diff options
Diffstat (limited to 'lib/model.py')
| -rw-r--r-- | lib/model.py | 126 |
1 files changed, 6 insertions, 120 deletions
diff --git a/lib/model.py b/lib/model.py index baa22da..1c19f14 100644 --- a/lib/model.py +++ b/lib/model.py @@ -151,41 +151,11 @@ class AnalyticModel: threshold = (self.attr_by_name[name][attr].stats.std_param_lut,) else: threshold = 0 - with_function_leaves = bool( - int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1")) - ) - with_nonbinary_nodes = bool( - int(os.getenv("DFATOOL_DTREE_NONBINARY_NODES", "1")) - ) - with_sklearn_cart = bool( - int(os.getenv("DFATOOL_DTREE_SKLEARN_CART", "0")) - ) - with_sklearn_decart = bool( - int(os.getenv("DFATOOL_DTREE_SKLEARN_DECART", "0")) - ) - 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}, ignore_irrelevant_parameters={ignore_irrelevant_parameters}, loss_ignore_scalar={loss_ignore_scalar})" - ) + logger.debug(f"build_dtree({name}, {attr}, threshold={threshold})") self.build_dtree( name, attr, threshold=threshold, - with_function_leaves=with_function_leaves, - with_nonbinary_nodes=with_nonbinary_nodes, - with_sklearn_cart=with_sklearn_cart, - with_sklearn_decart=with_sklearn_decart, - with_lmt=with_lmt, - with_xgboost=with_xgboost, - ignore_irrelevant_parameters=ignore_irrelevant_parameters, - loss_ignore_scalar=loss_ignore_scalar, ) self.fit_done = True @@ -327,28 +297,6 @@ class AnalyticModel: for name in self.names: for attr in self.attr_by_name[name].keys(): if tree_required[name].get(attr, False): - with_function_leaves = bool( - int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1")) - ) - with_nonbinary_nodes = bool( - int(os.getenv("DFATOOL_DTREE_NONBINARY_NODES", "1")) - ) - with_sklearn_cart = bool( - int(os.getenv("DFATOOL_DTREE_SKLEARN_CART", "0")) - ) - with_sklearn_decart = bool( - int(os.getenv("DFATOOL_DTREE_SKLEARN_DECART", "0")) - ) - 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")) - ) threshold = self.attr_by_name[name][attr].stats.std_param_lut if ( self.dtree_max_std @@ -357,21 +305,9 @@ class AnalyticModel: ): threshold = self.dtree_max_std[name][attr] logger.debug( - 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, - attr, - threshold=threshold, - with_function_leaves=with_function_leaves, - with_nonbinary_nodes=with_nonbinary_nodes, - with_sklearn_cart=with_sklearn_cart, - with_sklearn_decart=with_sklearn_decart, - with_lmt=with_lmt, - with_xgboost=with_xgboost, - ignore_irrelevant_parameters=ignore_irrelevant_parameters, - loss_ignore_scalar=loss_ignore_scalar, + f"build_dtree({name}, {attr}, threshold={threshold})" ) + self.build_dtree(name, attr, threshold=threshold) else: self.attr_by_name[name][attr].set_data_from_paramfit(paramfit) @@ -450,20 +386,7 @@ class AnalyticModel: return detailed_results, raw_results return detailed_results - def build_dtree( - self, - name, - attribute, - threshold=100, - with_function_leaves=False, - with_nonbinary_nodes=True, - with_sklearn_cart=False, - with_sklearn_decart=False, - with_lmt=False, - with_xgboost=False, - ignore_irrelevant_parameters=True, - loss_ignore_scalar=False, - ): + def build_dtree(self, name, attribute, threshold=100, **kwargs): if name not in self.attr_by_name: self.attr_by_name[name] = dict() @@ -481,15 +404,8 @@ class AnalyticModel: self.attr_by_name[name][attribute].build_dtree( self.by_name[name]["param"], self.by_name[name][attribute], - with_function_leaves=with_function_leaves, - with_nonbinary_nodes=with_nonbinary_nodes, - with_sklearn_cart=with_sklearn_cart, - with_sklearn_decart=with_sklearn_decart, - with_lmt=with_lmt, - with_xgboost=with_xgboost, - ignore_irrelevant_parameters=ignore_irrelevant_parameters, - loss_ignore_scalar=loss_ignore_scalar, threshold=threshold, + **kwargs, ) def to_dref( @@ -779,41 +695,11 @@ class PTAModel(AnalyticModel): threshold = (self.attr_by_name[name][attr].stats.std_param_lut,) else: threshold = 0 - with_function_leaves = bool( - int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1")) - ) - with_nonbinary_nodes = bool( - int(os.getenv("DFATOOL_DTREE_NONBINARY_NODES", "1")) - ) - with_sklearn_cart = bool( - int(os.getenv("DFATOOL_DTREE_SKLEARN_CART", "0")) - ) - with_sklearn_decart = bool( - int(os.getenv("DFATOOL_DTREE_SKLEARN_DECART", "0")) - ) - 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}, ignore_irrelevant_parameters={ignore_irrelevant_parameters}, loss_ignore_scalar={loss_ignore_scalar})" - ) + logger.debug(f"build_dtree({name}, {attr}, threshold={threshold})") self.build_dtree( name, attr, threshold=threshold, - with_function_leaves=with_function_leaves, - with_nonbinary_nodes=with_nonbinary_nodes, - with_sklearn_cart=with_sklearn_cart, - with_sklearn_decart=with_sklearn_decart, - with_lmt=with_lmt, - with_xgboost=with_xgboost, - ignore_irrelevant_parameters=ignore_irrelevant_parameters, - loss_ignore_scalar=loss_ignore_scalar, ) self.fit_done = True |