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observe
- for the same batch of samples, sometimes all results were 0.5, when I were retrying to predict later, the results seems to be normal. Specially, in multi proceess (using bash ‘&’), the-always-0.5 will happen by large probability(1/22), each process has two predictor, the the-always-0.5 appears in second predictor.
- for one sample, sometimes the result was 0.209669 and sometimes the result was 0.201879, is this stable or not ?
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env (for predict)
- os: CentOS release 6.7
- python: Python 3.5.2 |Anaconda 4.2.0 (64-bit)
- gcc: GCC 4.4.7
- xgboost: 0.7 (using pip to install)
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detail
I used same python env, but trained and predicted on diffrent machines, trained on CentOS Linux release 7.2.1511(GCC version 4.8.5), did the os or gcc cause the problem?
class XGBModel():
def __init__(self, model_path):
self.model_path = model_path
self.model = self._load(self.model_path)
def _load(self, path):
with open(path, 'rb') as fr:
data = pickle.load(fr)
return data
def predict(self, libsvm_filename):
dtest = xgb.DMatrix(libsvm_filename)
pred = self.model.predict(dtest)
return pred