text: train_ratio = 0.9
  use_dict = True
  use_scaler = False
  init_emb = False
  split_contins = True
  samp_size = 100000
  # samp_size = 0
  
  import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K
  import matplotlib.pyplot as plt, xgboost, operator, random, pickle, os
  from sklearn_pandas import DataFrameMapper
  from sklearn.preprocessing import LabelEncoder, Imputer, StandardScaler
  from keras.models import Model
  from keras.layers import merge, Input
  from keras.layers.core import Dense, Activation, Reshape, Flatten, Dropout
  from keras.layers.embeddings import Embedding
  from keras.optimizers import Adam
  from keras.layers.normalization import BatchNormalization
  from keras.regularizers import l2
  
  # from keras import initializations  # Keras 2 does not use this module
  np.set_printoptions(4)
  
  cfg = K.tf.ConfigProto()
  cfg.gpu_options.allow_growth = True
  K.set_session(K.tf.Session(config=cfg))
  
  os.chdir('data/rossman')
  cat_var_dict = {'Store': 50, 'DayOfWeek': 6, 'Year': 2, 'Month': 6,
                  'Day': 10, 'StateHoliday': 3, 'CompetitionMonthsOpen': 2,
                  'Promo2Weeks': 1, 'StoreType': 2, 'Assortment': 3, 'PromoInterval': 3,
                  'CompetitionOpenSinceYear': 4, 'Promo2SinceYear': 4, 'State': 6,
                  'Week': 2, 'Events': 4, 'Promo_fw': 1,
                  'Promo_bw': 1, 'StateHoliday_fw': 1,
                  'StateHoliday_bw': 1, 'SchoolHoliday_fw': 1,
                  'SchoolHoliday_bw': 1}
  
  cats, contins = [o for n, o in np.load('vars.npz').items()]
  y = np.load('deps.npz').items()[0][1]
  
  if samp_size != 0:
      np.random.seed(42)
      idxs = sorted(np.random.choice(len(y), samp_size, replace=False))
      cats = cats[idxs]
      contins = contins[idxs]
      y = y[idxs]
  
  n = len(y)
  train_size = int(n * train_ratio)
  
  contins_trn_orig, contins_val_orig = contins[:train_size], contins[train_size:]
  cats_trn, cats_val = cats[:train_size], cats[train_size:]
  y_trn, y_val = y[:train_size], y[train_size:]
  
  contin_map_fit = pickle.load(open('contin_maps.pickle', 'rb'))
  cat_map_fit = pickle.load(open('cat_maps.pickle', 'rb'))
  
  
  def cat_map_info(feat): return feat[0], len(feat[1].classes_)
  
  
  co_enc = StandardScaler().fit(contins_trn_orig)
  tf_contins_trn = co_enc.transform(contins_trn_orig)
  tf_contins_val = co_enc.transform(contins_val_orig)
  
  """
  def rmspe(y_pred, targ = y_valid_orig):
      return math.sqrt(np.square((targ - y_pred)/targ).mean())
  def log_max_inv(preds, mx = max_log_y): return np.exp(preds * mx)
  def normalize_inv(preds): return preds * ystd + ymean
  """
  
  
  def split_cols(arr): return np.hsplit(arr, arr.shape[1])
  
  
  def emb_init(shape, name=None):
      # - In Keras 2 the "initializations" module is not available.
      # - To keep here the custom initializer the code from Keras 1 "uniform" initializer is exploited
      # return initializations.uniform(shape, scale=0.6/shape[1], name=name)
      return K.variable(np.random.uniform(low=-2 / (shape[1] + 1), high=2 / (shape[1] + 1), size=shape),
                        name=name)
  
  
  def get_emb(feat):
      name, c = cat_map_info(feat)
      if use_dict:
          c2 = cat_var_dict[name]
      else:
          c2 = (c + 2) // 3
          if c2 > 50: c2 = 50
      inp = Input((1,), dtype='int64', name=name + '_in')
      if init_emb:
          u = Flatten(name=name + '_flt')(Embedding(c, c2, input_length=1)(inp))
      else:
          u = Flatten(name=name + '_flt')(Embedding(c, c2, input_length=1, init=emb_init)(inp))
      return inp, u
  
  
  def get_contin(feat):
      name = feat[0][0]
      inp = Input((1,), name=name + '_in')
      return inp, Dense(1, name=name + '_d')(inp)
  
  
  def split_data():
      if split_contins:
          map_train = split_cols(cats_trn) + split_cols(contins_trn)
          map_valid = split_cols(cats_val) + split_cols(contins_val)
      else:
          map_train = split_cols(cats_trn) + [contins_trn]
          map_valid = split_cols(cats_val) + [contins_val]
      return (map_train, map_valid)
  
  
  def get_contin_one():
      n_contin = contins_trn.shape[1]
      contin_inp = Input((n_contin,), name='contin')
      contin_out = BatchNormalization()(contin_inp)
      return contin_inp, contin_out
  
  
  def train(model, map_train, map_valid, bs=128, ne=10):
      return model.fit(map_train, y_trn, batch_size=bs, nb_epoch=ne,
                       verbose=0, validation_data=(map_valid, y_val))
  
  
  def get_model():
      if split_contins:
          conts = [get_contin(feat) for feat in contin_map_fit.features]
          cont_out = [d for inp, d in conts]
          cont_inp = [inp for inp, d in conts]
      else:
          contin_inp, contin_out = get_contin_one()
          cont_out = [contin_out]
          cont_inp = [contin_inp]
  
      embs = [get_emb(feat) for feat in cat_map_fit.features]
      x = merge([emb for inp, emb in embs] + cont_out, mode='concat')
  
      x = Dropout(0.02)(x)
      x = Dense(1000, activation='relu', init='uniform')(x)
      x = Dense(500, activation='relu', init='uniform')(x)
      x = Dense(1, activation='sigmoid')(x)
  
      model = Model([inp for inp, emb in embs] + cont_inp, x)
      model.compile('adam', 'mean_absolute_error')
      # model.compile(Adam(), 'mse')
      return model
  
  
  for split_contins in [True, False]:
      for use_dict in [True, False]:
          for use_scaler in [True, False]:
              for init_emb in [True, False]:
                  print({'split_contins': split_contins, 'use_dict': use_dict,
                         'use_scaler': use_scaler, 'init_emb': init_emb})
                  if use_scaler:
                      contins_trn = tf_contins_trn
                      contins_val = tf_contins_val
                  else:
                      contins_trn = contins_trn_orig
                      contins_val = contins_val_orig
  
                  map_train, map_valid = split_data()
                  model = get_model()
                  hist = np.array(train(model, map_train, map_valid, 128, 10)
                                  .history['val_loss'])
                  print(hist)
                  print(hist.min())
  