Source code for neural_networks.neural_network_decode

#!/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()