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--- courses:be5b33prg:homeworks:spam:step2 [2015/11/25 16:29]
xposik [Preparation]
+++ courses:be5b33prg:homeworks:spam:step2 [2015/11/25 16:33]
xposik [Preparation]
@@ Line 8: / Line 8: @@
     * what these abbreviations mean for the spam filtering problem, and
     * what we need to know to be able to compute them.
+  * See the documentation for ''[[https://docs.python.org/3.4/library/collections.html#collections.namedtuple|namedtuple]]''.
 ===== Specifications =====
@@ Line 27: / Line 28: @@
   * Function ''compute_confusion_matrix()'' represents the basis for evaluation of the filter performance.
   * The function can be used in the following way:<code python>
-    >>> cm1 = compute_confusion_matrix({}, {})
+>>> cm1 = compute_confusion_matrix({}, {})
-    >>> print(cm1)
+>>> print(cm1)
-    ConfMat(tp=0, tn=0, fp=0, fn=0)
+ConfMat(tp=0, tn=0, fp=0, fn=0)
 </code>or<code python>
-    >>> truth_dict = {'em1': 'SPAM', 'em2': 'SPAM', 'em3': 'OK', 'em4':'OK'}
+>>> truth_dict = {'em1': 'SPAM', 'em2': 'SPAM', 'em3': 'OK', 'em4':'OK'}
-    >>> pred_dict = {'em1': 'SPAM', 'em2': 'OK', 'em3': 'OK', 'em4':'SPAM'}
+>>> pred_dict = {'em1': 'SPAM', 'em2': 'OK', 'em3': 'OK', 'em4':'SPAM'}
-    >>> cm2 = compute_confusion_matrix(truth_dict, pred_dict, pos_tag='SPAM', neg_tag='OK')
+>>> cm2 = compute_confusion_matrix(truth_dict, pred_dict, pos_tag='SPAM', neg_tag='OK')
-    >>> print(cm2)
+>>> print(cm2)
-    ConfMat(tp=1, tn=1, fp=1, fn=1)
+ConfMat(tp=1, tn=1, fp=1, fn=1)
 </code>