def make_agent() -> EcommerceAgent:
"""Make an agent
Returns:
agent: created Ecommerce agent
"""
config_path = find_config('tfidf_retrieve')
skill = build_model(config_path)
agent = EcommerceAgent(skills=[skill])
return agent
def make_agent() -> EcommerceAgent:
"""Make an agent
Returns:
agent: created Ecommerce agent
"""
config_path = find_config('ecommerce_bot')
skill = build_model_from_config(config_path, as_component=True)
agent = EcommerceAgent(skills=[skill])
return agent
def make_agent() -> EcommerceAgent:
"""Make an agent
Returns:
agent: created Ecommerce agent
"""
config_path = find_config('tfidf_retrieve')
skill = build_model(config_path)
agent = EcommerceAgent(skills=[skill])
return agent
from deeppavlov.deep import find_config
from deeppavlov.core.commands.train import train_evaluate_model_from_config
from deeppavlov.core.commands.infer import interact_model
# PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_rus.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_wikitypos_en.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_kartaslov_ru.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/levenshtein_searcher.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_minimal.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_all.json'
# PIPELINE_CONFIG_PATH = 'configs/squad/squad.json'
# PIPELINE_CONFIG_PATH = 'configs/ranking/ranking_insurance.json'
# PIPELINE_CONFIG_PATH = 'configs/seq2seq_go_bot/bot_kvret.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/en_ranker_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ru_ranker_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/en_odqa_infer_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ru_odqa_infer_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ranker_test.json'
# PIPELINE_CONFIG_PATH = find_config('morpho_ru_syntagrus_train')
PIPELINE_CONFIG_PATH = find_config('morpho_ru_syntagrus_train_pymorphy')
if __name__ == '__main__':
train_evaluate_model_from_config(PIPELINE_CONFIG_PATH)
# interact_model(PIPELINE_CONFIG_PATH)
import argparse
from deeppavlov.models.morpho_tagger.common import predict_with_model
from deeppavlov.deep import find_config
from deeppavlov.download import deep_download
parser = argparse.ArgumentParser()
parser.add_argument("config_path", help="path to file with prediction configuration")
parser.add_argument("-d", "--download", action="store_true", help="download model components")
if __name__ == "__main__":
args = parser.parse_args()
config_path = find_config(args.config_path)
if args.download:
deep_download(['-c', config_path])
predict_with_model(config_path)
# PIPELINE_CONFIG_PATH = 'configs/classifiers/intents_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/classifiers/intents_snips.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_rus.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_wikitypos_en.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_kartaslov_ru.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/levenshtein_searcher.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_minimal.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_all.json'
# PIPELINE_CONFIG_PATH = 'configs/squad/squad.json'
# PIPELINE_CONFIG_PATH = 'configs/ranking/ranking_insurance.json'
# PIPELINE_CONFIG_PATH = 'configs/seq2seq_go_bot/bot_kvret.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/en_ranker_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ru_ranker_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/en_odqa_infer_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ru_odqa_infer_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ranker_test.json'
# PIPELINE_CONFIG_PATH = find_config('morpho_ru_syntagrus_train')
# PIPELINE_CONFIG_PATH = find_config('morpho_ru_syntagrus_train_pymorphy')
# PIPELINE_CONFIG_PATH = find_config('intents_dstc2_big')
PIPELINE_CONFIG_PATH = find_config('ecommerce_bot')
if __name__ == '__main__':
# train_evaluate_model_from_config(PIPELINE_CONFIG_PATH)
# start_model_server(PIPELINE_CONFIG_PATH)
interact_model(PIPELINE_CONFIG_PATH)
def main():
params_helper = ParamsSearch()
args = parser.parse_args()
is_loo = False
n_folds = None
if args.folds == 'loo':
is_loo = True
elif args.folds is None:
n_folds = None
elif args.folds.isdigit():
n_folds = int(args.folds)
else:
raise NotImplementedError('Not implemented this type of CV')
# read config
pipeline_config_path = find_config(args.config_path)
config_init = read_json(pipeline_config_path)
config = config_init.copy()
data = read_data_by_config(config)
target_metric = config_init['train']['metrics'][0]
# get all params for search
param_paths = list(params_helper.find_model_path(config, 'search_choice'))
param_values = []
param_names = []
for path in param_paths:
value = params_helper.get_value_from_config(config, path)
param_name = path[-1]
param_value_search = value['search_choice']
param_names.append(param_name)
param_values.append(param_value_search)
# find optimal params
if args.search_type == 'grid':
# generate params combnations for grid search
combinations = list(product(*param_values))
# calculate cv scores
scores = []
for comb in combinations:
config = config_init.copy()
for i, param_value in enumerate(comb):
config = params_helper.insert_value_or_dict_into_config(config, param_paths[i], param_value)
if (n_folds is not None) | is_loo:
# CV for model evaluation
score_dict = calc_cv_score(config=config, data=data, n_folds=n_folds, is_loo=is_loo)
score = score_dict[next(iter(score_dict))]
else:
# train/valid for model evaluation
data_to_evaluate = data.copy()
if len(data_to_evaluate['valid']) == 0:
data_to_evaluate['train'], data_to_evaluate['valid'] = train_test_split(data_to_evaluate['train'], test_size=0.2)
iterator = get_iterator_from_config(config, data_to_evaluate)
score = train_evaluate_model_from_config(config, iterator=iterator)['valid'][target_metric]
scores.append(score)
# get model with best score
best_params_dict = get_best_params(combinations, scores, param_names, target_metric)
log.info('Best model params: {}'.format(best_params_dict))
else:
raise NotImplementedError('Not implemented this type of search')
# save config
best_config = config_init.copy()
for i, param_name in enumerate(best_params_dict.keys()):
if param_name != target_metric:
best_config = params_helper.insert_value_or_dict_into_config(best_config, param_paths[i], best_params_dict[param_name])
best_model_filename = pipeline_config_path.replace('.json', '_cvbest.json')
save_json(best_config, best_model_filename)
log.info('Best model saved in json-file: {}'.format(best_model_filename))
from deeppavlov.core.commands.infer import interact_model
from utils.server_utils.server import start_model_server
# PIPELINE_CONFIG_PATH = 'configs/classifiers/intents_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/classifiers/intents_snips.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_rus.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_wikitypos_en.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_kartaslov_ru.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/levenshtein_searcher.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_minimal.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_all.json'
# PIPELINE_CONFIG_PATH = 'configs/squad/squad.json'
# PIPELINE_CONFIG_PATH = 'configs/ranking/ranking_insurance.json'
# PIPELINE_CONFIG_PATH = 'configs/seq2seq_go_bot/bot_kvret.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/en_ranker_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ru_ranker_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/en_odqa_infer_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ru_odqa_infer_prod.json'
# PIPELINE_CONFIG_PATH = 'configs/odqa/ranker_test.json'
# PIPELINE_CONFIG_PATH = find_config('morpho_ru_syntagrus_train')
# PIPELINE_CONFIG_PATH = find_config('morpho_ru_syntagrus_train_pymorphy')
PIPELINE_CONFIG_PATH = find_config('intents_dstc2_big')
if __name__ == '__main__':
train_evaluate_model_from_config(PIPELINE_CONFIG_PATH)
# start_model_server(PIPELINE_CONFIG_PATH)
# interact_model(PIPELINE_CONFIG_PATH)
