gen_bad_tabular

from fastai.tabular import *

def genNN(myDF, issue):  
  reframed = get_supervised_df(myDF, issue, 7)


  days = []
  myOrder = reframed.columns.to_list()

  for i in range(7):
    reframed["day%s"%i] = (reframed.index.weekday==i).astype(int)

  for i in range(7):
    days.append("day%s"%i)



  reframed = reframed[days+myOrder]


  procs = []
  dep_var = reframed.columns[-1]
  cont_names = reframed.columns[:-1].to_list()
  path = "/content/gdrive/My Drive/medium"

  trainSize = 45
  print(trainSize, "/", len(reframed), "in validation")

  test = TabularList.from_df(reframed[trainSize:].copy(), path=path)
  data = TabularList.from_df(reframed[:trainSize], path=path, cont_names=cont_names)
    
  data = data.split_by_idx(range(40,45))
  data = data.label_from_df(cols=dep_var, label_cls=FloatList)
  data = data.add_test(test).databunch(bs=40)
  
  y_range = [reframed[dep_var].min(), reframed[dep_var].max()]
  emb_szs = data.get_emb_szs()
  n_cont = len(data.cont_names)
  out_sz = data.c
  layers = [50, 25]
  model = TabularModel(emb_szs=emb_szs,
                       n_cont=n_cont,
                       out_sz=data.c,
                       layers=layers,
                       y_range=y_range)
  learn = Learner(data, model, metrics=rmse)
  
  return learn