diff --git a/extreme_fit/model/abstract_model.py b/extreme_fit/model/abstract_model.py
index 9aed76a7714e6d07122cd95b6f7c9dc7db1b50a5..ff4c43c1ce25869cb7bac632e550c2ca1575a8ae 100644
--- a/extreme_fit/model/abstract_model.py
+++ b/extreme_fit/model/abstract_model.py
@@ -8,7 +8,7 @@ class AbstractModel(object):
@property
def params_start_fit(self) -> dict:
- return self.merge_params(default_params=self.default_params, input_params=self.user_params_start_fit)
+ return self.default_params.copy()
@property
def params_sample(self) -> dict:
diff --git a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
index 831ae8a958326146be74936e77bcd56f7354478c..f418032c9231b6b838db5d3f7218af4926a7a8bf 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
@@ -57,7 +57,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
def ismev_gev_fit(self, x, df_coordinates_temp) -> ResultFromIsmev:
y = df_coordinates_temp.values
- res = safe_run_r_estimator(function=r('gev.fit'), use_start=self.use_start_value,
+ res = safe_run_r_estimator(function=r('gev.fit'),
xdat=x, y=y, mul=self.mul,
sigl=self.sigl, shl=self.shl)
return ResultFromIsmev(res, self.margin_function_start_fit.param_name_to_dims)
diff --git a/extreme_fit/model/margin_model/parametric_margin_model.py b/extreme_fit/model/margin_model/parametric_margin_model.py
index b27eeb14f6e6439c4a7d4db85702a674865dfa08..2e33984c3adcae59f09b0f56157eae6054656650 100644
--- a/extreme_fit/model/margin_model/parametric_margin_model.py
+++ b/extreme_fit/model/margin_model/parametric_margin_model.py
@@ -6,11 +6,11 @@ import pandas as pd
from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.function.margin_function.parametric_margin_function import \
ParametricMarginFunction
+from extreme_fit.model.margin_model.abstract_margin_model import AbstractMarginModel
from extreme_fit.model.margin_model.utils import MarginFitMethod
from extreme_fit.model.result_from_model_fit.result_from_spatial_extreme import ResultFromSpatialExtreme
-from extreme_fit.model.margin_model.abstract_margin_model import AbstractMarginModel
-from extreme_fit.model.utils import safe_run_r_estimator, r, get_coord, \
- get_margin_formula_spatial_extreme
+from extreme_fit.model.utils import r, get_coord, \
+ get_margin_formula_spatial_extreme, safe_run_r_estimator
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -45,7 +45,8 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
fit_params['temp.cov'] = get_coord(df_coordinates=df_coordinates_temp)
# Start parameters
coef_dict = self.margin_function_start_fit.coef_dict
- fit_params['start'] = r.list(**coef_dict)
- res = safe_run_r_estimator(function=r.fitspatgev, use_start=self.use_start_value, data=data,
+ # fit_params['start'] = r.list(**coef_dict)
+ res = safe_run_r_estimator(function=r.fitspatgev, data=data,
+ start_dict=coef_dict,
covariables=covariables, **fit_params)
return ResultFromSpatialExtreme(res)
diff --git a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
index d1942525fc617270ca395e20f33ef2b36b9f06ad..69f5e9cbda4639319a98d76b8dc9c065bfa0c95e 100644
--- a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
@@ -7,8 +7,8 @@ from rpy2.rinterface._rinterface import RRuntimeError
from extreme_fit.model.abstract_model import AbstractModel
from extreme_fit.model.result_from_model_fit.result_from_spatial_extreme import ResultFromSpatialExtreme
-from extreme_fit.model.utils import r, safe_run_r_estimator, get_coord, \
- get_margin_formula_spatial_extreme, SafeRunException
+from extreme_fit.model.utils import r, get_coord, \
+ get_margin_formula_spatial_extreme, SafeRunException, safe_run_r_estimator
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -58,7 +58,6 @@ class AbstractMaxStableModel(AbstractModel):
fit_params.update(margin_formulas)
if fitmaxstab_with_one_dimensional_data:
fit_params['iso'] = True
- fit_params['start'] = r.list(**start_dict)
fit_params['fit.marge'] = fit_marge
# Add some temporal covariates
@@ -69,7 +68,8 @@ class AbstractMaxStableModel(AbstractModel):
fit_params['temp.cov'] = get_coord(df_coordinates_temp)
# Run the fitmaxstab in R
- res = safe_run_r_estimator(function=r.fitmaxstab, use_start=self.use_start_value, data=data, coord=coord,
+ res = safe_run_r_estimator(function=r.fitmaxstab, data=data, coord=coord,
+ start_dict=start_dict,
**fit_params)
return ResultFromSpatialExtreme(res)
diff --git a/extreme_fit/model/max_stable_model/max_stable_models.py b/extreme_fit/model/max_stable_model/max_stable_models.py
index e0a973cc47c624e26c8498d3d517f8109dd801eb..bcc3ad8332b1bdb4de97a9f0003e50bcad8e094b 100644
--- a/extreme_fit/model/max_stable_model/max_stable_models.py
+++ b/extreme_fit/model/max_stable_model/max_stable_models.py
@@ -34,6 +34,7 @@ class BrownResnick(AbstractMaxStableModel):
'smooth': 0.5,
}
+
class Schlather(AbstractMaxStableModelWithCovarianceFunction):
def __init__(self, *args, **kwargs):
diff --git a/extreme_fit/model/utils.py b/extreme_fit/model/utils.py
index 7de527c0ed567c5799e02a22aac46b6ff7dac6c0..bec57774459a8352cb31cecba6351bc374686a78 100644
--- a/extreme_fit/model/utils.py
+++ b/extreme_fit/model/utils.py
@@ -1,3 +1,4 @@
+import copy
import io
import os.path as op
@@ -78,7 +79,23 @@ class SafeRunException(Exception):
pass
-def safe_run_r_estimator(function, data=None, use_start=False, max_ratio_between_two_extremes_values=10, maxit=1000000,
+def safe_run_r_estimator(function, data=None, start_dict=None, max_ratio_between_two_extremes_values=10, maxit=1000000,
+ **parameters) -> robjects.ListVector:
+ try:
+ return _safe_run_r_estimator(function, data, max_ratio_between_two_extremes_values, maxit, **parameters)
+ except SafeRunException as e:
+ if start_dict is not None:
+ for _ in range(5):
+ parameters['start'] = r.list(**start_dict)
+ try:
+ return _safe_run_r_estimator(function, data, max_ratio_between_two_extremes_values, maxit, **parameters)
+ except Exception:
+ continue
+ else:
+ raise e
+
+
+def _safe_run_r_estimator(function, data=None, max_ratio_between_two_extremes_values=10, maxit=1000000,
**parameters) -> robjects.ListVector:
if OptimizationConstants.USE_MAXIT:
# Add optimization parameters
@@ -107,25 +124,18 @@ def safe_run_r_estimator(function, data=None, use_start=False, max_ratio_between
warnings.warn(msg, WarningTooMuchZeroValues)
# Add data to the parameters
parameters['data'] = data
- # First run without using start value
- # Then if it crashes, use start value
+
run_successful = False
res = None
f = io.StringIO()
# Warning print will not work in this part
with redirect_stdout(f):
while not run_successful:
- current_parameter = parameters.copy()
- if not use_start and 'start' in current_parameter:
- current_parameter.pop('start')
try:
- res = function(**current_parameter) # type:
+ res = function(**parameters) # type:
run_successful = True
except (RRuntimeError, RRuntimeWarning) as e:
- if not use_start:
- use_start = True
- continue
- elif isinstance(e, RRuntimeError):
+ if isinstance(e, RRuntimeError):
raise SafeRunException('Some R exception have been launched at RunTime: \n {}'.format(e.__repr__()))
if isinstance(e, RRuntimeWarning):
warnings.warn(e.__repr__(), WarningWhileRunningR)
diff --git a/test/test_extreme_fit/test_estimator/test_max_stable_estimators.py b/test/test_extreme_fit/test_estimator/test_max_stable_estimators.py
index 0f6326745eb2f559d52c784641b71f8d823cb0fd..03e44b0a8e706d92d517bdd9c921f6fff650608e 100644
--- a/test/test_extreme_fit/test_estimator/test_max_stable_estimators.py
+++ b/test/test_extreme_fit/test_estimator/test_max_stable_estimators.py
@@ -67,8 +67,8 @@ class TestMaxStableEstimatorGaussFor3DCoordinates(TestMaxStableEstimators):
self.max_stable_models = load_test_max_stable_models()[:1]
def test_max_stable_estimators(self):
- with self.assertRaises(SafeRunException):
- self.fit_max_stable_estimator_for_all_coordinates()
+ self.fit_max_stable_estimator_for_all_coordinates()
+ self.assertTrue(True)
if __name__ == '__main__':