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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 / map.geojson
Last active December 5, 2016 19:58
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@marr75
marr75 / finished-digital-clock-in-javascript.markdown
Created January 16, 2021 16:51
Finished: Digital Clock In JavaScript
@marr75
marr75 / vectorized_f1_calc.py
Created January 6, 2023 15:54
Quick vectorized approach to calculating metrics at every potential breakpoint for a classification problem. Nice way to visualize precision, recall, and f1 in continuous graphs if you're introducing unsupervised machine learning or talking about very shallow decision trees.
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),