text: from __future__ import absolute_import, division, print_function
  
  import logging
  import numpy as np
  from collections import OrderedDict
  import torch
  
  from ..utils.ml.models.ratio import DenseSingleParameterizedRatioModel
  from ..utils.ml.eval import evaluate_ratio_model
  from ..utils.ml.utils import get_optimizer, get_loss
  from ..utils.various import load_and_check, shuffle, restrict_samplesize
  from ..utils.ml.trainer import SingleParameterizedRatioTrainer
  from .base import ConditionalEstimator, TheresAGoodReasonThisDoesntWork
  
  try:
      FileNotFoundError
  except NameError:
      FileNotFoundError = IOError
  
  logger = logging.getLogger(__name__)
  
  
  class ParameterizedRatioEstimator(ConditionalEstimator):
      """
      A neural estimator of the likelihood ratio as a function of the observation x as well as
      the numerator hypothesis theta. The reference (denominator) hypothesis is kept fixed at some
      reference value and NOT modeled by the network.
  
      Parameters
      ----------
      features : list of int or None, optional
          Indices of observables (features) that are used as input to the neural networks. If None, all observables
          are used. Default value: None.
  
      n_hidden : tuple of int, optional
          Units in each hidden layer in the neural networks. If method is 'nde' or 'scandal', this refers to the
          setup of each individual MADE layer. Default value: (100,).
  
      activation : {'tanh', 'sigmoid', 'relu'}, optional
          Activation function. Default value: 'tanh'.
  
  
      """
  
      def train(
          self,
          method,
          x,
          y,
          theta,
          r_xz=None,
          t_xz=None,
          x_val=None,
          y_val=None,
          theta_val=None,
          r_xz_val=None,
          t_xz_val=None,
          alpha=1.0,
          optimizer="amsgrad",
          n_epochs=50,
          batch_size=128,
          initial_lr=0.001,
          final_lr=0.0001,
          nesterov_momentum=None,
          validation_split=0.25,
          early_stopping=True,
          scale_inputs=True,
          shuffle_labels=False,
          limit_samplesize=None,
          memmap=False,
          verbose="some",
          scale_parameters=True,
          n_workers=8,
          clip_gradient=None,
          early_stopping_patience=None,
      ):
  
          """
          Trains the network.
  
          Parameters
          ----------
          method : str
              The inference method used for training. Allowed values are 'alice', 'alices', 'carl', 'cascal', 'rascal',
              and 'rolr'.
  
          x : ndarray or str
              Observations, or filename of a pickled numpy array.
  
          y : ndarray or str
              Class labels (0 = numeerator, 1 = denominator), or filename of a pickled numpy array.
  
          theta : ndarray or str
              Numerator parameter point, or filename of a pickled numpy array.
  
          r_xz : ndarray or str or None, optional
              Joint likelihood ratio, or filename of a pickled numpy array. Default value: None.
  
          t_xz : ndarray or str or None, optional
              Joint scores at theta, or filename of a pickled numpy array. Default value: None.
  
          x_val : ndarray or str or None, optional
              Validation observations, or filename of a pickled numpy array. If None
              and validation_split > 0, validation data will be randomly selected from the training data.
              Default value: None.
  
          y_val : ndarray or str or None, optional
              Validation labels (0 = numerator, 1 = denominator), or filename of a pickled numpy array. If None
              and validation_split > 0, validation data will be randomly selected from the training data.
              Default value: None.
  
          theta_val : ndarray or str or None, optional
              Validation numerator parameter points, or filename of a pickled numpy array. If None
              and validation_split > 0, validation data will be randomly selected from the training data.
              Default value: None.
  
          r_xz_val : ndarray or str or None, optional
              Validation joint likelihood ratio, or filename of a pickled numpy array. If None
              and validation_split > 0, validation data will be randomly selected from the training data.
              Default value: None.
  
          t_xz_val : ndarray or str or None, optional
              Validation joint scores at theta, or filename of a pickled numpy array. If None
              and validation_split > 0, validation data will be randomly selected from the training data.
              Default value: None.
  
          alpha : float, optional
              Hyperparameter weighting the score error in the loss function of the 'alices', 'rascal', and 'cascal'
              methods. Default value: 1.
  
          optimizer : {"adam", "amsgrad", "sgd"}, optional
              Optimization algorithm. Default value: "amsgrad".
  
          n_epochs : int, optional
              Number of epochs. Default value: 50.
  
          batch_size : int, optional
              Batch size. Default value: 128.
  
          initial_lr : float, optional
              Learning rate during the first epoch, after which it exponentially decays to final_lr. Default value:
              0.001.
  
          final_lr : float, optional
              Learning rate during the last epoch. Default value: 0.0001.
  
          nesterov_momentum : float or None, optional
              If trainer is "sgd", sets the Nesterov momentum. Default value: None.
  
          validation_split : float or None, optional
              Fraction of samples used  for validation and early stopping (if early_stopping is True). If None, the entire
              sample is used for training and early stopping is deactivated. Default value: 0.25.
  
          early_stopping : bool, optional
              Activates early stopping based on the validation loss (only if validation_split is not None). Default value:
              True.
  
          scale_inputs : bool, optional
              Scale the observables to zero mean and unit variance. Default value: True.
  
          shuffle_labels : bool, optional
              If True, the labels (`y`, `r_xz`, `t_xz`) are shuffled, while the observations (`x`) remain in their
              normal order. This serves as a closure test, in particular as cross-check against overfitting: an estimator
              trained with shuffle_labels=True should predict to likelihood ratios around 1 and scores around 0.
  
          limit_samplesize : int or None, optional
              If not None, only this number of samples (events) is used to train the estimator. Default value: None.
  
          memmap : bool, optional.
              If True, training files larger than 1 GB will not be loaded into memory at once. Default value: False.
  
          verbose : {"all", "many", "some", "few", "none}, optional
              Determines verbosity of training. Default value: "some".
  
          scale_parameters : bool, optional
              Whether parameters are rescaled to mean zero and unit variance before going into the neural network.
              Default value: True.
  
          Returns
          -------
          results: ndarray
              Results from SingleParameterizedRatioTrainer.train or DoubleParameterizedRatioTrainer.train for example
  
          """
  
          logger.info("Starting training")
          logger.info("  Method:                 %s", method)
          if method in ["cascal", "rascal", "alices"]:
              logger.info("  alpha:                  %s", alpha)
          logger.info("  Batch size:             %s", batch_size)
          logger.info("  Optimizer:              %s", optimizer)
          logger.info("  Epochs:                 %s", n_epochs)
          logger.info("  Learning rate:          %s initially, decaying to %s", initial_lr, final_lr)
          if optimizer == "sgd":
              logger.info("  Nesterov momentum:      %s", nesterov_momentum)
          logger.info("  Validation split:       %s", validation_split)
          logger.info("  Early stopping:         %s", early_stopping)
          logger.info("  Scale inputs:           %s", scale_inputs)
          logger.info("  Scale parameters:       %s", scale_parameters)
          logger.info("  Shuffle labels          %s", shuffle_labels)
          if limit_samplesize is None:
              logger.info("  Samples:                all")
          else:
              logger.info("  Samples:                %s", limit_samplesize)
  
          # Load training data
          logger.info("Loading training data")
          memmap_threshold = 1.0 if memmap else None
          theta = load_and_check(theta, memmap_files_larger_than_gb=memmap_threshold)
          x = load_and_check(x, memmap_files_larger_than_gb=memmap_threshold)
          y = load_and_check(y, memmap_files_larger_than_gb=memmap_threshold)
          r_xz = load_and_check(r_xz, memmap_files_larger_than_gb=memmap_threshold)
          t_xz = load_and_check(t_xz, memmap_files_larger_than_gb=memmap_threshold)
  
          self._check_required_data(method, r_xz, t_xz)
  
          # Infer dimensions of problem
          n_samples = x.shape[0]
          n_observables = x.shape[1]
          n_parameters = theta.shape[1]
          logger.info("Found %s samples with %s parameters and %s observables", n_samples, n_parameters, n_observables)
  
          # Limit sample size
          if limit_samplesize is not None and limit_samplesize < n_samples:
              logger.info("Only using %s of %s training samples", limit_samplesize, n_samples)
              x, theta, y, r_xz, t_xz = restrict_samplesize(limit_samplesize, x, theta, y, r_xz, t_xz)
  
          # Validation data
          external_validation = x_val is not None and y_val is not None and theta_val is not None
          if external_validation:
              theta_val = load_and_check(theta_val, memmap_files_larger_than_gb=memmap_threshold)
              x_val = load_and_check(x_val, memmap_files_larger_than_gb=memmap_threshold)
              y_val = load_and_check(y_val, memmap_files_larger_than_gb=memmap_threshold)
              r_xz_val = load_and_check(r_xz_val, memmap_files_larger_than_gb=memmap_threshold)
              t_xz_val = load_and_check(t_xz_val, memmap_files_larger_than_gb=memmap_threshold)
  
              logger.info("Found %s separate validation samples", x_val.shape[0])
  
              assert x_val.shape[1] == n_observables
              assert theta_val.shape[1] == n_parameters
              if r_xz is not None:
                  assert r_xz_val is not None, "When providing r_xz and sep. validation data, also provide r_xz_val"
              if t_xz is not None:
                  assert t_xz_val is not None, "When providing t_xz and sep. validation data, also provide t_xz_val"
  
          # Scale features
          if scale_inputs:
              self.initialize_input_transform(x, overwrite=False)
              x = self._transform_inputs(x)
              if external_validation:
                  x_val = self._transform_inputs(x_val)
          else:
              self.initialize_input_transform(x, False, overwrite=False)
  
          # Scale parameters
          if scale_parameters:
              logger.info("Rescaling parameters")
              self.initialize_parameter_transform(theta)
              theta = self._transform_parameters(theta)
              t_xz = self._transform_score(t_xz, inverse=False)
              if external_validation:
                  t_xz_val = self._transform_score(t_xz_val, inverse=False)
          else:
              self.initialize_parameter_transform(theta, False)
  
          # Shuffle labels
          if shuffle_labels:
              logger.info("Shuffling labels")
              y, r_xz, t_xz = shuffle(y, r_xz, t_xz)
  
          # Features
          if self.features is not None:
              x = x[:, self.features]
              logger.info("Only using %s of %s observables", x.shape[1], n_observables)
              n_observables = x.shape[1]
              if external_validation:
                  x_val = x_val[:, self.features]
  
          # Check consistency of input with model
          if self.n_observables is None:
              self.n_observables = n_observables
          if self.n_parameters is None:
              self.n_parameters = n_parameters
  
          if n_parameters != self.n_parameters:
              raise RuntimeError(
                  "Number of parameters does not match model: {} vs {}".format(n_parameters, self.n_parameters)
              )
          if n_observables != self.n_observables:
              raise RuntimeError(
                  "Number of observables does not match model: {} vs {}".format(n_observables, self.n_observables)
              )
  
          # Data
          data = self._package_training_data(method, x, theta, y, r_xz, t_xz)
          if external_validation:
              data_val = self._package_training_data(method, x_val, theta_val, y_val, r_xz_val, t_xz_val)
          else:
              data_val = None
  
          # Create model
          if self.model is None:
              logger.info("Creating model")
              self._create_model()
  
          # Losses
          loss_functions, loss_labels, loss_weights = get_loss(method, alpha)
  
          # Optimizer
          opt, opt_kwargs = get_optimizer(optimizer, nesterov_momentum)
  
          # Train model
          logger.info("Training model")
          trainer = SingleParameterizedRatioTrainer(self.model, n_workers=n_workers)
          result = trainer.train(
              data=data,
              data_val=data_val,
              loss_functions=loss_functions,
              loss_weights=loss_weights,
              loss_labels=loss_labels,
              epochs=n_epochs,
              batch_size=batch_size,
              optimizer=opt,
              optimizer_kwargs=opt_kwargs,
              initial_lr=initial_lr,
              final_lr=final_lr,
              validation_split=validation_split,
              early_stopping=early_stopping,
              verbose=verbose,
              clip_gradient=clip_gradient,
              early_stopping_patience=early_stopping_patience,
          )
          return result
  
      def evaluate_log_likelihood_ratio(self, x, theta, test_all_combinations=True, evaluate_score=False):
          """
          Evaluates the log likelihood ratio for given observations x betwen the given parameter point theta and the
          reference hypothesis.
  
          Parameters
          ----------
          x : str or ndarray
              Observations or filename of a pickled numpy array.
  
          theta : ndarray or str
              Parameter points or filename of a pickled numpy array.
  
          test_all_combinations : bool, optional
              If False, the number of samples in the observable and theta
              files has to match, and the likelihood ratio is evaluated only for the combinations
              `r(x_i | theta0_i, theta1_i)`. If True, `r(x_i | theta0_j, theta1_j)` for all pairwise combinations `i, j`
              are evaluated. Default value: True.
  
          evaluate_score : bool, optional
              Sets whether in addition to the likelihood ratio the score is evaluated. Default value: False.
  
          Returns
          -------
          log_likelihood_ratio : ndarray
              The estimated log likelihood ratio. If test_all_combinations is True, the result has shape
              `(n_thetas, n_x)`. Otherwise, it has shape `(n_samples,)`.
  
          score : ndarray or None
              None if evaluate_score is False. Otherwise the derived estimated score at `theta0`. If test_all_combinations
              is True, the result has shape `(n_thetas, n_x, n_parameters)`. Otherwise, it has shape
              `(n_samples, n_parameters)`.
  
          """
          if self.model is None:
              raise ValueError("No model -- train or load model before evaluating it!")
  
          # Load training data
          logger.debug("Loading evaluation data")
          x = load_and_check(x)
          theta = load_and_check(theta)
  
          # Scale observables
          x = self._transform_inputs(x)
          theta = self._transform_parameters(theta)
  
          # Restrict features
          if self.features is not None:
              x = x[:, self.features]
  
          all_log_r_hat = []
          all_t_hat = []
  
          if test_all_combinations:
              logger.debug("Starting ratio evaluation for %s x-theta combinations", len(theta) * len(x))
  
              for i, this_theta in enumerate(theta):
                  logger.debug("Starting ratio evaluation for thetas %s / %s: %s", i + 1, len(theta), this_theta)
                  _, log_r_hat, t_hat, _ = evaluate_ratio_model(
                      model=self.model,
                      method_type="parameterized_ratio",
                      theta0s=[this_theta],
                      theta1s=None,
                      xs=x,
                      evaluate_score=evaluate_score,
                  )
  
                  t_hat = self._transform_score(t_hat, inverse=True)
  
                  all_log_r_hat.append(log_r_hat)
                  all_t_hat.append(t_hat)
  
              all_log_r_hat = np.array(all_log_r_hat)
              all_t_hat = np.array(all_t_hat)
  
          else:
              logger.debug("Starting ratio evaluation")
              _, all_log_r_hat, all_t_hat, _ = evaluate_ratio_model(
                  model=self.model,
                  method_type="parameterized_ratio",
                  theta0s=theta,
                  theta1s=None,
                  xs=x,
                  evaluate_score=evaluate_score,
              )
  
              all_t_hat = self._transform_score(all_t_hat, inverse=True)
  
          logger.debug("Evaluation done")
          return all_log_r_hat, all_t_hat
  
      def evaluate_log_likelihood_ratio_torch(self, x, theta, test_all_combinations=True):
          """
          Evaluates the log likelihood ratio for given observations x betwen the given parameter point theta and the
          reference hypothesis.
  
          Parameters
          ----------
          x : torch.tensor
              Observations.
  
          theta : torch.tensor
              Parameter points.
  
          test_all_combinations : bool, optional
              If False, the number of samples in the observable and theta
              files has to match, and the likelihood ratio is evaluated only for the combinations
              `r(x_i | theta0_i, theta1_i)`. If True, `r(x_i | theta0_j, theta1_j)` for all pairwise combinations `i, j`
              are evaluated. Default value: True.
  
          Returns
          -------
          log_likelihood_ratio : torch.tensor
              The estimated log likelihood ratio. If test_all_combinations is True, the result has shape
              `(n_thetas, n_x)`. Otherwise, it has shape `(n_samples,)`.
  
          """
          if self.model is None:
              raise ValueError("No model -- train or load model before evaluating it!")
  
          # Scale observables and parameters
          x = self._transform_inputs(x)
          theta = self._transform_parameters(theta)
  
          # Eval
          if test_all_combinations:
              logger.debug("Starting ratio evaluation for %s x-theta combinations", len(theta) * len(x))
              all_log_r_hat = []
              for theta_ in theta:
                  theta_ = torch.stack([theta_ for _ in x])
                  _, log_r_hat, _ = self.model(theta_, x)
                  all_log_r_hat.append(log_r_hat[:, 0].unsqueeze(0))
              log_r_hat = torch.cat(all_log_r_hat, dim=0)
          else:
              logger.debug("Starting ratio evaluation")
              _, log_r_hat = self.model(theta, x)
  
          return log_r_hat
  
      def evaluate_log_likelihood(self, *args, **kwargs):
          raise TheresAGoodReasonThisDoesntWork(
              "This estimator can only estimate likelihood ratios, not the likelihood " "itself!"
          )
  
      def evaluate_score(self, x, theta, nuisance_mode="keep"):
          """
          Evaluates the scores for given observations x betwen at a given parameter point theta.
  
          Parameters
          ----------
          x : str or ndarray
              Observations or filename of a pickled numpy array.
  
          theta : ndarray or str
              Parameter points or filename of a pickled numpy array.
  
          nuisance_mode : {"auto", "keep", "profile", "project"}
              Decides how nuisance parameters are treated. If nuisance_mode is "keep", the
              returned score is always (n+k)-dimensional.
  
          Returns
          -------
          score : ndarray or None
              The estimated score at `theta`. If test_all_combinations is True, the
              result has shape `(n_thetas, n_x, n_parameters)`. Otherwise, it has shape
              `(n_samples, n_parameters)`.
  
          """
          if nuisance_mode == "keep":
              logger.debug("Keeping nuisance parameter in score")
          else:
              raise ValueError("Unknown nuisance_mode {}".format(nuisance_mode))
  
          _, all_t_hat = self.evaluate_log_likelihood_ratio(x, theta, test_all_combinations=False, evaluate_score=True)
          return all_t_hat
  
      def calculate_fisher_information(self, x, theta, weights=None, n_events=1, sum_events=True):
          fisher_information = super(ParameterizedRatioEstimator, self).calculate_fisher_information(
              x, theta, weights, n_events, sum_events
          )
          return fisher_information
  
      def evaluate(self, *args, **kwargs):
          return self.evaluate_log_likelihood_ratio(*args, **kwargs)
  
      def _create_model(self):
          self.model = DenseSingleParameterizedRatioModel(
              n_observables=self.n_observables,
              n_parameters=self.n_parameters,
              n_hidden=self.n_hidden,
              activation=self.activation,
              dropout_prob=self.dropout_prob,
          )
  
      @staticmethod
      def _check_required_data(method, r_xz, t_xz):
          if method in ["cascal", "alices", "rascal"] and t_xz is None:
              raise RuntimeError("Method {} requires joint score information".format(method))
          if method in ["rolr", "alice", "alices", "rascal"] and r_xz is None:
              raise RuntimeError("Method {} requires joint likelihood ratio information".format(method))
  
      @staticmethod
      def _package_training_data(method, x, theta, y, r_xz, t_xz):
          data = OrderedDict()
          data["x"] = x
          data["theta"] = theta
          data["y"] = y
          if method in ["rolr", "alice", "alices", "rascal"]:
              data["r_xz"] = r_xz
          if method in ["cascal", "alices", "rascal"]:
              data["t_xz"] = t_xz
          return data
  
      def _wrap_settings(self):
          settings = super(ParameterizedRatioEstimator, self)._wrap_settings()
          settings["estimator_type"] = "parameterized_ratio"
          return settings
  
      def _unwrap_settings(self, settings):
          super(ParameterizedRatioEstimator, self)._unwrap_settings(settings)
  
          estimator_type = str(settings["estimator_type"])
          if estimator_type != "parameterized_ratio":
              raise RuntimeError("Saved model is an incompatible estimator type {}.".format(estimator_type))
  