examples/estimator/classifier/SVC/js/basics.pct.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sklearn-porter\n",
    "\n",
    "Repository: [https://github.com/nok/sklearn-porter](https://github.com/nok/sklearn-porter)\n",
    "\n",
    "## SVC\n",
    "\n",
    "Documentation: [sklearn.svm.SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../../../..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((150, 4), (150,))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris_data = load_iris()\n",
    "\n",
    "X = iris_data.data\n",
    "y = iris_data.target\n",
    "\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC(C=1., gamma=0.001, kernel='rbf', random_state=0)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpile classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var SVC = function(nClasses, nRows, vectors, coefficients, intercepts, weights, kernel, gamma, coef0, degree) {\n",
      "\n",
      "    this.nClasses = nClasses;\n",
      "    this.classes = new Array(nClasses);\n",
      "    for (var i = 0; i < nClasses; i++) {\n",
      "        this.classes[i] = i;\n",
      "    }\n",
      "    this.nRows = nRows;\n",
      "    this.vectors = vectors;\n",
      "    this.coefficients = coefficients;\n",
      "    this.intercepts = intercepts;\n",
      "    this.weights = weights;\n",
      "    this.kernel = kernel.toUpperCase();\n",
      "    this.gamma = gamma;\n",
      "    this.coef0 = coef0;\n",
      "    this.degree = degree;\n",
      "\n",
      "    this.predict = function(features) {\n",
      "    \n",
      "        var kernels = new Array(vectors.length);\n",
      "        var kernel;\n",
      "        switch (this.kernel) {\n",
      "            case 'LINEAR':\n",
      "                // <x,x'>\n",
      "                for (var i = 0; i < this.vectors.length; i++) {\n",
      "                    kernel = 0.;\n",
      "                    for (var j = 0; j < this.vectors[i].length; j++) {\n",
      "                        kernel += this.vectors[i][j] * features[j];\n",
      "                    }\n",
      "                    kernels[i] = kernel;\n",
      "                }\n",
      "                break;\n",
      "            case 'POLY':\n",
      "                // (y<x,x'>+r)^d\n",
      "                for (var i = 0; i < this.vectors.length; i++) {\n",
      "                    kernel = 0.;\n",
      "                    for (var j = 0; j < this.vectors[i].length; j++) {\n",
      "                        kernel += this.vectors[i][j] * features[j];\n",
      "                    }\n",
      "                    kernels[i] = Math.pow((this.gamma * kernel) + this.coef0, this.degree);\n",
      "                }\n",
      "                break;\n",
      "            case 'RBF':\n",
      "                // exp(-y|x-x'|^2)\n",
      "                for (var i = 0; i < this.vectors.length; i++) {\n",
      "                    kernel = 0.;\n",
      "                    for (var j = 0; j < this.vectors[i].length; j++) {\n",
      "                        kernel += Math.pow(this.vectors[i][j] - features[j], 2);\n",
      "                    }\n",
      "                    kernels[i] = Math.exp(-this.gamma * kernel);\n",
      "                }\n",
      "                break;\n",
      "            case 'SIGMOID':\n",
      "                // tanh(y<x,x'>+r)\n",
      "                for (var i = 0; i < this.vectors.length; i++) {\n",
      "                    kernel = 0.;\n",
      "                    for (var j = 0; j < this.vectors[i].length; j++) {\n",
      "                        kernel += this.vectors[i][j] * features[j];\n",
      "                    }\n",
      "                    kernels[i] = Math.tanh((this.gamma * kernel) + this.coef0);\n",
      "                }\n",
      "                break;\n",
      "        }\n",
      "    \n",
      "        var starts = new Array(this.nRows);\n",
      "        for (var i = 0; i < this.nRows; i++) {\n",
      "            if (i != 0) {\n",
      "                var start = 0;\n",
      "                for (var j = 0; j < i; j++) {\n",
      "                    start += this.weights[j];\n",
      "                }\n",
      "                starts[i] = start;\n",
      "            } else {\n",
      "                starts[0] = 0;\n",
      "            }\n",
      "        }\n",
      "    \n",
      "        var ends = new Array(this.nRows);\n",
      "        for (var i = 0; i < this.nRows; i++) {\n",
      "            ends[i] = this.weights[i] + starts[i];\n",
      "        }\n",
      "    \n",
      "        if (this.nClasses == 2) {\n",
      "    \n",
      "            for (var i = 0; i < kernels.length; i++) {\n",
      "                kernels[i] = -kernels[i];\n",
      "            }\n",
      "    \n",
      "            var decision = 0.;\n",
      "            for (var k = starts[1]; k < ends[1]; k++) {\n",
      "                decision += kernels[k] * this.coefficients[0][k];\n",
      "            }\n",
      "            for (var k = starts[0]; k < ends[0]; k++) {\n",
      "                decision += kernels[k] * this.coefficients[0][k];\n",
      "            }\n",
      "            decision += this.intercepts[0];\n",
      "    \n",
      "            if (decision > 0) {\n",
      "                return 0;\n",
      "            }\n",
      "            return 1;\n",
      "    \n",
      "        }\n",
      "    \n",
      "        var decisions = new Array(this.intercepts.length);\n",
      "        for (var i = 0, d = 0, l = this.nRows; i < l; i++) {\n",
      "            for (var j = i + 1; j < l; j++) {\n",
      "                var tmp = 0.;\n",
      "                for (var k = starts[j]; k < ends[j]; k++) {\n",
      "                    tmp += this.coefficients[i][k] * kernels[k];\n",
      "                }\n",
      "                for (var k = starts[i]; k < ends[i]; k++) {\n",
      "                    tmp += this.coefficients[j - 1][k] * kernels[k];\n",
      "                }\n",
      "                decisions[d] = tmp + this.intercepts[d];\n",
      "                d++;\n",
      "            }\n",
      "        }\n",
      "    \n",
      "        var votes = new Array(this.intercepts.length);\n",
      "        for (var i = 0, d = 0, l = this.nRows; i < l; i++) {\n",
      "            for (var j = i + 1; j < l; j++) {\n",
      "                votes[d] = decisions[d] > 0 ? i : j;\n",
      "                d++;\n",
      "            }\n",
      "        }\n",
      "    \n",
      "        var amounts = new Array(this.nClasses).fill(0);\n",
      "        for (var i = 0, l = votes.length; i < l; i++) {\n",
      "            amounts[votes[i]] += 1;\n",
      "        }\n",
      "    \n",
      "        var classVal = -1, classIdx = -1;\n",
      "        for (var i = 0, l = amounts.length; i < l; i++) {\n",
      "            if (amounts[i] > classVal) {\n",
      "                classVal = amounts[i];\n",
      "                classIdx= i;\n",
      "            }\n",
      "        }\n",
      "        return this.classes[classIdx];\n",
      "    \n",
      "    }\n",
      "\n",
      "};\n",
      "\n",
      "if (typeof process !== 'undefined' && typeof process.argv !== 'undefined') {\n",
      "    if (process.argv.length - 2 === 4) {\n",
      "\n",
      "        // Features:\n",
      "        var features = process.argv.slice(2);\n",
      "\n",
      "        // Parameters:\n",
      "        var vectors = [[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2], [5.0, 3.6, 1.4, 0.2], [5.4, 3.9, 1.7, 0.4], [4.6, 3.4, 1.4, 0.3], [5.0, 3.4, 1.5, 0.2], [4.4, 2.9, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2], [4.8, 3.0, 1.4, 0.1], [4.3, 3.0, 1.1, 0.1], [5.8, 4.0, 1.2, 0.2], [5.7, 4.4, 1.5, 0.4], [5.4, 3.9, 1.3, 0.4], [5.1, 3.5, 1.4, 0.3], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [4.6, 3.6, 1.0, 0.2], [5.1, 3.3, 1.7, 0.5], [4.8, 3.4, 1.9, 0.2], [5.0, 3.0, 1.6, 0.2], [5.0, 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2], [5.2, 3.4, 1.4, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4], [5.2, 4.1, 1.5, 0.1], [5.5, 4.2, 1.4, 0.2], [4.9, 3.1, 1.5, 0.2], [5.0, 3.2, 1.2, 0.2], [5.5, 3.5, 1.3, 0.2], [4.9, 3.6, 1.4, 0.1], [4.4, 3.0, 1.3, 0.2], [5.1, 3.4, 1.5, 0.2], [5.0, 3.5, 1.3, 0.3], [4.5, 2.3, 1.3, 0.3], [4.4, 3.2, 1.3, 0.2], [5.0, 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3.0, 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2], [5.3, 3.7, 1.5, 0.2], [5.0, 3.3, 1.4, 0.2], [7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4.0, 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3], [6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1.0], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5.0, 2.0, 3.5, 1.0], [5.9, 3.0, 4.2, 1.5], [6.0, 2.2, 4.0, 1.0], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4], [5.6, 3.0, 4.5, 1.5], [5.8, 2.7, 4.1, 1.0], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1], [5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4.0, 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2], [6.4, 2.9, 4.3, 1.3], [6.6, 3.0, 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3.0, 5.0, 1.7], [6.0, 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1.0], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1.0], [5.8, 2.7, 3.9, 1.2], [6.0, 2.7, 5.1, 1.6], [5.4, 3.0, 4.5, 1.5], [6.0, 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5], [6.3, 2.3, 4.4, 1.3], [5.6, 3.0, 4.1, 1.3], [5.5, 2.5, 4.0, 1.3], [5.5, 2.6, 4.4, 1.2], [6.1, 3.0, 4.6, 1.4], [5.8, 2.6, 4.0, 1.2], [5.0, 2.3, 3.3, 1.0], [5.6, 2.7, 4.2, 1.3], [5.7, 3.0, 4.2, 1.2], [5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3.0, 1.1], [5.7, 2.8, 4.1, 1.3], [6.3, 3.3, 6.0, 2.5], [5.8, 2.7, 5.1, 1.9], [7.1, 3.0, 5.9, 2.1], [6.3, 2.9, 5.6, 1.8], [6.5, 3.0, 5.8, 2.2], [7.6, 3.0, 6.6, 2.1], [4.9, 2.5, 4.5, 1.7], [7.3, 2.9, 6.3, 1.8], [6.7, 2.5, 5.8, 1.8], [7.2, 3.6, 6.1, 2.5], [6.5, 3.2, 5.1, 2.0], [6.4, 2.7, 5.3, 1.9], [6.8, 3.0, 5.5, 2.1], [5.7, 2.5, 5.0, 2.0], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3.0, 5.5, 1.8], [7.7, 3.8, 6.7, 2.2], [7.7, 2.6, 6.9, 2.3], [6.0, 2.2, 5.0, 1.5], [6.9, 3.2, 5.7, 2.3], [5.6, 2.8, 4.9, 2.0], [7.7, 2.8, 6.7, 2.0], [6.3, 2.7, 4.9, 1.8], [6.7, 3.3, 5.7, 2.1], [7.2, 3.2, 6.0, 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3.0, 4.9, 1.8], [6.4, 2.8, 5.6, 2.1], [7.2, 3.0, 5.8, 1.6], [7.4, 2.8, 6.1, 1.9], [7.9, 3.8, 6.4, 2.0], [6.4, 2.8, 5.6, 2.2], [6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [7.7, 3.0, 6.1, 2.3], [6.3, 3.4, 5.6, 2.4], [6.4, 3.1, 5.5, 1.8], [6.0, 3.0, 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.7, 3.1, 5.6, 2.4], [6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.8, 3.2, 5.9, 2.3], [6.7, 3.3, 5.7, 2.5], [6.7, 3.0, 5.2, 2.3], [6.3, 2.5, 5.0, 1.9], [6.5, 3.0, 5.2, 2.0], [6.2, 3.4, 5.4, 2.3], [5.9, 3.0, 5.1, 1.8]];\n",
      "        var coefficients = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -0.0, -1.0, -0.0, -1.0, -0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -0.0, -0.0, -1.0, -1.0, -1.0, -0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -0.0, -0.0, -1.0, -1.0, -1.0, -0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0], [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]];\n",
      "        var intercepts = [0.04357001185417175, 0.11042118072509766, -0.0031709671020507812];\n",
      "        var weights = [50, 50, 50];\n",
      "\n",
      "        // Prediction:\n",
      "        var clf = new SVC(3, 3, vectors, coefficients, intercepts, weights, \"rbf\", 0.001, 0.0, 3);\n",
      "        var prediction = clf.predict(features);\n",
      "        console.log(prediction);\n",
      "\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from sklearn_porter import Porter\n",
    "\n",
    "porter = Porter(clf, language='js')\n",
    "output = porter.export()\n",
    "\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run classification in JavaScript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save classifier:\n",
    "# with open('SVC.js', 'w') as f:\n",
    "#     f.write(output)\n",
    "\n",
    "# Run classification:\n",
    "# if hash node 2/dev/null; then\n",
    "#     node SVC.js 1 2 3 4\n",
    "# fi"
   ]
  }
 ],
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