jubakit.anomaly のソースコード

# -*- coding: utf-8 -*-

from __future__ import absolute_import, division, print_function, unicode_literals

import jubatus
import jubatus.embedded

from .base import GenericSchema, BaseDataset, BaseService, GenericConfig
from .compat import *

[ドキュメント]class Schema(GenericSchema): """ Schema for Anomaly service. """ ID = 'i' FLAG = 'f'
[ドキュメント] def __init__(self, mapping, fallback=None): self._id_key = self._get_unique_mapping(mapping, fallback, self.ID, 'ID', True) self._flag_key = self._get_unique_mapping(mapping, fallback, self.FLAG, 'FLAG', True) super(Schema, self).__init__(mapping, fallback)
[ドキュメント] def transform(self, row): """ Anomaly schema transforms the row into Datum, its associated ID and flag. Flag can be a value of any type. It is provided for convenience to calculate precision. """ row_id = row.get(self._id_key, None) if row_id is not None: row_id = unicode_t(row_id) row_flag = row.get(self._flag_key, None) d = self._transform_as_datum(row, None, [self._id_key, self._flag_key]) return (row_id, row_flag, d)
[ドキュメント]class Dataset(BaseDataset): """ Dataset for Anomaly service. """ @classmethod def _predict(cls, row): return Schema.predict(row, False)
[ドキュメント]class Anomaly(BaseService): """ Anomaly service. """ @classmethod
[ドキュメント] def name(cls): return 'anomaly'
@classmethod def _client_class(cls): return jubatus.anomaly.client.Anomaly @classmethod def _embedded_class(cls): return jubatus.embedded.Anomaly
[ドキュメント] def add(self, dataset): """ Adds data points to the anomaly model using the given dataset and returns LOF scores. """ cli = self._client() for (idx, (row_id, row_flag, d)) in dataset: if row_id is not None: raise RuntimeError('ID-based datasets must use `overwrite` or `update` instead of `add`') result = cli.add(d) yield (idx, result.id, row_flag, result.score)
[ドキュメント] def add_bulk(self, dataset): """ Adds data points to the anomaly model using the given dataset and returns a list of data point IDs. """ cli = self._client() data = [d[1][2] for d in dataset] return cli.add_bulk(data)
[ドキュメント] def update(self, dataset): """ Updates data points in the anomaly model using the given dataset and returns LOF scores. """ cli = self._client() for (idx, (row_id, row_flag, d)) in dataset: if row_id is None: raise RuntimeError('Non ID-based datasets must use `add` instead of `update`') result = cli.update(row_id, d) yield (idx, row_id, row_flag, result)
[ドキュメント] def overwrite(self, dataset): """ Overwrites data points in the anomaly model using the given dataset and returns LOF scores. """ cli = self._client() for (idx, (row_id, row_flag, d)) in dataset: if row_id is None: raise RuntimeError('Non ID-based datasets must use `add` instead of `overwrite`') result = cli.overwrite(row_id, d) yield (idx, row_id, row_flag, result)
[ドキュメント] def calc_score(self, dataset): """ Calculates LOF scores for the given dataset. """ cli = self._client() for (idx, (row_id, row_flag, d)) in dataset: result = cli.calc_score(d) yield (idx, row_id, row_flag, result)
[ドキュメント]class Config(GenericConfig): """ Configuration to run Anomaly service. """ @classmethod
[ドキュメント] def methods(cls): return ['lof', 'light_lof']
@classmethod def _default_method(cls): return 'lof' @classmethod def _default_parameter(cls, method): params = { 'nearest_neighbor_num': 10, 'reverse_nearest_neighbor_num': 30, 'method': None, 'parameter': {}, 'ignore_kth_same_point': True, } if method == 'lof': params['method'] = 'inverted_index_euclid' elif method == 'light_lof': params['method'] = 'euclid_lsh' params['parameter'] = { 'threads': -1, # use number of logical CPU cores 'hash_num': 64, } else: raise RuntimeError('unknown method: {0}'.format(method)) return params