train_utils.py 6.09 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
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from typing import Tuple, Union, Any, List
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import deepof.hypermodels
import deepof.model_utils
import keras
import numpy as np
import os
import pickle
import tensorflow as tf


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def load_hparams(hparams):
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    """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)
    else:
        hparams = {
            "units_conv": 256,
            "units_lstm": 256,
            "units_dense2": 64,
            "dropout_rate": 0.25,
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            "encoding": 16,
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            "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,
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                [i for i in os.listdir(train_path) if i.endswith(".pkl")][0],
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            ),
            "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,
    )

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    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",
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    )

    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,
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    callbacks: List,
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) -> Union[bool, Tuple[Any, Any]]:
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    """Define the search space using keras-tuner and bayesian optimization

        Parameters:
            - train (np.array): dataset to train the model on
            - test (np.array): dataset to validate the model on
            - bayopt_trials (int): number of Bayesian optimization iterations to run
            - hypermodel (str): hypermodel to load. Must be one of S2SAE (plain autoencoder) or
            S2SGMVAE (Gaussian Mixture Variational autoencoder).
            - k (int) number of components of the Gaussian Mixture
            - kl_wu (int): number of epochs for KL divergence warm up
            - loss (str): one of [ELBO, MMD, ELBO+MMD]
            - mmd_wu (int): number of epochs for MMD warm up
            - overlap_loss (float): assigns as weight to an extra loss term which
            penalizes overlap between GM components
            - predictor (float): adds an extra regularizing neural network to the model,
            which tries to predict the next frame from the current one
            - project_name (str): ID of the current run
            - callbacks (list): list of callbacks for the training loop

        Returns:
            - best_hparams (dict): dictionary with the best retrieved hyperparameters
            - best_run (tf.keras.Model): trained instance of the best model found

    """

    tensorboard_callback, cp_callback, onecycle = callbacks
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    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.