def _make_feature_stats_proto(feature_name, value_count_list,
weighted_value_count_list, is_categorical,
num_top_values, num_rank_histogram_buckets):
"""Makes a FeatureNameStatistics proto containing top-k and uniques stats."""
# Create a FeatureNameStatistics proto that includes the unweighted top-k
# stats.
result = top_k_stats_generator.make_feature_stats_proto_with_topk_stats(
feature_name, value_count_list, is_categorical, False, num_top_values,
num_rank_histogram_buckets)
# If weights were provided, create another FeatureNameStatistics proto that
# includes the weighted top-k stats, and then copy those weighted top-k stats
# into the result proto.
if weighted_value_count_list:
weighted_result = (
top_k_stats_generator.make_feature_stats_proto_with_topk_stats(
feature_name, weighted_value_count_list, is_categorical, True,
num_top_values, num_rank_histogram_buckets))
result.string_stats.weighted_string_stats.CopyFrom(
weighted_result.string_stats.weighted_string_stats)
# Add the number of uniques to the FeatureNameStatistics proto.
result.string_stats.unique = len(value_count_list)
return result
def test_make_feature_stats_proto_with_topk_stats_weighted(self):
expected_result = text_format.Parse(
"""
name: 'fa'
type: STRING
string_stats {
weighted_string_stats {
top_values {
value: 'a'
frequency: 4
}
top_values {
value: 'c'
frequency: 3
}
top_values {
value: 'd'
frequency: 2
}
rank_histogram {
buckets {
low_rank: 0
high_rank: 0
label: "a"
sample_count: 4.0
}
buckets {
low_rank: 1
high_rank: 1
label: "c"
sample_count: 3.0
}
}
}
}""", statistics_pb2.FeatureNameStatistics())
value_counts = [('a', 4), ('c', 3), ('d', 2), ('b', 2)]
top_k_value_count_list = [
top_k_stats_generator.FeatureValueCount(value_count[0], value_count[1])
for value_count in value_counts
]
result = top_k_stats_generator.make_feature_stats_proto_with_topk_stats(
'fa', top_k_value_count_list, False, True, 3, 2)
compare.assertProtoEqual(self, result, expected_result)