haiku-lst 0.1.1.1

Short Text Classification

Introduce

A Modification of LibShortText and LIBLINEAR.

• Uses Wissen Text Analyzer

• Feature Selection

• API Exported by Skitai App Engine

• Win32 support (need MSVC)

Installation

git clone https://gitlab.com/hansroh/haiku
cd haiku
python setup.py build install

Basic Usage

import haiku

model_path = "./golforbed"
analyzer =  haiku.StandardAnalyzer (max_term = 200, stem_level = 2, make_lower_case = 1)

trainset = [
    ('Golf', "cloudy cold calm"),
    ('Golf', "sunny warm"),
    ('Bed', "rainy hot"),
    ('Golf', "sunny hot windy"),
    ('Bed', "windy cloudy cold"),
    ('Bed', "rainy cloudy cold"),
]

# training
h = haiku.Haiku (model_path, haiku.CL_L2, analyzer)
# pruning by document frequency and scoring by meth (FS_CF means category frequency)
h.select (data, mindf = 0, maxdf = 0, top = 0, meth = haiku.FS_CF)
# set training options: uni/bigram and feature representation
h.train (haiku.BIGRAM, haiku.FT_BIN)
h.close ()

# guessing
h = haiku.Haiku (model_path, haiku.CL_L2, analyzer)
h.load ()
print (h.guess ("sunny cold windy"))
h.close ()

Exporting API through Skitai App Engine

Place model data into app_root/resources/haikus/golforbed.

import haiku
import skitai

if __name__ == "__main__":

  pref = skitai.pref ()
  pref.config.resource_dir = skitai.joinpath ('resources')
  skitai.mount ("/", haiku, "app", pref)
  skitai.run (port = 5005)

Go to http://127.0.0.1:5000/haiku/golforbed/guess?q=sunny%20cold%20windy.