use this op.
+
+ Additionaly, it also returns the number of times each of the \
+ true classes and the sampled classes is expected to occur.
+
+ Parameters
+ ----------
+ true_classes : Symbol
+ The target classes in 1-D.
+ num_sampled: int
+ The number of classes to randomly sample.
+ range_max: int
+ The number of possible classes.
+
+ Returns
+ -------
+ samples: Symbol
+ The sampled candidate classes in 1-D `int64` dtype.
+ expected_count_true: Symbol
+ The expected count for true classes in 1-D `float64` dtype.
+ expected_count_sample: Symbol
+ The expected count for sampled candidates in 1-D `float64` dtype.
+
+ Examples
+ --------
+ >>> true_cls = mx.nd.array([3])
+ >>> samples, exp_count_true, exp_count_sample =
mx.nd.contrib.rand_zipfian(true_cls, 4, 5)
+ >>> samples
+ [1 3 3 3]
+ <NDArray 4 @cpu(0)>
+ >>> exp_count_true
+ [ 0.12453879]
+ <NDArray 1 @cpu(0)>
+ >>> exp_count_sample
+ [ 0.22629439 0.12453879 0.12453879 0.12453879]
+ <NDArray 4 @cpu(0)>
+ """
+ assert(isinstance(true_classes, Symbol)), "unexpected type %s" %
type(true_classes)
+ log_range = math.log(range_max + 1)
+ rand = uniform(0, log_range, shape=(num_sampled,), dtype='float64')
+ # make sure sampled_classes are in the range of [0, range_max)
+ sampled_classes = (rand.exp() - 1).astype('int64') % range_max
+
+ true_classes = true_classes.astype('float64')
+ expected_prob_true = ((true_classes + 2.0) / (true_classes + 1.0)).log() /
log_range
+ expected_count_true = expected_prob_true * num_sampled
+ # cast sampled classes to fp64 to avoid interget division
+ sampled_cls_fp64 = sampled_classes.astype('float64')
+ expected_prob_sampled = ((sampled_cls_fp64 + 2.0) / (sampled_cls_fp64 +
1.0)).log() / log_range
+ expected_count_sampled = expected_prob_sampled * num_sampled
+ return [sampled_classes, expected_count_true, expected_count_sampled]
Review comment:
why a list?
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
With regards,
Apache Git Services
