Python Options.get_pathname_option函数代码示例

def unlearn_compare(nsets, unsets):
    print options.display()

    spamdirs = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, nsets+1)]
    hamdirs = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, nsets+1)]
    spamhamdirs = zip(spamdirs, hamdirs)
    unspamdirs = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, unsets+1)]
    unhamdirs = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, unsets+1)]
    unspamhamdirs = zip(unspamdirs, unhamdirs)

    d = TestDriver.Driver()
    d.new_classifier()
    """
    for spamdir, hamdir in spamhamdirs:
        d.train(msgs.HamStream(hamdir, [hamdir]),
                msgs.SpamStream(spamdir, [spamdir]))
    """
    d.train(msgs.HamStream(hamdirs[0], [hamdirs[0]]),
            msgs.SpamStream(spamdirs[0], [spamdirs[0]]))
    d.train(msgs.HamStream(hamdirs[1], [hamdirs[1]]),
            msgs.SpamStream(spamdirs[1], [spamdirs[1]]))
    d.test(msgs.HamStream(hamdirs[2], [hamdirs[2]]),
           msgs.SpamStream(spamdirs[2], [spamdirs[2]]))
    d.finishtest()
    d.alldone()

    unlearn_driver(d, spamhamdirs, unspamhamdirs)

    def createWorkers(self):
        """Using the options that were initialised in __init__ and then
        possibly overridden by the driver code, create the Bayes object,
        the Corpuses, the Trainers and so on."""
        print "Loading database...",
        if self.isTest:
            self.useDB = "pickle"
            self.DBName = '_pop3proxy_test.pickle'   # This is never saved.
        if not hasattr(self, "DBName"):
            self.DBName, self.useDB = storage.database_type([])
        self.bayes = storage.open_storage(self.DBName, self.useDB)
        
        self.buildStatusStrings()

        # Don't set up the caches and training objects when running the self-test,
        # so as not to clutter the filesystem.
        if not self.isTest:
            def ensureDir(dirname):
                try:
                    os.mkdir(dirname)
                except OSError, e:
                    if e.errno != errno.EEXIST:
                        raise

            # Create/open the Corpuses.  Use small cache sizes to avoid hogging
            # lots of memory.
            sc = get_pathname_option("Storage", "spam_cache")
            hc = get_pathname_option("Storage", "ham_cache")
            uc = get_pathname_option("Storage", "unknown_cache")
            map(ensureDir, [sc, hc, uc])
            if self.gzipCache:
                factory = GzipFileMessageFactory()
            else:
                factory = FileMessageFactory()
            age = options["Storage", "cache_expiry_days"]*24*60*60
            self.spamCorpus = ExpiryFileCorpus(age, factory, sc,
                                               '[0123456789\-]*',
                                               cacheSize=20)
            self.hamCorpus = ExpiryFileCorpus(age, factory, hc,
                                              '[0123456789\-]*',
                                              cacheSize=20)
            self.unknownCorpus = ExpiryFileCorpus(age, factory, uc,
                                                  '[0123456789\-]*',
                                                  cacheSize=20)

            # Given that (hopefully) users will get to the stage
            # where they do not need to do any more regular training to
            # be satisfied with spambayes' performance, we expire old
            # messages from not only the trained corpora, but the unknown
            # as well.
            self.spamCorpus.removeExpiredMessages()
            self.hamCorpus.removeExpiredMessages()
            self.unknownCorpus.removeExpiredMessages()

            # Create the Trainers.
            self.spamTrainer = storage.SpamTrainer(self.bayes)
            self.hamTrainer = storage.HamTrainer(self.bayes)
            self.spamCorpus.addObserver(self.spamTrainer)
            self.hamCorpus.addObserver(self.hamTrainer)

def drive():
    print options.display()

    spam = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, 5)]
    ham = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, 5)]

    d = dictionarywriter.DictionaryWriter(150, 4)
    d.write()

    keep_going = True
    trial_number = 1

    au = ActiveUnlearnDriver.ActiveUnlearner([msgs.HamStream(ham[1], [ham[1]]),
                                              msgs.HamStream(ham[2], [ham[2]])],
                                             [msgs.SpamStream(spam[1], [spam[1]]),
                                              msgs.SpamStream(spam[3], [spam[3]])],
                                             msgs.HamStream(ham[0], [ham[0]]),
                                             msgs.SpamStream(spam[0], [spam[0]]),
                                             )
    with open("C:\Users\Alex\Desktop\dict_correlation_stats.txt", 'w') as outfile:

        while keep_going:
            chosen = set()
            current = au.select_initial()
            cluster = au.determine_cluster(current)
            chosen.add(current)
            au.driver.test(au.testing_ham, au.testing_spam)

            while not cluster:
                current = au.select_initial(chosen)
                cluster = au.determine_cluster(current)
                chosen.add(current)
                au.driver.test(au.testing_ham, au.testing_spam)

            cluster_list = list(cluster.cluster_set)

            dicts = au.driver.tester.train_examples[2]

            data = v_correlation(cluster_list, dicts)

            outfile.write("Trial " + str(trial_number) + " Percentage Overlap (Correlation): " + str(data))
            answer = raw_input("Keep going (y/n)? You have performed " + str(trial_number) + " trial(s) so far. ")

            valid_input = False

            while not valid_input:
                if answer == "n":
                    keep_going = False
                    valid_input = True

                elif answer == "y":
                    au.learn(cluster)
                    au.init_ground()
                    trial_number += 1
                    valid_input = True

                else:
                    print "Please enter either y or n."

    def __init__(self, spam_feature=None, ham_feature=None, inject_type=0):

        self.h_injected = get_pathname_option("TestDriver", "ham_directories") % 3 + "/"
        self.s_injected = get_pathname_option("TestDriver", "spam_directories") % 3 + "/"

        if inject_type is 0:
            self.feature = spam_feature
        elif inject_type is 1:
            self.feature = ham_feature

def main():
    ham = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, 5)]
    spam = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, 5)]

    t = TestDriver.Driver()
    t.train(msgs.HamStream(ham[0], [ham[0]]), msgs.SpamStream(spam[0], [spam[0]]))
    t.dict_test(msgs.HamStream(ham[2], [ham[2]]), msgs.SpamStream(spam[3], [spam[3]]))
    print "Test sizes: ", len(t.tester.truth_examples[0]), ", ", len(t.tester.truth_examples[1]), "\n"
    print "Detection rate:", t.tester.correct_classification_rate(), "\n"

def main():

    ham = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, 4)]
    spam = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, 4)]

    sizes = [0, 60, 120, 240, 480, 840, 1200, 2400, 3600, 4800, 6000]

    d = TestDriver.Driver()
    d.new_classifier()

    detection_rates = []
    target_rates    = []
    false_positives = []
    false_negatives = []
    unsures         = []

    for size in sizes:

        mislabeler = MislabeledFileMover(size)
        mislabeler.random_move_file()

        d.train(msgs.HamStream(ham[0], [ham[0]]),
            msgs.SpamStream(spam[0], [spam[0]]))
        d.test(msgs.HamStream(ham[1], [ham[1]]),
               msgs.SpamStream(spam[1], [spam[1]]))

        target_rate = d.tester.correct_classification_rate()
        target_rates.append(target_rate)

        d.train(msgs.HamStream(ham[2], [ham[2]]),
                msgs.SpamStream(spam[2], [spam[2]]))
        d.test(msgs.HamStream(ham[1], [ham[1]]),
               msgs.SpamStream(spam[1], [spam[1]]))

        detection_rate = d.tester.correct_classification_rate()
        detection_rates.append(detection_rate)

        fp = d.tester.nham_wrong
        false_positives.append(fp)
        fn = d.tester.nspam_wrong
        false_negatives.append(fn)
        unsure = d.tester.nham_unsure + d.tester.nspam_unsure
        unsures.append(unsure)

        d.untrain(msgs.HamStream(ham[0], [ham[0]]),
                  msgs.SpamStream(spam[0], [spam[0]]))
        d.untrain(msgs.HamStream(ham[2], [ham[2]]),
                  msgs.SpamStream(spam[2], [spam[2]]))

        mislabeler.reset()

    with open("/Users/AlexYang/Desktop/hamasspam.txt", 'w') as outfile:

        outfile.write(tabulate({"# of Mislabeled Words": sizes,
                                "Detection Rates": detection_rates,
                                "Target Rates": target_rates},
                               headers="keys", tablefmt="plain"))

    def create_workers(self):
        """Using the options that were initialised in __init__ and then
        possibly overridden by the driver code, create the Bayes object,
        the Corpuses, the Trainers and so on."""
        if self.is_test:
            self.use_db = "pickle"
            self.db_name = '_core_server.pickle'   # This is never saved.
        if not hasattr(self, "db_name"):
            self.db_name, self.use_db = storage.database_type([])
        self.bayes = storage.open_storage(self.db_name, self.use_db)

        # Load stats manager.
        self.stats = Stats.Stats(options,
                                 spambayes.message.Message().message_info_db)

        self.build_status_strings()

        # Don't set up the caches and training objects when running the
        # self-test, so as not to clutter the filesystem.
        if not self.is_test:
            # Create/open the Corpuses.  Use small cache sizes to avoid
            # hogging lots of memory.
            sc = get_pathname_option("Storage", "core_spam_cache")
            hc = get_pathname_option("Storage", "core_ham_cache")
            uc = get_pathname_option("Storage", "core_unknown_cache")
            for d in [sc, hc, uc]:
                storage.ensureDir(d)
            if self.gzip_cache:
                factory = GzipFileMessageFactory()
            else:
                factory = FileMessageFactory()
            age = options["Storage", "cache_expiry_days"]*24*60*60
            self.spamCorpus = ExpiryFileCorpus(age, factory, sc,
                                               '[0123456789\-]*',
                                               cacheSize=20)
            self.hamCorpus = ExpiryFileCorpus(age, factory, hc,
                                              '[0123456789\-]*',
                                              cacheSize=20)
            self.unknownCorpus = ExpiryFileCorpus(age, factory, uc,
                                                  '[0123456789\-]*',
                                                  cacheSize=20)

            # Given that (hopefully) users will get to the stage
            # where they do not need to do any more regular training to
            # be satisfied with spambayes' performance, we expire old
            # messages from not only the trained corpora, but the unknown
            # as well.
            self.spamCorpus.removeExpiredMessages()
            self.hamCorpus.removeExpiredMessages()
            self.unknownCorpus.removeExpiredMessages()

            # Create the Trainers.
            self.spam_trainer = storage.SpamTrainer(self.bayes)
            self.ham_trainer = storage.HamTrainer(self.bayes)
            self.spamCorpus.addObserver(self.spam_trainer)
            self.hamCorpus.addObserver(self.ham_trainer)

def drive(nsets):
    print options.display()

    spamdirs = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, nsets+1)]
    hamdirs = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, nsets+1)]

    d = TestDriver.Driver()
    d.new_classifier()
    d.train(msgs.HamStream(hamdirs[0], [hamdirs[0]]), msgs.SpamStream(spamdirs[0], [spamdirs[0]]))
    d.test(msgs.HamStream(hamdirs[1], [hamdirs[1]]), msgs.SpamStream(spamdirs[1], [spamdirs[1]]))
    d.finishtest()
    d.alldone()

def drive(nsets, decision):
    print options.display()

    spamdirs = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, nsets + 1)]
    hamdirs = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, nsets + 1)]

    spamfns = [(x, y, 1) for x in spamdirs for y in os.listdir(x)]
    hamfns = [(x, y, 0) for x in hamdirs for y in os.listdir(x)]

    nham = len(hamfns)
    nspam = len(spamfns)
    cc = CostCounter.nodelay()

    allfns = {}
    for fn in spamfns + hamfns:
        allfns[fn] = None

    d = hammie.open("weaktest.db", False)

    hamtrain = 0
    spamtrain = 0
    n = 0
    for dir, name, is_spam in allfns.iterkeys():
        n += 1
        m = msgs.Msg(dir, name).guts
        if debug > 1:
            print "trained:%dH+%dS" % (hamtrain, spamtrain)
        scr = d.score(m)
        if debug > 1:
            print "score:%.3f" % scr
        if not decision.tooearly():
            if is_spam:
                if debug > 0:
                    print "Spam with score %.2f" % scr
                cc.spam(scr)
            else:
                if debug > 0:
                    print "Ham with score %.2f" % scr
                cc.ham(scr)
        de = decision(scr, is_spam)
        if de == TRAIN_AS_SPAM:
            d.train_spam(m)
            spamtrain += 1
        elif de == TRAIN_AS_HAM:
            d.train_ham(m)
            hamtrain += 1
        if n % 100 == 0:
            print "%5d trained:%dH+%dS wrds:%d" % (n, hamtrain, spamtrain, len(d.bayes.wordinfo))
            print cc
    print "=" * 70
    print "%5d trained:%dH+%dS wrds:%d" % (n, hamtrain, spamtrain, len(d.bayes.wordinfo))
    print cc

def drive(nsets):
    print options.display()

    hamdirs  = [get_pathname_option("TestDriver", "ham_directories") % \
                i for i in range(1, nsets+1)]
    spamdirs = [get_pathname_option("TestDriver", "spam_directories") % \
                i for i in range(1, nsets+1)]

    d = TestDriver.Driver()
    # Train it on all sets except the first.
    d.train(msgs.HamStream("%s-%d" % (hamdirs[1], nsets),
                            hamdirs[1:], train=1),
            msgs.SpamStream("%s-%d" % (spamdirs[1], nsets),
                            spamdirs[1:], train=1))

    # Now run nsets times, predicting pair i against all except pair i.
    for i in range(nsets):
        h = hamdirs[i]
        s = spamdirs[i]
        hamstream = msgs.HamStream(h, [h], train=0)
        spamstream = msgs.SpamStream(s, [s], train=0)

        if i > 0:
            if options["CV Driver", "build_each_classifier_from_scratch"]:
                # Build a new classifier from the other sets.
                d.new_classifier()

                hname = "%s-%d, except %d" % (hamdirs[0], nsets, i+1)
                h2 = hamdirs[:]
                del h2[i]

                sname = "%s-%d, except %d" % (spamdirs[0], nsets, i+1)
                s2 = spamdirs[:]
                del s2[i]

                d.train(msgs.HamStream(hname, h2, train=1),
                        msgs.SpamStream(sname, s2, train=1))

            else:
                # Forget this set.
                d.untrain(hamstream, spamstream)

        # Predict this set.
        d.test(hamstream, spamstream)
        d.finishtest()

        if i < nsets - 1 and not options["CV Driver",
                                         "build_each_classifier_from_scratch"]:
            # Add this set back in.
            d.train(hamstream, spamstream)

    d.alldone()

def main():

    ham = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, 4)]
    spam = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, 4)]
    injected = get_pathname_option("TestDriver", "spam_directories") % 3

    au = ActiveUnlearnDriver.ActiveUnlearner([msgs.HamStream(ham[0], [ham[0]]), msgs.HamStream(ham[2], [ham[2]])],
                                             [msgs.SpamStream(spam[0], [spam[0]]), msgs.SpamStream(spam[2], [spam[2]])],
                                             msgs.HamStream(ham[1], [ham[1]]), msgs.SpamStream(spam[1], [spam[1]]))

    msg = choice(au.driver.tester.train_examples[2])    # Randomly chosen from Ham Set3

    original_rate = au.driver.tester.correct_classification_rate()
    cluster_sizes = []
    detection_rates = []
    target_cluster_rates = []

    sizes = []
    for i in range(150, 1050, 50):
        sizes.append(i)
    for i in range(1000, 15000, 1000):
        sizes.append(i)

    for size in sizes:
        cluster = ActiveUnlearnDriver.Cluster(msg, size, au, "extreme")
        print "Clustering with size " + str(cluster.size) + "..."
        cluster_sizes.append(size)
        detection_rates.append(au.detect_rate(cluster))
        target_cluster_rates.append(float(cluster.target_set3()) / float(cluster.size))

    file = open("/Users/AlexYang/Desktop/clues.txt", 'w')

    features = au.driver.classifier._getclues(msg)
    i = 1
    for feature in features:
        file.write(str(i) + ") ")
        file.write(str(feature) + "\n")
        i += 1

    with open("/Users/AlexYang/Desktop/clusterstats.txt", 'w') as outfile:

        outfile.write("Clustered around: " + msg.tag)
        outfile.write("\nOriginal Rate: " + str(original_rate) + "\n")

        outfile.write(tabulate({"Cluster Sizes": cluster_sizes,
                                "Detection Rates": detection_rates,
                                "% of Targets Clustered": target_cluster_rates},
                               headers="keys", tablefmt="plain"))

def database_type(opts):
    """Return the name of the database and the type to use.  The output of
    this function can be used as the db_type parameter for the open_storage
    function, for example:

        [standard getopts code]
        db_name, db_type = database_types(opts)
        storage = open_storage(db_name, db_type)

    The selection is made based on the options passed, or, if the
    appropriate options are not present, the options in the global
    options object.

    Currently supports:
       -p  :  pickle
       -d  :  dbm
    """
    nm, typ = None, None
    for opt, arg in opts:
        if _storage_options.has_key(opt):
            if nm is None and typ is None:
                nm, typ = arg, _storage_options[opt]
            else:
                raise MutuallyExclusiveError()
    if nm is None and typ is None:
        typ = options["Storage", "persistent_use_database"]
        if typ is True or typ == "True":
            typ = "dbm"
        elif typ is False or typ == "False":
            typ = "pickle"
        nm = get_pathname_option("Storage", "persistent_storage_file")
    return nm, typ

def main():
    print "Pickle is available."
    db = dumbdbm.open("dumbdb", "c")
    db["1"] = "1"
    db.close()
    dbstr = whichdb.whichdb("dumbdb")
    if dbstr:
        print "Dumbdbm is available."
    else:
        print "Dumbdbm is not available."

    db = dbhash.open("dbhash", "c")
    db["1"] = "1"
    db.close()
    dbstr = whichdb.whichdb("dbhash")
    if dbstr == "dbhash":
        print "Dbhash is available."
    else:
        print "Dbhash is not available."

    if bsddb is None:
        dbstr = ""
    else:
        db = bsddb.hashopen("bsddb3", "c")
        db["1"] = "1"
        db.close()
        dbstr = whichdb.whichdb("bsddb3")
    if dbstr == "dbhash":
        print "Bsddb[3] is available."
    else:
        print "Bsddb[3] is not available."

    print

    hammie = get_pathname_option("Storage", "persistent_storage_file")
    use_dbm = options["Storage", "persistent_use_database"]
    if not use_dbm:
        print "Your storage %s is a: pickle" % (hammie,)
        return

    if not os.path.exists(hammie):
        print "Your storage file does not exist yet."
        return
    db_type = whichdb.whichdb(hammie)
    if db_type == "dbhash":
        # could be dbhash or bsddb3
        # only bsddb3 has a __version__ attribute - old bsddb module does not
        if hasattr(bsddb, '__version__'):
            try:
                db = bsddb.hashopen(hammie, "r")
            except bsddb.error:
                pass
            else:
                db.close()
                print "Your storage", hammie, "is a: bsddb[3]"
                return
    elif db_type is None:
        print "Your storage %s is unreadable." % (hammie,)
    print "Your storage %s is a: %s" % (hammie, db_type)

def drive(num):
    print options.display()

    spamdirs = [get_pathname_option("TestDriver", "spam_directories") %
                i for i in range(1, 4)]
    hamdirs = [get_pathname_option("TestDriver", "ham_directories") %
               i for i in range(1, 4)]

    r = mislabeledfilemover.MislabeledFileMover(num)
    r.random_move_file()

    d = TestDriver.Driver()
    d.new_classifier()
    d.train(msgs.HamStream(hamdirs[0], [hamdirs[0]]),
            msgs.SpamStream(spamdirs[0], [spamdirs[0]]))
    d.train(msgs.HamStream(hamdirs[2], [hamdirs[2]]),
            msgs.SpamStream(spamdirs[2], [spamdirs[2]]))
    d.test(msgs.HamStream(hamdirs[1], [hamdirs[1]]),
           msgs.SpamStream(spamdirs[1], [spamdirs[1]]))

    guess = d.classifier.spamprob
    polluted = []
    for msg in msgs.HamStream(hamdirs[2], [hamdirs[2]]):
        msg.prob = guess(msg)
        polluted.append(msg)

    for msg in msgs.SpamStream(spamdirs[2], [spamdirs[2]]):
        msg.prob = guess(msg)
        polluted.append(msg)

    mislabeled = []
    for fp in d.tester.false_positives():
        mislabeled.append(fp)

    for fn in d.tester.false_negatives():
        mislabeled.append(fn)

    for unsure in d.unsure:
        mislabeled.append(unsure)

    d.finishtest()
    d.alldone()

    data = v_correlation(polluted, mislabeled)

    print "Percentage Overlap (Correlation): " + str(data)

def splice_set(n, dir_num=3):
    destination = get_pathname_option("TestDriver", "spam_directories") % dir_num + "/"
    dict_c = 1
    for dictionary in listdir(destination):
        print "Slicing dictionary", dict_c, "into", n, "parts"
        splice(destination + dictionary, n)
        remove(destination + dictionary)
        dict_c += 1