def test_Diversity_list_all_equals(self):
from Base.Evaluation.metrics import Diversity_MeanInterList
import scipy.sparse as sps
n_items = 3
n_users = 10
cutoff = min(5, n_items)
# create recommendation list
URM_predicted_row = []
URM_predicted_col = []
diversity_list = Diversity_MeanInterList(n_items, cutoff)
item_id_list = np.arange(0, n_items, dtype=np.int)
for n_user in range(n_users):
np.random.shuffle(item_id_list)
recommended = item_id_list[:cutoff]
URM_predicted_row.extend([n_user] * cutoff)
URM_predicted_col.extend(recommended)
diversity_list.add_recommendations(recommended)
object_diversity = diversity_list.get_metric_value()
URM_predicted_data = np.ones_like(URM_predicted_row)
URM_predicted_sparse = sps.csr_matrix(
(URM_predicted_data, (URM_predicted_row, URM_predicted_col)),
dtype=np.int)
co_counts = URM_predicted_sparse.dot(URM_predicted_sparse.T).toarray()
np.fill_diagonal(co_counts, 0)
all_user_couples_count = n_users**2 - n_users
diversity_cumulative = 1 - co_counts / cutoff
np.fill_diagonal(diversity_cumulative, 0)
diversity_cooccurrence = diversity_cumulative.sum(
) / all_user_couples_count
assert np.isclose(diversity_cooccurrence, object_diversity,
atol=1e-4), "metric incorrect"
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