#!/usr/bin/python3

import time


import psycopg2 
import psycopg2.extras as ppe

t0 = time.time()
db = psycopg2.connect("dbname=bayes")
csr = db.cursor(cursor_factory=ppe.DictCursor)
csr.execute(
        """
        delete from features
        """)
t1 = time.time()
print(t1 - t0, "deleted", csr.rowcount, "rows")
csr.execute(
        """
        insert into features(
            type, length, feature,
            spam_count, ham_count,
            spam_prob,
            interesting
        )
        with
            m as (
                select
                    count(*) filter(where type='spam') as spam_message_count,
                    count(*) filter(where type='ham') as ham_message_count
                from messages
            ),
            f as (
                select f.type, length, feature,
                    count(*) filter (where m.type = 'spam') as spam_count,
                    count(*) filter (where m.type = 'ham') as ham_count
                from message_features f join messages m on (f.message = m.id)
                group by f.type, length, feature
            ),
            f1 as (
                select
                    type, length, feature,
                    spam_count, ham_count,
                    spam_count::float8 / spam_message_count as spam_ratio,
                    ham_count::float8 / ham_message_count as ham_ratio
                from f, m
            ),
            p as (
                select
                    type, length, feature,
                    spam_count, ham_count,
                    case 
                        when spam_count + ham_count > 4 then spam_ratio / (spam_ratio + ham_ratio)
                    end as spam_prob
                from f1
            ),
            p1 as (
                select
                    type, length, feature,
                    spam_count, ham_count,
                    case
                        when spam_prob < 0.01 then 0.01
                        when spam_prob > 0.99 then 0.99
                        else spam_prob
                    end as spam_prob
                from p
            ),
            p2 as (
                select
                    type, length, feature,
                    spam_count, ham_count,
                    spam_prob,
                    abs(spam_prob - 0.5) as interesting
                from p1
            )
        select * from p2
        order by interesting desc
        """)
t1 = time.time()
print(t1 - t0, "inserted", csr.rowcount, "rows")
db.commit()