7d399c9daa
Add method to easily convert a StatCounter instance into a Python dict https://issues.apache.org/jira/browse/SPARK-6919 Note: This is my original work and the existing Spark license applies. Author: Erik Shilts <erik.shilts@opower.com> Closes #5516 from eshilts/statcounter-asdict.
159 lines
5 KiB
Python
159 lines
5 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This file is ported from spark/util/StatCounter.scala
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import copy
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import math
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try:
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from numpy import maximum, minimum, sqrt
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except ImportError:
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maximum = max
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minimum = min
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sqrt = math.sqrt
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class StatCounter(object):
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def __init__(self, values=None):
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if values is None:
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values = list()
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self.n = 0 # Running count of our values
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self.mu = 0.0 # Running mean of our values
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self.m2 = 0.0 # Running variance numerator (sum of (x - mean)^2)
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self.maxValue = float("-inf")
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self.minValue = float("inf")
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for v in values:
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self.merge(v)
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# Add a value into this StatCounter, updating the internal statistics.
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def merge(self, value):
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delta = value - self.mu
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self.n += 1
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self.mu += delta / self.n
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self.m2 += delta * (value - self.mu)
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self.maxValue = maximum(self.maxValue, value)
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self.minValue = minimum(self.minValue, value)
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return self
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# Merge another StatCounter into this one, adding up the internal statistics.
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def mergeStats(self, other):
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if not isinstance(other, StatCounter):
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raise Exception("Can only merge Statcounters!")
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if other is self: # reference equality holds
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self.merge(copy.deepcopy(other)) # Avoid overwriting fields in a weird order
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else:
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if self.n == 0:
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self.mu = other.mu
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self.m2 = other.m2
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self.n = other.n
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self.maxValue = other.maxValue
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self.minValue = other.minValue
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elif other.n != 0:
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delta = other.mu - self.mu
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if other.n * 10 < self.n:
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self.mu = self.mu + (delta * other.n) / (self.n + other.n)
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elif self.n * 10 < other.n:
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self.mu = other.mu - (delta * self.n) / (self.n + other.n)
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else:
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self.mu = (self.mu * self.n + other.mu * other.n) / (self.n + other.n)
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self.maxValue = maximum(self.maxValue, other.maxValue)
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self.minValue = minimum(self.minValue, other.minValue)
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self.m2 += other.m2 + (delta * delta * self.n * other.n) / (self.n + other.n)
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self.n += other.n
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return self
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# Clone this StatCounter
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def copy(self):
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return copy.deepcopy(self)
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def count(self):
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return int(self.n)
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def mean(self):
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return self.mu
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def sum(self):
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return self.n * self.mu
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def min(self):
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return self.minValue
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def max(self):
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return self.maxValue
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# Return the variance of the values.
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def variance(self):
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if self.n == 0:
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return float('nan')
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else:
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return self.m2 / self.n
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#
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# Return the sample variance, which corrects for bias in estimating the variance by dividing
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# by N-1 instead of N.
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#
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def sampleVariance(self):
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if self.n <= 1:
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return float('nan')
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else:
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return self.m2 / (self.n - 1)
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# Return the standard deviation of the values.
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def stdev(self):
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return sqrt(self.variance())
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#
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# Return the sample standard deviation of the values, which corrects for bias in estimating the
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# variance by dividing by N-1 instead of N.
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#
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def sampleStdev(self):
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return sqrt(self.sampleVariance())
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def asDict(self, sample=False):
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"""Returns the :class:`StatCounter` members as a ``dict``.
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>>> sc.parallelize([1., 2., 3., 4.]).stats().asDict()
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{'count': 4L,
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'max': 4.0,
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'mean': 2.5,
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'min': 1.0,
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'stdev': 1.2909944487358056,
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'sum': 10.0,
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'variance': 1.6666666666666667}
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"""
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return {
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'count': self.count(),
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'mean': self.mean(),
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'sum': self.sum(),
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'min': self.min(),
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'max': self.max(),
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'stdev': self.stdev() if sample else self.sampleStdev(),
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'variance': self.variance() if sample else self.sampleVariance()
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}
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def __repr__(self):
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return ("(count: %s, mean: %s, stdev: %s, max: %s, min: %s)" %
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(self.count(), self.mean(), self.stdev(), self.max(), self.min()))
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