def test_punctunation(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_remove_punctuation())
loader = DataPreprocess(config)
process = loader.process_item(TEST_DATA)
self.assertEqual("This isn t a TEST sentences ", process["data"])
def test_whitespace(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_remove_whitespace())
loader = DataPreprocess(config)
process = loader.process_item(" remove whitespace ")
self.assertEqual("remove whitespace", process["data"])
def test_lower(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_lowercase())
loader = DataPreprocess(config)
process = loader.process_item(TEST_DATA)
self.assertEqual(TEST_DATA.lower(), process["data"])
def test_csv_pipeline(self):
config = {
"data_loader": {
"name": "data_loader",
"type": "csv",
"file_path": "test_data/test.csv",
"columns": {
"id": "id",
"data": "text",
"additional_columns": ["username"]
},
},
"steps": [
{
"name": "normalize_text",
"type": "lowercase",
"log_level": "INFO"
},
],
}
loader = DataPreprocess(config)
data = []
for batch in loader.process_data():
for item in batch:
data.append(item["data"])
self.assertEqual(4, len(data))
def test_url(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_remove_urls())
loader = DataPreprocess(config)
process = loader.process_item("remove url http://www.google.com")
self.assertEqual("remove url ", process["data"])
def test_stopwords(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_lowercase())
config['steps'].append(templates.normalize_text_remove_stopwords())
loader = DataPreprocess(config)
process = loader.process_item(TEST_DATA)
self.assertEqual("isn't test sentences!", process["data"])
def test_lemmatizer(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_lowercase())
config['steps'].append(templates.normalize_text_lemmatizer())
loader = DataPreprocess(config)
process = loader.process_item("How many cities are there?")
self.assertEqual("how many city are there?", process["data"])
def test_contractions(self):
config = templates.pipeline()
config['data_loader'] = templates.data_loader_single_item_loader()
config['steps'].append(templates.normalize_text_lowercase())
config['steps'].append(templates.normalize_text_expand_contractions())
loader = DataPreprocess(config)
process = loader.process_item(TEST_DATA)
self.assertEqual("this is not a test sentences!", process["data"])
def __init__(self):
parser = argparse.ArgumentParser(description='List the content of a folder')
parser.add_argument('--text_file', type=str, help='File path to classify')
args = parser.parse_args()
self.text_file_path = args.text_file
self.df = None
self.X = None
self.word_to_vector_model_path = r'models/w2v.pkl'
self.dim_reduction_path = r'models/dim_reduction.pkl'
self.model_path = r'models/svc_model.pkl'
self.data_preprocessing = DataPreprocess()
self.build_features = BuildFeatures()
def main():
##################################################
# parse data from original data & construct images
##################################################
print(
"parsing data from log files which are generated by Atheros-CSI-TOOL\n"
)
data_generator = DataLogParser(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.nrx_max,
conf.nsubcarrier_max, conf.data_folder,
conf.log_folder, conf.skip_frames,
conf.time_offset_ratio, conf.day_conf,
conf.label)
data_generator.generate_image_no_label(conf.draw_date, conf.draw_label)
# train_data, test_data: classes (key: label, value: images under this label)
test_data = data_generator.get_data_no_label()
##################################################
# apply signal processing blocks to images
##################################################
print("Pre-processing data\n")
data_process = DataPreprocess(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.ntx, conf.nrx_max,
conf.nrx, conf.nsubcarrier_max,
conf.nsubcarrier, conf.data_shape_to_nn,
conf.data_folder, conf.label)
data_process.add_image_no_label(test_data)
data_process.signal_processing(conf.do_fft, conf.fft_shape)
data_process.prepare_shape()
final_test_data = data_process.get_data_no_label()
##################################################
# train or test data with neural netowrk
##################################################
nn_model = NeuralNetworkModel(conf.data_shape_to_nn, conf.abs_shape_to_nn,
conf.phase_shape_to_nn, conf.total_classes)
print("Get test result using existing model (in test mode)\n")
nn_model.load_model(conf.model_name)
for key in final_test_data:
plt.figure()
total_test = len(final_test_data[key])
cc = 1
for idx in final_test_data[key]:
# if want to output motion probability, please set output_label == False
result = nn_model.get_no_label_result(final_test_data[key][idx],
output_label=True)
plt.subplot(total_test, 1, cc)
plt.plot(result)
plt.title(idx)
plt.ylim(0, 1.05)
cc = cc + 1
plt.suptitle(key)
nn_model.end()
plt.show()
print("Done!")
def test_single_item_pipeline(self):
config = {
"data_loader": {
"type": "single_item"
},
"steps": [
{
"name": "normalize_text",
"type": "lowercase",
"log_level": "INFO"
},
],
}
loader = DataPreprocess(config)
process = loader.process_item(TEST_DATA)
self.assertEqual(TEST_DATA.lower(), process["data"])
def generate_evaluate(self, file_path):
while True:
for list_x, list_y, list_time_x, list_time_y in self.get_feature(
file_path):
list_x = DataPreprocess.add_feature(
file_path,
list_time_x) if self.tcn_add_features else list_x
yield np.array(list_x), np.array(list_y)
def execute():
test_data = prepare_test_data()
preprocessing = DataPreprocess(test_data, do_load_existing_tokenizer=True)
prediction = make_prediction(preprocessing)
roc_auc = evaluate_roc_auc(preprocessing, prediction > 0.5)
accuracy = evaluate_accuracy_score(preprocessing, prediction > 0.5)
print(f'Average ROC_AUC Score on Test Data: {roc_auc}')
print(f'Average Accuracy Score on Test Data: {accuracy}')
def execute():
# Import the training data csv file and save it into a dataframe
training_data = pd.read_csv(TRAINING_DATA_LOC)
preprocessing = DataPreprocess(training_data)
rnn_model, history = build_rnn_model(preprocessing.padded_data,
preprocessing.target_classes,
preprocessing.embedding_layer)
plot_training_history(rnn_model, history, preprocessing.padded_data,
preprocessing.target_classes)
def execute(data):
training_data = pd.read_csv(data)
preprocessing = DataPreprocess(training_data)
lstm_model, history = build_lstm_model(preprocessing.X_t,
preprocessing.target_classes,
preprocessing.embedding_layer)
plot_training_history(lstm_model,
history,
preprocessing.X_t,
preprocessing.target_classes)
def test_list_pipeline(self):
config = {
"data_loader": {
"type": "list"
},
"steps": [
{
"name": "normalize_text",
"type": "lowercase",
"log_level": "INFO"
},
],
}
loader = DataPreprocess(config)
data = []
for batch in loader.process_data(TEST_LIST):
for item in batch:
data.append(item["data"])
test = [item.lower() for item in TEST_LIST]
self.assertEqual(test, data)
def main():
logger.info(SEPARATOR)
configs = get_configs()
# Run data preprocess
if configs.data_preprocess_active:
logger.info(configs)
preprocess = DataPreprocess(configs)
preprocess.data_preprocess()
logger.info("Data preprocess finished!")
# Run train model
if configs.da_rnn_model_active:
logger.info(configs)
da_rnn_model = DaRnnModel(configs)
da_rnn_model.run()
logger.info("Da_rnnModel finished!")
if configs.xgboost_gridsearch_model_active:
xgboost_model = XgboostGridSearchModel(configs)
xgboost_model.run()
logger.info("XGboost finished!")
if configs.tcn_big_file_model_active:
tcn_model = TcnModel(configs)
tcn_model.run()
logger.info("TcnModel finished!")
def read_data_from_disk(self, queue): # optional pre-processing arguments
dataproc = DataPreprocess(queue,
coords=self.coord,
dataset_catgry=self.dataset_catgry,
dataset_type=self.data_type)
h, w = self.params['input_size']
dstep = self.params['dce_dstep']
if self.data_type == 1:
color_img = dataproc.load_colorimg(height=h, width=w, channels=3)
depth_gt = dataproc.load_depthgt(height=h, width=w, channels=1)
subsample_depth = dataproc.load_subsampledepth(height=h,
width=w,
channels=1)
if self.params['orig_normalizefac']:
depth_gt = tf.cast(depth_gt, tf.float32) * 100 / 256
subsample_depth = tf.cast(subsample_depth,
tf.float32) * 100 / 256
else:
depth_gt = tf.cast(depth_gt, tf.float32)
subsample_depth = tf.cast(subsample_depth, tf.float32)
color_img = tf.slice(color_img,
[self.params['truncated_height_start'], 0, 0],
[self.params['truncated_height_end'], w, 3])
depth_gt = tf.slice(depth_gt,
[self.params['truncated_height_start'], 0, 0],
[self.params['truncated_height_end'], w, 1])
subsample_depth = tf.slice(
subsample_depth, [self.params['truncated_height_start'], 0, 0],
[self.params['truncated_height_end'], w, 1])
if self.datainput in ['color_dc_dcclabels']:
color_img_processed = dataproc.preprocess_color(
color_img, self.params['coloraugmentflag'])
depth_gt = tf.cast(depth_gt, tf.float32)
if self.params['Gen_uniformsampflag']:
subsample_depth = dataproc.uniform_sampling(
depth_gt, self.params['Uniform_samp'])
subsampledepth_dcc = dataproc.depth_2_dcc_channelsgeneralize(
subsample_depth,
dstep,
self.params['depth_maxrange'],
spatial_dim=(self.params['truncated_height_end'], w))
depth_gt_dcc = dataproc.depth_2_dcc_channelsgeneralize(
depth_gt,
self.params['dce_dstep'],
self.params['depth_maxrange'],
spatial_dim=(self.params['truncated_height_end'], w))
#depth_gt_dcc = dataproc.depth_2_dcc_channelsgeneralize(depth_gt, dstep, self.params['depth_maxrange'], oorFlag = False, spatial_dim = (h,w))
depth_gt_dcc = tf.squeeze(depth_gt_dcc)
data_processed = tf.concat([
color_img_processed, subsampledepth_dcc, depth_gt_dcc, depth_gt
],
axis=2)
else:
ValueError('Data-Input Type is Unrecognized. Exiting ...\n')
if self.random_mirror:
data_processed = data_mirroring(data_processed)
if self.random_crop:
data, labels = self.random_crop_and_pad_data_and_labels(
data_processed, self.params['crop_size'][0],
self.params['crop_size'][1])
else:
data, labels = self.crop_pad_data_labels(
data_processed, self.params['crop_size'][0],
self.params['crop_size'][1])
return data, labels
def __init__(self):
self.model_path = r'models/svc_model.pkl'
self.data_preprocessing = DataPreprocess()
self.build_features = BuildFeatures()
self.X = None
self.y = None
class TrainModel:
def __init__(self):
self.model_path = r'models/svc_model.pkl'
self.data_preprocessing = DataPreprocess()
self.build_features = BuildFeatures()
self.X = None
self.y = None
def run(self):
# data preprocessing pipeline
self.data_preprocessing.load_csv()
self.data_preprocessing.clean_conversation()
self.data_preprocessing.extract_meaning_phrases()
self.data_preprocessing.group_convs_by_file_id()
self.data_preprocessing.rm_dups_phrases_in_same_conv()
self.X, self.y = self.data_preprocessing.get_X_y()
# with open('X.pkl', 'rb') as fp:
# self.X = pickle.load(fp)
# self.X = [list(a) for a in self.X]
#
# with open('y.pkl', 'rb') as fp:
# self.y = pickle.load(fp)
# Train and test set
X_train, X_test, y_train, y_test = train_test_split(self.X,
self.y,
test_size=0.1,
stratify=self.y)
# build features
# oversampling on training data only
X_train, y_train = self.build_features.oversampling_on_training_data(
X_train, y_train)
# X_train = [' '.join(a).replace('[PAD]', '').strip() for a in X_train]
# X_test = [' '.join(a).replace('[PAD]', '').strip() for a in X_test]
# Word to vectors
self.build_features.word_to_vectors_model(X_train)
X_train = self.build_features.word_to_vectors_transformed(X_train)
X_test = self.build_features.word_to_vectors_transformed(X_test)
# Dimenstion reduction technique.
self.build_features.dimension_reduction_model(X_train)
X_train = self.build_features.dimension_reduction_transformed(X_train)
X_test = self.build_features.dimension_reduction_transformed(X_test)
# train model
model = LinearSVC(random_state=25)
model.fit(X_train, y_train)
print('\n\n')
print('-*-' * 20)
print('Training accuracy: ', model.score(X_train, y_train) * 100)
print('Accuracy on unseen documents: ',
model.score(X_test, y_test) * 100)
print('-*-' * 20)
pickle.dump(model, open(self.model_path, 'wb')) # save
def main():
args = get_input_arguments()
training_mode = (args.mode == 'Y')
if args.mode not in ['Y', 'N']:
raise ValueError('Invalid input value for m should be either Y or N')
data_folder = conf.data_folder
if training_mode:
label = conf.train_label
print('in training mode')
print('training data from {} \nvalidation data from {}\n'.format(
conf.training_date, conf.training_validate_date))
print('training label is {}\n'.format(label))
data_folder += "training/"
else:
label = conf.test_label
print('in test mode')
print('test date from {}'.format(conf.test_date))
print('test label is {}\n'.format(label))
data_folder += "test/"
##################################################
# parse data from original data & construct images
##################################################
print(
"parsing data from log files which are generated by Atheros-CSI-TOOL\n"
)
data_generator = DataLogParser(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.nrx_max,
conf.nsubcarrier_max, data_folder,
conf.log_folder, conf.skip_frames,
conf.time_offset_ratio, conf.day_conf,
label)
train_date = conf.training_date if training_mode else []
if training_mode:
data_generator.generate_image(conf.training_date,
conf.training_validate_date)
else:
data_generator.generate_image([], conf.test_date)
# train_data, test_data: classes (key: label, value: images under this label)
train_data, test_data = data_generator.get_data()
##################################################
# apply signal processing blocks to images
##################################################
print("Pre-processing data\n")
data_process = DataPreprocess(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.ntx, conf.nrx_max,
conf.nrx, conf.nsubcarrier_max,
conf.nsubcarrier, conf.data_shape_to_nn,
data_folder, label)
data_process.load_image(training_mode, False, train_data, test_data)
data_process.signal_processing(conf.do_fft, conf.fft_shape)
data_process.prepare_shape()
x_train, y_train, x_test, y_test = data_process.get_data()
##################################################
# train or test data with neural netowrk
##################################################
nn_model = NeuralNetworkModel(conf.data_shape_to_nn, conf.abs_shape_to_nn,
conf.phase_shape_to_nn, conf.total_classes)
nn_model.add_data(x_train, y_train, x_test, y_test)
if training_mode:
print("Building a new model (in training mode)\n")
nn_model.cnn_model_abs_phase()
nn_model.fit_data(conf.epochs)
nn_model.save_model(conf.model_name)
else:
print("Get test result using existing model (in test mode)\n")
nn_model.load_model(conf.model_name)
result = nn_model.get_test_result(label)
# nn_model.save_result(result, conf.file_prefix+conf.test_result_filename)
nn_model.end()
print("Done!")
class Classifier:
def __init__(self):
parser = argparse.ArgumentParser(description='List the content of a folder')
parser.add_argument('--text_file', type=str, help='File path to classify')
args = parser.parse_args()
self.text_file_path = args.text_file
self.df = None
self.X = None
self.word_to_vector_model_path = r'models/w2v.pkl'
self.dim_reduction_path = r'models/dim_reduction.pkl'
self.model_path = r'models/svc_model.pkl'
self.data_preprocessing = DataPreprocess()
self.build_features = BuildFeatures()
def read_text_file(self):
with open(self.text_file_path) as fp:
text = [x.strip('\r\n') for x in fp.readlines()]
return text
def create_dataframe(self, text):
self.df = pd.DataFrame(text, columns=['conversation'])
def load_model(self):
return pickle.load(open(self.model_path, 'rb'))
def run(self):
text = self.read_text_file()
self.create_dataframe(text)
# data preprocessing pipeline
self.data_preprocessing.test_fill_df(self.df)
self.data_preprocessing.clean_conversation()
self.data_preprocessing.extract_meaning_phrases()
self.data_preprocessing.test_group_convs()
self.data_preprocessing.rm_dups_phrases_in_same_conv()
X_test = self.data_preprocessing.test_get_X()
print(len(X_test))
# Word to vectors
X_test = self.build_features.word_to_vectors_transformed(X_test)
# Dimenstion reduction technique.
X_test = self.build_features.dimension_reduction_transformed(X_test)
model = self.load_model()
print('-*-' * 20)
predicted_class = model.predict(X_test)
print('Result: ', predicted_class)
