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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from .utils import *
try:
import xgboost as xgb
except ImportError:
xgb = None
class XGBoostCostModel():
def __init__(self, loss_type, num_threads=None, log_interval=25, upper_model=None):
assert xgb is not None, "missing requirements, please install deepspeed w. 'autotuning_ml' extra."
self.loss_type = loss_type
if loss_type == "reg":
self.xgb_params = {
"max_depth": 3,
"gamma": 0.0001,
"min_child_weight": 1,
"subsample": 1.0,
"eta": 0.3,
"lambda": 1.0,
"alpha": 0,
"objective": "reg:linear",
}
elif loss_type == "rank":
self.xgb_params = {
"max_depth": 3,
"gamma": 0.0001,
"min_child_weight": 1,
"subsample": 1.0,
"eta": 0.3,
"lambda": 1.0,
"alpha": 0,
"objective": "rank:pairwise",
}
else:
raise RuntimeError("Invalid loss type: " + loss_type)
self.xgb_params["verbosity"] = 0
if num_threads:
self.xgb_params["nthread"] = num_threads
def fit(self, xs, ys):
x_train = np.array(xs, dtype=np.float32)
y_train = np.array(ys, dtype=np.float32)
y_max = np.max(y_train)
y_train = y_train / max(y_max, 1e-9)
index = np.random.permutation(len(x_train))
dtrain = xgb.DMatrix(x_train[index], y_train[index])
self.bst = xgb.train(self.xgb_params, dtrain)
def predict(self, xs):
features = xgb.DMatrix(xs)
return self.bst.predict(features)
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