# Copyright 2008 Matt Chaput. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY MATT CHAPUT ``AS IS'' AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
# EVENT SHALL MATT CHAPUT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# The views and conclusions contained in the software and documentation are
# those of the authors and should not be interpreted as representing official
# policies, either expressed or implied, of Matt Chaput.
"""Classes and functions for classifying and extracting information from
documents.
"""
from __future__ import annotations
import random
from collections import defaultdict
from math import log
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from collections.abc import Iterable
# Expansion models
[docs]
class ExpansionModel:
def __init__(self, doc_count: int, field_length: float) -> None:
self.N = doc_count
self.collection_total = field_length
if self.N:
self.mean_length = self.collection_total / self.N
else:
self.mean_length = 0
def normalizer(self, maxweight: float, top_total: float) -> float:
raise NotImplementedError
def score(
self, weight_in_top: float, weight_in_collection: float, top_total: float
) -> float:
raise NotImplementedError
[docs]
class Bo1Model(ExpansionModel):
def normalizer(self, maxweight: float, top_total: float) -> float:
if not maxweight:
return maxweight
f = maxweight / self.N
return (maxweight * log((1.0 + f) / f) + log(1.0 + f)) / log(2.0)
def score(
self, weight_in_top: float, weight_in_collection: float, top_total: float
) -> float:
f = weight_in_collection / self.N
return weight_in_top * log((1.0 + f) / f, 2) + log(1.0 + f, 2)
[docs]
class Bo2Model(ExpansionModel):
def normalizer(self, maxweight: float, top_total: float) -> float:
if not self.collection_total:
return maxweight
f = maxweight * self.N / self.collection_total
return maxweight * log((1.0 + f) / f, 2) + log(1.0 + f, 2)
def score(
self, weight_in_top: float, weight_in_collection: float, top_total: float
) -> float:
f = weight_in_top * top_total / self.collection_total
return weight_in_top * log((1.0 + f) / f, 2) + log(1.0 + f, 2)
[docs]
class KLModel(ExpansionModel):
def normalizer(self, maxweight: float, top_total: float) -> float:
if not self.collection_total:
return maxweight
return maxweight * log(self.collection_total / top_total) / log(2.0) * top_total
def score(
self, weight_in_top: float, weight_in_collection: float, top_total: float
) -> float:
wit_over_tt = weight_in_top / top_total
wic_over_ct = weight_in_collection / self.collection_total
if wit_over_tt < wic_over_ct:
return 0
else:
return wit_over_tt * log(
wit_over_tt / (weight_in_top / self.collection_total), 2
)
[docs]
class Expander:
"""Uses an ExpansionModel to expand the set of query terms based on the top
N result documents.
"""
def __init__(
self,
ixreader: Any,
fieldname: str,
model: type[ExpansionModel] | ExpansionModel = Bo1Model,
) -> None:
"""
:param reader: A :class: whoosh.reading.IndexReader object.
:param fieldname: The name of the field in which to search.
:param model: (classify.ExpansionModel) The model to use for expanding
the query terms. If you omit this parameter, the expander uses
:class:`Bo1Model` by default.
"""
self.ixreader = ixreader
self.fieldname = fieldname
doccount = self.ixreader.doc_count_all()
fieldlen = self.ixreader.field_length(fieldname)
if isinstance(model, ExpansionModel):
self.model = model
else:
self.model = model(doccount, fieldlen)
# Maps words to their weight in the top N documents.
self.topN_weight: dict[str, float] = defaultdict(float)
# Total weight of all terms in the top N documents.
self.top_total = 0.0
[docs]
def add(self, vector: Iterable[tuple[str, float]]) -> None:
"""Adds forward-index information about one of the "top N" documents.
:param vector: A series of (text, weight) tuples, such as is
returned by Reader.vector_as("weight", docnum, fieldname).
"""
total_weight = 0.0
topN_weight = self.topN_weight
for word, weight in vector:
total_weight += weight
topN_weight[word] += weight
self.top_total += total_weight
def add_document(self, docnum: int) -> None:
ixreader = self.ixreader
if self.ixreader.has_vector(docnum, self.fieldname):
self.add(ixreader.vector_as("weight", docnum, self.fieldname))
elif self.ixreader.schema[self.fieldname].stored:
self.add_text(ixreader.stored_fields(docnum).get(self.fieldname))
else:
raise Exception(
"Field %r in document %s is not vectored or stored"
% (self.fieldname, docnum)
)
def add_text(self, string: Any) -> None:
# Unfortunately since field.index() yields bytes texts, and we want
# unicode, we end up encoding and decoding unnecessarily.
#
# TODO: Find a way around this
field = self.ixreader.schema[self.fieldname]
from_bytes = field.from_bytes
self.add(
(from_bytes(text), weight) for text, _, weight, _ in field.index(string)
)
[docs]
def expanded_terms(
self, number: int, normalize: bool = True
) -> list[tuple[str, float]]:
"""Returns the N most important terms in the vectors added so far.
:param number: The number of terms to return.
:param normalize: Whether to normalize the weights.
:returns: A list of ("term", weight) tuples.
"""
model = self.model
fieldname = self.fieldname
ixreader = self.ixreader
field = ixreader.schema[fieldname]
tlist = []
maxweight = 0.0
# If no terms have been added, return an empty list
if not self.topN_weight:
return []
for word, weight in self.topN_weight.items():
btext = field.to_bytes(word)
if (fieldname, btext) in ixreader:
cf = ixreader.frequency(fieldname, btext)
score = model.score(weight, cf, self.top_total)
if score > maxweight:
maxweight = score
tlist.append((score, word))
if normalize:
norm = model.normalizer(maxweight, self.top_total)
else:
norm = maxweight
tlist = [(weight / norm, t) for weight, t in tlist]
tlist.sort(key=lambda x: (0 - x[0], x[1]))
return [(t, weight) for weight, t in tlist[:number]]
# Similarity functions
def shingles(input, size=2):
d = defaultdict(int)
for shingle in (input[i : i + size] for i in range(len(input) - (size - 1))):
d[shingle] += 1
return d.items()
def simhash(features, hashbits=32):
if hashbits == 32:
hashfn = hash
else:
def hashfn(s):
return _hash(s, hashbits)
vs = [0] * hashbits
for feature, weight in features:
h = hashfn(feature)
for i in range(hashbits):
if h & (1 << i):
vs[i] += weight
else:
vs[i] -= weight
out = 0
for i, v in enumerate(vs):
if v > 0:
out |= 1 << i
return out
def _hash(s, hashbits):
# A variable-length version of Python's builtin hash
if s == "":
return 0
else:
x = ord(s[0]) << 7
m = 1000003
mask = 2**hashbits - 1
for c in s:
x = ((x * m) ^ ord(c)) & mask
x ^= len(s)
if x == -1:
x = -2
return x
def hamming_distance(first_hash, other_hash, hashbits=32):
x = (first_hash ^ other_hash) & ((1 << hashbits) - 1)
tot = 0
while x:
tot += 1
x &= x - 1
return tot
# Clustering
def kmeans(data, k, t=0.0001, distfun=None, maxiter=50, centers=None):
"""
One-dimensional K-means clustering function.
:param data: list of data points.
:param k: number of clusters.
:param t: tolerance; stop if changes between iterations are smaller than
this value.
:param distfun: a distance function.
:param centers: a list of centroids to start with.
:param maxiter: maximum number of iterations to run.
"""
# Adapted from a C version by Roger Zhang, <rogerz@cs.dal.ca>
# http://cs.smu.ca/~r_zhang/code/kmeans.c
DOUBLE_MAX = 1.797693e308
n = len(data)
error = DOUBLE_MAX # sum of squared euclidean distance
counts = [0] * k # size of each cluster
labels = [0] * n # output cluster label for each data point
# c1 is an array of len k of the temp centroids
c1 = [0] * k
# choose k initial centroids
if centers:
c = centers
else:
c = random.sample(data, k)
niter = 0
# main loop
while True:
# save error from last step
old_error = error
error = 0
# clear old counts and temp centroids
for i in range(k):
counts[i] = 0
c1[i] = 0
for h in range(n):
# identify the closest cluster
min_distance = DOUBLE_MAX
for i in range(k):
distance = (data[h] - c[i]) ** 2
if distance < min_distance:
labels[h] = i
min_distance = distance
# update size and temp centroid of the destination cluster
c1[labels[h]] += data[h]
counts[labels[h]] += 1
# update standard error
error += min_distance
for i in range(k): # update all centroids
c[i] = c1[i] / counts[i] if counts[i] else c1[i]
niter += 1
if (abs(error - old_error) < t) or (niter > maxiter):
break
return labels, c
# Sliding window clusters
def two_pass_variance(data):
n = 0
sum1 = 0
sum2 = 0
for x in data:
n += 1
sum1 = sum1 + x
mean = sum1 / n
for x in data:
sum2 += (x - mean) * (x - mean)
variance = sum2 / (n - 1)
return variance
def weighted_incremental_variance(data_weight_pairs):
mean = 0
S = 0
sumweight = 0
for x, weight in data_weight_pairs:
temp = weight + sumweight
Q = x - mean
R = Q * weight / temp
S += sumweight * Q * R
mean += R
sumweight = temp
Variance = S / (sumweight - 1) # if sample is the population, omit -1
return Variance
def swin(data, size):
clusters = []
for i, left in enumerate(data):
j = i
right = data[j]
while j < len(data) - 1 and right - left < size:
j += 1
right = data[j]
v = 99999
if j - i > 1:
v = two_pass_variance(data[i : j + 1])
clusters.append((left, right, j - i, v))
clusters.sort(key=lambda x: (0 - x[2], x[3]))
return clusters