Mercurial > repos > bgruening > create_tool_recommendation_model
view optimise_hyperparameters.py @ 5:4f7e6612906b draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
author | bgruening |
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date | Fri, 06 May 2022 09:05:18 +0000 |
parents | afec8c595124 |
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""" Find the optimal combination of hyperparameters """ import numpy as np import utils from hyperopt import fmin, hp, STATUS_OK, tpe, Trials from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.layers import Dense, Dropout, Embedding, GRU, SpatialDropout1D from tensorflow.keras.models import Sequential from tensorflow.keras.optimizers import RMSprop 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( 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