marr75

with table_stats as (
select psut.relname,
  psut.n_live_tup,
  1.0 * psut.idx_scan / greatest(1, psut.seq_scan + psut.idx_scan) as index_use_ratio
from pg_stat_user_tables psut
order by psut.n_live_tup desc
),
table_io as (
select psiut.relname,
  sum(psiut.heap_blks_read) as table_page_read,

marr75

marr75

marr75

Finished: Digital Clock In JavaScript

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License.

marr75

def evaluate_threshold_binary_classification(x: pandas.Series, y: pandas.Series, reverse=False):
    """ Calculate quality metrics such as True Positive, True Negative, False Positive, False Negative, Precision, Recall, and F1
        for all possible thresholds of a metric x being used to predict a classification against a binary class, y.
        x: the independent variable we will use as a predictor, a continuos variable
        y: the dependent variable representing the class we are predicting, 0/1 or bool
        reverse: whether the x and y series have an inverse relationship
    """
    predictor = pd.DataFrame(
        {
            'discriminant': x * (1 and reverse or -1),