vendor/lib_lightgbm.so in lightgbm-0.2.7 vs vendor/lib_lightgbm.so in lightgbm-0.3.0

- old
+ new

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SZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17Featur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E__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3IL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ZZHM4LEZAXEZEZEZANct& IXB?HI,LXmmX88HILDX D DXDF<7EIK:MM9DX(D(EX?E?[]A\A]A^A_DAVIAAUDiATD9USTLcDaLWJ,H.M(D)HcIANLcZLAH)C HHHMAX A HHSM^L9HtLHc*HcAM^ZAA *HI,X]]HHSL9fDHcA#AsLcHIZA ZC2LcrC 2HI,XmmHHcE4EIK,1X}}J0L9uD9Lc҃HI)ѽLML$HcEEZDEIO,EXMEMJL9nHHtfHt0HcNEPEZHM4EXEHLc.E$(HEZBHMEX+E+HLc,.E4(HEZB /HI DX;D;HL9Lc4.A(AT(Lc\.Ad(At( ZHc\.B7ZZZHMAX +cBc +@c Hc Pc)c)Xc c `c c hcucuc c.c Ȫcg Ъcjتc cEc +pc +c c cc cC (cS0cc8c@cXcG`ckhcpcN xc + c + ccc c +c ȫcЫcc)ccc{c c +0c8ctPcthctctȬctctctHcV `cxcy c c%c%ccجc c +c +c +(c +@c +`c +c`cJ c8cPcXcchcJ pcccc ccbȭcAحcccc c1c)pc) coco(cc0c c 8cG cG cG 0cG cG xcG cG cG @cdcdcd8cdcdcdcdcd8cd0cdضcdcd(cdcdHc` +c` +c` +@c` +c` +c` +c` +c` +@c` +8c` +c` +c` +0c` +(c` +PcIcIcIHcIcIcIPcIcIcIHcIcIcI@cIcIcI8cIXcPc`c c hc pchccȱcpccchccc`cccXcpcxc pc c бc xc c ȳc pc c c hc c c `c c cc c(cc4Hc4c4c4Hc4c4c4Hc4c4c4@c4c4c48c4c4c4c4c;c cȮcЮc خcWcWc0c" c c c(c0cدcc0ccc0cسcc(cеcxc cȷcpcc8c@cXc `cShc pcxc,c +8c +cc)c +c ȯc Яccccc ccc cаc(cPcccXc c c `c c c Xc c c Pc c c Hc c `c c c hc c c `c c c Xc c c Pc c xcR(cRc8c cc@ccacc +cȰcذcxPchcpc_xc cȲcpccc cAc8ccشcc= +c= +c= +رcc(cгcxc cȵcpccchc`ccVcV8cVcV0cVصcVc c @c c c 8c c c 0c cc (c0c@c Hc# Xc#cc0 +cWвcزc6 Xc `ce hcxccnccccOcOcO c(c\0c(cжcPcVHcVcVcjcj(cjc/c cȴc$дccccoPcXcR `c~hc'pcC xcJcccc ccDcPc ccc'ccXȶc&HcUPc( XcS`chcpcxccзcطcc c cccc0c c @c1 +xc7cccIиcIcIcIcI`cI`cIc4c c hc Hc ȸc~c cG cZccccl cl cl cl cl cl cl cl `cl cl cYcYcYcYcYؽcYcYcYcYhcY cccccccccpc(cȻccccc0cd cd кcd лcd cd cd cd cd cd cd hcd 8ccغcػccccccc@cccccccccHcjcjcjcjcjcjcjcjcjPc` c` c` c` c` c` c` c` c` Xc%(c%c%c%ȼc%c%c%c%c%c%`c0cccмccccchc3 8c3 c3 c3 ؼc3 (c3 c3 c3 c3 c3 pc@cccc0cccccxc8cccc +Pc + c + c +c +@c +c +c +c8 (c8 c8 Hc8 c8 c8 cM `cM 0cM 0cM cM PcM (cM cM cM cM cChcC8cC8cCcCXcC0cCcCcCcCc`c8c cc Hc c hc @c (c c cPc cpcHc0cpcc^ȹcйc عc c( Hc(Xc:pc6xc +c c c} cc8cCȺc c +(c @cNHcPchc`c:xccc-c$cc@c`cw hcspclxcw cc0cPcpXc`c c hcpc68c6xccc`cc (c c ccv +cȽcнc@c cc cccccLcȾcؾcNcbc `c c8cc0c<HcXcwpc xc c[c4c4cc0c0cC c^ пcccbc c(cf 8c Hc Xchcxccc c +c +c cccc cF(c 8c Hc Xchcxc%cTcc!cCcfccccc(cO8c{HcXchcxc;ccccccccc$cc (c8cHc/Xc hc> xc] c~ c cOcncc +c c2 c] c(c 8c Hc3 +XchcxccccR co c cN +co +c +c +c (c'8cOHcXchcxcc.crccccQctccc6(c8cHcXchcxcccccCc}c`ccccy +(c`8c}HcXc +hc xc& cI cQcccc c c c< c (c8cHcXc hc%xcJcpc ccc4cc,cSczc(c8c+HcWXcBhcmxccc c]cccPcvccc (c8cHcXc'hcNxcvccf cc#cNc.cXc}cc(c +8c6 +Hc^ +Xchcxccc?cu c c cccc c +(cF 8cs Hc Xchcxc@clctc +c +c +c c*cJclc(c8cHcXc9hcpxccc_c c+cQc +c5 +c] +c| +c5(c;8cXHcXchcBxcncccccc! +cF +ce +c +c(c8cHcXchcxcccc5c\cxc(cFcncc + c + c +(c0cE 8c @c HcPcXc`chcpc xccckccccc/cccc0ccc2cIc cq(c.0c#8c@c$ HcSPc XcW `c< hcpccc c c Hc ccPccco ccc|c|c cxcXccN chc Xc c`ccDhcD0c8c1@cc pc cc4 +Xcpc: xccc +@cQXcM pcxc0ccoccc,c> c(c0c c @cHcPc Xcxc +c cpc ccHc c 8cHcPPcXc3`cjhcpckxc c0c"cccecc3ccc +c6c6c cacoc cDc +c( c(cK 0c8c5 @cHcPciXc`c hc$ +pcxcc5c cic ccc cLcncccc|cc&cDcqcc c(cZ 0c8cR@cHc Pc Xc&`chcpcxccscwccc ca cPccc ccc^cccc( co c(c8c @cHcPcXc`cm +hc,pc,xc]cc ccc?c cccucc c*cccqccccc 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LH-LHILIV(AH5IAFxAV`HILAdžMAƆAHILAdžI(AƆxA`HILAdžMAƆAHILAdžI(AƆxA`HILAdžLL-AƆIAMHLHL`AdžM(A`AƆxHAFLB(BxR`M9VfDI$L$M$pHL$LHL۱M$H$HLHAI۱I$h HLֱI$p HLfI~ֱI$f1H$$8fInL$L$0fH=L\$HDŽ$X$HH$H$H$HL$HDŽ$0L$L$H$L$L$$ɱL$0M H|$I$L$HHcjIA>ANIv`HIl$HfAv(fA~MN@}H}`MMFMnXLEAF}u(EF8EDE8fENHLM@LmXDMH +A^x]xE^yD]yI$Ht LcvEM$AHDŽ$fEfEf$XL$Icn~ŇHDŽ$HIl$HD$$Hu`D$(fMfU(DŽ$LUU]8HDŽ$ HDŽ$8HM@HDŽ$PD)$@H}HDŽ$H$`H$f$L$h$pD)$$x$$H$feHLEXH|$$L$茙I$DmxmyDHD$@$EnMcIM|$HIMWIw`AWH$fEW(IXfE_L$D$MG@D$Eg8$D$fEoHH$8H$@L$ D$(ʘAGxEOxLc$D~$D~$D$ ~$M~$I~$ L|$0fEbfD$X$$$L$$$(D~$0L$@D$8L\$8M$hA~$h$l\$Dt$@I$hA)t$pD~$`~$SH{^D$dfDb~$DNH$DT$ $~$$DC~$xHK $)$$)l$`D)L$PfDD$HfD;[S(K8cHHc$pL$fCXSZLHH|$(맱Hcl$ |$@LD~L$HLL$8D\$Dfot$pHfo|$`fDoD$P}L$ fDMHT$0fo$LM D]mu(}8DEH$YDuZLu^fMXLhT$ HHH5L$LD$ H$4躠D~{0~C@~S fDnSfDn[fDn#D{8CHfDnkDosS(D$LSPDCXfEb$HcsZH$fEbD$xfElL$H؉$pL)$fD$HD)$`H9H9sITH9tH$Hc$pHt$(LHHSfnMfnefnmfn]fbD~M fbfl)$ou$~}0D~E@DM(}8DEHD$$D$(H}PH$8DMXfD$XHcuZH$H$IM)II9=L9Hc$LLHH肥M$h H$`L$hL$McvH$fD$xfD$D$IMt$HI6Ht$I~`fD$MVA^MF@IFXEVE^(Ef8EnH$fAVx$xI$p DOEL +LL$0MH$HtߩH$HtͩL$H$I9t+H{`H螩HI9uH$HtH耩L$H$I9t"H}`HtWZHL9uH$HtH<Hh[]A\A]A^A_f.HI9cyfDHI9uDH$뇱H$HIMt$HHDUUHu`fDU(fD]LE@HEXEAVH}I~MI~`E^EV(De8ANEf8fDmHMF@IFXEnH<DMxENxDmyEnyFDI H9WH$HJfDL$IcHI\$HL$H{`LspLfD$H$ LcfD$kI)H$8Ds(L$HD{$HK@C8f$(HkXLL)SHI94LShLH)H9M9uHLPƱHk`JHDŽ$HDŽ$HDŽ$E1HDŽ$fED$ E11HD$0fAofAo1D~vfAoIH|$H)諤L$fDL)H$@舤L$@HtLnűLShH{`LH)I4I9tqLLH)IűHk`FHH9rSHIM9tHLH栱H{`Ht(LLs`HkpHkhD$XfDCxH蜱HL$옱H4H='Lt$ L$LØL H=&IH$IHt$]H$@Ht膥H$HuPH$ ±H$HtZH$HtHH$۶H|$ѶL)˱$IIH$H$HD$H$H$L$IHL$L$L$L$ILL$L$ZIH$tHJMH$t +H|$ -hHHt@f.@HHt4hu-SH@HHtH?H;胚H[f.f.DHSHH8HGHHGHGHGHGHGHG ұHIC@HCPHCXHHCXHCH[DHSHH8HGHHGHGHGHGHGHGѱHC@HCPHCXHHCXHCH[fAUATUHSHLcHHFHN1HPHH HGHxH?HEHUH{HHH@HHyHCHHC HCHC(HC0HC8HC@бHChLHC`HCPHCHChHCXHHxH訲H[]A\A]IL:HEHULHH@H1HCDZf.AWAVAUATLgxUSHLHH11ffXH`HCxHpHhLhHǃPHEH+L,HCH}HڱHCH{HLsHHC HPH@HC(HCxHHHHC0HCHC8HC@hϱChLLHCPHC`HHCHChHCXCH[]A\A]A^A_I IL9HEH+L,1HCHTLHHCx4LlƱf.fAUATUHSHLcHHFHHxHF1HH謰HEHUH{@HH@HHHCHHCHCHC HC(HC0HC8AαHjC`LHCXHCHHCHC`HCPHHxHH[]A\A]ILk8HEHULHH@H`űAWAVAUATLgpUSHLHL5 HIVHfHnfHnfl) $ׁHf1XHhHCp1fPH HǃHHhLhH}H+HL/?IF@fo $fH{@HCpLs CC C0ͱC`LLH!CHHCXHHCHC`HCPݮH[]A\A]A^A_IIL&7HEH+L,HLHHCpԃL ıf.fAUATUHSHHHFHN1HPHH HGHxHCHE 1HCH@H|HE(H$HEHU0H{PLcHH@HHE8HCHEHU@HH@HHEHHC HCH/HC(HHC0HCHC8HC@HCH˱HCpLHChHCXHCHCpHC`HHxHfH[]A\A]H IHEHUHH@H1HC±L5HHuWzL±f.DAWAVLAUATUSHLHL-2H+IU@HfHnfHnfl) $~H-%11Hff`LehL}HxHID$HǃXL#L<HCI|$HJHE 1HCH@H|HE(H+HEHU0fH{Pfo $LcH@HIEHIEhHKC C0C@ɱCpLLHCXHChHHCHCpHC`讫H[]A\A]A^A_H"I;HID$L#L<1HCHLHH蚀HL3HuHL{xfHeSHHHHHHCXH9t舚HH{8HH[PAUATUHSHLcHHFHN1HPHH HGHxHHEHUH{HHH@HHaHCHHC HCHC(HC0HC8HC@DȱHLHC`CPHChHCHChHCXHHxHUH[]A\A]ILHEHULHH@H1HCZf.AWAVAUATLgxUSHLH{Hpf1`HpHCxHHǃPHhLhǃXƃ\HEH+L,HCH}H肜HCH{HLsH+HC HPH@HC(HCxHHHHC0HCHC8HC@ƱHLCPHC`LHChHCHChHCX蛱H[]A\A]A^A_I ILQHEH+L,1HCH1LHHCx}LѽAUATUHSHLcHHFHHxHF1HH\HEHUH{@HH@HHHCHHCHCHC HC(HC0HC8űHLHCXCHHC`HCHC`HCPHHxHšH[]A\A]IL8HEHULHH@Hͼf.AWAVAUATLgpUSHLHL5=HIVHfHnfHnfl) $7yHf1XHhHCpH-HǃHHhLhǃPƃTH}H+HL/יIF@fo $fH{@HCpLs CC C0ZıHLCHHCXLHC`HCHC`HCPrH[]A\A]A^A_IILHEH+L,HLHHCp){LaAUATUHSHHHFHN1HPHH HGHxH㘱HE 1HCH@H|HE(HĘHEHU0H{PLcHH@HHE8HCHEHU@HH@HHEHHC HCHHC(HHC0HCHC8HC@HCH±H;LHChCXHCpHCHCpHC`HHxHH[]A\A]H IHEHUHH@H1HCLPHHuԝL칱f.fAWAVLAUATUSHLHL-*HIU@HfHnfHnfl) $TvH-f1HhLeL}xHƃdHID$HǃXǃ`L#L<HCI|$H▱HE 1HCH@H|HE(HÖHEHU0fH{Pfo $LcH@HIEHIEhHKC C0C@+HtLCXHChLHCpHCHCpHC`CH[]A\A]A^A_H"I;HID$L#L<1HCHLHHwH'LoHuHLf.@HSHHHHHHCXH9tؑH!H{8HH补H[鸑HHbasic_string: construction from null is not validstatic void yamc::rwlock::ReaderPrefer::release_wlock(state*)/__w/1/s/include/LightGBM/utils/yamc/yamc_rwlock_sched.hppThe number of features in data (%d) is not the same as it was in training data (%d). +You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.Unknown data type in RowFunctionFromCSRvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)Input data type error or field not foundTried to retrieve number of bins for feature index %d, but the valid feature indices are [0, %d].Unknown indptr type in LGBM_BoosterFreePredictSparseUnknown data type in LGBM_BoosterFreePredictSparseUnknown data type in RowFunctionFromDenseMatricCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . +Unknown data type in CSC matrixLGBM_GetSampleCount output is nullptr%s: __pos (which is %zu) > this->size() (which is %zu)static void yamc::rwlock::ReaderPrefer::acquire_rlock(state*)(s->rwcount & reader_mask) < reader_maskLGBM_SampleIndices output is nullptrThe number of columns should be greater than zero.The number of columns should be smaller than INT32_MAX.[%s]: does not tolerate element [#%i = Check failed: (num_used_row_indices) > (0) at %s, line %d . +used_row_indices should be sorted in SubsetCheck failed: (inner_data.first) < (num_col) at %s, line %d . +Continued train from model is not supported for c_api, +please use continued train with input scoreDo not support feature parallel in c apiOnly find one worker, will switch to serial tree learnerModel was trained on %d features, but got %d input features to predict.Expected '%s' at position %d but found '%s'Unknown matrix type in LGBM_BoosterPredictSparseOutputvector::_M_realloc_inserts->rwcount & writer_maskunknown exceptionField not found/__w/1/s/src/c_api.cpp'"basic_string::erase] outside [Used indices of subsetunordered_map::atdata cannot be null.serialcannot create std::vector larger than max_size()[LightGBM] [Fatal] %s +[LightGBM] [%s] WarningDebugInfocpuyamc::shared_lock<Mutex>::~shared_lock() [with Mutex = yamc::alternate::basic_shared_mutex<yamc::rwlock::ReaderPrefer>]/__w/1/s/include/LightGBM/utils/yamc/yamc_shared_lock.hppstatic bool yamc::rwlock::ReaderPrefer::release_rlock(state*)0 < (s->rwcount & reader_mask)pm_ != nullptrvector::reserveCannot change data_random_seed after constructed Dataset handle.Cannot change max_bin after constructed Dataset handle.Cannot change max_bin_by_feature after constructed Dataset handle.Cannot change bin_construct_sample_cnt after constructed Dataset handle.Cannot change min_data_in_bin after constructed Dataset handle.Cannot change use_missing after constructed Dataset handle.Cannot change zero_as_missing after constructed Dataset handle.Cannot change categorical_feature after constructed Dataset handle.Cannot change feature_pre_filter after constructed Dataset handle.Cannot change is_enable_sparse after constructed Dataset handle.Cannot change pre_partition after constructed Dataset handle.Cannot change enable_bundle after constructed Dataset handle.Cannot change header after constructed Dataset handle.Cannot change two_round after constructed Dataset handle.Cannot change label_column after constructed Dataset handle.Cannot change weight_column after constructed Dataset handle.Cannot change group_column after constructed Dataset handle.Cannot change ignore_column after constructed Dataset handle.Cannot change forced bins after constructed Dataset handle.Reducing `min_data_in_leaf` with `feature_pre_filter=true` may cause unexpected behaviour for features that were pre-filtered by the larger `min_data_in_leaf`. +You need to set `feature_pre_filter=false` to dynamically change the `min_data_in_leaf`.Cannot change linear_tree after constructed Dataset handle.Cannot change precise_float_parser after constructed Dataset handle.label_columnweight_columngroup_columnignore_columnforcedbins_filenamemin_data_in_leaflinear_treeprecise_float_parserCannot change num_class during trainingCannot change boosting during trainingCannot change metric during trainingUsing self-defined objective functionvector::_M_default_appendvector::_M_fill_insert/__w/1/s/src/application/predictor.hppCheck failed: (early_stop_freq) > (0) at %s, line %d . +Check failed: (early_stop_margin) >= (0) at %s, line %d . +Predicting SHAP feature contributions is not implemented for linear trees.multiclassPrediction results file %s cannot be createdCould not recognize the data format of data file %sSkipped header "%s" in file %sFeature (%s) appears more than one time.Feature (%s) is missed in data file. If it is weight/query/group/ignore_column, you can ignore this warning.Warning: last line of %s has no end of line, still using this lineCould not open %sbasic_string::substr ,basic_string::appendRead %.1f GBs from %s.Unknown data type in PredictSparseUnknown indptr type in PredictSparseCSRUnknown col_ptr type in PredictSparseCSCSize of feature_names error, should equal with total number of featuresDo not support special JSON characters in feature name.Found whitespace in feature_names, replace with underlinesN3fmt2v812format_errorEN8LightGBM8BoostingEN8LightGBM8GBDTBaseEN8LightGBM12BinaryWriterEN8LightGBM10ByteBufferENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS6_mEEEUlvE_EEEEEEZN8LightGBM9Predictor7PredictEPKcS2_bbbEUliRKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS9_EEE0_*Z31LGBM_BoosterPredictSparseOutputEUllE_*Z25LGBM_BoosterPredictForCSCEUliE_*Z30RowPairFunctionFromDenseMatricPKviiiiEUliE_*Z28RowPairFunctionFromDenseRowsPPKviiEUliE_ZN8LightGBM9PredictorC4EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE_ZN8LightGBM9PredictorC4EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE0_ZN8LightGBM9PredictorC4EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPS3_ISt13unordered_mapIidSt4hashIiESt8equal_toIiESaIS4_IKidEEESaISI_EEE1_ZN8LightGBM9PredictorC4EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE2_ZN8LightGBM9PredictorC4EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE3_ZN8LightGBM9Predictor7PredictEPKcS2_bbbEUlS2_PSt6vectorISt4pairIidESaIS5_EEE_ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKSt8functionIFviRKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS9_EEEEEUliiE_Z33RowFunctionFromDenseMatric_helperIfESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE_Z33RowFunctionFromDenseMatric_helperIfESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE0_Z33RowFunctionFromDenseMatric_helperIdESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE_Z33RowFunctionFromDenseMatric_helperIdESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE0_Z29IterateFunctionFromCSC_helperIfiESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIflESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIdiESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIdlESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKSt8functionIFviRKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS9_EEEERKS2_IFbiiEEEUlPKcmE_Z25RowFunctionFromCSR_helperIifiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIiflESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIidiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIidlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIlfiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIlflESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIldiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIldlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_?d $@@ p08{Gz?C>MbP?????????ffffff??LightGBM_model.tLightGBM_predictedict_result.txtgbdt_prediction.data_random_seedmax_bin_by_featubin_construct_sacategorical_featfeature_pre_filtis_enable_sparse8/__w/1/s/src/boosting/gbdt.hCheck failed: tree_idx >= 0 && static_cast<size_t>(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . -/__w/1/s/src/boosting/goss.hppCheck failed: (config_->top_rate + config_->other_rate) <= (1.0f) at %s, line %d . -Check failed: (num_tree_per_iteration_) == (num_class_) at %s, line %d . -Skipped header "%s" in file %sbasic_string::_S_construct null not validCheck failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . -Check failed: (gradients) == (nullptr) at %s, line %d . +Check failed: data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()) at %s, line %d . +Unknown token %s in data filevector::_M_range_insertUnknown boosting type %sTime for loading model: %f secondsUnknown model format or submodel type in model file %sRF mode do not support custom objective function, please use built-in objectives./__w/1/s/src/boosting/rf.hppCheck failed: (num_tree_per_iteration_) == (num_class_) at %s, line %d . + ]": doubleboolvector","Check failed: (gradients) == (nullptr) at %s, line %d . Check failed: (hessians) == (nullptr) at %s, line %d . -RF mode do not support custom objective function, please use built-in objectives.Time for loading model: %f secondsUnknown model format or submodel type in model file %sCheck failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at %s, line %d . -Check failed: config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f at %s, line %d . -Check failed: (train_data->metadata().init_score()) == (nullptr) at %s, line %d . -Check failed: hessians != nullptr && objective_function_ == nullptr at %s, line %d . -Check failed: hessians == nullptr at %s, line %d . -N3fmt2v712format_errorEN3fmt2v712system_errorEN8LightGBM4DARTEN8LightGBM4GOSSEN8LightGBM2RFEZN8LightGBM4GOSS7BaggingEiEUliiiPiS1_E_ZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_33333333)\(\p= -ףp= -x&1|?5^I ~:pΈҲ qh N]h㈵9ahVyc{-1Fw$zՔ!"Kga*m.:BGkI> O{ͷu9 idW>Pb6-?C,z MY@1eoT5$E!@pG8w=_$A|AѤ䥬4 )TTpi;wwIYePr&ޭԔ],`yOoy.P9kY 3333)\£p= -x&I ~:h 9ah1F!"*m.:I>8??<.AMbP??8GBDT::UpdateScoreGBDT::BoostingNo object function providedGBDT::Train.snapshot_iter_GBDT::BaggingGBDT::BoostFromAverageStart training from score %lfregression_l1quantilemapeIteration:, training : , valid_Use subset for baggingnonecudaGBDT::TrainOneIter/__w/1/s/include/LightGBM/tree.hCheck failed: (max_depth_) >= (0) at %s, line %d . +Check failed: (config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) || (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f) at %s, line %d . +Check failed: (config->data_sample_strategy) == (std::string("goss")) at %s, line %d . +Check failed: (train_data->metadata().init_score()) == (nullptr) at %s, line %d . +N8LightGBM4DARTEN8LightGBM2RFEZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_?RyXsd~QJ~Ô%ITP.5_]btiu<7~חA.AMbP?<?ScoreUpdater::Ad No objective function provided/__w/1/s/include/LightGBM/tree.hCheck failed: (max_depth_) >= (0) at %s, line %d . /__w/1/s/src/boosting/gbdt.cppCheck failed: (leaf_pred[i]) < (models_[model_index]->num_leaves()) at %s, line %d . -%f seconds elapsed, finished iteration %dRe-bagging, using %d data to trainDisabling boost_from_average in %s may cause the slow convergenceCheck failed: (tree_leaf_prediction.size()) > (0) at %s, line %d . +Check failed: (tree_leaf_prediction.size()) > (0) at %s, line %d . Check failed: (static_cast<size_t>(num_data_)) == (tree_leaf_prediction.size()) at %s, line %d . Check failed: (static_cast<size_t>(models_.size())) == (tree_leaf_prediction[0].size()) at %s, line %d . -Number of class for initial score errorEarly stopping at iteration %d, the best iteration round is %dOutput of best iteration round: +%f seconds elapsed, finished iteration %dDisabling boost_from_average in %s may cause the slow convergenceCheck failed: objective_function_ == nullptr at %s, line %d . +Check failed: (gradients_pointer_) == (gradients_.data()) at %s, line %d . +Check failed: (hessians_pointer_) == (hessians_.data()) at %s, line %d . +Stopped training because there are no more leaves that meet the split requirementsForced splits file includes feature index %d, but maximum feature index in dataset is %dCannot add validation data, since it has different bin mappers with training dataCannot reset training data, since new training data has different bin mappersCheck failed: (num_tree_per_iteration_) == (objective_function_->NumModelPerIteration()) at %s, line %d . +Cannot use ``monotone_constraints`` in %s objective, please disable it.Early stopping at iteration %d, the best iteration round is %dOutput of best iteration round: %sCheck failed: (static_cast<size_t>(train_data_->num_total_features())) == (config->monotone_constraints.size()) at %s, line %d . Check failed: (static_cast<size_t>(train_data_->num_total_features())) == (config->feature_contri.size()) at %s, line %d . -Cannot use ``monotone_constraints`` in %s objective, please disable it.train_data Can't be NULL at %s, line %d . -Check failed: (num_tree_per_iteration_) == (objective_function_->NumModelPerIteration()) at %s, line %d . -Cannot reset training data, since new training data has different bin mappersStopped training because there are no more leaves that meet the split requirementsCannot add validation data, since it has different bin mappers with training data*ZN8LightGBM4GBDT7BaggingEiEUliiiPiS1_E_*ZN8LightGBM4GBDT12TrainOneIterEPKfS2_EUlS2_iE_N8LightGBM8BoostingEN8LightGBM8GBDTBaseEN8LightGBM4GBDTE?Tree=#include "gbdt.h"#include <LightGBM/metric.h>#include <ctime>#include <sstream>#include <chrono>#include <string>#include <vector>#include <utility>namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBMregressionl2_rootroot_mean_squared_errorrmsemaemulticlassmulticlassovaovrxentropycross_entropyxentlambdacross_entropy_lambdarank_xendcgxe_ndcgxe_ndcg_martxendcg_martcustomnaregression_l2mean_absolute_errorsoftmaxmulticlass_ovananinfinfinityUnknown token %s in data filebasic_string::substrtruefalse"name":""version":""num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":"objective":"", -"average_output":",""feature_names":[""],"monotone_constraints":["feature_infos":},basic_string::erase{"min_value":"max_value":"values":[]}vector::reserve"values":["tree_info":["tree_index":"feature_importances":%lu#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifmonotone_constraintsWrong line at model file: %snum_classnum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesvector::_M_emplace_back_auxWrong size of feature_infosobjectivetree_sizes[linear_tree: end of parametersversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=feature_names=monotone_constraints=feature_infos=tree_sizes=end of treesfeature_importances: -parameters:v3Model format error, expect a tree here. met %s#include <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>double (*PredictTreePtr[])(const double*) = { int early_stop_round_counter = 0;std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);for (int i = 0; i < num_iteration_for_pred_; ++i) {for (int k = 0; k < num_tree_per_iteration_; ++k) {output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { void GBDT::PredictLeafIndex(const double* features, double *output) const {int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;for (int i = 0; i < total_tree; ++i) {output[i] = (*PredictTreeLeafPtr[i])(features);double (*PredictTreeLeafByMapPtr[])(const std::unordered_map<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Model file %s is not available for writesUnknown importance type: only support split=0 and gain=1mean_absolute_percentage_errorModel file doesn't specify the number of classesModel file doesn't specify the label indexModel file doesn't specify max_feature_idxModel file doesn't contain feature_namesWrong size of monotone_constraintsModel file doesn't contain feature_infosZN8LightGBM9ArrayArgsIiE8ArgMaxMTERKSt6vectorIiSaIiEEEUlimmE_$@d~QJחAu<7~u<7C _Map_base::atBinary early stopping needs predictions to be of length oneUnknown early stopping type: %sMulticlass early stopping needs predictions to be of length two or larger*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_/__w/1/s/src/io/bin.cppMultiValSparseBin::MergeData/__w/1/s/src/io/multi_val_dense_bin.hppCheck failed: (num_data_) == (num_used_indices) at %s, line %d . -Check failed: (max_bin) > (0) at %s, line %d . +train_data Can't be NULL at %s, line %d . +GBDT::UpdateScoreScoreUpdater::AddScoreGBDT::Train.snapshot_iter_GBDT::BoostFromAverageregression_l1Start training from score %lfquantilemapeGBDT::TrainOneIterIteration:, training : , valid_cudaNumber of class for initial score error{}nananinfinfinitycannot create std::deque larger than max_size()N8LightGBM12ScoreUpdaterEN8LightGBM4GBDTE*ZN8LightGBM4GBDT12TrainOneIterEPKfS2_EUlS2_iE_00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899?Model format error, expect a tree here. met %sModel file %s is not available for writesUnknown importance type: only support split=0 and gain=1#include <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>double (*PredictTreePtr[])(const double*) = { int early_stop_round_counter = 0;std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);for (int i = 0; i < num_iteration_for_pred_; ++i) {for (int k = 0; k < num_tree_per_iteration_; ++k) {output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { void GBDT::PredictLeafIndex(const double* features, double *output) const {int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;for (int i = 0; i < total_tree; ++i) {output[i] = (*PredictTreeLeafPtr[i])(features);double (*PredictTreeLeafByMapPtr[])(const std::unordered_map<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Model file doesn't specify the number of classesModel file doesn't specify the label indexModel file doesn't specify max_feature_idxModel file doesn't contain feature_namesWrong size of monotone_constraintsModel file doesn't contain feature_infosTree=#include "gbdt.h"#include <LightGBM/metric.h>#include <ctime>#include <sstream>#include <chrono>#include <string>#include <vector>#include <utility>namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBM#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endiftruefalse"name":""version":""num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":"objective":"", +"average_output":"feature_names":[""],"monotone_constraints":["feature_infos":},{"min_value":"max_value":"values":[]}"values":["tree_info":["tree_index":"feature_importances":monotone_constraintsWrong line at model file: %snum_classnum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesWrong size of feature_infosobjectivetree_sizesend of parameters[linear_tree: end of parserversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=feature_names=monotone_constraints=feature_infos=tree_sizes=end of treesfeature_importances: +parameters: +parser:v4regressionregression_l2l2_rootroot_mean_squared_errorrmsemean_absolute_errormaesoftmaxmulticlassovamulticlass_ovaovrxentropycross_entropyxentlambdacross_entropy_lambdarank_xendcgxe_ndcgxe_ndcg_martxendcg_martnonecustommean_absolute_percentage_errorZN8LightGBM9ArrayArgsIiE8ArgMaxMTERKSt6vectorIiSaIiEEEUlimmE_00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899u<7~u<7Binary early stopping needs predictions to be of length oneMulticlass early stopping needs predictions to be of length two or largerUnknown early stopping type: %s*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_Re-bagging, using %d data to train/__w/1/s/src/boosting/goss.hppCheck failed: (config_->top_rate + config_->other_rate) <= (1.0f) at %s, line %d . +Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . +Cannot use bagging in GOSSUsing GOSSUse subset for baggingN8LightGBM14SampleStrategyEN8LightGBM12GOSSStrategyEN8LightGBM21BaggingSampleStrategyEZN8LightGBM12GOSSStrategy7BaggingEiPNS_11TreeLearnerEPfS3_EUliiiPiS4_E_ZN8LightGBM21BaggingSampleStrategy7BaggingEiPNS_11TreeLearnerEPfS3_EUliiiPiS4_E_/__w/1/s/src/io/multi_val_dense_bin.hppCheck failed: (num_data_) == (num_used_indices) at %s, line %d . +/__w/1/s/src/io/bin.cppCheck failed: (max_bin) > (0) at %s, line %d . Check failed: (bin_upper_bound.size()) <= (static_cast<size_t>(max_bin)) at %s, line %d . Check failed: (num_bin_) <= (max_bin) at %s, line %d . -Met negative value in categorical features, will convert it to NaNMet categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zero/__w/1/s/src/io/multi_val_sparse_bin.hpp8N8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE??@ffffff?p}?multi_loglossserialcpuregresson_l1intermediatebasicmulti_errorauc_muvotingadvanced[boosting: [objective: [metric: [tree_learner: [device_type: binary_loglossmapkldivkullback_leiblerlambdarankmean_average_precisionboostinggbrtrandom_forestdevice_typeUnknown device type %stasktrainconvert_modelrefitUnknown task type %strainingpredictiontestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallel - '"Unknown parameter %sUnknown parameter: %s - AUC-mu matrix must have zeros on diagonal. Overwriting value in position %d of auc_mu_weights with 0.auc_mu_weights must have %d elements, but found %dAUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %d of auc_mu_weights.Number of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingMulticlass objective and metrics don't matchHistogram LRU queue was enabled (histogram_pool_size=%f). -Will disable this to reduce communication costsDon't support forcedsplits in %s tree learnerAccuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=%d).Although "deterministic" is set, the results ran by GPU may be non-deterministic.CUDA currently requires double precision calculations.Linear tree learner only works with CPU.Linear tree learner must be serial.zero_as_missing must be false when fitting linear trees.Cannot use regression_l1 objective when fitting linear trees.min_data_in_leaf has been increased to 2 because this is required when path smoothing is active.Cannot use "intermediate" or "advanced" monotone constraints in distributed learning, auto set to "basic" method.Cannot use "intermediate" or "advanced" monotone constraints with feature fraction different from 1, auto set monotone constraints to "basic" method.Monotone penalty greater than tree depth. Monotone features won't be used.Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. Will set min_data_in_leaf to 1.%s is set=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%sParameter %s should be of type int, got "%s"save_binary parameter set to true because task is save_binary??[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [deterministic: [force_col_wise: [force_row_wise: [histogram_pool_size: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [pos_bagging_fraction: [neg_bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_bynode: [feature_fraction_seed: [extra_trees: [extra_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [linear_lambda: [min_gain_to_split: [drop_rate: [max_drop: [skip_drop: [xgboost_dart_mode: [uniform_drop: [drop_seed: [top_rate: [other_rate: [min_data_per_group: [max_cat_threshold: [cat_l2: [cat_smooth: [max_cat_to_onehot: [top_k: [monotone_constraints: [monotone_penalty: [feature_contri: [forcedsplits_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [path_smooth: [interaction_constraints: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [data_random_seed: [is_enable_sparse: [enable_bundle: [use_missing: [zero_as_missing: [feature_pre_filter: [pre_partition: [two_round: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [forcedbins_filename: [precise_float_parser: [objective_seed: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [lambdarank_norm: [label_gain: [eval_at: [multi_error_top_k: [auc_mu_weights: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: [num_gpu: -+config_fileconfigtask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalidvalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationnum_iterationsn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorsmax_itershrinkage_ratelearning_rateetanum_leafnum_leavesmax_leavesmax_leafmax_leaf_nodestree_typetree_learner_typenum_threadnum_threadsnthreadnthreadsn_jobsdevicerandom_statehist_pool_sizehistogram_pool_sizemin_data_per_leafmin_data_in_leafmin_datamin_child_samplesmin_samples_leafmin_sum_hessian_per_leafmin_sum_hessian_in_leafmin_sum_hessianmin_hessianmin_child_weightpos_sub_rowpos_bagging_fractionpos_subsamplepos_baggingneg_sub_rowneg_bagging_fractionneg_subsampleneg_baggingsubsample_freqbagging_freqbagging_fraction_seedbagging_seedsub_featurefeature_fractioncolsample_bytreesub_feature_bynodefeature_fraction_bynodecolsample_bynodeextra_treeextra_treesearly_stopping_roundsearly_stopping_roundearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphalambda_l1l1_regularizationreg_lambdalambda_l2l2_regularizationmin_split_gainmin_gain_to_splitrate_dropdrop_ratetopkmcmonotone_constraintmonotonic_cstmonotone_constraining_methodmonotone_constraints_methodmc_methodmonotone_splits_penaltymonotone_penaltyms_penaltymc_penaltyfeature_contribfeature_contrifcfpfeature_penaltyfsforcedsplits_filenameforced_splits_filenameforced_splits_fileforced_splitsverboseverbositymodel_inputinput_modelmodel_inmodel_outputoutput_modelmodel_outsave_periodsnapshot_freqlinear_treeslinear_treemax_binsmax_binsubsample_for_binbin_construct_sample_cntdata_seeddata_random_seedis_sparseis_enable_sparseis_enable_bundleis_pre_partitiontwo_rounduse_two_round_loadinghas_headerlabel_columnweight_columngroup_columngroup_idquery_columnquery_idignore_featureignore_columnblacklistcat_featurecategorical_featurecategorical_columncat_columncategorical_featuresis_save_binaryis_save_binary_fileis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribpredict_resultoutput_resultprediction_resultpredict_nameprediction_namepred_namename_predconvert_model_filenum_classesis_unbalanceunbalanced_setsmetricsmetric_typesoutput_freqmetric_freqis_provide_training_metricis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinenum_machineslocal_portlocal_listen_portmachine_list_filemachine_list_filenamemachine_listmlistworkersstoddeterministicforce_col_wiseforce_row_wisemax_depthfeature_fraction_seedextra_seedfirst_metric_onlylinear_lambdamax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotrefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledpath_smoothinteraction_constraintssaved_feature_importance_typemax_bin_by_featuremin_data_in_binuse_missingzero_as_missingfeature_pre_filterforcedbins_filenameprecise_float_parserstart_iteration_predictnum_iteration_predictpredict_disable_shape_checkpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageobjective_seedscale_pos_weightsigmoidboost_from_averagereg_sqrtfair_cpoisson_max_delta_steptweedie_variance_powerlambdarank_truncation_levellambdarank_normlabel_gainmulti_error_top_kauc_mu_weightstime_outgpu_platform_idgpu_device_idgpu_use_dpnum_gpu[monotone_constraints_method: [cegb_penalty_feature_coupled: [saved_feature_importance_type: [lambdarank_truncation_level: Parameter %s should be of type double, got "%s"Parameter %s should be "true"/"+" or "false"/"-", got "%s"/__w/1/s/src/io/config_auto.cppCheck failed: (num_iterations) >= (0) at %s, line %d . +Met negative value in categorical features, will convert it to NaNMet categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zero/__w/1/s/src/io/multi_val_sparse_bin.hppN8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEE8??@ffffff?MultiValSparseBiseBin::MergeDatap}?AUC-mu matrix must have zeros on diagonal. Overwriting value in position %zu of auc_mu_weights with 0.Parameter %s should be of type int, got "%s"Number of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingMulticlass objective and metrics don't matchHistogram LRU queue was enabled (histogram_pool_size=%f). +Will disable this to reduce communication costsDon't support forcedsplits in %s tree learnerAccuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=%d).Although "deterministic" is set, the results ran by GPU may be non-deterministic.Quantized training is not supported by GPU tree learner. Switch to full precision training.Quantized training is not supported by CUDA tree learner. Switch to full precision training.Linear tree learner only works with CPU.Linear tree learner must be serial.zero_as_missing must be false when fitting linear trees.Cannot use regression_l1 objective when fitting linear trees.min_data_in_leaf has been increased to 2 because this is required when path smoothing is active.Cannot use "intermediate" or "advanced" monotone constraints in distributed learning, auto set to "basic" method.Cannot use "intermediate" or "advanced" monotone constraints with feature fraction different from 1, auto set monotone constraints to "basic" method.Monotone penalty greater than tree depth. Monotone features won't be used.Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. Will set min_data_in_leaf to 1.Found boosting=goss. For backwards compatibility reasons, LightGBM interprets this as boosting=gbdt, data_sample_strategy=goss.To suppress this warning, set data_sample_strategy=goss instead.auc_mu_weights must have %d elements, but found %zuAUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %zu of auc_mu_weights.save_binary parameter set to true because task is save_binarybinary_loglosslambdarankmapmean_average_precisionmulti_loglosskldivkullback_leiblerauc_muvotingregresson_l1intermediateadvancedbasicgbdtgoss[boosting: [objective: [metric: [tree_learner: [device_type: trainingpredictiontestconvert_modelrefitrefit_treeUnknown task type %sUnknown sample strategy %sUnknown device type %sgbrtdartrfrandom_forestfeature_paralleldata_parallelvoting_parallelUnknown tree learner type %sUnknown parameter %s + + { +} + "": [", " , "%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%s%s is set=%s, %s=%s will be ignored. Current value: %s=%sUnknown parameter: %sdata_sample_stra[monotone_constraints_method: [cegb_penalty_feature_coupled: [saved_feature_importance_type: [lambdarank_truncation_level: Parameter %s should be "true"/"+" or "false"/"-", got "%s"Parameter %s should be of type double, got "%s"/__w/1/s/src/io/config_auto.cppCheck failed: (num_iterations) >= (0) at %s, line %d . Check failed: (learning_rate) > (0.0) at %s, line %d . Check failed: (num_leaves) > (1) at %s, line %d . Check failed: (num_leaves) <= (131072) at %s, line %d . Check failed: (min_data_in_leaf) >= (0) at %s, line %d . Check failed: (min_sum_hessian_in_leaf) >= (0.0) at %s, line %d . @@ -4816,58 +5632,62 @@ Check failed: (multi_error_top_k) > (0) at %s, line %d . Check failed: (num_machines) > (0) at %s, line %d . Check failed: (local_listen_port) > (0) at %s, line %d . Check failed: (time_out) > (0) at %s, line %d . Check failed: (num_gpu) > (0) at %s, line %d . -_Dataset::sparse_bin_histogramtargetinit_scoreDataset::InitTrainDataset::dense_bin_histogramwtnum_features: %d +[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [deterministic: [force_col_wise: [force_row_wise: [histogram_pool_size: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [pos_bagging_fraction: [neg_bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_bynode: [feature_fraction_seed: [extra_trees: [extra_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [linear_lambda: [min_gain_to_split: [drop_rate: [max_drop: [skip_drop: [xgboost_dart_mode: [uniform_drop: [drop_seed: [top_rate: [other_rate: [min_data_per_group: [max_cat_threshold: [cat_l2: [cat_smooth: [max_cat_to_onehot: [top_k: [monotone_constraints: [monotone_penalty: [feature_contri: [forcedsplits_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [path_smooth: [interaction_constraints: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [data_random_seed: [is_enable_sparse: [enable_bundle: [use_missing: [zero_as_missing: [feature_pre_filter: [pre_partition: [two_round: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [forcedbins_filename: [precise_float_parser: [parser_config_file: [objective_seed: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [lambdarank_norm: [label_gain: [eval_at: [multi_error_top_k: [auc_mu_weights: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: [num_gpu: +task_typetaskobjective_typeappapplicationboosting_typeboostingboosttraintrain_datatrain_data_filevalidvalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationnum_iterationsn_iternum_treenum_treesnum_roundnum_roundsnroundsnum_boost_roundn_estimatorsmax_itershrinkage_ratelearning_rateetanum_leafnum_leavesmax_leavesmax_leafmax_leaf_nodestree_learnertree_typetree_learner_typenum_threadnum_threadsnthreadnthreadsn_jobsdevicedevice_typerandom_statehist_pool_sizehistogram_pool_sizemin_data_per_leafmin_datamin_child_samplesmin_samples_leafmin_sum_hessian_per_leafmin_sum_hessian_in_leafmin_sum_hessianmin_hessianmin_child_weightpos_subsamplepos_bagging_fractionneg_subsampleneg_bagging_fractionneg_baggingsubsample_freqbagging_freqbagging_fraction_seedbagging_seedsub_featurefeature_fractioncolsample_bytreesub_feature_bynodefeature_fraction_bynodecolsample_bynodeextra_treeextra_treesearly_stopping_roundsearly_stopping_roundearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphalambda_l1l1_regularizationreg_lambdalambda_l2l2_regularizationmin_split_gainmin_gain_to_splitrate_dropdrop_ratetopkmcmonotone_constraintmonotonic_cstmonotone_constraining_methodmonotone_constraints_methodmc_methodmonotone_splits_penaltymonotone_penaltyms_penaltymc_penaltyfeature_contrifeature_contribfcfpfeature_penaltyfsforcedsplits_filenameforced_splits_filenameforced_splits_fileforced_splitsverboseverbositymodel_inputinput_modelmodel_inmodel_outputoutput_modelmodel_outsave_periodsnapshot_freqlinear_treesmax_binsmax_binsubsample_for_binbin_construct_sample_cntdata_seeddata_random_seedis_sparseis_enable_sparseis_enable_bundleis_pre_partitiontwo_rounduse_two_round_loadinghas_headergroup_idquery_columnquery_idignore_featureblacklistcat_featurecategorical_featurecategorical_columncat_columncategorical_featuresis_save_binaryis_save_binary_fileis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribpredict_resultoutput_resultprediction_resultpredict_nameprediction_namepred_namename_predconvert_model_filenum_classesis_unbalanceunbalanced_setsmetricsmetric_typesoutput_freqmetric_freqis_provide_training_metricis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinenum_machineslocal_portlocal_listen_portmachine_list_filemachine_list_filenamemachine_listmlistworkersconfigdata_sample_strategyvector<string>force_col_wiseforce_row_wisefeature_fraction_seedextra_seedfirst_metric_onlylinear_lambdamax_dropskip_dropxgboost_dart_modeuniform_droptop_rateother_ratemin_data_per_groupcat_l2cat_smoothvector<int>vector<double>refit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledinteraction_constraintsvector<vector<int>>saved_feature_importance_typenum_grad_quant_binsquant_train_renew_leafstochastic_roundingmax_bin_by_featuremin_data_in_binuse_missingzero_as_missingparser_config_filestart_iteration_predictnum_iteration_predictpredict_disable_shape_checkpred_early_stoppred_early_stop_freqconvert_model_languageobjective_seedscale_pos_weightsigmoidreg_sqrtfair_cpoisson_max_delta_steplambdarank_truncation_levellambdarank_normlabel_gainauc_mu_weightstime_outgpu_platform_idgpu_device_idgpu_use_dpnum_gpustodpred_early_stop_marginboost_from_averagetweedie_variance_powermulti_error_top_kdeterministicmax_depthdrop_seedmax_cat_thresholdmax_cat_to_onehotpath_smoothuse_quantized_gradfeature_pre_filterdata_filenamepos_sub_rowpos_baggingneg_sub_rowpos_bagging_fracneg_bagging_fracmax_cat_thresholmax_cat_to_onehouse_quantized_grpredict_leaf_indpred_early_stop_boost_from_averatweedie_variancemulti_error_top_local_listen_porhistogram_pool_smin_data_in_leafmin_sum_hessian_bagging_fractionfeature_fractionearly_stopping_rfirst_metric_onlmin_gain_to_splixgboost_dart_modmin_data_per_gromonotone_constranstraints_methodmonotone_penaltyforcedsplits_filrefit_decay_ratecegb_penalty_splcegb_penalty_fealty_feature_lazy_feature_coupledinteraction_conssaved_feature_im_importance_typenum_grad_quant_bquant_train_renestochastic_roundprecise_float_pastart_iteration_num_iteration_prpredict_disable_able_shape_checkDataset::sparse_bin_histogram_merge/__w/1/s/include/LightGBM/feature_group.hCheck failed: is_multi_val_ at %s, line %d . +Check failed: !is_multi_val_ at %s, line %d . +Bug. There should be only one multi-val group.Dataset::GetMultiBinFromSparseFeatures: sparse rate %fCheck failed: (num_used_indices) == (num_data_) at %s, line %d . +Check failed: (num_data) > (0) at %s, line %d . +File %s exists, cannot save binary to itCannot write binary data to %s Saving data reference to binary bufferCheck failed: (static_cast<int>(bin_mappers->size())) == (1) at %s, line %d . +Check failed: static_cast<int>(most_freq_bins.size()) == ncol at %s, line %d . +Dataset::GetMultiBinFromAllFeatures: sparse rate %fCheck failed: (num_total_features_) == (static_cast<int>(bin_mappers->size())) at %s, line %d . +There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.Using sparse features with CUDA is currently not supported.Check failed: (static_cast<int>(bin_mappers->size())) == (num_feature) at %s, line %d . +Check failed: (static_cast<size_t>(num_total_features_)) == (io_config.max_bin_by_feature.size()) at %s, line %d . +Check failed: (*(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end()))) > (1) at %s, line %d . +Cannot add features from other Dataset with a different number of rowsCan only add features from other Dataset if both or neither have raw data.Check failed: other->is_multi_val_ at %s, line %d . +Check failed: num_groups_ == static_cast<int>(features_in_group.size()) at %s, line %d . +Find the same feature name (%s) in Dataset::AddFeaturesFrom, change its name to (%s)______LightGBM_Binary_Serialized_Token______ +______LightGBM_Binary_File_Token______ +Dataset::sparse_bin_histogramtargetnoname/__w/1/s/src/io/dataset.cppBinary file %s already exists.binSaving data to binary file %swtnum_features: %d num_total_features: %d num_groups: %d num_data: %d feature_names: max_bin_by_feature: %s, %d, forced_bins: -feature %d: %lf, NA, /__w/1/s/src/io/dataset.cppUsing Sparse Multi-Val BinUsing Dense Multi-Val BinnonameDataset::FastFeatureBundlingBinary file %s already exists.binSaving data to binary file %sDFind the same feature name (%s) in Dataset::AddFeaturesFrom, change its name to (%s)Dataset::ConstructHistogramsMultiValDataset::sparse_bin_histogram_mergeDataset::sparse_bin_histogram_moveDataset::PushDataToMultiValBin/__w/1/s/include/LightGBM/feature_group.hCheck failed: is_multi_val_ at %s, line %d . -Check failed: other->is_multi_val_ at %s, line %d . -Check failed: (num_used_indices) == (num_data_) at %s, line %d . -Dataset::GetMultiBinFromSparseFeaturesBug. There should be only one multi-val group.Dataset::GetMultiBinFromSparseFeatures: sparse rate %fDataset::GetMultiBinFromAllFeaturesCheck failed: static_cast<int>(most_freq_bins.size()) == ncol at %s, line %d . -Dataset::GetMultiBinFromAllFeatures: sparse rate %fDataset::TestMultiThreadingMethodCannot set both of `force_col_wise` and `force_row_wise` to `true` at the same timeinit for col-wise cost %f seconds, init for row-wise cost %f secondsAuto-choosing col-wise multi-threading, the overhead of testing was %f seconds. +feature %d: %lf, NA, Dv1Dataset::ConstructHistogramsMultiValDataset::sparse_bin_histogram_moveCannot set both of `force_col_wise` and `force_row_wise` to `true` at the same timeinit for col-wise cost %f seconds, init for row-wise cost %f secondsAuto-choosing col-wise multi-threading, the overhead of testing was %f seconds. You can set `force_col_wise=true` to remove the overhead.Auto-choosing row-wise multi-threading, the overhead of testing was %f seconds. You can set `force_row_wise=true` to remove the overhead. -And if memory is not enough, you can set `force_col_wise=true`.Check failed: (num_data) > (0) at %s, line %d . -File %s exists, cannot save binary to itCannot write binary data to %s Check failed: (num_total_features_) == (static_cast<int>(bin_mappers->size())) at %s, line %d . -There are no meaningful features, as all feature values are constant.Using sparse features with CUDA is currently not supported.Check failed: (static_cast<int>(bin_mappers->size())) == (num_feature) at %s, line %d . -Check failed: (static_cast<size_t>(num_total_features_)) == (io_config.max_bin_by_feature.size()) at %s, line %d . -Check failed: (*(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end()))) > (1) at %s, line %d . -Check failed: (static_cast<int>(bin_mappers->size())) == (1) at %s, line %d . -Cannot add features from other Dataset with a different number of rowsCan only add features from other Dataset if both or neither have raw data.Check failed: num_groups_ == static_cast<int>(features_in_group.size()) at %s, line %d . -______LightGBM_Binary_File_Token______ -*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE_*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE0_?Read %.1f GBs from %s.Data file %s is emptyCannot open data file %sbin_upper_boundColumn_name: ,Using column %s as weightUsing column %s as labelMaking second pass...Load from binary file %sCurrent query exceeds the range of the query file, -please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctThe output cannot be monotone with respect to categorical featuresUsing too small ``bin_construct_sample_cnt`` may encounter unexpected errors and poor accuracy.Size of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupDataset max_bin %d != config %dDataset min_data_in_bin %d != config %dDataset use_missing %d != config %dDataset zero_as_missing %d != config %dDataset bin_construct_sample_cnt %d != config %dDataset label_idx %d != config %dConfig label_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config weight_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config group_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config ignore_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Dataset max_bin_by_feature does not match with configRecommend use integer for label index when loading data from binary for sanity check.Warning: last line of %s has no end of line, still using this lineCould not read binary data from %sBinary file error: token has the wrong sizeBinary file error: header has the wrong sizeBinary file error: header is incorrect/__w/1/s/src/io/dataset_loader.cppCheck failed: (static_cast<size_t>(dataset->num_total_features_)) == (config_.max_bin_by_feature.size()) at %s, line %d . -Check failed: (*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end()))) > (1) at %s, line %d . -Binary file error: meta data has the wrong sizeBinary file error: meta data is incorrectBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %dBinary file error: row %d of raw data is incorrect, read count: %dInput file is not LightGBM binary fileCould not open %s. Will ignore.Check failed: forced_bins_json.is_array() at %s, line %d . -Check failed: (feature_num) < (num_total_features) at %s, line %d . -Feature %d is categorical. Will ignore forced bins for this feature.Size of feature_names error, should equal with total number of featuresFeature (%s) appears more than one time.Found whitespace in feature_names, replace with underlinesDo not support special JSON characters in feature name.Check failed: (static_cast<size_t>(num_col)) == (config_.max_bin_by_feature.size()) at %s, line %d . -Could not find label column %s in data file -or data file doesn't contain headerCould not find ignore column %s in data fileCould not find weight column %s in data fileUsing column %s as group/query idCould not find group/query column %s in data fileCould not find categorical_feature %s in data filelabel_column is not a number, +And if memory is not enough, you can set `force_col_wise=true`.Using Sparse Multi-Val BinUsing Dense Multi-Val Bin*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE_*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE0_Dataset::ConstructHistogramsMultDataset::sparse_bin_histogram_mebin_histogram_mose_bin_histogramDataset::PushDatataToMultiValBinDataset::InitTraDataset::GetMultiBinFromSparseFeDataset::dense_biBinFromAllFeatuDataset::TestMultiThreadingMethoDataset::FastFeatFeatureBundlingU v@X@?Current query exceeds the range of the query file, +please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctUsing too small ``bin_construct_sample_cnt`` may encounter unexpected errors and poor accuracy.Size of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupDataset was constructed with parameter max_bin=%d. It cannot be changed to %d when loading from binary file.Dataset was constructed with parameter min_data_in_bin=%d. It cannot be changed to %d when loading from binary file.Dataset was constructed with parameter use_missing=%d. It cannot be changed to %d when loading from binary file.Dataset was constructed with parameter zero_as_missing=%d. It cannot be changed to %d when loading from binary file.Dataset was constructed with parameter bin_construct_sample_cnt=%d. It cannot be changed to %d when loading from binary file.Parameter max_bin_by_feature cannot be changed when loading from binary file.Parameter label_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Parameter weight_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Parameter group_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Parameter ignore_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Parameter two_round works only in case of loading data directly from text file. It will be ignored when loading from binary file.Parameter header works only in case of loading data directly from text file. It will be ignored when loading from binary file.Categorical features with more bins than the configured maximum bin number found.For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.The output cannot be monotone with respect to categorical featuresInvalid parser config: %s. Please check if follow json format.Get raw column names from parser config.Could not find label column %s in data file +or data file doesn't contain headerlabel_column is not a number, if you want to use a column name, -please add the prefix "name:" to the column nameUsing column number %d as labelignore_column is not a number, +please add the prefix "name:" to the column nameUsing column number %d as labelCould not find ignore column %s in data fileignore_column is not a number, if you want to use a column name, -please add the prefix "name:" to the column nameweight_column is not a number, +please add the prefix "name:" to the column nameCould not find weight column %s in data fileweight_column is not a number, if you want to use a column name, -please add the prefix "name:" to the column nameUsing column number %d as weightgroup_column is not a number, +please add the prefix "name:" to the column nameUsing column number %d as weightCould not find group/query column %s in data fileUsing column %s as group/query idgroup_column is not a number, if you want to use a column name, -please add the prefix "name:" to the column nameUsing column number %d as group/query idcategorical_feature is not a number, +please add the prefix "name:" to the column nameUsing column number %d as group/query idCould not find categorical_feature %s in data filecategorical_feature is not a number, if you want to use a column name, -please add the prefix "name:" to the column nameCould not recognize data format of %sCheck failed: (dataset->num_total_features_) == (static_cast<int>(feature_names_.size())) at %s, line %d . +please add the prefix "name:" to the column name/__w/1/s/src/io/dataset_loader.cppCheck failed: (static_cast<size_t>(dataset->num_total_features_)) == (config_.max_bin_by_feature.size()) at %s, line %d . +Check failed: (*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end()))) > (1) at %s, line %d . +Binary definition file error: token has the wrong sizeInput file is not LightGBM binary reference fileUnexpected version of serialized binary data: %sLoaded reference dataset: %d features, %d num_dataCould not read binary data from %sBinary file error: token has the wrong sizeInput file is not LightGBM binary fileBinary file error: header has the wrong sizeBinary file error: header is incorrectBinary file error: meta data has the wrong sizeBinary file error: meta data is incorrectBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %zuBinary file error: row %d of raw data is incorrect, read count: %zuCould not recognize data format of %sCould not open %s. Will ignore.Check failed: forced_bins_json.is_array() at %s, line %d . +Check failed: (feature_num) < (num_total_features) at %s, line %d . +Feature %d is categorical. Will ignore forced bins for this feature.Check failed: (static_cast<size_t>(num_col)) == (config_.max_bin_by_feature.size()) at %s, line %d . +Check failed: (dataset->num_total_features_) == (static_cast<int>(feature_names_.size())) at %s, line %d . Check failed: label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_ at %s, line %d . Check failed: weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_ at %s, line %d . Check failed: group_idx_ < 0 || group_idx_ < dataset->num_total_features_ at %s, line %d . Construct bin mappers from text data time %.2f secondsUsing a query id without pre-partitioning the data file is not supported for distributed training. -Please use an additional query file or pre-partition the dataSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader23ExtractFeaturesFromFileEPKcPKNS_6ParserERKSt6vectorIiSaIiEEPNS_7DatasetEEUliRKS6_ISsSaISsEEE_St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EENSt6thread10_Impl_baseENSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS5_mEEEUlvE_vEEEEZN8LightGBM10TextReaderIiE9CountLineEvEUliPKcmE_ZN8LightGBM10TextReaderIiE12ReadAllLinesEvEUliPKcmE_ZN8LightGBM10TextReaderIiE18ReadAndFilterLinesERKSt8functionIFbiEEPSt6vectorIiSaIiEEEUliPKcmE_ZN8LightGBM10TextReaderIiE14SampleFromFileEPNS_6RandomEiPSt6vectorISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE23SampleAndFilterFromFileERKSt8functionIFbiEEPSt6vectorIiSaIiEEPNS_6RandomEiPS7_ISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE26ReadPartAndProcessParallelERKSt6vectorIiSaIiEERKSt8functionIFviRKS2_ISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKSt8functionIFviRKSt6vectorISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderIiE17ReadAllAndProcessERKSt8functionIFviPKcmEEEUlS4_mE_ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKSt8functionIFviRKSt6vectorISsSaISsEEEERKS2_IFbiiEEEUlPKcmE_St19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS6_mEEEUlvE_vEEEESaISF_ELN9__gnu_cxx12_Lock_policyE2EE>?rbwbhdfs://N8LightGBM17VirtualFileWriterEN8LightGBM17VirtualFileReaderEN8LightGBM9LocalFileE'%c' (%d)%.17g\\\"\b\f\n\r\t\u%04x\u2028\u2029Malformed commentUnexpected end of inputUnescaped in stringBad \u escape: Invalid escape character ]Expected JSON object, got Bad type for in /__w/1/s/src/io/json11.cppParse error: expected Invalid in numberExpected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected trailing Unexpected end of input after start of commentUnexpected end of input inside multi-line commentUnexpected end of input in stringCheck failed: (i) != (0) at %s, line %d . -Leading 0s not permitted in numbersAt least one digit required in fractional partAt least one digit required in exponentExceeded maximum nesting depthN6json119JsonValueEN6json115ValueILNS_4Json4TypeE1EdEEN6json1110JsonDoubleEN6json115ValueILNS_4Json4TypeE1EiEEN6json117JsonIntEN6json115ValueILNS_4Json4TypeE2EbEEN6json1111JsonBooleanEN6json115ValueILNS_4Json4TypeE3ESsEEN6json1110JsonStringEN6json115ValueILNS_4Json4TypeE4ESt6vectorIS1_SaIS1_EEEEN6json119JsonArrayEN6json115ValueILNS_4Json4TypeE5ESt3mapISsS1_St4lessISsESaISt4pairIKSsS1_EEEEEN6json1110JsonObjectEN6json115ValueILNS_4Json4TypeE0ENS_10NullStructEEEN6json118JsonNullESt23_Sp_counted_ptr_inplaceIN6json118JsonNullESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1111JsonBooleanESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonDoubleESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json117JsonIntESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonStringESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json119JsonArrayESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonObjectESaIS1_ELN9__gnu_cxx12_Lock_policyE2EELoading query weights...label cannot be nullptr.queryLoading query boundaries....weightLoading weights....initLoading initial scores...Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query filePlease note that `init_score` is not saved in binary file. -If you need it, please set it again after loading Dataset.Data partition error, data didn't match queriesCannot used query_id for distributed trainingNumber of queries in %s: %i. Average number of rows per query: %f.Weights size doesn't match data sizeQuery size doesn't match data sizeInitial score size doesn't match data sizeLength of weights is not same with #dataLength of label is not same with #dataSum of query counts is not same with #dataInvalid initial score file. Redundant or insufficient columnsZN8LightGBM10TextReaderImE12ReadAllLinesEvEUlmPKcmE_ZN8LightGBM10TextReaderImE17ReadAllAndProcessERKSt8functionIFvmPKcmEEEUlS4_mE_v~v*G*CData file %s doesn't exist. /__w/1/s/src/io/parser.cppno conversion to double for: %sconvert to double got underflow or overflow: %sData file %s couldn't be read.Check failed: (max_col_idx) > (0) at %s, line %d . -Input format error when parsing as TSVInput format error when parsing as CSVInput format error when parsing as LibSVMData file %s should have at least one line.Data file %s only has one line.Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported.Label should be the first column in a LibSVM fileData file %s doesn't contain a label column.-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G +Please use an additional query file or pre-partition the dataData file %s is emptyCannot open data file %sUsing column %s as labelUsing column %s as weightColumn_Making second pass...Load from binary file %s*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader23ExtractFeaturesFromFileEPKcPKNS_6ParserERKSt6vectorIiSaIiEEPNS_7DatasetEEUliRKS6_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISI_EEE_*ZNK8LightGBM13DatasetLoader29CheckCategoricalFeatureNumBinERKSt6vectorISt10unique_ptrINS_9BinMapperESt14default_deleteIS3_EESaIS6_EEiRKS1_IiSaIiEEEUlimmE_ZN8LightGBM10TextReaderIiE12ReadAllLinesEvEUliPKcmE_ZN8LightGBM10TextReaderIiE9CountLineEvEUliPKcmE_ZN8LightGBM10TextReaderIiE18ReadAndFilterLinesERKSt8functionIFbiEEPSt6vectorIiSaIiEEEUliPKcmE_ZN8LightGBM10TextReaderIiE14SampleFromFileEPNS_6RandomEiPSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EEEUliPKcmE_ZN8LightGBM10TextReaderIiE23SampleAndFilterFromFileERKSt8functionIFbiEEPSt6vectorIiSaIiEEPNS_6RandomEiPS7_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISI_EEEUliPKcmE_ZN8LightGBM10TextReaderIiE26ReadPartAndProcessParallelERKSt6vectorIiSaIiEERKSt8functionIFviRKS2_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISD_EEEEEUliiE_ZN8LightGBM10TextReaderIiE17ReadAllAndProcessERKSt8functionIFviPKcmEEEUlS4_mE_?N8LightGBM17VirtualFileWriterEN8LightGBM17VirtualFileReaderEN8LightGBM9LocalFileEhdfs://%.17g\\\"\b\f\n\r\t\u%04x\u2028\u2029'%c' (%d)Invalid in numberUnescaped in stringBad \u escape: Invalid escape character Expected JSON object, got Bad type for in /__w/1/s/src/io/json11.cppParse error: expected Expected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected trailing Check failed: (i) != (0) at %s, line %d . +Tl< jĬj(d += St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN24json11_internal_lightgbm9JsonValueEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE1EdEEN24json11_internal_lightgbm10JsonDoubleEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE1EiEEN24json11_internal_lightgbm7JsonIntEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE2EbEEN24json11_internal_lightgbm11JsonBooleanEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE3ENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN24json11_internal_lightgbm10JsonStringEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE4ESt6vectorIS1_SaIS1_EEEEN24json11_internal_lightgbm9JsonArrayEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE5ESt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES1_St4lessIS9_ESaISt4pairIKS9_S1_EEEEEN24json11_internal_lightgbm10JsonObjectEN24json11_internal_lightgbm5ValueILNS_4Json4TypeE0ENS_10NullStructEEEN24json11_internal_lightgbm8JsonNullESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm8JsonNullESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm11JsonBooleanESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm10JsonDoubleESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm7JsonIntESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm10JsonStringESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm9JsonArrayESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN24json11_internal_lightgbm10JsonObjectESaIvELN9__gnu_cxx12_Lock_policyE2EEUnexpected end of input after ststart of commentf input inside multi-line commenMalformed commenLeading 0s not permitted in numbAt least one digit required in f fractional partit required in ef input in strinExceeded maximumum nesting depthPassed null queriesCalculating query weights....weightLoading weights....querylabel cannot be nullptrPassed null weights.initLoading initial scores...Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query fileInserting query data into dataset with no queriesInserted query data is too large for datasetCalculating query boundaries...Data partition error, data didn't match queriesWeights size doesn't match data sizeQuery size doesn't match data sizeInitial score size doesn't match data sizeCannot used query_id for distributed trainingNumber of queries in %s: %i. Average number of rows per query: %f.Please note that `init_score` is not saved in binary file. +If you need it, please set it again after loading Dataset.Length of weights is not same with #dataLength of label is not same with #dataInserted label data is too large for datasetInserting weight data into dataset with no weightsInserted weight data is too large for datasetCalling Init() on Metadata weights that have already been initializedCalling Init() on Metadata initial scores that have already been initializedCalling Init() on Metadata queries that have already been initializedInserting initial score data into dataset with no initial scoresInserted initial score data is too large for datasetLength of metadata is too long to append #dataSum of query counts is not same with #dataInvalid initial score file. Redundant or insufficient columnsZN8LightGBM10TextReaderImE12ReadAllLinesEvEUlmPKcmE_ZN8LightGBM10TextReaderImE17ReadAllAndProcessERKSt8functionIFvmPKcmEEEUlS4_mE_*G*Ǚv~vCData file %s doesn't exist. /__w/1/s/src/io/parser.cppCustom parser class name: %sInput format error when parsing as LibSVMno conversion to double for: %sconvert to double got underflow or overflow: %sCannot find parser class '%s', please register first or check config format.Data file %s couldn't be read.Check failed: (max_col_idx) > (0) at %s, line %d . +/__w/1/s/include/LightGBM/utils/common.hCheck failed: config_json.is_object() at %s, line %d . +Data file %s should have at least one line.Data file %s only has one line.Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported.Label should be the first column in a LibSVM fileData file %s doesn't contain a label column.Input format error when parsing as CSVInput format error when parsing as TSVN8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G ni %Cp(W^j87.h3DJ!jzd)غ`Y3)$oVyԶӥlwHޔ3 M^Pa,\":1H cm̻, `%N *᮴ fMȟP(rbISz%Taw$jDk(sK2 CgN?͓A"xuRCkxk SVk#췆,2{S̰?h@ѡ¹ #2( @@ -4909,15 +5729,17 @@ ףp= ף@P$ (k@C*焑 1_.@v:k #NJbxz&n2xW ?hӠ@aQYȥo: '΄ x9? 6Ngɖ"E@|op+ŝL67V߄\l: {Η H½ Pv1P?%O7и'ƫCưo\{2~]n0b/5E=!֕C)L{QF$+v؈Sﶓh髤8AqfcQlN@< ePKПDǕh"!9oj%p E"&'O֪"Nj+w 3;LզIxŠ'V:qCN'1c$_E^69uD]ȩdLJ:݈MXd슠p`~Ȍ8eްݫ--{9.ڇz贙#"׬+֫* eǃR?Vʸ&ϫ ^xa Z^9wuܠLqm_:JIxX{-KqB]e wet _jhvjE¿;JЕ(aʩ]D&r<uX,iM.wzU?7ߎ~S^=lX![qgpdڲX4A4>QԞ2#Ek0SF|n_OIwmU c*O~MQ5Fe B‹_FiYW7/-k}{x 2cPME fzBΨ?]ߌG#٨l*CwyT^Tj4'RE]dB!ܺ~IriE֒Pś[[26hd#Dvja5ӨŹ7hdZk"!"k*=]S5Ȍ:Bxri %ь[e8y/JXg].\*߈=9tauGѹ"+_vI}Azdұ_F;#J ,(ن"y(+EWAu-/ӷ .|]|%94›.²}DKa2(x5˲az®k[rc? <<Ϗ( CYxķapeFYy "WˇuξTSܷ.$*(VyuUD`גjU'9pbCFTfAXjiQ.A"ZҪ/<Uj; VtubeDžl8Džinќ&E[`BrMx\ӛ Ζ3B)A> iyMh, XEa75 .|BǼ]R5VS!{Z֨Y dp>ԅ.7JŇԱ YJ^M:0ܵ$~sީq]V ukP/<R{ ܿ<Z !x\u\S٨|4EM +`]hk䤹Bx郮Ҁ0B$ZR0I<D|.ANyĢXˊ׵e󦑙?n̰ܔٜ =DLLvU_S,Uktwjۂ#-;#[Dz,4x@ УrČV<qe~1Vx5k\(3VFs,WЛgjB_wqBv/? Sc]ɞH|Y{ځo(1r}c<JAnJI+QEE;R -bڸ E}ajAQdһS8~cU4_^jIukS\ܺ)c᳹);b (3ʺ)2־ԩYpI0/j\&ҷ]ُ]X%2t.o?0:ͅϧz^KDfQ6^U@4õjP4cع^O 5bbLB},ݬ@!xP] RzRϖ|$ P~tґGQ.G嘡cD^/g?$@Y@@@@j@.AcAחAeA _BvH7BmB@0BļB4&k C7yAC؅W4vCNgmC=`XC@xDPKDMDN8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE/__w/1/s/src/io/train_share_states.cppCheck failed: cur_num_bin == feature_group->bin_offsets_.back() at %s, line %d . -333333?>%s:%d: assertion failed: %stoo large exponentnegative valueinvalid digit countempty buffersign == '+' || sign == '-'is_digit(*p)shift >= 0exp >= 0unaligned bigintsk is out of rangeshifting error detected"leaf_const":"leaf_features":["leaf_coeff":["leaf_features":[], +bڸ E}ajAQdһS8~cU4_^jIukS\ܺ)c᳹);b (3ʺ)2־ԩYpI0/j\&ҷ]ُ]X%2t.o?0:ͅϧz^KDfQ6^U@4õjP4cع^O 5bbLB},ݬ@!xP] RzRϖ|$ P~tґGQ.G嘡cD^/g?$@Y@@@@j@.AcAחAeA _BvH7BmB@0BļB4&k C7yAC؅W4vCNgmC=`XC@xDPKDMD +/__w/1/s/src/io/train_share_states.cppCheck failed: cur_num_bin == feature_group->bin_offsets_.back() at %s, line %d . +333333?>/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format-inl.hif (std::isnan(fval)) fval = 0.0;if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); }if (int_fval >= 0 && int_fval < 32 * ( + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { Check failed: (strs.size()) == (static_cast<size_t>(n)) at %s, line %d . +Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold fieldNumerical conversion failed. Buffer is too small."leaf_const":"leaf_features":["leaf_coeff":["leaf_features":[], "leaf_coeff":[] -if (fval <= || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":||"threshold":""decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","internal_value":"missing_type":"Zero","missing_type":"NaN","internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":string pointer is nullFailed to parse double: %sn is too largenum_catleaf_valueshrinkageis_linearleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typeleaf_constnum_featuresleaf_featuresleaf_coeffcat_boundariesexponent out of rangeNANINFinvalid type specifiernumber is too bigvalue is negativeinvalid number of digitsnegative widthwidth is not integerargument not foundinvalid format stringnegative precisionprecision is not integer0123456789ABCDEFinvalid fill character '{'invalid fillmissing precision specifiermissing '}' in format stringinvalid argument typeunknown format specifier{}{:.17g}{:g}num_leaves=num_cat=split_feature=split_gain=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=is_linear=leaf_const=num_features=leaf_features=leaf_coeff=shrinkage=/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format-inl.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/core.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format.hbigits_.capacity() <= bigits_capacityif (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { unmatched '}' in format string/__w/1/s/include/LightGBM/utils/common.hCheck failed: (strs.size()) == (static_cast<size_t>(n)) at %s, line %d . -Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold fieldcannot switch from manual to automatic argument indexingcannot switch from automatic to manual argument indexingformat specifier requires numeric argumentinvalid format specifier for charformat specifier requires signed argumentprecision not allowed for this argument typeNumerical conversion failed. Buffer is too small.@P ؑpP int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer<char>&) [with T = double]fmt::v7::detail::bigint::~bigint()fmt::v7::detail::bigint& fmt::v7::detail::bigint::operator<<=(int)void fmt::v7::detail::bigint::assign_pow10(int)int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer<char>&) [with T = long double]-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G +if (Tree::IsZero(fval)) {if (!Tree::IsZero(fval)) {if (fval <= if (std::isnan(fval)) {if (!std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":"threshold":"||"decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","missing_type":"Zero","missing_type":"NaN","internal_value":"internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":num_catleaf_valueFailed to parse double: %sshrinkageis_linearleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typeleaf_constnum_featuresleaf_featuresleaf_coeffcat_boundaries{:.17g}num_leaves=num_cat=split_feature=split_gain={:g}decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=is_linear=leaf_const=num_features=leaf_features=leaf_coeff=shrinkage=%s:%d: assertion failed: %snegative valueunaligned bigints/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/core.h-+  +  *ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE3_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE4_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE5_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE6_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE3_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE4_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE5_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE6_-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G ni %Cp(W^j87.h3DJ!jzd)غ`Y3)$oVyԶӥlwHޔ3 M^Pa,\":1H cm̻, `%N *᮴ fMȟP(rbISz%Taw$jDk(sK2 CgN?͓A"xuRCkxk SVk#췆,2{S̰?h@ѡ¹ #2( @@ -4959,5527 +5781,4472 @@ ףp= ף@P$ (k@C*焑 1_.@v:k #NJbxz&n2xW ?hӠ@aQYȥo: '΄ x9? 6Ngɖ"E@|op+ŝL67V߄\l: {Η H½ Pv1P?%O7и'ƫCưo\{2~]n0b/5E=!֕C)L{QF$+v؈Sﶓh髤8AqfcQlN@< ePKПDǕh"!9oj%p E"&'O֪"Nj+w 3;LզIxŠ'V:qCN'1c$_E^69uD]ȩdLJ:݈MXd슠p`~Ȍ8eްݫ--{9.ڇz贙#"׬+֫* eǃR?Vʸ&ϫ ^xa Z^9wuܠLqm_:JIxX{-KqB]e wet _jhvjE¿;JЕ(aʩ]D&r<uX,iM.wzU?7ߎ~S^=lX![qgpdڲX4A4>QԞ2#Ek0SF|n_OIwmU c*O~MQ5Fe B‹_FiYW7/-k}{x 2cPME fzBΨ?]ߌG#٨l*CwyT^Tj4'RE]dB!ܺ~IriE֒Pś[[26hd#Dvja5ӨŹ7hdZk"!"k*=]S5Ȍ:Bxri %ь[e8y/JXg].\*߈=9tauGѹ"+_vI}Azdұ_F;#J ,(ن"y(+EWAu-/ӷ .|]|%94›.²}DKa2(x5˲az®k[rc? <<Ϗ( CYxķapeFYy "WˇuξTSܷ.$*(VyuUD`גjU'9pbCFTfAXjiQ.A"ZҪ/<Uj; VtubeDžl8Džinќ&E[`BrMx\ӛ Ζ3B)A> iyMh, XEa75 .|BǼ]R5VS!{Z֨Y dp>ԅ.7JŇԱ YJ^M:0ܵ$~sީq]V ukP/<R{ ܿ<Z !x\u\S٨|4EM +`]hk䤹Bx郮Ҁ0B$ZR0I<D|.ANyĢXˊ׵e󦑙?n̰ܔٜ =DLLvU_S,Uktwjۂ#-;#[Dz,4x@ УrČV<qe~1Vx5k\(3VFs,WЛgjB_wqBv/? 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6Ngɖ"E@|op+ŝL67V߄\l: {Η H½ Pv1P?%O7и'ƫCưo\{2~]n0b/533333333)\(\p= +ףp= +x&1|?5^I ~:pΈҲ qh N]h㈵9ahVyc{-1Fw$zՔ!"Kga*m.:BGkI> O{ͷu9 idW>Pb6-?C,z MY@1eoT5$E!@pG8w=_$A|AѤ䥬4 )TTpi;wwIYePr&ޭԔ],`yOoy.P9kY 3333)\£p= +x&I ~:h 9ah1F!"*m.:I>`y`y`yigigig + + +6e 6e 6e 6e 6e +d'@Bʚ; TvHrN @zZƤ~o#]xEcd #NJ + + + +  G @ + .*Number of rows %i exceeds upper limit of %i for a querylabel should be int type (met %f) for ranking task, for the gain of label, please set the label_gain parameterLabel should be non-negative (met %f) for ranking taskLabel %zu is not less than the number of label mappings (%zu)/__w/1/s/src/metric/dcg_calculator.cppCheck failed: (ref_eval_at[i]) > (0) at %s, line %d . -'}&_] outside [auc[%s:%s]: sum-of-weights = %f%s offset term = %fmap@ndcg@poissonbinary_errorgammagamma_deviancetweediehuberfairmulti_error@[%s]: does not tolerate element [#%i = /__w/1/s/src/metric/xentropy_metric.hpplabel_ Can't be NULL at %s, line %d . -[%s:%s]: (metric) labels passed interval [0, 1] check[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) all weights must be positive[%s:%s]: (metric) at least one weight is negativeFor MAP metric, there should be query informationTotal groups: %d, total data: %dThe NDCG metric requires query information/__w/1/s/src/metric/regression_metric.hppCheck failed: (label) > (0) at %s, line %d . -InitInitInitN8LightGBM6MetricEN8LightGBM12BinaryMetricINS_19BinaryLoglossMetricEEEN8LightGBM19BinaryLoglossMetricEN8LightGBM12BinaryMetricINS_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM22AveragePrecisionMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM11AucMuMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_10RMSEMetricEEEN8LightGBM10RMSEMetricEN8LightGBM16RegressionMetricINS_8L2MetricEEEN8LightGBM8L2MetricEN8LightGBM16RegressionMetricINS_14QuantileMetricEEEN8LightGBM14QuantileMetricEN8LightGBM16RegressionMetricINS_8L1MetricEEEN8LightGBM8L1MetricEN8LightGBM16RegressionMetricINS_15HuberLossMetricEEEN8LightGBM15HuberLossMetricEN8LightGBM16RegressionMetricINS_14FairLossMetricEEEN8LightGBM14FairLossMetricEN8LightGBM16RegressionMetricINS_13PoissonMetricEEEN8LightGBM13PoissonMetricEN8LightGBM16RegressionMetricINS_10MAPEMetricEEEN8LightGBM10MAPEMetricEN8LightGBM16RegressionMetricINS_11GammaMetricEEEN8LightGBM11GammaMetricEN8LightGBM16RegressionMetricINS_19GammaDevianceMetricEEEN8LightGBM19GammaDevianceMetricEN8LightGBM16RegressionMetricINS_13TweedieMetricEEEN8LightGBM13TweedieMetricEN8LightGBM18CrossEntropyMetricEN8LightGBM24CrossEntropyLambdaMetricEN8LightGBM25KullbackLeiblerDivergenceE-q=;& .>DA@|=?Trying to bind port %d...0.0.0.0Binding port %d succeededBinding port %d failedSet TCP_NODELAY failedListening...Connected to rank %dSocket construction errorrank=Connecting to rank %d failed, waiting for %d millisecondsFinished linking network in %f secondsSet SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at leastSet SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at leastSocket accept error, %s (code: %d)Socket recv error, %s (code: %d)Socket send error, %s (code: %d)Machine list file %s doesn't existmachine_list size is larger than the parameter num_machines, ignoring redundant entriesCannot find any ip and port. -Please check machine_list_filename or machines parameterWorld size is larger than the machine_list size, change world size to %dMachine list file doesn't contain the local machineNSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS4_iEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS5_iEEEESaISC_ELN9__gnu_cxx12_Lock_policyE2EEff?Local rank: %d, total number of machines: %dPlease initialize the network interface firstNSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS4_iEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS5_iEUlvE_vEEEESaIS9_ELN9__gnu_cxx12_Lock_policyE2EEnum_class:sigmoid: sqrtContains only one class[%s]: sum of weights is zero[%s]: sum of labels is zeroRanking tasks require query information[%s:%s]: havg = %f -> initscore = %f[%s:%s]: pavg=%f -> initscore=%f[%s:%s]: pavg = %f -> initscore = %fNumber of positive: %d, number of negative: %d/__w/1/s/src/objective/xentropy_objective.hpp[%s:%s]: (objective) labels passed interval [0, 1] check[%s]: at least one weight is negative[%s]: at least one weight is non-positive[%s:%s]: min, max weights = %f, %f; ratio = %fSigmoid parameter %f should be greater than zeroCannot set is_unbalance and scale_pos_weight at the same timeCannot use sqrt transform in %s Regression, will auto disable it[%s]: at least one target label is negativeSome label values are < 1 in absolute value. MAPE is unstable with such values, so LightGBM rounds them to 1.0 when calculating MAPE.Objective should contain num_class fieldUnknown objective type name: %sLabel must be in [0, %d), but found %d in label/__w/1/s/src/objective/regression_objective.hppCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . -Sigmoid param %f should be greater than zeroCheck failed: (threshold) >= (weighted_cdf[pos - 1]) at %s, line %d . +'}&_aucFor MAP metric, there should be query informationTotal groups: %d, total data: %dThe NDCG metric requires query informationhuberfairpoissonbinary_errormulti_errorgammagamma_deviancetweedieInit[%s:%s]: sum-of-weights = %f/__w/1/s/src/metric/xentropy_metric.hpplabel_ Can't be NULL at %s, line %d . +[%s:%s]: (metric) labels passed interval [0, 1] check[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) all weights must be positive%s offset term = %f[%s:%s]: (metric) at least one weight is negative/__w/1/s/src/metric/regression_metric.hppCheck failed: (label) > (0) at %s, line %d . +multi_error@N8LightGBM6MetricEN8LightGBM12BinaryMetricINS_19BinaryLoglossMetricEEEN8LightGBM19BinaryLoglossMetricEN8LightGBM12BinaryMetricINS_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM22AveragePrecisionMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM11AucMuMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_10RMSEMetricEEEN8LightGBM10RMSEMetricEN8LightGBM16RegressionMetricINS_8L2MetricEEEN8LightGBM8L2MetricEN8LightGBM16RegressionMetricINS_14QuantileMetricEEEN8LightGBM14QuantileMetricEN8LightGBM16RegressionMetricINS_8L1MetricEEEN8LightGBM8L1MetricEN8LightGBM16RegressionMetricINS_15HuberLossMetricEEEN8LightGBM15HuberLossMetricEN8LightGBM16RegressionMetricINS_14FairLossMetricEEEN8LightGBM14FairLossMetricEN8LightGBM16RegressionMetricINS_13PoissonMetricEEEN8LightGBM13PoissonMetricEN8LightGBM16RegressionMetricINS_10MAPEMetricEEEN8LightGBM10MAPEMetricEN8LightGBM16RegressionMetricINS_11GammaMetricEEEN8LightGBM11GammaMetricEN8LightGBM16RegressionMetricINS_19GammaDevianceMetricEEEN8LightGBM19GammaDevianceMetricEN8LightGBM16RegressionMetricINS_13TweedieMetricEEEN8LightGBM13TweedieMetricEN8LightGBM18CrossEntropyMetricEN8LightGBM24CrossEntropyLambdaMetricEN8LightGBM25KullbackLeiblerDivergenceE|=& .>DA@-q=;average_precisiocross_entropy_lakullback_leibler?Connecting to rank %d failed, waiting for %d millisecondsFinished linking network in %f secondsSet SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at leastSet SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at leastSocket accept error, %s (code: %d)Socket recv error, %s (code: %d)Machine list file %s doesn't existmachine_list size is larger than the parameter num_machines, ignoring redundant entriesCannot find any ip and port. +Please check machine_list_filename or machines parameterWorld size is larger than the machine_list size, change world size to %zuSocket send error, %s (code: %d)Machine list file doesn't contain the local machineTrying to bind port %d...0.0.0.0Binding port %d failedBinding port %d succeededSet TCP_NODELAY failedListening...Connected to rank %drank=Socket construction errorNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN8LightGBM7LinkersEFviEPS4_iEEEEEEff?Please initialize the network interface firstLocal rank: %d, total number of machines: %dNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN8LightGBM7Linkers8SendRecvEiPciiS5_iEUlvE_EEEEEE/__w/1/s/src/objective/regression_objective.hppCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . +Sigmoid param %f should be greater than zeroUnknown objective type name: %sBoostFromScore[%s:%s]: havg = %f -> initscore = %f[%s:%s]: pavg=%f -> initscore=%f[%s:%s]: pavg = %f -> initscore = %fContains only one classNumber of positive: %d, number of negative: %dSigmoid parameter %f should be greater than zeroCannot set is_unbalance and scale_pos_weight at the same timeCannot use sqrt transform in %s Regression, will auto disable it sqrtsigmoid:num_class:Some label values are < 1 in absolute value. MAPE is unstable with such values, so LightGBM rounds them to 1.0 when calculating MAPE.[%s]: at least one target label is negative[%s]: sum of labels is zeroLabel must be in [0, %d), but found %d in labelRanking tasks require query information/__w/1/s/src/objective/xentropy_objective.hpp[%s:%s]: (objective) labels passed interval [0, 1] check[%s]: at least one weight is non-positive[%s:%s]: min, max weights = %f, %f; ratio = %f[%s]: at least one weight is negative[%s]: sum of weights is zeroObjective should contain num_class fieldCheck failed: (threshold) >= (weighted_cdf[pos - 1]) at %s, line %d . Check failed: (threshold) < (weighted_cdf[pos]) at %s, line %d . -BoostFromScoreInitBoostFromScoreInitBoostFromScoreN8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM16RankingObjectiveEN8LightGBM14LambdarankNDCGEN8LightGBM10RankXENDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC1ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC1ERKNS_6ConfigESt8functionIFbfEEEUlfE_ZN8LightGBM9ArrayArgsIfE8ArgMaxMTERKSt6vectorIfSaIfEEEUlimmE_ZN8LightGBM9ArrayArgsIdE8ArgMaxMTERKSt6vectorIdSaIdEEEUlimmE_?qq C<ư>0y$k CII@@z?????vector<bool>::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: (tree->num_leaves()) <= (data_partition_->num_leaves()) at %s, line %d . -CUDA Tree Learner was not enabled in this build. -Please recompile with CMake option -DUSE_CUDA=1GPU Tree Learner was not enabled in this build. -Please recompile with CMake option -DUSE_GPU=1N8LightGBM15CUDATreeLearnerEN8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_N8LightGBM26FeatureParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEE/__w/1/s/external_libs/eigen/Eigen/src/Core/util/Memory.h(size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator."/__w/1/s/external_libs/eigen/Eigen/src/Core/PlainObjectBase.h((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0/__w/1/s/external_libs/eigen/Eigen/src/LU/FullPivLU.hm_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseCoeffsBase.h/__w/1/s/external_libs/eigen/Eigen/src/Core/PermutationMatrix.hi>=0 && j>=0 && i<size() && j<size()m_isInitialized && "LU is not initialized."/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseBase.hrows == this->rows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."/__w/1/s/src/treelearner/linear_tree_learner.h(!(RowsAtCompileTime!=Dynamic) || (rows==RowsAtCompileTime)) && (!(ColsAtCompileTime!=Dynamic) || (cols==ColsAtCompileTime)) && (!(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic) || (rows<=MaxRowsAtCompileTime)) && (!(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic) || (cols<=MaxColsAtCompileTime)) && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array."/__w/1/s/external_libs/eigen/Eigen/src/Core/MapBase.h(dataPtr == 0) || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))/__w/1/s/external_libs/eigen/Eigen/src/Core/Block.hstartRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols(i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows()) ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols()))/__w/1/s/external_libs/eigen/Eigen/src/Core/util/XprHelper.h/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseNullaryOp.hrows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)/__w/1/s/external_libs/eigen/Eigen/src/Core/AssignEvaluator.hdst.rows() == src.rows() && dst.cols() == src.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/util/BlasUtil.hm_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest()/__w/1/s/external_libs/eigen/Eigen/src/Core/Redux.hthis->rows()>0 && this->cols()>0 && "you are using an empty matrix"/__w/1/s/external_libs/eigen/Eigen/src/Core/Visitor.hrow >= 0 && row < rows() && col >= 0 && col < cols()rows()==other.rows() && cols()==other.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/ProductEvaluators.hdst.rows() == dstRows && dst.cols() == dstCols/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseBinaryOp.haLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)/__w/1/s/external_libs/eigen/Eigen/src/Core/SolveTriangular.hderived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols()))/__w/1/s/external_libs/eigen/Eigen/src/Core/Product.hlhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.hdst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/SolverBase.hderived().m_isInitialized && "Solver is not initialized."(Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b"No further splits with positive gain, best gain: %fTrained a tree with leaves = %d and depth = %dDetected that num_threads changed during training (from %d to %d), it may cause unexpected errors.index >= 0 && index < size()v == T(Value)incr==1SerialTreeLearner::Train+0+`+0+,0+`+0+0+0+0+`+0+0+0+0+0,0+0+0+0+0+0+0+P,@,0++0+0+0++0+0++0+0+0++0+0+0+0+0+0+0+0+p+`,p++ , +0+,p++ +++++p,,,+,,,0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+`+0+0+0+0+0++`++0+0+0+0+0+0+0+0+0+0+0+0+0+0+0++p++ ++++ + +p++ +++ +++++0+0+,Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = double; Eigen::Index = long int]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; T1 = double; T2 = double]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Rhs = Eigen::Matrix<double, -1, -1>; Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::Scalar = double]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >, const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1> >; T1 = double; T2 = double]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]const PermutationQType& Eigen::FullPivLU<MatrixType>::permutationQ() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::FullPivLU<MatrixType>::PermutationQType = Eigen::PermutationMatrix<-1, -1, int>]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 2u]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; SrcXprType = Eigen::Matrix<double, -1, -1>; Functor = Eigen::internal::sub_assign_op<double, double>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false> >, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::Scalar = double]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::Product<Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1>; T1 = double; T2 = double]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 1; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 0; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::gemm_pack_rhs<Scalar, Index, DataMapper, nr, 0, Conjugate, PanelMode>::operator()(Scalar*, const DataMapper&, Index, Index, Index, Index) [with Scalar = double; Index = long int; DataMapper = Eigen::internal::blas_data_mapper<double, long int, 0, 0, 1>; int nr = 4; bool Conjugate = false; bool PanelMode = true]Eigen::internal::blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, 1>::blas_data_mapper(Scalar*, Index, Index) [with Scalar = double; Index = long int; int StorageOrder = 0; int AlignmentType = 0]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 5u]void Eigen::DenseBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::PermutationMatrix<-1, -1, int>; _Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >; int Option = 2; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >]const PermutationPType& Eigen::FullPivLU<MatrixType>::permutationP() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::FullPivLU<MatrixType>::PermutationPType = Eigen::PermutationMatrix<-1, -1, int>]Eigen::Index Eigen::FullPivLU<MatrixType>::rank() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]void Eigen::SolverBase<Derived>::_check_solve_assertion(const Rhs&) const [with bool Transpose_ = false; Rhs = Eigen::MatrixBase<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> > >; Derived = Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> >]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_identity_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; RhsType = const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 1; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Matrix<double, -1, -1>]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 0; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Matrix<double, -1, -1>]const Eigen::Inverse<Eigen::FullPivLU<MatrixType> > Eigen::FullPivLU<MatrixType>::inverse() const [with _MatrixType = Eigen::Matrix<double, -1, -1>]Derived& Eigen::PermutationBase<Derived>::applyTranspositionOnTheRight(Eigen::Index, Eigen::Index) [with Derived = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; SrcXprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1> >, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false> >; Functor = Eigen::internal::sub_assign_op<double, double>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, 1>; Eigen::Index = long int]static void Eigen::internal::Assignment<DstXprType, Eigen::Product<Lhs, Rhs, Options>, Eigen::internal::sub_assign_op<Scalar, Scalar>, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::run(DstXprType&, const SrcXprType&, const Eigen::internal::sub_assign_op<Scalar, Scalar>&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Lhs = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>; Rhs = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>; int Options = 0; Scalar = double; Eigen::internal::Assignment<DstXprType, Eigen::Product<Lhs, Rhs, Options>, Eigen::internal::sub_assign_op<Scalar, Scalar>, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::SrcXprType = Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>, 0>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::DenseBase<Derived>::swap(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>]void Eigen::DenseBase<Derived>::swap(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::maxCoeff(IndexType*, IndexType*) const [with IndexType = long int; Derived = Eigen::CwiseUnaryOp<Eigen::internal::scalar_score_coeff_op<double>, const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, -1, 1, true>; typename Eigen::internal::traits<T>::Scalar = double]Eigen::internal::variable_if_dynamic<T, Value>::variable_if_dynamic(T) [with T = long int; int Value = 1]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_max_op<double, double>; Derived = Eigen::PartialReduxExpr<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, Eigen::internal::member_sum<double, double>, 0>; typename Eigen::internal::traits<T>::Scalar = double]void Eigen::FullPivLU<MatrixType>::computeInPlace() [with _MatrixType = Eigen::Matrix<double, -1, -1>]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::Matrix<double, -1, -1>; T1 = double; T2 = double]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, 1, -1>; Eigen::Index = long int]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, -1, 1>; Eigen::Index = long int]Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = double; Eigen::Index = long int]void* Eigen::internal::aligned_malloc(std::size_t)void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]N8LightGBM17LinearTreeLearnerEj@<SerialTreeLearner::ConstructHistogramsSerialTreeLearner::BeforeFindBestSplit/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: (num_machines) > (1) at %s, line %d . +N8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM16RankingObjectiveEN8LightGBM14LambdarankNDCGEN8LightGBM10RankXENDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC4ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC4ERKNS_6ConfigESt8functionIFbfEEEUlfE_ZN8LightGBM9ArrayArgsIfE8ArgMaxMTERKSt6vectorIfSaIfEEEUlimmE_ZN8LightGBM9ArrayArgsIdE8ArgMaxMTERKSt6vectorIdSaIdEEEUlimmE_II@?0y$k C<qq Cư>@z?vector<bool>::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: (tree->num_leaves()) <= (data_partition_->num_leaves()) at %s, line %d . +/__w/1/s/src/treelearner/data_parallel_tree_learner.cppCheck failed: (smaller_leaf_num_bits) <= (16) at %s, line %d . +Check failed: (larger_leaf_num_bits) <= (16) at %s, line %d . +GPU Tree Learner was not enabled in this build. +Please recompile with CMake option -DUSE_GPU=1N8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_DataParallelTreeLearner::ReduceHeHistogram::Copyistogram::ReduceN8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEEN8LightGBM19GradientDiscretizerE*ZN8LightGBM19GradientDiscretizer19DiscretizeGradientsEiPKfS2_EUliiiE_?A;GradientDiscretizer::RenewIntGrantGradTreeOutputvoid Eigen::internal::gemm_pack_rhs<Scalar, Index, DataMapper, nr, 0, Conjugate, PanelMode>::operator()(Scalar*, const DataMapper&, Index, Index, Index, Index) [with Scalar = double; Index = long int; DataMapper = Eigen::internal::blas_data_mapper<double, long int, 0, 0, 1>; int nr = 4; bool Conjugate = false; bool PanelMode = true]/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)Eigen::internal::blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, 1>::blas_data_mapper(Scalar*, Index, Index) [with Scalar = double; Index = long int; int StorageOrder = 0; int AlignmentType = 0]/__w/1/s/external_libs/eigen/Eigen/src/Core/util/BlasUtil.hvoid* Eigen::internal::aligned_malloc(std::size_t)/__w/1/s/external_libs/eigen/Eigen/src/Core/util/Memory.h(size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator."Detected that num_threads changed during training (from %d to %d), it may cause unexpected errors.No further splits with positive gain, best gain: %fTrained a tree with leaves = %d and depth = %dEigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 1; Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; Rhs = Eigen::Matrix<double, -1, -1>]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; RhsType = const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>; Lhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; Rhs = Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>]/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseBinaryOp.haLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_identity_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]void Eigen::SolverBase<Derived>::_check_solve_assertion(const Rhs&) const [with bool Transpose_ = false; Rhs = Eigen::MatrixBase<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> > >; Derived = Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> >]/__w/1/s/external_libs/eigen/Eigen/src/Core/SolverBase.hderived().m_isInitialized && "Solver is not initialized."(Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b"Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false>; PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; Rhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]incr==1\,t,,t,,,,t,,,,,,,,,,,,<,,,,,,,,,,,,,,,,\\,\,,,,,,,,,,,,,,,,,t,,,,,\t,,,,,,,,,,,,,,,\\<,,tRG0$&\\<||D&| |\|D|||D|&|]/__w/1/s/src/treelearner/linear_tree_learner.hvoid Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/PlainObjectBase.h(!(RowsAtCompileTime!=Dynamic) || (rows==RowsAtCompileTime)) && (!(ColsAtCompileTime!=Dynamic) || (cols==ColsAtCompileTime)) && (!(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic) || (rows<=MaxRowsAtCompileTime)) && (!(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic) || (cols<=MaxColsAtCompileTime)) && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array."void Eigen::FullPivLU<MatrixType>::computeInPlace() [with _MatrixType = Eigen::Matrix<double, -1, -1>]/__w/1/s/external_libs/eigen/Eigen/src/LU/FullPivLU.hm_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest()typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_max_op<double, double, 0>; Derived = Eigen::PartialReduxExpr<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, Eigen::internal::member_sum<double, double>, 0>; typename Eigen::internal::traits<T>::Scalar = double]/__w/1/s/external_libs/eigen/Eigen/src/Core/Redux.hthis->rows()>0 && this->cols()>0 && "you are using an empty matrix"typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, -1, 1, true>; typename Eigen::internal::traits<T>::Scalar = double]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, -1, 1>; Eigen::Index = long int]((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, 1, -1>; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; PointerType = double*; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/MapBase.h(dataPtr == 0) || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/Block.hstartRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockColstypename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::maxCoeff(IndexType*, IndexType*) const [with int NaNPropagation = 0; IndexType = long int; Derived = Eigen::CwiseUnaryOp<Eigen::internal::scalar_score_coeff_op<double>, const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false> >; typename Eigen::internal::traits<T>::Scalar = double]/__w/1/s/external_libs/eigen/Eigen/src/Core/Visitor.hEigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Scalar = double; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseCoeffsBase.hrow >= 0 && row < rows() && col >= 0 && col < cols()Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int](i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows()) ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols()))Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = false; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Derived& Eigen::PermutationBase<Derived>::applyTranspositionOnTheRight(Eigen::Index, Eigen::Index) [with Derived = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/PermutationMatrix.hi>=0 && j>=0 && i<size() && j<size()Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false> >, -1, 1, true>; PointerType = double*; Eigen::Index = long int]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 2]/__w/1/s/external_libs/eigen/Eigen/src/Core/SolveTriangular.hderived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols()))void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 5]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Scalar = double]/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.hdst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols()Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>; PointerType = const double*; Eigen::Index = long int]typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType Eigen::MatrixBase<Derived>::dot(const Eigen::MatrixBase<OtherDerived>&) const [with OtherDerived = Eigen::Block<const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>, -1, 1, true>; Derived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>; typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType = double; typename Eigen::internal::traits<OtherDerived>::Scalar = double; typename Eigen::internal::traits<T>::Scalar = double]/__w/1/s/external_libs/eigen/Eigen/src/Core/Dot.htypename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >, const Eigen::Block<const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType Eigen::MatrixBase<Derived>::dot(const Eigen::MatrixBase<OtherDerived>&) const [with OtherDerived = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Derived = Eigen::Block<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>, 1, -1, true>; typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType = double; typename Eigen::internal::traits<OtherDerived>::Scalar = double; typename Eigen::internal::traits<T>::Scalar = double]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>, 1, -1, true> >, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]size() == other.size()Eigen::Index Eigen::FullPivLU<MatrixType>::rank() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]m_isInitialized && "LU is not initialized."Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseNullaryOp.hrows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::PermutationMatrix<-1, -1, int>; _Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >; int Option = 2; Lhs = Eigen::PermutationMatrix<-1, -1, int>; Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >]/__w/1/s/external_libs/eigen/Eigen/src/Core/Product.hlhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 0; Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; SrcXprType = Eigen::Matrix<double, -1, -1>; Functor = sub_assign_op<double, double>]/__w/1/s/external_libs/eigen/Eigen/src/Core/AssignEvaluator.hdst.rows() == src.rows() && dst.cols() == src.cols()const Eigen::FullPivLU<MatrixType>::PermutationQType& Eigen::FullPivLU<MatrixType>::permutationQ() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; PermutationQType = Eigen::PermutationMatrix<-1, -1, int>]void Eigen::DenseBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::Index = long int]/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseBase.hrows == this->rows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType Eigen::MatrixBase<Derived>::dot(const Eigen::MatrixBase<OtherDerived>&) const [with OtherDerived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Derived = Eigen::Block<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false>, 1, -1, true>; typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType = double; typename Eigen::internal::traits<OtherDerived>::Scalar = double; typename Eigen::internal::traits<T>::Scalar = double]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false>, 1, -1, true> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]static Eigen::internal::redux_impl<Func, Evaluator, 0, 0>::Scalar Eigen::internal::redux_impl<Func, Evaluator, 0, 0>::run(const Evaluator&, const Func&, const XprType&) [with XprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false>, 1, -1, true> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> >; Func = Eigen::internal::scalar_sum_op<double, double>; Evaluator = Eigen::internal::redux_evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false>, 1, -1, true> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> > >; Scalar = double]xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix"void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, true>; PointerType = const double*; Eigen::Index = long int]typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType Eigen::MatrixBase<Derived>::dot(const Eigen::MatrixBase<OtherDerived>&) const [with OtherDerived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, true>; Derived = Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false>; typename Eigen::ScalarBinaryOpTraits<typename Eigen::internal::traits<T>::Scalar, typename Eigen::internal::traits<OtherDerived>::Scalar>::ReturnType = double; typename Eigen::internal::traits<OtherDerived>::Scalar = double; typename Eigen::internal::traits<T>::Scalar = double]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false> >, const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]static Eigen::internal::redux_impl<Func, Evaluator, 0, 0>::Scalar Eigen::internal::redux_impl<Func, Evaluator, 0, 0>::run(const Evaluator&, const Func&, const XprType&) [with XprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false> >, const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, true> >; Func = Eigen::internal::scalar_sum_op<double, double>; Evaluator = Eigen::internal::redux_evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_conj_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >, 1, -1, false> >, const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, true> > >; Scalar = double]const Eigen::Inverse<Eigen::FullPivLU<MatrixType> > Eigen::FullPivLU<MatrixType>::inverse() const [with _MatrixType = Eigen::Matrix<double, -1, -1>]m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 0; Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Rhs = Eigen::Matrix<double, -1, -1>]Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Scalar = double; Eigen::Index = long int]index >= 0 && index < size()N8LightGBM17LinearTreeLearnerE<SerialTreeLearne/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: (num_machines) > (1) at %s, line %d . Check failed: (bag_cnt) == (num_data_) at %s, line %d . share_state_ Can't be NULL at %s, line %d . -Check failed: (data_partition_->num_leaves()) >= (tree->num_leaves()) at %s, line %d . -SerialTreeLearner::BeforeTrainInvalid categorical threshold split'Forced Split' will be ignored since the gain getting worse.cegb_penalty_feature_lazy should be the same size as feature number.cegb_penalty_feature_coupled should be the same size as feature number.SerialTreeLearner::FindBestSplitsFromHistogramsGet historical Histogram for leaf %d failed, will skip the ``RecomputeBestSplitForLeaf``Check failed: (best_split_info.left_count) > (0) at %s, line %d . +Check failed: (smaller_hist_bits) <= (16) at %s, line %d . +Check failed: (larger_hist_bits) <= (16) at %s, line %d . +Check failed: (data_partition_->num_leaves()) >= (tree->num_leaves()) at %s, line %d . +Check failed: (num_features_) == (train_data_->num_features()) at %s, line %d . +cegb_penalty_feature_coupled should be the same size as feature number.cegb_penalty_feature_lazy should be the same size as feature number.Number of data points in the train set: %d, number of used features: %dCheck failed: (train_data->num_features()) > (0) at %s, line %d . +Get historical Histogram for leaf %d failed, will skip the ``RecomputeBestSplitForLeaf``Check failed: (best_split_info.left_count) > (0) at %s, line %d . Check failed: (best_split_info.right_count) > (0) at %s, line %d . -/__w/1/s/src/treelearner/feature_histogram.hppCheck failed: (cache_size_) >= (2) at %s, line %d . -Check failed: (train_data->num_features()) > (0) at %s, line %d . -Number of data points in the train set: %d, number of used features: %dCheck failed: (num_features_) == (train_data_->num_features()) at %s, line %d . -DataPartition::Splitvector<bool>::_M_insert_auxleftrightSerialTreeLearner::SplitInnerTotal Bins %dSt22_Maybe_get_result_typeILb1ESt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEESt22_Weak_result_type_implISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEEN8LightGBM15BasicConstraintESt17_Weak_result_typeISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEEN8LightGBM11TreeLearnerEN8LightGBM17FeatureConstraintEN8LightGBM15ConstraintEntryEN8LightGBM20BasicConstraintEntryEN8LightGBM26AdvancedFeatureConstraintsEN8LightGBM19LeafConstraintsBaseEN8LightGBM23AdvancedConstraintEntryEN8LightGBM20BasicLeafConstraintsEN8LightGBM27IntermediateLeafConstraintsEN8LightGBM23AdvancedLeafConstraintsEN8LightGBM17SerialTreeLearnerEZN8LightGBM13DataPartition5SplitEiPKNS_7DatasetEiPKjibiEUliiiPiS6_E_St5_BindIFSt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEPS2_St12_PlaceholderILi1EESC_ILi2EESC_ILi3EESC_ILi4EESC_ILi5EESC_ILi6EEEEZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS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exceptionField not found/__w/1/s/src/c_api.cpppm_ != nullptrUsed indices of subsets->rwcount & writer_maskThe number of features in data (%d) is not the same as it was in training data (%d). -You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.Feature (%s) is missed in data file. If it is weight/query/group/ignore_column, you can ignore this warning.Input data type error or field not foundUnknown indptr type in LGBM_BoosterFreePredictSparseUnknown data type in LGBM_BoosterFreePredictSparseUnknown data type in RowFunctionFromDenseMatricCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . -Unknown data type in CSC matrix/__w/1/s/include/LightGBM/utils/yamc/yamc_shared_lock.hpp/__w/1/s/include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp0 < (s->rwcount & reader_mask)Unknown data type in RowFunctionFromCSR(s->rwcount & reader_mask) < reader_maskCannot change forced bins after constructed Dataset handle.Cannot change data_random_seed after constructed Dataset handle.Cannot change max_bin after constructed Dataset handle.Cannot change max_bin_by_feature after constructed Dataset handle.Cannot change bin_construct_sample_cnt after constructed Dataset handle.Cannot change min_data_in_bin after constructed Dataset handle.Cannot change use_missing after constructed Dataset handle.Cannot change zero_as_missing after constructed Dataset handle.Cannot change categorical_feature after constructed Dataset handle.Cannot change feature_pre_filter after constructed Dataset handle.Cannot change is_enable_sparse after constructed Dataset handle.Cannot change pre_partition after constructed Dataset handle.Cannot change enable_bundle after constructed Dataset handle.Cannot change header after constructed Dataset handle.Cannot change two_round after constructed Dataset handle.Cannot change label_column after constructed Dataset handle.Cannot change weight_column after constructed Dataset handle.Cannot change group_column after constructed Dataset handle.Cannot change ignore_column after constructed Dataset handle.Reducing `min_data_in_leaf` with `feature_pre_filter=true` may cause unexpected behaviour for features that were pre-filtered by the larger `min_data_in_leaf`. -You need to set `feature_pre_filter=false` to dynamically change the `min_data_in_leaf`.Cannot change linear_tree after constructed Dataset handle.Cannot change precise_float_parser after constructed Dataset handle.LGBM_GetSampleCount output is nullptrCheck failed: (num_used_row_indices) > (0) at %s, line %d . -used_row_indices should be sorted in SubsetLGBM_SampleIndices output is nullptrThe number of columns should be greater than zero.The number of columns should be smaller than INT32_MAX.Check failed: (inner_data.first) < (num_col) at %s, line %d . -Using self-defined objective functionContinued train from model is not supported for c_api, -please use continued train with input scoreDo not support feature parallel in c apiOnly find one worker, will switch to serial tree learnerPrediction results file %s cannot be createdCould not recognize the data format of data file %sCannot change num_class during trainingCannot change boosting during trainingCannot change metric during training/__w/1/s/src/application/predictor.hppCheck failed: (early_stop_freq) > (0) at %s, line %d . -Check failed: (early_stop_margin) >= (0) at %s, line %d . -Predicting SHAP feature contributions is not implemented for linear trees.Unknown data type in PredictSparseUnknown col_ptr type in PredictSparseCSCUnknown indptr type in PredictSparseCSRUnknown matrix type in LGBM_BoosterPredictSparseOutputstatic bool yamc::rwlock::ReaderPrefer::release_rlock(yamc::rwlock::ReaderPrefer::state*)yamc::shared_lock<Mutex>::~shared_lock() [with Mutex = yamc::alternate::basic_shared_mutex<yamc::rwlock::ReaderPrefer>]static void yamc::rwlock::ReaderPrefer::acquire_rlock(yamc::rwlock::ReaderPrefer::state*)static void 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EEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE3_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE4_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE5_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE6_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUllddhhiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumric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d +CAG N +CAF L +AAB @x|AAG d +CAG N +CAF L +AAB @AAG d +CAG N +CAF L +AAB @AAG d +CAG N +CAF L +AAB @DpAAG d +CAG N +CAF L +AAB @AAG d +CAG N +CAF L +AAB @(AAG d +CAG N +CAF L +AAB @AAG d +CAG N +CAF L +AAB @TAAG d +CAG N +CAF L +AAB @AAG d +CAG N +CAF L +AAB @hAAG d +CAG N +CAF L +AAB @ AAG d +CAG N +CAF L +AAB @dAAG d +CAG N +CAF L +AAB @LAAG d +CAG N +CAF L +AAB @AAG d +CAG N +CAF L +AAB 00KADD T +GDH QAA8dPBAD v +DBM A +ABD 8ԮBAD v +DBM A +ABD DH BGE A(E0O +0A(A BBBA ,[LA H<5KBB A(D0F +(A BBBG t(A BBBEH0I +(A BBBH V +(A BBBK HhBBB B(A0D8D@W +8D0A(B BBBK 8BFA A(D@G +(A ABBE T fLh BBB E(A0D8G +8D0A(B BBBB (YP HBBB B(A0D8GP +8D0A(B BBBE L BBB B(A0D8DP~ +8A0A(B BBBA pYI_ P,D(/AAD  +AAA LpU AIA p`J +r,BDA  +ABA pLEJI B(A0A8 +0A(B BBBA q +0F(B BBBA D +0A(D BBBA H$tBBB B(A0A8D` +8A0A(B BBBB Px= BEB B(A0A8G  +8A0A(B BBBA W  <F,>Fh?$$BPGJ +AB LI\(M BEB E(A0D8DRvAL +8A0A(B BBBD \R^DphBHB A(A0J +0A(A BBBA P$TBBB B(D0D8G +8A0A(B BBBD x,WP]qBBB B(A0D8J +8D0A(B BBBC WRP|a}6BBB E(A0A8J  +8D0A(B 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