コード例 #1
0
ファイル: api.py プロジェクト: mmerce/python
        def _get_fields_key(resource):
            """Returns the fields key from a resource dict

            """
            if resource['code'] in [HTTP_OK, HTTP_ACCEPTED]:
                if (MODEL_RE.match(resource_id) or
                        ANOMALY_RE.match(resource_id)):
                    return resource['object']['model']['model_fields']
                elif CLUSTER_RE.match(resource_id):
                    return resource['object']['clusters']['fields']
                elif CORRELATION_RE.match(resource_id):
                    return resource['object']['correlations']['fields']
                elif STATISTICAL_TEST_RE.match(resource_id):
                    return resource['object']['statistical_tests']['fields']
                elif STATISTICAL_TEST_RE.match(resource_id):
                    return resource['object']['statistical_tests']['fields']
                elif LOGISTIC_REGRESSION_RE.match(resource_id):
                    return resource['object']['logistic_regression']['fields']
                elif ASSOCIATION_RE.match(resource_id):
                    return resource['object']['associations']['fields']
                elif TOPIC_MODEL_RE.match(resource_id):
                    return resource['object']['topic_model']['fields']
                elif SAMPLE_RE.match(resource_id):
                    return dict([(field['id'], field) for field in
                                 resource['object']['sample']['fields']])
                else:
                    return resource['object']['fields']
            return None
コード例 #2
0
        def _get_fields_key(resource):
            """Returns the fields key from a resource dict

            """
            if resource['code'] in [HTTP_OK, HTTP_ACCEPTED]:
                if (MODEL_RE.match(resource_id) or
                        ANOMALY_RE.match(resource_id)):
                    return resource['object']['model']['model_fields']
                elif CLUSTER_RE.match(resource_id):
                    return resource['object']['clusters']['fields']
                elif CORRELATION_RE.match(resource_id):
                    return resource['object']['correlations']['fields']
                elif STATISTICAL_TEST_RE.match(resource_id):
                    return resource['object']['statistical_tests']['fields']
                elif LOGISTIC_REGRESSION_RE.match(resource_id):
                    return resource['object']['logistic_regression']['fields']
                elif ASSOCIATION_RE.match(resource_id):
                    return resource['object']['associations']['fields']
                elif TOPIC_MODEL_RE.match(resource_id):
                    return resource['object']['topic_model']['fields']
                elif TIME_SERIES_RE.match(resource_id):
                    return resource['object']['time_series']['fields']
                elif DEEPNET_RE.match(resource_id):
                    return resource['object']['deepnet']['fields']
                elif SAMPLE_RE.match(resource_id):
                    return dict([(field['id'], field) for field in
                                 resource['object']['sample']['fields']])
                elif PCA_RE.match(resource_id):
                    return dict([(field['id'], field) for field in
                                 resource['object']['pca']['fields']])
                elif LINEAR_REGRESSION_RE.match(resource_id):
                    return resource['object']['linear_regression']['fields']
                else:
                    return resource['object']['fields']
            return None
コード例 #3
0
    def pprint(self, resource, out=sys.stdout):
        """Pretty prints a resource or part of it.

        """

        if (isinstance(resource, dict)
                and 'object' in resource
                and 'resource' in resource):

            resource_id = resource['resource']
            if (SOURCE_RE.match(resource_id) or DATASET_RE.match(resource_id)
                    or MODEL_RE.match(resource_id)
                    or EVALUATION_RE.match(resource_id)
                    or ENSEMBLE_RE.match(resource_id)
                    or CLUSTER_RE.match(resource_id)
                    or ANOMALY_RE.match(resource_id)
                    or TOPIC_MODEL_RE.match(resource_id)
                    or LOGISTIC_REGRESSION_RE.match(resource_id)
                    or TIME_SERIES_RE.match(resource_id)
                    or DEEPNET_RE.match(resource_id)
                    or FUSION_RE.match(resource_id)
                    or PCA_RE.match(resource_id)
                    or LINEAR_REGRESSION_RE.match(resource_id)
                    or OPTIML_RE.match(resource_id)):
                out.write("%s (%s bytes)\n" % (resource['object']['name'],
                                               resource['object']['size']))
            elif PREDICTION_RE.match(resource['resource']):
                objective_field_name = (
                    resource['object']['fields'][
                        resource['object']['objective_fields'][0]]['name'])
                input_data = {}
                for key, value in resource['object']['input_data'].items():
                    try:
                        name = resource['object']['fields'][key]['name']
                    except KeyError:
                        name = key
                    input_data[name] = value

                prediction = (
                    resource['object']['prediction'][
                        resource['object']['objective_fields'][0]])
                out.write("%s for %s is %s\n" % (objective_field_name,
                                                 input_data,
                                                 prediction))
            out.flush()
        else:
            pprint.pprint(resource, out, indent=4)
コード例 #4
0
ファイル: api.py プロジェクト: bigmlcom/python
    def pprint(self, resource, out=sys.stdout):
        """Pretty prints a resource or part of it.

        """

        if (isinstance(resource, dict)
                and 'object' in resource
                and 'resource' in resource):

            resource_id = resource['resource']
            if (SOURCE_RE.match(resource_id) or DATASET_RE.match(resource_id)
                    or MODEL_RE.match(resource_id)
                    or EVALUATION_RE.match(resource_id)
                    or ENSEMBLE_RE.match(resource_id)
                    or CLUSTER_RE.match(resource_id)
                    or ANOMALY_RE.match(resource_id)
                    or TOPIC_MODEL_RE.match(resource_id)
                    or LOGISTIC_REGRESSION_RE.match(resource_id)
                    or TIME_SERIES_RE.match(resource_id)
                    or DEEPNET_RE.match(resource_id)
                    or FUSION_RE.match(resource_id)
                    or PCA_RE.match(resource_id)
                    or LINEAR_REGRESSION_RE.match(resource_id)
                    or OPTIML_RE.match(resource_id)):
                out.write("%s (%s bytes)\n" % (resource['object']['name'],
                                               resource['object']['size']))
            elif PREDICTION_RE.match(resource['resource']):
                objective_field_name = (
                    resource['object']['fields'][
                        resource['object']['objective_fields'][0]]['name'])
                input_data = {}
                for key, value in resource['object']['input_data'].items():
                    try:
                        name = resource['object']['fields'][key]['name']
                    except KeyError:
                        name = key
                    input_data[name] = value

                prediction = (
                    resource['object']['prediction'][
                        resource['object']['objective_fields'][0]])
                out.write("%s for %s is %s\n" % (objective_field_name,
                                                 input_data,
                                                 prediction))
            out.flush()
        else:
            pprint.pprint(resource, out, indent=4)