Makoto YUI myui

WITH data as (
  select 25.7724247 as lat, -80.1854473 as lon, 10 as zoom
  union all
  select 25.7724247 as lat, -80.1854473 as lon, 15 as zoom
)
select 
   map_url(lat,lon,zoom) as osm_url,
   map_url(lat,lon,zoom,'-type googlemaps') as gmap_url,
 tile(lat,lon,zoom) as tile_number

select feature_hashing(array("userid#4505:3.3","movieid#2331:4.999", "movieid#2331"));

["1828616:3.3","6238429:4.999","6238429"]

SELECT
  features_hashing(
 array_concat(

select
  recent1[3] as url,
  regexp_extract(
    recent1[3],
    '^https?://[^/]+(/[^/]+){0,2}'
  ) as extracted
from
  pre_hivemall_ac

SET sampleRate=0.1; -- in range (0.0-1.0]

select
    field1, field2, field3, ..., fieldN, state
from (
    select
        field1, field2, field3, ..., fieldN, state,
        count(*) over (partition by state) as state_cnt,
 rank() over (partition by state order by rand()) as state_rank

Data preparation

-- set mapred.max.split.size=128000000;
set hive.input.format=org.apache.hadoop.hive.ql.io.CombineHiveInputFormat;
set hive.tez.input.format=org.apache.hadoop.hive.ql.io.CombineHiveInputFormat;
set hive.mapjoin.smalltable.filesize=30000000;
-- set hive.optimize.s3.query=true;
set hive.exec.dynamic.partition.mode=nonstrict; 
set hive.optimize.sort.dynamic.partition=false;

	/*
	* 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

	use news20;

	set hivemall.smile.nprocs=4;

	drop table rf_model;
	create table rf_model
	as
	select train_randomforest_classifier(features,convert_label(label),'-trees 50 -seed 71')
	from train;

	digraph Tree {
	node [shape=box, style="filled, rounded", color="black", fontname=helvetica];
	edge [fontname=helvetica];
	0 [label=<pclass = 1.0>, fillcolor="#00000000"];
	1 [label=<sex = 0.0>, fillcolor="#00000000"];
	0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"];
	2 [label=<age ≤ 43.0>, fillcolor="#00000000"];
	1 -> 2;
	3 [label=<cabin = 0.0>, fillcolor="#00000000"];
	2 -> 3;

	import numpy as np
	from numpy import array
	from scipy.sparse import coo_matrix

	row = np.array([82, 8, 56, 86, 48, 20, 22, 98, 65, 98])
	col = np.array([80, 13, 51, 74, 16, 40, 64, 57, 40, 30])
	data = np.array([0.6805822, 0.23116356, 0.8527678, 0.3512172, 0.03575957, 0.2307719, 0.6499588, 0.66925836, 0.8698123, 0.041548133])

	coo = coo_matrix((data, (row, col)), shape=(100, 100))

	-- fitting
	select
	label, word, avg(lambda) as lambda
	from (
	select
	train_lda(feature, "-topic 2 -iter 20")
	as (label, word, lambda)
	from
	data
	) t1