Source code for neural_networks.classification_neural_network

#!/usr/bin/env python3

"""Module containing the ClassificationNeuralNetwork 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 sklearn.preprocessing import scale
from sklearn.model_selection import train_test_split
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow import math
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, getHeader, getTargetValue, getFeatures, getIndependentVarsList, getWeight, plotResultsClassMultCM, plotResultsClassBinCM


[docs]class ClassificationNeuralNetwork(BiobbObject): """ | biobb_ml ClassificationNeuralNetwork | Wrapper of the TensorFlow Keras Sequential method for classification. | Trains and tests a given dataset and save the complete model for a Neural Network Classification. Visit the `Sequential documentation page <https://www.tensorflow.org/api_docs/python/tf/keras/Sequential>`_ in the TensorFlow Keras official website for further information. Args: input_dataset_path (str): Path to the input dataset. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/neural_networks/dataset_classification.csv>`_. Accepted formats: csv (edam:format_3752). output_model_path (str): Path to the output model file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/neural_networks/ref_output_model_classification.h5>`_. Accepted formats: h5 (edam:format_3590). output_test_table_path (str) (Optional): Path to the test table file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/neural_networks/ref_output_test_classification.csv>`_. Accepted formats: csv (edam:format_3752). output_plot_path (str) (Optional): Loss, accuracy and MSE plots. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/neural_networks/ref_output_plot_classification.png>`_. Accepted formats: png (edam:format_3603). properties (dic - Python dictionary object containing the tool parameters, not input/output files): * **features** (*dict*) - ({}) Independent variables or columns from your dataset you want to train. You can specify either a list of columns names from your input dataset, a list of columns indexes or a range of columns indexes. Formats: { "columns": ["column1", "column2"] } or { "indexes": [0, 2, 3, 10, 11, 17] } or { "range": [[0, 20], [50, 102]] }. In case of mulitple formats, the first one will be picked. * **target** (*dict*) - ({}) Dependent variable you want to predict from your dataset. You can specify either a column name or a column index. Formats: { "column": "column3" } or { "index": 21 }. In case of mulitple formats, the first one will be picked. * **weight** (*dict*) - ({}) Weight variable from your dataset. You can specify either a column name or a column index. Formats: { "column": "column3" } or { "index": 21 }. In case of multiple formats, the first one will be picked. * **validation_size** (*float*) - (0.2) [0~1|0.05] Represents the proportion of the dataset to include in the validation split. It should be between 0.0 and 1.0. * **test_size** (*float*) - (0.1) [0~1|0.05] Represents the proportion of the dataset to include in the test split. It should be between 0.0 and 1.0. * **hidden_layers** (*list*) - (None) List of dictionaries with hidden layers values. Format: [ { 'size': 50, 'activation': 'relu' } ]. * **output_layer_activation** (*string*) - ("softmax") Activation function to use in the output layer. Values: sigmoid (Sigmoid activation function: sigmoid[x] = 1 / [1 + exp[-x]]), tanh (Hyperbolic tangent activation function), relu (Applies the rectified linear unit activation function), softmax (Softmax converts a real vector to a vector of categorical probabilities). * **optimizer** (*string*) - ("Adam") Name of optimizer instance. Values: Adadelta (Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: the continual decay of learning rates throughout training and the need for a manually selected global learning rate), Adagrad (Adagrad is an optimizer with parameter-specific learning rates; which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives; the smaller the updates), Adam (Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments), Adamax (It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam; specially in models with embeddings), Ftrl (Optimizer that implements the FTRL algorithm), Nadam (Much like Adam is essentially RMSprop with momentum; Nadam is Adam with Nesterov momentum), RMSprop (Optimizer that implements the RMSprop algorithm), SGD (Gradient descent -with momentum- optimizer). * **learning_rate** (*float*) - (0.02) [0~100|0.01] Determines the step size at each iteration while moving toward a minimum of a loss function * **batch_size** (*int*) - (100) [0~1000|1] Number of samples per gradient update. * **max_epochs** (*int*) - (100) [0~1000|1] Number of epochs to train the model. As the early stopping is enabled, this is a maximum. * **normalize_cm** (*bool*) - (False) Whether or not to normalize the confusion matrix. * **random_state** (*int*) - (5) [1~1000|1] Controls the shuffling applied to the data before applying the split. . * **scale** (*bool*) - (False) Whether or not to scale the input dataset. * **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.classification_neural_network import classification_neural_network prop = { 'features': { 'columns': [ 'column1', 'column2', 'column3' ] }, 'target': { 'column': 'target' }, 'validation_size': 0.2, 'test_size': .33, 'hidden_layers': [ { 'size': 10, 'activation': 'relu' }, { 'size': 8, 'activation': 'relu' } ], 'optimizer': 'Adam', 'learning_rate': 0.01, 'batch_size': 32, 'max_epochs': 150 } classification_neural_network(input_dataset_path='/path/to/myDataset.csv', output_model_path='/path/to/newModel.h5', output_test_table_path='/path/to/newTable.csv', output_plot_path='/path/to/newPlot.png', properties=prop) Info: * wrapped_software: * name: TensorFlow Keras Sequential * version: >2.1.0 * license: MIT * ontology: * name: EDAM * schema: http://edamontology.org/EDAM.owl """ def __init__(self, input_dataset_path, output_model_path, output_test_table_path=None, output_plot_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_dataset_path": input_dataset_path}, "out": {"output_model_path": output_model_path, "output_test_table_path": output_test_table_path, "output_plot_path": output_plot_path} } # Properties specific for BB self.features = properties.get('features', {}) self.target = properties.get('target', {}) self.weight = properties.get('weight', {}) self.validation_size = properties.get('validation_size', 0.1) self.test_size = properties.get('test_size', 0.1) self.hidden_layers = properties.get('hidden_layers', []) self.output_layer_activation = properties.get('output_layer_activation', 'softmax') self.optimizer = properties.get('optimizer', 'Adam') self.learning_rate = properties.get('learning_rate', 0.02) self.batch_size = properties.get('batch_size', 100) self.max_epochs = properties.get('max_epochs', 100) self.normalize_cm = properties.get('normalize_cm', False) self.random_state = properties.get('random_state', 5) self.scale = properties.get('scale', False) 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_dataset_path"] = check_input_path(self.io_dict["in"]["input_dataset_path"], "input_dataset_path", False, out_log, self.__class__.__name__) self.io_dict["out"]["output_model_path"] = check_output_path(self.io_dict["out"]["output_model_path"], "output_model_path", False, out_log, self.__class__.__name__) self.io_dict["out"]["output_test_table_path"] = check_output_path(self.io_dict["out"]["output_test_table_path"], "output_test_table_path", True, out_log, self.__class__.__name__) self.io_dict["out"]["output_plot_path"] = check_output_path(self.io_dict["out"]["output_plot_path"], "output_plot_path", True, out_log, self.__class__.__name__)
[docs] def build_model(self, input_shape, output_size): """ Builds Neural network according to hidden_layers property """ # create model model = Sequential([]) # if no hidden_layers provided, create manually a hidden layer with default values if not self.hidden_layers: self.hidden_layers = [{'size': 50, 'activation': 'relu'}] # generate hidden_layers for i, layer in enumerate(self.hidden_layers): if i == 0: model.add(Dense(layer['size'], activation=layer['activation'], kernel_initializer='he_normal', input_shape=input_shape)) # 1st hidden layer else: model.add(Dense(layer['size'], activation=layer['activation'], kernel_initializer='he_normal')) model.add(Dense(output_size, activation=self.output_layer_activation)) # output layer return model
[docs] @launchlogger def launch(self) -> int: """Execute the :class:`ClassificationNeuralNetwork <neural_networks.classification_neural_network.ClassificationNeuralNetwork>` neural_networks.classification_neural_network.ClassificationNeuralNetwork 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 dataset fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log) if 'columns' in self.features: labels = getHeader(self.io_dict["in"]["input_dataset_path"]) skiprows = 1 else: labels = None skiprows = None data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels) targets_list = data[getTargetValue(self.target)].to_numpy() X = getFeatures(self.features, data, self.out_log, self.__class__.__name__) fu.log('Features: [%s]' % (getIndependentVarsList(self.features)), self.out_log, self.global_log) # target # y = getTarget(self.target, data, self.out_log, self.__class__.__name__) fu.log('Target: %s' % (str(getTargetValue(self.target))), self.out_log, self.global_log) # weights if self.weight: w = getWeight(self.weight, data, self.out_log, self.__class__.__name__) # shuffle dataset fu.log('Shuffling dataset', self.out_log, self.global_log) shuffled_indices = np.arange(X.shape[0]) np.random.shuffle(shuffled_indices) np_X = X.to_numpy() shuffled_X = np_X[shuffled_indices] shuffled_y = targets_list[shuffled_indices] if self.weight: shuffled_w = w[shuffled_indices] # train / test split fu.log('Creating train and test sets', self.out_log, self.global_log) arrays_sets = (shuffled_X, shuffled_y) # if user provide weights if self.weight: arrays_sets = arrays_sets + (shuffled_w,) X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(*arrays_sets, test_size=self.test_size, random_state=self.random_state) else: X_train, X_test, y_train, y_test = train_test_split(*arrays_sets, test_size=self.test_size, random_state=self.random_state) # scale dataset if self.scale: fu.log('Scaling dataset', self.out_log, self.global_log) X_train = scale(X_train) # build model fu.log('Building model', self.out_log, self.global_log) model = self.build_model((X_train.shape[1],), np.unique(y_train).size) # model summary stringlist = [] 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) # get optimizer mod = __import__('tensorflow.keras.optimizers', fromlist=[self.optimizer]) opt_class = getattr(mod, self.optimizer) opt = opt_class(lr=self.learning_rate) # compile model model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy', 'mse']) # fitting fu.log('Training model', self.out_log, self.global_log) # set an early stopping mechanism # set patience=2, to be a bit tolerant against random validation loss increases early_stopping = EarlyStopping(patience=2) if self.weight: sample_weight = w_train class_weight = [] else: # TODO: class_weight not working since TF 2.4.1 update # fu.log('No weight provided, class_weight will be estimated from the target data', self.out_log, self.global_log) fu.log('No weight provided', self.out_log, self.global_log) sample_weight = None class_weight = [] # compute_class_weight('balanced', np.unique(y_train), y_train) print(class_weight) # fit the model mf = model.fit(X_train, y_train, class_weight=class_weight, sample_weight=sample_weight, batch_size=self.batch_size, epochs=self.max_epochs, callbacks=[early_stopping], validation_split=self.validation_size, verbose=1) fu.log('Total epochs performed: %s' % len(mf.history['loss']), self.out_log, self.global_log) train_metrics = pd.DataFrame() train_metrics['metric'] = ['Train loss', ' Train accuracy', 'Train MSE', 'Validation loss', 'Validation accuracy', 'Validation MSE'] train_metrics['coefficient'] = [mf.history['loss'][-1], mf.history['accuracy'][-1], mf.history['mse'][-1], mf.history['val_loss'][-1], mf.history['val_accuracy'][-1], mf.history['val_mse'][-1]] fu.log('Training metrics\n\nTRAINING METRICS TABLE\n\n%s\n' % train_metrics, self.out_log, self.global_log) # confusion matrix train_predictions = model.predict(X_train) train_predictions = np.around(train_predictions, decimals=2) norm_pred = [] [norm_pred.append(np.argmax(pred, axis=0)) for pred in train_predictions] cnf_matrix_train = math.confusion_matrix(y_train, norm_pred).numpy() np.set_printoptions(precision=2) if self.normalize_cm: cnf_matrix_train = cnf_matrix_train.astype('float') / cnf_matrix_train.sum(axis=1)[:, np.newaxis] cm_type = 'NORMALIZED CONFUSION MATRIX' else: cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION' fu.log('Calculating confusion matrix for training dataset\n\n%s\n\n%s\n' % (cm_type, cnf_matrix_train), self.out_log, self.global_log) # testing if self.scale: X_test = scale(X_test) fu.log('Testing model', self.out_log, self.global_log) test_loss, test_accuracy, test_mse = model.evaluate(X_test, y_test) test_metrics = pd.DataFrame() test_metrics['metric'] = ['Test loss', ' Test accuracy', 'Test MSE'] test_metrics['coefficient'] = [test_loss, test_accuracy, test_mse] fu.log('Testing metrics\n\nTESTING METRICS TABLE\n\n%s\n' % test_metrics, self.out_log, self.global_log) # predict data from X_test test_predictions = model.predict(X_test) test_predictions = np.around(test_predictions, decimals=2) tpr = tuple(map(tuple, test_predictions)) test_table = pd.DataFrame() test_table['P' + np.array2string(np.unique(y_train))] = tpr test_table['target'] = y_test fu.log('TEST DATA\n\n%s\n' % test_table, self.out_log, self.global_log) # confusion matrix norm_pred = [] [norm_pred.append(np.argmax(pred, axis=0)) for pred in test_predictions] cnf_matrix_test = math.confusion_matrix(y_test, norm_pred).numpy() np.set_printoptions(precision=2) if self.normalize_cm: cnf_matrix_test = cnf_matrix_test.astype('float') / cnf_matrix_test.sum(axis=1)[:, np.newaxis] cm_type = 'NORMALIZED CONFUSION MATRIX' else: cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION' fu.log('Calculating confusion matrix for testing dataset\n\n%s\n\n%s\n' % (cm_type, cnf_matrix_test), self.out_log, self.global_log) # save test data if (self.io_dict["out"]["output_test_table_path"]): fu.log('Saving testing data to %s' % self.io_dict["out"]["output_test_table_path"], self.out_log, self.global_log) test_table.to_csv(self.io_dict["out"]["output_test_table_path"], index=False, header=True) # create test plot if (self.io_dict["out"]["output_plot_path"]): vs = np.unique(targets_list) vs.sort() if len(vs) > 2: plot = plotResultsClassMultCM(mf.history, cnf_matrix_train, cnf_matrix_test, self.normalize_cm, vs) fu.log('Saving confusion matrix plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log) else: plot = plotResultsClassBinCM(mf.history, train_predictions, test_predictions, y_train, y_test, cnf_matrix_train, cnf_matrix_test, self.normalize_cm, vs) fu.log('Saving binary classifier evaluator plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log) plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150) # save model and parameters vs = np.unique(targets_list) vs.sort() vars_obj = { 'features': self.features, 'target': self.target, 'scale': self.scale, 'vs': vs.tolist(), 'type': 'classification' } variables = json.dumps(vars_obj) fu.log('Saving model to %s' % self.io_dict["out"]["output_model_path"], self.out_log, self.global_log) with h5py.File(self.io_dict["out"]["output_model_path"], mode='w') as f: hdf5_format.save_model_to_hdf5(model, f) f.attrs['variables'] = variables # 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 classification_neural_network(input_dataset_path: str, output_model_path: str, output_test_table_path: str = None, output_plot_path: str = None, properties: dict = None, **kwargs) -> int: """Execute the :class:`AutoencoderNeuralNetwork <neural_networks.classification_neural_network.AutoencoderNeuralNetwork>` class and execute the :meth:`launch() <neural_networks.classification_neural_network.AutoencoderNeuralNetwork.launch>` method.""" return ClassificationNeuralNetwork(input_dataset_path=input_dataset_path, output_model_path=output_model_path, output_test_table_path=output_test_table_path, output_plot_path=output_plot_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 Sequential method.", 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_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.') required_args.add_argument('--output_model_path', required=True, help='Path to the output model file. Accepted formats: h5.') parser.add_argument('--output_test_table_path', required=False, help='Path to the test table file. Accepted formats: csv.') parser.add_argument('--output_plot_path', required=False, help='Loss, accuracy and MSE plots. Accepted formats: png.') args = parser.parse_args() args.config = args.config or "{}" properties = settings.ConfReader(config=args.config).get_prop_dic() # Specific call of each building block classification_neural_network(input_dataset_path=args.input_dataset_path, output_model_path=args.output_model_path, output_test_table_path=args.output_test_table_path, output_plot_path=args.output_plot_path, properties=properties)
if __name__ == '__main__': main()