#! /usr/local/bin/python2.3 from spambayes import mboxutils from spambayes import storage from spambayes.Options import options from spambayes.tokenizer import tokenize try: True, False except NameError: # Maintain compatibility with Python 2.2 True, False = 1, 0 class Hammie: """A spambayes mail filter. This implements the basic functionality needed to score, filter, or train. """ def __init__(self, bayes): self.bayes = bayes def _scoremsg(self, msg, evidence=False): """Score a Message. msg can be a string, a file object, or a Message object. Returns the probability the message is spam. If evidence is true, returns a tuple: (probability, clues), where clues is a list of the words which contributed to the score. """ return self.bayes.spamprob(tokenize(msg), evidence) def formatclues(self, clues, sep="; "): """Format the clues into something readable.""" return sep.join(["%r: %.2f" % (word, prob) for word, prob in clues if (word[0] == '*' or prob <= options["Headers", "clue_mailheader_cutoff"] or prob >= 1.0 - options["Headers", "clue_mailheader_cutoff"])]) def score(self, msg, evidence=False): """Score (judge) a message. msg can be a string, a file object, or a Message object. Returns the probability the message is spam. If evidence is true, returns a tuple: (probability, clues), where clues is a list of the words which contributed to the score. """ return self._scoremsg(msg, evidence) def filter(self, msg, header=None, spam_cutoff=None, ham_cutoff=None, debugheader=None, debug=None, train=None): """Score (judge) a message and add a disposition header. msg can be a string, a file object, or a Message object. Optionally, set header to the name of the header to add, and/or spam_cutoff/ham_cutoff to the probability values which must be met or exceeded for a message to get a 'Spam' or 'Ham' classification. An extra debugging header can be added if 'debug' is set to True. The name of the debugging header is given as 'debugheader'. If 'train' is True, also train on the result of scoring the message (ie. train as ham if it's ham, train as spam if it's spam). If the message already has a trained header, it will be untrained first. You'll want to be very dilligent about retraining mistakes if you use this option. All defaults for optional parameters come from the Options file. Returns the same message with a new disposition header. """ if header == None: header = options["Headers", "classification_header_name"] if spam_cutoff == None: spam_cutoff = options["Categorization", "spam_cutoff"] if ham_cutoff == None: ham_cutoff = options["Categorization", "ham_cutoff"] if debugheader == None: debugheader = options["Headers", "evidence_header_name"] if debug == None: debug = options["Headers", "include_evidence"] if train == None: train = options["Hammie", "train_on_filter"] msg = mboxutils.get_message(msg) try: del msg[header] except KeyError: pass if train: self.untrain_from_header(msg) prob, clues = self._scoremsg(msg, True) if prob < ham_cutoff: is_spam = False disp = options["Headers", "header_ham_string"] elif prob > spam_cutoff: is_spam = True disp = options["Headers", "header_spam_string"] else: is_spam = False disp = options["Headers", "header_unsure_string"] if train: self.train(msg, is_spam, True) basic_disp = disp disp += "; %.*f" % (options["Headers", "header_score_digits"], prob) if options["Headers", "header_score_logarithm"]: if prob<=0.005 and prob>0.0: import math x=-math.log10(prob) disp += " (%d)"%x if prob>=0.995 and prob<1.0: import math x=-math.log10(1.0-prob) disp += " (%d)"%x del msg[header] msg.add_header(header, disp) # Obey notate_to and notate_subject. for header in ('to', 'subject'): if basic_disp in options["Headers", "notate_"+header]: orig = msg[header] del msg[header] msg[header] = "%s,%s" % (basic_disp, orig) if debug: disp = self.formatclues(clues) del msg[debugheader] msg.add_header(debugheader, disp) result = mboxutils.as_string(msg, unixfrom=(msg.get_unixfrom() is not None)) return result def train(self, msg, is_spam, add_header=False): """Train bayes with a message. msg can be a string, a file object, or a Message object. is_spam should be 1 if the message is spam, 0 if not. If add_header is True, add a header with how it was trained (in case we need to untrain later) """ self.bayes.learn(tokenize(msg), is_spam) if add_header: if is_spam: trained = options["Headers", "header_spam_string"] else: trained = options["Headers", "header_ham_string"] del msg[options["Headers", "trained_header_name"]] msg.add_header(options["Headers", "trained_header_name"], trained) def untrain(self, msg, is_spam): """Untrain bayes with a message. msg can be a string, a file object, or a Message object. is_spam should be True if the message is spam, False if not. """ self.bayes.unlearn(tokenize(msg), is_spam) def untrain_from_header(self, msg): """Untrain bayes based on X-Spambayes-Trained header. msg can be a string, a file object, or a Message object. If no such header is present, nothing happens. If add_header is True, add a header with how it was trained (in case we need to untrain later) """ msg = mboxutils.get_message(msg) trained = msg.get(options["Headers", "trained_header_name"]) if not trained: return del msg[options["Headers", "trained_header_name"]] if trained == options["Headers", "header_ham_string"]: self.untrain_ham(msg) elif trained == options["Headers", "header_spam_string"]: self.untrain_spam(msg) else: raise ValueError('%s header value unrecognized' % options["Headers", "trained_header_name"]) def train_ham(self, msg, add_header=False): """Train bayes with ham. msg can be a string, a file object, or a Message object. If add_header is True, add a header with how it was trained (in case we need to untrain later) """ self.train(msg, False, add_header) def train_spam(self, msg, add_header=False): """Train bayes with spam. msg can be a string, a file object, or a Message object. If add_header is True, add a header with how it was trained (in case we need to untrain later) """ self.train(msg, True, add_header) def untrain_ham(self, msg): """Untrain bayes with a message previously trained as ham. msg can be a string, a file object, or a Message object. """ self.untrain(msg, False) def untrain_spam(self, msg): """Untrain bayes with a message previously traned as spam. msg can be a string, a file object, or a Message object. """ self.untrain(msg, True) def store(self): """Write out the persistent store. This makes sure the persistent store reflects what is currently in memory. You would want to do this after a write and before exiting. """ self.bayes.store() def open(filename, useDB="dbm", mode='r'): """Open a file, returning a Hammie instance. mode is used as the flag to open DBDict objects. 'c' for read-write (create if needed), 'r' for read-only, 'w' for read-write. """ return Hammie(storage.open_storage(filename, useDB, mode)) if __name__ == "__main__": # Everybody's used to running hammie.py. Why mess with success? ;) import hammiebulk hammiebulk.main()