def __generate_feature_matrix(self):
feature_matrix = pd.DataFrame()
es = Es(settings.hosts, settings.index_name, settings.index_type)
doc_count = es.count()
## Populate the matrix
print("Total number of features: {}".format(len(self.spamwords)))
for i in range(len(self.spamwords)):
spamword = self.spamwords[i]
result_id_list, result_score_list = es.get_documents_for_query(
spamword)
if (len(result_id_list) == 0):
print("No documents are present for spamword {}".format(
spamword))
result_list = [0] * doc_count
for i in range(len(result_id_list)):
result_list[int(result_id_list[i]) - 1] = result_score_list[i]
feature_matrix[spamword] = result_list
## Print purpose
if (i % 10 == 0):
print("Feature matrix for {} words done".format(i))
print(feature_matrix.index.values)
## Get the train and test list
id_list, filename_list, label_list, split_list = es.get_all_documents()
label_list = [1 if x == "spam" else 0 for x in label_list]
## Divide into train-test set
train_row_ids = []
test_row_ids = []
train_index_list = []
train_labels = []
test_index_list = []
test_labels = []
for i in range(len(split_list)):
if (split_list[i] == 'train'):
train_row_ids.append(int(id_list[i]) - 1)
train_index_list.append(filename_list[i])
train_labels.append(label_list[i])
else:
test_row_ids.append(int(id_list[i]) - 1)
test_index_list.append(filename_list[i])
test_labels.append(label_list[i])
print("Number of documents in train: {}".format(len(train_row_ids)))
print("Number of documents in test: {}".format(len(test_row_ids)))
## Divide the feature_matrix into train and test
train_feature_matrix = feature_matrix.loc[train_row_ids, :]
train_feature_matrix.index = train_index_list
test_feature_matrix = feature_matrix.loc[test_row_ids, :]
test_feature_matrix.index = test_index_list
print("Shape of train feature matrix: {}".format(
train_feature_matrix.shape))
print("Shape of test feature matrix: {}".format(
test_feature_matrix.shape))
return (train_feature_matrix, train_labels, test_feature_matrix,
test_labels, train_row_ids, test_row_ids)
class Spam_Classifier_Unigrams():
es = None
unigrams_dict = {}
def __init__(self):
print(" In the constructor")
self.es = Es(settings.hosts, settings.index_name, settings.index_type)
def __load_unigram(self, create_unigrams_dict):
## Save the unigrams_dict
if (create_unigrams_dict == True):
self.unigrams_dict = self.es.get_all_vocabulary()
util.dump_pickle_file(settings.unigram_filename,
self.unigrams_dict)
else:
self.unigrams_dict = util.load_pickle_file(
settings.unigram_filename)
def __generate_feature_matrix(self, label_dict):
start_calculation = timeit.default_timer()
feature_sparse_matrix = dok_matrix(
(len(label_dict), len(self.unigrams_dict)), dtype=np.float32)
count = 0
## Loop over all the documents
for id in label_dict.keys():
if (count % 1000 == 0):
print(
"Generating features for {} documents done".format(count))
doc_term_vector = self.es.term_vectors(id)
if "body" in doc_term_vector[
"term_vectors"] and "terms" in doc_term_vector[
"term_vectors"]["body"]:
tokens = doc_term_vector["term_vectors"]["body"]["terms"].keys(
)
for token in tokens:
## Get the token number
token_number = self.unigrams_dict[token]
tf = doc_term_vector["term_vectors"]["body"]["terms"][
token]["term_freq"]
feature_sparse_matrix[count, token_number - 1] = tf
count = count + 1
stop_calculation = timeit.default_timer()
print("Time taken to generate feature matrix: " +
str(stop_calculation - start_calculation))
return (feature_sparse_matrix)
def __get_feature_matrix(self):
start_calculation = timeit.default_timer()
## Label dict
label_train_dict = {}
label_test_dict = {}
## Get all documents and get the train and test list
id_list, filename_list, label_list, split_list = self.es.get_all_documents(
)
label_list = [1 if x == "spam" else 0 for x in label_list]
for i in range(len(split_list)):
if (split_list[i] == 'train'):
label_train_dict[id_list[i]] = label_list[i]
else:
label_test_dict[id_list[i]] = label_list[i]
print("Generating feature matrix for train documents")
feature_sparse_train_matrix = self.__generate_feature_matrix(
label_train_dict)
print("Generating feature matrix for test documents")
feature_sparse_test_matrix = self.__generate_feature_matrix(
label_test_dict)
train_csr = feature_sparse_train_matrix.tocsr()
train_labels = list(label_train_dict.values())
test_csr = feature_sparse_test_matrix.tocsr()
test_labels = list(label_test_dict.values())
stop_calculation = timeit.default_timer()
print("Time taken to generate feature matrix: " +
str(stop_calculation - start_calculation))
return (train_csr, train_labels, test_csr, test_labels)
def __classification(self, train_df, train_labels, test_df, test_labels):
logistic_regression_accuracy = ml_models.logistic_regression(
train_df, train_labels, test_df, test_labels)
print("Logistic Regression Accuracy {}".format(
logistic_regression_accuracy))
decision_tree_accuracy = ml_models.decision_tree(
train_df, train_labels, test_df, test_labels)
print("Decision Tree Accuracy {}".format(decision_tree_accuracy))
accuracy = max(logistic_regression_accuracy, decision_tree_accuracy)
return (accuracy)
def run(self, create_unigrams_dict=False):
start_calculation = timeit.default_timer()
## Load unigram
self.__load_unigram(create_unigrams_dict)
## Generate feature matrix
train_csr, train_labels, test_csr, test_labels = self.__get_feature_matrix(
)
## Run the classification algorithm
accuracy = self.__classification(train_csr, train_labels, test_csr,
test_labels)
print("Accuracy is {}".format(accuracy))
stop_calculation = timeit.default_timer()
print("Time taken to run spam classifier userwords: " +
str(stop_calculation - start_calculation))
