text: #!/usr/bin/env python
  # coding: utf-8
  from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from __future__ import unicode_literals
  
  # Command line :
  # python -m benchmark.S3D2.PIVOT-Prior
  
  import os
  import logging
  from config import SEED
  from config import _ERROR
  from config import _TRUTH
  
  import numpy as np
  import pandas as pd
  
  from visual.misc import set_plot_config
  set_plot_config()
  
  from ..common import load_estimations
  from ..common import load_conditional_estimations
  from utils.log import set_logger
  from utils.log import flush
  from utils.log import print_line
  from utils.model import get_model
  from utils.model import get_optimizer
  from utils.model import train_or_load_pivot
  from utils.evaluation import evaluate_classifier
  from utils.evaluation import evaluate_neural_net
  from utils.evaluation import evaluate_config
  from utils.evaluation import evaluate_summary_computer
  from utils.evaluation import evaluate_minuit
  from utils.evaluation import evaluate_estimator
  from utils.evaluation import evaluate_conditional_estimation
  from utils.images import gather_images
  
  from visual.misc import plot_params
  
  from problem.synthetic3D import S3D2Config as Config
  from problem.synthetic3D import get_minimizer
  from problem.synthetic3D import get_minimizer_no_nuisance
  from problem.synthetic3D import Generator
  from problem.synthetic3D import Parameter
  from problem.synthetic3D import param_generator
  from problem.synthetic3D import S3D2NLL as NLLComputer
  
  from visual.special.synthetic3D import plot_nll_around_min
  
  from model.pivot import PivotClassifier
  from model.criterion.weighted_criterion import WeightedCrossEntropyLoss
  from model.criterion.weighted_criterion import WeightedGaussEntropyLossN
  from model.summaries import ClassifierSummaryComputer
  from ..my_argparser import PIVOT_parse_args
  
  from archi.classic import L4 as ARCHI
  
  
  from .common import N_BINS
  
  DATA_NAME = 'S3D2'
  BENCHMARK_NAME = DATA_NAME+'-prior'
  N_ITER = 30
  N_AUGMENT = 5
  
  
  def build_model(args, i_cv):
      args.net = ARCHI(n_in=3, n_out=2, n_unit=args.n_unit)
      args.adv_net = ARCHI(n_in=2, n_out=4, n_unit=args.n_unit)
      args.net_optimizer = get_optimizer(args)
      args.adv_optimizer = get_optimizer(args, args.adv_net)
      args.net_criterion = WeightedCrossEntropyLoss()
      args.adv_criterion = WeightedGaussEntropyLossN(n_out=2)
      model = get_model(args, PivotClassifier)
      model.set_info(DATA_NAME, BENCHMARK_NAME, i_cv)
      return model
  
  
  class TrainGenerator:
      def __init__(self, param_generator, data_generator, n_bunch=1000):
          self.param_generator = param_generator
          self.data_generator = data_generator
          self.n_bunch = n_bunch
  
      def generate(self, n_samples):
          n_bunch_samples = n_samples // self.n_bunch
          params = [self.param_generator().clone_with(mu=Config.CALIBRATED.mu) for i in range(self.n_bunch)]
          data = [self.data_generator.generate(*parameters, n_samples=n_bunch_samples) for parameters in params]
          X = np.concatenate([X for X, y, w in data], axis=0)
          y = np.concatenate([y for X, y, w in data], axis=0)
          w = np.concatenate([w for X, y, w in data], axis=0)
          z = np.array([p.nuisance_parameters for p in params])
          z = z.reshape(len(params), -1)
          z = np.repeat(z, n_bunch_samples, axis=0)
          return X, y, z, w
  
  
  
  # =====================================================================
  # MAIN
  # =====================================================================
  def main():
      # BASIC SETUP
      logger = set_logger()
      args = PIVOT_parse_args(main_description="Training launcher for Gradient boosting on S3D2 benchmark")
      logger.info(args)
      flush(logger)
      # INFO
      model = build_model(args, -1)
      os.makedirs(model.results_directory, exist_ok=True)
      config = Config()
      config_table = evaluate_config(config)
      config_table.to_csv(os.path.join(model.results_directory, 'config_table.csv'))
      # RUN
      if not args.conditional_only:
          eval_table = get_eval_table(args, model.results_directory)
      if not args.estimate_only:
          eval_conditional = get_eval_conditional(args, model.results_directory)
      if not args.estimate_only and not args.conditional_only:
          eval_table = pd.concat([eval_table, eval_conditional], axis=1)
          # EVALUATION
          print_line()
          print_line()
          print(eval_table)
          print_line()
          print_line()
          eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv'))
      gather_images(model.results_directory)
  
  
  def get_eval_table(args, results_directory):
      logger = logging.getLogger()
      if args.load_run:
          logger.info(f'Loading previous runs [{args.start_cv},{args.end_cv}[')
          estimations = load_estimations(results_directory, start_cv=args.start_cv, end_cv=args.end_cv)
      else:
          logger.info(f'Running runs [{args.start_cv},{args.end_cv}[')
          estimations = [run_estimation(args, i_cv) for i_cv in range(args.start_cv, args.end_cv)]
          estimations = pd.concat(estimations, ignore_index=True)
      estimations.to_csv(os.path.join(results_directory, 'estimations.csv'))
      # EVALUATION
      eval_table = evaluate_estimator(Config.INTEREST_PARAM_NAME, estimations)
      print_line()
      print_line()
      print(eval_table)
      print_line()
      print_line()
      eval_table.to_csv(os.path.join(results_directory, 'estimation_evaluation.csv'))
      return eval_table
  
  
  def get_eval_conditional(args, results_directory):
      logger = logging.getLogger()
      if args.load_run:
          logger.info(f'Loading previous runs [{args.start_cv},{args.end_cv}[')
          conditional_estimations = load_conditional_estimations(results_directory, start_cv=args.start_cv, end_cv=args.end_cv)
      else:
          logger.info(f'Running runs [{args.start_cv},{args.end_cv}[')
          conditional_estimations = [run_conditional_estimation(args, i_cv) for i_cv in range(args.start_cv, args.end_cv)]
          conditional_estimations = pd.concat(conditional_estimations, ignore_index=True)
      conditional_estimations.to_csv(os.path.join(results_directory, 'conditional_estimations.csv'))
      # EVALUATION
      eval_conditional = evaluate_conditional_estimation(conditional_estimations, interest_param_name=Config.INTEREST_PARAM_NAME)
      print_line()
      print_line()
      print(eval_conditional)
      print_line()
      print_line()
      eval_conditional.to_csv(os.path.join(results_directory, 'conditional_evaluation.csv'))
      return eval_conditional
  
  
  def run_estimation(args, i_cv):
      logger = logging.getLogger()
      print_line()
      logger.info('Running iter n°{}'.format(i_cv))
      print_line()
  
      result_row = {'i_cv': i_cv}
  
      # LOAD/GENERATE DATA
      logger.info('Set up data generator')
      config = Config()
      seed = SEED + i_cv * 5
      train_generator = Generator(seed)
      train_generator = TrainGenerator(param_generator, train_generator)
      valid_generator = Generator(seed+1)
      test_generator  = Generator(seed+2)
  
      # SET MODEL
      logger.info('Set up classifier')
      model = build_model(args, i_cv)
      os.makedirs(model.results_path, exist_ok=True)
      flush(logger)
  
      # TRAINING / LOADING
      train_or_load_pivot(model, train_generator, config.N_TRAINING_SAMPLES*N_AUGMENT, retrain=args.retrain)
  
      # CHECK TRAINING
      logger.info('Generate validation data')
      X_valid, y_valid, w_valid = valid_generator.generate(*config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES)
  
      result_row.update(evaluate_neural_net(model, prefix='valid'))
      result_row.update(evaluate_classifier(model, X_valid, y_valid, w_valid, prefix='valid'))
  
      # MEASUREMENT
      evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='')
      iter_results = [run_estimation_iter(model, result_row, i, test_config, valid_generator, test_generator, n_bins=N_BINS)
                      for i, test_config in enumerate(config.iter_test_config())]
      result_table = pd.DataFrame(iter_results)
      result_table.to_csv(os.path.join(model.results_path, 'estimations.csv'))
      logger.info('Plot params')
      param_names = config.PARAM_NAMES
      for name in param_names:
          plot_params(name, result_table, title=model.full_name, directory=model.results_path)
  
      logger.info('DONE')
      return result_table
  
  
  def run_estimation_iter(model, result_row, i_iter, config, valid_generator, test_generator, n_bins=N_BINS):
      logger = logging.getLogger()
      logger.info('-'*45)
      logger.info(f'iter : {i_iter}')
      flush(logger)
  
      iter_directory = os.path.join(model.results_path, f'iter_{i_iter}')
      os.makedirs(iter_directory, exist_ok=True)
      result_row['i'] = i_iter
      result_row['n_test_samples'] = config.N_TESTING_SAMPLES
      suffix = f'-mu={config.TRUE.mu:1.2f}_r={config.TRUE.r}_lambda={config.TRUE.lam}'
  
      logger.info('Generate testing data')
      test_generator.reset()
      X_test, y_test, w_test = test_generator.generate(*config.TRUE, n_samples=config.N_TESTING_SAMPLES)
      # PLOT SUMMARIES
      evaluate_summary_computer(model, X_test, y_test, w_test, n_bins=n_bins, prefix='', suffix=suffix, directory=iter_directory)
  
      logger.info('Set up NLL computer')
      compute_summaries = ClassifierSummaryComputer(model, n_bins=n_bins)
      compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config)
      # NLL PLOTS
      plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix)
  
      # MINIMIZE NLL
      logger.info('Prepare minuit minimizer')
      minimizer = get_minimizer(compute_nll, config.CALIBRATED, config.CALIBRATED_ERROR)
      result_row.update(evaluate_minuit(minimizer, config.TRUE, iter_directory, suffix=suffix))
      return result_row.copy()
  
  
  def run_conditional_estimation(args, i_cv):
      logger = logging.getLogger()
      print_line()
      logger.info('Running iter n°{}'.format(i_cv))
      print_line()
  
      result_row = {'i_cv': i_cv}
  
      # LOAD/GENERATE DATA
      logger.info('Set up data generator')
      config = Config()
      seed = SEED + i_cv * 5
      train_generator = Generator(seed)
      train_generator = TrainGenerator(param_generator, train_generator)
      valid_generator = Generator(seed+1)
      test_generator  = Generator(seed+2)
  
      # SET MODEL
      logger.info('Set up classifier')
      model = build_model(args, i_cv)
      os.makedirs(model.results_path, exist_ok=True)
      flush(logger)
  
      # TRAINING / LOADING
      train_or_load_pivot(model, train_generator, config.N_TRAINING_SAMPLES*N_AUGMENT, retrain=args.retrain)
  
      # CHECK TRAINING
      logger.info('Generate validation data')
      X_valid, y_valid, w_valid = valid_generator.generate(*config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES)
  
      result_row.update(evaluate_neural_net(model, prefix='valid'))
      result_row.update(evaluate_classifier(model, X_valid, y_valid, w_valid, prefix='valid'))
  
      # MEASUREMENT
      evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='')
      iter_results = [run_conditional_estimation_iter(model, result_row, i, test_config, valid_generator, test_generator, n_bins=N_BINS)
                      for i, test_config in enumerate(config.iter_test_config())]
  
      conditional_estimate = pd.concat(iter_results)
      conditional_estimate['i_cv'] = i_cv
      fname = os.path.join(model.results_path, "conditional_estimations.csv")
      conditional_estimate.to_csv(fname)
      logger.info('DONE')
      return conditional_estimate
  
  
  def run_conditional_estimation_iter(model, result_row, i_iter, config, valid_generator, test_generator, n_bins=N_BINS):
      logger = logging.getLogger()
      logger.info('-'*45)
      logger.info(f'iter : {i_iter}')
      flush(logger)
  
      iter_directory = os.path.join(model.results_path, f'iter_{i_iter}')
      os.makedirs(iter_directory, exist_ok=True)
  
      logger.info('Generate testing data')
      test_generator.reset()
      X_test, y_test, w_test = test_generator.generate(*config.TRUE, n_samples=config.N_TESTING_SAMPLES, no_grad=True)
      # SUMMARIES
      logger.info('Set up NLL computer')
      compute_summaries = model.summary_computer(n_bins=n_bins)
      compute_nll = NLLComputer(compute_summaries, valid_generator, X_test, w_test, config=config)
  
      # MEASURE STAT/SYST VARIANCE
      logger.info('MEASURE STAT/SYST VARIANCE')
      conditional_results = make_conditional_estimation(compute_nll, config)
      fname = os.path.join(iter_directory, "no_nuisance.csv")
      conditional_estimate = pd.DataFrame(conditional_results)
      conditional_estimate['i'] = i_iter
      conditional_estimate.to_csv(fname)
  
      return conditional_estimate
  
  
  def make_conditional_estimation(compute_nll, config):
      results = []
      for j, nuisance_parameters in enumerate(config.iter_nuisance()):
          compute_nll_no_nuisance = lambda mu : compute_nll(*nuisance_parameters, mu)
          minimizer = get_minimizer_no_nuisance(compute_nll_no_nuisance, config.CALIBRATED, config.CALIBRATED_ERROR)
          results_row = evaluate_minuit(minimizer, config.TRUE, do_hesse=False)
          results_row['j'] = j
          for name, value in zip(config.CALIBRATED.nuisance_parameters_names, nuisance_parameters):
              results_row[name] = value
              results_row[name+_TRUTH] = config.TRUE[name]
          results.append(results_row)
      return results
  
  
  if __name__ == '__main__':
      main()
  