ma_cisco_malware/hyperband.py

# -*- coding: utf-8 -*-
# implementation of hyperband:
# https://arxiv.org/pdf/1603.06560.pdf
import logging
import random
from math import ceil, log
from random import random as rng
from time import ctime, time

import joblib
import numpy as np
from keras.callbacks import EarlyStopping

import models
from main import create_model

logger = logging.getLogger('logger')


def sample_params(param_distribution: dict):
    p = {}
    for key, val in param_distribution.items():
        p[key] = random.choice(val)
    return p


class Hyperband:
    def __init__(self, param_distribution, X, y, max_iter=81, savefile=None):
        self.get_params = lambda: sample_params(param_distribution)

        self.max_iter = max_iter  # maximum iterations per configuration
        self.eta = 3  # defines configuration downsampling rate (default = 3)

        self.logeta = lambda x: log(x) / log(self.eta)
        self.s_max = int(self.logeta(self.max_iter))
        self.B = (self.s_max + 1) * self.max_iter

        self.results = []  # list of dicts
        self.counter = 0
        self.best_loss = np.inf
        self.best_counter = -1

        self.savefile = savefile
        
        self.X = X
        self.y = y
    
    def try_params(self, n_iterations, params):
        n_iterations = int(round(n_iterations))
        embedding, model, new_model = models.get_models_by_params(params)

        model = create_model(model, params["model_output"])
        new_model = create_model(new_model, params["model_output"])

        if params["type"] in ("inter", "staggered"):
            model = new_model

        callbacks = [EarlyStopping(monitor='val_loss',
                                   patience=5,
                                   verbose=False)]
        
        model.compile(optimizer='adam',
                      loss='binary_crossentropy',
                      metrics=['accuracy'])

        history = model.fit(self.X,
                            self.y,
                            batch_size=params["batch_size"],
                            epochs=n_iterations,
                            callbacks=callbacks,
                            shuffle=True,
                            validation_split=0.4)

        return {"loss": np.min(history.history['val_loss']),
                "early_stop": len(history.history["loss"]) < n_iterations}
    
    # can be called multiple times
    def run(self, skip_last=0, dry_run=False):
    
        for s in reversed(range(self.s_max + 1)):
        
            # initial number of configurations
            n = int(ceil(self.B / self.max_iter / (s + 1) * self.eta ** s))
        
            # initial number of iterations per config
            r = self.max_iter * self.eta ** (-s)
        
            # n random configurations
            random_configs = [self.get_params() for _ in range(n)]
            
            for i in range((s + 1) - int(skip_last)):  # changed from s + 1
    
                # Run each of the n configs for <iterations>
                # and keep best (n_configs / eta) configurations
    
                n_configs = n * self.eta ** (-i)
                n_iterations = r * self.eta ** (i)
    
                logger.info("\n*** {} configurations x {:.1f} iterations each".format(
                        n_configs, n_iterations))
                
                val_losses = []
                early_stops = []
    
                for t in random_configs:
                    
                    self.counter += 1
                    logger.info("\n{} | {} | lowest loss so far: {:.4f} (run {})\n".format(
                            self.counter, ctime(), self.best_loss, self.best_counter))
                    
                    start_time = time()

                    if dry_run:
                        result = {'loss': rng(), 'log_loss': rng(), 'auc': rng()}
                    else:
                        result = self.try_params(n_iterations, t)  # <---

                    assert (type(result) == dict)
                    assert ('loss' in result)

                    seconds = int(round(time() - start_time))
                    logger.info("\n{} seconds.".format(seconds))

                    loss = result['loss']
                    val_losses.append(loss)

                    early_stop = result.get('early_stop', False)
                    early_stops.append(early_stop)

                    # keeping track of the best result so far (for display only)
                    # could do it be checking results each time, but hey
                    if loss < self.best_loss:
                        self.best_loss = loss
                        self.best_counter = self.counter

                    result['counter'] = self.counter
                    result['seconds'] = seconds
                    result['params'] = t
                    result['iterations'] = n_iterations

                    self.results.append(result)
    
                # select a number of best configurations for the next loop
                # filter out early stops, if any
                indices = np.argsort(val_losses)
                random_configs = [random_configs[i] for i in indices if not early_stops[i]]
                random_configs = random_configs[0:int(n_configs / self.eta)]
        
            if self.savefile:
                joblib.dump(self.results, self.savefile)
        
        return self.results