# Path: disentanglement_lib/evaluation/metrics/utils.py
# def generate_batch_factor_code(ground_truth_data, representation_function,
#                                num_points, random_state, batch_size):
# def split_train_test(observations, train_percentage):
# def obtain_representation(observations, representation_function, batch_size):
# def discrete_mutual_info(mus, ys):
# def discrete_entropy(ys):
# def make_discretizer(target, num_bins=gin.REQUIRED,
#                      discretizer_fn=gin.REQUIRED):
# def _histogram_discretize(target, num_bins=gin.REQUIRED):
# def normalize_data(data, mean=None, stddev=None):
# def make_predictor_fn(predictor_fn=gin.REQUIRED):
# def logistic_regression_cv():
# def gradient_boosting_classifier():


from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf
                artifact_dir=None,
                num_train=gin.REQUIRED,
                batch_size=16):
  """Computes the mutual information gap.

  Args:
    ground_truth_data: GroundTruthData to be sampled from.
    representation_function: Function that takes observations as input and
      outputs a dim_representation sized representation for each observation.
    random_state: Numpy random state used for randomness.
    artifact_dir: Optional path to directory where artifacts can be saved.
    num_train: Number of points used for training.
    batch_size: Batch size for sampling.

  Returns:
    Dict with average mutual information gap.
  """
  del artifact_dir
  logging.info("Generating training set.")
  mus_train, ys_train = utils.generate_batch_factor_code(
      ground_truth_data, representation_function, num_train,
      random_state, batch_size)
  assert mus_train.shape[1] == num_train
  return _compute_mig(mus_train, ys_train)


def _compute_mig(mus_train, ys_train):
  """Computes score based on both training and testing codes and factors."""
  score_dict = {}
  discretized_mus = utils.make_discretizer(mus_train)

# Path: disentanglement_lib/evaluation/metrics/utils.py
# def generate_batch_factor_code(ground_truth_data, representation_function,
#                                num_points, random_state, batch_size):
# def split_train_test(observations, train_percentage):
# def obtain_representation(observations, representation_function, batch_size):
# def discrete_mutual_info(mus, ys):
# def discrete_entropy(ys):
# def make_discretizer(target, num_bins=gin.REQUIRED,
#                      discretizer_fn=gin.REQUIRED):
# def _histogram_discretize(target, num_bins=gin.REQUIRED):
# def normalize_data(data, mean=None, stddev=None):
# def make_predictor_fn(predictor_fn=gin.REQUIRED):
# def logistic_regression_cv():
# def gradient_boosting_classifier():


# Path: disentanglement_lib/evaluation/metrics/mig.py
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf

                artifact_dir=None,
                num_train=gin.REQUIRED,
                batch_size=16):
  """Computes the mutual information gap.

  Args:
    ground_truth_data: GroundTruthData to be sampled from.
    representation_function: Function that takes observations as input and
      outputs a dim_representation sized representation for each observation.
    random_state: Numpy random state used for randomness.
    artifact_dir: Optional path to directory where artifacts can be saved.
    num_train: Number of points used for training.
    batch_size: Batch size for sampling.

  Returns:
    Dict with average mutual information gap.
  """
  del artifact_dir
  logging.info("Generating training set.")
  mus_train, ys_train = utils.generate_batch_factor_code(
      ground_truth_data, representation_function, num_train,
      random_state, batch_size)
  assert mus_train.shape[1] == num_train
  return _compute_mig(mus_train, ys_train)


def _compute_mig(mus_train, ys_train):
  """Computes score based on both training and testing codes and factors."""
  score_dict = {}
  discretized_mus = utils.make_discretizer(mus_train)

  m = utils.discrete_mutual_info(discretized_mus, ys_train)