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""" |
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Summary: |
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Function generate_models from modelgen.py generates and compiles models |
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Function train_models_on_samples trains those models |
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Function plotTrainingProcess plots the training process |
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Function find_best_architecture is wrapper function that combines |
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these steps |
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Example function calls in 'EvaluateDifferentModels.ipynb' |
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""" |
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import numpy as np |
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from matplotlib import pyplot as plt |
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from . import modelgen |
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from sklearn import neighbors, metrics |
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import warnings |
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import json |
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import os |
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def train_models_on_samples(X_train, y_train, X_val, y_val, models, |
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nr_epochs=5, subset_size=100, verbose=True, |
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outputfile=None): |
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""" |
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Given a list of compiled models, this function trains |
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them all on a subset of the train data. If the given size of the subset is |
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smaller then the size of the data, the complete data set is used. |
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Parameters |
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---------- |
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X_train : numpy array of shape (num_samples, num_timesteps, num_channels) |
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The input dataset for training |
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y_train : numpy array of shape (num_samples, num_classes) |
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The output classes for the train data, in binary format |
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X_val : numpy array of shape (num_samples_val, num_timesteps, num_channels) |
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The input dataset for validation |
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y_val : numpy array of shape (num_samples_val, num_classes) |
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The output classes for the validation data, in binary format |
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models : list of model, params, modeltypes |
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List of keras models to train |
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nr_epochs : int, optional |
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nr of epochs to use for training one model |
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subset_size : |
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The number of samples used from the complete train set |
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subsize_set : int, optional |
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number of samples to use from the training set for training the models |
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verbose : bool, optional |
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flag for displaying verbose output |
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outputfile : str, optional |
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File location to store the model results |
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Returns |
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---------- |
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histories : list of Keras History objects |
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train histories for all models |
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val_accuracies : list of floats |
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validation accuraracies of the models |
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val_losses : list of floats |
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validation losses of the models |
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""" |
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# if subset_size is smaller then X_train, this will work fine |
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X_train_sub = X_train[:subset_size,:,:] |
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y_train_sub = y_train[:subset_size,:] |
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histories = [] |
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val_accuracies = [] |
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val_losses = [] |
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for i, (model, params, model_types) in enumerate(models): |
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if verbose: |
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print('Training model %d'%i, model_types) |
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history = model.fit(X_train_sub, y_train_sub, |
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nb_epoch=nr_epochs, batch_size=20, |
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# see comment on subsize_set |
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validation_data=(X_val, y_val), |
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verbose=verbose) |
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histories.append(history) |
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val_accuracies.append(history.history['val_acc'][-1]) |
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val_losses.append(history.history['val_loss'][-1]) |
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if outputfile is not None: |
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storetrainhist2json(params, model_types, |
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history.history, outputfile) |
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return histories, val_accuracies, val_losses |
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def storetrainhist2json(params, model_type, history, outputfile): |
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""" |
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This function stores the model parameters, the loss and accuracy history |
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of one model in a JSON file. It appends the model information to the |
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existing models in the file. |
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Parameters |
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---------- |
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params : dictionary with parameters for one model |
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model_type : Keras model object for one model |
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history : dictionary with training history from one model |
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outputfile : str of path where the json file needs to be stored |
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""" |
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jsondata = params.copy() |
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for k in jsondata.keys(): |
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if isinstance(jsondata[k], np.ndarray): |
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jsondata[k] = jsondata[k].tolist() |
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jsondata['train_acc'] = history['acc'] |
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jsondata['train_loss'] = history['loss'] |
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jsondata['val_acc'] = history['val_acc'] |
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jsondata['val_loss'] = history['val_loss'] |
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jsondata['modeltype'] = model_type |
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jsondata['modeltype'] = model_type |
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if os.path.isfile(outputfile): |
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with open(outputfile, 'r') as outfile: |
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previousdata = json.load(outfile) |
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else: |
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previousdata = [] |
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previousdata.append(jsondata) |
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with open(outputfile, 'w') as outfile: |
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json.dump(previousdata, outfile, sort_keys = True, |
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indent = 4, ensure_ascii=False) |
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116
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def plotTrainingProcess(history, name='Model', ax=None): |
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""" |
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This function plots the loss and accuracy on the train and validation set, |
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for each epoch in the history of one model. |
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Parameters |
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---------- |
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history : keras History object for one model |
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The history object of the training process corresponding to one model |
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""" |
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if ax is None: |
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fig, ax = plt.subplots() |
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ax2 = ax.twinx() |
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LN = len(history.history['val_loss']) |
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val_loss, = ax.plot(range(LN), history.history['val_loss'], 'g--', |
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label='validation loss') |
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train_loss, = ax.plot(range(LN), history.history['loss'], 'g-', |
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label='train loss') |
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val_acc, = ax2.plot(range(LN), history.history['val_acc'], 'b--', |
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label='validation accuracy') |
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train_acc, = ax2.plot(range(LN), history.history['acc'], 'b-', |
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label='train accuracy') |
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ax.set_xlabel('epoch') |
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ax.set_ylabel('loss', color='g') |
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ax2.set_ylabel('accuracy', color='b') |
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plt.legend(handles=[val_loss, train_loss, val_acc, train_acc], |
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loc=2, bbox_to_anchor=(1.1, 1)) |
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plt.title(name) |
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def find_best_architecture(X_train, y_train, X_val, y_val, verbose=True, |
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149
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number_of_models=5, nr_epochs=5, subset_size=100, |
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outputpath=None, **kwargs |
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): |
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""" |
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Tries out a number of models on a subsample of the data, |
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and outputs the best found architecture and hyperparameters. |
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156
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Parameters |
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157
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---------- |
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X_train : numpy array of shape (num_samples, num_timesteps, num_channels) |
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159
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The input dataset for training |
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y_train : numpy array of shape (num_samples, num_classes) |
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161
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The output classes for the train data, in binary format |
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162
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X_val : numpy array of shape (num_samples_val, num_timesteps, num_channels) |
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163
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The input dataset for validation |
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164
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y_val : numpy array of shape (num_samples_val, num_classes) |
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165
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The output classes for the validation data, in binary format |
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166
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verbose : bool, optional |
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167
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flag for displaying verbose output |
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168
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number_of_models : int |
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169
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The number of models to generate and test |
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nr_epochs : int |
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The number of epochs that each model is trained |
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subset_size : int |
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The size of the subset of the data that is used for finding |
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the optimal architecture |
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**kwargs: key-value parameters |
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parameters for generating the models |
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(see docstring for modelgen.generate_models) |
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179
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Returns |
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180
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---------- |
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181
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best_model : Keras model |
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182
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Best performing model, already trained on a small sample data set. |
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183
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best_params : dict |
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184
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Dictionary containing the hyperparameters for the best model |
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185
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best_model_type : str |
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186
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Type of the best model |
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187
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knn_acc : float |
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188
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accuaracy for kNN prediction on validation set |
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189
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""" |
|
190
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1 |
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models = modelgen.generate_models(X_train.shape, y_train.shape[1], |
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191
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number_of_models=number_of_models, |
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192
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**kwargs) |
|
193
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1 |
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histories, val_accuracies, val_losses = train_models_on_samples(X_train, |
|
194
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y_train, |
|
195
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|
X_val, |
|
196
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y_val, |
|
197
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models, |
|
198
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nr_epochs, |
|
199
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subset_size=subset_size, |
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|
200
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verbose=verbose, |
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|
201
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outputfile=outputpath) |
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|
202
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1 |
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best_model_index = np.argmax(val_accuracies) |
|
203
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1 |
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best_model, best_params, best_model_type = models[best_model_index] |
|
204
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1 |
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knn_acc = kNN_accuracy( |
|
205
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X_train[:subset_size,:,:], y_train[:subset_size,:], X_val, y_val) |
|
|
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|
206
|
1 |
|
if verbose: |
|
207
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for i in range(len(models)): # now one plot per model, ultimately we |
|
208
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# may want all models in one plot to allow for direct comparison |
|
209
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|
name = str(models[i][1]) |
|
210
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|
plotTrainingProcess(histories[i], name) |
|
211
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|
print('Best model: model ', best_model_index) |
|
212
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|
print('Model type: ', best_model_type) |
|
213
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|
|
print('Hyperparameters: ', best_params) |
|
214
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|
|
print('Accuracy on validation set: ', val_accuracies[best_model_index]) |
|
215
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|
|
print('Accuracy of kNN on validation set', knn_acc) |
|
216
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|
217
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1 |
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if val_accuracies[best_model_index] < knn_acc: |
|
218
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warnings.warn('Best model not better than kNN: ' + |
|
219
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|
str(val_accuracies[best_model_index]) + ' vs ' + |
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220
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str(knn_acc) |
|
221
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) |
|
222
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1 |
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return best_model, best_params, best_model_type, knn_acc |
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223
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|
224
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|
225
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1 |
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def kNN_accuracy(X_train, y_train, X_val, y_val, k=1): |
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|
226
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""" |
|
227
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|
Performs k-Neigherst Neighbors and returns the accuracy score. |
|
228
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|
229
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Parameters |
|
230
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|
|
---------- |
|
231
|
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|
X_train : numpy array |
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232
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Train set of shape (num_samples, num_timesteps, num_channels) |
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233
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y_train : numpy array |
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234
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Class labels for train set |
|
235
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|
X_val : numpy array |
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236
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Validation set of shape (num_samples, num_timesteps, num_channels) |
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237
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y_val : numpy array |
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238
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Class labels for validation set |
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239
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|
k : int |
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240
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number of neighbors to use for classifying |
|
241
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|
242
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|
|
Returns |
|
243
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|
|
------- |
|
244
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|
|
accuracy: float |
|
245
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|
|
accuracy score on the validation set |
|
246
|
|
|
""" |
|
247
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1 |
|
num_samples, num_timesteps, num_channels = X_train.shape |
|
248
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1 |
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clf = neighbors.KNeighborsClassifier(k) |
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249
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1 |
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clf.fit( |
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250
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X_train.reshape( |
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251
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num_samples, |
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252
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num_timesteps * |
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253
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num_channels), |
|
254
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y_train) |
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255
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1 |
|
num_samples, num_timesteps, num_channels = X_val.shape |
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256
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1 |
|
val_predict = clf.predict( |
|
257
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|
X_val.reshape(num_samples, |
|
258
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|
num_timesteps * num_channels)) |
|
259
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|
return metrics.accuracy_score(val_predict, y_val) |
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NLeSC /
mcfly
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