prf_val, df_val = eval_plot.evaluate_dbm(model_RNN, data, 'val_ids')
df_all = pd.concat([df_tr, df_val, df_test], axis=1)
dict_df_prf_mod['Epoch' + str(iter_)] = df_all
print('==' * 5 + "Epoch No:" + str(iter_) + "==" * 5)
print("Training Loss: " + str(total_loss))
print("==" * 4)
print("Train: " + str(prf_tr))
print(df_tr)
print("--" * 4)
print("Val: " + str(prf_val))
print(df_val)
print("--" * 4)
print("Test: " + str(prf_test))
print(df_test)
print('==' * 40)
print('\n')
if (save_flag):
torch.save(model_RNN,
'./student_life/models/' + model_name + str(iter_) + '.pt')
pickle.dump(
dict_df_prf_mod,
open(
'./student_life/results/dict_prf_' + model_name + str(iter_) +
'.pkl', 'wb'))
eval_plot.plot_graphs(
dict_df_prf_mod, 'F-score',
'./student_life/plots/' + model_name + str(iter_) + '.png', 0,
iter_ + 1, model_name)
print "=="*4
print "Train: " + str(prf_tr)
print df_tr
print "--"*4
print "Val: " + str(prf_val)
print df_val
print "--"*4
print "Test: " + str(prf_test)
print df_test
print '=='*40
print '\n'
if(save_flag):
torch.save(model_RNN, '../../Models/'+model_name+str(iter_)+'.pt')
pickle.dump(dict_df_prf_mod, open('../../Results/dict_prf_'+model_name+str(iter_)+'.pkl','wb'))
eval_plot.plot_graphs(dict_df_prf_mod, 'F-score',
'../../Plots/'+model_name+str(iter_)+'.png',
0, iter_+1,
model_name)
# In[9]:
eval_plot.plot_graphs(dict_df_prf_mod, 'F-score',
'../../Plots/'+model_name+str(iter_)+'.png',
0, iter_,
model_name)
# In[ ]:
print('==' * 5 + "Epoch No:" + str(iter_) + "==" * 5)
print("Training Loss: " + str(total_loss))
print("==" * 4)
print("Train: " + str(prf_tr))
print(df_tr)
print("--" * 4)
print("Val: " + str(prf_val))
print(df_val)
print("--" * 4)
print("Test: " + str(prf_test))
print(df_test)
print('==' * 40)
print('\n')
if (save_flag):
torch.save(model,
savepath + "/Models/" + model_name + str(iter_) + '.pt')
pickle.dump(
dict_df_prf_mod,
open(
savepath + '/Results/dict_prf_' + model_name + str(iter_) +
'.pkl', 'wb'))
# eval_plot.plot_graphs(dict_df_prf_mod, 'F-score', savepath+'/Plots/'+model_name+str(iter_)+'.png', 0, iter_+1, model_name)
eval_plot.plot_graphs(dict_df_prf_mod, 'F-score',
savepath + '/Plots/' + model_name + str(iter_) + '.png',
0, iter_, model_name)
# f.close()