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configfromfile.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# author Romaric Besançon ([email protected])
# date Tue Nov 10 2020
# copyright Copyright (C) 2020 by CEA - LIST
#
import os,sys,argparse,codecs
import copy
import yaml
import jsonschema
from jsonschema import Draft4Validator, validators
import logging
from tfner import general_utils
from tfner.config import Config
class ConfigException(Exception):
pass
def extend_with_default(validator_class):
validate_properties = validator_class.VALIDATORS["properties"]
def set_defaults(validator, properties, instance, schema):
for property_, subschema in properties.items():
if "default" in subschema and not isinstance(instance, list):
if subschema["default"] == "null":
instance.setdefault(property_, None)
else:
instance.setdefault(property_, subschema["default"])
for error in validate_properties(
validator, properties, instance, schema,
):
yield error
return validators.extend(
validator_class, {"properties": set_defaults},
)
FillDefaultValidatingDraft4Validator = extend_with_default(Draft4Validator)
ConfigSchema = {
"$schema": "http://json-schema.org/schema#",
"title": "Configuration for NER BiLSTM-CRF model",
"type": "object",
"properties": {
"language": { "type": "string", "description": "language of the model."},
"dir_output": { "type": "string", "description": "output directory.","default":"results"},
"dim_word": { "type": "integer", "description": "dimension of the the word embeddings.","default":300},
"dim_char": { "type": "integer", "description": "dimension of the the character embeddings.","default":100},
"filename_glove": { "type": "string", "description": "path to glove embeddings"},
"filename_trimmed": { "type": ["string","null"], "description": "path to trimmed embeddings (created)","default":"null"},
"use_pretrained": { "type": "boolean", "description": "indicates if pretrained embeddings are used","default":True},
"filename_dev": { "type": "string", "description": "path to IOB file containing training data"},
"filename_train": { "type": "string", "description": "path to IOB file containing training data"},
"filename_test": { "type": "string", "description": "path to IOB file containing training data"},
# vocab (created from dataset with build_data.py)
"filename_words": { "type": "string", "description": "path to output file containing training words", "default":"data/IOB1/eng/words.txt"},
"filename_tags": { "type": "string", "description": "path to output file containing training tags", "default":"data/IOB1/eng/tags.txt"},
"filename_chars": { "type": "string", "description": "path to output file containing training chars", "default":"data/IOB1/eng/chars.txt"},
"dir_resources": { "type": "string", "description": "data directory", "default":"data/IOB1/eng/"},
"dir_model": { "type": "string", "description": "name of output file containing the model weights (relative to dir_output)", "default":"model.weights"},
"path_log": { "type": "string", "description": "name of output file containing the log (relative to dir_output)", "default":"log.txt"},
"input_graph": { "type": "string", "description": "path to output file containing training chars (relative to dir_output)", "default":"input_graph.pb"},
"output_graph": { "type": "string", "description": "path to output file containing training chars (relative to dir_output)", "default":"output_graph.pb" },
# parameters for the NN model and training
"max_iter": { "type": ["string","null"], "description": "if not null, max number of examples in Dataset", "default":"null" },
"train_embeddings": { "type": "boolean", "description": "indicates if pretrained embeddings are used","default":False},
"nepochs": { "type": "integer", "description": "max number of epochs","default":100},
"dropout": { "type": "number", "description": "dropout probability","default":0.5},
"batch_size": { "type": "integer", "description": "batch size","default":20},
"lr_method": { "type": "string", "description": "optimizer","default":"adam"},
"lr": { "type": "number", "description": "learning rate","default":0.001},
"lr_decay": { "type": "number", "description": "learning rate decay","default":0.9},
"clip": { "type": "number", "description": "if negative, no clipping","default":-1},
"nepoch_no_imprv": { "type": "integer", "description": "nb epochs without improvement","default":3},
"hidden_size_char": { "type": "integer", "description": "size of lstm on chars","default":100},
"hidden_size_lstm": { "type": "integer", "description": "size of lstm on word embeddings","default":300},
"use_crf": { "type": "boolean", "description": "use the CRF","default":True},
"use_chars": { "type": "boolean", "description": "use character representations","default":True}
}
}
class ModelConfig(Config):
def __init__(self,config_file=None,config_data=None):
if config_file is None and config_data is None:
sys.stderr.write("Error: ModelConfig needs a config file or a config data object\n")
return
if config_file is not None:
try:
with open(config_file) as f:
# use safe_load instead load
cfg = yaml.load(f,Loader=yaml.Loader)
ok,err=self.validate(cfg)
if not ok:
msg="Error in config file: %s"%err
logging.error(msg)
raise ConfigException(msg)
self.__dict__.update(**cfg)
except IOError:
logging.error("Failed to open file %s"%f)
raise ConfigException("Failed to open file %s"%f)
except yaml.parser.ParserError as e:
logging.error("Failed to parse file %s: %s"%(f,str(e)))
raise ConfigException(str(e))
if config_data is not None:
# copy data to validate and set default
cfg=copy.copy(config_data)
ok,err=self.validate(cfg)
if not ok:
msg="Error in configuration data: %s"%err
logging.error(msg)
raise ConfigException(msg)
self.__dict__.update(**cfg)
# validate paths
tmp_filename_glove=self.validate_path(self.filename_glove)
if tmp_filename_glove is None:
msg="Could not read GloVe embeddings : filename_glove: %s"%self.filename_glove
logging.error(msg)
raise ConfigException(msg)
self.filename_glove = tmp_filename_glove
if not self.filename_trimmed:
self.filename_trimmed=self.filename_glove+".trimmed.npz"
# paths relative to output directory
self.dir_output=self.validate_path(self.dir_output,create=True)
self.dir_model=os.path.join(self.dir_output,self.dir_model)
self.path_log=os.path.join(self.dir_output,self.path_log)
self.input_graph=os.path.join(self.dir_output,self.input_graph)
self.output_graph=os.path.join(self.dir_output,self.output_graph)
# create instance of logger
self.logger = general_utils.get_logger(self.path_log)
def validate(self,data):
try:
#jsonschema.validate(instance=data, schema=ConfigSchema)
FillDefaultValidatingDraft4Validator(ConfigSchema).validate(data)
except jsonschema.exceptions.ValidationError as err:
logging.error(err)
return False,err.message
return True,""
def validate_path(self,path,create=False):
if path is not None:
#if self.working_dir is not None:
# path=os.path.join(self.working_dir,path)
# expand environment variables
path=os.path.expandvars(path)
if not os.path.exists(path):
if create:
os.makedirs(path)
return path
else:
logging.error("path does not exist: %s"%path)
return None
return path