comparison optimise_hyperparameters.py @ 6:e94dc7945639 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 24bab7a797f53fe4bcc668b18ee0326625486164

5:4f7e6612906b

"""
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