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feat: xgboost gpu accelerate #359

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Sep 26, 2024
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feat: xgboost gpu accelerate #359

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bf05923
gpu accelerate
TPLin22 Sep 26, 2024
8ebdf40
delete nn of sf-crime
TPLin22 Sep 26, 2024
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6 changes: 4 additions & 2 deletions ...aggle/experiment/feedback-prize-english-language-learning_template/model/model_xgboost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,9 +21,11 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
"""Define and train the model. Merge feature_select"""
X_train = select(X_train)

xgb_estimator = xgb.XGBRegressor(n_estimators=500, random_state=0, objective="reg:squarederror")
xgb_estimator = xgb.XGBRegressor(
n_estimators=500, random_state=0, objective="reg:squarederror", tree_method="gpu_hist", device="cuda"
)

model = MultiOutputRegressor(xgb_estimator, n_jobs=2)
model = MultiOutputRegressor(xgb_estimator, n_jobs=-1)

if is_sparse_df(X_train):
X_train = X_train.sparse.to_coo()
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2 changes: 2 additions & 0 deletions .../scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/model_xgboost.py
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Expand Up @@ -22,6 +22,8 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
"objective": "multi:softmax", # Use softmax for multi-class classification
"num_class": len(set(y_train)), # Number of classes
"nthread": -1,
"tree_method": "gpu_hist",
"device": "cuda",
}
num_round = 100

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2 changes: 2 additions & 0 deletions .../kaggle/experiment/optiver-realized-volatility-prediction_template/model/model_xgboost.py
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Expand Up @@ -18,6 +18,8 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
params = {
"objective": "reg:squarederror", # Use squared error for regression
"nthread": -1,
"tree_method": "gpu_hist",
"device": "cuda",
}
num_round = 200

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4 changes: 3 additions & 1 deletion rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/model_xgboost.py
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Expand Up @@ -24,7 +24,9 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
params = {
"objective": "multi:softprob",
"num_class": num_classes,
"nthred": -1,
"nthread": -1,
"tree_method": "gpu_hist",
"device": "cuda",
}
num_round = 100

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2 changes: 2 additions & 0 deletions rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/model_xgboost.py
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Expand Up @@ -21,6 +21,8 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
# TODO: for quick running....
params = {
"nthread": -1,
"tree_method": "gpu_hist",
"device": "cuda",
}
num_round = 180

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4 changes: 3 additions & 1 deletion rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/model_xgboost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,10 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
params = {
"objective": "reg:squarederror", # Use squared error for regression
"nthread": -1,
"tree_method": "gpu_hist",
"device": "cuda",
}
num_round = 200
num_round = 10

evallist = [(dtrain, "train"), (dvalid, "eval")]
bst = xgb.train(params, dtrain, num_round, evallist)
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79 changes: 0 additions & 79 deletions rdagent/scenarios/kaggle/experiment/sf-crime_template/model/model_nn.py
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This file was deleted.

4 changes: 3 additions & 1 deletion rdagent/scenarios/kaggle/experiment/sf-crime_template/model/model_xgboost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,9 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
params = {
"objective": "multi:softprob",
"num_class": num_classes,
"nthred": -1,
"nthread": -1,
"tree_method": "hist",
"device": "cuda",
}
num_round = 100

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2 changes: 2 additions & 0 deletions rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/model_xgboost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,8 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
# TODO: for quick running....
params = {
"nthread": -1,
"tree_method": "gpu_hist",
"device": "cuda",
}
num_round = 100

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