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Code Issues Packages Projects Releases Wiki Activity

fix bug in feature selection (#398)

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This commit is contained in:
XianBW
2024-09-30 01:21:45 +08:00
committed by GitHub
parent 7fc53940fd
commit 455e73858e
25 changed files with 25 additions and 25 deletions
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rdagent/scenarios/kaggle/developer/coder.py
+1 -1
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@@ -29,7 +29,7 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
{% if feature_index_list is not none %}
X = X.loc[:, X.columns.levels[0][{{feature_index_list}}].tolist()]
{% endif %}
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
"""
rdagent/scenarios/kaggle/experiment/covid19-global-forecasting-week-1_template/model/select_xgboost.py
+1 -1
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@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_nn.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_nn.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_nn.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_randomforest.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X
rdagent/scenarios/kaggle/experiment/statoil-iceberg-classifier-challenge_template/model/select_xgboost.py
+1 -1
View File
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
# For now, we assume all features are relevant. This can be expanded to feature selection logic.
if X.columns.nlevels == 1:
return X
X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
return X