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 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