e4488b9b68416439a226b48b74c22ae84c129db1,magpie/api.py,,batch_test,#Any#Any#Any#Any#Any#Any#,148

Before Change

    // Load the model
    model = load_from_disk(model_path)

    precision_list = []
    recall_list = []
    f1_list = []

    if verbose:
        print("Batches:", end=" ")

    no_more_samples = False
    batch_number = 0
    while not no_more_samples:
        batch_number += 1

        batch = []
        for i in xrange(batch_size):
            try:
                batch.append(doc_generator.next())
            except StopIteration:
                no_more_samples = True
                break

        X, answers, kw_vector = build_test_matrices(
            batch,
            model,
            testset_path,
            ontology,
        )

        // Predict
        y_pred = model.scale_and_predict(X)

        // Evaluate the results
        precision, recall = evaluate_results(
            y_pred,
            kw_vector,
            answers,
        )

        f1 = (2 * precision * recall) / (precision + recall)

        precision_list.append(precision)
        recall_list.append(recall)
        f1_list.append(f1)

        if verbose:

After Change

    start_time = time.clock()

    all_metrics = ["map", "mrr", "ndcg", "r_prec", "p_at_3", "p_at_5"]
    metrics_agg = {m: [] for m in all_metrics}

    if verbose:
        print("Batches:", end=" ")

    no_more_samples = False
    batch_number = 0
    while not no_more_samples:
        batch_number += 1

        batch = []
        for i in xrange(batch_size):
            try:
                batch.append(doc_generator.next())
            except StopIteration:
                no_more_samples = True
                break

        X, answers, kw_vector = build_test_matrices(
            batch,
            model,
            testset_path,
            ontology,
        )

        // Predict
        y_pred = model.scale_and_predict_confidence(X)

        // Evaluate the results
        metrics = evaluate_results(
            y_pred,
            kw_vector,
            answers,
        )
        for k, v in metrics.iteritems():
            metrics_agg[k].append(v)

        if verbose:
            sys.stdout.write(b".")
            sys.stdout.flush()

    if verbose:
        print()
        print("Testing finished in: {0:.2f}s".format(time.clock() - start_time))

    return {k: np.mean(v) for k, v in metrics_agg.iteritems()}


def train(

Instances