diff options
Diffstat (limited to 'lib/parameters.py')
| -rw-r--r-- | lib/parameters.py | 23 |
1 files changed, 16 insertions, 7 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index dd435e6..0f23ef8 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -920,6 +920,7 @@ class ModelAttribute: function_str, self.param_names, self.arg_count, + n_samples=self.data.shape[0], # fit_by_param=fit_result, ) x.fit(self.by_param) @@ -1031,7 +1032,9 @@ class ModelAttribute: logger.warning( f"Cannot generate CART for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" ) - self.model_function = df.StaticFunction(np.mean(data)) + self.model_function = df.StaticFunction( + np.mean(data), n_samples=len(data) + ) return logger.debug("Fitting sklearn CART ...") cart.fit(fit_parameters, data) @@ -1065,7 +1068,9 @@ class ModelAttribute: logger.warning( f"Cannot run XGBoost for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" ) - self.model_function = df.StaticFunction(np.mean(data)) + self.model_function = df.StaticFunction( + np.mean(data), n_samples=len(data) + ) return xgb.fit(fit_parameters, np.reshape(data, (-1, 1))) self.model_function = df.XGBoostFunction( @@ -1090,14 +1095,18 @@ class ModelAttribute: logger.warning( f"Cannot generate LMT for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" ) - self.model_function = df.StaticFunction(np.mean(data)) + self.model_function = df.StaticFunction( + np.mean(data), n_samples=len(data) + ) return logger.debug("Fitting LMT ...") try: lmt.fit(fit_parameters, data) except np.linalg.LinAlgError as e: logger.error(f"LMT generation for {self.name} {self.attr} failed: {e}") - self.model_function = df.StaticFunction(np.mean(data)) + self.model_function = df.StaticFunction( + np.mean(data), n_samples=len(data) + ) return logger.debug("Fitted LMT") self.model_function = df.LMTFunction( @@ -1156,7 +1165,7 @@ class ModelAttribute: param_count = nonarg_count + self.arg_count # TODO eigentlich muss threshold hier auf Basis der aktuellen Messdatenpartition neu berechnet werden if param_count == 0 or np.std(data) <= threshold: - return df.StaticFunction(np.mean(data)) + return df.StaticFunction(np.mean(data), n_samples=len(data)) # sf.value_error["std"] = np.std(data) loss = list() @@ -1294,7 +1303,7 @@ class ModelAttribute: paramfit.fit() ma.set_data_from_paramfit(paramfit) return ma.model_function - return df.StaticFunction(np.mean(data)) + return df.StaticFunction(np.mean(data), n_samples=len(data)) split_feasible = True if loss_ignore_scalar: @@ -1365,4 +1374,4 @@ class ModelAttribute: assert len(child.values()) >= 2 - return df.SplitFunction(np.mean(data), symbol_index, child) + return df.SplitFunction(np.mean(data), symbol_index, child, n_samples=len(data)) |