#!/usr/bin/env python
# Copyright 2016 DIANA-HEP
#
# 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.
import json
import math
import numbers
import struct
from histogrammar.defs import *
from histogrammar.util import *
class Average(Factory, Container):
"""Accumulate the weighted mean of a given quantity.
Uses the numerically stable weighted mean algorithm described in `"Incremental calculation of weighted mean and variance," <http://www-uxsup.csx.cam.ac.uk/~fanf2/hermes/doc/antiforgery/stats.pdf>`_ Tony Finch, *Univeristy of Cambridge Computing Service,* 2009.
"""
@staticmethod
def ed(entries, mean):
"""Create an Average that is only capable of being added.
Parameters:
entries (float): the number of entries.
mean (float): the mean.
"""
if not isinstance(entries, numbers.Real) and entries not in ("nan", "inf", "-inf"):
raise TypeError("entries ({0}) must be a number".format(entries))
if not isinstance(mean, numbers.Real) and entries not in ("nan", "inf", "-inf"):
raise TypeError("mean ({0}) must be a number".format(mean))
if entries < 0.0:
raise ValueError("entries ({0}) cannot be negative".format(entries))
out = Average(None)
out.entries = float(entries)
out.mean = float(mean)
return out.specialize()
@staticmethod
def ing(quantity):
"""Synonym for ``__init__``."""
return Average(quantity)
def __init__(self, quantity):
"""Create an Average that is capable of being filled and added.
Parameters:
quantity (function returning float): computes the quantity of interest from the data.
Other parameters:
entries (float): the number of entries, initially 0.0.
mean (float): the running mean, initially NaN.
"""
self.quantity = serializable(quantity)
self.entries = 0.0
self.mean = float("nan")
super(Average, self).__init__()
self.specialize()
@inheritdoc(Container)
def zero(self): return Average(self.quantity)
@inheritdoc(Container)
def __add__(self, other):
if isinstance(other, Average):
out = Average(self.quantity)
out.entries = self.entries + other.entries
if self.entries == 0.0:
out.mean = other.mean
elif other.entries == 0.0:
out.mean = self.mean
else:
out.mean = (self.entries*self.mean + other.entries*other.mean)/(self.entries + other.entries)
return out.specialize()
else:
raise ContainerException("cannot add {0} and {1}".format(self.name, other.name))
@inheritdoc(Container)
def __iadd__(self, other):
both = self + other
self.entries = both.entries
self.mean = both.mean
return self
@inheritdoc(Container)
def __mul__(self, factor):
if math.isnan(factor) or factor <= 0.0:
return self.zero()
else:
out = self.zero()
out.entries = factor * self.entries
out.mean = self.mean
return out.specialize()
@inheritdoc(Container)
def __rmul__(self, factor):
return self.__mul__(factor)
@inheritdoc(Container)
def fill(self, datum, weight=1.0):
self._checkForCrossReferences()
if weight > 0.0:
q = self.quantity(datum)
if not isinstance(q, numbers.Real):
raise TypeError("function return value ({0}) must be boolean or number".format(q))
# no possibility of exception from here on out (for rollback)
if self.entries == 0.0:
self.mean = q
self.entries += weight
if math.isnan(self.mean) or math.isnan(q):
self.mean = float("nan")
elif math.isinf(self.mean) or math.isinf(q):
if math.isinf(self.mean) and math.isinf(q) and self.mean * q < 0.0:
self.mean = float("nan") # opposite-sign infinities is bad
elif math.isinf(q):
self.mean = q # mean becomes infinite with sign of q
else:
pass # mean is already infinite
if math.isinf(self.entries) or math.isnan(self.entries):
self.mean = float("nan") # non-finite denominator is bad
else: # handle finite case
delta = q - self.mean
shift = delta * weight / self.entries
self.mean += shift
def _cppGenerateCode(self, parser, generator, inputFieldNames, inputFieldTypes, derivedFieldTypes, derivedFieldExprs, storageStructs, initCode, initPrefix, initIndent, fillCode, fillPrefix, fillIndent, weightVars, weightVarStack, tmpVarTypes):
return self._c99GenerateCode(parser, generator, inputFieldNames, inputFieldTypes, derivedFieldTypes, derivedFieldExprs, storageStructs, initCode, initPrefix, initIndent, fillCode, fillPrefix, fillIndent, weightVars, weightVarStack, tmpVarTypes)
def _c99GenerateCode(self, parser, generator, inputFieldNames, inputFieldTypes, derivedFieldTypes, derivedFieldExprs, storageStructs, initCode, initPrefix, initIndent, fillCode, fillPrefix, fillIndent, weightVars, weightVarStack, tmpVarTypes):
initCode.append(" " * initIndent + self._c99ExpandPrefix(*initPrefix) + ".entries = 0.0;")
initCode.append(" " * initIndent + self._c99ExpandPrefix(*initPrefix) + ".mean = 0.0;")
normexpr = self._c99QuantityExpr(parser, generator, inputFieldNames, inputFieldTypes, derivedFieldTypes, derivedFieldExprs, None)
fillCode.append(" " * fillIndent + self._c99ExpandPrefix(*fillPrefix) + ".entries += " + weightVarStack[-1] + ";")
delta = "delta_" + str(len(tmpVarTypes))
tmpVarTypes[delta] = "double"
shift = "shift_" + str(len(tmpVarTypes))
tmpVarTypes[shift] = "double"
fillCode.append("""{indent}if (std::isnan({mean}) || std::isnan({q})) {{
{indent} {mean} = NAN;
{indent}}}
{indent}else if (std::isinf({mean}) || std::isinf({q})) {{
{indent} if (std::isinf({mean}) && std::isinf({q}) && {mean} * {q} < 0.0)
{indent} {mean} = NAN;
{indent} else if (std::isinf({q}))
{indent} {mean} = {q};
{indent} else
{indent} {{ }}
{indent} if (std::isinf({entries}) || std::isnan({entries}))
{indent} {mean} = NAN;
{indent}}}
{indent}else {{
{indent} {delta} = {q} - {mean};
{indent} {shift} = {delta} * {weight} / {entries};
{indent} {mean} += {shift};
{indent}}}""".format(indent = " " * fillIndent,
entries = self._c99ExpandPrefix(*fillPrefix) + ".entries",
mean = self._c99ExpandPrefix(*fillPrefix) + ".mean",
q = normexpr,
delta = delta,
shift = shift,
weight = weightVarStack[-1]))
storageStructs[self._c99StructName()] = """
typedef struct {{
double entries;
double mean;
}} {0};
""".format(self._c99StructName())
def _clingUpdate(self, filler, *extractorPrefix):
obj = self._clingExpandPrefix(filler, *extractorPrefix)
entries = self.entries + obj.entries
if self.entries == 0.0:
mean = obj.mean
elif obj.entries == 0.0:
mean = self.mean
else:
mean = (self.entries*self.mean + obj.entries*obj.mean)/(self.entries + obj.entries)
self.entries = entries
self.mean = mean
def _c99StructName(self):
return "Av"
def _cudaGenerateCode(self, parser, generator, inputFieldNames, inputFieldTypes, derivedFieldTypes, derivedFieldExprs, storageStructs, initCode, initPrefix, initIndent, fillCode, fillPrefix, fillIndent, combineCode, totalPrefix, itemPrefix, combineIndent, jsonCode, jsonPrefix, jsonIndent, weightVars, weightVarStack, tmpVarTypes, suppressName):
initCode.append(" " * initIndent + self._c99ExpandPrefix(*initPrefix) + ".entries = 0.0f;")
initCode.append(" " * initIndent + self._c99ExpandPrefix(*initPrefix) + ".sum = 0.0f;")
normexpr = self._cudaQuantityExpr(parser, generator, inputFieldNames, inputFieldTypes, derivedFieldTypes, derivedFieldExprs, None)
fillCode.append(" " * fillIndent + "atomicAdd(&" + self._c99ExpandPrefix(*fillPrefix) + ".entries, " + weightVarStack[-1] + ");")
fillCode.append(" " * fillIndent + "atomicAdd(&" + self._c99ExpandPrefix(*fillPrefix) + ".sum, " + weightVarStack[-1] + " * " + normexpr + ");")
combineCode.append(" " * combineIndent + "atomicAdd(&" + self._c99ExpandPrefix(*totalPrefix) + ".entries, " + self._c99ExpandPrefix(*itemPrefix) + ".entries);")
combineCode.append(" " * combineIndent + "atomicAdd(&" + self._c99ExpandPrefix(*totalPrefix) + ".sum, " + self._c99ExpandPrefix(*itemPrefix) + ".sum);")
jsonCode.append(" " * jsonIndent + "fprintf(out, \"{\\\"entries\\\": \");")
jsonCode.append(" " * jsonIndent + "floatToJson(out, " + self._c99ExpandPrefix(*jsonPrefix) + ".entries);")
jsonCode.append(" " * jsonIndent + "fprintf(out, \", \\\"mean\\\": \");")
jsonCode.append(" " * jsonIndent + "if (" + self._c99ExpandPrefix(*jsonPrefix) + ".entries == 0.0f)")
jsonCode.append(" " * jsonIndent + " fprintf(out, \"\\\"nan\\\"\");")
jsonCode.append(" " * jsonIndent + "else")
jsonCode.append(" " * jsonIndent + " floatToJson(out, " + self._c99ExpandPrefix(*jsonPrefix) + ".sum / " + self._c99ExpandPrefix(*jsonPrefix) + ".entries);")
if suppressName or self.quantity.name is None:
jsonCode.append(" " * jsonIndent + "fprintf(out, \"}\");")
else:
jsonCode.append(" " * jsonIndent + "fprintf(out, \", \\\"name\\\": " + json.dumps(json.dumps(self.quantity.name))[1:-1] + "}\");")
storageStructs[self._c99StructName()] = """
typedef struct {{
float entries;
float sum;
}} {0};
""".format(self._c99StructName())
def _cudaUnpackAndFill(self, data, bigendian, alignment):
format = "<ff"
objentries, objsum = struct.unpack(format, data[:struct.calcsize(format)])
entries = self.entries + objentries
if self.entries == 0.0:
if objentries == 0.0:
mean = float("nan")
else:
mean = objsum / objentries
elif objentries == 0.0:
mean = self.mean
else:
mean = (self.entries*self.mean + objsum)/(self.entries + objentries)
self.entries = entries
self.mean = mean
return data[struct.calcsize(format):]
def _numpy(self, data, weights, shape):
q = self.quantity(data)
self._checkNPQuantity(q, shape)
self._checkNPWeights(weights, shape)
weights = self._makeNPWeights(weights, shape)
# no possibility of exception from here on out (for rollback)
ca, ma = self.entries, self.mean
if ca == 0.0:
ma = 0.0
import numpy
selection = weights > 0.0
q = q[selection]
weights = weights[selection]
self.entries += float(weights.sum())
ca_plus_cb = self.entries
if math.isinf(ca_plus_cb):
self.mean = float("nan")
elif ca_plus_cb > 0.0:
mb = numpy.average(q, weights=weights)
self.mean = float((ca*ma + (ca_plus_cb - ca)*mb) / ca_plus_cb)
def _sparksql(self, jvm, converter):
return converter.Average(self.quantity.asSparkSQL())
@property
def children(self):
"""List of sub-aggregators, to make it possible to walk the tree."""
return []
@inheritdoc(Container)
def toJsonFragment(self, suppressName): return maybeAdd({
"entries": floatToJson(self.entries),
"mean": floatToJson(self.mean),
}, name=(None if suppressName else self.quantity.name))
@staticmethod
@inheritdoc(Factory)
def fromJsonFragment(json, nameFromParent):
if isinstance(json, dict) and hasKeys(json.keys(), ["entries", "mean"], ["name"]):
if json["entries"] in ("nan", "inf", "-inf") or isinstance(json["entries"], numbers.Real):
entries = float(json["entries"])
else:
raise JsonFormatException(json["entries"], "Average.entries")
if isinstance(json.get("name", None), basestring):
name = json["name"]
elif json.get("name", None) is None:
name = None
else:
raise JsonFormatException(json["name"], "Average.name")
if json["mean"] in ("nan", "inf", "-inf") or isinstance(json["mean"], numbers.Real):
mean = float(json["mean"])
else:
raise JsonFormatException(json["mean"], "Average.mean")
out = Average.ed(entries, mean)
out.quantity.name = nameFromParent if name is None else name
return out.specialize()
else:
raise JsonFormatException(json, "Average")
def __repr__(self):
return "<Average mean={0}>".format(self.mean)
def __eq__(self, other):
return isinstance(other, Average) and self.quantity == other.quantity and numeq(self.entries, other.entries) and numeq(self.mean, other.mean)
def __ne__(self, other): return not self == other
def __hash__(self):
return hash((self.quantity, self.entries, self.mean))
Factory.register(Average)
