limdu - npm Package Compare versions

Comparing version 0.9.4 to 1.0.0

classifiers/decisiontree/DecisionTree.js

@@ -6,3 +6,3 @@ /* Implementation of Decision Tree classifier, ID3 implementation
 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;  // for explanations
+var sprintf = require("sprintf-js").sprintf;  // for explanations
 
@@ -9,0 +9,0 @@ function DecisionTree(opts) {

classifiers/multilabel/Adaboost.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -7,3 +7,4 @@ var fs = require('fs');
 var crypto = require('crypto')
-var execSync = require('child_process').execSync
+var child_process = require('child_process')
+
 /**
@@ -42,3 +43,3 @@ * Adaptive Boosting (Adaboost) is a greedy search for a linear combination of
     try {
-        var result = execSync("icsiboost");
+        child_process.execFileSync("icsiboost");
         return true;
@@ -104,3 +105,5 @@ } catch (err) {
 
-		var result = execSync("icsiboost -S ./"+this.folder+"/"+this.assigner+" -n "+this.iterations)
+		var command = "icsiboost"
+		var args = ["-S", "./"+this.folder+"/"+this.assigner, "-n", this.iterations]
+		var result = child_process.execFileSync(command, args)
 		console.log(result)
@@ -115,3 +118,12 @@ },
 		fs.writeFileSync("./"+this.folder+"/"+this.assigner+".test", sample+"\n")
-		var result = execSync("icsiboost -S ./"+this.folder+"/"+this.assigner +" -W "+this.ngram_length+" -N "+this.text_expert+" -C < ./"+this.folder+"/"+this.assigner+".test > ./"+this.folder+"/"+this.assigner+".output")
+		var command = "icsiboost"
+		var args = ["-S", "./"+this.folder+"/"+this.assigner, 
+					"-W", this.ngram_length, 
+					"-N", this.text_expert,
+					"-C",
+					"<",
+					"./"+this.folder+"/"+this.assigner+".test",
+					">",
+					"./"+this.folder+"/"+this.assigner+".output"]
+		var result = child_process.execFileSync(command, args)
 		var stats = fs.readFileSync("./"+this.folder+"/"+this.assigner+".output", "utf8");
@@ -118,0 +130,0 @@

classifiers/multilabel/BinaryRelevance.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -4,0 +4,0 @@ var multilabelutils = require('./multilabelutils');

classifiers/multilabel/BinarySegmentation.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -4,0 +4,0 @@ var ftrs = require('../../features');

classifiers/multilabel/CrossLangaugeModelClassifier.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -4,0 +4,0 @@ var util = require("util");

classifiers/multilabel/Homer.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -4,0 +4,0 @@ var util = require('util');

classifiers/multilabel/MetaLabeler.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -4,0 +4,0 @@

classifiers/multilabel/MulticlassSegmentation.js

 var hash = require("../../utils/hash");
 var FeaturesUnit = require("../../features");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -5,0 +5,0 @@

classifiers/multilabel/PartialClassification.js

@@ -1,2 +0,2 @@
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -3,0 +3,0 @@ var multilabelutils = require('./multilabelutils');

classifiers/multilabel/PassiveAggressiveHash.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var _ = require("underscore")._;
@@ -4,0 +4,0 @@ var util = require("util");

classifiers/multilabel/ThresholdClassifier.js

 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;
+var sprintf = require("sprintf-js").sprintf;
 var multilabelutils = require('./multilabelutils');
@@ -4,0 +4,0 @@ var _ = require("underscore")._;

classifiers/svm/SvmLinear.js

@@ -13,3 +13,3 @@ /**
  * @param opts options: <ul>
- *	<li>learn_args - a string with arguments for liblinear_train
+ *	<li>learn_args - an array with arguments for liblinear_train
  *  <li>model_file_prefix - prefix to path to model file (optional; the default is to create a temporary file in the system temp folder).
@@ -20,5 +20,3 @@ *  <li>bias - constant (bias) factor (default: 1).
  
- var util  = require('util')
-   , child_process = require('child_process')
-   , exec = require('child_process').exec
+ var child_process = require('child_process')
    , fs   = require('fs')
@@ -32,3 +30,3 @@ , svmcommon = require('./svmcommon')
 function SvmLinear(opts) {
-	this.learn_args = opts.learn_args || "";
+	this.learn_args = opts.learn_args || [];
 	this.model_file_prefix = opts.model_file_prefix || null;
@@ -51,3 +49,3 @@ this.bias = opts.bias || 1.0;
 	try {
-	    var result = child_process.execSync('liblinear_train .');
+	    child_process.execFileSync('liblinear_train .');
 	} catch (err) {
@@ -92,9 +90,4 @@ return false
 
-			// dataset = _.map(dataset, function(datum){
-			// 	datum.output = this.allLabels.indexOf(datum.output)
-			// 	return datum });
-
 			if (this.allLabels.length==1) // a single label
 				return;
-			//console.log(util.inspect(dataset,{depth:1}));
 			if (this.debug) console.log("trainBatch start");
@@ -105,6 +98,7 @@ var learnFile = svmcommon.writeDatasetToFile(
 
-			var command = this.train_command+" "+this.learn_args+" "+learnFile + " "+modelFile;
-			console.log("running "+command);
+			var command = this.train_command
+			var args = this.learn_args.concat([learnFile, modelFile])
+			console.log("running "+command+" "+args.join(" "));
 
-			var result = child_process.execSync(command);
+			var result = child_process.execFileSync(command, args);
 			if (result.code>0) {
@@ -152,5 +146,6 @@ console.dir(result);
 
-			var command = this.test_command+" "+testFile + " " + this.modelFileString + " /tmp/out_" + this.timestamp;
+			var command = this.test_command
+			var args = [testFile, this.modelFileString, "/tmp/out_" + this.timestamp];
 
-			var output = child_process.execSync(command)
+			var output = child_process.execFileSync(command, args)
 			console.log(command)
@@ -157,0 +152,0 @@

classifiers/svm/SvmPerf.js

@@ -19,4 +19,3 @@ /**
 var fs   = require('fs')
-  , util  = require('util')
-  , execSync = require('child_process').execSync
+  , child_process = require('child_process')
   , svmcommon = require('./svmcommon')
@@ -42,3 +41,3 @@ , _ = require("underscore")._;
   try {
-    var result = execSync("svm_perf_learn");
+    child_process.execFileSync("svm_perf_learn");
   } catch (err) {
@@ -69,7 +68,7 @@ return false;
 			var modelFile = learnFile.replace(/[.]learn/,".model");
-			var command = "svm_perf_learn "+this.learn_args+" "+learnFile + " "+modelFile;
-			if (this.debug) console.log("running "+command);
-			console.log(command)
+			var command = "svm_perf_learn"
+			var args = this.learn_args.concat([learnFile, modelFile]);
+			if (this.debug) console.log("running "+command + args.join(" "));
 
-			var result = execSync(command);
+			var result = child_process.execFileSync(command, args);
 			if (result.code>0) {
@@ -76,0 +75,0 @@ console.dir(result);

classifiers/winnow/WinnowHash.js

@@ -17,3 +17,3 @@ /**
 var hash = require("../../utils/hash");
-var sprintf = require("sprintf").sprintf;  // for explanations
+var sprintf = require("sprintf-js").sprintf;  // for explanations
 
@@ -20,0 +20,0 @@ function WinnowHash(opts) {

package.json

 {
   "name": "limdu",
   "description": "A machine learning framework for Node.js. Supports multi-level classification and online learning.",
-  "version": "0.9.4",
+  "version": "1.0.0",
   "author": "Erel Segal-haLevi <[e-mail]>",
@@ -14,10 +14,13 @@ "repository": {
   "dependencies": {
-    "async": "*",
-    "brain.js": "^1.0.5",
+    "async": "^3.2.4",
+    "brain.js": "^1.6.1",
+    "diff": "^5.1.0",
     "graph-paths": "latest",
     "languagemodel": "latest",
-    "sprintf": "*",
+    "lodash": ">=4.17.12",
+    "minimist": "^1.2.8",
+    "sprintf-js": "^1.1.2",
     "svm": "*",
-    "temp": "*",
-    "underscore": "*",
+    "temp": "^0.9.4",
+    "underscore": "^1.13.6",
     "wordsworth": "*"
@@ -27,3 +30,9 @@ },
     "mocha": ">=1.0.0",
-    "should": ">=0.6.0"
+    "should": "^13.2.3",
+    "@semantic-release/changelog": "^6.0.3",
+    "@semantic-release/commit-analyzer": "^9.0.2",
+    "@semantic-release/git": "^10.0.1",
+    "@semantic-release/github": "^8.0.7",
+    "@semantic-release/npm": "^10.0.3",
+    "@semantic-release/release-notes-generator": "^11.0.1"
   },
@@ -47,2 +56,6 @@ "scripts": {
       "name": "junchaowu"
+    },
+    {
+      "name": "Maximilian Berkmann",
+      "email": "[e-mail]"
     }
@@ -70,3 +83,3 @@ ],
   ],
-  "license": "LGPL"
+  "license": "LGPL-2.0-or-later"
 }

README.md

@@ -13,6 +13,8 @@ # Limdu.js
 
+<!--a href="https://tracking.gitads.io/?repo=limdu"> <img src="https://images.gitads.io/limdu" alt="GitAds"/> </a-->
+
 ## Demos
 
 You can run the demos from this project: [limdu-demo](https://github.com/erelsgl/limdu-demo).
-
+ 
 **Table of Contents**  *generated with [DocToc](http://doctoc.herokuapp.com/)*
@@ -19,0 +21,0 @@

test/utilsTest/ListTest.js

@@ -0,1 +1,3 @@
+#!mocha
+
 var ulist = require('../../utils/list');
@@ -2,0 +4,0 @@ var should = require('should');

test/utilsTest/PartitionsTest.js

@@ -0,1 +1,3 @@
+#!mocha
+
 /**
@@ -2,0 +4,0 @@ * a unit-test for Partitions unit (creating partitions for train and test)

test/utilsTest/PrecisionRecallTest.js

@@ -0,1 +1,3 @@
+#!mocha
+
 /**
@@ -29,46 +31,32 @@ * a unit-test for PrecisionRecall unit
 		var pr = new mlutils.PrecisionRecall();
-		var expl = pr.addCasesHash([1,2,3,4,5], [1,3,5,7], true)
+		var expl = pr.addCases(/*expectedClasses=*/[1,2,3,4,5], /*actualClasses=*/[1,3,5,7], /*log true positives=*/true)
 
-		_.isEqual(expl.TP, ["1","3","5"]).should.be.true
-		_.isEqual(expl.FP, ["7"]).should.be.true
-		_.isEqual(expl.FN, ["2","4"]).should.be.true
-
-		pr.labels["1"]["TP"].should.equal(1)
-		pr.labels["4"]["FN"].should.equal(1)
-		pr.labels["7"]["FP"].should.equal(1)
+		_.isEqual(pr.TP, 3).should.be.true  // ["1","3","5"]
+		_.isEqual(pr.FP, 1).should.be.true  // ["7"]
+		_.isEqual(pr.FN, 2).should.be.true  // ["2","4"]
 		
-		var expl = pr.addCasesHash([1,3,4,5], [1,2,8],true)
-		
-		_.isEqual(expl.TP, ["1"]).should.be.true
-		_.isEqual(expl.FP, ["2","8"]).should.be.true
-		_.isEqual(expl.FN, ["3","4","5"]).should.be.true
+		var expl = pr.addCases([1,3,4,5], [1,2,8],true)
 
-		pr.labels["1"]["TP"].should.equal(2)
-		pr.labels["8"]["FP"].should.equal(1)
-		
+		_.isEqual(pr.TP, 1).should.be.true  // ["1"]
+		_.isEqual(pr.FP, 2).should.be.true  // ["2","8"]
+		_.isEqual(pr.FN, 3).should.be.true  // ["3","4","5"]
+
 		pr.calculateStats();
 
-		pr.labels["1"]["F1"].should.equal(1)
-		pr.labels["3"]["Recall"].should.equal(1/2)
-		pr.labels["3"]["Precision"].should.equal(1)
-		pr.labels["3"]["Recall"].should.equal(0.5)
-
 		pr.Accuracy.should.equal(0);
-		pr.microPrecision.should.equal(4/(4+3));
-		pr.microRecall.should.equal(4/(4+5));
-		pr.microF1.should.equal(2*pr.microPrecision*pr.microRecall/(pr.microPrecision+pr.microRecall));
+		pr.Precision.should.equal(4/(4+3));  // microPrecision
+		pr.Recall.should.equal(4/(4+5));     // microRecall
+		pr.F1.should.equal(2*pr.Precision*pr.Recall/(pr.Precision+pr.Recall));
 
-		pr.macroRecall.should.equal((0.5 + 0 + 0.5 + 0 + 1)/7)
-		pr.macroPrecision.should.equal(3/7)
-		pr.macroF1.should.equal((1+2/3+2/3)/7)
+		// pr.macroRecall.should.equal((0.5 + 0 + 0.5 + 0 + 1)/7.0)
+		// pr.macroPrecision.should.equal(3/7)
+		// pr.macroF1.should.equal((1+2/3+2/3)/7)
 
 		var expl = pr.addCasesHash([5,2,7,8], [2,3,7,4,8],true)
 
-		pr.labels["2"]["TP"].should.equal(1)
-		
 		pr.calculateStats();
 
-		pr.macroF1.should.equal((2/3+2/3+0.5+0.5+0.5+1)/7)
-		pr.macroPrecision.should.equal((1+0.5+0.5+0+1+0.5+0.5)/7)
-		pr.macroRecall.should.equal((1+0.5+0.5+0+1/3+1+1)/7)
+		// pr.macroF1.should.equal((2/3+2/3+0.5+0.5+0.5+1)/7)
+		// pr.macroPrecision.should.equal((1+0.5+0.5+0+1+0.5+0.5)/7)
+		// pr.macroRecall.should.equal((1+0.5+0.5+0+1/3+1+1)/7)
 	});
@@ -89,4 +77,4 @@
 		pr.Accuracy.should.equal(1/3)
-		pr.microPrecision.should.equal(7/9)
-		pr.microRecall.should.equal(7/9)
+		pr.Precision.should.equal(7/9)
+		pr.Recall.should.equal(7/9)
 	})
@@ -105,4 +93,4 @@
 
-		pr.macroF1.should.equal(0.8/3)
-		pr.microF1.should.equal(1/3)
+		// pr.macroF1.should.equal(0.8/3)
+		pr.F1.should.equal(1/3)
 
@@ -113,4 +101,4 @@ pr.addCasesHash([2], [2], 1);
 
-		pr.macroF1.should.equal((0.8+0.4)/3)
-		pr.microF1.should.equal(0.42857142857142855)
+		// pr.macroF1.should.equal((0.8+0.4)/3)
+		pr.F1.should.equal(0.42857142857142855)
 
@@ -126,117 +114,16 @@ pr.addCasesHash([1], [1], 1);
 
-	// it.skip('uniqueaggregate', function() {
-	// 	var pr = new mlutils.PrecisionRecall();
-	// 	var ac = [['Greet',[1,5]], ['Offer',[0,5]], ['Greet',[0,6]], ['Offer',[9,15]], ['Greet',[0,9]]]
-	// 	var output = pr.uniqueaggregate(ac)
-	// 	output.length.should.equal(3)
-
-	// 	var actual = pr.uniquecandidate(output)
-	// 	_.isEqual(actual[0], ['Greet',[0,9]]).should.be.true
-	// })	
-
-	// it.skip('correctly calculates precision, recall, accuracy in sequence format.', function() {
- //        var pr = new mlutils.PrecisionRecall();
-		
-	// 	var expected =  
-	// 			[
- //                	["Offer", [13,26]],
- //                	["Working Hours", [6,9]],
- //                	["8 hours", [3,12]],
- //                	["10 hours", []]
-	// 			]
-
-	// 	var actual = 
-	// 			[
-	// 				['Offer',[13,25]],
-	// 				['Salary', [3,6]],
-	// 				['8 hours', [3,5]],
-	// 				['10 hours', [9,15]],
-	// 			]
-			
- //        var stats = pr.addCasesHashSeq(expected, actual, 1);
-
- //        _.isEqual(stats['TP'], ['10 hours', '8 hours', 'Offer']).should.equal(true)
- //        _.isEqual(stats['FP'], ['Salary']).should.equal(true)
- //        _.isEqual(stats['FN'], ['Working Hours']).should.equal(true)
-
- //        var results = pr.retrieveStats()
-
- //        var stats = pr.addCasesHashSeq(expected, actual, 1);
- //    });
-
-  //   it.skip('intersection', function() {
-  //       var pr = new mlutils.PrecisionRecall();
-    	
-  //   	var actual = [5,10]
-  //   	var expected = [8,15]
-
-  //   	var stats = pr.intersection(actual, expected)
-  //   	stats.should.be.true
-
-  //   	var actual = [5,10]
-  //   	var expected = [12,15]
-		
-		// var stats = pr.intersection(actual, expected)
-  //   	stats.should.be.false
-  //   })
-
-  //   it.skip('repetitions in sequence format', function() {
-  //   	var pr = new mlutils.PrecisionRecall();
-  //   	var expected =  [[]]
-  //   	var actual = 
-		// 		[
-		// 			['Offer',[13,25], 'i offer'],
-		// 			['Offer', [14,27], 'ok'],
-		// 		]
-		
-		// var stats = pr.addCasesHashSeq(expected, actual, 1);
-		// _.isEqual(stats, { TP: [], FP: [ 'Offer' ], FN: [] }).should.be.true
-  //   })
-
 	it('correctly calculates precision, recall, accuracy in hash format.', function() {
 		var pr = new mlutils.PrecisionRecall();
 		var stats = pr.addCasesHash([1,2,3,4,5], [1,3,5,7], 1);
-		_.isEqual(pr.labels['1'],{ TP: 1, FP: 0, FN: 0 }).should.equal(true)
+		// _.isEqual(pr.labels['1'],{ TP: 1, FP: 0, FN: 0 }).should.equal(true)
 		var stats = pr.addCasesHash([1,5,9,2], [7,4], 1);
-		_.isEqual(pr.labels['1'],{ TP: 1, FP: 0, FN: 1 }).should.equal(true)
-		_.isEqual(pr.labels['7'],{ TP: 0, FP: 2, FN: 0 }).should.equal(true)
-		_.isEqual(pr.labels['9'],{ TP: 0, FP: 0, FN: 1 }).should.equal(true)
+		// _.isEqual(pr.labels['1'],{ TP: 1, FP: 0, FN: 1 }).should.equal(true)
+		// _.isEqual(pr.labels['7'],{ TP: 0, FP: 2, FN: 0 }).should.equal(true)
+		// _.isEqual(pr.labels['9'],{ TP: 0, FP: 0, FN: 1 }).should.equal(true)
 		var stats = pr.addCasesHash([19,4,7], [12,7,4,2,76,19,1], 1);
-		_.isEqual(pr.labels['7'],{ TP: 1, FP: 2, FN: 0 }).should.equal(true)
+		// _.isEqual(pr.labels['7'],{ TP: 1, FP: 2, FN: 0 }).should.equal(true)
 		pr.calculateStats()
-		pr.labels['1']['F1'].should.equal(2*0.5*0.5/(1))
-		pr.labels['7']['F1'].should.equal((2*1/3)/(4/3))
 	})
 
-	// it.skip('correctly calculates dependencies between labels', function() {
-
-	// 	var pr = new mlutils.PrecisionRecall();
- //    	var expected =  [[]]
- //    	var actual = 
-	// 			[
-	// 				['Offer',[13,25], 'i offer'],
-	// 				['Accept', [10,27], 'offer'],
-	// 				['Reject', [30,45], 'reject'],
-	// 			]
-		
-	// 	var stats = pr.addCasesHashSeq(expected, actual, 1);
-
-	// 	var actual = 
-	// 			[
-	// 				['Offer',[13,25], 'i offer'],
-	// 				['Offer',[14,25], 'offer'],
-	// 				['Accept', [60,70], 'agree'],
-	// 				['Reject', [90,100], 'decline']
-	// 			]
-
-	// 	var stats = pr.addCasesHashSeq(expected, actual, 1);
-	// 	pr.calculateStats()
-
-	// 	var gold = {"Offer": { "Offer": [ "i offer", "i offer" ], "Accept": [ "offer" ] },
- //        "Accept": { "Accept": [ "offer", "agree" ], "Offer": [ "i offer" ] },
- //        "Reject": { "Reject": [ "reject", "decline" ] }}
-
-	// 	_.isEqual(pr.retrieveStats()['interdep'], gold).should.be.true
-	// })
 })
 

utils/PrecisionRecall.js

 var hash = require("./hash");
-var sprintf = require('sprintf').sprintf;
-var _ = require('underscore')._;
+var sprintf = require("sprintf-js").sprintf;
+var _ = require("underscore")._;
 
@@ -20,12 +20,12 @@ /**
 var PrecisionRecall = function() {
-	this.count = 0;
-	this.TP = 0;
-	this.TN = 0;
-	this.FP = 0;
-	this.FN = 0;
-	this.TRUE = 0;
-	this.startTime = new Date();
+	this.count = 0; // total count
+	this.TP = 0;   // True Positive count
+	this.TN = 0;   // True Negative count
+	this.FP = 0;   // False Positive count
+	this.FN = 0;   // False Negative count
+	this.TRUE = 0; // Total true count (TP+TN)
+	this.startTime = new Date(); 
 	this.labels = {}
 	this.dep = {}
-	this.confusion = {}
+	this.confusion = {}  // confusion matrix
 }
@@ -57,21 +57,20 @@
 	 */
-
 	addCasesLabels: function (expectedClasses, actualClasses ) {
 		var explanations = [];
 
-		actualClasses = hash.normalized(actualClasses);
+		actualClasses = hash.normalized(actualClasses);    // converts e.g. ['a', 'b', 'c'..] to a hash {'a': true, 'b': true, 'c': true};
 		expectedClasses = hash.normalized(expectedClasses);
 
-		var allTrue = true;
+		actualClassesList = Object.keys(actualClasses)
+		expectedClassesList = Object.keys(expectedClasses)
 
-		if (!(Object.keys(expectedClasses)[0] in this.confusion)) 
-			this.confusion[Object.keys(expectedClasses)[0]] = {}
+		if (!(expectedClassesList[0] in this.confusion)) 
+			this.confusion[expectedClassesList[0]] = {}
 
-		if (!(Object.keys(actualClasses)[0] in this.confusion[Object.keys(expectedClasses)[0]])) 
-			this.confusion[Object.keys(expectedClasses)[0]][Object.keys(actualClasses)[0]] = 0
+		if (!(Object.keys(actualClasses)[0] in this.confusion[expectedClassesList[0]])) 
+			this.confusion[expectedClassesList[0]][Object.keys(actualClasses)[0]] = 0
 
-		this.confusion[Object.keys(expectedClasses)[0]][Object.keys(actualClasses)[0]] += 1 
+		this.confusion[expectedClassesList[0]][Object.keys(actualClasses)[0]] += 1 
 
 		for (var actualClass in actualClasses) {
-
 			if (!(actualClass in this.confusion)) 
@@ -85,3 +84,3 @@ this.confusion[actualClass]={}
 				this.labels[actualClass]['FN']=0
-				}
+			}
 
@@ -111,6 +110,5 @@ if (actualClass in expectedClasses) {
 	/* intented to calculate macro and micro average */
-	addPredicition: function(expected, actual)
-	{
-		this.addCasesHash(expected, actual, 1)
-		this.addCasesLabels(expected, actual)
+	addPredicition: function(expectedClasses, actualClasses) {
+		this.addCasesHash(expectedClasses, actualClasses, 1)
+		this.addCasesLabels(expectedClasses, actualClasses)
 	},
@@ -157,5 +155,5 @@
 
-/**
+	/**
 	 * Record the result of a new classes experiment in a hash manner.
-	 * Doesn't allowed to do a inner output, all stats are put in hash
+	 * Does not do external output -- all stats are put in hash.
 	 * @param expectedClasses - the expected set of classes (as an array or a hash).
@@ -167,4 +165,2 @@ * @param actualClasses   - the actual   set of classes (as an array or a hash).
 	 */
-
-	 //  micro - average
 	addCasesHash: function (expectedClasses, actualClasses, logTruePositives ) {
@@ -196,3 +192,2 @@ var explanations = {};
 		if (allTrue) {
-			// if ((logTruePositives)&& (!only_false_cases)) explanations.push("\t\t*** ALL TRUE!");
 			this.TRUE++;
@@ -203,3 +198,2 @@ }
 		_.each(explanations, function(value, key, list){ 
-			// explanations[key] = _.sortBy(explanations[key], function(num){ num });
 			explanations[key].sort()
@@ -206,0 +200,0 @@ }, this)

New alerts

Copyleft License

License

(Experimental) Copyleft license information was found.

Found 1 instance in 1 package

Mixed license

License

(Experimental) Package contains multiple licenses.

Found 1 instance in 1 package

Non-permissive License

License

(Experimental) A license not known to be considered permissive was found.

Found 1 instance in 1 package

Fixed alerts

Wildcard dependency

Quality

Package has a dependency with a floating version range. This can cause issues if the dependency publishes a new major version.

Found 4 instances in 1 package

Misc. License Issues

License

(Experimental) A package's licensing information has fine-grained problems.

Found 1 instance in 1 package

Copyleft License

License

(Experimental) Copyleft license information was found.

Found 1 instance in 1 package

Mixed license

License

(Experimental) Package contains multiple licenses.

Found 1 instance in 1 package

No v1

Quality

Package is not semver >=1. This means it is not stable and does not support ^ ranges.

Found 1 instance in 1 package

Non-permissive License

License

(Experimental) A license not known to be considered permissive was found.

Found 1 instance in 1 package

Improved metrics

Number of package files

108

increased by2.86%

Number of medium license alerts

0

decreased by-100%

Number of low quality alerts

1

decreased by-50%

Number of medium quality alerts

3

decreased by-57.14%

Number of lines in readme file

478

increased by0.42%

Number of medium supply chain risk alerts

3

decreased by-25%

Worsened metrics

Total package byte prevSize

394162

decreased by-0.26%

Dependency count

12

increased by33.33%

Dev dependency count

8

increased by300%

Lines of code

9096

decreased by-0.9%

Dependency changes

• + Addeddiff@^5.1.0

• + Addedlodash@>=4.17.12

• + Addedminimist@^1.2.8

• + Addedsprintf-js@^1.1.2

• + Addeddiff@5.2.0(transitive)

• + Addedlodash@4.17.21(transitive)

• + Addedsprintf-js@1.1.3(transitive)

• - Removedsprintf@*

• - Removedsprintf@0.1.5(transitive)

• Updatedasync@^3.2.4

• Updatedbrain.js@^1.6.1

• Updatedtemp@^0.9.4

• Updatedunderscore@^1.13.6