stats.py

来自「用python实现的邮件过滤器」· Python 代码 · 共 391 行 · 第 1/2 页

                              data["num_spam_correct"]
        data["num_incorrect"] = data["num_trained_spam_fn"] + \
                                data["num_trained_ham_fp"]
        data["perc_correct"] = 100.0 * data["num_correct"] / \
                               data["num_seen"]
        data["perc_incorrect"] = 100.0 * data["num_incorrect"] / \
                                 data["num_seen"]
        data["perc_fp"] = 100.0 * data["num_trained_ham_fp"] / \
                          data["num_seen"]
        data["perc_fn"] = 100.0 * data["num_trained_spam_fn"] / \
                          data["num_seen"]
        data["num_unsure_trained_ham"] = data["num_trained_ham"] - \
                                         data["num_trained_ham_fp"]
        data["num_unsure_trained_spam"] = data["num_trained_spam"] - \
                                          data["num_trained_spam_fn"]
        data["num_unsure_not_trained"] = data["num_unsure"] - \
                                         data["num_unsure_trained_ham"] - \
                                         data["num_unsure_trained_spam"]
        if data["num_unsure"]:
            data["perc_unsure_trained_ham"] = 100.0 * \
                                              data["num_unsure_trained_ham"] / \
                                              data["num_unsure"]
            data["perc_unsure_trained_spam"] = 100.0 * \
                                               data["num_unsure_trained_spam"] / \
                                               data["num_unsure"]
            data["perc_unsure_not_trained"] = 100.0 * \
                                              data["num_unsure_not_trained"] / \
                                              data["num_unsure"]
        data["total_ham"] = data["num_ham_correct"] + \
                            data["num_trained_ham"]
        data["total_spam"] = data["num_spam_correct"] + \
                             data["num_trained_spam"]
        if data["total_ham"]:
            data["perc_ham_incorrect"] = 100.0 * \
                                         data["num_trained_ham_fp"] / \
                                         data["total_ham"]
            data["perc_ham_unsure"] = 100.0 * \
                                      data["num_unsure_trained_ham"] / \
                                      data["total_ham"]
            data["perc_ham_incorrect_or_unsure"] = \
                100.0 * (data["num_trained_ham_fp"] +
                         data["num_unsure_trained_ham"]) / \
                         data["total_ham"]
        if data["total_spam"]:
            data["perc_spam_correct"] = 100.0 * data["num_spam_correct"] / \
                                        data["total_spam"]
            data["perc_spam_unsure"] = 100.0 * \
                                       data["num_unsure_trained_spam"] / \
                                       data["total_spam"]
            data["perc_spam_correct_or_unsure"] = \
                100.0 * (data["num_spam_correct"] + \
                         data["num_unsure_trained_spam"]) / \
                         data["total_spam"]

        fp_cost = self.options["TestDriver", "best_cutoff_fp_weight"]
        fn_cost = self.options["TestDriver", "best_cutoff_fn_weight"]
        unsure_cost = self.options["TestDriver",
                                   "best_cutoff_unsure_weight"]
        data["total_cost"] = data["num_trained_ham_fp"] * fp_cost + \
                             data["num_trained_spam_fn"] * fn_cost + \
                             data["num_unsure"] * unsure_cost
        # If there was no filtering done, what would the cost have been?
        # (Assuming that any spam in the inbox earns the cost of a fn)
        no_filter_cost = data["num_spam"] * fn_cost
        data["cost_savings"] = no_filter_cost - data["total_cost"]

        return data

    def _AddPercentStrings(self, data, dp):
        data["perc_ham_s"] = "%%(perc_ham).%df%%(perc)s" % (dp,)
        data["perc_spam_s"] = "%%(perc_spam).%df%%(perc)s" % (dp,)
        data["perc_unsure_s"] = "%%(perc_unsure).%df%%(perc)s" % (dp,)
        data["perc_correct_s"] = "%%(perc_correct).%df%%(perc)s" % (dp,)
        data["perc_incorrect_s"] = "%%(perc_incorrect).%df%%(perc)s" % (dp,)
        data["perc_fp_s"] = "%%(perc_fp).%df%%(perc)s" % (dp,)
        data["perc_fn_s"] = "%%(perc_fn).%df%%(perc)s" % (dp,)
        data["perc_spam_correct_s"] = "%%(perc_spam_correct).%df%%(perc)s" \
                                      % (dp,)
        data["perc_spam_unsure_s"] = "%%(perc_spam_unsure).%df%%(perc)s" \
                                     % (dp,)
        data["perc_spam_correct_or_unsure_s"] = \
              "%%(perc_spam_correct_or_unsure).%df%%(perc)s" % (dp,)
        data["perc_ham_incorrect_s"] = "%%(perc_ham_incorrect).%df%%(perc)s" \
                                       % (dp,)
        data["perc_ham_unsure_s"] = "%%(perc_ham_unsure).%df%%(perc)s" \
                                    % (dp,)
        data["perc_ham_incorrect_or_unsure_s"] = \
              "%%(perc_ham_incorrect_or_unsure).%df%%(perc)s" % (dp,)
        data["perc_unsure_trained_ham_s"] = \
              "%%(perc_unsure_trained_ham).%df%%(perc)s" % (dp,)
        data["perc_unsure_trained_spam_s"] = "%%(perc_unsure_trained_spam).%df%%(perc)s" \
                                             % (dp,)
        data["perc_unsure_not_trained_s"] = "%%(perc_unsure_not_trained).%df%%(perc)s" \
                                            % (dp,)
        data["perc"] = "%"
        return data

    def GetStats(self, use_html=False, session_only=False, decimal_points=1):
        """Return a description of the statistics.

        If session_only is True, then only a description of the statistics
        since we were last reset.  Otherwise, lifetime statistics (i.e.
        those including the ones loaded).

        Users probably care most about persistent statistics, so present
        those by default.  If session-only stats are desired, then a
        special call to here can be made.

        The percentages will be accurate to the given number of decimal
        points.

        If use_html is True, then the returned data is marked up with
        appropriate HTML, otherwise it is plain text.
        """
        chunks = []
        push = chunks.append

        if session_only:
            data = {}
            data["num_seen"] = self.num_ham + self.num_spam + \
                               self.num_unsure
            data["num_ham"] = self.num_ham
            data["num_spam"] = self.num_spam
            data["num_unsure"] = self.num_unsure
            data["num_trained_ham"] = self.num_trained_ham
            data["num_trained_ham_fp"] = self.num_trained_ham_fp
            data["num_trained_spam"] = self.num_trained_spam
            data["num_trained_spam_fn"] = self.num_trained_spam_fn
        else:
            data = self._CombineSessionAndTotal()

        push(_("Messages classified: %d") % (data["num_seen"],))
        if data["num_seen"] == 0:
            return chunks

        data = self._CalculateAdditional(data)
        format_dict = self._AddPercentStrings(data, decimal_points)
        
        # Possibly use HTML for tabs.
        if use_html:
            format_dict["tab"] = "    "
        else:
            format_dict["tab"] = "\t"

        push((_("%(tab)sGood:%(tab)s%(num_ham)d (%(perc_ham_s)s)") \
             % format_dict) % format_dict)
        push((_("%(tab)sSpam:%(tab)s%(num_spam)d (%(perc_spam_s)s)") \
             % format_dict) % format_dict)
        push((_("%(tab)sUnsure:%(tab)s%(num_unsure)d (%(perc_unsure_s)s)") \
             % format_dict) % format_dict)
        push("")

        push((_("Classified correctly:%(tab)s%(num_correct)d (%(perc_correct_s)s of total)") \
             % format_dict) % format_dict)
        push((_("Classified incorrectly:%(tab)s%(num_incorrect)d (%(perc_incorrect_s)s of total)") \
             % format_dict) % format_dict)
        if format_dict["num_incorrect"]:
            push((_("%(tab)sFalse positives:%(tab)s%(num_trained_ham_fp)d (%(perc_fp_s)s of total)") \
                 % format_dict) % format_dict)
            push((_("%(tab)sFalse negatives:%(tab)s%(num_trained_spam_fn)d (%(perc_fn_s)s of total)") \
                 % format_dict) % format_dict)
        push("")
        
        push(_("Manually classified as good:%(tab)s%(num_trained_ham)d") % format_dict)
        push(_("Manually classified as spam:%(tab)s%(num_trained_spam)d") % format_dict)
        push("")

        if format_dict["num_unsure"]:
            push((_("Unsures trained as good:%(tab)s%(num_unsure_trained_ham)d (%(perc_unsure_trained_ham_s)s of unsures)") \
                 % format_dict) % format_dict)
            push((_("Unsures trained as spam:%(tab)s%(num_unsure_trained_spam)d (%(perc_unsure_trained_spam_s)s of unsures)") \
                 % format_dict) % format_dict)
            push((_("Unsures not trained:%(tab)s%(tab)s%(num_unsure_not_trained)d (%(perc_unsure_not_trained_s)s of unsures)") \
                 % format_dict) % format_dict)
            push("")

        if format_dict["total_spam"]:
            push((_("Spam correctly identified:%(tab)s%(perc_spam_correct_s)s (+ %(perc_spam_unsure_s)s unsure)") \
                 % format_dict) % format_dict)
        if format_dict["total_ham"]:
            push((_("Good incorrectly identified:%(tab)s%(perc_ham_incorrect_s)s (+ %(perc_ham_unsure_s)s unsure)") \
                 % format_dict) % format_dict)
        if format_dict["total_spam"] or format_dict["total_ham"]:
            push("")

        push(_("Total cost of spam:%(tab)s$%(total_cost).2f") % format_dict)
        push(_("SpamBayes savings:%(tab)s$%(cost_savings).2f") % format_dict)

        return chunks


if __name__=='__main__':
    s = Stats()
    print "\n".join(s.GetStats())