def load_forms(plot_type):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
data_setting = io.load(plot_type+'_data_setting')
config = io.load(plot_type+'_config')
interaction_config = io.load(plot_type+'_interaction_config')
if data_setting is None or config is None:
raise ValueError("Cannot load forms from redis, cache missing")
if interaction_config is None:
return data_setting, config
else:
return data_setting, config, interaction_config
def request_training():
setting = io.load('MLM::training::setting')
config = io.load('MLM::training::config')
setting = form_dicts_to_dict(setting)
config = form_dicts_to_dict(config)
if setting['data']['value'] == '' or setting['model']['value'] == '':
return flask.jsonify(result=False)
data_frame_ids = [int(_id) for _id in str(setting['data']['value']).split(",")]
models = str(setting['model']['value']).split(",")
data_frames = [data_pool.load_training(_id) for _id in data_frame_ids]
for data_frame in data_frames:
for model in models:
train(data_frame, model, config)
return flask.jsonify(result=True)
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 load_interaction_config(plot_type):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
interaction_config = io.load(plot_type + '_interaction_config')
if interaction_config is None:
raise ValueError("Cannot load forms from redis, cache missing")
return interaction_config
def load_data_setting(plot_type):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
data_setting = io.load(plot_type + '_data_setting')
if data_setting is None:
raise ValueError("Cannot load forms from redis, cache missing")
return data_setting
def interactive_query_cache(interactive_plot_type):
(data_setting, config,
interaction_config) = load_forms(interactive_plot_type)
return flask.jsonify(
dict(selected_frame_id=io.load('selected_frame_id'),
data_setting=data_setting,
config=config,
interation_config=interaction_config))
def fetch():
global connector
cfg_mysql = io.load('MLV::config::cfg_mysql')
if not cfg_mysql:
return "No MySQL config dict provided"
connector = Connector(cfg_mysql)
connector.open_conn()
html = flask.render_template('fetching/fetch.html', )
return html
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 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 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)
def get_config():
config = io.load('MLM::training::config')
return flask.jsonify(result=config)
def get_data_setting():
setting = io.load('MLM::training::setting')
return flask.jsonify(result=setting)
def __init__(self):
self.pool = io.load(self.name_prefix) if io.load(
self.name_prefix) else []
def load_selected_frame_id(plot_type):
if not check_plot_type(plot_type):
raise ValueError("Invalid Plot Type")
selected_frame_id = io.load(plot_type+'_selected_frame_id')
return selected_frame_id
def static_query_cache(static_plot_type):
(data_setting, config) = load_forms(static_plot_type)
return flask.jsonify(
dict(selected_frame_id=io.load('selected_frame_id'),
data_setting=data_setting,
config=config))