jubaclient
Jubatus CLI client (unofficial)
Quick Start
npm install -g jubaclient
jubaclassifier -f ./config.json -D
echo '[ [ [ "baz", [ [ [ "foo", "bar" ] ] ] ] ] ]' | \
jubaclient classifier train | jq '.'
Requires
Installation
npm install -g jubaclient
Usage
jubaclient service method [-p port] [-h host] [-n name] [-t timeoutSeconds]
The jubaclient
command requests JSON received from standard input with the specified method to the Jubatus server, and returns the response to the standard output.
The command line options are as follows:
service
: sevice name (classifier
, nearest_neighbor
, etc.)method
: service method (get_status
, train
, get_k_center
, etc.)-p port
: port number (default 9190
)-h host
: hostname (default localhost
)-n name
: name of target cluster (default ''
)-t timeoutSeconds
: timeout (default 0
)
Examples
- #save(id)
echo '[ "jubaclient_save_1" ]' | jubaclient classifier save
- #get_status()
echo '[]' | jubaclient classifier get_status | jq '.'
- #get_config()
echo '[]' | jubaclient classifier get_config | jq '.|fromjson'
- classifier#train(data)
jubaclient classifier train <<EOF | jq '.'
[ [ [ "corge", [ [ [ "message", "<p>foo</p>" ] ] ] ] ] ]
[ [ [ "corge", [ [ [ "message", "<p>bar</p>" ] ] ] ] ] ]
[ [ [ "corge", [ [ [ "message", "<p>baz</p>" ] ] ] ] ] ]
[ [ [ "grault", [ [ [ "message", "<p>qux</p>" ] ] ] ] ] ]
[ [ [ "grault", [ [ [ "message", "<p>quux</p>" ] ] ] ] ] ]
EOF
- classifier#classify(data)
jubaclient classifier classify <<EOF | jq '.'
[ [ [ [ [ "message", "<b>quuz</b>" ] ] ] ] ]
EOF
Tutorial
Classifier
See also http://jubat.us/en/tutorial/classifier.html
-
start jubaclassifier
process.
jubaclassifier -D --configpath gender.json
-
train
cat train.csv \
| jq -RcM 'split(",")|[[[.[0],[[["hair",.[1]],["top",.[2]],["bottom",.[3]]],[["height",(.[4]|tonumber)]]]]]]' \
| jubaclient classifier train
-
classify
cat classify.csv \
| jq -RcM 'split(",")|[[[[["hair",.[0]],["top",.[1]],["bottom",.[2]]],[["height",(.[3]|tonumber)]]]]]' \
| jubaclient classifier classify \
| jq '.[]|max_by(.[1])'
configure: gender.json
{
"method": "AROW",
"converter": {
"num_filter_types": {}, "num_filter_rules": [],
"string_filter_types": {}, "string_filter_rules": [],
"num_types": {}, "num_rules": [],
"string_types": {
"unigram": { "method": "ngram", "char_num": "1" }
},
"string_rules": [
{ "key": "*", "type": "unigram", "sample_weight": "bin", "global_weight": "bin" }
]
},
"parameter": { "regularization_weight" : 1.0 }
}
training data: train.csv
male,short,sweater,jeans,1.70
female,long,shirt,skirt,1.56
male,short,jacket,chino,1.65
female,short,T shirt,jeans,1.72
male,long,T shirt,jeans,1.82
female,long,jacket,skirt,1.43
test data: classify.csv
short,T shirt,jeans,1.81
long,shirt,skirt,1.50