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import json
import os
import _jsonnet
from seq2struct import datasets
from seq2struct.utils import registry
def compute_metrics(config_path, config_args, section, inferred_path,logdir=None):
if config_args:
config = json.loads(_jsonnet.evaluate_file(config_path, tla_codes={'args': config_args}))
else:
config = json.loads(_jsonnet.evaluate_file(config_path))
print(f"Eval Dataset val(data val paths):{config['data']['val']['paths']}")
print(f"Eval Dataset val(data val tables_paths):{config['data']['val']['tables_paths']}\n")
if 'model_name' in config and logdir:
logdir = os.path.join(logdir, config['model_name'])
if logdir:
inferred_path = inferred_path.replace('__LOGDIR__', logdir)
inferred = open(inferred_path, encoding='utf8')
data = registry.construct('dataset', config['data'][section])
metrics = data.Metrics(data)
inferred_lines = list(inferred)
if len(inferred_lines) < len(data):
raise Exception('Not enough inferred: {} vs {}'.format(len(inferred_lines),
len(data)))
for line in inferred_lines:
infer_results = json.loads(line)
if infer_results['beams']:
inferred_code = infer_results['beams'][0]['inferred_code']
else:
inferred_code = None
if 'index' in infer_results:
metrics.add(data[infer_results['index']], inferred_code)
else:
metrics.add(None, inferred_code, obsolete_gold_code=infer_results['gold_code'])
return logdir, metrics.finalize()
def compute_metrics2(config_path, config_args, section, inferred_path, val_data_path, logdir=None):
if config_args:
config = json.loads(_jsonnet.evaluate_file(config_path, tla_codes={'args': config_args}))
else:
config = json.loads(_jsonnet.evaluate_file(config_path))
#use the command line validation data path
config['data']['val']['paths'][0] = val_data_path + "dev.json"
config['data']['val']['tables_paths'][0] = val_data_path + "tables.json"
print(f"Eval Dataset val(data val paths):{config['data']['val']['paths']}")
print(f"Eval Dataset val(data val tables_paths):{config['data']['val']['tables_paths']}\n")
if 'model_name' in config and logdir:
logdir = os.path.join(logdir, config['model_name'])
if logdir:
inferred_path = inferred_path.replace('__LOGDIR__', logdir)
inferred = open(inferred_path, encoding='utf8')
data = registry.construct('dataset', config['data'][section])
metrics = data.Metrics(data)
inferred_lines = list(inferred)
if len(inferred_lines) < len(data):
raise Exception('Not enough inferred: {} vs {}'.format(len(inferred_lines),
len(data)))
for line in inferred_lines:
infer_results = json.loads(line)
if infer_results['beams']:
inferred_code = infer_results['beams'][0]['inferred_code']
else:
inferred_code = None
if 'index' in infer_results:
metrics.add(data[infer_results['index']], inferred_code)
else:
metrics.add(None, inferred_code, obsolete_gold_code=infer_results['gold_code'])
return logdir, metrics.finalize() |