ENH: adding possibility to compute confusion matrix

scikits/learn/confusion.py

@@ -0,0 +1,65 @@
+import numpy as np
+
+def confusion_matrix(y, y_):
+    """
+    compute confusion matrix
+    to evaluate the accuracy of a classification result
+
+    By definition a confusion matrix cm is such that
+
+    cm[i,j] is equal to the number of observations known to be in group i
+    but predicted to be in group j
+
+    Parameters
+    ==========
+
+    y : array
+        true targets
+
+    y_ : array
+        estimated targets
+
+    """
+
+    # removing possible NaNs in targets (they are ignored)
+    clean_y = y[np.isfinite(y)].ravel()
+    clean_y_ = y_[np.isfinite(y_)].ravel()
+
+    labels = np.r_[np.unique(clean_y).ravel(),np.unique(clean_y_).ravel()]
+    labels = np.unique(labels)
+    n_labels = labels.size
+
+    cm = np.empty((n_labels,n_labels))
+    for i, label_i in enumerate(labels):
+        for j, label_j in enumerate(labels):
+            cm[i,j] = np.sum(np.logical_and(y==label_i, y_==label_j))
+
+    return cm
+
+if __name__ == '__main__':
+    import pylab as pl
+    from scikits.learn import svm, datasets
+    import random
+    random.seed(0)
+
+    # import some data to play with
+    iris = datasets.load_iris()
+    X = iris.data
+    y = iris.target
+    n_samples, n_features = X.shape
+    p = range(n_samples)
+    random.shuffle(p)
+    X, y = X[p], y[p]
+    half = int(n_samples/2)
+
+    classifier = svm.SVC(kernel='linear')
+    y_ = classifier.fit(X[:half],y[:half]).predict(X[half:])
+
+    cm = confusion_matrix(y[half:], y_)
+
+    print cm
+
+    pl.matshow(cm)
+    pl.title('Confusion matrix')
+    pl.colorbar()
+    pl.show()