Python Bunch.predicted方法代码示例

# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import predicted [as 别名]
def main():
    accuracies = defaultdict(lambda: [])

    aucs = defaultdict(lambda: [])

    x_axis = defaultdict(lambda: [])

    vct = CountVectorizer(encoding='ISO-8859-1', min_df=5, max_df=1.0, binary=True, ngram_range=(1, 3),
                          token_pattern='\\b\\w+\\b', tokenizer=StemTokenizer())
    vct_analizer = vct.build_tokenizer()
    print("Start loading ...")
    # data fields: data, bow, file_names, target_names, target

    ########## NEWS GROUPS ###############
    # easy to hard. see "Less is More" paper: http://axon.cs.byu.edu/~martinez/classes/678/Presentations/Clawson.pdf
    categories = [['alt.atheism', 'talk.religion.misc'],
                  ['comp.graphics', 'comp.windows.x'],
                  ['comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware'],
                  ['rec.sport.baseball', 'sci.crypt']]

    min_size = max(100, args.fixk)

    fixk_saved = "{0}{1}.p".format(args.train, args.fixk)

    try:
        fixk_file = open(fixk_saved, "rb")
        data = pickle.load(fixk_file)
    except IOError:
        data = load_dataset(args.train, args.fixk, categories[0], vct, min_size, percent=.5)
        fixk_file = open(fixk_saved, "wb")
        pickle.dump(data, fixk_file)

    # data = load_dataset(args.train, args.fixk, categories[0], vct, min_size)

    print("Data %s" % args.train)
    print("Data size %s" % len(data.train.data))

    parameters = parse_parameters_mat(args.cost_model)

    print "Cost Parameters %s" % parameters

    cost_model = set_cost_model(args.cost_function, parameters=parameters)
    print "\nCost Model: %s" % cost_model.__class__.__name__


    #### STUDENT CLASSIFIER
    clf = linear_model.LogisticRegression(penalty="l1", C=1)
    print "\nStudent Classifier: %s" % clf

    #### EXPERT CLASSIFIER

    exp_clf = linear_model.LogisticRegression(penalty='l1', C=.3)
    exp_clf.fit(data.test.bow, data.test.target)
    expert = baseexpert.NeutralityExpert(exp_clf, threshold=args.neutral_threshold,
                                         cost_function=cost_model.cost_function)
    print "\nExpert: %s " % expert

    #### ACTIVE LEARNING SETTINGS
    step_size = args.step_size
    bootstrap_size = args.bootstrap
    evaluation_points = 200

    print("\nExperiment: step={0}, BT={1}, plot points={2}, fixk:{3}, minsize:{4}".format(step_size, bootstrap_size,
                                                                                          evaluation_points, args.fixk,
                                                                                          min_size))
    print ("Cheating experiment - use full uncertainty query k words")
    t0 = time.time()
    ### experiment starts
    tx =[]
    tac = []
    tau = []
    for t in range(args.trials):
        trial_accu =[]

        trial_aucs = []

        trial_x_axis = []
        print "*" * 60
        print "Trial: %s" % t

        student = randomsampling.UncertaintyLearner(model=clf, accuracy_model=None, budget=args.budget, seed=t)
        print "\nStudent: %s " % student
        train_indices = []
        train_x = []
        train_y = []
        pool = Bunch()
        pool.data = data.train.bow.tocsr()   # full words, for training
        pool.fixk = data.train.bowk.tocsr()  # k words BOW for querying
        pool.target = data.train.target
        pool.predicted = []
        pool.kwords = np.array(data.train.kwords)  # k words
        pool.remaining = set(range(pool.data.shape[0]))  # indices of the pool

        bootstrapped = False

        current_cost = 0
        iteration = 0
        while 0 < student.budget and len(pool.remaining) > step_size and iteration <= args.maxiter:

            if not bootstrapped:
#.........这里部分代码省略.........

# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import predicted [as 别名]

#.........这里部分代码省略.........

    print("\nExperiment: step={0}, BT={1}, plot points={2}, fixk:{3}, minsize:{4}".format(step_size, bootstrap_size,
                                                                                          evaluation_points, args.fixk,
                                                                                          min_size))
    print ("Anytime active learning experiment - use objective function to pick data")
    t0 = time.time()
    tac = []
    tau = []
    ### experiment starts
    for t in range(args.trials):
        trial_accu = []

        trial_aucs = []

        print "*" * 60
        print "Trial: %s" % t

        student = get_student(clf, cost_model, sent_clf, sent_detector, vct)
        student.human_mode = args.expert == 'human'

        print "\nStudent: %s " % student

        train_indices = []
        neutral_data = []  # save the xik vectors
        train_x = []
        train_y = []
        neu_x = []  # data to train the classifier
        neu_y = np.array([])

        pool = Bunch()
        pool.data = data.train.bow.tocsr()  # full words, for training
        pool.text = data.train.data
        pool.target = data.train.target
        pool.predicted = []
        pool.remaining = set(range(pool.data.shape[0]))  # indices of the pool

        bootstrapped = False
        current_cost = 0
        iteration = 0
        query_index = None
        query_size = None
        oracle_answers = 0
        calibrated=args.calibrate
        while 0 < student.budget and len(pool.remaining) > step_size and iteration <= args.maxiter:
            util = []

            if not bootstrapped:
                ## random from each bootstrap
                bt = randomsampling.BootstrapFromEach(t * 10)

                query_index = bt.bootstrap(pool=pool, k=bootstrap_size)
                bootstrapped = True
                query = pool.data[query_index]
                print "Bootstrap: %s " % bt.__class__.__name__
                print
            else:

                chosen = student.pick_next(pool=pool, step_size=step_size)

                query_index = [x for x, y in chosen]  # document id of chosen instances
                query = [y[0] for x, y in chosen]  # sentence of the document

                query_size = [1] * len(query_index)

            ground_truth = pool.target[query_index]

# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import predicted [as 别名]

#.........这里部分代码省略.........
    elif "direct" in args.expert:
        expert = baseexpert.LookUpExpert(accuracy_value=accu_parameters, cost_function=cost_model.cost_function)
    else:
        raise Exception("We need a defined cost function options [fixed|log|linear]")
        #expert = baseexpert.TrueOracleExpert(cost_function=cost_model.cost_function)
    print "\nExpert: %s " % expert

    #### ACTIVE LEARNING SETTINGS
    step_size = args.step_size
    bootstrap_size = args.bootstrap
    evaluation_points = 200
    eval_range = 1 if (args.budget / evaluation_points) <= 0 else args.budget / evaluation_points
    print("\nExperiment: step={0}, BT={1}, plot points={2}, fixk:{3}, minsize:{4}".format(step_size, bootstrap_size,
                                                                                          evaluation_points, args.fixk,
                                                                                          50))

    t0 = time.time()
    ### experiment starts
    for t in range(args.trials):
        print "*" * 60
        print "Trial: %s" % t
        # TODO shuffle the data??
        #student = baselearner.BaseLearner(model=clf, cost_model=cost_model, accuracy_model=accuracy_model, budget=args.budget,
        #                                  seed=t)
        student = randomsampling.RandomSamplingLearner(model=clf, accuracy_model=None, budget=args.budget, seed=t)
        print "\nStudent: %s " % student
        train_indices = []
        train_x = []
        train_y = []
        pool = Bunch()
        pool.data = data.train.bow.tocsr()   # full words, for training
        pool.fixk = data.train.bowk.tocsr()  # k words BOW for querying
        pool.target = data.train.target
        pool.predicted = []
        pool.kwords = np.array(data.train.kwords)  # k words
        pool.remaining = set(range(pool.data.shape[0]))  # indices of the pool

        #for x in pool.fixk:
        #    print x.todense().sum()

        bootstrapped = False

        current_cost = 0
        iteration = 0
        while 0 < student.budget and len(pool.remaining) > step_size and iteration <= args.maxiter:

            if not bootstrapped:
                ## random bootstrap
                #bt = randomsampling.BootstrapRandom(random_state=t * 10)

                ## random from each bootstrap
                bt = randomsampling.BootstrapFromEach(t * 10)

                query_index = bt.bootstrap(pool=pool, k=bootstrap_size)
                bootstrapped = True
                print "Bootstrap: %s " % bt.__class__.__name__
                print
            else:
                query_index = student.pick_next(pool=pool, k=step_size)

            query = pool.fixk[query_index]  # query with k words

            query_size = [len(vct_analizer(x)) for x in pool.kwords[query_index]]

            #if query_size[0] >50:
            #    print "*** %s" % pool.kwords[query_index]

# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import predicted [as 别名]

#.........这里部分代码省略.........
    for t in range(args.trials):
        trial_accu = []

        trial_aucs = []

        print "*" * 60
        print "Trial: %s" % t
        if args.student in "anyunc":
            student = randomsampling.AnytimeLearner(model=clf, accuracy_model=None, budget=args.budget, seed=t, vcn=vct,
                                                    subpool=250, cost_model=cost_model)
        elif args.student in "lambda":
            student = randomsampling.AnytimeLearnerDiff(model=clf, accuracy_model=None, budget=args.budget, seed=t, vcn=vct,
                                                    subpool=250, cost_model=cost_model, lambda_value=args.lambda_value)
        elif args.student in "anyzero":
            student = randomsampling.AnytimeLearnerZeroUtility(model=clf, accuracy_model=None, budget=args.budget, seed=t, vcn=vct,
                                                    subpool=250, cost_model=cost_model)
        else:
            raise ValueError("Oops! We do not know that anytime strategy. Try again.")

        print "\nStudent: %s " % student
        train_indices = []
        neutral_text = []  # save the raw text of the queries
        neutral_data = []  # save the xik vectors
        train_x = []
        train_y = []
        neu_x = [] # data to train the classifier
        neu_y = np.array([])

        pool = Bunch()
        pool.data = data.train.bow.tocsr()   # full words, for training
        pool.text = data.train.data
        # pool.fixk = data.train.bowk.tocsr()  # k words BOW for querying
        pool.target = data.train.target
        pool.predicted = []
        # pool.kwords = np.array(data.train.kwords)  # k words
        pool.remaining = set(range(pool.data.shape[0]))  # indices of the pool

        bootstrapped = False

        current_cost = 0
        iteration = 0
        query_index = None
        query_size = None
        while 0 < student.budget and len(pool.remaining) > step_size and iteration <= args.maxiter:
            util = []
            if not bootstrapped:
                ## random from each bootstrap
                bt = randomsampling.BootstrapFromEach(t * 10)

                query_index = bt.bootstrap(pool=pool, k=bootstrap_size)
                bootstrapped = True
                query = pool.data[query_index]
                print "Bootstrap: %s " % bt.__class__.__name__
                print
            else:
                # print "pick instance"

                ## chose returns: index, k
                ## util returns: utility, k, unc
                query_chosen, util = student.pick_next(pool=pool, step_size=step_size)
                query_index = [a for a, b in query_chosen]
                query_size = [b for a, b in query_chosen]

                # query = pool.fixk[query_index]  # query with k words
                qk = []
                for q, k in query_chosen:

# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import predicted [as 别名]

#.........这里部分代码省略.........
    print("\nExperiment: step={0}, BT={1}, plot points={2}, fixk:{3}, minsize:{4}".format(step_size, bootstrap_size,
                                                                                          evaluation_points, args.fixk,
                                                                                          50))

    t0 = time.time()
    tac = []
    tau = []
    ### experiment starts
    for t in range(args.trials):
        trial_accu = []

        trial_aucs = []

        print "*" * 60
        print "Trial: %s" % t
        if  args.student in "unc":
            student = randomsampling.UncertaintyLearner(model=clf, accuracy_model=None, budget=args.budget, seed=t,
                                                        subpool=250)
        else:
            student = randomsampling.RandomSamplingLearner(model=clf, accuracy_model=None, budget=args.budget, seed=t)

        print "\nStudent: %s " % student

        train_indices = []
        train_x = []
        train_y = []
        pool = Bunch()
        pool.data = data.train.bow.tocsr()   # full words, for training
        if args.fixk is None:
            pool.fixk = data.train.bow.tocsr()
        else:
            pool.fixk = data.train.bowk.tocsr()  # k words BOW for querying
        pool.target = data.train.target
        pool.predicted = []
        # pool.kwords = np.array(data.train.kwords)  # k words
        pool.remaining = set(range(pool.data.shape[0]))  # indices of the pool


        bootstrapped = False

        current_cost = 0
        iteration = 0
        while 0 < student.budget and len(pool.remaining) > step_size and iteration <= args.maxiter:

            if not bootstrapped:
                ## random bootstrap
                #bt = randomsampling.BootstrapRandom(random_state=t * 10)

                ## random from each bootstrap
                bt = randomsampling.BootstrapFromEach(t * 10)

                query_index = bt.bootstrap(pool=pool, k=bootstrap_size)
                bootstrapped = True
                print "Bootstrap: %s " % bt.__class__.__name__
                print
            else:
                query_index = student.pick_next(pool=pool, k=step_size)

            # query = pool.fixk[query_index]  # query with k words
            query = pool.data[query_index]
            # print query_index
            # query_size = [len(vct_analizer(x)) for x in pool.kwords[query_index]]
            query_size = [1]*query.shape[0]

            ground_truth = pool.target[query_index]