More changes than I should be making in a commit

src/edu/stanford/nlp/classify/Classifier.java

@@ -1,7 +1,9 @@
 package edu.stanford.nlp.classify;
 
 import edu.stanford.nlp.ling.Datum;
+import edu.stanford.nlp.ling.RVFDatum;
 import edu.stanford.nlp.stats.Counter;
+import edu.stanford.nlp.util.Pair;
 
 import java.io.Serializable;
 import java.util.Collection;
@@ -25,4 +27,70 @@ public interface Classifier<L, F> extends Serializable {
   public Counter<L> scoresOf(Datum<L, F> example);
 
   public Collection<L> labels();
+
+  /**
+   * Evaluates the precision and recall of this classifier against a dataset, and the target label.
+   *
+   * @param testData The dataset to evaluate the classifier on.
+   * @param targetLabel The target label (e.g., for relation extraction, this is the relation we're interested in).
+   * @return A pair of the precision (first) and recall (second) of the classifier on the target label.
+   */
+  public default Pair<Double, Double> evaluatePrecisionAndRecall(GeneralDataset<L, F> testData, L targetLabel) {
+    if (targetLabel == null) {
+      throw new IllegalArgumentException("Must supply a target label to compute precision and recall against");
+    }
+    // Variables to count
+    int numCorrectAndTarget = 0;
+    int numTargetGuess = 0;
+    int numTargetGold = 0;
+    // Iterate over dataset
+    for (RVFDatum<L, F> datum : testData) {
+      // Get the gold label
+      L label = datum.label();
+      if (label == null) {
+        throw new IllegalArgumentException("Cannot compute precision and recall on unlabelled dataset. Offending datum: " + datum);
+      }
+      // Get the guess label
+      L guess = classOf(datum);
+      // Compute statistics on datum
+      if (label.equals(targetLabel)) {
+        numTargetGold += 1;
+      }
+      if (guess.equals(targetLabel)) {
+        numTargetGuess += 1;
+        if (guess.equals(label)) {
+          numCorrectAndTarget += 1;
+        }
+      }
+    }
+    // Aggregate statistics
+    double precision = numTargetGuess == 0 ? 0.0 : ((double) numCorrectAndTarget) / ((double) numTargetGuess);
+    double recall = numTargetGold == 0 ? 1.0 : ((double) numCorrectAndTarget) / ((double) numTargetGold);
+    return Pair.makePair(precision, recall);
+  }
+
+  /**
+   * Evaluate the accuracy of this classifier on the given dataset.
+   *
+   * @param testData The dataset to evaluate the classifier on.
+   * @return The accuracy of the classifier on the given dataset.
+   */
+  public default double evaluateAccuracy(GeneralDataset<L, F> testData) {
+    int numCorrect = 0;
+    for (RVFDatum<L, F> datum : testData) {
+      // Get the gold label
+      L label = datum.label();
+      if (label == null) {
+        throw new IllegalArgumentException("Cannot compute precision and recall on unlabelled dataset. Offending datum: " + datum);
+      }
+      // Get the guess
+      L guess = classOf(datum);
+      // Compute statistics
+      if (label.equals(guess)) {
+        numCorrect += 1;
+      }
+    }
+    return ((double) numCorrect) / ((double) testData.size);
+  }
+
 }