我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用metrics.Metrics()。
def debug_mixture_classifier(opts, step, probs, points, num_plot=320, real=True): """Small debugger for the mixture classifier's output. """ num = len(points) if len(probs) != num: return if num < 2 * num_plot: return sorted_vals_and_ids = sorted(zip(probs, range(num))) if real: correct = sorted_vals_and_ids[-num_plot:] wrong = sorted_vals_and_ids[:num_plot] else: correct = sorted_vals_and_ids[:num_plot] wrong = sorted_vals_and_ids[-num_plot:] correct_ids = [_id for val, _id in correct] wrong_ids = [_id for val, _id in wrong] idstring = 'real' if real else 'fake' logging.debug('Correctly classified %s points probs:' %\ idstring) logging.debug([val[0] for val, _id in correct]) logging.debug('Incorrectly classified %s points probs:' %\ idstring) logging.debug([val[0] for val, _id in wrong]) metrics = metrics_lib.Metrics() metrics.make_plots(opts, step, None, points[correct_ids], prefix='c_%s_correct_' % idstring) metrics.make_plots(opts, step, None, points[wrong_ids], prefix='c_%s_wrong_' % idstring)
def debug_updated_weights(opts, steps, weights, data): """ Various debug plots for updated weights of training points. """ assert data.num_points == len(weights), 'Length mismatch' ws_and_ids = sorted(zip(weights, range(len(weights)))) num_plot = 20 * 16 if num_plot > len(weights): return ids = [_id for w, _id in ws_and_ids[:num_plot]] plot_points = data.data[ids] metrics = metrics_lib.Metrics() metrics.make_plots(opts, steps, None, plot_points, prefix='d_least_') ids = [_id for w, _id in ws_and_ids[-num_plot:]] plot_points = data.data[ids] metrics = metrics_lib.Metrics() metrics.make_plots(opts, steps, None, plot_points, prefix='d_most_') plt.clf() ax1 = plt.subplot(211) ax1.set_title('Weights over data points') plt.plot(range(len(weights)), sorted(weights)) plt.axis([0, len(weights), 0., 2. * np.max(weights)]) if data.labels is not None: all_labels = np.unique(data.labels) w_per_label = -1. * np.ones(len(all_labels)) for _id, y in enumerate(all_labels): w_per_label[_id] = np.sum( weights[np.where(data.labels == y)[0]]) ax2 = plt.subplot(212) ax2.set_title('Weights over labels') plt.scatter(range(len(all_labels)), w_per_label, s=30) filename = 'data_w{:02d}.png'.format(steps) create_dir(opts['work_dir']) plt.savefig(o_gfile((opts["work_dir"], filename), 'wb'))
def _train_internal(self, opts): """Train a GAN model. """ batches_num = self._data.num_points / opts['batch_size'] train_size = self._data.num_points counter = 0 logging.debug('Training GAN') for _epoch in xrange(opts["gan_epoch_num"]): for _idx in xrange(batches_num): data_ids = np.random.choice(train_size, opts['batch_size'], replace=False, p=self._data_weights) batch_images = self._data.data[data_ids].astype(np.float) batch_noise = utils.generate_noise(opts, opts['batch_size']) # Update discriminator parameters for _iter in xrange(opts['d_steps']): _ = self._session.run( self._d_optim, feed_dict={self._real_points_ph: batch_images, self._noise_ph: batch_noise}) # Update generator parameters for _iter in xrange(opts['g_steps']): _ = self._session.run( self._g_optim, feed_dict={self._noise_ph: batch_noise}) counter += 1 if opts['verbose'] and counter % opts['plot_every'] == 0: metrics = Metrics() points_to_plot = self._run_batch( opts, self._G, self._noise_ph, self._noise_for_plots[0:320]) data_ids = np.random.choice(train_size, 320, replace=False, p=self._data_weights) metrics.make_plots( opts, counter, self._data.data[data_ids], points_to_plot, prefix='sample_e%04d_mb%05d_' % (_epoch, _idx))
def __init__(self, metric_names): self.model_name = P.MODEL_ID self.setup_folders() initialize_logger(os.path.join(self.model_folder, 'log.txt').format(self.model_name)) P.write_to_file(os.path.join(self.model_folder, 'config.ini')) logging.info(P.to_string()) self.train_metrics = metrics.Metrics('train', metric_names, P.N_CLASSES) self.val_metrics = metrics.Metrics('validation', metric_names, P.N_CLASSES) self.epoch = -1
def _train_internal(self, opts): """Train a GAN model. """ batches_num = self._data.num_points / opts['batch_size'] train_size = self._data.num_points counter = 0 logging.debug('Training GAN') for _epoch in xrange(opts["gan_epoch_num"]): for _idx in TQDM(opts, xrange(batches_num), desc='Epoch %2d/%2d' %\ (_epoch+1, opts["gan_epoch_num"])): data_ids = np.random.choice(train_size, opts['batch_size'], replace=False, p=self._data_weights) batch_images = self._data.data[data_ids].astype(np.float) batch_noise = utils.generate_noise(opts, opts['batch_size']) # Update discriminator parameters for _iter in xrange(opts['d_steps']): _ = self._session.run( self._d_optim, feed_dict={self._real_points_ph: batch_images, self._noise_ph: batch_noise}) # Roll back discriminator_cp's variables self._session.run(self._roll_back) # Unrolling steps for _iter in xrange(opts['unrolling_steps']): self._session.run( self._d_optim_cp, feed_dict={self._real_points_ph: batch_images, self._noise_ph: batch_noise}) # Update generator parameters for _iter in xrange(opts['g_steps']): _ = self._session.run( self._g_optim, feed_dict={self._noise_ph: batch_noise}) counter += 1 if opts['verbose'] and counter % opts['plot_every'] == 0: metrics = Metrics() points_to_plot = self._run_batch( opts, self._G, self._noise_ph, self._noise_for_plots[0:320]) data_ids = np.random.choice(train_size, 320, replace=False, p=self._data_weights) metrics.make_plots( opts, counter, self._data.data[data_ids], points_to_plot, prefix='sample_e%04d_mb%05d_' % (_epoch, _idx))
def _train_internal(self, opts): """Train a GAN model. """ batches_num = self._data.num_points / opts['batch_size'] train_size = self._data.num_points counter = 0 logging.debug('Training GAN') for _epoch in xrange(opts["gan_epoch_num"]): for _idx in xrange(batches_num): # logging.debug('Step %d of %d' % (_idx, batches_num ) ) data_ids = np.random.choice(train_size, opts['batch_size'], replace=False, p=self._data_weights) batch_images = self._data.data[data_ids].astype(np.float) batch_noise = utils.generate_noise(opts, opts['batch_size']) # Update discriminator parameters for _iter in xrange(opts['d_steps']): _ = self._session.run( self._d_optim, feed_dict={self._real_points_ph: batch_images, self._noise_ph: batch_noise, self._is_training_ph: True}) # Update generator parameters for _iter in xrange(opts['g_steps']): _ = self._session.run( self._g_optim, feed_dict={self._noise_ph: batch_noise, self._is_training_ph: True}) counter += 1 if opts['verbose'] and counter % opts['plot_every'] == 0: logging.debug( 'Epoch: %d/%d, batch:%d/%d' % \ (_epoch+1, opts['gan_epoch_num'], _idx+1, batches_num)) metrics = Metrics() points_to_plot = self._run_batch( opts, self._G, self._noise_ph, self._noise_for_plots[0:320], self._is_training_ph, False) metrics.make_plots( opts, counter, None, points_to_plot, prefix='sample_e%04d_mb%05d_' % (_epoch, _idx)) if opts['early_stop'] > 0 and counter > opts['early_stop']: break
def __init__(self, db, config): self.database = db self.config = config super(PokemonGoBot, self).__init__() self.fort_timeouts = dict() self.pokemon_list = json.load( open(os.path.join(_base_dir, 'data', 'pokemon.json')) ) self.item_list = json.load(open(os.path.join(_base_dir, 'data', 'items.json'))) # @var Metrics self.metrics = Metrics(self) self.latest_inventory = None self.cell = None self.recent_forts = [None] * config.forts_max_circle_size self.tick_count = 0 self.softban = False self.start_position = None self.last_map_object = None self.last_time_map_object = 0 self.logger = logging.getLogger(type(self).__name__) self.alt = self.config.gps_default_altitude # Make our own copy of the workers for this instance self.workers = [] # Theading setup for file writing self.web_update_queue = Queue.Queue(maxsize=1) self.web_update_thread = threading.Thread(target=self.update_web_location_worker) self.web_update_thread.start() # Heartbeat limiting self.heartbeat_threshold = self.config.heartbeat_threshold self.heartbeat_counter = 0 self.last_heartbeat = time.time() self.capture_locked = False # lock catching while moving to VIP pokemon client_id_file_path = os.path.join(_base_dir, 'data', 'mqtt_client_id') saved_info = shelve.open(client_id_file_path) key = 'client_id'.encode('utf-8') if key in saved_info: self.config.client_id = saved_info[key] else: self.config.client_id = str(uuid.uuid4()) saved_info[key] = self.config.client_id saved_info.close()
