How to use begin_context method in unittest-xml-reporting

Best Python code snippet using unittest-xml-reporting_python

seq_embed.py

Source:seq_embed.py Github

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1"""2x-vector categorical embeddings3"""4from __future__ import absolute_import5from __future__ import print_function6from __future__ import division7from six.moves import xrange8import logging9import numpy as np10from keras import backend as K11from keras import optimizers12from keras import objectives13from keras.layers import Input, Concatenate, MaxPooling1D14from keras.models import Model, load_model, model_from_json15from .. import objectives as hyp_obj16from ..keras_utils import *17from ..layers import *18from ..losses import categorical_mbr19from ...hyp_model import HypModel20class SeqEmbed(HypModel):21 def __init__(self, enc_net, pt_net,22 loss='categorical_crossentropy',23 pooling='mean+std',24 left_context=0,25 right_context=0,26 begin_context=None,27 end_context=None,28 enc_downsampling=None,29 **kwargs):30 super(SeqEmbed, self).__init__(**kwargs)31 self.enc_net = enc_net32 self.pt_net = pt_net33 self.pooling = pooling34 self.loss = loss35 self.model = None36 self.pool_net = None37 38 self.left_context = left_context39 self.right_context = right_context40 self.begin_context = left_context if begin_context is None else begin_context41 self.end_context = right_context if end_context is None else end_context42 self._enc_downsampling = enc_downsampling43 self.max_seq_length = None44 45 @property46 def x_dim(self):47 return self.enc_net.get_input_shape_at(0)[-1]48 @property49 def num_classes(self):50 return self.pt_net.get_output_shape_at(0)[-1]51 52 @property53 def pool_in_dim(self):54 return self.enc_net.get_output_shape_at(0)[-1]55 56 @property57 def pool_out_dim(self):58 return self.pt_net.get_input_shape_at(0)[-1]59 60 @property61 def in_length(self):62 if self.max_seq_length is None:63 return self.enc_net.get_input_shape_at(0)[-2]64 return self.max_seq_length65 66 @property67 def pool_in_length(self):68 pool_length = self.enc_net.get_output_shape_at(0)[-2]69 if pool_length is None:70 in_length = self.in_length71 if in_length is None:72 return None73 x = Input(shape=(in_length, self.x_dim))74 net = Model(x, self.enc_net(x))75 pool_length = net.get_output_shape_at(0)[-2]76 return pool_length77 78 @property79 def enc_downsampling(self):80 if self._enc_downsampling is None:81 assert self.in_length is not None82 assert self.pool_in_length is not None83 r = self.in_length/​self.pool_in_length84 assert np.ceil(r) == np.floor(r)85 self._enc_downsampling = int(r)86 return self._enc_downsampling87 88 def _apply_pooling(self, x, mask):89 90 if self.pooling == 'mean+std':91 pool = Concatenate(axis=-1, name='pooling')(92 GlobalWeightedMeanStdPooling1D(name='mean--std')([x, mask]))93 elif self.pooling == 'mean+logvar':94 pool = Concatenate(axis=-1, name='pooling')(95 GlobalWeightedMeanLogVarPooling1D(name='mean--logvar')([x, mask]))96 elif self.pooling == 'mean':97 pool = GlobalWeightedAveragePooling1D(name='pooling')([x, mask])98 else:99 raise ValueError('Invalid pooling %s' % self.pooling)100 return pool101 102 def compile(self, metrics=None, **kwargs):103 if self.loss == 'categorical_mbr':104 loss = categorical_mbr105 else:106 loss = self.loss107 108 if metrics is None:109 self.model.compile(loss=loss, **kwargs)110 else:111 self.model.compile(loss=loss,112 metrics=metrics,113 weighted_metrics=metrics, **kwargs)114 115 def freeze_enc_net(self):116 self.enc_net.trainable = False117 118 def freeze_enc_net_layers(self, layers):119 for layer_name in layers:120 self.enc_net.get_layer(layer_name).trainable = False121 122 def freeze_pt_net_layers(self, layers):123 for layer_name in layers:124 self.pt_net.get_layer(layer_name).trainable = False125 126 def build(self, max_seq_length=None):127 if max_seq_length is None:128 max_seq_length = self.enc_net.get_input_shape_at(0)[-2]129 self.max_seq_length = max_seq_length130 x = Input(shape=(max_seq_length, self.x_dim,))131 mask = CreateMask(0)(x)132 frame_embed = self.enc_net(x)133 dec_ratio = int(max_seq_length/​frame_embed._keras_shape[1])134 if dec_ratio > 1:135 mask = MaxPooling1D(dec_ratio, padding='same')(mask)136 137 pool = self._apply_pooling(frame_embed, mask)138 y = self.pt_net(pool)139 self.model = Model(x, y)140 self.model.summary()141 142 143 def build_embed(self, layers):144 frame_embed = Input(shape=(None, self.pool_in_dim,))145 mask = Input(shape=(None,))146 pool = self._apply_pooling(frame_embed, mask)147 148 outputs = []149 for layer_name in layers:150 embed_i = Model(self.pt_net.get_input_at(0),151 self.pt_net.get_layer(layer_name).get_output_at(0))(pool)152 outputs.append(embed_i)153 self.pool_net = Model([frame_embed, mask], outputs)154 self.pool_net.summary()155 156 def predict_embed(self, x, **kwargs):157 in_seq_length = self.in_length158 pool_seq_length = self.pool_in_length159 r = self.enc_downsampling160 161 assert np.ceil(self.left_context/​r) == np.floor(self.left_context/​r)162 assert np.ceil(self.right_context/​r) == np.floor(self.right_context/​r)163 assert np.ceil(self.begin_context/​r) == np.floor(self.begin_context/​r)164 assert np.ceil(self.end_context/​r) == np.floor(self.end_context/​r) 165 pool_begin_context = int(self.begin_context/​r)166 pool_end_context = int(self.end_context/​r)167 pool_left_context = int(self.left_context/​r)168 pool_right_context = int(self.right_context/​r)169 in_length = x.shape[-2]170 pool_length = int(in_length/​r)171 in_shift = in_seq_length - self.left_context - self.right_context172 pool_shift = int(in_shift/​r)173 174 y = np.zeros((pool_length, self.pool_in_dim), dtype=float_keras())175 mask = np.ones((1, pool_length), dtype=float_keras())176 mask[0,:pool_begin_context] = 0177 mask[0,pool_length - pool_end_context:] = 0178 num_batches = max(int(np.ceil((in_length-in_seq_length)/​in_shift+1)), 1)179 x_i = np.zeros((1,in_seq_length, x.shape[-1]), dtype=float_keras())180 j_in = 0181 j_out = 0182 for i in xrange(num_batches):183 k_in = min(j_in+in_seq_length, in_length)184 k_out = min(j_out+pool_seq_length, pool_length)185 l_in = k_in - j_in186 l_out = k_out - j_out187 x_i[0,:l_in] = x[j_in:k_in]188 y_i = self.enc_net.predict(x_i, batch_size=1, **kwargs)[0]189 y[j_out:k_out] = y_i[:l_out]190 j_in += in_shift191 j_out += pool_shift192 if i==0:193 j_out += pool_left_context194 logging.debug(pool_seq_length)195 logging.debug(pool_left_context)196 logging.debug(pool_right_context)197 logging.debug(pool_begin_context)198 logging.debug(pool_end_context)199 logging.debug('embed2 %d %d %d' % (pool_length, j_out-pool_shift, j_out-pool_shift+l_out))200 y = np.expand_dims(y, axis=0)201 embeds = self.pool_net.predict([y, mask], batch_size=1, **kwargs)202 return np.hstack(tuple(embeds))203 204 @property205 def embed_dim(self):206 if self.pool_net is None:207 return None208 embed_dim=0209 for node in xrange(len(self.pool_net._inbound_nodes)):210 output_shape = self.pool_net.get_output_shape_at(node)211 if isinstance(output_shape, list):212 for shape in output_shape:213 embed_dim += shape[-1]214 else:215 embed_dim += output_shape[-1]216 return embed_dim217 218 def build_eval(self):219 frame_embed = Input(shape=(None, self.pool_in_dim,))220 mask = Input(shape=(None,))221 pool = self._apply_pooling(frame_embed, mask)222 223 score = self.pt_net(pool)224 self.pool_net = Model([frame_embed, mask], score)225 self.pool_net.summary()226 227 def predict_eval(self, x, **kwargs):228 return np.log(self.predict_embed(x, **kwargs)+1e-10)229 230 231 def fit(self, x, y, **kwargs):232 self.model.fit(x, y, **kwargs)233 234 def fit_generator(self, generator, steps_per_epoch, **kwargs):235 self.model.fit_generator(generator, steps_per_epoch, **kwargs)236 237 def get_config(self):238 config = { 'pooling': self.pooling,239 'loss': self.loss,240 'left_context': self.left_context,241 'right_context': self.right_context,242 'begin_context': self.begin_context,243 'end_context': self.end_context}244 base_config = super(SeqEmbed, self).get_config()245 return dict(list(base_config.items()) + list(config.items()))246 247 def save(self, file_path):248 file_model = '%s.json' % (file_path)249 with open(file_model, 'w') as f:250 f.write(self.to_json())251 252 file_model = '%s.enc.h5' % (file_path)253 self.enc_net.save(file_model)254 file_model = '%s.pt.h5' % (file_path)255 self.pt_net.save(file_model)256 257 @classmethod258 def load(cls, file_path):259 file_config = '%s.json' % (file_path) 260 config = SeqEmbed.load_config(file_config)261 262 file_model = '%s.enc.h5' % (file_path)263 enc_net = load_model(file_model, custom_objects=get_keras_custom_obj())264 file_model = '%s.pt.h5' % (file_path)265 pt_net = load_model(file_model, custom_objects=get_keras_custom_obj())266 filter_args = ('loss', 'pooling',267 'left_context', 'right_context',268 'begin_context', 'end_context', 'name')269 kwargs = {k: config[k] for k in filter_args if k in config }270 return cls(enc_net, pt_net, **kwargs)271 272 273 274 @staticmethod275 def filter_args(prefix=None, **kwargs):276 if prefix is None:277 p = ''278 else:279 p = prefix + '_'280 valid_args = ('pooling', 'left_context', 'right_context',281 'begin_context', 'end_context')282 return dict((k, kwargs[p+k])283 for k in valid_args if p+k in kwargs)284 285 286 @staticmethod287 def add_argparse_args(parser, prefix=None):288 if prefix is None:289 p1 = '--'290 p2 = ''291 else:292 p1 = '--' + prefix + '-'293 p2 = prefix + '_'294 parser.add_argument(p1+'pooling', dest=p2+'pooling', default='mean+std',295 choices=['mean+std', 'mean+logvar', 'mean'])296 parser.add_argument(p1+'left-context', dest=(p2+'left_context'),297 default=0, type=int)298 parser.add_argument(p1+'right-context', dest=(p2+'right_context'),299 default=0, type=int)300 parser.add_argument(p1+'begin-context', dest=(p2+'begin_context'),301 default=None, type=int)302 parser.add_argument(p1+'end-context', dest=(p2+'end_context'),...

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builder_test.py

Source:builder_test.py Github

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...64 """65 def setUp(self):66 self.builder = builder.TestXMLBuilder()67 self.doc = self.builder._xml_doc68 self.builder.begin_context('testsuites', 'name')69 self.valid_chars = u'выбор'70 self.invalid_chars = '\x01'71 self.invalid_chars_replace = u'\ufffd'72 def test_root_has_no_parent(self):73 self.assertIsNone(self.builder.current_context().parent)74 def test_current_context_tag(self):75 self.assertEqual(self.builder.context_tag(), 'testsuites')76 def test_begin_nested_context(self):77 root = self.builder.current_context()78 self.builder.begin_context('testsuite', 'name')79 self.assertEqual(self.builder.context_tag(), 'testsuite')80 self.assertIs(self.builder.current_context().parent, root)81 def test_end_inexistent_context(self):82 self.builder = builder.TestXMLBuilder()83 self.assertFalse(self.builder.end_context())84 self.assertEqual(len(self.doc.childNodes), 0)85 def test_end_root_context(self):86 root = self.builder.current_context()87 self.assertTrue(self.builder.end_context())88 self.assertIsNone(self.builder.current_context())89 # No contexts left90 self.assertFalse(self.builder.end_context())91 doc_children = self.doc.childNodes92 self.assertEqual(len(doc_children), 1)93 self.assertEqual(len(doc_children[0].childNodes), 0)94 self.assertEqual(doc_children[0].tagName, root.element_tag())95 def test_end_nested_context(self):96 self.builder.begin_context('testsuite', 'name')97 self.builder.current_context()98 self.assertTrue(self.builder.end_context())99 # Only updates the document when all contexts end100 self.assertEqual(len(self.doc.childNodes), 0)101 def test_end_all_context_stack(self):102 root = self.builder.current_context()103 self.builder.begin_context('testsuite', 'name')104 nested = self.builder.current_context()105 self.assertTrue(self.builder.end_context())106 self.assertTrue(self.builder.end_context())107 # No contexts left108 self.assertFalse(self.builder.end_context())109 root_child = self.doc.childNodes110 self.assertEqual(len(root_child), 1)111 self.assertEqual(root_child[0].tagName, root.element_tag())112 nested_child = root_child[0].childNodes113 self.assertEqual(len(nested_child), 1)114 self.assertEqual(nested_child[0].tagName, nested.element_tag())115 def test_append_valid_unicode_cdata_section(self):116 self.builder.append_cdata_section('tag', self.valid_chars)117 self.builder.end_context()118 root_child = self.doc.childNodes[0]119 cdata_container = root_child.childNodes[0]120 self.assertEqual(cdata_container.tagName, 'tag')121 cdata = cdata_container.childNodes[0]122 self.assertEqual(cdata.data, self.valid_chars)123 def test_append_invalid_unicode_cdata_section(self):124 self.builder.append_cdata_section('tag', self.invalid_chars)125 self.builder.end_context()126 root_child = self.doc.childNodes[0]127 cdata_container = root_child.childNodes[0]128 cdata = cdata_container.childNodes[0]129 self.assertEqual(cdata.data, self.invalid_chars_replace)130 def test_append_cdata_closing_tags_into_cdata_section(self):131 self.builder.append_cdata_section('tag', ']]>')132 self.builder.end_context()133 root_child = self.doc.childNodes[0]134 cdata_container = root_child.childNodes[0]135 self.assertEqual(len(cdata_container.childNodes), 2)136 self.assertEqual(cdata_container.childNodes[0].data, ']]')137 self.assertEqual(cdata_container.childNodes[1].data, '>')138 def test_append_tag_with_valid_unicode_values(self):139 self.builder.append('tag', self.valid_chars, attr=self.valid_chars)140 self.builder.end_context()141 root_child = self.doc.childNodes[0]142 tag = root_child.childNodes[0]143 self.assertEqual(tag.tagName, 'tag')144 self.assertEqual(tag.getAttribute('attr'), self.valid_chars)145 self.assertEqual(tag.childNodes[0].data, self.valid_chars)146 def test_append_tag_with_invalid_unicode_values(self):147 self.builder.append('tag', self.invalid_chars, attr=self.invalid_chars)148 self.builder.end_context()149 root_child = self.doc.childNodes[0]150 tag = root_child.childNodes[0]151 self.assertEqual(tag.tagName, 'tag')152 self.assertEqual(tag.getAttribute('attr'), self.invalid_chars_replace)153 self.assertEqual(tag.childNodes[0].data, self.invalid_chars_replace)154 def test_increment_root_context_counter(self):155 self.builder.increment_counter('tests')156 self.builder.end_context()157 root_child = self.doc.childNodes[0]158 self.assertEqual(root_child.tagName, 'testsuites')159 self.assertEqual(root_child.getAttribute('tests'), '1')160 def test_increment_nested_context_counter(self):161 self.builder.increment_counter('tests')162 self.builder.begin_context('testsuite', 'name')163 self.builder.increment_counter('tests')164 self.builder.end_context()165 self.builder.end_context()166 root_child = self.doc.childNodes[0]167 nested_child = root_child.childNodes[0]168 self.assertEqual(root_child.tagName, 'testsuites')169 self.assertEqual(nested_child.getAttribute('tests'), '1')170 self.assertEqual(root_child.getAttribute('tests'), '2')171 def test_finish_nested_context(self):172 self.builder.begin_context('testsuite', 'name')173 tree = ET.fromstring(self.builder.finish())174 self.assertEqual(tree.tag, 'testsuites')...

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