def run():
# Crop out facial images and retrieve features. Ideally, one only need to call this function once.
prepare_data.run()
# Make prediction by using different features
for facial_image_extension, feature_extension in itertools.product(\
prepare_data.FACIAL_IMAGE_EXTENSION_LIST, prepare_data.FEATURE_EXTENSION_LIST):
make_prediction(facial_image_extension, feature_extension)
print("All done!")
def run():
# Crop out facial images and retrieve features. Ideally, one only need to call this function once.
prepare_data.run()
# Make prediction by using different features
for facial_image_extension, feature_extension in itertools.product(\
prepare_data.FACIAL_IMAGE_EXTENSION_LIST, prepare_data.FEATURE_EXTENSION_LIST):
make_prediction(facial_image_extension, feature_extension)
print("All done!")
def run_all(userid_, songids_, K):
"""
runs the prepare_data functions
to create datasets and then runs the
knn C++ program to get results written to file and then
reads the file written to by running the C++ program
Return: Returns a dict with the songids and the corresponding
predictions
"""
filename_ = prepare_data.run(userid_, songids_)
num_queries = len(songids_)
outfile = DATADIR_ + userid_ + "_output"
command = (
"/home/tathagata/SmartPlayerModules/Audio/audiomodules/KNN/KNN/KNN_c/k-classifier\
"
+ filename_
+ " "
+ str(K)
+ "\
"
+ str(num_queries)
+ " > "
+ outfile
)
# subprocess.call blocks until command has completed
subprocess.call(command, 0, None, None, None, None, None, False, True)
return read_knn_out(outfile)
def run_all(userid_, songids_, K):
"""
runs the prepare_data functions
to create datasets and then runs the
knn C++ program to get results written to file and then
reads the file written to by running the C++ program
Return: Returns a dict with the songids and the corresponding
predictions
"""
trainfile_, testfile_ = prepare_data.run(userid_, songids_)
print(trainfile_)
print(testfile_)
outfile = DATADIR_ + userid_ + '_output'
command = '/home/tathagata/SmartPlayerModules/Audio/audiomodules/KNN/KNN/KNN_c/knn ' + trainfile_ + ' ' + testfile_ + ' ' + str(K) + '\
> ' + outfile
# subprocess.call blocks until command has completed
subprocess.call(command, 0, None, None, None, None, None, False, True)
return read_knn_out(outfile)
def run_model_LR(Base_Dir_40p208, Base_Dir_share, date):
# sys.path.append(Base_Dir_40p208 + '/model_LR')
import prepare_data
import offline_exp
# conf = get_conf.run(Base_Dir_40p208, date, cid, bid, "1")
# if conf.has_key("model_LR"):
# conf = conf["model_LR"]
# else:
# print "run_model.py - run_model_LR() : cid-{0}, bid-{1} has no model_LR section.".format(cid, bid)
# exit()
conf = conf_dict
conf_format = {}
conf_format["General"] = {}
for k in conf:
if k == "select_features":
conf_format["General"][k] = conf[k]
continue
if k == "sample_proportion":
conf_format["General"][k] = conf[k]
continue
if k == "sample":
conf_format["General"][k] = conf[k]
continue
if k == "train_days":
conf_format["General"][k] = conf[k]
continue
if k == "test_days":
conf_format["General"][k] = conf[k]
continue
conf_format[k] = conf[k]
train_list, test_list = prepare_data.run(Base_Dir_40p208, Base_Dir_share, date, cid, bid, conf_format)
print train_list
print test_list
offline_exp.run(Base_Dir_40p208, date, cid, bid, conf_format, train_list, test_list)
def run_model_LR(Base_Dir_40p208, Base_Dir_share, cid, bid, date):
sys.path.append(Base_Dir_40p208 + '/model_LR')
import prepare_data
import offline_exp
conf = get_conf.run(Base_Dir_40p208, date, cid, bid, "1")
if conf.has_key("model_LR"):
conf = conf["model_LR"]
else:
print "run_model.py - run_model_LR() : cid-{0}, bid-{1} has no model_LR section.".format(cid, bid)
exit()
conf_format = {}
conf_format["General"] = {}
for k in conf:
if k == "select_features":
conf_format["General"][k] = conf[k]
continue
if k == "sample_proportion":
conf_format["General"][k] = conf[k]
continue
if k == "sample":
conf_format["General"][k] = conf[k]
continue
if k == "train_days":
conf_format["General"][k] = conf[k]
continue
if k == "test_days":
conf_format["General"][k] = conf[k]
continue
conf_format[k] = conf[k]
train_list, test_list = prepare_data.run(Base_Dir_40p208, Base_Dir_share, date, cid, bid, conf_format)
print train_list
print test_list
offline_exp.run(Base_Dir_40p208, date, cid, bid, conf_format, train_list, test_list)
