from scipy.sparse import csc_matrix, csr_matrix
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer, CountVectorizer
import numpy as np
from joblib import Parallel, delayed
import sklearn
import math
from scipy.stats import t
class ContTable:
def __init__(self, tp=0, tn=0, fp=0, fn=0):
self.tp=tp
self.tn=tn
self.fp=fp
self.fn=fn
def get_d(self): return self.tp + self.tn + self.fp + self.fn
def get_c(self): return self.tp + self.fn
def get_not_c(self): return self.tn + self.fp
def get_f(self): return self.tp + self.fp
def get_not_f(self): return self.tn + self.fn
def p_c(self): return (1.0*self.get_c())/self.get_d()
def p_not_c(self): return 1.0-self.p_c()
def p_f(self): return (1.0*self.get_f())/self.get_d()
def p_not_f(self): return 1.0-self.p_f()
def p_tp(self): return (1.0*self.tp) / self.get_d()
def p_tn(self): return (1.0*self.tn) / self.get_d()
def p_fp(self): return (1.0*self.fp) / self.get_d()
def p_fn(self): return (1.0*self.fn) / self.get_d()
def tpr(self):
c = 1.0*self.get_c()
return self.tp / c if c > 0.0 else 0.0
def fpr(self):
_c = 1.0*self.get_not_c()
return self.fp / _c if _c > 0.0 else 0.0
def __ig_factor(p_tc, p_t, p_c):
den = p_t * p_c
if den != 0.0 and p_tc != 0:
return p_tc * math.log(p_tc / den, 2)
else:
return 0.0
def information_gain(cell):
return __ig_factor(cell.p_tp(), cell.p_f(), cell.p_c()) + \
__ig_factor(cell.p_fp(), cell.p_f(), cell.p_not_c()) +\
__ig_factor(cell.p_fn(), cell.p_not_f(), cell.p_c()) + \
__ig_factor(cell.p_tn(), cell.p_not_f(), cell.p_not_c())
def squared_information_gain(cell):
return information_gain(cell)**2
def posneg_information_gain(cell):
ig = information_gain(cell)
if cell.tpr() < cell.fpr():
return -ig
else:
return ig
def pos_information_gain(cell):
if cell.tpr() < cell.fpr():
return 0
else:
return information_gain(cell)
def pointwise_mutual_information(cell):
return __ig_factor(cell.p_tp(), cell.p_f(), cell.p_c())
def gss(cell):
return cell.p_tp()*cell.p_tn() - cell.p_fp()*cell.p_fn()
def chi_square(cell):
den = cell.p_f() * cell.p_not_f() * cell.p_c() * cell.p_not_c()
if den==0.0: return 0.0
num = gss(cell)**2
return num / den
def conf_interval(xt, n):
if n>30:
z2 = 3.84145882069 # norm.ppf(0.5+0.95/2.0)**2
else:
z2 = t.ppf(0.5 + 0.95 / 2.0, df=max(n-1,1)) ** 2
p = (xt + 0.5 * z2) / (n + z2)
amplitude = 0.5 * z2 * math.sqrt((p * (1.0 - p)) / (n + z2))
return p, amplitude
def strength(minPosRelFreq, minPos, maxNeg):
if minPos > maxNeg:
return math.log(2.0 * minPosRelFreq, 2.0)
else:
return 0.0
#set cancel_features=True to allow some features to be weighted as 0 (as in the original article)
#however, for some extremely imbalanced dataset caused all documents to be 0
def conf_weight(cell, cancel_features=False):
c = cell.get_c()
not_c = cell.get_not_c()
tp = cell.tp
fp = cell.fp
pos_p, pos_amp = conf_interval(tp, c)
neg_p, neg_amp = conf_interval(fp, not_c)
min_pos = pos_p-pos_amp
max_neg = neg_p+neg_amp
den = (min_pos + max_neg)
minpos_relfreq = min_pos / (den if den != 0 else 1)
str_tplus = strength(minpos_relfreq, min_pos, max_neg);
if str_tplus == 0 and not cancel_features:
return 1e-20
return str_tplus;
def get_tsr_matrix(cell_matrix, tsr_score_funtion):
nC = len(cell_matrix)
nF = len(cell_matrix[0])
tsr_matrix = [[tsr_score_funtion(cell_matrix[c,f]) for f in range(nF)] for c in range(nC)]
return np.array(tsr_matrix)
def feature_label_contingency_table(positive_document_indexes, feature_document_indexes, nD):
tp_ = len(positive_document_indexes & feature_document_indexes)
fp_ = len(feature_document_indexes - positive_document_indexes)
fn_ = len(positive_document_indexes - feature_document_indexes)
tn_ = nD - (tp_ + fp_ + fn_)
return ContTable(tp=tp_, tn=tn_, fp=fp_, fn=fn_)
def category_tables(feature_sets, category_sets, c, nD, nF):
return [feature_label_contingency_table(category_sets[c], feature_sets[f], nD) for f in range(nF)]
def get_supervised_matrix(coocurrence_matrix, label_matrix, n_jobs=-1):
"""
Computes the nC x nF supervised matrix M where Mcf is the 4-cell contingency table for feature f and class c.
Efficiency O(nF x nC x log(S)) where S is the sparse factor
"""
nD, nF = coocurrence_matrix.shape
nD2, nC = label_matrix.shape
if nD != nD2:
raise ValueError('Number of rows in coocurrence matrix shape %s and label matrix shape %s is not consistent' %
(coocurrence_matrix.shape,label_matrix.shape))
def nonzero_set(matrix, col):
return set(matrix[:, col].nonzero()[0])
if isinstance(coocurrence_matrix, csr_matrix):
coocurrence_matrix = csc_matrix(coocurrence_matrix)
feature_sets = [nonzero_set(coocurrence_matrix, f) for f in range(nF)]
category_sets = [nonzero_set(label_matrix, c) for c in range(nC)]
cell_matrix = Parallel(n_jobs=n_jobs, backend="threading")(delayed(category_tables)(feature_sets, category_sets, c, nD, nF) for c in range(nC))
return np.array(cell_matrix)
class TSRweighting(BaseEstimator,TransformerMixin):
"""
Supervised Term Weighting function based on any Term Selection Reduction (TSR) function (e.g., information gain,
chi-square, etc.) or, more generally, on any function that could be computed on the 4-cell contingency table for
each category-feature pair.
The supervised_4cell_matrix (a CxF matrix containing the 4-cell contingency tables
for each category-feature pair) can be pre-computed (e.g., during the feature selection phase) and passed as an
argument.
When C>1, i.e., in multiclass scenarios, a global_policy is used in order to determine a single feature-score which
informs about its relevance. Accepted policies include "max" (takes the max score across categories), "ave" and "wave"
(take the average, or weighted average, across all categories -- weights correspond to the class prevalence), and "sum"
(which sums all category scores).
"""
def __init__(self, tsr_function, global_policy='max', supervised_4cell_matrix=None, sublinear_tf=True, norm='l2', min_df=3, n_jobs=-1):
if global_policy not in ['max', 'ave', 'wave', 'sum']: raise ValueError('Global policy should be in {"max", "ave", "wave", "sum"}')
self.tsr_function = tsr_function
self.global_policy = global_policy
self.supervised_4cell_matrix = supervised_4cell_matrix
self.sublinear_tf=sublinear_tf
self.norm=norm
self.min_df = min_df
self.n_jobs=n_jobs
def fit(self, X, y):
self.count_vectorizer = CountVectorizer(min_df=self.min_df)
X = self.count_vectorizer.fit_transform(X)
self.tf_vectorizer = TfidfTransformer(
norm=None, use_idf=False, smooth_idf=False, sublinear_tf=self.sublinear_tf).fit(X)
if len(y.shape) == 1:
y = np.expand_dims(y, axis=1)
nD, nC = y.shape
nF = len(self.tf_vectorizer.get_feature_names_out())
if self.supervised_4cell_matrix is None:
self.supervised_4cell_matrix = get_supervised_matrix(X, y, n_jobs=self.n_jobs)
else:
if self.supervised_4cell_matrix.shape != (nC, nF): raise ValueError("Shape of supervised information matrix is inconsistent with X and y")
tsr_matrix = get_tsr_matrix(self.supervised_4cell_matrix, self.tsr_function)
if self.global_policy == 'ave':
self.global_tsr_vector = np.average(tsr_matrix, axis=0)
elif self.global_policy == 'wave':
category_prevalences = [sum(y[:,c])*1.0/nD for c in range(nC)]
self.global_tsr_vector = np.average(tsr_matrix, axis=0, weights=category_prevalences)
elif self.global_policy == 'sum':
self.global_tsr_vector = np.sum(tsr_matrix, axis=0)
elif self.global_policy == 'max':
self.global_tsr_vector = np.amax(tsr_matrix, axis=0)
return self
def fit_transform(self, X, y):
return self.fit(X,y).transform(X)
def transform(self, X):
if not hasattr(self, 'global_tsr_vector'): raise NameError('TSRweighting: transform method called before fit.')
X = self.count_vectorizer.transform(X)
tf_X = self.tf_vectorizer.transform(X).toarray()
weighted_X = np.multiply(tf_X, self.global_tsr_vector)
if self.norm is not None and self.norm!='none':
weighted_X = sklearn.preprocessing.normalize(weighted_X, norm=self.norm, axis=1, copy=False)
return csr_matrix(weighted_X)