本文整理汇总了Python中sklearn.datasets.base.Bunch.text方法的典型用法代码示例。如果您正苦于以下问题：Python Bunch.text方法的具体用法？Python Bunch.text怎么用？Python Bunch.text使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.datasets.base.Bunch的用法示例。
在下文中一共展示了Bunch.text方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import text [as 别名]
#.........这里部分代码省略.........
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 ("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)
示例2: main
# 需要导入模块: from sklearn.datasets.base import Bunch [as 别名]
# 或者: from sklearn.datasets.base.Bunch import text [as 别名]
#.........这里部分代码省略.........
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 ("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
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