ce4a5ca7cab1ea4ae421fbcd3b28205b1e18158d,deepchem/models/tests/test_overfit.py,TestOverfitAPI,test_tf_skewed_missing_classification_overfit,#TestOverfitAPI#,475

Before Change

    possible.
    
    
    tasks = ["task0"]
    task_types = {task: "classification" for task in tasks}
    n_samples = 5120
    n_features = 6
    n_tasks = len(tasks)
    n_classes = 2
    
    // Generate dummy dataset
    np.random.seed(123)
    p = .002
    ids = np.arange(n_samples)
    X = np.random.rand(n_samples, n_features)
    y = np.random.binomial(1, p, size=(n_samples, n_tasks))
    w = np.ones((n_samples, n_tasks))
    y_flat, w_flat = np.squeeze(y), np.squeeze(w)
    y_nonzero = y_flat[w_flat != 0]
    num_nonzero = np.count_nonzero(y_nonzero)
    weight_nonzero = len(y_nonzero)/num_nonzero
    w_flat[y_flat != 0] = weight_nonzero
    w = np.reshape(w_flat, (n_samples, n_tasks))
  
    dataset = Dataset.from_numpy(self.train_dir, X, y, w, ids, tasks)

    model_params = {
      "layer_sizes": [1200],
      "dropouts": [.0],
      "learning_rate": 0.003,
      "momentum": .9,
      "batch_size": 75,
      "num_classification_tasks": 1,
      "num_classes": n_classes,
      "num_features": n_features,
      "weight_init_stddevs": [1.],
      "bias_init_consts": [1.],
      "nb_epoch": 250,
      "penalty": 0.0,
      "optimizer": "adam",
      "data_shape": dataset.get_data_shape()
    }

    verbosity = "high"
    classification_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
    model = TensorflowModel(

After Change

        n_tasks, n_features, self.model_dir, dropouts=[0.],
        learning_rate=0.003, weight_init_stddevs=[1.],
        batch_size=n_samples, verbosity=verbosity)
    model = TensorflowModel(tensorflow_model, self.model_dir)

    // Fit trained model
    model.fit(dataset, nb_epoch=50)
    model.save()

Instances