diff options
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/model.py | 22 | ||||
| -rw-r--r-- | lib/parameters.py | 18 |
2 files changed, 31 insertions, 9 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 diff --git a/lib/parameters.py b/lib/parameters.py index a615e5f..50a7ae8 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -866,6 +866,7 @@ class ModelAttribute: with_sklearn_cart=False, with_xgboost=False, with_lmt=False, + ignore_irrelevant_parameters=True, loss_ignore_scalar=False, threshold=100, ): @@ -980,6 +981,7 @@ class ModelAttribute: data, with_function_leaves=with_function_leaves, with_nonbinary_nodes=with_nonbinary_nodes, + ignore_irrelevant_parameters=ignore_irrelevant_parameters, loss_ignore_scalar=loss_ignore_scalar, threshold=threshold, ) @@ -990,6 +992,7 @@ class ModelAttribute: data, with_function_leaves=False, with_nonbinary_nodes=True, + ignore_irrelevant_parameters=True, loss_ignore_scalar=False, threshold=100, level=0, @@ -1050,12 +1053,13 @@ class ModelAttribute: ffs_feasible = True continue - std_by_param = _mean_std_by_param( - by_param, distinct_values_by_param_index, param_index - ) - if not _depends_on_param(None, std_by_param, std_lut): - loss.append(np.inf) - continue + if ignore_irrelevant_parameters: + std_by_param = _mean_std_by_param( + by_param, distinct_values_by_param_index, param_index + ) + if not _depends_on_param(None, std_by_param, std_lut): + loss.append(np.inf) + continue child_indexes = list() for value in unique_values: @@ -1141,6 +1145,8 @@ class ModelAttribute: child_data, with_function_leaves=with_function_leaves, with_nonbinary_nodes=with_nonbinary_nodes, + ignore_irrelevant_parameters=ignore_irrelevant_parameters, + loss_ignore_scalar=loss_ignore_scalar, threshold=threshold, level=level + 1, ) |