Source code for cornac.metrics.rating

# Copyright 2018 The Cornac Authors. All Rights Reserved.
#
# 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 numpy as np


class RatingMetric:
    """Rating Metric.

    Attributes
    ----------
    name: string,
        Name of the measure.

    type: string, value: 'rating'
        Type of the metric, e.g., "ranking", "rating".

    """

    def __init__(self, name=None):
        self.type = 'rating'
        self.name = name

    def compute(self, **kwargs):
        raise NotImplementedError()


[docs]class MAE(RatingMetric): """Mean Absolute Error. Attributes ---------- name: string, value: 'MAE' Name of the measure. """ def __init__(self): RatingMetric.__init__(self, name='MAE') def compute(self, gt_ratings, pd_ratings, weights=None, **kwargs): """Compute Mean Absolute Error. Parameters ---------- gt_ratings: Numpy array Ground-truth rating values. pd_ratings: Numpy array Predicted rating values. weights: Numpy array, optional, default: None Weights for rating values. **kwargs: For compatibility Returns ------- mae: A scalar. Mean Absolute Error. """ mae = np.average(np.abs(gt_ratings - pd_ratings), axis=0, weights=weights) return mae
[docs]class MSE(RatingMetric): """Mean Squared Error. Attributes ---------- name: string, value: 'MSE' Name of the measure. """ def __init__(self): RatingMetric.__init__(self, name='MSE') def compute(self, gt_ratings, pd_ratings, weights=None, **kwargs): """Compute Mean Squared Error. Parameters ---------- gt_ratings: Numpy array Ground-truth rating values. pd_ratings: Numpy array Predicted rating values. weights: Numpy array, optional, default: None Weights for rating values. **kwargs: For compatibility Returns ------- mse: A scalar. Mean Squared Error. """ mse = np.average((gt_ratings - pd_ratings) ** 2, axis=0, weights=weights) return mse
[docs]class RMSE(RatingMetric): """Root Mean Squared Error. Attributes ---------- name: string, value: 'RMSE' Name of the measure. """ def __init__(self): RatingMetric.__init__(self, name='RMSE') def compute(self, gt_ratings, pd_ratings, weights=None, **kwargs): """Compute Root Mean Squared Error. Parameters ---------- gt_ratings: Numpy array Ground-truth rating values. pd_ratings: Numpy array Predicted rating values. weights: Numpy array, optional, default: None Weights for rating values. **kwargs: For compatibility Returns ------- rmse: A scalar. Root Mean Squared Error. """ mse = np.average((gt_ratings - pd_ratings) ** 2, axis=0, weights=weights) return np.sqrt(mse)