#!/usr/bin/env python3
"""Module containing the DecodingNeuralNetwork class and the command line interface."""
import argparse
import h5py
# import json
import numpy as np
import pandas as pd
from biobb_common.generic.biobb_object import BiobbObject
from tensorflow.python.keras.saving import hdf5_format
from biobb_common.configuration import settings
from biobb_common.tools import file_utils as fu
from biobb_common.tools.file_utils import launchlogger
from biobb_ml.neural_networks.common import check_input_path, check_output_path
[docs]class DecodingNeuralNetwork(BiobbObject):
"""
| biobb_ml DecodingNeuralNetwork
| Wrapper of the TensorFlow Keras LSTM method for decoding.
| Decodes and predicts given a dataset and a model file compiled by an Autoencoder Neural Network. Visit the `LSTM documentation page <https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM>`_ in the TensorFlow Keras official website for further information.
Args:
input_decode_path (str): Path to the input decode dataset. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/neural_networks/dataset_decoder.csv>`_. Accepted formats: csv (edam:format_3752).
input_model_path (str): Path to the input model. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/neural_networks/input_model_decoder.h5>`_. Accepted formats: h5 (edam:format_3590).
output_decode_path (str): Path to the output decode file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/neural_networks/ref_output_decode_decoder.csv>`_. Accepted formats: csv (edam:format_3752).
output_predict_path (str) (Optional): Path to the output predict file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/neural_networks/ref_output_predict_decoder.csv>`_. Accepted formats: csv (edam:format_3752).
properties (dic - Python dictionary object containing the tool parameters, not input/output files):
* **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
* **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
Examples:
This is a use example of how to use the building block from Python::
from biobb_ml.neural_networks.neural_network_decode import neural_network_decode
prop = { }
neural_network_decode(input_decode_path='/path/to/myDecodeDataset.csv',
input_model_path='/path/to/newModel.h5',
output_decode_path='/path/to/newDecodeDataset.csv',
output_predict_path='/path/to/newPredictDataset.csv',
properties=prop)
Info:
* wrapped_software:
* name: TensorFlow Keras LSTM
* version: >2.1.0
* license: MIT
* ontology:
* name: EDAM
* schema: http://edamontology.org/EDAM.owl
"""
def __init__(self, input_decode_path, input_model_path, output_decode_path,
output_predict_path=None, properties=None, **kwargs) -> None:
properties = properties or {}
# Call parent class constructor
super().__init__(properties)
self.locals_var_dict = locals().copy()
# Input/Output files
self.io_dict = {
"in": {"input_decode_path": input_decode_path, "input_model_path": input_model_path},
"out": {"output_decode_path": output_decode_path, "output_predict_path": output_predict_path}
}
# Properties specific for BB
self.properties = properties
# Check the properties
self.check_properties(properties)
self.check_arguments()
[docs] def check_data_params(self, out_log, err_log):
""" Checks all the input/output paths and parameters """
self.io_dict["in"]["input_decode_path"] = check_input_path(self.io_dict["in"]["input_decode_path"], "input_decode_path", False, out_log, self.__class__.__name__)
self.io_dict["in"]["input_model_path"] = check_input_path(self.io_dict["in"]["input_model_path"], "input_model_path", False, out_log, self.__class__.__name__)
self.io_dict["out"]["output_decode_path"] = check_output_path(self.io_dict["out"]["output_decode_path"], "output_decode_path", False, out_log, self.__class__.__name__)
if self.io_dict["out"]["output_predict_path"]:
self.io_dict["out"]["output_predict_path"] = check_output_path(self.io_dict["out"]["output_predict_path"], "output_predict_path", False, out_log, self.__class__.__name__)
[docs] @launchlogger
def launch(self) -> int:
"""Execute the :class:`DecodingNeuralNetwork <neural_networks.neural_network_decode.DecodingNeuralNetwork>` neural_networks.neural_network_decode.DecodingNeuralNetwork object."""
# check input/output paths and parameters
self.check_data_params(self.out_log, self.err_log)
# Setup Biobb
if self.check_restart():
return 0
self.stage_files()
# load decode dataset
fu.log('Getting decode dataset from %s' % self.io_dict["in"]["input_decode_path"], self.out_log, self.global_log)
data_dec = pd.read_csv(self.io_dict["in"]["input_decode_path"])
seq_in = np.array(data_dec)
# reshape input into [samples, timesteps, features]
n_in = len(seq_in)
seq_in = seq_in.reshape((1, n_in, 1))
fu.log('Getting model from %s' % self.io_dict["in"]["input_model_path"], self.out_log, self.global_log)
with h5py.File(self.io_dict["in"]["input_model_path"], mode='r') as f:
# variables = f.attrs['variables']
new_model = hdf5_format.load_model_from_hdf5(f)
# get dictionary with variables
# vars_obj = json.loads(variables)
stringlist = []
new_model.summary(print_fn=lambda x: stringlist.append(x))
model_summary = "\n".join(stringlist)
fu.log('Model summary:\n\n%s\n' % model_summary, self.out_log, self.global_log)
# decoding / predicting
fu.log('Decoding / Predicting model', self.out_log, self.global_log)
yhat = new_model.predict(seq_in, verbose=1)
# decoding
decoding_table = pd.DataFrame()
decoding_table['reconstructed'] = np.squeeze(np.asarray(yhat[0][0]))
pd.set_option('display.float_format', lambda x: '%.5f' % x)
decoding_table = decoding_table.reset_index(drop=True)
fu.log('RECONSTRUCTION TABLE\n\n%s\n' % decoding_table, self.out_log, self.global_log)
fu.log('Saving reconstruction to %s' % self.io_dict["out"]["output_decode_path"], self.out_log, self.global_log)
decoding_table.to_csv(self.io_dict["out"]["output_decode_path"], index=False, header=True, float_format='%.5f')
if len(yhat) == 2:
# decoding
prediction_table = pd.DataFrame()
prediction_table['predicted'] = np.squeeze(np.asarray(yhat[1][0]))
pd.set_option('display.float_format', lambda x: '%.5f' % x)
prediction_table = prediction_table.reset_index(drop=True)
fu.log('PREDICTION TABLE\n\n%s\n' % prediction_table, self.out_log, self.global_log)
fu.log('Saving prediction to %s' % self.io_dict["out"]["output_predict_path"], self.out_log, self.global_log)
prediction_table.to_csv(self.io_dict["out"]["output_predict_path"], index=False, header=True, float_format='%.5f')
# Copy files to host
self.copy_to_host()
self.tmp_files.extend([
self.stage_io_dict.get("unique_dir")
])
self.remove_tmp_files()
self.check_arguments(output_files_created=True, raise_exception=False)
return 0
[docs]def neural_network_decode(input_decode_path: str, input_model_path: str, output_decode_path: str, output_predict_path: str = None, properties: dict = None, **kwargs) -> int:
"""Execute the :class:`DecodingNeuralNetwork <neural_networks.neural_network_decode.DecodingNeuralNetwork>` class and
execute the :meth:`launch() <neural_networks.neural_network_decode.DecodingNeuralNetwork.launch>` method."""
return DecodingNeuralNetwork(input_decode_path=input_decode_path,
input_model_path=input_model_path,
output_decode_path=output_decode_path,
output_predict_path=output_predict_path,
properties=properties, **kwargs).launch()
[docs]def main():
"""Command line execution of this building block. Please check the command line documentation."""
parser = argparse.ArgumentParser(description="Wrapper of the TensorFlow Keras LSTM method for decoding.", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
parser.add_argument('--config', required=False, help='Configuration file')
# Specific args of each building block
required_args = parser.add_argument_group('required arguments')
required_args.add_argument('--input_decode_path', required=True, help='Path to the input decode dataset. Accepted formats: csv.')
required_args.add_argument('--input_model_path', required=True, help='Path to the input model. Accepted formats: h5.')
required_args.add_argument('--output_decode_path', required=True, help='Path to the output decode file. Accepted formats: csv.')
parser.add_argument('--output_predict_path', required=False, help='Path to the output predict file. Accepted formats: csv.')
args = parser.parse_args()
args.config = args.config or "{}"
properties = settings.ConfReader(config=args.config).get_prop_dic()
# Specific call of each building block
neural_network_decode(input_decode_path=args.input_decode_path,
input_model_path=args.input_model_path,
output_decode_path=args.output_decode_path,
output_predict_path=args.output_predict_path,
properties=properties)
if __name__ == '__main__':
main()