def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
        """
        Evaluate the model by making predictions of target values and comparing
        these to actual values.

        Parameters
        ----------
        dataset : SFrame
            Dataset of new observations. Must include columns with the same
            names as the target and features used for model training. Additional
            columns are ignored.

        metric : str, optional
            Name of the evaluation metric.  Possible values are:

            - 'auto'             : Returns all available metrics.
            - 'accuracy'         : Classification accuracy (micro average).
            - 'auc'              : Area under the ROC curve (macro average)
            - 'precision'        : Precision score (macro average)
            - 'recall'           : Recall score (macro average)
            - 'f1_score'         : F1 score (macro average)
            - 'log_loss'         : Log loss
            - 'confusion_matrix' : An SFrame with counts of possible prediction/true label combinations.
            - 'roc_curve'        : An SFrame containing information needed for an ROC curve

            For more flexibility in calculating evaluation metrics, use the
            :class:`~turicreate.evaluation` module.

        verbose : bool, optional
            If True, prints progress updates and model details.

        batch_size : int, optional
            If you are getting memory errors, try decreasing this value. If you
            have a powerful computer, increasing this value may improve performance.

        Returns
        -------
        out : dict
            Dictionary of evaluation results where the key is the name of the
            evaluation metric (e.g. `accuracy`) and the value is the evaluation
            score.

        See Also
        ----------
        create, predict, classify

        Examples
        ----------
        .. sourcecode:: python

          >>> results = model.evaluate(data)
          >>> print results['accuracy']
        """

        import os, json, math

        if(batch_size < 1):
            raise ValueError("'batch_size' must be greater than or equal to 1")

        extracted_features = self._extract_features(dataset, verbose=verbose, batch_size=batch_size)
        extracted_features[self.target] = dataset[self.target]
        
        metrics = self.classifier.evaluate(extracted_features, metric=metric, with_predictions=True)
        predictions = metrics["predictions"]["probs"]
        state = self.__proxy__.get_state()
        labels = state["classes"]


        def entropy(probs):
            return _reduce(lambda x, y: x + (y*math.log(1/y, 2) if y > 0 else 0) , probs, 0) / math.log(len(probs),2)

        def confidence(probs):
            return max(probs)

        def relative_confidence(probs):
            lp = len(probs)
            return probs[lp-1] - probs[lp-2]

        def get_confusion_matrix(extended_test, labels):
            #Init a matrix
            sf_confusion_matrix = {'label':[], 'predicted_label':[], 'prob_default':[]}
            for target_l in labels:
                for predicted_l in labels:
                    sf_confusion_matrix['label'].append(target_l)
                    sf_confusion_matrix['predicted_label'].append(predicted_l)
                    sf_confusion_matrix['prob_default'].append(0)

            sf_confusion_matrix = _tc.SFrame(sf_confusion_matrix)
            sf_confusion_matrix = sf_confusion_matrix.join(extended_test.groupby(['label', 'predicted_label'], {'count' :_tc.aggregate.COUNT}), how='left', on=['label','predicted_label'])
            sf_confusion_matrix = sf_confusion_matrix.fillna('count', 0)

            label_column = _tc.SFrame({'label': extended_test['label']})
            predictions = extended_test['probs']
            for i in range(0, len(labels)):
                new_test_data = label_column.add_columns([predictions.apply(lambda probs: probs[i]), predictions.apply(lambda probs: labels[i])], ['prob','predicted_label'])
                if (i==0):
                    test_longer_form = new_test_data
                else:
                    test_longer_form = test_longer_form.append(new_test_data)

            if len(extended_test) is 0:
                sf_confusion_matrix = sf_confusion_matrix.rename({'prob_default': 'prob', 'label': 'target_label'})
            else:
                sf_confusion_matrix = sf_confusion_matrix.join(test_longer_form.groupby(['label', 'predicted_label'], {'prob': _tc.aggregate.SUM('prob')}), how='left', on=['label', 'predicted_label'])
                sf_confusion_matrix = sf_confusion_matrix.rename({'label': 'target_label'}).fillna('prob', 0)
            
            def wo_divide_by_zero(a,b):
                if b==0:
                    return None
                else:
                    return a*1.0/b

            sf_confusion_matrix['norm_prob'] = sf_confusion_matrix.join(sf_confusion_matrix.groupby('target_label', {'sum_prob': _tc.aggregate.SUM('prob')}),how='left').apply(lambda x: wo_divide_by_zero(x['prob'], x['sum_prob']))
            return sf_confusion_matrix.fillna('norm_prob', 0)


        def hclusterSort(vectors, dist_fn):
            distances = []
            vecs = list(vectors)[:]
            for i in range(0, len(vecs)):
                for j in range(i+1, len(vecs)):
                    distances.append({'from': vecs[i], 'to': vecs[j], 'dist': dist_fn(vecs[i], vecs[j])})
            distances = sorted(distances, key=lambda d: d['dist'])
            excluding_names = []

            while(len(distances) > 0):
                min_dist = distances[0]

                new_vec = {'name': str(min_dist['from']['name']) + '|'+ str(min_dist['to']['name']),
                        'members': min_dist['from'].get('members', [min_dist['from']]) + min_dist['to'].get('members',[min_dist['to']])}

                excluding_names = [min_dist['from']['name'], min_dist['to']['name']]

                vecs = filter(lambda v: v['name'] not in excluding_names, vecs)
                distances = filter(lambda dist: (dist['from']['name'] not in excluding_names) and (dist['to']['name'] not in excluding_names), distances)

                for v in vecs:
                    total = 0
                    for vi in v.get('members', [v]):
                        for vj in new_vec['members']:
                            total += dist_fn(vi, vj)
                    distances.append({'from': v, 'to': new_vec, 'dist': total/len(v.get('members', [v]))/len(new_vec['members'])})

                vecs.append(new_vec)
                distances = sorted(distances, key=lambda d: d['dist'])

            return vecs

        def l2Dist(v1, v2):
            dist = 0
            for i in range(0, len(v1['pos'])):
                dist +=  math.pow(v1['pos'][i] - v2['pos'][i], 2)
            return math.pow(dist, 0.5)

    

        evaluation_result = {k: metrics[k] for k in ['accuracy', 'f1_score', 'log_loss', 'precision', 'recall', 'auc']}
        evaluation_result['num_test_examples'] = len(dataset)
        for k in ['num_classes', 'num_features', 'input_image_shape', 'num_examples', 'training_loss', 'training_time', 'model', 'max_iterations']:
            evaluation_result[k] = getattr(self, k)
        
        # Extend the given test data
        extended_test = dataset.add_column(predictions, 'probs')
        extended_test['label'] = dataset[self.target]
        extended_test = extended_test.add_columns( [extended_test.apply(lambda d: labels[d['probs'].index(confidence(d['probs']))]),
            extended_test.apply(lambda d: entropy(d['probs'])),
            extended_test.apply(lambda d: confidence(d['probs'])),
            extended_test.apply(lambda d: relative_confidence(d['probs']))],
            ['predicted_label', 'entropy', 'confidence', 'relative_confidence'])
        extended_test = extended_test.add_column(extended_test.apply(lambda d: d['label'] == d['predicted_label']), 'correct')

        # Calculate the confusion matrix
        sf_conf_mat = get_confusion_matrix(extended_test, labels)
        confidence_threshold = 0.5
        hesitant_threshold = 0.2
        evaluation_result['confidence_threshold'] = confidence_threshold
        evaluation_result['hesitant_threshold'] = hesitant_threshold
        evaluation_result['confidence_metric_for_threshold'] = 'relative_confidence'
        sf_hesitant_conf_mat = get_confusion_matrix(extended_test[extended_test[evaluation_result['confidence_metric_for_threshold']] < hesitant_threshold], labels)
        sf_confidently_wrong_conf_mat = get_confusion_matrix(extended_test[(extended_test[evaluation_result['confidence_metric_for_threshold']] > confidence_threshold) & (extended_test['correct']==True)], labels)

        evaluation_result['conf_mat'] = list(sf_conf_mat)
        evaluation_result['hesitant_conf_mat'] = list(sf_hesitant_conf_mat)
        evaluation_result['confidently_wrong_conf_mat'] = list(sf_confidently_wrong_conf_mat)

        
        # Get sorted labels (sorted by hCluster)
        vectors = map(lambda l: {'name': l, 'pos':list(sf_conf_mat[sf_conf_mat['target_label']==l].sort('predicted_label')['norm_prob'])},
                    labels)
        evaluation_result['sorted_labels'] = hclusterSort(vectors, l2Dist)[0]['name'].split("|")

        # Get recall and precision per label
        per_l = extended_test.groupby(['label'], {'count': _tc.aggregate.COUNT, 'correct_count': _tc.aggregate.SUM('correct') })
        per_l['recall'] = per_l.apply(lambda l: l['correct_count']*1.0 / l['count'])

        per_pl = extended_test.groupby(['predicted_label'], {'predicted_count': _tc.aggregate.COUNT, 'correct_count': _tc.aggregate.SUM('correct') })
        per_pl['precision'] = per_pl.apply(lambda l: l['correct_count']*1.0 / l['predicted_count'])
        per_pl = per_pl.rename({'predicted_label': 'label'})
        evaluation_result['label_metrics'] = list(per_l.join(per_pl, on='label', how='outer').select_columns(['label', 'count', 'correct_count', 'predicted_count', 'recall', 'precision']))
        evaluation_result['labels'] = labels
        
        extended_test = extended_test.add_row_number('__idx').rename({'label': 'target_label'})
        evaluation_result['test_data'] = extended_test
        evaluation_result['feature'] = self.feature

        return _Evaluation(evaluation_result)
Evaluate the model by making predictions of target values and comparing
        these to actual values.

        Parameters
        ----------
        dataset : SFrame
            Dataset of new observations. Must include columns with the same
            names as the target and features used for model training. Additional
            columns are ignored.

        metric : str, optional
            Name of the evaluation metric.  Possible values are:

            - 'auto'             : Returns all available metrics.
            - 'accuracy'         : Classification accuracy (micro average).
            - 'auc'              : Area under the ROC curve (macro average)
            - 'precision'        : Precision score (macro average)
            - 'recall'           : Recall score (macro average)
            - 'f1_score'         : F1 score (macro average)
            - 'log_loss'         : Log loss
            - 'confusion_matrix' : An SFrame with counts of possible prediction/true label combinations.
            - 'roc_curve'        : An SFrame containing information needed for an ROC curve

            For more flexibility in calculating evaluation metrics, use the
            :class:`~turicreate.evaluation` module.

        verbose : bool, optional
            If True, prints progress updates and model details.

        batch_size : int, optional
            If you are getting memory errors, try decreasing this value. If you
            have a powerful computer, increasing this value may improve performance.

        Returns
        -------
        out : dict
            Dictionary of evaluation results where the key is the name of the
            evaluation metric (e.g. `accuracy`) and the value is the evaluation
            score.

        See Also
        ----------
        create, predict, classify

        Examples
        ----------
        .. sourcecode:: python

          >>> results = model.evaluate(data)
          >>> print results['accuracy']