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Darius Morawiec
committedDec 2, 2017
Add new templates, examples and tests for the LinearSVC classifier
·
v0.8.0v0.6.0
1 parent 2e98680 commit 562ed8c

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+642
-16
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‎examples/estimator/classifier/LinearSVC/java/basics.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {
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"scrolled": false
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},
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" }\n",
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"\n",
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" // Parameters:\n",
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" double[][] coefficients = {{0.18424209458473811, 0.45123000025163923, -0.80794587716737576, -0.45071660033253858}, {0.052877455748516447, -0.89214995228605254, 0.40398084459610972, -0.9376821661447452}, {-0.85070784319293802, -0.98670214922204336, 1.381010448739191, 1.8654095662423917}};\n",
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" double[] intercepts = {0.10956266406702335, 1.6636707776739579, -1.7096109416521363};\n",
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" double[][] coefficients = {{0.184242094585, 0.451230000252, -0.807945877167, -0.450716600333}, {0.0528774557485, -0.892149952286, 0.403980844596, -0.937682166145}, {-0.850707843193, -0.986702149222, 1.38101044874, 1.86540956624}};\n",
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" double[] intercepts = {0.109562664067, 1.66367077767, -1.70961094165};\n",
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"\n",
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" // Prediction:\n",
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" LinearSVC clf = new LinearSVC(coefficients, intercepts);\n",
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"\n",
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" }\n",
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" }\n",
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"}\n"
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"}\n",
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"CPU times: user 957 µs, sys: 951 µs, total: 1.91 ms\n",
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"Wall time: 1.08 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"\n",
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"from sklearn_porter import Porter\n",
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"\n",
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"porter = Porter(clf)\n",
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"output = porter.export()\n",
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"\n",
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"print(output)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run classification in Java:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Save the transpiled estimator:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"with open('LinearSVC.java', 'w') as f:\n",
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" f.write(output)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Compiling:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%%bash\n",
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"\n",
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"javac -cp . LinearSVC.java"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prediction:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2\n"
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]
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}
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],
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"source": [
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"%%bash\n",
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"\n",
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"java -cp . LinearSVC 1 2 3 4"
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]
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}
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],
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"metadata": {

‎examples/estimator/classifier/LinearSVC/java/basics_imported.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# sklearn-porter\n",
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"\n",
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"Repository: https://github.com/nok/sklearn-porter\n",
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"\n",
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"## LinearSVC\n",
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"\n",
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"Documentation: [sklearn.svm.LinearSVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Loading data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"((150, 4), (150,))\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"\n",
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"iris_data = load_iris()\n",
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"X = iris_data.data\n",
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"y = iris_data.target\n",
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"\n",
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"print(X.shape, y.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Train classifier:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
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" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
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" multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,\n",
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" verbose=0)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn import svm\n",
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"\n",
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"clf = svm.LinearSVC(C=1., random_state=0)\n",
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"clf.fit(X, y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Transpile classifier:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"import java.io.File;\n",
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"import java.io.FileNotFoundException;\n",
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"import java.util.*;\n",
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"import com.google.gson.Gson;\n",
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"\n",
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"\n",
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"class LinearSVC {\n",
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"\n",
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" private class Classifier {\n",
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" private double[][] coefficients;\n",
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" private double[] intercepts;\n",
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" }\n",
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"\n",
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" private Classifier clf;\n",
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"\n",
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" public LinearSVC(String file) throws FileNotFoundException {\n",
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" String jsonStr = new Scanner(new File(file)).useDelimiter(\"\\\\Z\").next();\n",
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" this.clf = new Gson().fromJson(jsonStr, Classifier.class);\n",
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" }\n",
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"\n",
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" public int predict(double[] features) {\n",
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" int classIdx = 0;\n",
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" double classVal = Double.NEGATIVE_INFINITY;\n",
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" for (int i = 0, il = this.clf.intercepts.length; i < il; i++) {\n",
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" double prob = 0.;\n",
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" for (int j = 0, jl = this.clf.coefficients[0].length; j < jl; j++) {\n",
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" prob += this.clf.coefficients[i][j] * features[j];\n",
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" }\n",
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" if (prob + this.clf.intercepts[i] > classVal) {\n",
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" classVal = prob + this.clf.intercepts[i];\n",
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" classIdx = i;\n",
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" }\n",
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" }\n",
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" return classIdx;\n",
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" }\n",
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"\n",
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" public static void main(String[] args) throws FileNotFoundException {\n",
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" if (args.length > 0 && args[0].endsWith(\".json\")) {\n",
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"\n",
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" // Features:\n",
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" double[] features = new double[args.length-1];\n",
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" for (int i = 1, l = args.length; i < l; i++) {\n",
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" features[i - 1] = Double.parseDouble(args[i]);\n",
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" }\n",
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"\n",
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" // Parameters:\n",
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" String modelData = args[0];\n",
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"\n",
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" // Estimators:\n",
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" LinearSVC clf = new LinearSVC(modelData);\n",
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"\n",
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" // Prediction:\n",
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" int prediction = clf.predict(features);\n",
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" System.out.println(prediction);\n",
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"\n",
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" }\n",
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" }\n",
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"}\n",
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"CPU times: user 1.1 ms, sys: 1.6 ms, total: 2.7 ms\n",
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"Wall time: 1.36 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"\n",
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"from sklearn_porter import Porter\n",
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"\n",
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"porter = Porter(clf)\n",
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"output = porter.export(export_data=True)\n",
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"\n",
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"print(output)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Parameters:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\"coefficients\": [[0.184242094585, 0.451230000252, -0.807945877167, -0.450716600333], [0.0528774557485, -0.892149952286, 0.403980844596, -0.937682166145], [-0.850707843193, -0.986702149222, 1.38101044874, 1.86540956624]], \"intercepts\": [0.109562664067, 1.66367077767, -1.70961094165]}"
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]
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}
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],
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"source": [
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"%%bash\n",
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"\n",
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"cat data.json"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hideOutput": false
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},
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"source": [
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"### Run classification in Java:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Save the transpiled estimator:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"with open('LinearSVC.java', 'w') as f:\n",
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" f.write(output)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Download the dependencies:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"--2017-12-02 17:35:06-- http://central.maven.org/maven2/com/google/code/gson/gson/2.8.2/gson-2.8.2.jar\n",
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"Resolving central.maven.org... 151.101.36.209\n",
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"Connecting to central.maven.org|151.101.36.209|:80... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 232932 (227K) [application/java-archive]\n",
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"Saving to: 'gson-2.8.2.jar'\n",
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"\n",
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" 0K .......... .......... .......... .......... .......... 21% 1.87M 0s\n",
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" 50K .......... .......... .......... .......... .......... 43% 3.28M 0s\n",
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" 100K .......... .......... .......... .......... .......... 65% 6.27M 0s\n",
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" 150K .......... .......... .......... .......... .......... 87% 3.97M 0s\n",
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" 200K .......... .......... ....... 100% 171M=0.06s\n",
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"\n",
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"2017-12-02 17:35:06 (3.62 MB/s) - 'gson-2.8.2.jar' saved [232932/232932]\n",
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"\n"
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]
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}
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],
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"source": [
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"%%bash\n",
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"\n",
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"wget http://central.maven.org/maven2/com/google/code/gson/gson/2.8.2/gson-2.8.2.jar"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Compiling:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%%bash\n",
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"\n",
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"javac -cp .:gson-2.8.2.jar LinearSVC.java"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prediction:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2\n"
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]
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}
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],
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"source": [
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"%%bash\n",
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"\n",
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"java -cp .:gson-2.8.2.jar LinearSVC data.json 1 2 3 4"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎examples/estimator/classifier/LinearSVC/java/basics_imported.py

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# -*- coding: utf-8 -*-
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from sklearn import svm
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from sklearn.datasets import load_iris
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from sklearn_porter import Porter
6+
7+
8+
iris_data = load_iris()
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X = iris_data.data
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y = iris_data.target
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clf = svm.LinearSVC(C=1., random_state=0)
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clf.fit(X, y)
14+
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porter = Porter(clf)
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output = porter.export(export_data=True)
17+
print(output)
18+
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"""
20+
import java.io.File;
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import java.io.FileNotFoundException;
22+
import java.util.*;
23+
import com.google.gson.Gson;
24+
25+
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class LinearSVC {
27+
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private class Classifier {
29+
private double[][] coefficients;
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private double[] intercepts;
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}
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private Classifier clf;
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public LinearSVC(String file) throws FileNotFoundException {
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String jsonStr = new Scanner(new File(file)).useDelimiter("\\Z").next();
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this.clf = new Gson().fromJson(jsonStr, Classifier.class);
38+
}
39+
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public int predict(double[] features) {
41+
int classIdx = 0;
42+
double classVal = Double.NEGATIVE_INFINITY;
43+
for (int i = 0, il = this.clf.intercepts.length; i < il; i++) {
44+
double prob = 0.;
45+
for (int j = 0, jl = this.clf.coefficients[0].length; j < jl; j++) {
46+
prob += this.clf.coefficients[i][j] * features[j];
47+
}
48+
if (prob + this.clf.intercepts[i] > classVal) {
49+
classVal = prob + this.clf.intercepts[i];
50+
classIdx = i;
51+
}
52+
}
53+
return classIdx;
54+
}
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56+
public static void main(String[] args) throws FileNotFoundException {
57+
if (args.length > 0 && args[0].endsWith(".json")) {
58+
59+
// Features:
60+
double[] features = new double[args.length-1];
61+
for (int i = 1, l = args.length; i < l; i++) {
62+
features[i - 1] = Double.parseDouble(args[i]);
63+
}
64+
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// Parameters:
66+
String modelData = args[0];
67+
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// Estimators:
69+
LinearSVC clf = new LinearSVC(modelData);
70+
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// Prediction:
72+
int prediction = clf.predict(features);
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System.out.println(prediction);
74+
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}
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}
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}
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"""

‎readme.md

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@@ -47,7 +47,7 @@ Transpile trained [scikit-learn](https://github.com/scikit-learn/scikit-learn) e
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</tr>
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<tr>
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<td><a href="http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html">svm.LinearSVC</a></td>
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<td align="center"><a href="examples/estimator/classifier/LinearSVC/java/basics.ipynb">✓</a></td>
50+
<td align="center"><a href="examples/estimator/classifier/LinearSVC/java/basics.ipynb">✓</a>, <a href="examples/estimator/classifier/LinearSVC/java/basics_imported.ipynb">✓ ᴵ</a></td>
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<td align="center"><a href="examples/estimator/classifier/LinearSVC/js/basics.ipynb">✓</a></td>
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<td align="center"><a href="examples/estimator/classifier/LinearSVC/c/basics.ipynb">✓</a></td>
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<td align="center"><a href="examples/estimator/classifier/LinearSVC/go/basics.ipynb">✓</a></td>

‎sklearn_porter/estimator/classifier/LinearSVC/__init__.py

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# -*- coding: utf-8 -*-
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import os
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import json
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from json import encoder
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from sklearn_porter.estimator.classifier.Classifier import Classifier
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target_method=target_method, **kwargs)
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self.estimator = estimator
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def export(self, class_name, method_name, **kwargs):
92+
def export(self, class_name, method_name,
93+
export_data=False, export_dir='.',
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**kwargs):
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"""
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Port a trained estimator to the syntax of a chosen programming language.
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@@ -153,9 +159,14 @@ def export(self, class_name, method_name, **kwargs):
153159
self.intercepts = inters
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155161
if self.target_method == 'predict':
156-
return self.predict()
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def predict(self):
162+
# Exported:
163+
if export_data and os.path.isdir(export_dir):
164+
self.export_data(export_dir)
165+
return self.predict('exported')
166+
# Separated:
167+
return self.predict('separated')
168+
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def predict(self, temp_type):
159170
"""
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Transpile the predict method.
161172
@@ -164,9 +175,25 @@ def predict(self):
164175
:return : string
165176
The transpiled predict method as string.
166177
"""
178+
# Exported:
179+
if temp_type == 'exported':
180+
temp = self.temp('exported.{}.class'.format(self.prefix))
181+
return temp.format(class_name=self.class_name,
182+
method_name=self.method_name)
183+
184+
# Separated
167185
self.method = self.create_method()
168-
output = self.create_class()
169-
return output
186+
return self.create_class()
187+
188+
def export_data(self, export_dir):
189+
model_data = {
190+
'coefficients': (self.estimator.coef_[0] if self.is_binary else self.estimator.coef_).tolist(),
191+
'intercepts': (self.estimator.intercept_[0] if self.is_binary else self.estimator.intercept_).tolist(),
192+
}
193+
encoder.FLOAT_REPR = lambda o: self.repr(o)
194+
path = os.path.join(export_dir, 'data.json')
195+
with open(path, 'w') as fp:
196+
json.dump(model_data, fp)
170197

171198
def create_method(self):
172199
"""
@@ -178,7 +205,7 @@ def create_method(self):
178205
The built method as string.
179206
"""
180207
n_indents = 0 if self.target_language in ['c', 'go'] else 1
181-
method_type = '{}.method'.format(self.prefix)
208+
method_type = 'separated.{}.method'.format(self.prefix)
182209
method_temp = self.temp(method_type, n_indents=n_indents, skipping=True)
183210
output = method_temp.format(**self.__dict__)
184211
return output
@@ -194,9 +221,9 @@ def create_class(self):
194221
"""
195222
if self.target_language in ['java', 'go']:
196223
n_indents = 1 if self.target_language == 'java' else 0
197-
class_head_temp = self.temp('{}.class'.format(self.prefix),
224+
class_head_temp = self.temp('separated.{}.class'.format(self.prefix),
198225
n_indents=n_indents, skipping=True)
199226
self.class_head = class_head_temp.format(**self.__dict__)
200227

201-
output = self.temp('class').format(**self.__dict__)
228+
output = self.temp('separated.class').format(**self.__dict__)
202229
return output

‎sklearn_porter/estimator/classifier/LinearSVC/templates/java/exported.binary.class.txt

+53
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
1+
import java.io.File;
2+
import java.io.FileNotFoundException;
3+
import java.util.*;
4+
import com.google.gson.Gson;
5+
6+
7+
class {class_name} {{
8+
9+
private class Classifier {{
10+
private double[] coefficients;
11+
private double intercepts;
12+
}}
13+
14+
private Classifier clf;
15+
16+
public {class_name}(String file) throws FileNotFoundException {{
17+
String jsonStr = new Scanner(new File(file)).useDelimiter("\\Z").next();
18+
this.clf = new Gson().fromJson(jsonStr, Classifier.class);
19+
}}
20+
21+
public int {method_name}(double[] features) {{
22+
double prob = 0.;
23+
for (int i = 0, il = this.clf.coefficients.length; i < il; i++) {{
24+
prob += this.clf.coefficients[i] * features[i];
25+
}}
26+
if (prob + this.clf.intercepts > 0) {{
27+
return 1;
28+
}}
29+
return 0;
30+
}}
31+
32+
public static void main(String[] args) throws FileNotFoundException {{
33+
if (args.length > 0 && args[0].endsWith(".json")) {{
34+
35+
// Features:
36+
double[] features = new double[args.length-1];
37+
for (int i = 1, l = args.length; i < l; i++) {{
38+
features[i - 1] = Double.parseDouble(args[i]);
39+
}}
40+
41+
// Parameters:
42+
String modelData = args[0];
43+
44+
// Estimators:
45+
{class_name} clf = new {class_name}(modelData);
46+
47+
// Prediction:
48+
int prediction = clf.{method_name}(features);
49+
System.out.println(prediction);
50+
51+
}}
52+
}}
53+
}}

‎sklearn_porter/estimator/classifier/LinearSVC/templates/java/exported.multi.class.txt

+58
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
1+
import java.io.File;
2+
import java.io.FileNotFoundException;
3+
import java.util.*;
4+
import com.google.gson.Gson;
5+
6+
7+
class {class_name} {{
8+
9+
private class Classifier {{
10+
private double[][] coefficients;
11+
private double[] intercepts;
12+
}}
13+
14+
private Classifier clf;
15+
16+
public {class_name}(String file) throws FileNotFoundException {{
17+
String jsonStr = new Scanner(new File(file)).useDelimiter("\\Z").next();
18+
this.clf = new Gson().fromJson(jsonStr, Classifier.class);
19+
}}
20+
21+
public int {method_name}(double[] features) {{
22+
int classIdx = 0;
23+
double classVal = Double.NEGATIVE_INFINITY;
24+
for (int i = 0, il = this.clf.intercepts.length; i < il; i++) {{
25+
double prob = 0.;
26+
for (int j = 0, jl = this.clf.coefficients[0].length; j < jl; j++) {{
27+
prob += this.clf.coefficients[i][j] * features[j];
28+
}}
29+
if (prob + this.clf.intercepts[i] > classVal) {{
30+
classVal = prob + this.clf.intercepts[i];
31+
classIdx = i;
32+
}}
33+
}}
34+
return classIdx;
35+
}}
36+
37+
public static void main(String[] args) throws FileNotFoundException {{
38+
if (args.length > 0 && args[0].endsWith(".json")) {{
39+
40+
// Features:
41+
double[] features = new double[args.length-1];
42+
for (int i = 1, l = args.length; i < l; i++) {{
43+
features[i - 1] = Double.parseDouble(args[i]);
44+
}}
45+
46+
// Parameters:
47+
String modelData = args[0];
48+
49+
// Estimators:
50+
{class_name} clf = new {class_name}(modelData);
51+
52+
// Prediction:
53+
int prediction = clf.{method_name}(features);
54+
System.out.println(prediction);
55+
56+
}}
57+
}}
58+
}}

‎tests/estimator/classifier/LinearSVC/LinearSVCJavaTest.py

+2-1
Original file line numberDiff line numberDiff line change
@@ -13,10 +13,11 @@
1313
from sklearn_porter import Porter
1414

1515
from tests.estimator.classifier.Classifier import Classifier
16+
from tests.estimator.classifier.ExportedData import ExportedData
1617
from tests.language.Java import Java
1718

1819

19-
class LinearSVCJavaTest(Java, Classifier, TestCase):
20+
class LinearSVCJavaTest(Java, Classifier, ExportedData, TestCase):
2021

2122
def setUp(self):
2223
super(LinearSVCJavaTest, self).setUp()

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