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q = calibrated probability 
  = p / (p + (1-p) / w)

https://pdfs.semanticscholar.org/daf9/ed5dc6c6bad5367d7fd8561527da30e9b8dd.pdf

where 
    p = predicted probability 
    w = negative down-sampling rate
 = (Neg/Neg+(Pos*k)) / (Neg/(Neg+Pos))

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69.613 129.070 52.111
70.670 128.161 52.446
72.303 128.450 52.853
73.759 127.522 51.786
74.085 129.067 53.352
74.561 134.031 50.992
74.911 134.944 50.744
75.205 129.162 52.800
75.395 129.711 52.844
75.554 132.642 51.427

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def auc(num_positives, num_negatives, predicted):
    l_sorted = sorted(range(len(predicted)),key=lambda i: predicted[i],
                      reverse=True)
    fp_cur = 0.0
    tp_cur = 0.0
    fp_prev = 0.0
    tp_prev = 0.0
    fp_sum = 0.0
    auc_tmp = 0.0
    last_score = float("nan")

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# -*- coding: utf-8 -*-

# sort list.txt | uniq | grep -v '#' | grep -v 'noreply' | grep -v 'local' | grep -e '\.' | grep -v 'internal' | grep -v 'contact'

import os
import sys
import requests
import time
from github3 import login
from tqdm import tqdm

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create table page (
  docid int,
  contents string
);

INSERT OVERWRITE TABLE page_exploded
select
  d.docid, 
  normalize_unicode(t.word) as word
from

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WITH term_frequency as (
  select
    docid, 
    word,
    freq
  from (
    select
      docid,
      tf(word) as word2freq
    from

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create table page (
  docid int,
  contents string
);

INSERT OVERWRITE TABLE page_exploded
select
  d.docid, 
  normalize_unicode(t.word) as word
from

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--------------------
Hivemall

Hivemall is a library for machine learning implemented as Hive
UDFs/UDAFs/UDTFs.

Hivemall has been incubating since 2016-09-13.

Three most important issues to address in the move towards graduation:

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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0

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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0