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main.py
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main.py
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from KNearestNeighbour import KNearestNeighbour
from DataHandler import DataHandler
from EvaluationMetrics import EvaluationMetrics
from Bagging import Bagging
from Svm import Svm
from LogisticRegression import LogisticRegression
from NaiveBayes import NaiveBayes
# Method to test basic KNN implementation with a dummy feature set and predictions set
def test_stub():
#train _ features
data_set = [(3, 2), (1,1), (4,4), (7,9), (11, 11)]
prediction_labels = [1,-1,1,-1,1]
dh = DataHandler('data/total-test.csv', 'prediction_label')
data_set = dh.get_k_best_features(2, data_set, prediction_labels)
knn = KNearestNeighbour(data_set, prediction_labels, 1)
print knn.predict((0,0))
# Method to test reading of numerical columns
def data_numeric_stub():
dh = DataHandler('data/total-test.csv', 'prediction_label')
headers, features, prediction_labels = dh.get_numeric_data_set()
knn = KNearestNeighbour(features, prediction_labels, 1)
print knn.predict((0, 0))
# Method to test reading of numerical columns with some columns that need to be ignored
def data_numeric__cols_stub():
dh = DataHandler('data/total-test.csv', 'prediction_label')
headers, features, prediction_labels = dh.get_numeric_data_set()
print(len(headers))
print (len(features[0]))
print headers
print features[0]
knn = KNearestNeighbour(features, prediction_labels, 1)
print knn.predict((0, ))
# Method to test tf-idf
def data_text_stub():
dh = DataHandler('data/train-set.csv', 'sentiment')
headers, features, prediction_labels = dh.get_textual_data_set()
review_text_index = headers.index('review_text')
review_text_list = [feature[review_text_index] for feature in features]
bow_headers, train_features = dh.convert_docs_to_bow(review_text_list)
# Method to write tf-idf features and prediction labels into a file for subsequent reading.
def data_write_train_stub():
dh = DataHandler('data/train-set.csv', 'sentiment')
headers, features, prediction_labels = dh.get_textual_data_set()
review_text_index = headers.index('review_text')
review_text_list = [feature[review_text_index] for feature in features]
bow_feature_names = dh.get_feature_set_for_documents(review_text_list)
print bow_feature_names
bow_features = dh.convert_docs_to_bow_for_features(review_text_list, bow_feature_names)
train_prediction_labels = dh.convert_sentiment_list_to_number(prediction_labels)
print len(bow_feature_names)
print len(bow_features[0])
print train_prediction_labels[0]
bow_feature_names.append("prediction_label")
dh.write_to_file('data/train-set-feature-engineered.csv', bow_features, bow_feature_names, train_prediction_labels)
# Method to write tf-idf features and prediction labels into a file for subsequent reading.
def data_write_test_stub():
dh = DataHandler('data/train-set.csv', 'sentiment')
headers, features, prediction_labels = dh.get_textual_data_set()
review_text_index = headers.index('review_text')
review_text_list = [feature[review_text_index] for feature in features]
bow_feature_names = dh.get_feature_set_for_documents(review_text_list)
dh = DataHandler('data/test-set.csv', 'sentiment')
headers, features, prediction_labels = dh.get_textual_data_set()
review_text_index = headers.index('review_text')
review_text_list = [feature[review_text_index] for feature in features]
bow_features = dh.convert_docs_to_bow_for_features(review_text_list, bow_feature_names)
test_prediction_labels = dh.convert_sentiment_list_to_number(prediction_labels)
print len(bow_feature_names)
print len(bow_features[0])
print test_prediction_labels[0]
bow_feature_names.append("prediction_label")
dh.write_to_file('data/test-set-feature-engineered.csv', bow_features, bow_feature_names, test_prediction_labels)
# Method to read tf-idf feature engineered files and perform KNN classification
def test_knn_on_review_data_set():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
knn = KNearestNeighbour(train_features, train_prediction_labels, 1)
dh_test = DataHandler('data/test-set-feature-engineered.csv', 'prediction_label')
headers, test_features, test_prediction_labels = dh_test.get_numeric_data_set()
print knn.predict(test_features[1])
print test_prediction_labels[1]
# This is the main method to evaluate knn on the data set.
def evaluate_knn():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
knn = KNearestNeighbour(train_features, train_prediction_labels, 5)
dh_test = DataHandler('data/test-set-feature-engineered.csv', 'prediction_label')
headers, test_features, test_prediction_labels = dh_test.get_numeric_data_set()
eval_metrics = EvaluationMetrics(knn, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
eval_metrics.compute_and_plot_auc(eval['predicted'], test_prediction_labels)
eval_metrics.compute_au_roc(eval['predicted'], test_prediction_labels)
# This is the main method to evaluate knn on the data set.
def evaluate_bagged_knn():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
bagged_knn = Bagging(train_features, train_prediction_labels, 3, 1)
dh_test = DataHandler('data/test-set-feature-engineered.csv', 'prediction_label')
headers, test_features, test_prediction_labels = dh_test.get_numeric_data_set()
eval_metrics = EvaluationMetrics(bagged_knn, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
eval_metrics.compute_and_plot_auc(eval['predicted'], test_prediction_labels)
eval_metrics.compute_au_roc(eval['predicted'], test_prediction_labels)
# This is the main method to evaluate knn on the data set.
def evaluate_svm():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
# Feature selection
train_features, selected_features = dh.get_k_best_features(len(train_features[0]), train_features, train_prediction_labels)
svm = Svm(train_features, train_prediction_labels, 20, 0)
svm.train()
dh_test = DataHandler('data/test-set-feature-engineered.csv', 'prediction_label')
headers, test_features, test_prediction_labels = dh_test.get_numeric_data_set()
# Feature selection
test_features = dh_test.get_new_feature_vec(test_features, selected_features)
eval_metrics = EvaluationMetrics(svm, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
eval_metrics.compute_and_plot_auc(eval['predicted'], test_prediction_labels)
eval_metrics.compute_au_roc(eval['predicted'], test_prediction_labels)
def tune_svm_using_10_fold():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
#train_features = dh.get_k_best_features(500, train_features, train_prediction_labels)
data_sets = dh.get_cross_validation_data_sets(10, train_features, train_prediction_labels)
accuracy = []
for data_set_number in data_sets:
data_set = data_sets.get(data_set_number)
training_set = data_set[0]
tuning_set = data_set[1]
train_features = training_set["data_points"]
train_prediction_labels = training_set["labels"]
# Feature selection
train_features, selected_features = dh.get_k_best_features(len(train_features[0]), train_features, train_prediction_labels)
test_features = tuning_set["data_points"]
test_prediction_labels = tuning_set["labels"]
# Feature selection
test_features = dh.get_new_feature_vec(test_features, selected_features)
svm = Svm(train_features, train_prediction_labels, 200, 1, 2)
svm.train()
eval_metrics = EvaluationMetrics(svm, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
accuracy.append(eval['accuracy'])
average_accuracy = sum(accuracy) / len(accuracy)
print average_accuracy
def tune_lr_using_10_fold():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
#train_features = dh.get_k_best_features(len(train_features[0]), train_features, train_prediction_labels)
data_sets = dh.get_cross_validation_data_sets(10, train_features, train_prediction_labels)
accuracy = []
for data_set_number in data_sets:
data_set = data_sets.get(data_set_number)
training_set = data_set[0]
tuning_set = data_set[1]
train_features = training_set["data_points"]
train_prediction_labels = training_set["labels"]
# Feature selection
train_features, selected_features = dh.get_k_best_features(len(train_features[0]), train_features, train_prediction_labels)
test_features = tuning_set["data_points"]
test_prediction_labels = tuning_set["labels"]
# Feature selection
test_features = dh.get_new_feature_vec(test_features, selected_features)
lr = LogisticRegression(train_features, train_prediction_labels, test_features, test_prediction_labels, 0.3)
eval_metrics = EvaluationMetrics(lr, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
accuracy.append(eval['accuracy'])
average_accuracy = sum(accuracy) / len(accuracy)
print average_accuracy
def tune_knn_using_10_fold():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
data_sets = dh.get_cross_validation_data_sets(10, train_features, train_prediction_labels)
accuracy = []
for data_set_number in data_sets:
data_set = data_sets.get(data_set_number)
training_set = data_set[0]
tuning_set = data_set[1]
train_features = training_set["data_points"]
train_prediction_labels = training_set["labels"]
test_features = tuning_set["data_points"]
test_prediction_labels = tuning_set["labels"]
knn = KNearestNeighbour(train_features, train_prediction_labels, 11)
eval_metrics = EvaluationMetrics(knn, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
accuracy.append(eval['accuracy'])
average_accuracy = sum(accuracy) / len(accuracy)
print average_accuracy
def tune_bagged_knn_using_10_fold():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
data_sets = dh.get_cross_validation_data_sets(10, train_features, train_prediction_labels)
accuracy = []
for data_set_number in data_sets:
data_set = data_sets.get(data_set_number)
training_set = data_set[0]
tuning_set = data_set[1]
train_features = training_set["data_points"]
train_prediction_labels = training_set["labels"]
test_features = tuning_set["data_points"]
test_prediction_labels = tuning_set["labels"]
bagged_knn = Bagging(train_features, train_prediction_labels, 4, 1)
eval_metrics = EvaluationMetrics(bagged_knn, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
accuracy.append(eval['accuracy'])
average_accuracy = sum(accuracy) / len(accuracy)
print average_accuracy
def evaluate_logistic_regression():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
dh_test = DataHandler('data/test-set-feature-engineered.csv', 'prediction_label')
headers, test_features, test_prediction_labels = dh_test.get_numeric_data_set()
print(len(test_prediction_labels))
lr = LogisticRegression(train_features, train_prediction_labels, test_features, test_prediction_labels, 0.3)
eval_metrics = EvaluationMetrics(lr, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
eval_metrics.compute_and_plot_auc(eval['predicted'], test_prediction_labels)
eval_metrics.compute_au_roc(eval['predicted'], test_prediction_labels)
def evaluate_naive_bayes():
dh = DataHandler('data/train-set-feature-engineered.csv', 'prediction_label')
headers, train_features, train_prediction_labels = dh.get_numeric_data_set()
print len(train_features[0])
dh_test = DataHandler('data/test-set-feature-engineered.csv', 'prediction_label')
headers, test_features, test_prediction_labels = dh_test.get_numeric_data_set()
nb = NaiveBayes(train_features, train_prediction_labels, test_features, test_prediction_labels, headers)
eval_metrics = EvaluationMetrics(nb, test_features, test_prediction_labels)
eval = eval_metrics.evaluate()
eval_metrics.compute_and_plot_auc(eval['predicted'], test_prediction_labels)
eval_metrics.compute_au_roc(eval['predicted'], test_prediction_labels)
# data_write_train_stub()
# data_write_test_stub()
#evaluate_logistic_regression()
#evaluate_knn()
# evaluate_bagged_knn()
#evaluate_svm()
#evaluate_naive_bayes()
#tune_nb_using_10_fold()
tune_lr_using_10_fold()
#tune_svm_using_10_fold()
#tune_knn_using_10_fold()
#tune_bagged_knn_using_10_fold()