How to use _config method in molecule

Best Python code snippet using molecule_python

config.py

Source:config.py

1from configparser import ConfigParser2import os3class myconf(ConfigParser):4    def __init__(self, defaults=None):5        ConfigParser.__init__(self, defaults=defaults)6    def optionxform(self, optionstr):7        return optionstr8class Configurable(myconf):9    def __init__(self, config_file):10        config = myconf()11        config.read(config_file)12        self._config = config13        print('Loaded config file sucessfully.')14        for section in config.sections():15            for k, v in config.items(section):16                print(k, ":", v)17        # if not os.path.isdir(self.save_dir):18        #     os.mkdir(self.save_dir)19        config.write(open(config_file, 'w'))20    # Data21    @property22    def word_Embedding(self):23        return self._config.getboolean('Data', 'word_Embedding')24    @property25    def freq_1_unk(self):26        return self._config.getboolean('Data', 'freq_1_unk')27    @property28    def word_Embedding_Path(self):29        return self._config.get('Data', 'word_Embedding_Path')30    @property31    def datafile_path(self):32        return self._config.get('Data', 'datafile_path')33    @property34    def name_trainfile(self):35        return self._config.get('Data', 'name_trainfile')36    @property37    def name_devfile(self):38        return self._config.get('Data', 'name_devfile')39    @property40    def name_testfile(self):41        return self._config.get('Data', 'name_testfile')42    @property43    def min_freq(self):44        return self._config.getint('Data', 'min_freq')45    @property46    def word_data(self):47        return self._config.getboolean('Data', 'word_data')48    @property49    def char_data(self):50        return self._config.getboolean('Data', 'char_data')51    @property52    def shuffle(self):53        return self._config.getboolean('Data', 'shuffle')54    @property55    def epochs_shuffle(self):56        return self._config.getboolean('Data', 'epochs_shuffle')57    @property58    def FIVE_CLASS_TASK(self):59        return self._config.getboolean('Data', 'FIVE_CLASS_TASK')    \60    @property61    def TWO_CLASS_TASK(self):62        return self._config.getboolean('Data', 'TWO_CLASS_TASK')63    # Save64    @property65    def snapshot(self):66        value = self._config.get('Save', 'snapshot')67        if value == "None" or value == "none":68            return None69        else:70            return value71    @property72    def predict(self):73        value = self._config.get('Save', 'predict')74        if value == "None" or value == "none":75            return None76        else:77            return value78    @property79    def test(self):80        return self._config.getboolean('Save', 'test')81    @property82    def save_dir(self):83        return self._config.get('Save', 'save_dir')84    @save_dir.setter85    def save_dir(self, value):86        self._config.set('Save', 'save_dir', str(value))87    @property88    def rm_model(self):89        return self._config.getboolean('Save', 'rm_model')90    # Model91    @property92    def static(self):93        return self._config.getboolean("Model", "static")94    @property95    def wide_conv(self):96        return self._config.getboolean("Model", "wide_conv")97    @property98    def CNN(self):99        return self._config.getboolean("Model", "CNN")100    @property101    def HighWay_CNN(self):102        return self._config.getboolean("Model", "HighWay_CNN")103    @property104    def CNN_MUI(self):105        return self._config.getboolean("Model", "CNN_MUI")106    @property107    def DEEP_CNN(self):108        return self._config.getboolean("Model", "DEEP_CNN")109    @property110    def DEEP_CNN_MUI(self):111        return self._config.getboolean("Model", "DEEP_CNN_MUI")112    @property113    def LSTM(self):114        return self._config.getboolean("Model", "LSTM")115    @property116    def GRU(self):117        return self._config.getboolean("Model", "GRU")118    @property119    def BiLSTM(self):120        return self._config.getboolean("Model", "BiLSTM")121    @property122    def BiLSTM_1(self):123        return self._config.getboolean("Model", "BiLSTM_1")124    @property125    def HighWay_BiLSTM_1(self):126        return self._config.getboolean("Model", "HighWay_BiLSTM_1")127    @property128    def CNN_LSTM(self):129        return self._config.getboolean("Model", "CNN_LSTM")130    @property131    def CNN_BiLSTM(self):132        return self._config.getboolean("Model", "CNN_BiLSTM")133    @property134    def CLSTM(self):135        return self._config.getboolean("Model", "CLSTM")136    @property137    def CBiLSTM(self):138        return self._config.getboolean("Model", "CBiLSTM")139    @property140    def CGRU(self):141        return self._config.getboolean("Model", "CGRU")142    @property143    def BiGRU(self):144        return self._config.getboolean("Model", "BiGRU")145    @property146    def CNN_BiGRU(self):147        return self._config.getboolean("Model", "CNN_BiGRU")148    @property149    def embed_dim(self):150        return self._config.getint("Model", "embed_dim")151    @property152    def lstm_hidden_dim(self):153        return self._config.getint("Model", "lstm_hidden_dim")154    @property155    def lstm_num_layers(self):156        return self._config.getint("Model", "lstm_num_layers")157    @property158    def batch_normalizations(self):159        return self._config.getboolean("Model", "batch_normalizations")160    @property161    def bath_norm_momentum(self):162        return self._config.getfloat("Model", "bath_norm_momentum")163    @property164    def batch_norm_affine(self):165        return self._config.getboolean("Model", "batch_norm_affine")166    @property167    def dropout(self):168        return self._config.getfloat("Model", "dropout")169    @property170    def dropout_embed(self):171        return self._config.getfloat("Model", "dropout_embed")172    @property173    def max_norm(self):174        value = self._config.get("Model", "max_norm")175        if value == "None" or value == "none":176            return None177        else:178            return value179    @property180    def clip_max_norm(self):181        return self._config.getint("Model", "clip_max_norm")182    @property183    def kernel_num(self):184        return self._config.getint("Model", "kernel_num")185    @property186    def kernel_sizes(self):187        value = self._config.get("Model", "kernel_sizes")188        # print(list(value))189        value = [int(k) for k in list(value) if k != ","]190        return value191    @kernel_sizes.setter192    def kernel_sizes(self, value):193        self._config.set("Model", "kernel_sizes", str(value))194    @property195    def init_weight(self):196        return self._config.getboolean("Model", "init_weight")197    @property198    def init_weight_value(self):199        return self._config.getfloat("Model", "init_weight_value")200    # Optimizer201    @property202    def learning_rate(self):203        return self._config.getfloat("Optimizer", "learning_rate")204    @property205    def Adam(self):206        return self._config.getboolean("Optimizer", "Adam")207    @property208    def SGD(self):209        return self._config.getboolean("Optimizer", "SGD")210    @property211    def Adadelta(self):212        return self._config.getboolean("Optimizer", "Adadelta")213    @property214    def momentum_value(self):215        return self._config.getfloat("Optimizer", "optim_momentum_value")216    @property217    def weight_decay(self):218        return self._config.getfloat("Optimizer", "weight_decay")219    # Train220    @property221    def num_threads(self):222        return self._config.getint("Train", "num_threads")223    @property224    def device(self):225        return self._config.getint("Train", "device")226    @property227    def cuda(self):228        return self._config.getboolean("Train", "cuda")229    @property230    def epochs(self):231        return self._config.getint("Train", "epochs")232    @property233    def batch_size(self):234        return self._config.getint("Train", "batch_size")235    @property236    def log_interval(self):237        return self._config.getint("Train", "log_interval")238    @property239    def test_interval(self):240        return self._config.getint("Train", "test_interval")241    @property242    def save_interval(self):...

configdict_test.py

Source:configdict_test.py

1# Copyright 2018 The TensorFlow Authors.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14"""Tests for config_util.configdict."""15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18from absl.testing import absltest19from tf_util import configdict20class ConfigDictTest(absltest.TestCase):21  def setUp(self):22    super(ConfigDictTest, self).setUp()23    self._config = configdict.ConfigDict({24        "int": 1,25        "float": 2.0,26        "bool": True,27        "str": "hello",28        "nested": {29            "int": 3,30        },31        "double_nested": {32            "a": {33                "int": 3,34            },35            "b": {36                "float": 4.0,37            }38        }39    })40  def testAccess(self):41    # Simple types.42    self.assertEqual(1, self._config.int)43    self.assertEqual(1, self._config["int"])44    self.assertEqual(2.0, self._config.float)45    self.assertEqual(2.0, self._config["float"])46    self.assertTrue(self._config.bool)47    self.assertTrue(self._config["bool"])48    self.assertEqual("hello", self._config.str)49    self.assertEqual("hello", self._config["str"])50    # Single nested config.51    self.assertEqual(3, self._config.nested.int)52    self.assertEqual(3, self._config["nested"].int)53    self.assertEqual(3, self._config.nested["int"])54    self.assertEqual(3, self._config["nested"]["int"])55    # Double nested config.56    self.assertEqual(3, self._config["double_nested"].a.int)57    self.assertEqual(3, self._config["double_nested"]["a"].int)58    self.assertEqual(3, self._config["double_nested"].a["int"])59    self.assertEqual(3, self._config["double_nested"]["a"]["int"])60    self.assertEqual(4.0, self._config.double_nested.b.float)61    self.assertEqual(4.0, self._config.double_nested["b"].float)62    self.assertEqual(4.0, self._config.double_nested.b["float"])63    self.assertEqual(4.0, self._config.double_nested["b"]["float"])64    # Nonexistent parameters.65    with self.assertRaises(AttributeError):66      _ = self._config.nonexistent67    with self.assertRaises(KeyError):68      _ = self._config["nonexistent"]69  def testSetAttribut(self):70    # Overwrite existing simple type.71    self._config.int = 4072    self.assertEqual(40, self._config.int)73    # Overwrite existing nested simple type.74    self._config.nested.int = 4075    self.assertEqual(40, self._config.nested.int)76    # Overwrite existing nested config.77    self._config.double_nested.a = {"float": 50.0}78    self.assertIsInstance(self._config.double_nested.a, configdict.ConfigDict)79    self.assertEqual(50.0, self._config.double_nested.a.float)80    self.assertNotIn("int", self._config.double_nested.a)81    # Set new simple type.82    self._config.int_2 = 1083    self.assertEqual(10, self._config.int_2)84    # Set new nested simple type.85    self._config.nested.int_2 = 2086    self.assertEqual(20, self._config.nested.int_2)87    # Set new nested config.88    self._config.double_nested.c = {"int": 30}89    self.assertIsInstance(self._config.double_nested.c, configdict.ConfigDict)90    self.assertEqual(30, self._config.double_nested.c.int)91  def testSetItem(self):92    # Overwrite existing simple type.93    self._config["int"] = 4094    self.assertEqual(40, self._config.int)95    # Overwrite existing nested simple type.96    self._config["nested"].int = 4097    self.assertEqual(40, self._config.nested.int)98    self._config.nested["int"] = 5099    self.assertEqual(50, self._config.nested.int)100    # Overwrite existing nested config.101    self._config.double_nested["a"] = {"float": 50.0}102    self.assertIsInstance(self._config.double_nested.a, configdict.ConfigDict)103    self.assertEqual(50.0, self._config.double_nested.a.float)104    self.assertNotIn("int", self._config.double_nested.a)105    # Set new simple type.106    self._config["int_2"] = 10107    self.assertEqual(10, self._config.int_2)108    # Set new nested simple type.109    self._config.nested["int_2"] = 20110    self.assertEqual(20, self._config.nested.int_2)111    self._config.nested["int_3"] = 30112    self.assertEqual(30, self._config.nested.int_3)113    # Set new nested config.114    self._config.double_nested["c"] = {"int": 30}115    self.assertIsInstance(self._config.double_nested.c, configdict.ConfigDict)116    self.assertEqual(30, self._config.double_nested.c.int)117  def testDelete(self):118    # Simple types.119    self.assertEqual(1, self._config.int)120    del self._config.int121    with self.assertRaises(AttributeError):122      _ = self._config.int123    with self.assertRaises(KeyError):124      _ = self._config["int"]125    self.assertEqual(2.0, self._config["float"])126    del self._config["float"]127    with self.assertRaises(AttributeError):128      _ = self._config.float129    with self.assertRaises(KeyError):130      _ = self._config["float"]131    # Nested config.132    self.assertEqual(3, self._config.nested.int)133    del self._config.nested134    with self.assertRaises(AttributeError):135      _ = self._config.nested136    with self.assertRaises(KeyError):137      _ = self._config["nested"]138if __name__ == "__main__":...

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