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Fix xgboost example for hyperparameter tuning #476

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merged 4 commits into from
Jan 15, 2019
Merged

Fix xgboost example for hyperparameter tuning #476

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9e1ee20
Fix xgboost for hp tuning
richardsliu Jan 14, 2019
8437ec9
Fix logging
richardsliu Jan 14, 2019
1b29c21
Merge remote-tracking branch 'upstream/master' into hp_tuning
richardsliu Jan 15, 2019
3859564
Fix pylint and log fmt
richardsliu Jan 15, 2019
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Fix pylint and log fmt
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@richardsliu
richardsliu committed Jan 15, 2019
commit 385956442234e15e761bd72f3402012ad40b45d9
14 changes: 8 additions & 6 deletions xgboost_ames_housing/housing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,8 @@
# limitations under the License.

import argparse
import joblib
import logging
import joblib
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
Expand Down Expand Up @@ -55,20 +55,20 @@ def train_model(train_X,
early_stopping_rounds=40,
eval_set=[(test_X, test_y)])

logging.info("Best RMSE on eval: {:.2f} with {} rounds".format(
model.best_score,
model.best_iteration+1))
logging.info("Best RMSE on eval: %.2f with %d rounds",
model.best_score,
model.best_iteration+1)
return model

def eval_model(model, test_X, test_y):
"""Evaluate the model performance."""
predictions = model.predict(test_X)
logging.info("mean_absolute_error={:.2f}".format(mean_absolute_error(predictions, test_y)))
logging.info("mean_absolute_error=%.2f", mean_absolute_error(predictions, test_y))

def save_model(model, model_file):
"""Save XGBoost model for serving."""
joblib.dump(model, model_file)
logging.info("Model export success {}".format(model_file))
logging.info("Model export success: %s", model_file)

def main(args):
(train_X, train_y), (test_X, test_y) = read_input(args.train_input)
Expand Down Expand Up @@ -117,5 +117,7 @@ def main(args):
default=50
)

logging.basicConfig(format='%(message)s')
logging.getLogger().setLevel(logging.INFO)
main_args = parser.parse_args()
main(main_args)