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courses:be5b33prg:homeworks:spam:step3 [2015/11/25 16:01]
xposik [Confusion Matrix] 		courses:be5b33prg:homeworks:spam:step3 [2015/12/14 14:14]
xposik [Function ''compute_quality_for_corpus()'']
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 ====== Spam filter - step 3 ====== ====== Spam filter - step 3 ======
-Create ​a set of classes and functions needed to evaluate the filter quality.+Create ​additional ​functions needed to evaluate the filter quality. 
  
-/** 
-<WRAP round download>​ 
-[[.unit_testing|Tests]] for step 3: {{:​courses:​a4b99rph:​cviceni:​spam:​test3_quality.zip|}} 
-</​WRAP>​ 
-**/ 
  
  
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 Task: Task:
   * Create function ''​quality_score(tp,​ tn, fp, fn)''​ in module ''​quality.py''​.   * Create function ''​quality_score(tp,​ tn, fp, fn)''​ in module ''​quality.py''​.
-  * Function computes the quality score defined during the lab.+  * Function computes the quality score defined during the lab (find it also [[courses:​be5b33prg:​homeworks:​spam:​evaluation#​filter_quality_assessment|here]]).
  
 ^ ''​quality_score(tp,​ tn, fp, fn) ''​ Compute the quality score based on the confusion matrix. ^^ ^ ''​quality_score(tp,​ tn, fp, fn) ''​ Compute the quality score based on the confusion matrix. ^^
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   * In module ''​quality.py'',​ create function ''​compute_quality_for_corpus(corpus_dir)''​ which evaluates the filter quality based on the information contained in files ''​!truth.txt''​ and ''​!prediction.txt''​ in the given corpus.   * In module ''​quality.py'',​ create function ''​compute_quality_for_corpus(corpus_dir)''​ which evaluates the filter quality based on the information contained in files ''​!truth.txt''​ and ''​!prediction.txt''​ in the given corpus.
   * The true and predicted classification can be read in the form of dictionaries using function ''​read_classification_from_file()''​.   * The true and predicted classification can be read in the form of dictionaries using function ''​read_classification_from_file()''​.
-  * The confusion matrix for the given corpus can be computed from the dictionaries using method ''​compute_from_dicts()'' ​of ''​BinaryConfusionMatrix''​ class.+  * The confusion matrix for the given corpus can be computed from the dictionaries using method ''​compute_confusion_matrix()'' ​function from step 2.
   * The quality score can be computed from the confusion matrix using function ''​quality_score()''​.   * The quality score can be computed from the confusion matrix using function ''​quality_score()''​.
  
courses/be5b33prg/homeworks/spam/step3.txt · Last modified: 2015/12/14 14:24 by xposik