def run_permutation_test_bow_vs_doc2vec(train, test, bow_kernel,
doc2vec_kernel, doc2vec_train_X,
doc2vec_train_Y, doc2vec_model):
""" Run a permutation test to compare bow and doc2vec svms. """
# prepare data for both svms
train_Y = train['sentiment'].to_numpy()
test_Y = test['sentiment'].to_numpy()
bow_train_X, vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
frequency=False)
bow_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=vectorizer)
doc2vec_test_X = get_doc2vec_data(test['review'].values, doc2vec_model)
# build models and predict
bow_svm = build_svm_classifier(bow_train_X,
train_Y,
kernel=bow_kernel,
gamma='scale')
doc2vec_svm = build_svm_classifier(doc2vec_train_X,
doc2vec_train_Y,
kernel=doc2vec_kernel,
gamma='scale')
# delete big variables no longer used
del bow_train_X
return permutation_test(test_Y, bow_svm.predict(bow_test_X),
doc2vec_svm.predict(doc2vec_test_X))
def run_permutation_test_bow_lowercase(train, test, kernel):
train_Y = train['sentiment'].to_numpy()
test_Y = test['sentiment'].to_numpy()
bow_train_X, vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
frequency=False)
bow_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=vectorizer)
low_bow_train_X, low_vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
lowercase=False,
frequency=False)
low_bow_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=low_vectorizer)
svm1 = build_svm_classifier(bow_train_X,
train_Y,
kernel=kernel,
gamma='scale')
svm2 = build_svm_classifier(low_bow_train_X,
train_Y,
kernel=kernel,
gamma='scale')
return permutation_test(test_Y, svm1.predict(bow_test_X),
svm2.predict(low_bow_test_X))
def run_permutation_test_bow_with_uni_and_bigrams(train, test, kernel):
train_Y = train['sentiment'].to_numpy()
test_Y = test['sentiment'].to_numpy()
bi_train_X, bi_vectorizer = get_bow_vectors(train['review'].values,
min_count=7,
max_frac=0.5,
frequency=False,
bigrams=True)
bi_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=bi_vectorizer)
uni_train_X, uni_vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
frequency=False,
bigrams=False)
uni_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=uni_vectorizer)
svm1 = build_svm_classifier(uni_train_X,
train_Y,
kernel=kernel,
gamma='scale')
svm2 = build_svm_classifier(np.concatenate((uni_train_X, bi_train_X),
axis=1),
train_Y,
kernel=kernel,
gamma='scale')
return permutation_test(
test_Y, svm1.predict(uni_test_X),
svm2.predict(np.concatenate((uni_test_X, bi_test_X), axis=1)))
def run_permutation_test_bow_with_bigrams(train, test, kernel):
""" Run permutation test of difference between using only bigrmas and only unigrams. """
train_Y = train['sentiment'].to_numpy()
test_Y = test['sentiment'].to_numpy()
bi_train_X, bi_vectorizer = get_bow_vectors(train['review'].values,
min_count=7,
max_frac=0.5,
frequency=False,
bigrams=True)
bi_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=bi_vectorizer)
uni_train_X, uni_vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
frequency=False)
uni_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=uni_vectorizer)
svm1 = build_svm_classifier(bi_train_X,
train_Y,
kernel=kernel,
gamma='scale')
svm2 = build_svm_classifier(uni_train_X,
train_Y,
kernel=kernel,
gamma='scale')
return permutation_test(test_Y, svm1.predict(bi_test_X),
svm2.predict(uni_test_X))
def run_permutation_test_two_different_doc2vecs(train, test, kernel1, kernel2,
model1, model2, d2v_X_1,
d2v_X_2, d2v_Y1, d2v_Y2):
""" Run a permutation test to compare bow and doc2vec svms. """
# prepare test data for both svms
test_X1 = get_doc2vec_data(test['review'].values, model1)
test_X2 = get_doc2vec_data(test['review'].values, model2)
# build models and predict
svm1 = build_svm_classifier(d2v_X_1, d2v_Y1, kernel=kernel1, gamma='scale')
svm2 = build_svm_classifier(d2v_X_2, d2v_Y2, kernel=kernel2, gamma='scale')
test_Y = test['sentiment'].to_numpy()
return permutation_test(test_Y, svm1.predict(test_X1),
svm2.predict(test_X2))
def run_permutation_test_two_doc2vec_kernels(train, test, kernel1, kernel2,
model, d2v_X, d2v_Y, **kwargs):
""" Run a permutation test to compare bow and doc2vec svms. """
# prepare test data
test_X = get_doc2vec_data(test['review'].values, model)
# build models and predict
svm1 = build_svm_classifier(d2v_X,
d2v_Y,
kernel=kernel1,
gamma='scale',
**kwargs)
svm2 = build_svm_classifier(d2v_X, d2v_Y, kernel=kernel2, gamma='scale')
test_Y = test['sentiment'].to_numpy()
return permutation_test(test_Y, svm1.predict(test_X), svm2.predict(test_X))
def run_permutation_concatenated_vs_simple_doc2vec(train, test, kernel1,
kernel2, model1, model2,
d2v_X_1, d2v_X_2, d2v_Y):
""" Run a permutation test to compare bow and doc2vec svms. """
# prepare test data for both svms
test_X1 = get_doc2vec_data(test['review'].values, model1)
test_X2 = get_doc2vec_data(test['review'].values, model2)
test_concat_X = np.concatenate((test_X1, test_X2), axis=1)
# build models and predict
svm1 = build_svm_classifier(d2v_X_1, d2v_Y, kernel=kernel1, gamma='scale')
svm2 = build_svm_classifier(np.concatenate((d2v_X_1, d2v_X_2), axis=1),
d2v_Y,
kernel=kernel2,
gamma='scale')
test_Y = test['sentiment'].to_numpy()
return permutation_test(test_Y, svm1.predict(test_X1),
svm2.predict(test_concat_X))
def run_permutation_test_bow_feature_presence_vs_frequency(
train, test, kernel):
train_Y = train['sentiment'].to_numpy()
test_Y = test['sentiment'].to_numpy()
X_pres, pres_vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
frequency=False)
X_pres_test, _ = get_bow_vectors(test['review'].values,
vectorizer=pres_vectorizer)
X, vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5)
X_test, _ = get_bow_vectors(test['review'].values, vectorizer=vectorizer)
svm1 = build_svm_classifier(X_pres, train_Y, kernel=kernel, gamma='scale')
svm2 = build_svm_classifier(X, train_Y, kernel=kernel, gamma='scale')
return permutation_test(test_Y, svm1.predict(X_pres_test),
svm2.predict(X_test))
def run_permutation_test_two_bow_kernels(train, test, kernel1, kernel2):
""" Run permutation test of difference between two kernels when using BOW vectors. """
train_Y = train['sentiment'].to_numpy()
test_Y = test['sentiment'].to_numpy()
bow_train_X, vectorizer = get_bow_vectors(train['review'].values,
min_count=4,
max_frac=0.5,
frequency=False)
bow_test_X, _ = get_bow_vectors(test['review'].values,
vectorizer=vectorizer)
svm1 = build_svm_classifier(bow_train_X,
train_Y,
kernel=kernel1,
gamma='scale')
svm2 = build_svm_classifier(bow_train_X,
train_Y,
kernel=kernel2,
gamma='scale')
return permutation_test(test_Y, svm1.predict(bow_test_X),
svm2.predict(bow_test_X))
