def api_test(experiment_id_number):
"""Prepare an experiment already uploaded to the db
to be run against the model"""
clean_experiment_number = int(experiment_id_number)
try:
query = db.session.query(Sensor)
df = pd.read_sql_query(query.statement, query.session.bind)
df2 = df[df['experiment_id'] == clean_experiment_number]
current_app.logger.debug(df2)
current_app.logger.debug('run resolve_acc_gyro')
df2 = resolve_acc_gyro_db(df2)
current_app.logger.debug(df2)
current_app.logger.debug('run create_rm_feature')
df2 = create_rm_feature(df2, TIME_SEQUENCE_LENGTH)
current_app.logger.debug(df2)
test_data = blank_filter(df2)
current_app.logger.debug(test_data)
# TODO: NEED TO MAKE SURE FEATURE NUMBER IS THE SAME
#Xt = df2.drop(['state', 'index'], axis=1)
Xt = polynomial_features.fit_transform(test_data)
current_app.logger.debug(Xt)
return Xt
except Exception as e:
current_app.logger.debug('error: {}'.format(e))
abort(500)
def api_test(experiment_id_number):
"""Prepare an experiment already uploaded to the db
to be run against the model"""
clean_experiment_number = int(experiment_id_number)
try:
query = db.session.query(Sensor)
df = pd.read_sql_query(query.statement, query.session.bind)
df2 = df[df['experiment_id']==clean_experiment_number]
current_app.logger.debug(df2)
current_app.logger.debug('run resolve_acc_gyro')
df2 = resolve_acc_gyro_db(df2)
current_app.logger.debug(df2)
current_app.logger.debug('run create_rm_feature')
df2 = create_rm_feature(df2, TIME_SEQUENCE_LENGTH)
current_app.logger.debug(df2)
test_data = blank_filter(df2)
current_app.logger.debug(test_data)
# TODO: NEED TO MAKE SURE FEATURE NUMBER IS THE SAME
#Xt = df2.drop(['state', 'index'], axis=1)
Xt = polynomial_features.fit_transform(test_data)
current_app.logger.debug(Xt)
return Xt
except Exception as e:
current_app.logger.debug('error: {}'.format(e))
abort(500)
def prep_test(el_file):
el_file = DIR + '/data/test_cases/' + el_file
df = pd.DataFrame()
df = pd.read_csv(el_file, index_col=None, header=0)
df = resolve_acc_gyro(df)
df = create_rm_feature(df, TIME_SEQUENCE_LENGTH)
test_data = blank_filter(df)
return test_data
def prep_test(el_file):
el_file = DIR + '/data/test_cases/' + el_file
df = pd.DataFrame()
df = pd.read_csv(el_file, index_col=None, header=0)
df = resolve_acc_gyro(df)
df = create_rm_feature(df, TIME_SEQUENCE_LENGTH)
test_data = blank_filter(df)
return test_data
def combine_setState_createFeatures(directory, state):
"""
convenience method to combine three steps in one function:
(1) combine multiple csv files, (2) set their movement state for training,
(3) combine to create time sequences and add features
"""
combined_data = combine_csv(directory)
combined_data_updated = set_state(combined_data, state) # TO CHECK: probably not necessary
feature_training_data = create_rm_feature(combined_data_updated, TIME_SEQUENCE_LENGTH)
ready_training_data = set_state(feature_training_data, state)
return ready_training_data
def combine_setState_createFeatures(directory, state):
"""
convenience method to combine three steps in one function:
(1) combine multiple csv files, (2) set their movement state for training,
(3) combine to create time sequences and add features
"""
combined_data = combine_csv(directory)
combined_data_updated = set_state(
combined_data, state) # TO CHECK: probably not necessary
feature_training_data = create_rm_feature(combined_data_updated,
TIME_SEQUENCE_LENGTH)
ready_training_data = set_state(feature_training_data, state)
return ready_training_data
