Add prediction in the used programming language

nok · Dec 4, 2017 · 65439b9 · 65439b9
1 parent 6da2f7c
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diff --git a/examples/estimator/classifier/AdaBoostClassifier/js/basics_embedded.ipynb b/examples/estimator/classifier/AdaBoostClassifier/js/basics_embedded.ipynb
@@ -96,32 +96,6 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.48 ms, sys: 2.33 ms, total: 5.8 ms\n",
-      "Wall time: 3.99 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from sklearn_porter import Porter\n",
-    "\n",
-    "porter = Porter(clf, language='js')\n",
-    "output = porter.export()\n",
-    "\n",
-    "with open('AdaBoostClassifier.js', 'w') as f:\n",
-    "    f.write(output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -383,14 +357,21 @@
       "        console.log(prediction);\n",
       "\n",
       "    }\n",
-      "}"
+      "}\n",
+      "CPU times: user 2.7 ms, sys: 1.61 ms, total: 4.31 ms\n",
+      "Wall time: 2.94 ms\n"
      ]
     }
    ],
    "source": [
-    "%%bash\n",
+    "%%time\n",
     "\n",
-    "cat AdaBoostClassifier.js"
+    "from sklearn_porter import Porter\n",
+    "\n",
+    "porter = Porter(clf, language='js')\n",
+    "output = porter.export()\n",
+    "\n",
+    "print(output)"
    ]
   },
   {
@@ -400,6 +381,32 @@
     "### Run classification in JavaScript:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the transpiled estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('AdaBoostClassifier.js', 'w') as f:\n",
+    "    f.write(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prediction:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,

diff --git a/examples/estimator/classifier/AdaBoostClassifier/js/basics_imported.ipynb b/examples/estimator/classifier/AdaBoostClassifier/js/basics_imported.ipynb
@@ -96,39 +96,6 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.98 ms, sys: 1.49 ms, total: 3.47 ms\n",
-      "Wall time: 2.37 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from sklearn_porter import Porter\n",
-    "\n",
-    "porter = Porter(clf, language='js')\n",
-    "output = porter.export(export_data=True)\n",
-    "\n",
-    "with open('AdaBoostClassifier.js', 'w') as f:\n",
-    "    f.write(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classifier:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -241,17 +208,26 @@
       "        // Prediction:\n",
       "        clf.predict(features).then(function(prediction) {\n",
       "            console.log(prediction);\n",
+      "        }, function(error) {\n",
+      "            console.log(error);\n",
       "        });\n",
       "\n",
       "    }\n",
-      "}"
+      "}\n",
+      "CPU times: user 2.71 ms, sys: 2.25 ms, total: 4.96 ms\n",
+      "Wall time: 3.57 ms\n"
      ]
     }
    ],
    "source": [
-    "%%bash\n",
+    "%%time\n",
+    "\n",
+    "from sklearn_porter import Porter\n",
     "\n",
-    "cat AdaBoostClassifier.js"
+    "porter = Porter(clf, language='js')\n",
+    "output = porter.export(export_data=True)\n",
+    "\n",
+    "print(output)"
    ]
   },
   {
@@ -263,14 +239,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[{\"indices\": [3, -2, 3, 2, 3, -2, -2, 3, -2, -2, 2, 0, -2, -2, -2], \"thresholds\": [0.800000011921, -2.0, 1.75, 4.94999980927, 1.65000009537, -2.0, -2.0, 1.54999995232, -2.0, -2.0, 4.85000038147, 5.94999980927, -2.0, -2.0, -2.0], \"classes\": [[0.333333333333, 0.333333333333, 0.333333333333], [0.333333333333, 0.0, 0.0], [0.0, 0.333333333333, 0.333333333333], [0.0, 0.326666666667, 0.0333333333333], [0.0, 0.313333333333, 0.00666666666667], [0.0, 0.313333333333, 0.0], [0.0, 0.0, 0.00666666666667], [0.0, 0.0133333333333, 0.0266666666667], [0.0, 0.0, 0.02], [0.0, 0.0133333333333, 0.00666666666667], [0.0, 0.00666666666667, 0.3], [0.0, 0.00666666666667, 0.0133333333333], [0.0, 0.00666666666667, 0.0], [0.0, 0.0, 0.0133333333333], [0.0, 0.0, 0.286666666667]], \"childrenRight\": [2, -1, 10, 7, 6, -1, -1, 9, -1, -1, 14, 13, -1, -1, -1], \"childrenLeft\": [1, -1, 3, 4, 5, -1, -1, 8, -1, -1, 11, 12, -1, -1, -1]}, {\"indices\": [2, 2, -2, 3, 0, -2, -2, 1, -2, -2, -2], \"thresholds\": [5.14999961853, 2.45000004768, -2.0, 1.75, 4.94999980927, -2.0, -2.0, 3.15000009537, -2.0, -2.0, -2.0], \"classes\": [[8.3290724464e-05, 0.499957521731, 0.499959187545], [8.3290724464e-05, 0.499957521731, 2.66530318285e-05], [8.3290724464e-05, 0.0, 0.0], [0.0, 0.499957521731, 2.66530318285e-05], [0.0, 0.499955855916, 4.99744346784e-06], [0.0, 1.66581448928e-06, 1.66581448928e-06], [0.0, 0.499954190102, 3.33162897856e-06], [0.0, 1.66581448928e-06, 2.16555883606e-05], [0.0, 0.0, 1.99897738714e-05], [0.0, 1.66581448928e-06, 1.66581448928e-06], [0.0, 0.0, 0.499932534513]], \"childrenRight\": [10, 3, -1, 7, 6, -1, -1, 9, -1, -1, -1], \"childrenLeft\": [1, 2, -1, 4, 5, -1, -1, 8, -1, -1, -1]}, {\"indices\": [3, 2, 3, -2, -2, -2, 2, 3, 0, -2, -2, -2, -2], \"thresholds\": [1.54999995232, 4.94999980927, 0.800000011921, -2.0, -2.0, -2.0, 5.14999961853, 1.84999990463, 5.40000009537, -2.0, -2.0, -2.0, -2.0], \"classes\": [[2.67881771865e-08, 0.499919588597, 0.500080384615], [2.67881771865e-08, 0.000184731094993, 0.499696643102], [2.67881771865e-08, 0.000184731094993, 0.0], [2.67881771865e-08, 0.0, 0.0], [0.0, 0.000184731094993, 0.0], [0.0, 0.0, 0.499696643102], [0.0, 0.499734857502, 0.000383741512437], [0.0, 0.499734857502, 0.00022295245966], [0.0, 0.499734857502, 0.000111475694067], [0.0, 0.0, 0.000111473015249], [0.0, 0.499734857502, 2.67881771865e-09], [0.0, 0.0, 0.000111476765594], [0.0, 0.0, 0.000160789052777]], \"childrenRight\": [6, 5, 4, -1, -1, -1, 12, 11, 10, -1, -1, -1, -1], \"childrenLeft\": [1, 2, 3, -1, -1, -1, 7, 8, 9, -1, -1, -1, -1]}, {\"indices\": [3, 3, 2, 3, -2, -2, -2, 0, 1, -2, -2, -2, 1, -2, 2, -2, -2], \"thresholds\": [1.75, 1.54999995232, 4.94999980927, 0.800000011921, -2.0, -2.0, -2.0, 6.94999980927, 2.59999990463, -2.0, -2.0, -2.0, 3.15000009537, -2.0, 4.94999980927, -2.0, -2.0], \"classes\": [[9.25765397376e-11, 0.499136211999, 0.500863787909], [9.25765397376e-11, 0.499024872662, 0.00172782900859], [9.25765397376e-11, 6.38407213652e-07, 0.00172688816469], [9.25765397376e-11, 6.38407213652e-07, 0.0], [9.25765397376e-11, 0.0, 0.0], [0.0, 6.38407213652e-07, 0.0], [0.0, 0.0, 0.00172688816469], [0.0, 0.499024234255, 9.40843895869e-07], [0.0, 0.499024234255, 3.85236589785e-07], [0.0, 0.0, 3.85236589785e-07], [0.0, 0.499024234255, 0.0], [0.0, 0.0, 5.55607306084e-07], [0.0, 0.000111339336392, 0.4991359589], [0.0, 0.0, 0.499135573641], [0.0, 0.000111339336392, 3.85258808154e-07], [0.0, 0.000111339336392, 0.0], [0.0, 0.0, 3.85258808154e-07]], \"childrenRight\": [12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 14, -1, 16, -1, -1], \"childrenLeft\": [1, 2, 3, 4, -1, -1, -1, 8, 9, -1, -1, -1, 13, -1, 15, -1, -1]}]"
+      "[{\"childrenLeft\": [1, -1, 3, 4, 5, -1, -1, 8, -1, -1, 11, 12, -1, -1, -1], \"childrenRight\": [2, -1, 10, 7, 6, -1, -1, 9, -1, -1, 14, 13, -1, -1, -1], \"classes\": [[0.333333333333, 0.333333333333, 0.333333333333], [0.333333333333, 0.0, 0.0], [0.0, 0.333333333333, 0.333333333333], [0.0, 0.326666666667, 0.0333333333333], [0.0, 0.313333333333, 0.00666666666667], [0.0, 0.313333333333, 0.0], [0.0, 0.0, 0.00666666666667], [0.0, 0.0133333333333, 0.0266666666667], [0.0, 0.0, 0.02], [0.0, 0.0133333333333, 0.00666666666667], [0.0, 0.00666666666667, 0.3], [0.0, 0.00666666666667, 0.0133333333333], [0.0, 0.00666666666667, 0.0], [0.0, 0.0, 0.0133333333333], [0.0, 0.0, 0.286666666667]], \"indices\": [3, -2, 3, 2, 3, -2, -2, 3, -2, -2, 2, 0, -2, -2, -2], \"thresholds\": [0.800000011921, -2.0, 1.75, 4.94999980927, 1.65000009537, -2.0, -2.0, 1.54999995232, -2.0, -2.0, 4.85000038147, 5.94999980927, -2.0, -2.0, -2.0]}, {\"childrenLeft\": [1, 2, -1, 4, 5, -1, -1, 8, -1, -1, -1], \"childrenRight\": [10, 3, -1, 7, 6, -1, -1, 9, -1, -1, -1], \"classes\": [[8.3290724464e-05, 0.499957521731, 0.499959187545], [8.3290724464e-05, 0.499957521731, 2.66530318285e-05], [8.3290724464e-05, 0.0, 0.0], [0.0, 0.499957521731, 2.66530318285e-05], [0.0, 0.499955855916, 4.99744346784e-06], [0.0, 1.66581448928e-06, 1.66581448928e-06], [0.0, 0.499954190102, 3.33162897856e-06], [0.0, 1.66581448928e-06, 2.16555883606e-05], [0.0, 0.0, 1.99897738714e-05], [0.0, 1.66581448928e-06, 1.66581448928e-06], [0.0, 0.0, 0.499932534513]], \"indices\": [2, 2, -2, 3, 0, -2, -2, 1, -2, -2, -2], \"thresholds\": [5.14999961853, 2.45000004768, -2.0, 1.75, 4.94999980927, -2.0, -2.0, 3.15000009537, -2.0, -2.0, -2.0]}, {\"childrenLeft\": [1, 2, 3, -1, -1, -1, 7, 8, 9, -1, -1, -1, -1], \"childrenRight\": [6, 5, 4, -1, -1, -1, 12, 11, 10, -1, -1, -1, -1], \"classes\": [[2.67881771865e-08, 0.499919588597, 0.500080384615], [2.67881771865e-08, 0.000184731094993, 0.499696643102], [2.67881771865e-08, 0.000184731094993, 0.0], [2.67881771865e-08, 0.0, 0.0], [0.0, 0.000184731094993, 0.0], [0.0, 0.0, 0.499696643102], [0.0, 0.499734857502, 0.000383741512437], [0.0, 0.499734857502, 0.00022295245966], [0.0, 0.499734857502, 0.000111475694067], [0.0, 0.0, 0.000111473015249], [0.0, 0.499734857502, 2.67881771865e-09], [0.0, 0.0, 0.000111476765594], [0.0, 0.0, 0.000160789052777]], \"indices\": [3, 2, 3, -2, -2, -2, 2, 3, 0, -2, -2, -2, -2], \"thresholds\": [1.54999995232, 4.94999980927, 0.800000011921, -2.0, -2.0, -2.0, 5.14999961853, 1.84999990463, 5.40000009537, -2.0, -2.0, -2.0, -2.0]}, {\"childrenLeft\": [1, 2, 3, 4, -1, -1, -1, 8, 9, -1, -1, -1, 13, -1, 15, -1, -1], \"childrenRight\": [12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 14, -1, 16, -1, -1], \"classes\": [[9.25765397376e-11, 0.499136211999, 0.500863787909], [9.25765397376e-11, 0.499024872662, 0.00172782900859], [9.25765397376e-11, 6.38407213652e-07, 0.00172688816469], [9.25765397376e-11, 6.38407213652e-07, 0.0], [9.25765397376e-11, 0.0, 0.0], [0.0, 6.38407213652e-07, 0.0], [0.0, 0.0, 0.00172688816469], [0.0, 0.499024234255, 9.40843895869e-07], [0.0, 0.499024234255, 3.85236589785e-07], [0.0, 0.0, 3.85236589785e-07], [0.0, 0.499024234255, 0.0], [0.0, 0.0, 5.55607306084e-07], [0.0, 0.000111339336392, 0.4991359589], [0.0, 0.0, 0.499135573641], [0.0, 0.000111339336392, 3.85258808154e-07], [0.0, 0.000111339336392, 0.0], [0.0, 0.0, 3.85258808154e-07]], \"indices\": [3, 3, 2, 3, -2, -2, -2, 0, 1, -2, -2, -2, 1, -2, 2, -2, -2], \"thresholds\": [1.75, 1.54999995232, 4.94999980927, 0.800000011921, -2.0, -2.0, -2.0, 6.94999980927, 2.59999990463, -2.0, -2.0, -2.0, 3.15000009537, -2.0, 4.94999980927, -2.0, -2.0]}]"
      ]
     }
    ],
@@ -282,42 +258,40 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "hideCode": true
-   },
+   "metadata": {},
    "source": [
-    "File size:"
+    "### Run classification in JavaScript:"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.0K\tdata.json\n"
-     ]
-    }
-   ],
    "source": [
-    "%%bash\n",
-    "\n",
-    "du -h data.json"
+    "Save the transpiled estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('AdaBoostClassifier.js', 'w') as f:\n",
+    "    f.write(output)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Run classification in JavaScript:"
+    "Prediction:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -331,7 +305,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "127.0.0.1 - - [24/Nov/2017 01:01:32] \"GET /data.json HTTP/1.1\" 200 -\n"
+      "127.0.0.1 - - [04/Dec/2017 21:09:25] \"GET /data.json HTTP/1.1\" 200 -\n"
      ]
     }
    ],

diff --git a/examples/estimator/classifier/BernoulliNB/js/basics.ipynb b/examples/estimator/classifier/BernoulliNB/js/basics.ipynb
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
@@ -133,28 +133,86 @@
       "        var features = process.argv.slice(2);\n",
       "\n",
       "        // Parameters:\n",
-      "        var priors = [-1.0986122886681096, -1.0986122886681096, -1.0986122886681096];\n",
-      "        var negProbs = [[-3.9512437185814138, -3.9512437185814138, -3.9512437185814138, -3.9512437185814138], [-3.9512437185814138, -3.9512437185814138, -3.9512437185814138, -3.9512437185814138], [-3.9512437185814138, -3.9512437185814138, -3.9512437185814138, -3.9512437185814138]];\n",
-      "        var delProbs = [[3.931825632724312, 3.931825632724312, 3.931825632724312], [3.931825632724312, 3.931825632724312, 3.931825632724312], [3.931825632724312, 3.931825632724312, 3.931825632724312], [3.931825632724312, 3.931825632724312, 3.931825632724312]];\n",
+      "        var priors = [-1.09861228867, -1.09861228867, -1.09861228867];\n",
+      "        var negProbs = [[-3.95124371858, -3.95124371858, -3.95124371858, -3.95124371858], [-3.95124371858, -3.95124371858, -3.95124371858, -3.95124371858], [-3.95124371858, -3.95124371858, -3.95124371858, -3.95124371858]];\n",
+      "        var delProbs = [[3.93182563272, 3.93182563272, 3.93182563272], [3.93182563272, 3.93182563272, 3.93182563272], [3.93182563272, 3.93182563272, 3.93182563272], [3.93182563272, 3.93182563272, 3.93182563272]];\n",
       "\n",
       "        // Estimator:\n",
       "        var clf = new BernoulliNB(priors, negProbs, delProbs);\n",
       "        var prediction = clf.predict(features);\n",
       "        console.log(prediction);\n",
       "\n",
       "    }\n",
-      "}\n"
+      "}\n",
+      "CPU times: user 855 µs, sys: 802 µs, total: 1.66 ms\n",
+      "Wall time: 858 µs\n"
      ]
     }
    ],
    "source": [
+    "%%time\n",
+    "\n",
     "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the transpiled estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('BernoulliNB.js', 'w') as f:\n",
+    "    f.write(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prediction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "if hash node 2/dev/null; then\n",
+    "    node BernoulliNB.js 1 2 3 4\n",
+    "fi"
+   ]
   }
  ],
  "metadata": {