diff options
| -rw-r--r-- | lib/functions.py | 23 | ||||
| -rw-r--r-- | lib/parameters.py | 23 |
2 files changed, 29 insertions, 17 deletions
diff --git a/lib/functions.py b/lib/functions.py index 4be32c6..1b8612e 100644 --- a/lib/functions.py +++ b/lib/functions.py @@ -177,11 +177,12 @@ class ModelFunction: :type value_error: dict, optional """ - def __init__(self, value): + def __init__(self, value, n_samples=None): # a model always has a static (median/mean) value. For StaticFunction, it's the only data point. # For more complex models, it's usede both as fallback in case the model cannot predict the current # parameter combination, and for use cases requiring static models self.value = value + self.n_samples = n_samples # A ModelFunction may track its own accuracy, both of the static value and of the eval() method. # However, it does not specify how the accuracy was calculated (e.g. which data was used and whether cross-validation was performed) @@ -213,6 +214,7 @@ class ModelFunction: """Convert model to JSON.""" ret = { "value": self.value, + "n_samples": self.n_samples, "valueError": self.value_error, "functionError": self.function_error, } @@ -308,8 +310,8 @@ class StaticFunction(ModelFunction): class SplitFunction(ModelFunction): - def __init__(self, value, param_index, child): - super().__init__(value) + def __init__(self, value, param_index, child, **kwargs): + super().__init__(value, **kwargs) self.param_index = param_index self.child = child @@ -416,8 +418,8 @@ class SplitFunction(ModelFunction): class SubstateFunction(ModelFunction): - def __init__(self, value, sequence_by_count, count_model, sub_model): - super().__init__(value) + def __init__(self, value, sequence_by_count, count_model, sub_model, **kwargs): + super().__init__(value, **kwargs) self.sequence_by_count = sequence_by_count self.count_model = count_model self.sub_model = sub_model @@ -475,8 +477,8 @@ class SKLearnRegressionFunction(ModelFunction): always_predictable = True has_eval_arr = True - def __init__(self, value, regressor, categorial_to_index, ignore_index): - super().__init__(value) + def __init__(self, value, regressor, categorial_to_index, ignore_index, **kwargs): + super().__init__(value, **kwargs) self.regressor = regressor self.categorial_to_index = categorial_to_index self.ignore_index = ignore_index @@ -734,8 +736,8 @@ class XGBoostFunction(SKLearnRegressionFunction): class FOLFunction(ModelFunction): always_predictable = True - def __init__(self, value, parameters, num_args=0): - super().__init__(value) + def __init__(self, value, parameters, num_args=0, **kwargs): + super().__init__(value, **kwargs) self.parameter_names = parameters self._num_args = num_args self.fit_success = False @@ -904,6 +906,7 @@ class AnalyticFunction(ModelFunction): num_args=0, regression_args=None, fit_by_param=None, + **kwargs, ): """ Create a new AnalyticFunction object from a function string. @@ -923,7 +926,7 @@ class AnalyticFunction(ModelFunction): both for function usage and least squares optimization. If unset, defaults to [1, 1, 1, ...] """ - super().__init__(value) + super().__init__(value, **kwargs) self._parameter_names = parameters self._num_args = num_args self.model_function = function_str 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)) |