def main():
print ("Generating language models....")
trainAACleanLM = LanguageModel(CLEAN_TRAIN_AA_FILE)
trainAAInsultLM = LanguageModel(INSULT_TRAIN_AA_FILE)
trainABCleanLM = LanguageModel(CLEAN_TRAIN_AB_FILE)
trainABInsultLM = LanguageModel(INSULT_TRAIN_AB_FILE)
testCleanLM = LanguageModel(CLEAN_TEST_FILE)
testInsultLM = LanguageModel(INSULT_TEST_FILE)
trainLabels = np.array(([0] * trainABCleanLM.getDocCount()) + ([1] * trainABInsultLM.getDocCount()))
testLabels = np.array(([0] * testCleanLM.getDocCount()) + ([1] * testInsultLM.getDocCount()))
### Just baseline probabilities
print ("Running baseline....")
NB = baselineNaiveBayes(trainAACleanLM, trainAAInsultLM)
print ("\tTraining NB....")
NB.train()
print ("\tTesting NB....")
totalNBMatrix = np.array(NB.genProbs(trainABCleanLM.getSents(), trainABInsultLM.getSents()))
trainMatrix = totalNBMatrix
testMatrix = np.array(NB.genProbs(testCleanLM.getSents(), testInsultLM.getSents()))
clf = LogisticRegression()
print ("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print ("\tTesting SVM....")
output1 = clf.predict(testMatrix).tolist()
### Baseline + PoS Features
print ("Running baseline + PoS Features....")
cleanPosMatrix = trainABCleanLM.getPosMatrix()
insultPosMatrix = trainABInsultLM.getPosMatrix()
testCleanPosMatrix = testCleanLM.getPosMatrix()
testInsultPosMatrix = testInsultLM.getPosMatrix()
posFeatures = np.array(cleanPosMatrix + insultPosMatrix)
testPosFeatures = np.array(testCleanPosMatrix + testInsultPosMatrix)
trainMatrix = np.hstack((trainMatrix, posFeatures))
testMatrix = np.hstack((testMatrix, testPosFeatures))
clf = LogisticRegression()
print ("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print ("\tTesting SVM....")
output2 = clf.predict(testMatrix).tolist()
### Baseline + PoS Features + TF-IDF Features (TODO Arun)
print("Running baseline + PoS Features + TF-IDF Features")
# generate list of features with TFIDF, using trainABCleanLM and trainABInsultLM
# trainMatrix = np.hstack((trainMatrix, the new thing you just generated))
# do same for testMatrix
# clf = svm.SVC()
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output3 = clf.predict(testMatrix).tolist()
# then update the output_file.txt thing below
tfidf_train_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM, trainABCleanLM, trainABInsultLM)
tfidf_test_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM, testCleanLM, testInsultLM)
print tfidf_test_features.shape, tfidf_train_features.shape
print testMatrix.shape, trainMatrix.shape
trainMatrix = np.hstack((trainMatrix, tfidf_train_features))
testMatrix = np.hstack((testMatrix, tfidf_test_features))
clf = LogisticRegression()
print ("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print ("\tTesting SVM....")
output3 = clf.predict(testMatrix).tolist()
### SENTIMENT ###
print("Running baseline + PoS Features + TF-IDF Features + Sentiment Features")
s = Sentiment()
clean_train = np.array(s.get_clean_train_vector())
insult_train = np.array(s.get_insult_train_vector())
sentiment_train_features = np.concatenate((clean_train, insult_train), axis=0)
shape = sentiment_train_features.shape
sentiment_train_features = sentiment_train_features.reshape((shape[0], 1))
print sentiment_train_features.shape
clean_test = np.array(s.get_clean_test_vector())
insult_test = np.array(s.get_insult_test_vector())
sentiment_test_features = np.concatenate((clean_test, insult_test), axis=0)
shape = sentiment_test_features.shape
sentiment_test_features = sentiment_test_features.reshape((shape[0], 1))
print sentiment_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
clf = LogisticRegression()
print ("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print ("\tTesting SVM....")
output4 = clf.predict(testMatrix).tolist()
### MISSPELLINGS ###
print("Running baseline + PoS Features + TF-IDF Features + Sentiment Features + Misspellings features")
m = Misspellings()
clean_train = np.array(m.get_clean_misspellings(False))
insult_train = np.array(m.get_insult_misspellings(False))
misspellings_train_features = np.concatenate((clean_train, insult_train), axis=0)
shape = misspellings_train_features.shape
misspellings_train_features = misspellings_train_features.reshape((shape[0], 1))
print misspellings_train_features.shape
clean_test = np.array(m.get_clean_misspellings())
insult_test = np.array(m.get_insult_misspellings())
misspellings_test_features = np.concatenate((clean_test, insult_test), axis=0)
shape = misspellings_test_features.shape
misspellings_test_features = misspellings_test_features.reshape((shape[0], 1))
print misspellings_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
clf = LogisticRegression()
print ("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print ("\tTesting SVM....")
output5 = clf.predict(testMatrix).tolist()
with open('LOG_REG_output_file_w_SB.txt', 'w+') as f:
f.write("Output 1\n")
f.write("{}\n".format(output1))
interpret_results(output1, testLabels, f)
f.write("\nOutput 2\n")
f.write("{}\n".format(output2))
interpret_results(output2, testLabels, f)
f.write("\nOutput 3\n")
f.write("{}\n".format(output3))
interpret_results(output3, testLabels, f)
f.write("Output 4\n")
f.write("{}\n".format(output4))
interpret_results(output4, testLabels, f)
f.write("Output 5\n")
f.write("{}\n".format(output5))
interpret_results(output5, testLabels, f)
def main():
print("Generating language models....")
trainAACleanLM = LanguageModel(CLEAN_TRAIN_AA_FILE)
trainAAInsultLM = LanguageModel(INSULT_TRAIN_AA_FILE)
trainABCleanLM = LanguageModel(CLEAN_TRAIN_AB_FILE)
trainABInsultLM = LanguageModel(INSULT_TRAIN_AB_FILE)
testCleanLM = LanguageModel(CLEAN_TEST_FILE)
testInsultLM = LanguageModel(INSULT_TEST_FILE)
trainLabels = np.array(([0] * trainABCleanLM.getDocCount()) +
([1] * trainABInsultLM.getDocCount()))
testLabels = np.array(([0] * testCleanLM.getDocCount()) +
([1] * testInsultLM.getDocCount()))
### Just baseline probabilities
print("Running baseline....")
NB = baselineNaiveBayes(trainAACleanLM, trainAAInsultLM)
print("\tTraining NB....")
NB.train()
print("\tTesting NB....")
totalNBMatrix = np.array(
NB.genProbs(trainABCleanLM.getSents(), trainABInsultLM.getSents()))
trainMatrix = totalNBMatrix
testMatrix = np.array(
NB.genProbs(testCleanLM.getSents(), testInsultLM.getSents()))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output1 = clf.predict(testMatrix).tolist()
## Baseline + PoS Features
print("Running baseline + PoS Features....")
cleanPosMatrix = trainABCleanLM.getPosMatrix()
insultPosMatrix = trainABInsultLM.getPosMatrix()
testCleanPosMatrix = testCleanLM.getPosMatrix()
testInsultPosMatrix = testInsultLM.getPosMatrix()
posFeatures = np.array(cleanPosMatrix + insultPosMatrix)
testPosFeatures = np.array(testCleanPosMatrix + testInsultPosMatrix)
trainMatrix = np.hstack((trainMatrix, posFeatures))
testMatrix = np.hstack((testMatrix, testPosFeatures))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output2 = clf.predict(testMatrix).tolist()
### Baseline + PoS Features + TF-IDF Features (TODO Arun)
print("Running baseline + PoS Features + TF-IDF Features")
# generate list of features with TFIDF, using trainABCleanLM and trainABInsultLM
# trainMatrix = np.hstack((trainMatrix, the new thing you just generated))
# do same for testMatrix
# clf = svm.SVC()
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output3 = clf.predict(testMatrix).tolist()
# then update the output_file.txt thing below
tfidf_train_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM,
trainABCleanLM,
trainABInsultLM)
tfidf_test_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM,
testCleanLM, testInsultLM)
print tfidf_test_features.shape, tfidf_train_features.shape
print testMatrix.shape, trainMatrix.shape
trainMatrix = np.hstack((trainMatrix, tfidf_train_features))
testMatrix = np.hstack((testMatrix, tfidf_test_features))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output3 = clf.predict(testMatrix).tolist()
### SENTIMENT ###
print(
"Running baseline + PoS Features + TF-IDF Features + Sentiment Features"
)
s = Sentiment()
clean_train = np.array(s.get_clean_train_vector())
insult_train = np.array(s.get_insult_train_vector())
sentiment_train_features = np.concatenate((clean_train, insult_train),
axis=0)
shape = sentiment_train_features.shape
sentiment_train_features = sentiment_train_features.reshape((shape[0], 1))
print sentiment_train_features.shape
clean_test = np.array(s.get_clean_test_vector())
insult_test = np.array(s.get_insult_test_vector())
sentiment_test_features = np.concatenate((clean_test, insult_test), axis=0)
shape = sentiment_test_features.shape
sentiment_test_features = sentiment_test_features.reshape((shape[0], 1))
print sentiment_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output4 = clf.predict(testMatrix).tolist()
### MISSPELLINGS ###
print(
"Running baseline + PoS Features + TF-IDF Features + Sentiment Features + Misspellings features"
)
m = Misspellings()
clean_train = np.array(m.get_clean_misspellings(False))
insult_train = np.array(m.get_insult_misspellings(False))
misspellings_train_features = np.concatenate((clean_train, insult_train),
axis=0)
shape = misspellings_train_features.shape
misspellings_train_features = misspellings_train_features.reshape(
(shape[0], 1))
print misspellings_train_features.shape
clean_test = np.array(m.get_clean_misspellings())
insult_test = np.array(m.get_insult_misspellings())
misspellings_test_features = np.concatenate((clean_test, insult_test),
axis=0)
shape = misspellings_test_features.shape
misspellings_test_features = misspellings_test_features.reshape(
(shape[0], 1))
print misspellings_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
clf = svm.SVC(kernel='linear')
print("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print("\tTesting SVM....")
output5 = clf.predict(testMatrix).tolist()
index_shuf = range(len(trainMatrix))
trainMatrix_shuf = []
trainLabel_shuf = []
shuffle(index_shuf)
for i in index_shuf:
trainMatrix_shuf.append(trainMatrix[i])
trainLabel_shuf.append(trainLabels[i])
train_sizes, train_scores, valid_scores = learning_curve(
svm.SVC(),
trainMatrix_shuf,
trainLabel_shuf,
train_sizes=[100, 300, 500, 700, 900],
cv=2)
average_train_scores = [sum(i) / float(len(i)) for i in train_scores]
average_valid_scores = [sum(i) / float(len(i)) for i in valid_scores]
plt.plot(train_sizes, average_train_scores)
plt.plot(train_sizes, average_valid_scores)
plt.legend(['Training score', 'Cross-validation score'],
loc='center left',
bbox_to_anchor=(0.85, 0.5))
plt.ylabel('Score')
plt.xlabel('Training examples')
plt.show()
# with open('SVM_output_file_with_SB.txt', 'w+') as f:
# f.write("Output 1\n")
# f.write("{}\n".format(output1))
# interpret_results(output1, testLabels, f)
# f.write("\nOutput 2\n")
# f.write("{}\n".format(output2))
# interpret_results(output2, testLabels, f)
# f.write("\nOutput 3\n")
# f.write("{}\n".format(output3))
# interpret_results(output3, testLabels, f)
# f.write("Output 4\n")
# f.write("{}\n".format(output4))
# interpret_results(output4, testLabels, f)
# f.write("Output 5\n")
# f.write("{}\n".format(output5))
# interpret_results(output5, testLabels, f)
get_pca_graph(trainMatrix,
trainLabels,
"train_pca.png",
title="PCA of Training Set")
get_pca_graph(testMatrix,
testLabels,
"test_pca.png",
title="PCA of Test Set")
get_pca_graph(trainMatrix,
trainLabels,
"train_pca2.png",
title="PCA of Training Set (Insults Only)",
plot_negative=False)
get_pca_graph(testMatrix,
testLabels,
"test_pca2.png",
title="PCA of Test Set (Insults Only)",
plot_negative=False)
def main():
print("Generating language models....")
trainAACleanLM = LanguageModel(CLEAN_TRAIN_AA_FILE)
trainAAInsultLM = LanguageModel(INSULT_TRAIN_AA_FILE)
trainABCleanLM = LanguageModel(CLEAN_TRAIN_AB_FILE)
trainABInsultLM = LanguageModel(INSULT_TRAIN_AB_FILE)
testCleanLM = LanguageModel(CLEAN_TEST_FILE)
testInsultLM = LanguageModel(INSULT_TEST_FILE)
trainLabels = np.array(([0] * trainABCleanLM.getDocCount()) +
([1] * trainABInsultLM.getDocCount()))
testLabels = np.array(([0] * testCleanLM.getDocCount()) +
([1] * testInsultLM.getDocCount()))
### Just baseline probabilities
print("Running baseline....")
NB = baselineNaiveBayes(trainAACleanLM, trainAAInsultLM)
print("\tTraining NB....")
NB.train()
print("\tTesting NB....")
totalNBMatrix = np.array(
NB.genProbs(trainABCleanLM.getSents(), trainABInsultLM.getSents()))
trainMatrix = totalNBMatrix
testMatrix = np.array(
NB.genProbs(testCleanLM.getSents(), testInsultLM.getSents()))
clf = RandomForestClassifier()
print("\tTraining random forest....")
clf.fit(trainMatrix, trainLabels)
print("\tTesting random forest....")
output1 = clf.predict(testMatrix).tolist()
### Baseline + PoS Features
print("Running baseline + PoS Features....")
cleanPosMatrix = trainABCleanLM.getPosMatrix()
insultPosMatrix = trainABInsultLM.getPosMatrix()
testCleanPosMatrix = testCleanLM.getPosMatrix()
testInsultPosMatrix = testInsultLM.getPosMatrix()
posFeatures = np.array(cleanPosMatrix + insultPosMatrix)
testPosFeatures = np.array(testCleanPosMatrix + testInsultPosMatrix)
trainMatrix = np.hstack((trainMatrix, posFeatures))
testMatrix = np.hstack((testMatrix, testPosFeatures))
clf = RandomForestClassifier()
print("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print("\tTesting SVM....")
output2 = clf.predict(testMatrix).tolist()
### Baseline + PoS Features + TF-IDF Features (TODO Arun)
print("Running baseline + PoS Features + TF-IDF Features")
# generate list of features with TFIDF, using trainABCleanLM and trainABInsultLM
# trainMatrix = np.hstack((trainMatrix, the new thing you just generated))
# do same for testMatrix
# clf = svm.SVC()
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output3 = clf.predict(testMatrix).tolist()
# then update the output_file.txt thing below
tfidf_train_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM,
trainABCleanLM,
trainABInsultLM)
tfidf_test_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM,
testCleanLM, testInsultLM)
print tfidf_test_features.shape, tfidf_train_features.shape
print testMatrix.shape, trainMatrix.shape
trainMatrix = np.hstack((trainMatrix, tfidf_train_features))
testMatrix = np.hstack((testMatrix, tfidf_test_features))
clf = RandomForestClassifier()
print("\tTraining random forest....")
clf.fit(trainMatrix, trainLabels)
print("\tTesting random forest....")
output3 = clf.predict(testMatrix).tolist()
### SENTIMENT ###
print(
"Running baseline + PoS Features + TF-IDF Features + Sentiment Features"
)
s = Sentiment()
clean_train = np.array(s.get_clean_train_vector())
insult_train = np.array(s.get_insult_train_vector())
sentiment_train_features = np.concatenate((clean_train, insult_train),
axis=0)
shape = sentiment_train_features.shape
sentiment_train_features = sentiment_train_features.reshape((shape[0], 1))
print sentiment_train_features.shape
clean_test = np.array(s.get_clean_test_vector())
insult_test = np.array(s.get_insult_test_vector())
sentiment_test_features = np.concatenate((clean_test, insult_test), axis=0)
shape = sentiment_test_features.shape
sentiment_test_features = sentiment_test_features.reshape((shape[0], 1))
print sentiment_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
clf = RandomForestClassifier()
print("\tTraining random forest....")
clf.fit(trainMatrix, trainLabels)
print("\tTesting random forest....")
output4 = clf.predict(testMatrix).tolist()
### MISSPELLINGS ###
print(
"Running baseline + PoS Features + TF-IDF Features + Sentiment Features + Misspellings features"
)
m = Misspellings()
clean_train = np.array(m.get_clean_misspellings(False))
insult_train = np.array(m.get_insult_misspellings(False))
misspellings_train_features = np.concatenate((clean_train, insult_train),
axis=0)
shape = misspellings_train_features.shape
misspellings_train_features = misspellings_train_features.reshape(
(shape[0], 1))
print misspellings_train_features.shape
clean_test = np.array(m.get_clean_misspellings())
insult_test = np.array(m.get_insult_misspellings())
misspellings_test_features = np.concatenate((clean_test, insult_test),
axis=0)
shape = misspellings_test_features.shape
misspellings_test_features = misspellings_test_features.reshape(
(shape[0], 1))
print misspellings_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
clf = RandomForestClassifier()
print("\tTraining random forest....")
clf.fit(trainMatrix, trainLabels)
print("\tTesting forest....")
output5 = clf.predict(testMatrix).tolist()
with open('RANDOM_FOREST_output_file_without_SB.txt', 'w+') as f:
f.write("Output 1\n")
f.write("{}\n".format(output1))
interpret_results(output1, testLabels, f)
f.write("\nOutput 2\n")
f.write("{}\n".format(output2))
interpret_results(output2, testLabels, f)
f.write("\nOutput 3\n")
f.write("{}\n".format(output3))
interpret_results(output3, testLabels, f)
f.write("Output 4\n")
f.write("{}\n".format(output4))
interpret_results(output4, testLabels, f)
f.write("Output 5\n")
f.write("{}\n".format(output5))
interpret_results(output5, testLabels, f)
def main():
print ("Generating language models....")
trainAACleanLM = LanguageModel(CLEAN_TRAIN_AA_FILE)
trainAAInsultLM = LanguageModel(INSULT_TRAIN_AA_FILE)
trainABCleanLM = LanguageModel(CLEAN_TRAIN_AB_FILE)
trainABInsultLM = LanguageModel(INSULT_TRAIN_AB_FILE)
testCleanLM = LanguageModel(CLEAN_TEST_FILE)
testInsultLM = LanguageModel(INSULT_TEST_FILE)
trainLabels = np.array(([0] * trainABCleanLM.getDocCount()) + ([1] * trainABInsultLM.getDocCount()))
testLabels = np.array(([0] * testCleanLM.getDocCount()) + ([1] * testInsultLM.getDocCount()))
### Just baseline probabilities
print ("Running baseline....")
NB = baselineNaiveBayes(trainAACleanLM, trainAAInsultLM)
print ("\tTraining NB....")
NB.train()
print ("\tTesting NB....")
totalNBMatrix = np.array(NB.genProbs(trainABCleanLM.getSents(), trainABInsultLM.getSents()))
trainMatrix = totalNBMatrix
testMatrix = np.array(NB.genProbs(testCleanLM.getSents(), testInsultLM.getSents()))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output1 = clf.predict(testMatrix).tolist()
## Baseline + PoS Features
print ("Running baseline + PoS Features....")
cleanPosMatrix = trainABCleanLM.getPosMatrix()
insultPosMatrix = trainABInsultLM.getPosMatrix()
testCleanPosMatrix = testCleanLM.getPosMatrix()
testInsultPosMatrix = testInsultLM.getPosMatrix()
posFeatures = np.array(cleanPosMatrix + insultPosMatrix)
testPosFeatures = np.array(testCleanPosMatrix + testInsultPosMatrix)
trainMatrix = np.hstack((trainMatrix, posFeatures))
testMatrix = np.hstack((testMatrix, testPosFeatures))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output2 = clf.predict(testMatrix).tolist()
### Baseline + PoS Features + TF-IDF Features (TODO Arun)
print("Running baseline + PoS Features + TF-IDF Features")
# generate list of features with TFIDF, using trainABCleanLM and trainABInsultLM
# trainMatrix = np.hstack((trainMatrix, the new thing you just generated))
# do same for testMatrix
# clf = svm.SVC()
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output3 = clf.predict(testMatrix).tolist()
# then update the output_file.txt thing below
tfidf_train_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM, trainABCleanLM, trainABInsultLM)
tfidf_test_features = tfidf.make_feature_vectors(trainAACleanLM,
trainAAInsultLM, testCleanLM, testInsultLM)
print tfidf_test_features.shape, tfidf_train_features.shape
print testMatrix.shape, trainMatrix.shape
trainMatrix = np.hstack((trainMatrix, tfidf_train_features))
testMatrix = np.hstack((testMatrix, tfidf_test_features))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output3 = clf.predict(testMatrix).tolist()
### SENTIMENT ###
print("Running baseline + PoS Features + TF-IDF Features + Sentiment Features")
s = Sentiment()
clean_train = np.array(s.get_clean_train_vector())
insult_train = np.array(s.get_insult_train_vector())
sentiment_train_features = np.concatenate((clean_train, insult_train), axis=0)
shape = sentiment_train_features.shape
sentiment_train_features = sentiment_train_features.reshape((shape[0], 1))
print sentiment_train_features.shape
clean_test = np.array(s.get_clean_test_vector())
insult_test = np.array(s.get_insult_test_vector())
sentiment_test_features = np.concatenate((clean_test, insult_test), axis=0)
shape = sentiment_test_features.shape
sentiment_test_features = sentiment_test_features.reshape((shape[0], 1))
print sentiment_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
# clf = svm.SVC(kernel='linear')
# print ("\tTraining SVM....")
# clf.fit(trainMatrix, trainLabels)
# print ("\tTesting SVM....")
# output4 = clf.predict(testMatrix).tolist()
### MISSPELLINGS ###
print("Running baseline + PoS Features + TF-IDF Features + Sentiment Features + Misspellings features")
m = Misspellings()
clean_train = np.array(m.get_clean_misspellings(False))
insult_train = np.array(m.get_insult_misspellings(False))
misspellings_train_features = np.concatenate((clean_train, insult_train), axis=0)
shape = misspellings_train_features.shape
misspellings_train_features = misspellings_train_features.reshape((shape[0], 1))
print misspellings_train_features.shape
clean_test = np.array(m.get_clean_misspellings())
insult_test = np.array(m.get_insult_misspellings())
misspellings_test_features = np.concatenate((clean_test, insult_test), axis=0)
shape = misspellings_test_features.shape
misspellings_test_features = misspellings_test_features.reshape((shape[0], 1))
print misspellings_test_features.shape
trainMatrix = np.hstack((trainMatrix, sentiment_train_features))
testMatrix = np.hstack((testMatrix, sentiment_test_features))
clf = svm.SVC(kernel='linear')
print ("\tTraining SVM....")
clf.fit(trainMatrix, trainLabels)
print ("\tTesting SVM....")
output5 = clf.predict(testMatrix).tolist()
index_shuf = range(len(trainMatrix))
trainMatrix_shuf = []
trainLabel_shuf = []
shuffle(index_shuf)
for i in index_shuf:
trainMatrix_shuf.append(trainMatrix[i])
trainLabel_shuf.append(trainLabels[i])
train_sizes, train_scores, valid_scores = learning_curve(svm.SVC(), trainMatrix_shuf, trainLabel_shuf, train_sizes=[100, 300, 500, 700, 900], cv=2)
average_train_scores = [sum(i)/float(len(i)) for i in train_scores]
average_valid_scores = [sum(i)/float(len(i)) for i in valid_scores]
plt.plot(train_sizes, average_train_scores)
plt.plot(train_sizes, average_valid_scores)
plt.legend(['Training score', 'Cross-validation score'], loc='center left', bbox_to_anchor=(0.85, 0.5))
plt.ylabel('Score')
plt.xlabel('Training examples')
plt.show()
# with open('SVM_output_file_with_SB.txt', 'w+') as f:
# f.write("Output 1\n")
# f.write("{}\n".format(output1))
# interpret_results(output1, testLabels, f)
# f.write("\nOutput 2\n")
# f.write("{}\n".format(output2))
# interpret_results(output2, testLabels, f)
# f.write("\nOutput 3\n")
# f.write("{}\n".format(output3))
# interpret_results(output3, testLabels, f)
# f.write("Output 4\n")
# f.write("{}\n".format(output4))
# interpret_results(output4, testLabels, f)
# f.write("Output 5\n")
# f.write("{}\n".format(output5))
# interpret_results(output5, testLabels, f)
get_pca_graph(trainMatrix, trainLabels, "train_pca.png", title="PCA of Training Set")
get_pca_graph(testMatrix, testLabels, "test_pca.png", title="PCA of Test Set")
get_pca_graph(trainMatrix, trainLabels, "train_pca2.png", title="PCA of Training Set (Insults Only)", plot_negative=False)
get_pca_graph(testMatrix, testLabels, "test_pca2.png", title="PCA of Test Set (Insults Only)", plot_negative=False)