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train_and_test.py
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train_and_test.py
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import numpy as np
import matplotlib.pyplot as plt
import csv
import pickle
import random as random
import pickle as cPickle
from sklearn import svm
from scipy.sparse import csr_matrix
from sklearn.metrics import classification_report
from features.vectorizer import PolitenessFeatureVectorizer
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn import cross_validation
stack_data=pickle.load(open('data/se_parsed.p'))
wiki_data=pickle.load(open('data/wiki_parsed.p'))
def chuckNeutralRequests(docs):
print "Chucking neutral requests"
vals = docs.values()
vals.sort(key=lambda l: l['score'])
# l = []
# for v in vals:
# l.append(v['score'])
# l = np.array(l)
# print l
n = len(vals)
# n1 = vals[0:n/4]
# n2 = vals[(3*n/4):]
# print n, len(n1), len(n2)
return vals[0:n/4] + vals[(3*n/4):]
# raw_input()
def documents2feature_vectors(documents):
vectorizer = PolitenessFeatureVectorizer()
fks = False
X, y = [], []
for d in documents:
fs = vectorizer.features(d)
if not fks:
fks = sorted(fs.keys())
fv = [fs[f] for f in fks]
# If politeness score > 0.0,
# the doc is polite, class=1
l = 1 if d['score'] > 0.0 else 0
X.append(fv)
y.append(l)
X = csr_matrix(np.asarray(X))
y = np.asarray(y)
return X, y
bow = 10
def crossdomain(documents_stack, documents_wiki):
print "Cross Domain"
# documents_stack=stack_data.values()
# documents_wiki=wiki_data.values()
PolitenessFeatureVectorizer.generate_bow_features(documents_stack, bow)
X_stack, y_stack = documents2feature_vectors(documents_stack)
X_wiki, y_wiki = documents2feature_vectors(documents_wiki)
print "Fitting"
clf = svm.SVC(C=0.02, kernel='linear', probability=True)
# clf = RandomForestClassifier(n_estimators=50)
clf.fit(X_stack, y_stack)
y_pred = clf.predict(X_wiki)
print "Trained on Stack and results predicted for wiki"
# Test
#print(classification_report(y_wiki, y_pred))
print(clf.score(X_wiki, y_wiki))
print "------------------------------------------------------"
PolitenessFeatureVectorizer.generate_bow_features(documents_wiki, bow)
X_stack, y_stack = documents2feature_vectors(documents_stack)
X_wiki, y_wiki = documents2feature_vectors(documents_wiki)
print "Fitting"
clf = svm.SVC(C=0.02, kernel='linear', probability=True)
# clf = RandomForestClassifier(n_estimators=50)
clf.fit(X_wiki, y_wiki)
y_pred = clf.predict(X_stack)
print "Trained on wiki and results predicted for stack"
# Test
#print(classification_report(y_stack, y_pred))
print(clf.score(X_stack, y_stack))
print "------------------------------------------------------"
def indomain(documents_stack, documents_wiki):
print "In Domain"
PolitenessFeatureVectorizer.generate_bow_features(documents_stack, bow)
X_stack, y_stack = documents2feature_vectors(documents_stack)
print "Fitting"
clf = svm.SVC(C=0.02, kernel='linear', probability=True)
scores = cross_validation.cross_val_score(clf, X_stack, y_stack, cv=10)
print "In doman for stack"
print scores
print np.mean(scores)
print "------------------------------------------------------"
PolitenessFeatureVectorizer.generate_bow_features(documents_wiki, bow)
X_wiki, y_wiki = documents2feature_vectors(documents_wiki)
print "Fitting"
clf = svm.SVC(C=0.02, kernel='linear', probability=True)
scores = cross_validation.cross_val_score(clf, X_wiki, y_wiki, cv=10)
print "In doman for wiki"
print scores
print "Mean: ", np.mean(scores)
top_stack_data = chuckNeutralRequests(stack_data)
top_wiki_data = chuckNeutralRequests(wiki_data)
#crossdomain(top_stack_data, top_wiki_data)
indomain(top_stack_data, top_wiki_data)