def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = data["steering_angle"]
# The current throttle of the car
throttle = data["throttle"]
# The current speed of the car
speed = data["speed"]
# The current image from the center camera of the car
imgString = data["image"]
image = Image.open(BytesIO(base64.b64decode(imgString)))
image_array = np.asarray(image)
image_array = functions.preprocess(image_array)
image = np.array([image_array])
steering_angle = float(
model.predict(image_array[None, :, :, :], batch_size=1))
throttle = controller.update(float(speed))
print(steering_angle, throttle)
send_control(steering_angle, throttle)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
# NOTE: DON'T EDIT THIS.
sio.emit('manual', data={}, skip_sid=True)
def main():
# Load training data from csv
filename = 'train_data.csv'
raw_data = np.loadtxt(filename, dtype=np.str, delimiter=",")
image_paths = raw_data[:, 0]
labels = raw_data[:, 1]
# One-hot encode labels
lb = preprocessing.LabelBinarizer()
one_hot_labels = lb.fit_transform(labels)
# Save transformation matrix
functions.save_object(lb, 'one-hot-matrix.pkl')
# Load training images from directory
images = []
for path in image_paths:
images.append(imread(path))
images = np.array(images)
# pre-process images
images = np.array([functions.preprocess(img) for img in images])
# Split data into training and validation sets
x_train, x_valid, y_train, y_valid = train_test_split(images, one_hot_labels, test_size=VALIDATION_SPLIT)
# Augment training set with rotated and flipped images
x_train, y_train = functions.augment_dataset(x_train, y_train)
def preprocessing_test(mat):
"""
Checks if a matrix matches its original version after being preprocessed and reverse_preprocessed.
"""
means, stds, maxes, temp = f.preprocess(mat)
temp2 = f.reverse_preprocess(means, stds, maxes, temp)
diff = np.round(mat - temp2, 10)
return np.all(diff == 0.)
def main():
positive_set = 'test_extractions/bc_samples.txt' #'test_extractions/test-neural-hash-samples.txt'
negative_set = 'test_extractions/bc_grounds.txt' #'test_extractions/test-neural-hash-ground.txt'
analogy_list = functions.get_list_re(positive_set)
non_analogy_list = functions.get_list_re(negative_set)
samples = [(text, 'YES') for text in analogy_list] + [(text, 'NO') for text in non_analogy_list]
train_data, train_labels, test_data, test_labels = functions.preprocess(samples, 0.5)
pipeline = []
classifiers = ['svc', 'linearsvc', 'nusvc', 'naive', 'maxEnt', 'neural']
classifiers2 = ['neural']
representations = ['tfidf', 'count', 'hash']
representations2 = ['hash']
for classifier in classifiers:
for representation in representations:
pipeline = (Pipeline([(representation, helpers.get_function(representation)),
(classifier, helpers.get_function(classifier)),]))
parameters = helpers.generate_parameters(representation, classifier)
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, error_score=-1)
print("Performing grid search...")
print("pipeline:", [name for name, _ in pipeline.steps])
print("parameters:")
pprint(parameters)
t0 = time()
grid_search.fit(train_data, train_labels)
print("done in %0.3fs" % (time() - t0))
print()
print("Best score: %0.3f" % grid_search.best_score_)
print("Best parameters set:")
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print("\t%s: %r" % (param_name, best_parameters[param_name]))
print()
print("Getting the confusion matrix for the best estimator:")
prediction = grid_search.best_estimator_.predict(test_data)
matrix = confusion_matrix(test_labels, prediction, labels = ['YES', 'NO'])
precision, recall, f_measure = functions.fmeasure(matrix)
accuracy = accuracy_from_matrix(matrix)
print("Accuracy ", accuracy)
print("Precision, recall, f-score:")
print(precision, recall, f_measure)
print(matrix)
print()
def analogy_pipeline(positive_set,
negative_set,
percent_test,
representation,
classifier,
seed,
extra={"sub_class": ""},
timer=1000000000):
start = time.time()
# Read in the set of positive examples
analogy_list = functions.get_list_re(positive_set)
# Read in the set of negative examples
non_analogy_list = functions.get_list_re(negative_set)
# Randomly divide them into a training set and a test set
nan_set = functions.read_CSV('corpora/dmb_open_test.csv', 1)
samples = [(text, 'YES') for text in analogy_list] + [
(text, 'NO') for text in non_analogy_list
] + [(txt, 'NO') for txt in nan_set]
bt_parsed = functions.readCSV('base_target.csv', 1)
extra = functions.set_extra(extra)
num_samples = min(len(analogy_list), len(non_analogy_list))
# Run classifier, generate results based on the value passed in for representation
beginTimer = time.time()
train_data, train_labels, test_data, test_labels = functions.preprocess(
bt_parsed, percent_test, seed, num_samples,
'test_main_interface_output')
# Make sure the classifier runs within a set time
seed = (seed - 1000) / 30
dic = {'data': []}
for dat in test_data:
dic['data'].append(dat)
pd.DataFrame(dic, columns=['data']).to_csv('./testing/test_set' +
str(int(seed)) + '.csv')
# train_data = functions.strip_id(train_data)
# test_data = functions.strip_id(test_data)
score, matrix, precision, recall, f_measure = functions.classify_pipeline(
train_data, train_labels, test_data, test_labels, classifier,
representation, seed, extra, timer)
print(score)
print(matrix)
print(precision, recall, f_measure)
return score, f_measure
def main():
# Load one-hot-encoding matrix
labeler = functions.load_object('one-hot-matrix.pkl')
# Load the trained model
model = load_model('model.h5')
# Define the webcam object
cam = cv2.VideoCapture(0)
while (True):
# Capture video frame
ret, frame = cam.read()
# Preprocess frame
image = functions.preprocess(frame)
# Predict object in frame
logits = model.predict(image, batch_size=1)
# Decode logits
result = labeler.inverse_transform(logits)
# Check the environment
warnings.simplefilter('ignore')
np.random.seed(0)
if six.PY3:
tff.framework.set_default_executor(tff.framework.create_local_executor())
tff.federated_computation(
lambda: 'The tensorflow federated environment is correctly setup!')()
# Load the data
emnist_train, emnist_test = load_data(path)
# Generate sample batch
example_dataset = emnist_train.create_tf_dataset_for_client(
emnist_train.client_ids[1])
example_element = iter(example_dataset).next()
preprocessed_example_dataset = preprocess(example_dataset, NUM_EPOCHS,
SHUFFLE_BUFFER, BATCH_SIZE)
sample_batch = tf.nest.map_structure(lambda x: x.numpy(),
iter(preprocessed_example_dataset).next())
# Create federated data for each client
sample_clients = emnist_train.client_ids[0:NUM_CLIENTS]
federated_train_data = make_federated_data(emnist_train, sample_clients,
NUM_EPOCHS, SHUFFLE_BUFFER,
BATCH_SIZE)
# Function to create tff,learning instances
def model_fn():
keras_model = create_compiled_keras_model()
return tff.learning.from_compiled_keras_model(keras_model, sample_batch)
def extract_patches(self, h5db, new_folder):
print 'OpenSlide needed to extract patches.'
return None
'''
for centre in self.centres:
print('[cnn][patch_extraction] Selected Centre: ', centre)
# each centre may have more than one annotation XML file, so here we retrieve
# a list of all the XMLs related to the current centre
annotation_list = np.sort(self.get_annotation_list(centre, self.xml_source_fld))
# for each XML file in the annotation list
# we want to extract tumor and normal patches
for xml_file in annotation_list:
files_counter +=1 # variable to shape the final data vector
'''
print('[debug] ', self.name)
print('[debug] ', self.settings)
self.set_files_counter(self.count_annotation_files())
print('[dataset] {0} [extract_patches] {1} total annotation files.'.
format(self.name, self.files_counter))
for centre in self.centres:
annotation_list = self.get_annotation_list(centre)
for xml_file in annotation_list:
slide_path = self.get_wsi_path(centre, xml_file)
xml_path = os.path.join(self.xml_source_fld, xml_file)
# retrieving the information about the file analysed.
# info is a dictionary with the following keys:
# info['centre'], current centre number
# info['patient'], current patient number
# info['node'], current WSI node
info = self.get_info(xml_path, centre)
#functions.setDBHierarchy(h5db, self.settings,info)
if info['patient'] == '008_Mask.tif':
continue
if xml_path != None: ## add check slide is open and ok
# preprocess takes the WSI path, and the slide_level and returns the
# the WSI openslide obj, the tumor annotation mask, the WSI image
# and the tumor contours
if self.name == 'camelyon16':
print('import openslides')
#slide = openslide.OpenSlide(slide_path)
#rgb_im = np.array(slide.read_region((0,0),7,slide.level_dimensions[7]))
#mask_file = xml_path+'Tumor_{}_Mask.tif'.format(info['patient'])
#import pdb; pdb.set_trace()
annotations = np.asarray(
openslide.OpenSlide(xml_path).read_region(
(0, 0), 7, slide.level_dimensions[7]))
annotations_mask = annotations[:, :, 0]
#import pdb; pdb.set_trace()
im_contour = rgb_im
else:
import pdb
pdb.set_trace()
slide, annotations_mask, rgb_im, im_contour = functions.preprocess(
slide_path,
xml_path,
slide_level=self.settings['slide_level'])
tum_patch_list, tum_patch_point = integral.patch_sampling_using_integral(
slide, self.settings['slide_level'], annotations_mask,
self.settings['patch_size'],
self.settings['n_samples'])
# conversion of the lists to np arrays
tum_patch_array = np.asarray(tum_patch_list)
#import pdb; pdb.set_trace()
tum_locations = np.array(tum_patch_point)
# storage in the HDF5 db
self.store(h5db, info, tum_patch_array, tum_locations,
'tumor')
# reverting the tumor mask to find normal tissue and extract patches
# Note :
# normal_mask = tissu mask(morp_im) - tummor mask(annotations_mask)
##### restart from here ##
morp_im = functions.get_morp_im(rgb_im)
normal_im = morp_im - annotations_mask ## np.min(normal_im) := -1.0
normal_im = normal_im == 1.0
normal_im = (normal_im).astype(int)
# sampling normal patches with uniform distribution
nor_patch_list, nor_patch_point = integral.patch_sampling_using_integral(
slide, self.settings['slide_level'], normal_im,
self.settings['patch_size'],
self.settings['n_samples'])
nor_patch_array = np.asarray(nor_patch_list)
normal_patches_locations = np.array(nor_patch_point)
# storing the normal patches and their locations
self.store(h5db, info, nor_patch_array, nor_patch_point,
'normal')
''' Visualisation '''
# plotting the tumor locations in the XML file
# Drawing the normal patches sampling points
# tumor_locations.png shows the tumor patches locations in red
# and the normal patches locations in green
tumor_locations_im = rgb_im
plt.figure()
plt.imshow(tumor_locations_im)
for p_x, p_y in normal_patches_locations:
plt.scatter(p_y, p_x, c='g')
#cv2.circle(tumor_locations_im,(p_y,p_x),30,(0,255,0),10)
for p_x, p_y in tum_locations:
plt.scatter(p_y, p_x, c='r')
#cv2.circle(tumor_locations_im,(p_y,p_x),30,(255,0,0), 10)
print(
'[cnn][patch_extraction] Saving tumor locations image')
plt.savefig(
os.path.join(
new_folder,
'level{}_centre{}_patient{}_node{}_tumor_locations.png'
.format(self.settings['slide_level'],
info['centre'], info['patient'],
info['node'])))
plt.close()
#print('Saving tumor locations image')
#plt.savefig('tumor_locations_patient0{}_node{}'.format(info['patient'], info['node']))
print(
'[cnn][patch_extraction] Saving annotation mask and normal tissue mask'
)
plt.figure()
plt.imshow(annotations_mask)
plt.savefig(
os.path.join(
new_folder,
'level{}_centre{}_patient{}_node{}_annotation_mask.png'
.format(self.settings['slide_level'],
info['centre'], info['patient'],
info['node'])))
plt.close()
plt.figure()
plt.imshow(normal_im)
plt.savefig(
os.path.join(
new_folder,
'level{}_centre{}_patient{}_node{}_normal_tissue_mask.png'
.format(self.settings['slide_level'],
info['centre'], info['patient'],
info['node'])))
plt.close()
plt.close('all')
self.tum_counter += len(tum_patch_array)
self.nor_counter += len(nor_patch_array)
#self.nor_counter = 0
return
def analogy_trial(positive_set,
negative_set,
percent_test,
representation,
classifier,
extra={"sub_class": ""},
timer=1000000000,
comment=""):
caller = inspect.stack()[1][3]
start = time.time()
# Read in the set of positive examples
analogy_list = functions.get_list_re(positive_set)
# Read in the set of negative examples
non_analogy_list = functions.get_list_re(negative_set)
# Randomly divide them into a training set and a test set
samples = [(text, 'YES')
for text in analogy_list] + [(text, 'NO')
for text in non_analogy_list]
extra = functions.set_extra(extra)
# Run classifier, generate results based on the value passed in for representation
beginTimer = time.time()
now = time.strftime("%c")
currentTime = now
now = now.replace(" ", "_")
now = now.replace(":", "")
train_data, train_labels, test_data, test_labels = functions.preprocess(
samples, percent_test, caller)
# Make sure the classifier runs within a set time
try:
score, matrix, precision, recall, f_measure = functions.classify(
train_data, train_labels, test_data, test_labels, classifier,
representation, extra, timer)
# catch the timeout error
except timeout.TimeoutError:
print("Classifier timeout.")
print("Output error in log.")
algoTime = time.time() - beginTimer
runTime = time.time() - start
outputData = [
currentTime, positive_set, negative_set, percent_test,
representation, classifier, extra, "", "", "", "", "", "", "",
"Algorithm Timeout"
]
else:
algoTime = time.time() - beginTimer
runTime = time.time() - start
outputData = [
currentTime, positive_set, negative_set, percent_test,
representation, classifier, extra, score, matrix, precision,
recall, f_measure, runTime, algoTime, comment
]
# Store results
outputResults(outputData)
if caller != "test_main_interface_output":
print("Successfully logged trial results")
outputData = outputData[7:-3]
outputData[1] = outputData[1].tolist()
return outputData
def extract_patches(self):
"""
(more doc please)
"""
errors = 0
warnings = 0
settings = self.config['settings']
for centre in self.centres:
for patient in self.get_patients(centre):
self.logger.info('processing patient: {}'.format(patient))
slide_path = self.get_wsi_path(centre, patient)
xml_path = self.get_annotation_path(centre, patient)
info = self.get_info(centre, patient)
pat_res_dir = self.make_patient_dir(info)
if not pat_res_dir:
self.logger.error(
"patient {}: problems with results dir...".format(
patient))
errors += 1
continue
h5db_path = os.path.join(pat_res_dir, self.h5db_bname + '.h5')
try:
h5db = hd.File(h5db_path, 'w')
except Exception as e:
self.logger.error(
"patient {}: can't open my H5 DB '{}': {} ".format(
patient, h5db_path, e))
errors += 1
continue
slide, annotations_mask, rgb_im, im_contour = preprocess(
slide_path, xml_path, slide_level=settings['slide_level'])
# reverting the tumor mask to find normal tissue and extract patches
# Note :
# normal_mask = tissu mask(morp_im) - tummor mask(annotations_mask)
morp_im = get_morp_im(rgb_im)
normal_im = morp_im - annotations_mask # np.min(normal_im) := -1.0
normal_im = normal_im == 1.0
normal_im = (normal_im).astype(int)
# masks are the same for any sample batch ;-)
# [TO-DO] make switchable from config/CL
plt.figure()
plt.imshow(annotations_mask)
img_file = self.get_image_fname(pat_res_dir, 'annotation_mask',
info)
plt.savefig(img_file)
plt.close()
self.logger.info(
'patient {}: Annotation mask image saved to: {}'.format(
patient, img_file))
plt.figure()
plt.imshow(normal_im)
img_file = self.get_image_fname(pat_res_dir,
'normal_tissue_mask', info)
plt.savefig(img_file)
plt.close()
self.logger.info(
'patient {}: Normal tissue mask image saved to: {}'.format(
patient, img_file))
opts = dict(
map(lambda k: (k, settings[k]), (
'area_overlap',
'bad_batch_size',
'gray_threshold',
'margin_width_x',
'margin_width_y',
'method',
'n_samples',
'patch_size',
'slide_level',
'white_level',
'white_threshold',
'white_threshold_incr',
'white_threshold_max',
)))
# batch sample & store -- keep it small to avoid OOM! In
# "linear" sampling mode, more batches might be needed, so go
# for a run and get the extracted pathes and the last
# index. Loop until no patches come out
# [TO-DO] store info in _per-patient_ H5 DB
# a patient case (:= slide) the tumor annotation mask is
# usually (much) smaller than the normal tissue mask, thus a
# different number of batches is needed to extract all the
# tumor and normal patches. So we compute then normal tissue
# mask once. Apart from that, there's no relation between
# tumor and normal patches, hence we batch-loop two times: a
# first time for the tumor case and a second time for the
# normal case. N.B. In 'random' sampling mode, just one batch
# is ever done.
index = 0 # ignored in 'random' mode -- only one batch done
tum_patch_point = []
bcnt_t, bcnt_n = 0, 0
last_idx_t = last_idx_n = -1
if settings['window']:
self.logger.info(
"patient {}: restricting nonzero points range to {}%, {}%"
.format(patient, settings['window'][0],
settings['window'][1]))
nzx_n, nzy_n = integral.nonzero_range(normal_im,
settings['window'])
# *** Warning! *** Split loops doesn't work if we want to show
# images: there's data dependency on "normal_patches_locations".
# normal tissue
while (True):
self.logger.info(
"patient {}: >>> [normal] starting batch {}".format(
patient, bcnt_n))
opts['start_idx'] = last_idx_n + 1
nor_patch_list, nor_patch_point, last_idx_n = integral.patch_sampling(
slide, normal_im, nzx_n, nzy_n, **opts)
if nor_patch_point and nor_patch_list:
nor_patch_array = np.asarray(nor_patch_list)
normal_patches_locations = np.array(nor_patch_point)
self.store_patient(info, nor_patch_array,
nor_patch_point, 'normal', h5db,
bcnt_n)
else:
self.logger.info(
'patient {}: batch {}: no (more) normal patches'.
format(patient, bcnt_n))
break
self.nor_counter += len(nor_patch_array)
self.logger.info(
"patient {}: <<< [normal] done batch {}".format(
patient, bcnt_n))
if last_idx_n == None:
# in 'random' method, this tells us that we're done sampling
break
bcnt_n += 1
# {end-while}
# TO-DO: batch runs should be better encapsulated (aux fun/method)...
# tumors masks are usually too small for windowed sampling, so
# take the full range
nzx_t, nzy_t = integral.nonzero_range(annotations_mask, [])
while (True):
self.logger.info(
"patient {}: >>> [tumor] starting batch {}".format(
patient, bcnt_t))
opts['start_idx'] = last_idx_t + 1
tum_patch_list, tum_patch_point, last_idx_t = integral.patch_sampling(
slide, annotations_mask, nzx_t, nzy_t, **opts)
if tum_patch_list and tum_patch_point:
tum_patch_array = np.asarray(tum_patch_list)
tum_locations = np.array(tum_patch_point)
self.store_patient(info, tum_patch_array,
tum_locations, 'tumor', h5db,
bcnt_t)
else:
self.logger.info(
'patient {}: batch {}: no (more) tumor patches'.
format(patient, bcnt_t))
break
if opts['method'] == 'random':
if bcnt_n != bcnt_t:
self.logger.error(
"[BUG] Can't make scatter image(s): batch count mismatch"
)
errors += 1
else:
# plotting the tumor locations in the XML file Drawing the
# normal patches sampling points tumor_locations.png shows the
# tumor patches locations in red and the normal patches
# locations in green
tumor_locations_im = rgb_im
plt.figure()
plt.imshow(tumor_locations_im)
# Warning! Data dependency on previous normal batch run
for p_x, p_y in normal_patches_locations:
plt.scatter(p_y, p_x, c='g')
for p_x, p_y in tum_locations:
plt.scatter(p_y, p_x, c='r')
img_file = self.get_image_fname(
pat_res_dir, 'tumor_locations', info, bcnt_t)
plt.savefig(img_file)
plt.close()
self.logger.info(
'patient {}: batch {}: tumor locations image saved to: {}'
.format(patient, bcnt_t, img_file))
self.tum_counter += len(tum_patch_array)
self.logger.info(
"patient {}: <<< [tumor] done batch {}".format(
patient, bcnt_t))
if last_idx_t == None:
# in 'random' method, this tells us that we're done sampling
break
bcnt_t += 1
# {end-while}
h5db.close()
self.logger.info(
"patient {}: processed in {} (normal) + {} (tumor) batches"
.format(patient, bcnt_n, bcnt_t))
self.logger.info("patient {}: data saved to H5 DB: {}".format(
patient, h5db_path))
# {end-for-patient}
# {end-for-centre}
self.report['errors'] = errors
self.report['warnings'] = warnings
else:
logging.basicConfig(level=logging.INFO)
logging.info(
f"using {m}.{e} model to calculate submitid {i}") if v else None
# load word embedding model
start = datetime.now()
vectors = load_model(m, e)
logging.info(f"model loaded in {datetime.now() - start}") if v else None
# get source code and problem text from database that corresponds with input submit ID
code, problem = get(i)
# preprocessing includes normalization and tokenization
logging.info("preprocessing code and problem text...") if v else None
problem_processed, comments_processed, code_only = preprocess(
problem, code)
# count words in code comment
comment_word_count_raw = 0
for line in comments_processed:
comment_word_count_raw += len(line)
logging.info("preprocessing finished") if v else None
# calculate code density
logging.info("calculating code density...") if v else None
comment_line_density, comment_char_density = calculate_density(
comments_processed, code_only)
logging.info("finished calculating") if v else None
# calculate code header score
logging.info("calculating header score...") if v else None
header_score = calculate_header_score(comments_processed)
def main():
positive_set = '../latest_analogy/test_extractions/bc_samples.txt' #'test_extractions/test-neural-hash-samples.txt'
negative_set = '../latest_analogy/test_extractions/bc_grounds.txt' #'test_extractions/test-neural-hash-ground.txt'
analogy_list = functions.get_list_re(positive_set)
non_analogy_list = functions.get_list_re(negative_set)
samples = [(text, 1)
for text in analogy_list] + [(text, 0)
for text in non_analogy_list]
train_data, train_labels, test_data, test_labels = functions.preprocess(
samples, 0.15)
overlap_input = [('LP', 'count'), ('TSVM', 'tfidf')]
rng = np.random.RandomState(42)
random_unlabeled_points = rng.rand(len(train_labels)) < 0.7
train_labels = np.array(train_labels)
test_labels = np.array(test_labels)
train_labels[random_unlabeled_points] = -1
train_data = np.array(train_data)
prediction_second_input = []
pipeline = []
no_as_yes = [] # predictions with label NO classified with label YES
yes_as_no = [] # predictions with label YES classified with label NO
count = 0
for element in overlap_input:
pipeline = helpers.get_function(element[0])
representation = helpers.get_function(element[1])
parameters = helpers.get_parameters(element[0])
train_set = representation.fit_transform(train_data).toarray()
test_set = representation.transform(test_data).toarray()
grid_search = GridSearchCV(pipeline,
parameters,
n_jobs=-1,
verbose=10,
error_score=-1)
grid_search.fit(train_set, train_labels)
if count == 0:
prediction = grid_search.best_estimator_.predict(test_set)
matrix = confusion_matrix(test_labels, prediction, labels=[1, 0])
else:
prediction_second_input = grid_search.best_estimator_.predict(
test_set)
matrix = confusion_matrix(test_labels,
prediction_second_input,
labels=[1, 0])
count += 1
print(matrix)
for i in range(len(test_labels)):
#print(test_labels[i], prediction[i], prediction_second_input[i])
if (test_labels[i] != prediction[i]) and (
prediction[i] == prediction_second_input[i]):
if test_labels[i] == 0:
no_as_yes.append(test_data[i])
else:
yes_as_no.append(test_data[i])
print("Overlapping NO as YES:")
l1 = len(no_as_yes)
print("Number: ", l1)
for i in range(l1):
print(no_as_yes[i])
print("Overlapping YES as NO:")
l2 = len(yes_as_no)
print("Number: ", l2)
for i in range(l2):
print(yes_as_no[i])
from scikitTSVM import SKTSVM
import warnings
warnings.filterwarnings("ignore", category=PendingDeprecationWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning)
tsvm = SKTSVM(probability=False, C=0.01, gamma=1.0, kernel='linear', lamU=1.0)
percent_test = 0.15
positive_set = 'data/bc_samples.txt'
negative_set = 'data/bc_grounds.txt'
unlabeled_set = 'data/unlabeled-data.csv'
analogy_list = functions.get_list_re(positive_set)
non_analogy_list = functions.get_list_re(negative_set)
unlabeled_list = functions.get_list_re(unlabeled_set)
samples = [(text, 1) for text in analogy_list] + [(text, 0)
for text in non_analogy_list]
train_data, train_labels, test_data, test_labels = functions.preprocess(
samples, percent_test)
j = 0
for sample in unlabeled_list:
if j <= 20000:
train_data.append(sample)
train_labels.append(-1)
j += 1
train_labels = np.array(train_labels)
test_labels = np.array(test_labels)
train_data = np.array(train_data)
TfidfVect = TfidfVectorizer(tokenizer=lambda doc: doc, lowercase=False)
train_set = TfidfVect.fit_transform(train_data).toarray()
test_set = TfidfVect.transform(test_data).toarray()
# Label Propagation
"""
# Couple of ways included to create the pandas dataframe, one from sqlite, one via path, and finally one via github
# Sqlite option
# # songs_df = pd.read_sql_table('songs', 'sqlite:///db.sqlite3')
# path option
# songs_df = pd.read_csv('../Data/SpotifyAudioFeaturesApril2019_duplicates_removed.csv')
# github option
infile = "https://raw.githubusercontent.com/spotify-recommendation-engine-3/data_science/master/Data/SpotifyAudioFeaturesApril2019_duplicates_removed.csv"
songs_df = pd.read_csv(infile)
y = songs_df[songs_df.columns[:3]]
X = songs_df[songs_df.columns[3:]]
my_model = create_model(preprocess(X))
@app.route('/', methods=['GET', 'POST'])
def plot_png():
fig = create_figure()
output = io.BytesIO()
FigureCanvas(fig).print_png(output)
return Response(output.getvalue(), mimetype='image/png')
def create_figure():
song_df = songs_df.sample()
song_df = song_df.iloc[:, 3:]
songs_to_plot = suggest_songs(song_df, songs_df, y, my_model)
fig = Figure(figsize=(9, 9), edgecolor='gray')
