<jupyter_start><jupyter_text>Next set of params..... {'latent_sz': 30, 'batch_sz': 3, 'epochs': 50, 'learning_rate': 0.0006260860555585102}Trn_Spearman:  0.7525 +/-0.0128 | Val_Spearman:  0.2228 +/-0.1453Trn_ACCLoss:   0.0245 +/-0.0040 | Val_ACCLoss:   0.0924 +/-0.0526Iteration No: 62 ended. Search finished for the next optimal point.Time taken: 530.1786Function value obtained: 0.0924 Next set of params..... {'latent_sz': 30, 'batch_sz': 40, 'epochs': 29, 'learning_rate': 0.0023670978180025}Trn_Spearman:  0.7219 +/-0.0134 | Val_Spearman:  0.2374 +/-0.1504Trn_ACCLoss:   0.0289 +/-0.0040 | Val_ACCLoss:   0.0908 +/-0.0486Iteration No: 69 ended. Search finished for the next optimal point.Time taken: 42.5270Function value obtained: 0.0908 Next set of params..... {'latent_sz': 30, 'batch_sz': 20, 'epochs': 43, 'learning_rate': 0.0017902338191623233}Trn_Spearman:  0.7426 +/-0.0126 | Val_Spearman:  0.2303 +/-0.1365Trn_ACCLoss:   0.0257 +/-0.0040 | Val_ACCLoss:   0.0927 +/-0.0517Iteration No: 73 ended. Search finished for the next optimal point.Time taken: 99.7439Function value obtained: 0.0927<jupyter_code>%%time
import lightgbm
from sklearn.model_selection import RepeatedKFold
kfolds = RepeatedKFold(10, n_repeats=10, random_state=42)
params = {'latent_sz': 30, 'batch_sz': 40, 'epochs': 29, 
          'learning_rate': 0.0023670978180025}
results, models = cv_neuralnet(X, y, y_scores, kfolds, params, 
                               verbose_folds=True)
acc = pd.read_csv('results/ACC_scores.csv')
acc['Neural Net'] = results[:,3]
acc.to_csv('results/results_neuralnet.csv', index=False)
acc = pd.melt(acc, value_vars=['Random', 'AR', 'AR Manhattan', 'KNN', 
                               'KNN Manhattan', 'LGBM', 'Tuned LGBM', 'Neural Net'], 
              var_name='Ranking Method', value_name='ACC Loss')

fig, ax = plt.subplots(figsize=(10,5))
sns.boxplot(x='Ranking Method', y='ACC Loss', data=acc)
%%time
import lightgbm
from sklearn.model_selection import RepeatedKFold
kfolds = RepeatedKFold(10, n_repeats=10, random_state=42)
params = {'latent_sz': 40, 'batch_sz': 40, 'epochs': 29, 
          'learning_rate': 0.0023670978180025}
results, models = cv_neuralnet(X, y, y_scores, kfolds, params, 
                               verbose_folds=True)<jupyter_output>Fold   1 | Trn_Spearman:  0.7251 | Val_Spearman:  0.2885 | Trn_ACCLoss:  0.0319 | Val_ACCLoss:  0.0778
Fold   2 | Trn_Spearman:  0.7189 | Val_Spearman:  0.0650 | Trn_ACCLoss:  0.0183 | Val_ACCLoss:  0.2213
Fold   3 | Trn_Spearman:  0.7662 | Val_Spearman: -0.0238 | Trn_ACCLoss:  0.0267 | Val_ACCLoss:  0.0671
Fold   4 | Trn_Spearman:  0.7160 | Val_Spearman:  0.2509 | Trn_ACCLoss:  0.0319 | Val_ACCLoss:  0.0853
Fold   5 | Trn_Spearman:  0.7206 | Val_Spearman:  0.2509 | Trn_ACCLoss:  0.0330 | Val_ACCLoss:  0.0539
Fold   6 | Trn_Spearman:  0.7303 | Val_Spearman:  0.3755 | Trn_ACCLoss:  0.0255 | Val_ACCLoss:  0.0846
Fold   7 | Trn_Spearman:  0.7392 | Val_Spearman:  0.1832 | Trn_ACCLoss:  0.0286 | Val_ACCLoss:  0.1711
Fold   8 | Trn_Spearman:  0.7373 | Val_Spearman:  0.2289 | Trn_ACCLoss:  0.0260 | Val_ACCLoss:  0.0706
Fold   9 | Trn_Spearman:  0.7168 | Val_Spearman:  0.2179 | Trn_ACCLoss:  0.0260 | Val_ACCLoss:  0.0605
Fold  10 | Trn_Spearman:  0.7440 | Val_Spearman:  0.1429 | Trn_ACCLoss:  [...]
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