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How to use is_uint method in pandera
Best Python code snippet using pandera_python
MPM.py
Source:MPM.py 
1# 2021.03.242# @yifan3# most probable mode for bitstream encoding4# codeword should be sorted maybe in a similar mannar like HEVC's angular prediction5# or it should be sorted based on the context? smooth -> texture6# based on statics, we got the probability 7import numpy as np8import copy9from myIO import bits2int, int2bits10from mylearner import myLearner11# encode uses idx with shape (H, W)12# if index in MPM_list, send the index in MPM_list13# else, send 3 and the index in the codeword_list which contains all other codeword expect those in MPM_list14class MPM():15 def __init__(self, n_codeword, MPM_init_list):16 self.n_MPM = len(MPM_init_list)17 self.n_codeword = n_codeword18 self.length = (int)(np.log2(n_codeword-4))+119 self.MPM_init_list = MPM_init_list20 #self.MPM_init_list.sort()21 self.MPM_list = self.MPM_init_list22 self.codeword_list = [] # code word not in current MPM_list23 self.stream = '' # encoding bit stream <string> with 0/1 in it24 self.ct = 0 # decoding bit position indicater25 26 def get_MPM(self, prev_idx):27 ct = 228 self.MPM_list = copy.deepcopy(self.MPM_init_list)29 for i in range(len(prev_idx)):30 if prev_idx[i] not in self.MPM_list:31 self.MPM_list[ct] = prev_idx[i]32 ct -= 1 33 self.MPM_list.sort()34 self.codeword_list = []35 for i in range(self.n_codeword):36 if i not in self.MPM_list:37 self.codeword_list.append(i)38 39 def encode_one_idx(self, prev_idx, cur_idx):40 self.get_MPM(prev_idx)41 for i in range(len(self.MPM_list)):42 if self.MPM_list[i] == cur_idx:43 return i, i44 for i in range(len(self.codeword_list)):45 if self.codeword_list[i] == cur_idx:46 return i, 347 assert (False), 'err'48 def get_prev(self, idx, i, j):49 if i < 0 or j < 0 or i >= idx.shape[0] or j >= idx.shape[1]:50 return self.MPM_init_list[0]51 else:52 return idx[i, j]53 54 def encode(self, idx):55 self.stream = ''56 idx = idx.astype('int16')57 for i in range(idx.shape[0]):58 for j in range(idx.shape[1]):59 prev_idx = [self.get_prev(idx, i-1, j-1),60 self.get_prev(idx, i-1, j),61 self.get_prev(idx, i, j-1)]62 val, mode = self.encode_one_idx(prev_idx, idx[i,j])63 self.stream += int2bits(mode, 2, is_uint=True, return_string=True)64 if mode == 3: 65 self.stream += int2bits(val, self.length, is_uint=True, return_string=True)66 return self.stream67 68 def decode_one_idx(self, prev_idx, mode):69 self.get_MPM(prev_idx)70 if mode == 3:71 idx = bits2int(self.stream[self.ct:self.ct+self.length], is_uint=True)72 self.ct += self.length73 return self.codeword_list[idx]74 else:75 return self.MPM_list[mode]76 77 def decode(self, stream, H, W):78 self.stream = stream79 idx = np.zeros((H, W))80 self.ct = 081 for i in range(idx.shape[0]):82 for j in range(idx.shape[1]):83 prev_idx = [self.get_prev(idx, i-1, j-1),84 self.get_prev(idx, i-1, j),85 self.get_prev(idx, i, j-1)]86 mode = bits2int(self.stream[self.ct:self.ct+2], is_uint=True)87 self.ct += 288 idx[i,j] = self.decode_one_idx(prev_idx, mode)89 return idx.astype('int16')90 91# fit uses idx with shape (K, H, W)92# encode uses idx with shape (H, W)93# apply ML to the idea,94# use previous index as feature and current index as label to train the learner95# if predict correctly, send 0, else send 1 and the corret index96# fitting time is too large97class ML_MPM(MPM):98 def __init__(self, learner, n_codeword, MPM_init_list):99 super().__init__(n_codeword, MPM_init_list)100 self.learner = myLearner(learner, n_codeword)101 self.length = (int)(np.log2(n_codeword-1))+1102 103 def fit(self, idx):104 prev, cur = [], []105 idx = idx.astype('int16')106 for k in range(idx.shape[0]):107 for i in range(idx.shape[1]):108 for j in range(idx.shape[2]):109 prev.append( [self.get_prev(idx[k], i-1, j-1),110 self.get_prev(idx[k], i-1, j),111 self.get_prev(idx[k], i, j-1),112 self.get_prev(idx[k], i-1, j+1)])113 cur.append(idx[k,i,j]) 114 prev = np.array(prev).reshape(-1, 4)115 cur = np.array(cur).reshape(-1,1)116 self.learner.fit(prev, cur)117 print('fit score:', self.learner.score(prev, cur))118 print('fit score top3',self.learner.topNscore(prev, cur, 3))119 return self120 121 def encode_one_idx(self, prev_idx, cur_idx):122 prev_idx = np.array(prev_idx).reshape(1, len(prev_idx))123 pred = self.learner.predict(prev_idx).reshape(-1)124 if pred[0] == cur_idx:125 return 0, 0126 else:127 return cur_idx, 1 128 129 def encode(self, idx):130 self.stream = ''131 idx = idx.astype('int16')132 x, y = [], []133 for i in range(idx.shape[0]):134 for j in range(idx.shape[1]):135 prev_idx = [self.get_prev(idx, i-1, j-1),136 self.get_prev(idx, i-1, j),137 self.get_prev(idx, i, j-1),138 self.get_prev(idx, i-1, j+1)]139 x.append(prev_idx)140 y.append(idx[i,j])141 x, y = np.array(x), np.array(y).reshape(-1)142 px = self.learner.predict(x).reshape(-1)143 print('test score',self.learner.score(x, y))144 print('test score top3',self.learner.topNscore(x, y, 3))145 for i in range(len(px)):146 if px[i] == y[i]:147 mode = 0148 else:149 mode = 1150 self.stream += int2bits(mode, 1, is_uint=True, return_string=True)151 if mode == 1: 152 self.stream += int2bits(y[i], self.length, is_uint=True, return_string=True)153 return self.stream154 155 def decode_one_idx(self, prev_idx):156 mode = bits2int(self.stream[self.ct:self.ct+1], is_uint=True)157 self.ct += 1158 if mode == 1:159 idx = bits2int(self.stream[self.ct:self.ct+self.length], is_uint=True)160 self.ct += self.length161 return idx162 else:163 pred = self.learner.predict(np.array(prev_idx).reshape(1,-1))164 return pred165 166 def decode(self, stream, H, W, raw_idx=None):167 self.stream = stream168 idx = np.zeros((H, W))169 self.ct = 0170 prev_idx = []171 for i in range(idx.shape[0]):172 for j in range(idx.shape[1]):173 prev_idx = [self.get_prev(idx, i-1, j-1),174 self.get_prev(idx, i-1, j),175 self.get_prev(idx, i, j-1),176 self.get_prev(idx, i-1, j+1)]177 idx[i,j] = self.decode_one_idx(prev_idx)178 if raw_idx is not None:179 assert(idx[i,j] == raw_idx[i,j]), 'Decoding Error!'180 return idx.astype('int16')181class ML_MPM3(ML_MPM):182 def __init__(self, learner, n_codeword, MPM_init_list):183 super().__init__(learner, n_codeword, MPM_init_list)184 self.length = (int)(np.log2(n_codeword-4))+1185 def encode(self, idx):186 self.stream = ''187 idx = idx.astype('int16')188 x, y = [], []189 for i in range(idx.shape[0]):190 for j in range(idx.shape[1]):191 prev_idx = [self.get_prev(idx, i-1, j-1),192 self.get_prev(idx, i-1, j),193 self.get_prev(idx, i, j-1),194 self.get_prev(idx, i-1, j+1)]195 x.append(prev_idx)196 y.append(idx[i,j])197 x, y = np.array(x), np.array(y).reshape(-1)198 px = self.learner.predict_proba(x)199 idx = np.argsort(px, axis=1)200 self.MPM_list = idx[:, -3:]201 print('test score',self.learner.score(x, y))202 print('test score top3',self.learner.topNscore(x, y, 3))203 for i in range(len(self.MPM_list)):204 tmp = copy.deepcopy(self.MPM_list[i])205 tmp.sort()206 mode, c = 3, 0207 for j in range(len(tmp)):208 if tmp[j] == y[i]:209 mode = j210 if y[i] > tmp[j]:211 c += 1212 self.stream += int2bits(mode, 2, is_uint=True, return_string=True)213 if mode == 3: 214 self.stream += int2bits(y[i]-c, self.length, is_uint=True, return_string=True)215 return self.stream216 217 def decode_one_idx(self, prev_idx):218 mode = bits2int(self.stream[self.ct:self.ct+2], is_uint=True)219 self.ct += 2220 prob = self.learner.predict_proba(np.array(prev_idx).reshape(1,-1)).reshape(-1)221 pred = np.argsort(prob)[-3:]222 pred.sort()223 if mode == 3:224 codeword = []225 for i in range(self.n_codeword):226 if i not in pred:227 codeword.append(i)228 idx = bits2int(self.stream[self.ct:self.ct+self.length], is_uint=True)229 self.ct += self.length230 return codeword[idx]231 else:232 return pred[mode]233 234 def decode(self, stream, H, W, raw_idx=None):235 self.stream = stream236 idx = np.zeros((H, W))237 self.ct = 0238 for i in range(idx.shape[0]):239 for j in range(idx.shape[1]):240 prev_idx = [self.get_prev(idx, i-1, j-1),241 self.get_prev(idx, i-1, j),242 self.get_prev(idx, i, j-1),243 self.get_prev(idx, i-1, j+1)]244 idx[i,j] = self.decode_one_idx(prev_idx)245 if raw_idx is not None:246 assert(idx[i,j] == raw_idx[i,j]), 'Decoding Error!'247 return idx.astype('int16')248def Check(idx, didx):249 for i in range(idx.shape[0]):250 for j in range(idx.shape[1]):251 if idx[i,j] != didx[i,j]:252 err_list = 'idx: '+str(idx[i,j])+', become: '+str(didx[i,j])+', pos: ('+str(i)+','+str(j)+')'253 print('Error!')254 print(err_list)255 assert (False), "Not Match!"256class ML_MPMa(MPM):257 def __init__(self, learner, n_codeword, MPM_init_list):258 super().__init__(n_codeword, MPM_init_list)259 self.learner = myLearner(learner, n_codeword)260 self.length = (int)(np.log2(n_codeword-5))+1261 262 def fit(self, idx):263 prev, cur = [], []264 idx = idx.astype('int16')265 for k in range(idx.shape[0]):266 for i in range(idx.shape[1]):267 for j in range(idx.shape[2]):268 prev.append( [self.get_prev(idx[k], i-1, j-1),269 self.get_prev(idx[k], i-1, j),270 self.get_prev(idx[k], i, j-1),271 self.get_prev(idx[k], i-1, j+1)])272 cur.append(idx[k,i,j]) 273 prev = np.array(prev).reshape(-1, 4)274 cur = np.array(cur).reshape(-1,1)275 self.learner.fit(prev, cur)276 print('fit score:', self.learner.score(prev, cur))277 print('fit score top4',self.learner.topNscore(prev, cur, 4))278 return self 279 280 def encode_fail(self, sprob, cur_idx):281 a = sprob[-4:-1]282 a.sort()283 b = sprob[:-4]284 b.sort() 285 mode = 3286 for i in range(len(a)):287 if a[i] == cur_idx:288 mode = i289 self.stream += int2bits(mode, 2, is_uint=True, return_string=True)290 if mode == 3:291 for i in range(len(b)):292 if cur_idx == b[i]:293 self.stream += int2bits(i, self.length, is_uint=True, return_string=True)294 return295 assert (False), 'Error, missing codeword!'296 else:297 pass298 def encode(self, idx):299 self.stream = ''300 idx = idx.astype('int16')301 x, y = [], []302 for i in range(idx.shape[0]):303 for j in range(idx.shape[1]):304 prev_idx = [self.get_prev(idx, i-1, j-1),305 self.get_prev(idx, i-1, j),306 self.get_prev(idx, i, j-1),307 self.get_prev(idx, i-1, j+1)]308 x.append(prev_idx)309 y.append(idx[i,j])310 x, y = np.array(x), np.array(y).reshape(-1)311 prob = self.learner.predict_proba(x)312 sprob = np.argsort(prob, axis=1)313 px = sprob[:, -1].reshape(-1)314 print('test score',self.learner.score(x, y))315 print('test score top4',self.learner.topNscore(x, y, 4))316 for i in range(len(px)):317 if px[i] == y[i]:318 mode = 0319 else:320 mode = 1321 self.stream += int2bits(mode, 1, is_uint=True, return_string=True)322 if mode == 1: 323 self.encode_fail(sprob[i], y[i])324 return self.stream325 def decode_fail(self, idx):326 mode = bits2int(self.stream[self.ct:self.ct+2], is_uint=True)327 self.ct += 2 328 if mode < 3:329 a = idx[-4:-1]330 a.sort()331 return a[mode]332 else:333 b = idx[:-4]334 b.sort()335 val = bits2int(self.stream[self.ct:self.ct+self.length], is_uint=True)336 self.ct += self.length337 return b[val]338 def decode_one_idx(self, prev_idx):339 mode = bits2int(self.stream[self.ct:self.ct+1], is_uint=True)340 self.ct += 1341 prob = self.learner.predict_proba(np.array(prev_idx).reshape(1,-1)).reshape(-1)342 if mode == 1:343 idx = self.decode_fail(np.argsort(prob))344 return idx345 else:346 return np.argmax(prob)347 348 def decode(self, stream, H, W, raw_idx=None):349 self.stream = stream350 idx = np.zeros((H, W)).astype('int16')351 self.ct = 0352 prev_idx = []353 for i in range(idx.shape[0]):354 for j in range(idx.shape[1]):355 prev_idx = [self.get_prev(idx, i-1, j-1),356 self.get_prev(idx, i-1, j),357 self.get_prev(idx, i, j-1),358 self.get_prev(idx, i-1, j+1)]359 idx[i,j] = self.decode_one_idx(prev_idx)360 if raw_idx is not None:361 assert((int)(idx[i,j]) == raw_idx[i,j]), 'Decoding Error!'+str(i)+'_'+str(j)+'_'+str((int)(idx[i,j]))+'_'+str(raw_idx[i,j])...
common_verify.py
Source:common_verify.py
1r'''2>>> is_UInt(0)3True4>>> is_UInt(-1)5False6>>> is_UInt([])7False8>>> is_UInt(0, TypeError)9True10#doctest: +IGNORE_EXCEPTION_DETAIL11>>> is_UInt([], TypeError)12Traceback (most recent call last):13 ...14TypeError: <class 'list'> is not <class 'int'>15>>> is_pair(())16False17>>> is_pair((1, 2))18True19>>> is_Sequence([1, ''], TypeError)20True21>>> is_Sequence({})22False23>>> is_Sequence.of([1, ''], is_UInt)24False25>>> is_Sequence.of([1, 0], is_UInt)26True27>>> is_sorted_sequence([])28True29>>> is_sorted_sequence([None])30True31>>> is_sorted_sequence([1,2])32True33>>> is_sorted_sequence([1,1])34True35>>> is_sorted_sequence([1,2,3])36True37>>> is_sorted_sequence([2,1])38False39>>> is_sorted_sequence.of([1, 2], is_UInt)40True41>>> is_sorted_sequence(['', 'a'])42True43>>> is_sorted_sequence.of(['', 'a'], is_UInt)44False45>>> is_sorted_sequence.of([0, 'a'], is_UInt)46False47>>> is_sorted_sequence([0, 'a'])48Traceback (most recent call last):49 ...50TypeError: '<=' not supported between instances of 'int' and 'str'51>>> is_strict_sorted_sequence([2,2])52False53>>> is_strict_sorted_sequence([1,2])54True55>>> has_attrs([], attrs='__getitem__ index'.split())56True57>>> has_attrs([], attrs=[])58True59>>> has_attrs([], attrs=['xxxxxx'])60False61'''62__all__ = '''63 is_int64 is_UInt65 is_tuple66 is_pair67 is_str68 is_Hashable69 is_Sequence70 is_Set71 is_Mapping72 is_sorted_sequence73 is_strict_sorted_sequence74 has_attrs75 '''.split()76from .Verify import Verify77from .VerifyType import VerifyType, VerifyType__static78from .VerifyContainer import VerifyContainerType79from collections.abc import Sequence, Set, Mapping, Hashable80class is_int(VerifyType__static):81 types = int82class is_UInt(is_int):83 def _iter_verify_object_(self, obj):84 yield obj >= 0, lambda:'i < 0: {}'.format(obj)85 yield from super()._iter_verify_object_(obj)86class is_tuple(VerifyType__static):87 types = tuple88class is_pair(is_tuple):89 def _iter_verify_object_(self, obj):90 yield len(obj) == 2, lambda: 'len != 2: {}'.format(len(obj))91 yield from super()._iter_verify_object_(obj)92class is_str(VerifyType__static):93 types = str94class is_Hashable(VerifyType__static):95 types = Hashable96class is_Sequence(VerifyContainerType):97 types = Sequence98class is_Set(VerifyContainerType):99 types = Set100class is_Mapping(VerifyContainerType):101 types = Mapping102class is_sorted_sequence(is_Sequence):103 def _iter_verify_object_(self, obj):104 ls = obj105 prevs = iter(ls)106 succs = iter(ls)107 for _ in succs: break108 for i, prev, succ in zip(range(len(ls)), prevs, succs):109 yield (prev <= succ, lambda: 'not sorted sequence at {idx}: '110 'not ls[{idx}] <= ls[{idx1}]; '111 'ls[{idx}]={lsI!r}; ls[{idx1}]={lsI1!r}; ls={ls!r}'112 .format(idx=i, idx1=i+1, lsI=ls[i], lsI1=ls[i+1], ls=ls)113 )114 yield from super()._iter_verify_object_(obj)115class is_strict_sorted_sequence(is_Sequence):116 def _iter_verify_object_(self, obj):117 ls = obj118 prevs = iter(ls)119 succs = iter(ls)120 for _ in succs: break121 for i, prev, succ in zip(range(len(ls)), prevs, succs):122 yield (prev < succ, lambda: 'not sorted sequence at {idx}: '123 'not ls[{idx}] < ls[{idx1}]; '124 'ls[{idx}]={lsI!r}; ls[{idx1}]={lsI1!r}; ls={ls!r}'125 .format(idx=i, idx1=i+1, lsI=ls[i], lsI1=ls[i+1], ls=ls)126 )127 yield from super()._iter_verify_object_(obj)128class has_attrs(Verify):129 def __init__(self, *, attrs, **kwargs):130 attrs = self.attrs = tuple(sorted(set(attrs)))131 assert is_Sequence.of(attrs, is_str, TypeError)132 if not all(attr.isidentifier() for attr in attrs): raise ValueError133 super().__init__(**kwargs)134 def _iter_verify_object_(self, obj):135 for attr in self.attrs:136 yield (hasattr(obj, attr), lambda:137 'has not attr {!r}: {!r}'.format(attr, obj)138 )139 yield from super()._iter_verify_object_(obj)140if __name__ == "__main__":141 import doctest142 doctest.testmod()143 #doctest: +ELLIPSIS144 #doctest: +NORMALIZE_WHITESPACE145 #doctest: +IGNORE_EXCEPTION_DETAIL...
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