diff options
Diffstat (limited to 'lib/model.py')
| -rw-r--r-- | lib/model.py | 17 |
1 files changed, 6 insertions, 11 deletions
diff --git a/lib/model.py b/lib/model.py index e908af4..bb4a45b 100644 --- a/lib/model.py +++ b/lib/model.py @@ -5,6 +5,7 @@ import numpy as np from scipy import optimize from sklearn.metrics import r2_score from multiprocessing import Pool +from .automata import PTA from .functions import analytic from .functions import AnalyticFunction from .parameters import ParamStats @@ -700,7 +701,6 @@ class PTAModel: arg_count, traces=[], ignore_trace_indexes=[], - discard_outliers=None, function_override={}, use_corrcoef=False, pta=None, @@ -716,13 +716,6 @@ class PTAModel: arg_count -- function arguments, as returned by pta_trace_to_aggregate traces -- list of preprocessed DFA traces, as returned by RawData.get_preprocessed_data() ignore_trace_indexes -- list of trace indexes. The corresponding traces will be ignored. - discard_outliers -- currently not supported: threshold for outlier detection and removel (float). - Outlier detection is performed individually for each state/transition in each trace, - so it only works if the benchmark ran several times. - Given "data" (a set of measurements of the same thing, e.g. TX duration in the third benchmark trace), - "m" (the median of all attribute measurements with the same parameters, which may include data from other traces), - a data point X is considered an outlier if - | 0.6745 * (X - m) / median(|data - m|) | > discard_outliers . function_override -- dict of overrides for automatic parameter function generation. If (state or transition name, model attribute) is present in function_override, the corresponding text string is the function used for analytic (parameter-aware/fitted) @@ -749,7 +742,6 @@ class PTAModel: ) self.cache = {} np.seterr("raise") - self._outlier_threshold = discard_outliers self.function_override = function_override.copy() self.pta = pta self.ignore_trace_indexes = ignore_trace_indexes @@ -940,13 +932,16 @@ class PTAModel: static_quality = self.assess(static_model) param_model, param_info = self.get_fitted() analytic_quality = self.assess(param_model) - self.pta.update( + pta = self.pta + if pta is None: + pta = PTA(self.states(), parameters=self._parameter_names) + pta.update( static_model, param_info, static_error=static_quality["by_name"], analytic_error=analytic_quality["by_name"], ) - return self.pta.to_json() + return pta.to_json() def states(self): """Return sorted list of state names.""" |