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WIP: Scala Notebooks for examples #446

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378 changes: 378 additions & 0 deletions saul-notebooks/getting_started/SpamClassifier.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting Started with Saul\n",
"\n",
"We will look at a Spam Classification task where we try to classify email documents as SPAM or HAM. This notebook will work through the steps in creating and running the Spam Classifier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step -1 - Jupyter-Scala integration for Saul"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"34 new artifact(s)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"34 new artifacts in macro\n",
"34 new artifacts in runtime\n",
"34 new artifacts in compile\n"
]
},
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classpath.addRepository(\"http://cogcomp.cs.illinois.edu/m2repo\")\n",
"classpath.add(\"edu.illinois.cs.cogcomp\" %% \"saul\" % \"0.5.5\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 0 - Spam Data\n",
"\n",
"something about how the data looks like"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36mscala.io.Source\u001b[0m\n",
"\u001b[32mimport \u001b[36mjava.io.File\u001b[0m\n",
"\u001b[36mspamDataBasePath\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"../../data/EmailSpam/\"\u001b[0m\n",
"\u001b[36mtrainDataPath\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"../../data/EmailSpam/train/\"\u001b[0m\n",
"\u001b[36mtestDataPath\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"../../data/EmailSpam/test/\"\u001b[0m\n",
"\u001b[36mdir\u001b[0m: \u001b[32mjava\u001b[0m.\u001b[32mio\u001b[0m.\u001b[32mFile\u001b[0m = ../../data/EmailSpam/train/ham"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import scala.io.Source\n",
"import java.io.File\n",
"\n",
"val spamDataBasePath = \"../../data/EmailSpam/\"\n",
"val trainDataPath = spamDataBasePath + \"train/\"\n",
"val testDataPath = spamDataBasePath + \"test/\"\n",
"\n",
"val dir = new File(trainDataPath + \"ham\")\n",
"require(dir.exists() && dir.isDirectory())\n",
"\n",
"// val sampleDoc = dir.listFiles.filter(_.isFile).head\n",
"// $println(\"Sample Document:\")\n",
"// Source.fromFile(sampleDoc).getLines.foreach(println(_))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1 - Data Reader\n",
"\n",
"We create a reader that parses each document and parses it into required classes. \n",
"\n",
"We define an Email as a collection of words and its label."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mclass \u001b[36mEmail\u001b[0m\n",
"defined \u001b[32mobject \u001b[36mDataReader\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"case class Email(val words: Seq[String], val label: String)\n",
"\n",
"object DataReader {\n",
" def apply(dirName: String, label: String): Iterable[Email] = {\n",
" val dir = new File(dirName)\n",
" require(dir.exists() && dir.isDirectory)\n",
" \n",
" dir.listFiles\n",
" .filter(_.isFile)\n",
" .flatMap(file => parseEmail(file.getAbsolutePath, label))\n",
" }\n",
" \n",
" private def parseEmail(fileName: String, label: String): Option[Email] = {\n",
" val source = Source.fromFile(fileName)\n",
" if (source.hasNext) {\n",
" val words = source.getLines\n",
" .flatMap(_.split(\"\\\\s+\"))\n",
" .toSeq\n",
" Some(Email(words, label))\n",
" } else {\n",
" None\n",
" }\n",
" }\n",
"}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2 - DataModel (Entities, Features)\n",
"\n",
"Where we define the DataModel"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36medu.illinois.cs.cogcomp.saul.datamodel.DataModel\u001b[0m\n",
"defined \u001b[32mobject \u001b[36mSpamDataModel\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import edu.illinois.cs.cogcomp.saul.datamodel.DataModel\n",
"\n",
"object SpamDataModel extends DataModel {\n",
" val email = node[Email]\n",
" \n",
" // Features\n",
" val words = property(email) { \n",
" doc: Email => doc.words.toList\n",
" }\n",
" \n",
" val bigrams = property(email) {\n",
" doc: Email => doc.words.sliding(2).map(_.mkString(\"-\")).toList\n",
" }\n",
" \n",
" val spamLabel = property(email) {\n",
" doc: Email => doc.label\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3 - Classifier"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36medu.illinois.cs.cogcomp.lbjava.learn.SupportVectorMachine\u001b[0m\n",
"\u001b[32mimport \u001b[36medu.illinois.cs.cogcomp.saul.classifier.Learnable\u001b[0m\n",
"\u001b[32mimport \u001b[36mSpamDataModel._\u001b[0m\n",
"defined \u001b[32mobject \u001b[36mSpamClassifier\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import edu.illinois.cs.cogcomp.lbjava.learn.SupportVectorMachine\n",
"import edu.illinois.cs.cogcomp.saul.classifier.Learnable\n",
"import SpamDataModel._\n",
"\n",
"object SpamClassifier extends Learnable(email) {\n",
" def label = spamLabel\n",
" override lazy val classifier = new SupportVectorMachine()\n",
" override def feature = using(words, bigrams)\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4 - App (Train, Test)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO [2016-11-06 18:30:11,121] cmd4$$user$SpamClassifier$: Learnable: Learn with data of size 9\n",
"INFO [2016-11-06 18:30:11,122] cmd4$$user$SpamClassifier$: Training: 30 iterations remain.\n",
"INFO [2016-11-06 18:30:11,123] cmd4$$user$SpamClassifier$: Training: 30 iterations remain.\n",
"INFO [2016-11-06 18:30:11,248] cmd4$$user$SpamClassifier$: Training: 20 iterations remain.\n",
"INFO [2016-11-06 18:30:11,348] cmd4$$user$SpamClassifier$: Training: 10 iterations remain.\n",
" Label Precision Recall F1 LCount PCount\n",
"-----------------------------------------------\n",
"ham 100.000 60.000 75.000 5 3\n",
"spam 71.429 100.000 83.333 5 7\n",
"-----------------------------------------------\n",
"Accuracy 80.000 - - - 10\n"
]
},
{
"data": {
"text/plain": [
"\u001b[36mtrainData\u001b[0m: \u001b[32mIterable\u001b[0m[\u001b[32mEmail\u001b[0m] = \u001b[33mArraySeq\u001b[0m(\n",
" \u001b[33mEmail\u001b[0m(\n",
" \u001b[33mStream\u001b[0m(\n",
" \u001b[32m\"Subject:\"\u001b[0m,\n",
" \u001b[32m\"double\"\u001b[0m,\n",
" \u001b[32m\"your\"\u001b[0m,\n",
" \u001b[32m\"life\"\u001b[0m,\n",
" \u001b[32m\"insurance\"\u001b[0m,\n",
" \u001b[32m\"at\"\u001b[0m,\n",
" \u001b[32m\"no\"\u001b[0m,\n",
" \u001b[32m\"extra\"\u001b[0m,\n",
" \u001b[32m\"cost\"\u001b[0m,\n",
" \u001b[32m\"!\"\u001b[0m,\n",
" \u001b[32m\"29155\"\u001b[0m,\n",
" \u001b[32m\"the\"\u001b[0m,\n",
" \u001b[32m\"lowest\"\u001b[0m,\n",
" \u001b[32m\"life\"\u001b[0m,\n",
" \u001b[32m\"insurance\"\u001b[0m,\n",
" \u001b[32m\"quotes\"\u001b[0m,\n",
" \u001b[32m\"without\"\u001b[0m,\n",
"\u001b[33m...\u001b[0m\n",
"\u001b[36mtestData\u001b[0m: \u001b[32mIterable\u001b[0m[\u001b[32mEmail\u001b[0m] = \u001b[33mArraySeq\u001b[0m(\n",
" \u001b[33mEmail\u001b[0m(\n",
" \u001b[33mStream\u001b[0m(\n",
" \u001b[32m\"Subject:\"\u001b[0m,\n",
" \u001b[32m\"slotting\"\u001b[0m,\n",
" \u001b[32m\"order\"\u001b[0m,\n",
" \u001b[32m\"confirmation\"\u001b[0m,\n",
" \u001b[32m\"may\"\u001b[0m,\n",
" \u001b[32m\"18\"\u001b[0m,\n",
" \u001b[32m\",\"\u001b[0m,\n",
" \u001b[32m\"2004\"\u001b[0m,\n",
" \u001b[32m\"etacitne\"\u001b[0m,\n",
" \u001b[32m\"{\"\u001b[0m,\n",
" \u001b[32m\"%\"\u001b[0m,\n",
" \u001b[32m\"begin\"\u001b[0m,\n",
" \u001b[32m\"_\"\u001b[0m,\n",
" \u001b[32m\"split\"\u001b[0m,\n",
" \u001b[32m\"76\"\u001b[0m,\n",
" \u001b[32m\"%\"\u001b[0m,\n",
" \u001b[32m\"}\"\u001b[0m,\n",
"\u001b[33m...\u001b[0m\n",
"\u001b[36mres6_5\u001b[0m: \u001b[32medu\u001b[0m.\u001b[32millinois\u001b[0m.\u001b[32mcs\u001b[0m.\u001b[32mcogcomp\u001b[0m.\u001b[32msaul\u001b[0m.\u001b[32mclassifier\u001b[0m.\u001b[32mResults\u001b[0m = \u001b[33mResults\u001b[0m(\n",
" \u001b[33mArray\u001b[0m(\n",
" \u001b[33mResultPerLabel\u001b[0m(\n",
" \u001b[32m\"ham\"\u001b[0m,\n",
" \u001b[32m0.7499999999999999\u001b[0m,\n",
" \u001b[32m1.0\u001b[0m,\n",
" \u001b[32m0.6\u001b[0m,\n",
" \u001b[33mArray\u001b[0m(\u001b[32m\"ham\"\u001b[0m, \u001b[32m\"spam\"\u001b[0m),\n",
" \u001b[32m5\u001b[0m,\n",
" \u001b[32m3\u001b[0m,\n",
" \u001b[32m3\u001b[0m\n",
" ),\n",
" \u001b[33mResultPerLabel\u001b[0m(\n",
" \u001b[32m\"spam\"\u001b[0m,\n",
" \u001b[32m0.8333333333333333\u001b[0m,\n",
" \u001b[32m0.7142857142857143\u001b[0m,\n",
" \u001b[32m1.0\u001b[0m,\n",
" \u001b[33mArray\u001b[0m(\u001b[32m\"ham\"\u001b[0m, \u001b[32m\"spam\"\u001b[0m),\n",
" \u001b[32m5\u001b[0m,\n",
" \u001b[32m7\u001b[0m,\n",
"\u001b[33m...\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val trainData = DataReader(trainDataPath + \"spam\", \"spam\") ++ DataReader(trainDataPath + \"ham\", \"ham\")\n",
"val testData = DataReader(testDataPath + \"spam\", \"spam\") ++ DataReader(testDataPath + \"ham\", \"ham\")\n",
"\n",
"SpamDataModel.email.populate(trainData)\n",
"SpamDataModel.email.populate(testData, train = false)\n",
"\n",
"SpamClassifier.learn(30)\n",
"SpamClassifier.test()"
]
}
],
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