train_utils.py 4.92 KB
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# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""

Simple utility functions used in deepof example scripts. These are not part of the main package

"""
from datetime import datetime

from kerastuner import BayesianOptimization
from typing import Tuple, Union, Any
import deepof.hypermodels
import deepof.model_utils
import keras
import numpy as np
import os
import pickle
import tensorflow as tf


def load_hparams(hparams, encoding):
    """Loads hyperparameters from a custom dictionary pickled on disc.
    Thought to be used with the output of hyperparameter_tuning.py"""

    if hparams is not None:
        with open(hparams, "rb") as handle:
            hparams = pickle.load(handle)
        hparams["encoding"] = encoding
    else:
        hparams = {
            "units_conv": 256,
            "units_lstm": 256,
            "units_dense2": 64,
            "dropout_rate": 0.25,
            "encoding": encoding,
            "learning_rate": 1e-3,
        }
    return hparams


def load_treatments(train_path):
    """Loads a dictionary containing the treatments per individual,
    to be loaded as metadata in the coordinates class"""
    try:
        with open(
            os.path.join(
                train_path,
                [i for i in os.listdir(train_path) if i.endswith(".pickle")][0],
            ),
            "rb",
        ) as handle:
            treatment_dict = pickle.load(handle)
    except IndexError:
        treatment_dict = None

    return treatment_dict


def get_callbacks(
    X_train: np.array,
    batch_size: int,
    variational: bool,
    predictor: float,
    k: int,
    loss: str,
    kl_wu: int,
    mmd_wu: int,
) -> Tuple:
    """Generates callbacks for model training, including:
        - run_ID: run name, with coarse parameter details;
        - tensorboard_callback: for real-time visualization;
        - cp_callback: for checkpoint saving,
        - onecycle: for learning rate scheduling"""

    run_ID = "{}{}{}{}{}{}_{}".format(
        ("GMVAE" if variational else "AE"),
        ("P" if predictor > 0 and variational else ""),
        ("_components={}".format(k) if variational else ""),
        ("_loss={}".format(loss) if variational else ""),
        ("_kl_warmup={}".format(kl_wu) if variational else ""),
        ("_mmd_warmup={}".format(mmd_wu) if variational else ""),
        datetime.now().strftime("%Y%m%d-%H%M%S"),
    )

    log_dir = os.path.abspath("logs/fit/{}".format(run_ID))
    tensorboard_callback = keras.callbacks.TensorBoard(
        log_dir=log_dir, histogram_freq=1, profile_batch=2,
    )

    cp_callback = (
        tf.keras.callbacks.ModelCheckpoint(
            "./logs/checkpoints/" + run_ID + "/cp-{epoch:04d}.ckpt",
            verbose=1,
            save_best_only=False,
            save_weights_only=True,
            save_freq="epoch",
        ),
    )

    onecycle = deepof.model_utils.one_cycle_scheduler(
        X_train.shape[0] // batch_size * 250, max_rate=0.005,
    )

    return run_ID, tensorboard_callback, cp_callback, onecycle


def tune_search(
    train: np.array,
    test: np.array,
    bayopt_trials: int,
    hypermodel: str,
    k: int,
    kl_wu: int,
    loss: str,
    mmd_wu: int,
    overlap_loss: float,
    predictor: float,
    project_name: str,
    tensorboard_callback: tf.keras.callbacks,
) -> Union[bool, Tuple[Any, Any]]:
    """Define the search space using keras-tuner and bayesian optimization"""

    if hypermodel == "S2SAE":
        hypermodel = deepof.hypermodels.SEQ_2_SEQ_AE(input_shape=train.shape)

    elif hypermodel == "S2SGMVAE":
        hypermodel = deepof.hypermodels.SEQ_2_SEQ_GMVAE(
            input_shape=train.shape,
            loss=loss,
            number_of_components=k,
            kl_warmup_epochs=kl_wu,
            mmd_warmup_epochs=mmd_wu,
            predictor=predictor,
            overlap_loss=overlap_loss,
        )
    else:
        return False

    tuner = BayesianOptimization(
        hypermodel,
        max_trials=bayopt_trials,
        executions_per_trial=1,
        objective="val_mae",
        seed=42,
        directory="BayesianOptx",
        project_name=project_name,
    )

    print(tuner.search_space_summary())

    tuner.search(
        train,
        train,
        epochs=30,
        validation_data=(test, test),
        verbose=1,
        batch_size=256,
        callbacks=[
            tensorboard_callback,
            tf.keras.callbacks.EarlyStopping("val_mae", patience=5),
        ],
    )

    print(tuner.results_summary())
    best_hparams = tuner.get_best_hyperparameters(num_trials=1)[0]
    best_run = tuner.hypermodel.build(best_hparams)

    return best_hparams, best_run


# TODO:
#    - load_treatments should be part of the main data module. If available in the main directory,
#    a table (preferrable in csv) should be loaded as metadata of the coordinates automatically.
#    This becomes particularly important por the supervised models that include phenotype classification
#    alongside the encoding.