text: # -*- coding: utf-8 -*-
  import time
  import json
  
  import numpy as np
  from hyperopt import STATUS_OK as HOPT_STATUS_OK
  
  import buffalo.data
  from buffalo.misc import aux, log
  from buffalo.data.base import Data
  from buffalo.algo._als import CyALS
  from buffalo.evaluate import Evaluable
  from buffalo.algo.options import ALSOption
  from buffalo.algo.optimize import Optimizable
  from buffalo.algo.tensorflow._als import TFALS
  from buffalo.data.buffered_data import BufferedDataMatrix
  from buffalo.algo.base import Algo, Serializable, TensorboardExtention
  
  try:
      from buffalo.algo.cuda._als import CyALS as CuALS
  except Exception as e:
      log.get_logger("system").error(f"ImportError CuALS, no cuda library exists. error message: {e}")
      CuALS = lambda x: ()
  
  
  class ALS(Algo, ALSOption, Evaluable, Serializable, Optimizable, TensorboardExtention):
      """Python implementation for C-ALS.
  
      Implementation of Collaborative Filtering for Implicit Feedback datasets.
  
      Reference: http://yifanhu.net/PUB/cf.pdf"""
      def __init__(self, opt_path=None, *args, **kwargs):
          Algo.__init__(self, *args, **kwargs)
          ALSOption.__init__(self, *args, **kwargs)
          Evaluable.__init__(self, *args, **kwargs)
          Serializable.__init__(self, *args, **kwargs)
          Optimizable.__init__(self, *args, **kwargs)
          if opt_path is None:
              opt_path = ALSOption().get_default_option()
  
          self.logger = log.get_logger('ALS')
          self.opt, self.opt_path = self.get_option(opt_path)
          self.obj = CuALS() if self.opt.accelerator else CyALS()
          assert self.obj.init(bytes(self.opt_path, 'utf-8')),\
              'cannot parse option file: %s' % opt_path
          self.data = None
          data = kwargs.get('data')
          data_opt = self.opt.get('data_opt')
          data_opt = kwargs.get('data_opt', data_opt)
          if data_opt:
              self.data = buffalo.data.load(data_opt)
              self.data.create()
          elif isinstance(data, Data):
              self.data = data
          self.logger.info('ALS(%s)' % json.dumps(self.opt, indent=2))
          if self.data:
              self.logger.info(self.data.show_info())
              assert self.data.data_type in ['matrix']
  
      @staticmethod
      def new(path, data_fields=[]):
          return ALS.instantiate(ALSOption, path, data_fields)
  
      def set_data(self, data):
          assert isinstance(data, aux.data.Data), 'Wrong instance: {}'.format(type(data))
          self.data = data
  
      def normalize(self, group='item'):
          if group == 'item' and not self.opt._nrz_Q:
              self.Q = self._normalize(self.Q)
              self.opt._nrz_Q = True
          elif group == 'user' and not self.opt._nrz_P:
              self.P = self._normalize(self.P)
              self.opt._nrz_P = True
  
      def initialize(self):
          super().initialize()
          self.init_factors()
  
      def init_factors(self):
          assert self.data, 'Data is not setted'
          self.vdim = self.obj.get_vdim() if self.opt.accelerator else self.opt.d
          header = self.data.get_header()
          for name, rows in [('P', header['num_users']), ('Q', header['num_items'])]:
              setattr(self, name, None)
              setattr(self, name, np.abs(np.random.normal(scale=1.0/(self.opt.d ** 2),
                                         size=(rows, self.vdim)).astype("float32")))
          self.P[:, self.opt.d:] = 0.0
          self.Q[:, self.opt.d:] = 0.0
          self.obj.initialize_model(self.P, self.Q)
  
      def _get_topk_recommendation(self, rows, topk, pool=None):
          p = self.P[rows]
          topks = super()._get_topk_recommendation(p, self.Q, pool, topk, self.opt.num_workers)
          return zip(rows, topks)
  
      def _get_most_similar_item(self, col, topk, pool):
          return super()._get_most_similar_item(col, topk, self.Q, self.opt._nrz_Q, pool)
  
      def get_scores(self, row_col_pairs):
          rets = {(r, c): self.P[r].dot(self.Q[c]) for r, c in row_col_pairs}
          return rets
  
      def _get_buffer(self):
          buf = BufferedDataMatrix()
          buf.initialize(self.data)
          return buf
  
      def _iterate(self, buf, group='rowwise'):
          header = self.data.get_header()
          # end = header['num_users'] if group == 'rowwise' else header['num_items']
          int_group = 0 if group == 'rowwise' else 1
          st = time.time()
          self.obj.precompute(int_group)
          el, st = time.time() - st, time.time()
          loss_nume, loss_deno = 0.0, 0.0
          update_t, feed_t, updated = el, 0, 0
          buf.set_group(group)
          with log.pbar(log.DEBUG, desc='%s' % group,
                        total=header['num_nnz'], mininterval=30) as pbar:
              for sz in buf.fetch_batch():
                  updated += sz
                  start_x, next_x, indptr, keys, vals = buf.get()
                  _feed_t, st = time.time() - st, time.time()
  
                  _loss_nume, _loss_deno = self.obj.partial_update(start_x, next_x, indptr, keys, vals, int_group)
                  loss_nume += _loss_nume
                  loss_deno += _loss_deno
  
                  _update_t, st = time.time() - st, time.time()
                  pbar.update(sz)
                  feed_t += _feed_t
                  update_t += _update_t
          self.logger.debug(f'{group} updated: processed({updated}) elapsed(data feed: {feed_t:0.3f}s update: {update_t:0.03}s)')
          return loss_nume, loss_deno
  
      def train(self):
          if self.opt.accelerator:
              for attr in ["P", "Q"]:
                  F = getattr(self, attr)
                  if F.shape[1] < self.vdim:
                      _F = np.empty(shape=(F.shape[0], self.vdim), dtype=np.float32)
                      _F[:, :self.P.shape[1]] = F
                      _F[:, self.opt.d: ] = 0.0
                      setattr(self, attr, _F)
              self.obj.initialize_model(self.P, self.Q)
  
          buf = self._get_buffer()
          if self.opt.accelerator:
              lindptr, rindptr, batch_size = buf.get_indptrs()
              self.obj.set_placeholder(lindptr, rindptr, batch_size)
  
          best_loss, rmse, self.validation_result = 987654321.0, None, {}
          self.prepare_evaluation()
          self.initialize_tensorboard(self.opt.num_iters)
          full_st = time.time()
          for i in range(self.opt.num_iters):
              start_t = time.time()
  
              _loss_nume1, _loss_deno1 = self._iterate(buf, group='rowwise')
              _loss_nume2, _loss_deno2 = self._iterate(buf, group='colwise')
              loss_nume = _loss_nume1 + _loss_nume2
              loss_deno = _loss_deno1 + _loss_deno2
  
              train_t = time.time() - start_t
              rmse = (loss_nume / (loss_deno + self.opt.eps)) ** 0.5
              metrics = {'train_loss': rmse}
              if self.opt.validation and self.opt.evaluation_on_learning and self.periodical(self.opt.evaluation_period, i):
                  start_t = time.time()
                  self.validation_result = self.get_validation_results()
                  vali_t = time.time() - start_t
                  val_str = ' '.join([f'{k}:{v:0.5f}' for k, v in self.validation_result.items()])
                  self.logger.info(f'Validation: {val_str} Elapsed {vali_t:0.3f} secs')
                  metrics.update({'val_%s' % k: v
                                  for k, v in self.validation_result.items()})
              self.logger.info('Iteration %d: RMSE %.3f Elapsed %.3f secs' % (i + 1, rmse, train_t))
              self.update_tensorboard_data(metrics)
              best_loss = self.save_best_only(rmse, best_loss, i)
              if self.early_stopping(rmse):
                  break
          full_el = time.time() - full_st
          self.logger.info(f"elapsed for full epochs: {full_el:.2f} sec")
          if self.opt.accelerator and self.opt.d < self.vdim:
              self.P = self.P[:, :self.opt.d]
              self.Q = self.Q[:, :self.opt.d]
          ret = {'train_loss': rmse}
          ret.update({'val_%s' % k: v
                      for k, v in self.validation_result.items()})
          self.finalize_tensorboard()
          return ret
  
      def _optimize(self, params):
          self._optimize_params = params
          for name, value in params.items():
              assert name in self.opt, 'Unexepcted parameter: {}'.format(name)
              setattr(self.opt, name, value)
          with open(self._temporary_opt_file, 'w') as fout:
              json.dump(self.opt, fout, indent=2)
          assert self.obj.init(bytes(self._temporary_opt_file, 'utf-8')),\
              'cannot parse option file: %s' % self._temporary_opt_file
          self.logger.info(params)
          self.initialize()
          loss = self.train()
          loss['eval_time'] = time.time()
          loss['loss'] = loss.get(self.opt.optimize.loss)
          if any([metric in self.opt.optimize.loss for metric in ['ndcg', 'map', 'accracy']]):
              loss['loss'] *= -1
          # TODO: deal with failture of training
          loss['status'] = HOPT_STATUS_OK
          self._optimize_loss = loss
          return loss
  
      def _get_feature(self, index, group='item'):
          if group == 'item':
              return self.Q[index]
          elif group == 'user':
              return self.P[index]
          return None
  
      def _get_data(self):
          data = super()._get_data()
          data.extend([('opt', self.opt),
                       ('Q', self.Q),
                       ('P', self.P)])
          return data
  
      def get_evaluation_metrics(self):
          return ['train_loss', 'val_rmse', 'val_ndcg', 'val_map', 'val_accuracy', 'val_error']
  