Overhaul logistic regression scripts; fix a number of bugs.

methods/matlab/LOGISTIC_REGRESSION.m

@@ -26,7 +26,7 @@ function logistic_regression(cmd)
 X = csvread(regressorsFile{:});
 
 if isempty(responsesFile)
-  y = X(:,end);
+  y = X(:,end) + 1; % We have to increment because labels must be positive.
   X = X(:,1:end-1);
 else
   y = csvread(responsesFile{:});
@@ -47,7 +47,7 @@ function logistic_regression(cmd)
 disp(sprintf('[INFO ]   total_time: %fs', toc(total_time)))
 
 if ~isempty(testFile)
-    csvwrite('predictions.csv', idx);
+    csvwrite('predictions.csv', idx - 1); % Subtract extra label bit.
     csvwrite('matlab_lr_probs.csv', predictions);
 end
 

methods/matlab/logistic_regression.py

@@ -79,7 +79,7 @@ def RunMetrics(self, options):
 
     # If the dataset contains two files then the second file is the test
     # file. In this case we add this to the command line.
-    if len(self.dataset) == 2:
+    if len(self.dataset) >= 2:
       inputCmd = "-i " + self.dataset[0] + " -t " + self.dataset[1]
     else:
       inputCmd = "-i " + self.dataset[0]
@@ -111,11 +111,15 @@ def RunMetrics(self, options):
       truelabels = np.genfromtxt(self.dataset[2], delimiter = ',')
       metrics['Runtime'] = timer.total_time
       confusionMatrix = Metrics.ConfusionMatrix(truelabels, predictions)
-      metrics['ACC'] = Metrics.AverageAccuracy(confusionMatrix)
-      metrics['MCC'] = Metrics.MCCMultiClass(confusionMatrix)
-      metrics['Precision'] = Metrics.AvgPrecision(confusionMatrix)
-      metrics['Recall'] = Metrics.AvgRecall(confusionMatrix)
-      metrics['MSE'] = Metrics.SimpleMeanSquaredError(truelabels, predictions)
+
+      metrics['Avg Accuracy'] = Metrics.AverageAccuracy(confusionMatrix)
+      metrics['MultiClass Precision'] = Metrics.AvgPrecision(confusionMatrix)
+      metrics['MultiClass Recall'] = Metrics.AvgRecall(confusionMatrix)
+      metrics['MultiClass FMeasure'] = Metrics.AvgFMeasure(confusionMatrix)
+      metrics['MultiClass Lift'] = Metrics.LiftMultiClass(confusionMatrix)
+      metrics['MultiClass MCC'] = Metrics.MCCMultiClass(confusionMatrix)
+      metrics['MultiClass Information'] = Metrics.AvgMPIArray(confusionMatrix, truelabels, predictions)
+      metrics['Simple MSE'] = Metrics.SimpleMeanSquaredError(truelabels, predictions)
 
       Log.Info(("total time: %fs" % (metrics['Runtime'])), self.verbose)
 

methods/milk/logistic_regression.py

@@ -80,6 +80,9 @@ def RunLogisticRegressionMilk():
         self.model = self.BuildModel()
         with totalTimer:
           self.model = self.model.train(trainData, labels)
+          if len(self.dataset) > 1:
+            # We get back probabilities; cast these to classes.
+            self.predictions = np.greater(self.model.apply(testData), 0.5)
       except Exception as e:
         return -1
 
@@ -112,4 +115,19 @@ def RunMetrics(self, options):
 
     # Datastructure to store the results.
     metrics = {'Runtime' : results}
+
+    if len(self.dataset) >= 3:
+      truelabels = LoadDataset(self.dataset[2])
+
+      confusionMatrix = Metrics.ConfusionMatrix(truelabels, self.predictions)
+
+      metrics['Avg Accuracy'] = Metrics.AverageAccuracy(confusionMatrix)
+      metrics['MultiClass Precision'] = Metrics.AvgPrecision(confusionMatrix)
+      metrics['MultiClass Recall'] = Metrics.AvgRecall(confusionMatrix)
+      metrics['MultiClass FMeasure'] = Metrics.AvgFMeasure(confusionMatrix)
+      metrics['MultiClass Lift'] = Metrics.LiftMultiClass(confusionMatrix)
+      metrics['MultiClass MCC'] = Metrics.MCCMultiClass(confusionMatrix)
+      metrics['MultiClass Information'] = Metrics.AvgMPIArray(confusionMatrix, truelabels, self.predictions)
+      metrics['Simple MSE'] = Metrics.SimpleMeanSquaredError(truelabels, self.predictions)
+
     return metrics

methods/mlpack/logistic_regression.py

@@ -98,7 +98,7 @@ def OptionsToStr(self, options):
       optionsStr = "-e " + str(options.pop("epsilon"))
     if "max_iterations" in options:
       optionsStr = optionsStr + " -n " + str(options.pop("max_iterations"))
-    if "optimizer" in options:
+    if "algorithm" in options:
       optionsStr = optionsStr + " -O " + str(options.pop("optimizer"))
     if "step_size" in options:
       optionsStr = optionsStr + " -s " + str(options.pop("step_size"))

methods/scikit/logistic_regression.py

@@ -85,7 +85,7 @@ def RunLogisticRegressionScikit():
       # Use the last row of the training set as the responses.
       X, y = SplitTrainData(self.dataset)
       if "algorithm" in options:
-        self.opts["algorithm"] = str(options.pop("algorithm"))
+        self.opts["solver"] = str(options.pop("algorithm"))
       if "epsilon" in options:
         self.opts["epsilon"] = float(options.pop("epsilon"))
       if "max_iterations" in options:

methods/shogun/logistic_regression.py

@@ -51,6 +51,7 @@ def __init__(self, dataset, timeout=0, verbose=True):
     self.predictions = None
     self.z = 1
     self.model = None
+    self.max_iter = None
 
   '''
   Build the model for the Logistic Regression.
@@ -63,6 +64,8 @@ def BuildModel(self, data, responses):
     # Create and train the classifier.
     model = MulticlassLogisticRegression(self.z, RealFeatures(data.T),
         MulticlassLabels(responses))
+    if self.max_iter is not None:
+      model.set_max_iter(self.max_iter);
     model.train()
     return model
 
@@ -87,6 +90,10 @@ def RunLogisticRegressionShogun():
         # Use the last row of the training set as the responses.
         X, y = SplitTrainData(self.dataset)
 
+        # Get the maximum number of iterations.
+        if "max_iterations" in options:
+          self.max_iter = int(options.pop("max_iterations"))
+
         # Get the regularization value.
         if "lambda" in options:
           self.z = float(options.pop("lambda"))

methods/weka/logistic_regression.py

@@ -69,6 +69,11 @@ def __del__(self):
   def RunMetrics(self, options):
     Log.Info("Perform Logistic Regression.", self.verbose)
 
+    maxIterStr = ""
+    if 'max_iterations' in options:
+      maxIterStr = " -m " + str(options['max_iterations']) + " "
+      options.pop('max_iterations')
+
     if len(options) > 0:
       Log.Fatal("Unknown parameters: " + str(options))
       raise Exception("unknown parameters")
@@ -79,8 +84,8 @@ def RunMetrics(self, options):
 
     # Split the command using shell-like syntax.
     cmd = shlex.split("java -classpath " + self.path + "/weka.jar" +
-        ":methods/weka" + " LOGISTICREGRESSION -t " + self.dataset[0] + " -T " +
-        self.dataset[1])
+        ":methods/weka" + " LogisticRegression -t " + self.dataset[0] + " -T " +
+        self.dataset[1] + maxIterStr)
 
     # Run command with the nessecary arguments and return its output as a byte
     # string. We have untrusted input so we disable all shell based features.
@@ -105,11 +110,14 @@ def RunMetrics(self, options):
       truelabels = np.genfromtxt(self.dataset[2], delimiter = ',')
       metrics['Runtime'] = timer.total_time
       confusionMatrix = Metrics.ConfusionMatrix(truelabels, predictions)
-      metrics['ACC'] = Metrics.AverageAccuracy(confusionMatrix)
-      metrics['MCC'] = Metrics.MCCMultiClass(confusionMatrix)
-      metrics['Precision'] = Metrics.AvgPrecision(confusionMatrix)
-      metrics['Recall'] = Metrics.AvgRecall(confusionMatrix)
-      metrics['MSE'] = Metrics.SimpleMeanSquaredError(truelabels, predictions)
+      metrics['Avg Accuracy'] = Metrics.AverageAccuracy(confusionMatrix)
+      metrics['MultiClass Precision'] = Metrics.AvgPrecision(confusionMatrix)
+      metrics['MultiClass Recall'] = Metrics.AvgRecall(confusionMatrix)
+      metrics['MultiClass FMeasure'] = Metrics.AvgFMeasure(confusionMatrix)
+      metrics['MultiClass Lift'] = Metrics.LiftMultiClass(confusionMatrix)
+      metrics['MultiClass MCC'] = Metrics.MCCMultiClass(confusionMatrix)
+      metrics['MultiClass Information'] = Metrics.AvgMPIArray(confusionMatrix, truelabels, predictions)
+      metrics['Simple MSE'] = Metrics.SimpleMeanSquaredError(truelabels, predictions)
 
       Log.Info(("total time: %fs" % (metrics['Runtime'])), self.verbose)
 

methods/weka/src/LogisticRegression.java

@@ -8,6 +8,7 @@
 import java.io.IOException;
 import weka.core.*;
 import weka.core.converters.ConverterUtils.DataSource;
+import weka.core.converters.CSVLoader;
 import weka.filters.Filter;
 import weka.filters.unsupervised.attribute.NumericToNominal;
 
@@ -29,7 +30,8 @@ public class LogisticRegression {
           + "                the last row of the input file.\n\n"
           + "Options:\n\n"
           + "-t [string]   Optional file containing containing\n"
-          + "              test dataset");
+          + "              test dataset\n"
+          + "-m [int]      Maximum number of iterations\n");
 
   public static HashMap<Integer, Double> createClassMap(Instances Data) {
    HashMap<Integer, Double> classMap = new HashMap<Integer, Double>();
@@ -69,6 +71,8 @@ public static void main(String args[]) {
 
       // Load input dataset.
       DataSource source = new DataSource(regressorsFile);
+      if (source.getLoader() instanceof CSVLoader)
+        ((CSVLoader) source.getLoader()).setNoHeaderRowPresent(true);
       Instances data = source.getDataSet();
 
       // Transform numeric class to nominal class because the
@@ -81,12 +85,19 @@ public static void main(String args[]) {
       nm.setInputFormat(data);
       data = Filter.useFilter(data, nm);
 
+      boolean hasMaxIters = false;
+      int maxIter = Integer.parseInt(Utils.getOption('m', args));
+      if (maxIter != 0)
+        hasMaxIters = true;
+
       // Did the user pass a test file?
       String testFile = Utils.getOption('t', args);
       Instances testData = null;
       if (testFile.length() != 0)
       {
         source = new DataSource(testFile);
+        if (source.getLoader() instanceof CSVLoader)
+          ((CSVLoader) source.getLoader()).setNoHeaderRowPresent(true);
         testData = source.getDataSet();
 
         // Weka makes the assumption that the structure of the training and test
@@ -122,6 +133,8 @@ public static void main(String args[]) {
       // Perform Logistic Regression.
       timer.StartTimer("total_time");
       weka.classifiers.functions.Logistic model = new weka.classifiers.functions.Logistic();
+      if (hasMaxIters)
+        model.setMaxIts(maxIter);
       model.buildClassifier(data);
 
       // Use the testdata to evaluate the modell.
@@ -140,7 +153,7 @@ public static void main(String args[]) {
           }
           FileWriter writer = new FileWriter(probabs.getName(), false);
 
-          File predictions = new File("weka_lr_predictions.csv");
+          File predictions = new File("weka_predicted.csv");
           if(!predictions.exists()) {
             predictions.createNewFile();
           }

util/timer.py

@@ -64,10 +64,10 @@ def timeout(fun, timeout=9000):
     p.join()
 
     Log.Warn("Script timed out after " + str(timeout) + " seconds")
-    return -2
+    return [-2]
   else:
     try:
       r = q.get(timeout=3)
     except Exception as e:
-      r = -1
+      r = [-1]
     return r