def set_data_setting():
setting = io.load('MLM::training::setting')
setting_input = flask.request.args
setting = process_form_input('setting', setting, setting_input)
print(setting)
io.save('MLM::training::setting', setting)
return flask.jsonify(result=True)
def save(self, frame):
if not isinstance(frame, ClassifierList):
raise ValueError("ClassifierPool only accept ClassifierList")
io.raw_save(self.get_redis_key(len(self.pool)), frame.to_pickle())
_id = len(self.pool)
self.pool.append(self.get_redis_key(len(self.pool)))
io.save(self.name_prefix, self.pool)
return _id
def get_features():
selected_feature_table_id = int(
flask.request.args.get('selected_feature_table_id', None))
selected_feature_table = feature_table_frame.publish_dict(
)[selected_feature_table_id]['feature_table']
io.save('selected_feature_table', selected_feature_table)
global feature_id_frame
feature_id_frame = connector.get_feature_ids(selected_feature_table)
return flask.jsonify(result=feature_id_frame.publish_dicts())
def save(self, frame):
if not isinstance(frame, FeatureFrame):
raise ValueError("FramePool only accept FeatureFrame")
io.raw_save(self.get_redis_key(len(self.pool)),
frame.to_msgpack(compress='zlib'))
_id = len(self.pool)
self.pool.append(self.get_redis_key(len(self.pool)))
io.save(self.name_prefix, self.pool)
return _id
def save(self, frame):
if not isinstance(frame, DataFrame):
raise ValueError("FramePool only accept FeatureFrame")
io.raw_save(self.get_redis_key(len(self.pool)),
frame.to_msgpack(compress='zlib'))
_id = len(self.pool)
self.pool.append(
dict(key=self.get_redis_key(_id),
is_splitted=False,
training_rows=[],
frac=None,
splitted_timestamp=None))
io.save(self.name_prefix, self.pool)
return _id
def split(self, _id, frac):
if _id < 0 or _id >= len(self.pool):
raise ValueError("Invalid FeatureFrame id")
frame = pandas.read_msgpack(io.raw_load(self.get_redis_key(_id)))
self.pool[_id]['is_splitted'] = True
self.pool[_id]['frac'] = frac
self.pool[_id]['splitted_timestamp'] = datetime.now()
training_rows = []
nweek = max(frame['feature_week'].unique())
for week in range(1, nweek + 1):
idx = frame[frame['feature_week'] == week].index.tolist()
training_rows.extend(random.sample(idx, int(frac * len(idx))))
self.pool[_id]['training_rows'] = training_rows
io.save(self.name_prefix, self.pool)
return DataFrame(frame=frame)
def set_selected_features():
selected_feature_id_ids = str(
flask.request.args.get('selected_feature_id_ids', None)).split(',')
selected_feature_id_ids = [
int(selected_id) for selected_id in selected_feature_id_ids
]
selected_feature_ids = [
feature_id_frame.publish_dict()[selected_id]['feature_id']
for selected_id in selected_feature_id_ids
]
selected_feature_names = [
feature_id_frame.publish_dict()[selected_id]['feature_name']
for selected_id in selected_feature_id_ids
]
io.save('selected_feature_ids', selected_feature_ids)
io.save('selected_feature_names', selected_feature_names)
selected_feature_table = str(io.load('selected_feature_table'))
return flask.jsonify(result=connector.save_features(
selected_feature_table, selected_feature_ids, selected_feature_names))
def training():
global data_pool
global classifier_pool
data_pool = DataPool()
classifier_pool = ClassifierPool()
setting = initialize_form_dicts(FORM_DICT['setting'])
config = initialize_form_dicts(FORM_DICT['config'])
io.save('MLM::training::setting', setting)
io.save('MLM::training::config', config)
setting = io.load('MLM::training::setting')
config = io.load('MLM::training::config')
if setting is None or config is None:
raise ValueError("Cannot load forms from redis, cache missing")
html = flask.render_template(
'training/training.html',
setting=setting,
config=config,
)
return html
def initialize_forms(plot_type):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
data_setting = initialize_form_dicts(FORM_DICTS[plot_type]['data_setting'])
config = initialize_form_dicts(FORM_DICTS[plot_type]['config'])
io.save(plot_type+'_data_setting', data_setting)
io.save(plot_type+'_config', config)
if 'interaction_config' in FORM_DICTS[plot_type]:
interaction_config = initialize_form_dicts(FORM_DICTS[plot_type]['interaction_config'])
io.save(plot_type+'_interaction_config', interaction_config)
def set_config():
config = io.load('MLM::training::config')
config_input = flask.request.args
config = process_form_input('config', config, config_input)
io.save('MLM::training::config', config)
return flask.jsonify(result=True)
app.register_blueprint(training_api, url_prefix='/training')
app.register_blueprint(frames_api, url_prefix='/frames')
app.register_blueprint(classifier_api, url_prefix='/classifiers')
# open redis connection
rds = redis.StrictRedis(host='localhost', port=6379, db=1)
@app.route('/')
def index():
html = flask.render_template('index.html', )
return html
if __name__ == "__main__":
rds.flushall()
io.connect(rds)
if len(sys.argv) < 2:
cfg = config.ConfigParser()
else:
cfg = config.ConfigParser(sys.argv[1])
if not cfg.is_valid():
sys.exit("Config file is invalid.")
cfg_mysql = cfg.get_or_query_mysql()
io.save('MLM::config::cfg_mysql', cfg_mysql)
data_pool = DataPool()
app.run(host='0.0.0.0')
def save_interaction_config(plot_type, interaction_config):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
io.save(plot_type + '_interaction_config', interaction_config)
def save_config(plot_type, config):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
io.save(plot_type + '_config', config)
def save_data_setting(plot_type, data_setting):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
io.save(plot_type + '_data_setting', data_setting)
def save_selected_frame_id(plot_type, selected_frame_id):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
io.save(plot_type + '_selected_frame_id', selected_frame_id)
def get_features():
io.save('selected_feature_table', 'user_long_feature')
global feature_id_frame
feature_id_frame = connector.get_feature_ids('user_long_feature')
return flask.jsonify(result=feature_id_frame.publish_dicts())
