1.5.3 +1.5.1
/tmp/ipykernel_1883/116020630.py:6: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.
+ df = pd.DataFrame({'y_test': y_test[:10].to_numpy(), 'y_pred': housing_predictions[:10]}, index=[x for x in range(10)])
+
+/tmp/ipykernel_1883/3630218259.py:15: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.
+ df = pd.DataFrame({'y_test': y_test[:10].to_numpy(
+
+[CV] END max_features=1.0, min_samples_split=2, n_estimators=25; total time= 1.7s +[CV] END max_features=1.0, min_samples_split=2, n_estimators=25; total time= 1.6s
[CV] END max_features=1.0, min_samples_split=5, n_estimators=100; total time= 6.1s +[CV] END max_features=1.0, min_samples_split=5, n_estimators=100; total time= 6.2s
[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 1.8s +[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 1.9s
/tmp/ipykernel_1883/3630218259.py:15: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.
+ df = pd.DataFrame({'y_test': y_test[:10].to_numpy(
+
+<matplotlib.collections.PathCollection at 0x7f8a13c44af0>+
<matplotlib.collections.PathCollection at 0x7f7a4eeb7a30>
[CV] END max_depth=5, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 2.0s +[CV] END max_depth=5, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 2.1s
[CV] END max_depth=5, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 2.0s +[CV] END max_depth=5, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 2.1s
[CV] END max_depth=2, max_features=1.0, min_samples_leaf=5, n_estimators=150; total time= 1.4s +[CV] END max_depth=2, max_features=1.0, min_samples_leaf=5, n_estimators=150; total time= 1.3s
[flaml.automl.logger: 04-09 14:51:36] {1680} INFO - task = classification
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[flaml.automl.logger: 04-09 14:51:36] {1691} INFO - Evaluation method: cv
+[flaml.automl.logger: 04-09 14:52:48] {1691} INFO - Evaluation method: cv
[flaml.automl.logger: 04-09 14:51:36] {1789} INFO - Minimizing error metric: 1-accuracy
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[flaml.automl.logger: 04-09 14:51:36] {1901} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'lrl1']
+[flaml.automl.logger: 04-09 14:52:48] {1901} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'lrl1']
[flaml.automl.logger: 04-09 14:51:36] {2219} INFO - iteration 0, current learner lgbm
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[flaml.automl.logger: 04-09 14:51:36] {2345} INFO - Estimated sufficient time budget=1454s. Estimated necessary time budget=34s.
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[flaml.automl.logger: 04-09 14:52:37] {2631} INFO - retrained model: LGBMClassifier(colsample_bytree=0.6782006405163307,
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learning_rate=0.08222714104485472, max_bin=511,
min_child_samples=5, n_estimators=1, n_jobs=-1, num_leaves=254,
reg_alpha=0.003934614746573571, reg_lambda=0.003560646472734122,
@@ -10714,14 +10738,14 @@ AutoML with auto-sklearn
-[flaml.automl.logger: 04-09 14:52:37] {1931} INFO - fit succeeded
+[flaml.automl.logger: 04-09 14:53:49] {1931} INFO - fit succeeded
[flaml.automl.logger: 04-09 14:52:37] {1932} INFO - Time taken to find the best model: 45.995359897613525
+[flaml.automl.logger: 04-09 14:53:49] {1932} INFO - Time taken to find the best model: 46.7029709815979