Mercurial > repos > bgruening > create_tool_recommendation_model
view optimise_hyperparameters.py @ 4:afec8c595124 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
author | bgruening |
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date | Tue, 07 Jul 2020 03:25:49 -0400 |
parents | 5b3c08710e47 |
children | 4f7e6612906b |
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""" Find the optimal combination of hyperparameters """ import numpy as np from hyperopt import fmin, tpe, hp, STATUS_OK, Trials from keras.models import Sequential from keras.layers import Dense, GRU, Dropout from keras.layers.embeddings import Embedding from keras.layers.core import SpatialDropout1D from keras.optimizers import RMSprop from keras.callbacks import EarlyStopping import utils class HyperparameterOptimisation: def __init__(self): """ Init method. """ def train_model(self, config, reverse_dictionary, train_data, train_labels, test_data, test_labels, tool_tr_samples, class_weights): """ Train a model and report accuracy """ # convert items to integer l_batch_size = list(map(int, config["batch_size"].split(","))) l_embedding_size = list(map(int, config["embedding_size"].split(","))) l_units = list(map(int, config["units"].split(","))) # convert items to float l_learning_rate = list(map(float, config["learning_rate"].split(","))) l_dropout = list(map(float, config["dropout"].split(","))) l_spatial_dropout = list(map(float, config["spatial_dropout"].split(","))) l_recurrent_dropout = list(map(float, config["recurrent_dropout"].split(","))) optimize_n_epochs = int(config["optimize_n_epochs"]) # get dimensions dimensions = len(reverse_dictionary) + 1 best_model_params = dict() early_stopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, min_delta=1e-1, restore_best_weights=True) # specify the search space for finding the best combination of parameters using Bayesian optimisation params = { "embedding_size": hp.quniform("embedding_size", l_embedding_size[0], l_embedding_size[1], 1), "units": hp.quniform("units", l_units[0], l_units[1], 1), "batch_size": hp.quniform("batch_size", l_batch_size[0], l_batch_size[1], 1), "learning_rate": hp.loguniform("learning_rate", np.log(l_learning_rate[0]), np.log(l_learning_rate[1])), "dropout": hp.uniform("dropout", l_dropout[0], l_dropout[1]), "spatial_dropout": hp.uniform("spatial_dropout", l_spatial_dropout[0], l_spatial_dropout[1]), "recurrent_dropout": hp.uniform("recurrent_dropout", l_recurrent_dropout[0], l_recurrent_dropout[1]) } def create_model(params): model = Sequential() model.add(Embedding(dimensions, int(params["embedding_size"]), mask_zero=True)) model.add(SpatialDropout1D(params["spatial_dropout"])) model.add(GRU(int(params["units"]), dropout=params["dropout"], recurrent_dropout=params["recurrent_dropout"], return_sequences=True, activation="elu")) model.add(Dropout(params["dropout"])) model.add(GRU(int(params["units"]), dropout=params["dropout"], recurrent_dropout=params["recurrent_dropout"], return_sequences=False, activation="elu")) model.add(Dropout(params["dropout"])) model.add(Dense(2 * dimensions, activation="sigmoid")) optimizer_rms = RMSprop(lr=params["learning_rate"]) batch_size = int(params["batch_size"]) model.compile(loss=utils.weighted_loss(class_weights), optimizer=optimizer_rms) print(model.summary()) model_fit = model.fit_generator( utils.balanced_sample_generator( train_data, train_labels, batch_size, tool_tr_samples, reverse_dictionary ), steps_per_epoch=len(train_data) // batch_size, epochs=optimize_n_epochs, callbacks=[early_stopping], validation_data=(test_data, test_labels), verbose=2, shuffle=True ) return {'loss': model_fit.history["val_loss"][-1], 'status': STATUS_OK, 'model': model} # minimize the objective function using the set of parameters above trials = Trials() learned_params = fmin(create_model, params, trials=trials, algo=tpe.suggest, max_evals=int(config["max_evals"])) best_model = trials.results[np.argmin([r['loss'] for r in trials.results])]['model'] # set the best params with respective values for item in learned_params: item_val = learned_params[item] best_model_params[item] = item_val return best_model_params, best_model