Based on this article: http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-word2vec/ I am trying to implement a gensim word2vec model with the pretrained vectors of GloVe in a text classification task. However, I would like to do FeatureSelection also in my text data. I tried multiple sequences in the pipeline but i get fast a memory error which points to the transform part of TfidfEmbeddingVectorizer.
return np.array([
np.mean([self.word2vec[w] * self.word2weight[w]
for w in words if w in self.word2vec] or
[np.zeros(self.dim)], axis=0)
for words in X
If I replace the TfidfEmbeddingVectorizer class with a regular TfIdfVectorizer it works properly. Is there a way I could combine SelectFromModel and W2vec in the pipeline?
from sklearn.base import BaseEstimator, TransformerMixin
import pandas as pd
import numpy as np
import itertools
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import precision_recall_fscore_support as score, f1_score
import pickle
from sklearn.externals import joblib
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.pipeline import FeatureUnion
from sklearn.feature_extraction import DictVectorizer
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.svm import LinearSVC
import gensim
import collections
class ItemSelector(BaseEstimator, TransformerMixin):
def __init__(self, column):
self.column = column
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X):
return (X[self.column])
class TextStats(BaseEstimator, TransformerMixin):
"""Extract features from each document for DictVectorizer"""
def fit(self, x, y=None):
return self
def transform(self, posts):
return [{'REPORT_M': text}
for text in posts]
class TfidfEmbeddingVectorizer(object):
def __init__(self, word2vec):
self.word2vec = word2vec
self.word2weight = None
self.dim = len(word2vec.values())
def fit(self, X, y):
tfidf = TfidfVectorizer(analyzer=lambda x: x)
tfidf.fit(X)
# if a word was never seen - it must be at least as infrequent
# as any of the known words - so the default idf is the max of
# known idf's
max_idf = max(tfidf.idf_)
self.word2weight = collections.defaultdict(
lambda: max_idf,
[(w, tfidf.idf_[i]) for w, i in tfidf.vocabulary_.items()])
return self
def transform(self, X):
return np.array([
np.mean([self.word2vec[w] * self.word2weight[w]
for w in words if w in self.word2vec] or
[np.zeros(self.dim)], axis=0)
for words in X
])
# training model
def train(data_train, data_val):
with open("glove.6B/glove.6B.50d.txt", "rb") as lines:
w2v = {line.split()[0]: np.array(map(float, line.split()[1:]))
for line in lines}
classifier = Pipeline([
('union', FeatureUnion([
('text', Pipeline([
('selector', ItemSelector(column='TEXT')),
("word2vec vectorizer", TfidfEmbeddingVectorizer(w2v)),
('feature_selection', SelectFromModel(LinearSVC(penalty="l1", dual=False),threshold=0.01))
])),
('category', Pipeline([
('selector', ItemSelector(column='category')),
('stats', TextStats()),
('vect', DictVectorizer())
]))
])),
('clf',ExtraTreesClassifier(n_estimators=200, max_depth=500, min_samples_split=6, class_weight= 'balanced'))])
classifier.fit(data_train,data_train.CLASSES)
predicted = classifier.predict(data_val)
