def save(self, file_path): save_path = Path(file_path) mkdir(save_path) param_path = Path(save_path.joinpath("params.json")) with open(param_path, "w") as fp: fp.write(json.dumps(self.params)) word_index_path = Path(save_path.joinpath("word2idx.json")) with open(word_index_path, "w") as fp: fp.write(json.dumps(self.word2idx)) tag_index_path = Path(save_path.joinpath("tag2idx.json")) with open(tag_index_path, "w") as fp: fp.write(json.dumps(self.tag2idx)) if self.params["use_chars"]: char_index_path = Path(save_path.joinpath("char2idx.json")) with open(char_index_path, "w") as fp: fp.write(json.dumps(self.char2idx)) if self.params["use_pos_tags"]: pos_index_path = Path(save_path.joinpath("pos2idx.json")) with open(pos_index_path, "w") as fp: fp.write(json.dumps(self.pos2idx)) if self.params["use_word_emb"]: word_emb_path = Path(save_path.joinpath("word_emb.csv")) np.savetxt(fname=str(word_emb_path), X=self.word_vectors, delimiter=",")
def save(self, file_path): """ Saves models to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) for _, model in self.pipeline.steps: model.save(save_path.joinpath(model.name))
def save(self, file_path): save_path = Path(file_path) mkdir(save_path) model_save_path = save_path.joinpath("word2vec.model") config_save_path = save_path.joinpath("word2vec.config") self.model.save(str(model_save_path)) self.config.save(config_save_path)
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) model_save_path = save_path.joinpath("CRF.model") config_save_path = save_path.joinpath("CRF.config") joblib.dump(self.crf, model_save_path) self.config.save(config_save_path)
def save(self, file_path): """ Saves an ensemble of models to the local disk, provided a file path. """ save_path = os.path.join(file_path, "Ensemble_NER") mkdir(save_path) for model in self.models: model.save(save_path)
def save(self, file_path): """ Saves an ensemble of models to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) for model in self.models: model.save(save_path.joinpath(model.name))
def save(self, file_path): save_path = Path(file_path) mkdir(save_path) words_path = save_path.joinpath("words.dict") # save dictionaries # we don't save examples for now joblib.dump(self.word_vectorizer, words_path)
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) model_save_path = save_path.joinpath("RF.model") config_save_path = save_path.joinpath("RF.config") joblib.dump(self.rf, model_save_path) self.feature_extractor.save(save_path.joinpath(self.feature_extractor.name)) self.config.save(config_save_path)
def test_mkdir(): directory1 = "data" directory2 = "data/foo" mkdir(directory1) mkdir(directory2) assert_true(os.path.exists(directory1)) assert_true(os.path.exists(directory2))
def save(self, file_path): save_path = Path(file_path) mkdir(save_path) # save config config_save_path = save_path.joinpath("context.config") self.config.save(config_save_path) # save dictionaries for i in self.encoders: words_path = save_path.joinpath("f{}.dict".format(i)) joblib.dump(self.encoders[i], words_path)
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) model_save_path = save_path.joinpath("BiLSTM.model") mkdir(model_save_path) config_save_path = save_path.joinpath("BiLSTM.config") weights_save_path = model_save_path.joinpath("weights.h5") params_save_path = model_save_path.joinpath("params.json") preproc_save_path = model_save_path.joinpath("preprocessor.pickle") self.model.save(weights_save_path, params_save_path, preproc_save_path) self.config.save(config_save_path)
def test_rmdir(): directory1 = "data" directory2 = "data/foo" mkdir(directory1) mkdir(directory2) rmdir(directory2) rmdir(directory1) assert_false(os.path.exists(directory1)) assert_false(os.path.exists(directory2))
def save(self, file_path): save_path = Path(file_path) mkdir(save_path) words_path = save_path.joinpath("words.dict") labels_path = save_path.joinpath("labels.dict") pos_path = save_path.joinpath("pos.dict") dep_path = save_path.joinpath("dep.dict") # save dictionaries # we don't save examples for now joblib.dump(self.word_vectorizer, words_path) joblib.dump(self.label_vectorizer, labels_path) joblib.dump(self.pos_vectorizer, pos_path) joblib.dump(self.dep_vectorizer, dep_path)
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) model_save_path = save_path.joinpath("KerasNER.model") config_save_path = save_path.joinpath("KerasNER.config") arch_save_path = save_path.joinpath("KerasNER.json") encoder_save_path = save_path.joinpath("encoder") with open(arch_save_path, 'w') as f: params = self.model.to_json() json.dump(json.loads(params), f, sort_keys=True, indent=4) self.model.save_weights(model_save_path) self.config.save(config_save_path) self.p.save(encoder_save_path)
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = os.path.join(file_path, "CRF_NER") model_filename = "CRF.model" model_save_path = os.path.join(save_path, model_filename) metadata_filename = "CRF_metadata.json" metadata_save_path = os.path.join(save_path, metadata_filename) mkdir(save_path) joblib.dump(self.crf, model_save_path) with open(metadata_save_path, "w") as fp: fp.write(json.dumps({"entity_label": self.entity_label}))
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = Path(file_path) mkdir(save_path) model_save_path = save_path.joinpath("KerasNER.model") config_save_path = save_path.joinpath("KerasNER.config") arch_save_path = save_path.joinpath("KerasNER.json") encoder_save_path = save_path.joinpath("encoder") if self.config.get_parameter("use_crf"): save_load_utils.save_all_weights(self.model, str(model_save_path)) else: self.model.save(str(model_save_path)) self.config.save(config_save_path) # human-readable model architecture in json with open(arch_save_path, "w") as wf: wf.write(self.model.to_json()) self.encoder.save(encoder_save_path)
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ save_path = os.path.join(file_path, "BiLSTM_NER") model_filename = "BiLSTM.model" model_save_path = os.path.join(save_path, model_filename) metadata_filename = "BiLSTM_metadata.json" metadata_save_path = os.path.join(save_path, metadata_filename) mkdir(save_path) mkdir(model_save_path) self.model.save(model_save_path) with open(metadata_save_path, "w") as fp: fp.write( json.dumps({ "entity_label": self.entity_label, "label_map": self._label_map }))
def save(self, file_path): """ Saves a model to the local disk, provided a file path. """ mkdir(file_path) self.svm.save(file_path.joinpath(self.svm.name))
def __init__(self, path_to_folder): self.path_to_folder = path_to_folder mkdir(self.path_to_folder)
def save(self, file_path): save_path = Path(file_path) mkdir(save_path) self.word_vectorizer.save(save_path) self.pos_vectorizer.save(save_path) self.char_vectorizer.save(save_path)
def __init__(self, path_to_files): path_to_files = Path(path_to_files) mkdir(path_to_files) self.path = path_to_files