new_contents: import os
  import time
  import json
  import numpy as np
  import pandas as pd
  from supervised.automl import AutoML
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import log_loss
  
  from sklearn.model_selection import train_test_split
  from pmlb import fetch_data, classification_dataset_names
  
  results = json.load(open("results.json"))
  
  for classification_dataset in classification_dataset_names:
  
      X, y = fetch_data(classification_dataset, return_X_y=True)
      if X.shape[0] < 1000:
          continue
      if classification_dataset in [r["dataset"] for r in results]:
          continue
      print(classification_dataset, X.shape, y[:5], np.unique(y))
  
      train_X, test_X, train_y, test_y = train_test_split(
          X, y, test_size=0.25, stratify=y, random_state=12
      )
      ml_task = "binary_classification"
      if len(np.unique(y)) > 2:
          ml_task = "multiclass_classification"
  
      mlp = AutoML(
          algorithms=["MLP"],
          mode="Perform",
          explain_level=0,
          train_ensemble=False,
          golden_features=False,
          features_selection=False,
          ml_task=ml_task,
      )
      nn = AutoML(
          algorithms=["Neural Network"],
          mode="Perform",
          explain_level=0,
          train_ensemble=False,
          golden_features=False,
          features_selection=False,
          ml_task=ml_task,
      )
  
      mlp.fit(train_X, train_y)
      mlp_time = np.round(time.time() - mlp._start_time, 2)
      nn.fit(train_X, train_y)
      nn_time = np.round(time.time() - nn._start_time, 2)
  
      mlp_ll = log_loss(test_y, mlp.predict_proba(test_X))
      nn_ll = log_loss(test_y, nn.predict_proba(test_X))
  
      print(classification_dataset, X.shape, np.unique(y), mlp_ll, nn_ll)
  
      results += [
          {
              "dataset": classification_dataset,
              "nrows": X.shape[0],
              "ncols": X.shape[1],
              "n_classes": len(np.unique(y)),
              "mlp_logloss": mlp_ll,
              "nn_logloss": nn_ll,
              "mlp_time": mlp_time,
              "nn_time": nn_time,
          }
      ]
  
      with open("results.json", "w") as fout:
          fout.write(json.dumps(results, indent=4))
  
subject: Create benchmark_classification.py