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

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'),...

builder_test.py

Source:builder_test.py

...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'Ð²ÑÐ±Ð¾Ñ'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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