#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import sys
from bigdl.util.common import JavaValue
from zoo.common.utils import callZooFunc
if sys.version >= '3':
long = int
unicode = str
[docs]class Ranker(JavaValue):
"""
Base class for Ranking models (e.g., TextMatcher and Ranker) that
provides validation methods with different metrics.
"""
[docs] def evaluate_ndcg(self, x, k, threshold=0.0):
"""
Evaluate using normalized discounted cumulative gain on TextSet.
:param x: TextSet. Each TextFeature should contain Sample with batch features and labels.
In other words, each Sample should be a batch of records having both positive
and negative labels.
:param k: Positive int. Rank position.
:param threshold: Float. If label > threshold, then it will be considered as
a positive record. Default is 0.0.
:return: Float. NDCG result.
"""
return callZooFunc(self.bigdl_type, "evaluateNDCG",
self.value, x, k, threshold)
[docs] def evaluate_map(self, x, threshold=0.0):
"""
Evaluate using mean average precision on TextSet.
:param x: TextSet. Each TextFeature should contain Sample with batch features and labels.
In other words, each Sample should be a batch of records having both positive
and negative labels.
:param threshold: Float. If label > threshold, then it will be considered as
a positive record. Default is 0.0.
:return: Float. MAP result.
"""
return callZooFunc(self.bigdl_type, "evaluateMAP",
self.value, x, threshold)