====== Spam filter - step 3 ======
Create additional functions needed to evaluate the filter quality.







===== Function ''quality_score()'' =====
Task:
  * Create function ''quality_score(tp, tn, fp, fn)'' in module ''quality.py''.
  * 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. ^^
^ Inputs | 4 nonnegative integers for TP, TN, FP, FN. |
^ Outputs | A number between 0 and 1 showing the prediction quality measure. |

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===== Function ''compute_quality_for_corpus()'' =====
Task:
  * 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 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()''.

Why do we need it?
  * To compute the quality of a filter and to rank them.

^ ''compute_quality_for_corpus(corpus_dir)'' ^ Compute the quality of predictions for given corpus. ^
^ Inputs | A corpus directory evaluated by a filter (i.e. a directory containing ''!truth.txt'' and ''!prediction.txt'' files). |
^ Outputs | Quality of the filter as a number between 0 and 1. |

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