def train():
train_dataset = YoutubeTrendingDataset("./data/train.csv")
train_dataloader = DataLoader(train_dataset, batch_size=1)
preprocess_dict = {
"publish_time": [TimeStampPreprocessing],
"likes": [FrequencyPreprocessing],
"category_id": [CategoricalPrerprocessing]
}
preprocessor = Preprocessor(preprocess_dict)
for batch_ix, data in enumerate(train_dataloader):
train_x, train_y = preprocessor.run(data)
def test_smoke(self):
"""
Smoke test - check that the preprocessor runs without exploding
"""
pp = Preprocessor(file_name=self.file_name,
input_dir=self.input_dir,
output_dir=self.output_dir)
pp.process()
out_dir_files = os.listdir(self.output_dir)
for file_name in out_dir_files:
name = utilities.path.get_name(self.file_name,
extension=False)
if name in file_name:
file_path = os.path.join(self.output_dir, file_name)
doc = Document.from_json(file_path)
self.assertNotEqual(doc.pre_file_name, self.file_name)
self.assertEqual(doc.file_name, 'test_preprocessed/lorem.json')
def by_infile(self, infile):
try:
shutil.rmtree(self.OUTPUT_DIR)
except:
pass
self.db_open()
json_data = self.get_events_from_infile(infile)
# build preprocessor
ppr = Preprocessor()
# Process raw data
#X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y, events_found = ppr.get_from_json(self.DIMENSION, json_data)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(
X, Y, 0.0, 1.0)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY,
self.INPUT_DIR)
# build adapter
adapter = MACAdapter(self.INPUT_DIR, self.DIMENSION, self.FOLDS)
# build model
convnet = ConvNet(self.DIMENSION)
# build server
server = ConvNetServer(adapter,
self.OUTPUT_DIR,
batch_size=self.BATCH_SIZE,
verbose=True,
use=True)
x, durs, _ = server.get_testing_batch()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
convnet.restore(sess, self.INITIAL_WEIGHTS)
predictions = sess.run((convnet.predictor),
feed_dict={
convnet.x: x,
convnet.durs: durs
})
# Get event ids
_, _, ids = adapter.get_ids()
results = [{
"eventID": int(ids[i]),
"ml": {
"aircraftProbability":
round(np.around(predictions[i][0], decimals=4), 4),
"model":
self.MODEL
}
} for i in range(0, len(ids))]
for result in results:
self.insert_result_for_event(result)
self.db_close()
# Set output path
if args.out_path:
out_path = Path(args.out_path)
else:
out_path = Path('data_' + str(patch_size) + '/test/masks_predicted_' +
time.strftime("%y%m%d-%H%M%S"))
if not out_path.exists():
out_path.mkdir(parents=True)
# log all arguments including default ones
with open(Path(out_path, 'options.json'), 'w') as f:
f.write(json.dumps(vars(args)))
# Preprocessing
preprocessor = Preprocessor()
if args.denoise:
preprocessor.add_filter(
filter.get_denoise_filter(args.denoise, args.denoise_parms))
# get loss function from function name
loss_function = get_loss_function(args.loss, args.loss_parms)
if 'bayes' in args.model or 'uncert' in args.model:
mc_iterations = args.mc_iterations
else: # set Nr iterations to 1 for regular u-net
mc_iterations = 1
# 2-Stage Optimization Process
if args.model == 'two_stage':
#1st Stage
def main():
input_dir = ""
output_dir = ""
max_length_sentence = 100
lc = False
verbose = False
try:
opts, args = getopt.getopt(sys.argv[1:], "hvls:i:o:", [
"help", "verbose", "lowercase", "max_sen_length=", "input_dir=",
"output_dir="
])
except getopt.GetoptError as err:
print(str(err))
usage()
sys.exit(2)
for o, a in opts:
if o in ("-h", "--help"):
usage()
sys.exit()
elif o in ("-v", "--verbose"):
verbose = True
elif o in ("-s", "--max_sen_length"):
max_length_sentence = int(a)
elif o in ("-l", "--lowercase"):
lc = True
elif o in ("-i", "--input_dir"):
input_dir = os.path.expanduser(a)
elif o in ("-o", "--output_dir"):
output_dir = os.path.expanduser(a)
else:
assert False, "unhandled option"
if not os.path.exists(input_dir):
print("input directory does not exists... exiting")
sys.exit()
if output_dir == "":
output_dir = input_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if verbose:
print("directory {} created".format(output_dir))
path_trainset = os.path.join(input_dir, TRAIN_NAME)
path_testset = os.path.join(input_dir, TEST_NAME)
if not os.path.exists(path_trainset):
print("training set file is absent ({})".format(path_trainset))
sys.exit()
if not os.path.exists(path_testset):
print("test set file is absent ({})".format(path_testset))
sys.exit()
path_save_voc_w = os.path.join(output_dir, VOCABULARY_NAME)
path_save_voc_c = os.path.join(output_dir, CLASSES_NAME)
prep_train = Preprocessor(path_trainset)
prep_test = Preprocessor(path_testset, train=False)
size_train = len(prep_train)
size_test = len(prep_test)
if verbose:
print("Starting pre-processing on files of {} sentences".format(
size_train + size_test))
voc_w, voc_c, max_length = get_vocabulary([prep_train, prep_test], verbose)
voc_w.add(PAD)
write_vocabulary(voc_w, path_save_voc_w)
write_vocabulary(voc_c, path_save_voc_c)
embeddings_size = len(voc_w)
del voc_w, voc_c, prep_train
vocab_words = load_vocabulary(path_save_voc_w)
# Loading vocabularies as dictionary
if verbose:
print(
"\nvocabulary loaded back ... {} words "
"(might be different from before due to utf-8 encoding issues...)".
format(len(vocab_words)))
vocab_classes = load_vocabulary(path_save_voc_c)
max_length = min(max_length, max_length_sentence)
processing_words = get_word_preprocessing(vocab_words,
max_length=max_length)
processing_class = get_classes_preprocessing(vocab_classes)
prep_to_int_train = Preprocessor(path_trainset,
processing_words=processing_words,
processing_class=processing_class)
prep_to_int_test = Preprocessor(path_testset,
train=False,
processing_words=processing_words,
processing_class=processing_class)
train, _ = fill_matrix(size_train,
max_length,
prep_to_int_train,
train=True)
test, ids_test = fill_matrix(size_test,
max_length,
prep_to_int_test,
train=False)
np.save(os.path.join(output_dir, TRAIN_OUTPUT_NAME), train)
np.save(os.path.join(output_dir, TEST_OUTPUT_NAME), test)
with codecs.open(os.path.join(output_dir, IDS_OUTPUT_NAME),
"w",
encoding='utf-8') as f:
f.write("\n".join(ids_test))
path_w2v = os.path.join(input_dir, EMBEDDINGS_INPUT_NAME)
path_ngrams_w2v = os.path.join(input_dir, EMBEDDINGS_INPUT_NAME_NG)
path_save_embeddings = os.path.join(output_dir, EMBEDDINGS_OUTPUT_NAME)
min_n = 3
max_n = 6
create_embeddings(vocab_words, embeddings_size, path_w2v, path_ngrams_w2v,
path_save_embeddings, min_n, max_n)
from utilities import file_io
from sklearn.feature_extraction import FeatureHasher
from sklearn.neighbors import KNeighborsClassifier
import time
from preprocessing.preprocessor import Preprocessor
from LyricsProcessor import LyricsProcessor
if __name__ == "__main__":
#chunks = file_io.read_lastfm_user_art_file("data/userid-timestamp-artid-artname-traid-traname.tsv")
chunks = file_io.read_lastfm_user_art_file("data/test_shorter.tsv")
# read songs
vectorizer = FeatureHasher()
pre = Preprocessor(chunks, vectorizer)
songs = pre.read_songs(20)
print(songs)
# reset file reader
#chunks = file_io.read_lastfm_user_art_file("data/tmp.tsv")
#pre.reset_file_reader(chunks)
# read user song mapping
pre.read_user_songs(1000)
# convert to user-song matrix
X = pre.get_user_song_matrix()
start_time = time.time()
clf = KNeighborsClassifier(n_neighbors=1)
clf.fit(X, list(range(X.shape[0])))
print(clf.predict(pre.user_song_dict["user_000001"]))
type=int,
default=1,
help="# of nearest neighbors for classification")
ap.add_argument(
"-j",
"--jobs",
type=int,
default=-1,
help="# of jobs for k-NN distance (-1 uses all available cores)")
args = vars(ap.parse_args())
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
sp = Preprocessor(32, 32)
sdl = DatasetLoader(preprocessors=[sp])
(data, labels) = sdl.load(imagePaths, verbose=1000)
data = data.reshape((data.shape[0], 3072))
print("[INFO] features matrix: {:.1f}MB".format(data.nbytes / (1024 * 1000.0)))
le = LabelEncoder()
labels = le.fit_transform(labels)
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
#k-NN
print("[INFO] evaluating k-NN classifier...")
def main(argv):
"""Main entrypoint for the anonymization tool"""
# Default parameters
configuration_file = ''
input_file = ''
use_cache = True
weight = 0.5
strategy = "gdf"
result_dir = None
# Read and set tool parameters
try:
opts, _ = getopt.getopt(argv, "c:i:r:w:v", ["config=", "input=", "weight=", "result_dir=", "verbose"])
except getopt.GetoptError:
logger.error('experiment_runner.py -c <config_file> -i <input_file> -w <relational_weight>')
sys.exit(2)
for opt, arg in opts:
if opt in ("-c", "--config"):
configuration_file = arg
if opt in ("-i", "--input"):
input_file = arg
base = os.path.basename(input_file)
if not result_dir:
result_dir = os.path.splitext(base)[0]
if opt in ("-w", "--weight"):
weight = float(arg)
strategy = "mondrian"
if opt in ("-r", "--result_dir"):
result_dir = arg
if opt in ("-v", "--verbose"):
logging.getLogger().setLevel(logging.DEBUG)
result_path = Path("experiment_results") / result_dir
result_path.mkdir(parents=True, exist_ok=True)
# Let's get started
logger.info("Anonymizing input file %s", input_file)
# Initialize and read configuration
configuration_reader = ConfigurationReader()
config = configuration_reader.read(configuration_file)
# Read data using data types defined in the configuration
data_reader = DataReader(config)
df = data_reader.read(input_file)
# Initialize the sensitive terms recognizer
sensitive_terms_recognizer = SensitiveTermsRecognizer(config, use_cache)
# Initialize the preprocessor (preprocessor is stateful, so pass df at the beginning)
pp = Preprocessor(sensitive_terms_recognizer, config, df)
# Run through preprocessing of dataframe: Data cleansing, analysis of textual attributes, resolving of redundant information, and compression
pp.clean_textual_attributes()
pp.analyze_textual_attributes()
pp.find_redundant_information()
pp.compress()
# Get sensitive terms dictionary and preprocessed dataframe
terms = pp.get_sensitive_terms()
df = pp.get_df()
# Initialize the anonymization kernel by providing the sensitive terms dictionary, the configuration, the sensitive terms recognizer, and the preprocessor
kernel = AnonymizationKernel(terms, config, sensitive_terms_recognizer, pp)
unanonymized = df
# Determine k values for experiment
k_values = [2, 3, 4, 5, 10, 20, 50]
biases = config.get_biases()
# Set strategy names
if strategy == "mondrian":
strategy_name = "mondrian-{}".format(weight)
elif strategy == "gdf":
strategy_name = strategy
# Parameters for calculating metrics
quasi_identifiers = config.get_quasi_identifiers()
textual_attribute_mapping = pp.get_textual_attribute_mapping()
# Prepare dataframes and json to store experiment results
total_information_loss = pd.DataFrame(index=k_values, columns=[strategy_name])
total_information_loss.index.name = 'k'
relational_information_loss = pd.DataFrame(index=k_values, columns=[strategy_name])
relational_information_loss.index.name = 'k'
textual_information_loss = pd.DataFrame(index=k_values, columns=[strategy_name])
textual_information_loss.index.name = 'k'
detailed_loss_level_0 = [k for k in textual_attribute_mapping]
detailed_loss_level_1 = set()
for k in textual_attribute_mapping:
for e in textual_attribute_mapping[k]:
detailed_loss_level_1.add(e.replace("{}_".format(k), ''))
detailed_loss_level_1 = ["total"] + list(detailed_loss_level_1)
detailed_textual_information_loss = pd.DataFrame(index=k_values, columns=pd.MultiIndex.from_product([detailed_loss_level_0, detailed_loss_level_1]))
detailed_textual_information_loss.index.name = 'k'
partition_sizes = {}
partition_sizes[strategy_name] = {}
partition_splits = {}
partition_splits[strategy_name] = {}
# Let's start the experiments
for k in k_values:
logger.info("-------------------------------------------------------------------------------")
logger.info("Anonymizing dataset with k=%d and strategy %s", k, strategy_name)
# Anonymize dataset for a specific k
anonymized_df, partitions, partition_split_statistics = kernel.anonymize_quasi_identifiers(df, k, strategy, biases, weight)
# Calculating the total, relational, and textual information loss based on the original and anonymized data frame
total_il, relational_il, textual_il = calculate_normalized_certainty_penalty(unanonymized, anonymized_df, quasi_identifiers, textual_attribute_mapping)
# Calculating the mean and std for partition size as well as split statistics
mean_partition_size = calculate_mean_partition_size(partitions)
std_partition_size = calculate_std_partition_size(partitions)
if partition_split_statistics:
number_of_relational_splits, number_of_textual_splits = get_partition_split_share(partition_split_statistics, textual_attribute_mapping)
# Notify about the results
logger.info("Information loss for relational attributes is %4.4f", relational_il)
if textual_il:
logger.info("Information loss for textual attribute is %4.4f", textual_il["total"])
logger.info("Total information loss is %4.4f", total_il)
logger.info("Ended up with %d partitions with a mean size of %.2f and a std of %.2f", len(partitions), mean_partition_size, std_partition_size)
if partition_split_statistics:
logger.info("Split %d times on a relational attribute", number_of_relational_splits)
logger.info("Split %d times on a textual attribute", number_of_textual_splits)
# Store experiment results
total_information_loss.at[k, strategy_name] = total_il
relational_information_loss.at[k, strategy_name] = relational_il
if textual_il:
textual_information_loss.at[k, strategy_name] = textual_il["total"]
for key in textual_il:
if isinstance(textual_il[key], dict):
for subkey in textual_il[key]:
if subkey == "total":
detailed_textual_information_loss.at[k, (key, "total")] = textual_il[key]["total"]
else:
entity_type = subkey.replace("{}_".format(key), '')
detailed_textual_information_loss.at[k, (key, entity_type)] = textual_il[key][subkey]
partition_sizes[strategy_name][k] = get_partition_lengths(partitions)
if partition_split_statistics:
partition_splits[strategy_name][k] = {
"relational": number_of_relational_splits,
"textual": number_of_textual_splits
}
# Define file info
if strategy == "mondrian":
file_info = str(weight).replace(".", "_")
elif strategy == "gdf":
file_info = strategy
# Save the experiment results
with open(result_path / 'partition_distribution_{}.json'.format(file_info), 'w') as f:
json.dump(partition_sizes, f, ensure_ascii=False)
if partition_split_statistics:
with open(result_path / 'partition_splits_{}.json'.format(file_info), 'w') as f:
json.dump(partition_splits, f, ensure_ascii=False)
total_information_loss.to_csv(result_path / "total_information_loss_{}.csv".format(file_info))
relational_information_loss.to_csv(result_path / "relational_information_loss_{}.csv".format(file_info))
if textual_il:
textual_information_loss.to_csv(result_path / "textual_information_loss_{}.csv".format(file_info))
detailed_textual_information_loss.to_csv(result_path / "detailed_textual_information_loss_{}.csv".format(file_info))
def __predict_output__():
plt.interactive(False)
cfg = Configuration()
GPU = True
if GPU != True:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Input Path
root_dir = os.path.dirname(os.path.abspath(__file__))
image_path = cfg.image_path
json_path = os.path.join(root_dir, cfg.input_filename)
testingset = os.path.join(root_dir, 'testingset')
Preprocessor.__generate_kijiji_set__(root_dir, image_path, json_path,
testingset, 'model')
# ------------------generator to compile training data of kijiji dataset----------------------------------------
image_path = os.path.join(root_dir, 'testingset')
data_path = glob(image_path + "/*")
# Image Segmentation Parameters
model_path = os.path.expanduser(cfg.model_path)
assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.'
anchors_path = os.path.expanduser(cfg.anchors_path)
classes_path = os.path.expanduser(cfg.classes_path)
test_path = os.path.expanduser(cfg.test_path)
output_path = os.path.expanduser(cfg.segmented_output_path)
json_path = os.path.expanduser(cfg.json_output)
if not os.path.exists(output_path):
print('Creating output path {}'.format(output_path))
os.mkdir(output_path)
sess = K.get_session()
class_names = Preprocessor.__return_class_names__(classes_path)
anchors = Preprocessor.__return_anchors__(anchors_path)
yolo_model = load_model(model_path)
# Verify model, anchors, and classes are compatible
num_classes = len(class_names)
num_anchors = len(anchors)
info = 'Mismatch between model and given anchor and class sizes. ' \
'Specify matching anchors and classes with --anchors_path and --classes_path flags.'
model_output_channels = yolo_model.layers[-1].output_shape[-1]
assert model_output_channels == num_anchors * (num_classes + 5), info
print('{} model, anchors, and classes loaded.'.format(model_path))
# Check if model is fully convolutional, assuming channel last order.
model_image_size = yolo_model.layers[0].input_shape[1:3]
is_fixed_size = model_image_size != (None, None)
# Generate Colors for drawing bounding boxes
hsv_tuples, colors = Preprocessor.__generate_colors_for_bounding_boxes__(
class_names)
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=cfg.score_threshold,
iou_threshold=cfg.iou_threshold)
# Load Images from the root folder
input_images_model_1, all_images, data_path, data_path_with_image_name = Preprocessor.__load_image_data_thumbnails__(
data_path,
cfg.compressed_image_height,
cfg.compressed_image_width,
cfg.compressed_channel,
cfg.number_of_categories,
cfg.number_of_images_per_category,
root_dir,
is_fixed_size,
model_image_size,
sess,
yolo_model,
input_image_shape,
boxes,
scores,
classes,
cfg.font_path,
class_names,
colors,
output_path,
json_path,
test_path,
True, # Segmentation Flag
False, # Edge-detection Flag
True, # Extract object Flag
False) # Gray Scale Flag
input_images_model_2, all_images, data_path, data_path_with_image_name = Preprocessor.__load_image_data_thumbnails__(
data_path, cfg.compressed_image_height, cfg.compressed_image_width,
cfg.compressed_channel, cfg.number_of_categories,
cfg.number_of_images_per_category, root_dir, is_fixed_size,
model_image_size, sess, yolo_model, input_image_shape, boxes,
scores, classes, cfg.font_path, class_names, colors, output_path,
json_path, test_path, False, True, False, False)
input_images_model_3, all_images, data_path, data_path_with_image_name = Preprocessor.__load_image_data_thumbnails__(
data_path, cfg.image_height, cfg.image_width, cfg.channel,
cfg.number_of_categories, cfg.number_of_images_per_category,
root_dir, is_fixed_size, model_image_size, sess, yolo_model,
input_image_shape, boxes, scores, classes, cfg.font_path,
class_names, colors, output_path, json_path, test_path, False,
False, False, False)
input_shape = [
cfg.compressed_image_height, cfg.compressed_image_width,
cfg.compressed_channel
]
input_shape_3 = [cfg.image_height, cfg.image_width, cfg.channel]
# load (pre-trained) weights for model_1
print('-' * 30)
print('Loading model weights...\n')
weight_folder = cfg.model_1_save # the path where the model weights are stored
weight_file = 'model_1.h5'
model_1 = Preprocessor.__load_model_weights__(weight_folder,
weight_file, input_shape,
input_shape_3, "Model_1")
# load (pre-trained) weights for model_2
print('-' * 30)
print('Loading model weights...\n')
weight_folder = cfg.model_2_save # the path where the model weights are stored
weight_file = 'model_2.h5'
model_2 = Preprocessor.__load_model_weights__(weight_folder,
weight_file, input_shape,
input_shape_3, "Model_2")
# load (pre-trained) weights for model_2
print('-' * 30)
print('Loading model weights...\n')
weight_folder = cfg.model_3_save # the path where the model weights are stored
weight_file = 'model_3.h5'
model_3 = Preprocessor.__load_model_weights__(weight_folder,
weight_file, input_shape,
input_shape_3, "Model_3")
print(root_dir)
print(os.path.join(root_dir, cfg.output_model_1))
output_path_model_1 = os.path.join(root_dir + cfg.output_model_1)
output_path_model_2 = os.path.join(root_dir + cfg.output_model_2)
output_path_model_3 = os.path.join(root_dir + cfg.output_model_3)
Preprocessor.__create_output_directories__(output_path_model_1)
Preprocessor.__create_output_directories__(output_path_model_2)
Preprocessor.__create_output_directories__(output_path_model_3)
features_from_model_1 = Preprocessor.__get_score_model__(
model_1, input_images_model_1, output_path_model_1)
features_from_model_2 = Preprocessor.__get_score_model__(
model_2, input_images_model_2, output_path_model_2)
features_from_model_3 = Preprocessor.__get_score_model__(
model_3, input_images_model_3, output_path_model_3)
features_from_model_1 = Preprocessor.__flatten_img_data__(
features_from_model_1)
features_from_model_2 = Preprocessor.__flatten_img_data__(
features_from_model_2)
features_from_model_3 = Preprocessor.__flatten_img_data__(
features_from_model_3)
fused_features = np.concatenate([
features_from_model_1, features_from_model_2, features_from_model_3
],
axis=1)
fused_features = [
Preprocessor.__binarize__(features) for features in fused_features
]
counter_for_predictions = 0
sub_average_precision_make, sub_average_precision_color = [], []
sub_average_precision_body, sub_average_precision_model = [], []
cum_average_precision_make, cum_average_precision_color = [], []
cum_average_precision_body, cum_average_precision_model = [], []
precision_at_3_5_10_all = ''.join(cfg.precision_counter).split(',')
while counter_for_predictions <= 2:
test_image_idx = int(len(input_images_model_1) * random())
if test_image_idx < len(data_path_with_image_name):
idx_closest = Preprocessor.__get_closest_images__(
test_image_idx, fused_features, cfg.number_of_predictions)
test_image = Preprocessor.__get_concatenated_images__(
data_path_with_image_name, [test_image_idx],
cfg.compressed_image_width)
results_image = Preprocessor.__get_concatenated_images__(
data_path_with_image_name, idx_closest,
cfg.compressed_image_width)
source_category = str(
data_path_with_image_name[test_image_idx]).split('/')
similar_image = []
similar_idx_closest = []
for counter_for_recommendations in range(0, len(idx_closest)):
category = str(data_path_with_image_name[
idx_closest[counter_for_recommendations]]).split('/')
if str(source_category[-2]).strip() == str(
category[-2].strip()):
similar_image.append(data_path_with_image_name[
idx_closest[counter_for_recommendations]])
similar_idx_closest.append(
idx_closest[counter_for_recommendations])
print("Test Image ID:", test_image_idx)
print("\n")
print("Closest Images ID:", idx_closest)
print("\n")
print("Similar Images ID", similar_idx_closest)
print("\n")
precision_per_make, precision_per_color = [], []
precision_per_body_wise, precision_per_model_wise = [], []
results_image_recommendations = []
for i in range(0, len(precision_at_3_5_10_all)):
results_image_recommendations = Preprocessor.__get_concatenated_images__(
data_path_with_image_name, similar_idx_closest,
cfg.compressed_image_width)
list_of_similar_image_names = Preprocessor.__return_image_names__(
data_path_with_image_name, similar_idx_closest)
name_of_test_image = Preprocessor.__return_image_names__(
data_path_with_image_name, [test_image_idx])
dict_of_attributes_of_similar_images = Preprocessor.__get_attributes_list__(
list_of_similar_image_names,
os.path.join(root_dir, cfg.input_filename))
dict_of_attributes_of_test_image = Preprocessor.__get_attributes_list__(
name_of_test_image,
os.path.join(root_dir, cfg.input_filename))
similar_make_wise = Preprocessor.__get_similar__(
dict_of_attributes_of_test_image,
dict_of_attributes_of_similar_images[:int(
precision_at_3_5_10_all[i])], 'make')
similar_color_wise = Preprocessor.__get_similar__(
dict_of_attributes_of_test_image,
dict_of_attributes_of_similar_images[:int(
precision_at_3_5_10_all[i])], 'color')
similar_body_wise = Preprocessor.__get_similar__(
dict_of_attributes_of_test_image,
dict_of_attributes_of_similar_images[:int(
precision_at_3_5_10_all[i])], 'body')
similar_model_wise = Preprocessor.__get_similar__(
dict_of_attributes_of_test_image,
dict_of_attributes_of_similar_images[:int(
precision_at_3_5_10_all[i])], 'model')
precision_per_make.append(
float(
float(len(similar_make_wise)) /
int(precision_at_3_5_10_all[i])))
precision_per_color.append(
float(
float(len(similar_color_wise)) /
int(precision_at_3_5_10_all[i])))
precision_per_body_wise.append(
float(
float(len(similar_body_wise)) /
int(precision_at_3_5_10_all[i])))
precision_per_model_wise.append(
float(
float(len(similar_model_wise)) /
int(precision_at_3_5_10_all[i])))
sub_average_precision_make.append(precision_per_make)
sub_average_precision_color.append(precision_per_color)
sub_average_precision_body.append(precision_per_body_wise)
sub_average_precision_model.append(precision_per_model_wise)
imsave('test.png', test_image)
imsave('recommendations.png', results_image_recommendations)
imsave('total_results.png', results_image)
counter_for_predictions += 1
time.sleep(1)
else:
print("Index is out of bound")
cum_average_precision_make.append(
map(Preprocessor.__mean__, zip(*sub_average_precision_make)))
cum_average_precision_color.append(
map(Preprocessor.__mean__, zip(*sub_average_precision_color)))
cum_average_precision_body.append(
map(Preprocessor.__mean__, zip(*sub_average_precision_body)))
cum_average_precision_model.append(
map(Preprocessor.__mean__, zip(*sub_average_precision_model)))
print("\n \n \n")
print(
"-----------------------------------------------------------------------------------"
)
print("Average Precision Make-Wise", precision_at_3_5_10_all,
map(Preprocessor.__mean__, zip(*cum_average_precision_make)))
print("Average Precision Color-Wise", precision_at_3_5_10_all,
map(Preprocessor.__mean__, zip(*cum_average_precision_color)))
print("Average Precision Body-Wise", precision_at_3_5_10_all,
map(Preprocessor.__mean__, zip(*cum_average_precision_body)))
print("Average Precision Model-Wise", precision_at_3_5_10_all,
map(Preprocessor.__mean__, zip(*cum_average_precision_model)))
writer = csv.writer(open(os.path.join(root_dir, 'results.csv'), 'w'))
writer.writerow([
"Make-Wise: Precision at 3", "Make-Wise: Precision at 5",
"Make-Wise: Precision at 10"
])
for row in zip(*cum_average_precision_make):
writer.writerow(row)
writer.writerow('\n')
writer.writerow([
"Color-Wise: Precision at 3", "Color-Wise: Precision at 5",
"Color-Wise: Precision at 10"
])
for row in zip(*cum_average_precision_color):
writer.writerow(row)
writer.writerow('\n')
writer.writerow([
"Body-Wise: Precision at 3", "Body-Wise: Precision at 5",
"Body-Wise: Precision at 10"
])
for row in zip(*cum_average_precision_body):
writer.writerow(row)
writer.writerow('\n')
writer.writerow([
"Model-Wise: Precision at 3", "Model-Wise: Precision at 5",
"Model-Wise: Precision at 10"
])
for row in zip(*cum_average_precision_model):
writer.writerow(row)
writer.writerow('\n')
from model.nn import BasicNeuralNetwork
from preprocessing.preprocessor import Preprocessor
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.decomposition import PCA, FastICA
BASE_DIR = 'data/'
#########################
# Preprocess data
#########################
train = pd.read_csv(BASE_DIR + 'train.csv')
test = pd.read_csv(BASE_DIR + 'test.csv')
preprocessor = Preprocessor(magicFeature=True)
train_p, test_p = preprocessor.transform(train, test)
#########################
# Create models
#########################
gb = GradientBoostingRegressor(n_estimators=1000,
max_features=0.95,
learning_rate=0.005,
max_depth=4)
las = Lasso(alpha=5)
lgb = {
'objective': 'regression',
'metric': 'rmse',
'boosting': 'gbdt',
def main(argv):
"""Main entrypoint for the anonymization tool"""
# Default parameters
configuration_file = ''
input_file = ''
output_file = ''
use_cache = False
# Read and set tool parameters
try:
opts, _ = getopt.getopt(
argv, "c:i:o:vs",
["config=", "input=", "output=", "verbose", "use_chached_docs"])
except getopt.GetoptError:
logger.error(
'main.py -c <config_file> -i <input_file> -o <output_file>')
sys.exit(2)
for opt, arg in opts:
if opt in ("-c", "--config"):
configuration_file = arg
if opt in ("-i", "--input"):
input_file = arg
if opt in ("-o", "--output"):
output_file = arg
if opt in ("-s", "--use_chached_docs"):
use_cache = True
if opt in ("-v", "--verbose"):
logging.getLogger().setLevel(logging.DEBUG)
# Let's get started
logger.info("Anonymizing input file %s", input_file)
# Initialize and read configuration
configuration_reader = ConfigurationReader()
config = configuration_reader.read(configuration_file)
# Read data using data types defined in the configuration
data_reader = DataReader(config)
df = data_reader.read(input_file)
# Initialize the sensitive terms recognizer
sensitive_terms_recognizer = SensitiveTermsRecognizer(config, use_cache)
# Initialize the preprocessor (preprocessor is stateful, so pass df at the beginning)
pp = Preprocessor(sensitive_terms_recognizer, config, df)
# Run through preprocessing of dataframe: Data cleansing, analysis of textual attributes, resolving of redundant information, and compression
pp.clean_textual_attributes()
pp.analyze_textual_attributes()
pp.find_redundant_information()
pp.compress()
# Get sensitive terms dictionary and preprocessed dataframe
terms = pp.get_sensitive_terms()
df = pp.get_df()
# Initialize the anonymization kernel by providing the sensitive terms dictionary, the configuration, the sensitive terms recognizer, and the preprocessor
kernel = AnonymizationKernel(terms, config, sensitive_terms_recognizer, pp)
# Save the unanonymized dataframe for later
unanonymized_df = df.copy()
# Parameters for anonymization
k = config.parameters["k"]
strategy = config.parameters["strategy"]
biases = config.get_biases()
relational_weight = config.get_relational_weight()
# Anonymize quasi identifier (applying k-anonymity) and recode textual attributes
anonymized_df, partitions, partition_split_statistics = kernel.anonymize_quasi_identifiers(
df, k, strategy, biases, relational_weight)
anonymized_df = kernel.recode_textual_attributes(anonymized_df)
# Parameters for calculating metrics
quasi_identifiers = config.get_quasi_identifiers()
textual_attribute_mapping = pp.get_textual_attribute_mapping()
# Calculating the total, relational, and textual information loss based on the original and anonymized data frame
total_information_loss, relational_information_loss, textual_information_loss = calculate_normalized_certainty_penalty(
unanonymized_df, anonymized_df, quasi_identifiers,
textual_attribute_mapping)
# Calculating the mean and std for partition size as well as split statistics
mean_partition_size = calculate_mean_partition_size(partitions)
std_partition_size = calculate_std_partition_size(partitions)
if partition_split_statistics:
number_of_relational_splits, number_of_textual_splits = get_partition_split_share(
partition_split_statistics, textual_attribute_mapping)
# Notify about the results
logger.info("Information loss for relational attributes is %4.4f",
relational_information_loss)
if textual_information_loss:
logger.info("Information loss for textual attribute is %4.4f",
textual_information_loss["total"])
logger.info("Total information loss is %4.4f", total_information_loss)
logger.info(
"Ended up with %d partitions with a mean size of %.2f and a std of %.2f",
len(partitions), mean_partition_size, std_partition_size)
if partition_split_statistics:
logger.info("Split %d times on a relational attribute",
number_of_relational_splits)
logger.info("Split %d times on a textual attribute",
number_of_textual_splits)
# Initialize the postprocessor with the config and the preprocessor
post_processor = PostProcessor(config, pp)
# Perform post processing actions on the anonymized data frame
anonymized_df = post_processor.clean(anonymized_df)
anonymized_df = post_processor.uncompress(anonymized_df)
anonymized_df = post_processor.pretty(anonymized_df)
# Don't forget to drop the direct identifiers since they are now not needed anymore
anonymized_df = kernel.remove_direct_identifier(anonymized_df)
# Notify and save
logger.info("Saving anonymized file to %s", output_file)
anonymized_df.to_csv(output_file, index=False)
from metrics import evaluation
from utilities import cluster_cf
if __name__ == "__main__":
start_time = time.time()
np.set_printoptions(threshold=np.nan)
vectorizer = DictVectorizer()
# reset file reader
chunks = file_io.read_lastfm_user_art_file("data/halfid_20%_train.tsv")
valid_songs = [] # don't filter with valid songs
valid_songs = file_io.get_all_valid_songs('data/song_word2vec_whole_truncate_60000_new.csv')
pre = Preprocessor(chunks, vectorizer, valid_songs)
pre.reset_file_reader(chunks)
# read user song mapping
pre.read_user_songs(3000000)
# convert to user-song matrix
X = pre.get_user_song_matrix()
print("non zeros: {0}".format(X.count_nonzero()))
print("pre-processed in {0:.2f} sec".format(time.time() - start_time))
#cluster_cf.cluster_usr(X, k=5)
print("non zeros: {0}".format(X.count_nonzero()))
pred = recommendation.predict_by_user(X)
#pred = recommendation.predict_by_factorize(X)
recommended = recommendation.recommend_all(X, pred, masked=False)
print('export_path = {}\n'.format(export_path))
if os.path.isdir(export_path):
print('\nAlready saved a model, cleaning up\n')
from keras import backend
sess = backend.get_session()
tf.compat.v1.saved_model.simple_save(
sess,
export_path,
inputs = {'input_image':model.input},
outputs = {t.name:t for t in model.outputs})
# working with dataload_ecg
df_train, df_test = Preprocessor.load_mitbih("")
df_train_ecg, df_test_ecg = Preprocessor.load_ecg("")
df_train.append(df_train_ecg)
df_test.append(df_test_ecg)
X_train, y_train, X_val, y_val, X_test, y_test = Preprocessor.CNN_preprocessor(df_train, df_test, 800, 2000)
# define model
n_obs, feature, depth = X_train.shape
model = CNNModel.get_model(n_obs, feature, depth)
h_params = CNNModel.CNN_hyperparameters(n_obs)
model.compile(loss = h_params ['loss'], optimizer = h_params ['optimizer'], metrics = h_params ['metrics'])
history = model.fit(X_train, y_train,
OUTPUT_DIR = "use_out"
# dummy to make the network happy
BATCH_SIZE = None
'''
Network
'''
# file location of weights to restore from (i.e. weights/model1.ckpt)
INITIAL_WEIGHTS = 'checkpoints/cvd_model.ckpt'
'''
SCRIPT
'''
# Only run if this is the main module to be run
if __name__ == '__main__':
# build preprocessor
ppr = Preprocessor()
# Process raw data
X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(X, Y, 0.0, 1.0)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY, INPUT_DIR)
# build adapter
adapter = MACAdapter(INPUT_DIR, DIMENSION, FOLDS)
# build model
convnet = ConvNet(DIMENSION)
# build server
if not Path(args.gt_path).exists():
print(args.gt_path + " does not exist")
exit(-1)
if args.uncert_path:
if not Path(args.uncert_path).exists():
print(args.uncert_path + " does not exist")
exit(-1)
uncert_path = Path(args.uncert_path)
else:
uncert_path = None
model_path = Path(args.model_path)
options = json.load(open(Path(model_path, 'options.json'), 'r'))
# Setup Preprocessing filters
preprocessor = Preprocessor()
if 'denoise' in options:
if 'denoise_parms' in options:
preprocessor.add_filter(
filter.get_denoise_filter(options['denoise']))
else:
preprocessor.add_filter(
filter.get_denoise_filter(options['denoise'],
options['denoise_parms']))
if 'contrast' in options and options['contrast']:
clahe = cv2.createCLAHE(
clipLimit=2.0,
tileGridSize=(25, 25)) # CLAHE adaptive contrast enhancement
preprocessor.add_filter(clahe.apply)
def compare_preprocessing():
# Loading train and test data:
all_categories = [
'alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x',
'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball',
'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med',
'sci.space', 'soc.religion.christian', 'talk.politics.guns',
'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc'
]
print("Loading 20 newsgroups...")
newsgroups_train = fetch_20newsgroups(subset='train',
remove=('headers', 'footers',
'quotes'),
categories=all_categories)
newsgroups_test = fetch_20newsgroups(subset='test',
remove=('headers', 'footers',
'quotes'),
categories=all_categories)
print("{} training documents loaded.".format(
newsgroups_train.filenames.shape[0]))
print("Buidling Preprocessor combinations...")
# flags: special_character_removal, number_removal, url_email_removal, stopword_removal, lower, stemming, lemmatize
num_of_preprocessor_flags = 7
# Creates a list of all possible permutations of a boolean list with the length of number of flags
booleans = [
False, True
] # Creates a list of all possible permutations of a boolean list
flags_list = [
list(b)
for b in itertools.product(booleans, repeat=num_of_preprocessor_flags)
]
invalid_flags = []
for i in range(len(flags_list)):
if flags_list[i][5] and flags_list[i][
6]: # Removes simultaneous Stemming and Lemmatization
invalid_flags.append(flags_list[i])
elif flags_list[i][5] and not flags_list[i][
4]: # Remove Stemming without lowercase (lowercase is inbuilt)
invalid_flags.append(flags_list[i])
flags_list = [x for x in flags_list if x not in invalid_flags]
print("{} Combinations built.".format(len(flags_list)))
# Initialize vectorizer, machine learning algorithm and data frame to store the results
vectorizer = TfidfVectorizer(analyzer="word",
tokenizer=dummy,
lowercase=False,
preprocessor=dummy,
stop_words=None)
clf = MultinomialNB(alpha=.01)
columns = [
'Special Character Removal', 'Number Removal',
'URL and E-Mail Removal', 'Stopword Removal', 'Lowercase', 'Stemming',
'Lemmatization', 'Unique Words', 'Accuracy'
]
rows = []
for flags in flags_list: # loops through all combinations
prep = Preprocessor(special_character_removal=flags[0],
number_removal=flags[1],
url_email_removal=flags[2],
stopword_removal=flags[3],
lower=flags[4],
stemming=flags[5],
lemmatize=flags[6])
preprocessed_train_data = [
prep.preprocess(d) for d in newsgroups_train.data
]
preprocessed_test_data = [
prep.preprocess(d) for d in newsgroups_test.data
]
vectors = vectorizer.fit_transform(preprocessed_train_data)
# Train machine learning model
clf.fit(vectors, newsgroups_train.target)
# Transform test data to the model fitted to the training data
vectors_test = vectorizer.transform(preprocessed_test_data)
# Evaluate
pred = clf.predict(vectors_test)
vocab = vectors.shape[1]
accuracy = metrics.accuracy_score(newsgroups_test.target, pred)
rows.append([
flags[0], flags[1], flags[2], flags[3], flags[4], flags[5],
flags[6], vocab, accuracy
])
print(
"Spec: {} , Numbers: {} , EmailUrl: {} , SWR: {}, low: {}, Stem: {} , Lem: {} -> Vocab: {}, Acc: {}"
.format(flags[0], flags[1], flags[2], flags[3], flags[4], flags[5],
flags[6], vocab, accuracy))
# Organize data frame and save the results
df = pd.DataFrame(np.array(rows), columns=columns)
df = df.sort_values(by=['Accuracy'], ascending=False)
pprint(df)
df.to_csv('results.csv', sep=';')
if __name__ == "__main__":
csrc_path = argv[0]
with open(csrc_path) as fl:
avr_code = gcc.compile(fl.read())
with open("lib/avrheader.sap") as fl:
avrheader = fl.read()
translator = Translator(avrheader)
sap = translator.to_sap(avr_code)
with open("build/build.sap.superset", "w") as fl:
fl.write(sap)
proc = Preprocessor()
proc.load_extension("ext/sapplus.json")
sap = proc.preprocess(sap)
with open("build/build.sap", "w") as fl:
fl.write(sap)
out = assemble(sap)
if out.success:
pass
else:
print("SAP output did not compile successfully")
# The location in which to save the model
SAVE_NAME = "example_training.ckpt"
'''
SCRIPT
'''
# Only run if this is the main module to be run
if __name__ == '__main__':
# JSON object returned from api_call
# replace this with however you would like it to work in production
json_data = json.load(open(EXAMPLE_FILE))
# NOTE if events in json object have neither "aircraft" nor "community" fields
# in they will be labeled as community for training - probably try to avoid this
# build preprocessor
ppr = Preprocessor()
# Process raw data
#X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y, events_found = ppr.get_from_json(DIMENSION, json_data)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
# Shove all events into the "training" subdirectory
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(X, Y, 1.0, 0.0)
# Store events in intermediate directory (will be deleted on subsequent trainings)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY, INPUT_DIR)
# build adapter
adapter = MACAdapter(INPUT_DIR, DIMENSION, FOLDS)
# build model
"""
Demo file for usage of the Preprocessor class
Initialize the object with the required parameters, then
call the process_images() method
"""
import sys, os
lib_path = os.path.abspath(os.path.join(__file__, '../..'))
sys.path.append(lib_path)
from preprocessing.preprocessor import Preprocessor
# inputs
size = 299
data_dir = './data/'
annotations_dir = './annotations/'
dest_dir = './data_preprocessed_{}/'.format(size)
train_annotations = '{}train2017.json'.format(annotations_dir)
val_annotations = '{}val2017.json'.format(annotations_dir)
preprocessor_train = Preprocessor(data_dir, train_annotations, (size, size))
preprocessor_train.process_images(dest_dir)
preprocessor_val = Preprocessor(data_dir, val_annotations, (size, size))
preprocessor_val.process_images(dest_dir)
print("Preprocessing in", dest_dir, "completed.")
def __run_training__():
cfg = Configuration()
# These variable would be parametrized
GPU = True
if GPU != True:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Input Path
root_dir = os.path.dirname(os.path.abspath(__file__))
image_path = cfg.image_path
json_path = os.path.join(root_dir, cfg.input_filename)
trainingset = os.path.join(root_dir, 'trainingset')
Preprocessor.__generate_kijiji_set__(root_dir, image_path, json_path,
trainingset, 'make')
# --------------------------------------------------------------------------------------------------------------
image_path = os.path.join(root_dir, 'trainingset')
data_path = glob(image_path + "/*")
# Image Segmentation Parameters
model_path = os.path.expanduser(cfg.model_path)
assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.'
anchors_path = os.path.expanduser(cfg.anchors_path)
classes_path = os.path.expanduser(cfg.classes_path)
test_path = os.path.expanduser(cfg.test_path)
output_path = os.path.expanduser(cfg.segmented_output_path)
json_path = os.path.expanduser(cfg.json_output)
if not os.path.exists(output_path):
print('Creating output path {}'.format(output_path))
os.mkdir(output_path)
sess = K.get_session()
class_names = Preprocessor.__return_class_names__(classes_path)
anchors = Preprocessor.__return_anchors__(anchors_path)
yolo_model = load_model(model_path)
# Verify model, anchors, and classes are compatible
num_classes = len(class_names)
num_anchors = len(anchors)
info = 'Mismatch between model and given anchor and class sizes. ' \
'Specify matching anchors and classes with --anchors_path and --classes_path flags.'
model_output_channels = yolo_model.layers[-1].output_shape[-1]
assert model_output_channels == num_anchors * (num_classes + 5), info
print('{} model, anchors, and classes loaded.'.format(model_path))
# Check if model is fully convolutional, assuming channel last order.
model_image_size = yolo_model.layers[0].input_shape[1:3]
is_fixed_size = model_image_size != (None, None)
# Generate Colors for drawing bounding boxes
hsv_tuples, colors = Preprocessor.__generate_colors_for_bounding_boxes__(
class_names)
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=cfg.score_threshold,
iou_threshold=cfg.iou_threshold)
# Load Images from the root folder
input_images_model_1, all_images, data_path, data_path_with_image_name = Preprocessor.__load_image_data_thumbnails__(
data_path,
cfg.compressed_image_height,
cfg.compressed_image_width,
cfg.compressed_channel,
cfg.number_of_categories,
cfg.number_of_images_per_category,
root_dir,
is_fixed_size,
model_image_size,
sess,
yolo_model,
input_image_shape,
boxes,
scores,
classes,
cfg.font_path,
class_names,
colors,
output_path,
json_path,
test_path,
True, # Segmentation Flag
False, # Edge-detection Flag
True, # Extract object Flag
False) # Gray Scale Flag
input_images_model_2, all_images, data_path, data_path_with_image_name = Preprocessor.__load_image_data_thumbnails__(
data_path,
cfg.compressed_image_height,
cfg.compressed_image_width,
cfg.compressed_channel,
cfg.number_of_categories,
cfg.number_of_images_per_category,
root_dir,
is_fixed_size,
model_image_size,
sess,
yolo_model,
input_image_shape,
boxes,
scores,
classes,
cfg.font_path,
class_names,
colors,
output_path,
json_path,
test_path,
False, # Segmentation Flag
True, # Edge-detection Flag
False, # Extract object Flag
False) # Gray Scale Flag
input_images_model_3, all_images, data_path, data_path_with_image_name = Preprocessor.__load_image_data_thumbnails__(
data_path,
cfg.image_height,
cfg.image_width,
cfg.channel,
cfg.number_of_categories,
cfg.number_of_images_per_category,
root_dir,
is_fixed_size,
model_image_size,
sess,
yolo_model,
input_image_shape,
boxes,
scores,
classes,
cfg.font_path,
class_names,
colors,
output_path,
json_path,
test_path,
False, # Segmentation Flag
False, # Edge-detection Flag
False, # Extract object Flag
False) # Gray Scale Flag
input_shape = [
cfg.compressed_image_height, cfg.compressed_image_width,
cfg.compressed_channel
]
input_shape_3 = [cfg.image_height, cfg.image_width, cfg.channel]
# Model Save Paths
model_1_save_path = os.path.join(root_dir + cfg.model_1_save)
model_2_save_path = os.path.join(root_dir + cfg.model_2_save)
model_3_save_path = os.path.join(root_dir + cfg.model_3_save)
Preprocessor.__create_output_directories__(model_1_save_path)
Preprocessor.__create_output_directories__(model_2_save_path)
Preprocessor.__create_output_directories__(model_3_save_path)
# Instantiating the training class
train = Train(input_images_model_1, input_images_model_2,
input_images_model_3, input_shape, input_shape_3,
cfg.batch_size, cfg.epochs, model_1_save_path,
model_2_save_path, model_3_save_path)
# Output Path
output_path_model_1 = os.path.join(root_dir + cfg.output_model_1)
output_path_model_2 = os.path.join(root_dir + cfg.output_model_2)
output_path_model_3 = os.path.join(root_dir + cfg.output_model_3)
Preprocessor.__create_output_directories__(output_path_model_1)
Preprocessor.__create_output_directories__(output_path_model_2)
Preprocessor.__create_output_directories__(output_path_model_3)
# FCN Model
model_1 = train.__train_model_1__()
# VGG Model
model_2 = train.__train_model_2__()
# Inception-v3
model_3 = train.__train_model_3__()
features_from_model_1 = Preprocessor.__get_score_model__(
model_1, input_images_model_1, output_path_model_1)
features_from_model_2 = Preprocessor.__get_score_model__(
model_2, input_images_model_2, output_path_model_2)
features_from_model_3 = Preprocessor.__get_score_model__(
model_3, input_images_model_3, output_path_model_3)
print("Output FeatureMap For Model 1 \n")
print(features_from_model_1.shape)
print("\n")
print("Output FeatureMap For Model 2 \n")
print(features_from_model_2.shape)
print("\n")
print("Output FeatureMap For Model 3 \n")
print(features_from_model_3.shape)
print("\n")
