my first python submission!

jpoirier/classification_utilities.py

@@ -0,0 +1,76 @@
+import numpy as np
+
+def display_cm(cm, labels, hide_zeros=False,
+                             display_metrics=False):
+    """Display confusion matrix with labels, along with
+       metrics such as Recall, Precision and F1 score.
+       Based on Zach Guo's print_cm gist at
+       https://gist.github.com/zachguo/10296432
+    """
+
+    precision = np.diagonal(cm)/cm.sum(axis=0).astype('float')
+    recall = np.diagonal(cm)/cm.sum(axis=1).astype('float')
+    F1 = 2 * (precision * recall) / (precision + recall)
+
+    precision[np.isnan(precision)] = 0
+    recall[np.isnan(recall)] = 0
+    F1[np.isnan(F1)] = 0
+
+    total_precision = np.sum(precision * cm.sum(axis=1)) / cm.sum(axis=(0,1))
+    total_recall = np.sum(recall * cm.sum(axis=1)) / cm.sum(axis=(0,1))
+    total_F1 = np.sum(F1 * cm.sum(axis=1)) / cm.sum(axis=(0,1))
+    #print total_precision
+
+    columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length
+    empty_cell = " " * columnwidth
+    # Print header
+    print("    " + " Pred", end=' ')
+    for label in labels: 
+        print("%{0}s".format(columnwidth) % label, end=' ')
+    print("%{0}s".format(columnwidth) % 'Total')
+    print("    " + " True")
+    # Print rows
+    for i, label1 in enumerate(labels):
+        print("    %{0}s".format(columnwidth) % label1, end=' ')
+        for j in range(len(labels)): 
+            cell = "%{0}d".format(columnwidth) % cm[i, j]
+            if hide_zeros:
+                cell = cell if float(cm[i, j]) != 0 else empty_cell
+            print(cell, end=' ')
+        print("%{0}d".format(columnwidth) % sum(cm[i,:]))
+
+    if display_metrics:
+        print()
+        print("Precision", end=' ')
+        for j in range(len(labels)):
+            cell = "%{0}.2f".format(columnwidth) % precision[j]
+            print(cell, end=' ')
+        print("%{0}.2f".format(columnwidth) % total_precision)
+        print("   Recall", end=' ')
+        for j in range(len(labels)):
+            cell = "%{0}.2f".format(columnwidth) % recall[j]
+            print(cell, end=' ')
+        print("%{0}.2f".format(columnwidth) % total_recall)
+        print("       F1", end=' ')
+        for j in range(len(labels)):
+            cell = "%{0}.2f".format(columnwidth) % F1[j]
+            print(cell, end=' ')
+        print("%{0}.2f".format(columnwidth) % total_F1)
+
+
+def display_adj_cm(
+        cm, labels, adjacent_facies, hide_zeros=False, 
+        display_metrics=False):
+    """This function displays a confusion matrix that counts 
+       adjacent facies as correct.
+    """
+    adj_cm = np.copy(cm)
+
+    for i in np.arange(0,cm.shape[0]):
+        for j in adjacent_facies[i]:
+            adj_cm[i][i] += adj_cm[i][j]
+            adj_cm[i][j] = 0.0
+
+    display_cm(adj_cm, labels, hide_zeros, 
+                             display_metrics)
+