def _create_empty_metrics_dict(cutoff_list, n_items, n_users, URM_train, URM_test, ignore_items, ignore_users, diversity_similarity_object):
empty_dict = {}
# global_RMSE_object = RMSE(URM_train + URM_test)
for cutoff in cutoff_list:
cutoff_dict = {}
for metric in EvaluatorMetrics:
if metric == EvaluatorMetrics.COVERAGE_ITEM:
cutoff_dict[metric.value] = Coverage_Item(n_items, ignore_items)
elif metric == EvaluatorMetrics.DIVERSITY_GINI:
cutoff_dict[metric.value] = Gini_Diversity(n_items, ignore_items)
elif metric == EvaluatorMetrics.SHANNON_ENTROPY:
cutoff_dict[metric.value] = Shannon_Entropy(n_items, ignore_items)
elif metric == EvaluatorMetrics.COVERAGE_USER:
cutoff_dict[metric.value] = Coverage_User(n_users, ignore_users)
elif metric == EvaluatorMetrics.DIVERSITY_MEAN_INTER_LIST:
cutoff_dict[metric.value] = Diversity_MeanInterList(n_items, cutoff)
elif metric == EvaluatorMetrics.DIVERSITY_HERFINDAHL:
cutoff_dict[metric.value] = Diversity_Herfindahl(n_items, ignore_items)
elif metric == EvaluatorMetrics.NOVELTY:
cutoff_dict[metric.value] = Novelty(URM_train)
elif metric == EvaluatorMetrics.AVERAGE_POPULARITY:
cutoff_dict[metric.value] = AveragePopularity(URM_train)
elif metric == EvaluatorMetrics.MAP:
cutoff_dict[metric.value] = MAP()
elif metric == EvaluatorMetrics.MRR:
cutoff_dict[metric.value] = MRR()
# elif metric == EvaluatorMetrics.RMSE:
# cutoff_dict[metric.value] = global_RMSE_object
elif metric == EvaluatorMetrics.DIVERSITY_SIMILARITY:
if diversity_similarity_object is not None:
cutoff_dict[metric.value] = copy.deepcopy(diversity_similarity_object)
else:
cutoff_dict[metric.value] = 0.0
empty_dict[cutoff] = cutoff_dict
return empty_dict
def create_empty_metrics_dict(n_items, n_users, URM_train, ignore_items,
ignore_users, cutoff,
diversity_similarity_object):
empty_dict = {}
# from Base.Evaluation.ResultMetric import ResultMetric
# empty_dict = ResultMetric()
for metric in EvaluatorMetrics:
if metric == EvaluatorMetrics.COVERAGE_ITEM:
empty_dict[metric.value] = Coverage_Item(n_items, ignore_items)
elif metric == EvaluatorMetrics.DIVERSITY_GINI:
empty_dict[metric.value] = Gini_Diversity(n_items, ignore_items)
elif metric == EvaluatorMetrics.SHANNON_ENTROPY:
empty_dict[metric.value] = Shannon_Entropy(n_items, ignore_items)
elif metric == EvaluatorMetrics.COVERAGE_USER:
empty_dict[metric.value] = Coverage_User(n_users, ignore_users)
elif metric == EvaluatorMetrics.DIVERSITY_MEAN_INTER_LIST:
empty_dict[metric.value] = Diversity_MeanInterList(n_items, cutoff)
elif metric == EvaluatorMetrics.DIVERSITY_HERFINDAHL:
empty_dict[metric.value] = Diversity_Herfindahl(
n_items, ignore_items)
elif metric == EvaluatorMetrics.NOVELTY:
empty_dict[metric.value] = Novelty(URM_train)
elif metric == EvaluatorMetrics.AVERAGE_POPULARITY:
empty_dict[metric.value] = AveragePopularity(URM_train)
elif metric == EvaluatorMetrics.MAP:
empty_dict[metric.value] = MAP()
elif metric == EvaluatorMetrics.MRR:
empty_dict[metric.value] = MRR()
elif metric == EvaluatorMetrics.DIVERSITY_SIMILARITY:
if diversity_similarity_object is not None:
empty_dict[metric.value] = copy.deepcopy(
diversity_similarity_object)
else:
empty_dict[metric.value] = 0.0
return empty_dict
def test_Gini_Index(self):
from Base.Evaluation.metrics import Gini_Diversity
n_items = 1000
gini_index = Gini_Diversity(n_items, ignore_items=np.array([]))
gini_index.recommended_counter = np.ones(n_items)
assert np.isclose(1.0, gini_index.get_metric_value(), atol=1e-2), "Gini_Index metric incorrect"
gini_index.recommended_counter = np.ones(n_items)*1e-12
gini_index.recommended_counter[0] = 1.0
assert np.isclose(0.0, gini_index.get_metric_value(), atol=1e-2), "Gini_Index metric incorrect"
