Source code for cornac.experiment.experiment

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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# ============================================================================

from .result import ExperimentResult
from .result import CVExperimentResult
from ..metrics.rating import RatingMetric
from ..metrics.ranking import RankingMetric
from ..models.recommender import Recommender

[docs]class Experiment: """ Experiment Class Parameters ---------- eval_method: :obj:`<cornac.eval_methods.BaseMethod>`, required The evaluation method (e.g., RatioSplit). models: array of :obj:`<cornac.models.Recommender>`, required A collection of recommender models to evaluate, e.g., [C2PF, HPF, PMF]. metrics: array of :obj:{`<cornac.metrics.RatingMetric>`, `<cornac.metrics.RankingMetric>`}, required A collection of metrics to use to evaluate the recommender models, \ e.g., [NDCG, MRR, Recall]. user_based: bool, optional, default: True This parameter is only useful if you are considering rating metrics. When True, first the average performance \ for every user is computed, then the obtained values are averaged to return the final result. If `False`, results will be averaged over the number of ratings. result: array of :obj:`<cornac.experiment.result.Result>`, default: None This attribute contains the results per-model of your experiment, initially it is set to None. """ def __init__(self, eval_method, models, metrics, user_based=True, verbose=False): self.eval_method = eval_method self.models = self._validate_models(models) self.metrics = self._validate_metrics(metrics) self.user_based = user_based self.verbose = verbose self.result = None @staticmethod def _validate_models(input_models): if not hasattr(input_models, "__len__"): raise ValueError('models have to be an array but {}'.format(type(input_models))) valid_models = [] for model in input_models: if isinstance(model, Recommender): valid_models.append(model) return valid_models @staticmethod def _validate_metrics(input_metrics): if not hasattr(input_metrics, "__len__"): raise ValueError('metrics have to be an array but {}'.format(type(input_metrics))) valid_metrics = [] for metric in input_metrics: if isinstance(metric, RatingMetric) or isinstance(metric, RankingMetric): valid_metrics.append(metric) return valid_metrics def _create_result(self): from ..eval_methods.cross_validation import CrossValidation if isinstance(self.eval_method, CrossValidation): self.result = CVExperimentResult() else: self.result = ExperimentResult() def run(self): self._create_result() for model in self.models: model_result = self.eval_method.evaluate(model=model, metrics=self.metrics, user_based=self.user_based) self.result.append(model_result) print('\n{}'.format(self.result))