147 lines
5.2 KiB
Python
147 lines
5.2 KiB
Python
# -*- coding: utf-8 -*-
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# implementation of hyperband:
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# https://arxiv.org/pdf/1603.06560.pdf
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import logging
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import random
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from math import ceil, log
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from random import random as rng
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from time import ctime, time
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import joblib
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import numpy as np
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from keras.callbacks import EarlyStopping
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import models
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logger = logging.getLogger('cisco_logger')
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def sample_params(param_distribution: dict):
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p = {}
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for key, val in param_distribution.items():
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p[key] = random.choice(val)
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return p
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class Hyperband:
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def __init__(self, param_distribution, X, y, max_iter=81, savefile=None):
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self.get_params = lambda: sample_params(param_distribution)
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self.max_iter = max_iter # maximum iterations per configuration
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self.eta = 3 # defines configuration downsampling rate (default = 3)
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self.logeta = lambda x: log(x) / log(self.eta)
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self.s_max = int(self.logeta(self.max_iter))
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self.B = (self.s_max + 1) * self.max_iter
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self.results = [] # list of dicts
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self.counter = 0
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self.best_loss = np.inf
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self.best_counter = -1
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self.savefile = savefile
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self.X = X
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self.y = y
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def try_params(self, n_iterations, params):
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n_iterations = int(round(n_iterations))
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model = models.get_models_by_params(params)
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callbacks = [EarlyStopping(monitor='val_loss',
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patience=5,
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verbose=False)]
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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history = model.fit(self.X,
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self.y[0] if params["model_output"] == "client" else self.y,
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batch_size=params["batch_size"],
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epochs=n_iterations,
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callbacks=callbacks,
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shuffle=True,
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validation_split=0.4)
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return {"loss": np.min(history.history['val_loss']),
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"early_stop": len(history.history["loss"]) < n_iterations,
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"stop_after": len(history.history["val_loss"])}
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# can be called multiple times
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def run(self, skip_last=0, dry_run=False):
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for s in reversed(range(self.s_max + 1)):
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# initial number of configurations
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n = int(ceil(self.B / self.max_iter / (s + 1) * self.eta ** s))
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# initial number of iterations per config
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r = self.max_iter * self.eta ** (-s)
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# n random configurations
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random_configs = [self.get_params() for _ in range(n)]
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for i in range((s + 1) - int(skip_last)): # changed from s + 1
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# Run each of the n configs for <iterations>
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# and keep best (n_configs / eta) configurations
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n_configs = n * self.eta ** (-i)
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n_iterations = r * self.eta ** (i)
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logger.info("*** {} configurations x {:.1f} iterations each".format(
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n_configs, n_iterations))
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val_losses = []
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early_stops = []
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for t in random_configs:
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self.counter += 1
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logger.info("Config {} | {} | lowest loss so far: {:.4f} (run {})".format(
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self.counter, ctime(), self.best_loss, self.best_counter))
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start_time = time()
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if dry_run:
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result = {'loss': rng(), 'log_loss': rng(), 'auc': rng()}
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else:
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result = self.try_params(n_iterations, t) # <---
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assert (type(result) == dict)
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assert ('loss' in result)
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seconds = int(round(time() - start_time))
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logger.info("{} seconds.".format(seconds))
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loss = result['loss']
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val_losses.append(loss)
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early_stop = result.get('early_stop', False)
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early_stops.append(early_stop)
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# keeping track of the best result so far (for display only)
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# could do it be checking results each time, but hey
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if loss < self.best_loss:
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self.best_loss = loss
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self.best_counter = self.counter
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result['counter'] = self.counter
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result['seconds'] = seconds
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result['params'] = t
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result['iterations'] = n_iterations
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self.results.append(result)
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# select a number of best configurations for the next loop
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# filter out early stops, if any
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indices = np.argsort(val_losses)
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random_configs = [random_configs[i] for i in indices if not early_stops[i]]
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random_configs = random_configs[0:int(n_configs / self.eta)]
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if self.savefile:
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joblib.dump(self.results, self.savefile)
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return self.results
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