diff options
Diffstat (limited to 'lib/model.py')
| -rw-r--r-- | lib/model.py | 22 |
1 files changed, 19 insertions, 3 deletions
diff --git a/lib/model.py b/lib/model.py index 3b1279f..227a323 100644 --- a/lib/model.py +++ b/lib/model.py @@ -162,11 +162,14 @@ class AnalyticModel: ) with_lmt = bool(int(os.getenv("DFATOOL_DTREE_LMT", "0"))) with_xgboost = bool(int(os.getenv("DFATOOL_USE_XGBOOST", "0"))) + ignore_irrelevant_parameters = bool( + int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "1")) + ) loss_ignore_scalar = bool( int(os.getenv("DFATOOL_DTREE_LOSS_IGNORE_SCALAR", "0")) ) logger.debug( - f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves}, with_nonbinary_nodes={with_nonbinary_nodes}, loss_ignore_scalar={loss_ignore_scalar})" + f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves}, with_nonbinary_nodes={with_nonbinary_nodes}, ignore_irrelevant_parameters={ignore_irrelevant_parameters}, loss_ignore_scalar={loss_ignore_scalar})" ) self.build_dtree( name, @@ -177,6 +180,7 @@ class AnalyticModel: with_sklearn_cart=with_sklearn_cart, with_lmt=with_lmt, with_xgboost=with_xgboost, + ignore_irrelevant_parameters=ignore_irrelevant_parameters, loss_ignore_scalar=loss_ignore_scalar, ) self.fit_done = True @@ -330,6 +334,11 @@ class AnalyticModel: ) with_lmt = bool(int(os.getenv("DFATOOL_DTREE_LMT", "0"))) with_xgboost = bool(int(os.getenv("DFATOOL_USE_XGBOOST", "0"))) + ignore_irrelevant_parameters = bool( + int( + os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "1") + ) + ) loss_ignore_scalar = bool( int(os.getenv("DFATOOL_DTREE_LOSS_IGNORE_SCALAR", "0")) ) @@ -341,7 +350,7 @@ class AnalyticModel: ): threshold = self.dtree_max_std[name][attr] logger.debug( - f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves}, with_nonbinary_nodes={with_nonbinary_nodes}, loss_ignore_scalar={loss_ignore_scalar})" + f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves}, ignore_irrelevant_parameters={ignore_irrelevant_parameters}, with_nonbinary_nodes={with_nonbinary_nodes}, loss_ignore_scalar={loss_ignore_scalar})" ) self.build_dtree( name, @@ -352,6 +361,7 @@ class AnalyticModel: with_sklearn_cart=with_sklearn_cart, with_lmt=with_lmt, with_xgboost=with_xgboost, + ignore_irrelevant_parameters=ignore_irrelevant_parameters, loss_ignore_scalar=loss_ignore_scalar, ) else: @@ -433,6 +443,7 @@ class AnalyticModel: with_sklearn_cart=False, with_lmt=False, with_xgboost=False, + ignore_irrelevant_parameters=True, loss_ignore_scalar=False, ): @@ -457,6 +468,7 @@ class AnalyticModel: with_sklearn_cart=with_sklearn_cart, with_lmt=with_lmt, with_xgboost=with_xgboost, + ignore_irrelevant_parameters=ignore_irrelevant_parameters, loss_ignore_scalar=loss_ignore_scalar, threshold=threshold, ) @@ -759,11 +771,14 @@ class PTAModel(AnalyticModel): ) with_lmt = bool(int(os.getenv("DFATOOL_DTREE_LMT", "0"))) with_xgboost = bool(int(os.getenv("DFATOOL_USE_XGBOOST", "0"))) + ignore_irrelevant_parameters = bool( + int(os.getenv("DFATOOL_DTREE_IGNORE_IRRELEVANT_PARAMS", "1")) + ) loss_ignore_scalar = bool( int(os.getenv("DFATOOL_DTREE_LOSS_IGNORE_SCALAR", "0")) ) logger.debug( - f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves}, with_nonbinary_nodes={with_nonbinary_nodes}, loss_ignore_scalar={loss_ignore_scalar})" + f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves}, with_nonbinary_nodes={with_nonbinary_nodes}, ignore_irrelevant_parameters={ignore_irrelevant_parameters}, loss_ignore_scalar={loss_ignore_scalar})" ) self.build_dtree( name, @@ -774,6 +789,7 @@ class PTAModel(AnalyticModel): with_sklearn_cart=with_sklearn_cart, with_lmt=with_lmt, with_xgboost=with_xgboost, + ignore_irrelevant_parameters=ignore_irrelevant_parameters, loss_ignore_scalar=loss_ignore_scalar, ) self.fit_done = True |