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Takuya Kitazawa takuti

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takuti / activities.csv
Last active January 28, 2021 22:20
Fitbit exported data (2020)
Date Calories Burned Steps Distance Floors Minutes Sedentary Minutes Lightly Active Minutes Fairly Active Minutes Very Active Activity Calories
2020-01-01 2,313 9,179 6.52 4 901 152 4 51 916
2020-01-02 2,367 10,634 7.56 17 718 206 24 23 1,032
2020-01-03 2,366 10,002 7.01 5 789 241 15 10 1,033
2020-01-04 2,740 15,201 10.72 15 622 315 42 16 1,542
2020-01-05 2,346 9,737 6.83 13 847 232 20 9 1,023
2020-01-06 2,714 15,106 10.68 12 662 238 29 67 1,461
2020-01-07 2,486 12,440 8.83 5 587 218 11 49 1,158
2020-01-08 2,869 19,895 14 6 648 181 48 107 1,599
2020-01-09 2,495 9,844 6.97 5 766 251 7 22 1,136
We can't make this file beautiful and searchable because it's too large.
time,user_id,source,conversion
1590863740,xobepz7opw,direct,0
1590863754,vpo60mcha1,facebook,0
1590864169,89u9knmqni,direct,0
1590864169,cdmgdvf6oo,google,0
1590864380,h0czqgvxbg,google,0
1590864409,cj98eurd91,google,0
1590864574,fqu9t0sd02,facebook,0
1590864646,89pp6xf2pb,google,0
1590864929,k1jtp0bz2j,facebook,0
@takuti
takuti / td-wf-partial-upload.sh
Created June 16, 2019 07:03
Uploading specific files in a folder to TD Workflow
#!/bin/bash
project_name=foo
project_files=(
"config/"
"scripts/"
"queries/"
"workflow1.dig"
"workflow2.dig"

Assume we have a binary classifier that gives the probability of being a positive sample in the [0.0, 1.0] range. Area Under the ROC Curve (AUC) quantitatively measures the accuracy of prediction made by such a classification model. Intuitively, what AUC does is to make sure if positive (i.e., label=1) samples in a validation set get higher probability of being positive than negative ones.

The AUC metric eventually gives a single value in [0.0, 1.0]. When we have five test samples sorted by their prediction results as follows, we can see that the classifier put higher probability to all positive samples, #1, #2, and #4, than the others. We define the best scenario as an AUC of 1.0.

Test sample # Probability of label=1 True label
1 0.8 1
2 0.7 1
4 0.6 1
3 0.5 0

Generic functions

  • convert_label(const int|const float) - Convert from -1|1 to 0.0f|1.0f, or from 0.0f|1.0f to -1|1

  • each_top_k(int K, Object group, double cmpKey, *) - Returns top-K values (or tail-K values when k is less than 0)

  • generate_series(const int|bigint start, const int|bigint end) - Generate a series of values, from start to end. A similar function to PostgreSQL's generate_serics. http://www.postgresql.org/docs/current/static/functions-srf.html

    select generate_series(1,9);
    
    
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@takuti
takuti / Dockerfile
Created January 21, 2018 23:12
Mock dockerfile for takuti.me
FROM node:alpine
ENV HUGO_VERSION=0.30.2
ADD https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_${HUGO_VERSION}_Linux-64bit.tar.gz /tmp
ADD . /src
WORKDIR /src
RUN \
# install hugo
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@takuti
takuti / jawikicorpus.py
Last active May 27, 2019 02:49 — forked from yuku/jawikicorpus.py
gensimに日本語Wikipediaを取り込むためのスクリプト
# coding: utf-8
"""USAGE: %(program)s WIKI_XML_DUMP OUTPUT_PREFIX
"""
import logging
import os.path
import sys
import gensim.corpora.wikicorpus as wikicorpus