diff --git a/experiment/simulation/abstract_simulation.py b/experiment/simulation/abstract_simulation.py
index 4319f7842c346a6db8145aa9e4cabd449e6e9637..3e99de35bc1a743721fae71f5d918fc3bdcac190 100644
--- a/experiment/simulation/abstract_simulation.py
+++ b/experiment/simulation/abstract_simulation.py
@@ -1,4 +1,11 @@
import os
+from typing import List
+
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+import matplotlib.cm as cmx
+import numpy as np
import os.path as op
import pickle
@@ -8,12 +15,15 @@ import numpy as np
import seaborn as sns
from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
+from extreme_estimator.extreme_models.margin_model.margin_function.abstract_margin_function import \
+ AbstractMarginFunction
from extreme_estimator.extreme_models.margin_model.margin_function.combined_margin_function import \
CombinedMarginFunction
from extreme_estimator.extreme_models.margin_model.margin_function.utils import error_dict_between_margin_functions
from extreme_estimator.gev_params import GevParams
from spatio_temporal_dataset.dataset.abstract_dataset import get_subset_dataset
from spatio_temporal_dataset.dataset.simulation_dataset import SimulatedDataset
+from spatio_temporal_dataset.slicer.split import split_to_display_kwargs
from utils import get_full_path, get_display_name_from_object_type
SIMULATION_RELATIVE_PATH = op.join('local', 'simulation')
@@ -21,14 +31,15 @@ SIMULATION_RELATIVE_PATH = op.join('local', 'simulation')
class AbstractSimulation(object):
- def __init__(self):
- self.nb_fit = 1
- self.margin_function_fitted_list = None
+ def __init__(self, nb_fit=1):
+ self.nb_fit = nb_fit
+ self.margin_function_fitted_list = None # type: List[AbstractMarginFunction]
self.full_dataset = None
self.error_dict_all = None
self.margin_function_sample = None
self.mean_error_dict = None
- self.mean_margin_function_fitted = None
+ self.mean_margin_function_fitted = None # type: AbstractMarginFunction
+ self.estimator_name = ''
def fit(self, estimator_class, show=True):
assert estimator_class not in self.already_fitted_estimator_names, \
@@ -75,7 +86,18 @@ class AbstractSimulation(object):
self.margin_function_sample = self.full_dataset.margin_model.margin_function_sample
fig, axes = self.load_fig_and_axes()
- for estimator_name in estimator_names:
+
+ # Binary color should
+ values = np.linspace(0, 1, len(estimator_names))
+ jet = plt.get_cmap('jet')
+ cNorm = matplotlib.colors.Normalize(vmin=0, vmax=values[-1])
+ scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
+ colors = [scalarMap.to_rgba(value) for value in values]
+
+ for j, (estimator_name, color) in enumerate(zip(estimator_names, colors)):
+ self.j = j
+ self.color = color
+ self.estimator_name = estimator_name
self.margin_function_fitted_list = self.load_fitted_margins_pickles(estimator_name)
self.error_dict_all = [error_dict_between_margin_functions(reference=self.margin_function_sample,
@@ -89,6 +111,11 @@ class AbstractSimulation(object):
self.mean_error_dict = error_dict_between_margin_functions(self.margin_function_sample,
self.mean_margin_function_fitted)
self.visualize(fig, axes, show=False)
+
+ title = self.main_title
+ for j, estimator_name in enumerate(estimator_names):
+ title += '\n y{}: {}'.format(j, estimator_name)
+ fig.suptitle(title)
plt.show()
@property
@@ -137,15 +164,17 @@ class AbstractSimulation(object):
# todo: fix binary color problem
self.margin_function_sample.set_datapoint_display_parameters(split, datapoint_marker=marker,
- filter=data_filter)
+ filter=data_filter,
+ color='black',
+ datapoint_display=True)
self.margin_function_sample.visualize_single_param(gev_value_name, ax, show=False)
# Display the individual fitted curve
- self.mean_margin_function_fitted.color = 'lightskyblue'
for m in self.margin_function_fitted_list:
+ m.set_datapoint_display_parameters(linewidth=0.1, color=self.color)
m.visualize_single_param(gev_value_name, ax, show=False)
# Display the mean fitted curve
- self.mean_margin_function_fitted.color = 'blue'
+ self.mean_margin_function_fitted.set_datapoint_display_parameters(color=self.color, linewidth=2)
self.mean_margin_function_fitted.visualize_single_param(gev_value_name, ax, show=False)
def score_graph(self, ax, gev_value_name):
@@ -156,10 +185,19 @@ class AbstractSimulation(object):
for split in self.full_dataset.splits:
ind = self.full_dataset.coordinates_index(split)
data = s.loc[ind].values
- sns.kdeplot(data, bw=0.5, ax=ax, label=split.name).set(xlim=0)
+ display_kwargs = split_to_display_kwargs(split)
+ print(split, 'train' in split.name)
+ if 'train' in split.name:
+ display_kwargs.update({"label": 'y' + str(self.j)})
+ markersize=3
+ else:
+ markersize = 10
+ ax.plot([data.mean()], [0], color=self.color, marker='o', markersize=markersize)
+ sns.kdeplot(data, bw=1, ax=ax, color=self.color, **display_kwargs).set(xlim=0)
ax.legend()
+
# X axis
- ax.set_xlabel('Absolute error in percentage')
+ ax.set_xlabel('Mean absolute error in %')
plt.setp(ax.get_xticklabels(), visible=True)
ax.xaxis.set_tick_params(labelbottom=True)
# Y axis
diff --git a/experiment/simulation/lin_space2_simulation.py b/experiment/simulation/lin_space2_simulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c28f8ce23a2c241378c9b508c9b426f23d54ee9d
--- /dev/null
+++ b/experiment/simulation/lin_space2_simulation.py
@@ -0,0 +1,41 @@
+from experiment.simulation.abstract_simulation import AbstractSimulation
+from extreme_estimator.estimator.full_estimator.full_estimator_for_simulation import FULL_ESTIMATORS_FOR_SIMULATION
+from extreme_estimator.estimator.margin_estimator.margin_estimator_for_simulation import \
+ MARGIN_ESTIMATORS_FOR_SIMULATION
+from extreme_estimator.extreme_models.margin_model.smooth_margin_model import ConstantMarginModel
+from extreme_estimator.extreme_models.max_stable_model.max_stable_models import Smith
+from extreme_estimator.gev_params import GevParams
+from spatio_temporal_dataset.coordinates.spatial_coordinates.coordinates_1D import LinSpaceSpatialCoordinates
+from spatio_temporal_dataset.dataset.simulation_dataset import FullSimulatedDataset
+
+
+class LinSpace3Simulation(AbstractSimulation):
+ FITTED_ESTIMATORS = []
+
+ def __init__(self, nb_fit=1):
+ super().__init__(nb_fit)
+ # Simulation parameters
+ self.nb_obs = 60
+ self.coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=100, train_split_ratio=0.75)
+ # MarginModel Linear with respect to the shape (from 0.01 to 0.02)
+ params_sample = {
+ (GevParams.GEV_LOC, 0): 1.0,
+ (GevParams.GEV_SHAPE, 0): 1.0,
+ (GevParams.GEV_SCALE, 0): 1.0,
+ }
+ self.margin_model = ConstantMarginModel(coordinates=self.coordinates, params_sample=params_sample)
+ self.max_stable_model = Smith()
+
+ def dump(self):
+ dataset = FullSimulatedDataset.from_double_sampling(nb_obs=self.nb_obs, margin_model=self.margin_model,
+ coordinates=self.coordinates,
+ max_stable_model=self.max_stable_model)
+ self._dump(dataset=dataset)
+
+
+if __name__ == '__main__':
+ simu = LinSpace3Simulation(nb_fit=7)
+ simu.dump()
+ for estimator_class in MARGIN_ESTIMATORS_FOR_SIMULATION + FULL_ESTIMATORS_FOR_SIMULATION:
+ simu.fit(estimator_class, show=False)
+ simu.visualize_comparison_graph()
diff --git a/experiment/simulation/lin_space_simulation.py b/experiment/simulation/lin_space_simulation.py
index 910eb3a3c3492713513951be1240018f1f471887..3d11e2dd9b6c1f48487291129de322507266c735 100644
--- a/experiment/simulation/lin_space_simulation.py
+++ b/experiment/simulation/lin_space_simulation.py
@@ -12,9 +12,8 @@ from spatio_temporal_dataset.dataset.simulation_dataset import FullSimulatedData
class LinSpaceSimulation(AbstractSimulation):
FITTED_ESTIMATORS = []
- def __init__(self):
- super().__init__()
- self.nb_fit = 2
+ def __init__(self, nb_fit=1):
+ super().__init__(nb_fit)
# Simulation parameters
self.nb_obs = 60
self.coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=21, train_split_ratio=0.75)
@@ -35,7 +34,7 @@ class LinSpaceSimulation(AbstractSimulation):
if __name__ == '__main__':
- simu = LinSpaceSimulation()
+ simu = LinSpaceSimulation(nb_fit=3)
simu.dump()
# for estimator_class in MARGIN_ESTIMATORS_FOR_SIMULATION + FULL_ESTIMATORS_FOR_SIMULATION:
# simu.fit(estimator_class, show=False)
diff --git a/extreme_estimator/extreme_models/abstract_model.py b/extreme_estimator/extreme_models/abstract_model.py
index 016362d0cd73c3bd81957346cca850108e9a0101..28e19653d23939caf41b5dd849e67c946752ff75 100644
--- a/extreme_estimator/extreme_models/abstract_model.py
+++ b/extreme_estimator/extreme_models/abstract_model.py
@@ -1,6 +1,6 @@
class AbstractModel(object):
- def __init__(self, use_start_value=True, params_start_fit=None, params_sample=None):
+ def __init__(self, use_start_value=False, params_start_fit=None, params_sample=None):
self.default_params_start_fit = None
self.default_params_sample = None
self.use_start_value = use_start_value
diff --git a/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py b/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
index e604abc6e5019b17235f8d01f3eb1047f7976935..26f2a98f5e6ba40c820b6f496438433218d65aca 100644
--- a/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
@@ -11,7 +11,7 @@ from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoo
class AbstractMarginModel(AbstractModel):
- def __init__(self, coordinates: AbstractCoordinates, use_start_value=True, params_start_fit=None, params_sample=None):
+ def __init__(self, coordinates: AbstractCoordinates, use_start_value=False, params_start_fit=None, params_sample=None):
super().__init__(use_start_value, params_start_fit, params_sample)
assert isinstance(coordinates, AbstractCoordinates), type(coordinates)
self.coordinates = coordinates
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
index a2475ef6780d449b147c53d5091fde7f1a91b453..1a75950badc61a2d5df40bfd6dc335ce6b7dc860 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
@@ -23,6 +23,7 @@ class AbstractMarginFunction(object):
self.datapoint_marker = 'o'
self.color = 'skyblue'
self.filter = None
+ self.linewidth = 1
self._grid_2D = None
self._grid_1D = None
@@ -45,10 +46,12 @@ class AbstractMarginFunction(object):
# Visualization function
- def set_datapoint_display_parameters(self, spatio_temporal_split, datapoint_marker, filter=None, color=None):
- self.datapoint_display = True
+ def set_datapoint_display_parameters(self, spatio_temporal_split=Split.all, datapoint_marker=None, filter=None, color=None,
+ linewidth=1, datapoint_display=False):
+ self.datapoint_display = datapoint_display
self.spatio_temporal_split = spatio_temporal_split
self.datapoint_marker = datapoint_marker
+ self.linewidth = linewidth
self.filter = filter
self.color = color
@@ -83,9 +86,10 @@ class AbstractMarginFunction(object):
if ax is None:
ax = plt.gca()
if self.datapoint_display:
- ax.plot(linspace, grid[gev_value_name], self.datapoint_marker, color=self.color)
+ ax.plot(linspace, grid[gev_value_name], marker=self.datapoint_marker, color=self.color)
else:
- ax.plot(linspace, grid[gev_value_name], color=self.color)
+ print('here')
+ ax.plot(linspace, grid[gev_value_name], color=self.color, linewidth=self.linewidth)
# X axis
ax.set_xlabel('coordinate X')
plt.setp(ax.get_xticklabels(), visible=True)
diff --git a/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py b/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
index 1655c1f0550a57cbd87c48e70e54a771ad4d7f33..b2089aa11935f80ea45d832cfa37052dadcc4ff9 100644
--- a/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
@@ -65,9 +65,8 @@ class LinearMarginModel(AbstractMarginModel):
data = np.transpose(maxima_gev)
covariables = get_coord(df_coordinates)
fit_params = get_margin_formula(self.margin_function_start_fit.form_dict)
- if self.use_start_value:
- fit_params['start'] = r.list(**self.margin_function_start_fit.coef_dict)
- res = safe_run_r_estimator(function=r.fitspatgev, data=data, covariables=covariables, **fit_params)
+ fit_params['start'] = r.list(**self.margin_function_start_fit.coef_dict)
+ res = safe_run_r_estimator(function=r.fitspatgev, use_start=self.use_start_value, data=data, covariables=covariables, **fit_params)
return retrieve_fitted_values(res)
diff --git a/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py b/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
index 2c3b4314b0a9743f760c97f7c1a123dd6fd3c33d..78ae219a174b9cd846fbc218196066789af4723d 100644
--- a/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
@@ -12,7 +12,7 @@ from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoo
class AbstractMaxStableModel(AbstractModel):
- def __init__(self, use_start_value=True, params_start_fit=None, params_sample=None):
+ def __init__(self, use_start_value=False, params_start_fit=None, params_sample=None):
super().__init__(use_start_value, params_start_fit, params_sample)
self.cov_mod = None
@@ -59,12 +59,11 @@ class AbstractMaxStableModel(AbstractModel):
fit_params.update(margin_formulas)
if fitmaxstab_with_one_dimensional_data:
fit_params['iso'] = True
- if self.use_start_value:
- fit_params['start'] = r.list(**start_dict)
+ fit_params['start'] = r.list(**start_dict)
fit_params['fit.marge'] = fit_marge
# Run the fitmaxstab in R
- res = safe_run_r_estimator(function=r.fitmaxstab, data=data, coord=coord, **fit_params)
+ res = safe_run_r_estimator(function=r.fitmaxstab, use_start=self.use_start_value, data=data, coord=coord, **fit_params)
return retrieve_fitted_values(res)
def rmaxstab(self, nb_obs: int, coordinates_values: np.ndarray) -> np.ndarray:
@@ -89,7 +88,7 @@ class CovarianceFunction(Enum):
class AbstractMaxStableModelWithCovarianceFunction(AbstractMaxStableModel):
- def __init__(self, use_start_value=True, params_start_fit=None, params_sample=None, covariance_function: CovarianceFunction = None):
+ def __init__(self, use_start_value=False, params_start_fit=None, params_sample=None, covariance_function: CovarianceFunction = None):
super().__init__(use_start_value, params_start_fit, params_sample)
assert covariance_function is not None
self.covariance_function = covariance_function
diff --git a/extreme_estimator/extreme_models/utils.py b/extreme_estimator/extreme_models/utils.py
index 0ea8f3283edc5938917a0560727a97d0bd3040a5..2d84f995292750b39c1bfc1411b7b5e08fb3ae80 100644
--- a/extreme_estimator/extreme_models/utils.py
+++ b/extreme_estimator/extreme_models/utils.py
@@ -29,15 +29,26 @@ def get_associated_r_file(python_filepath: str) -> str:
return r_filepath
-def safe_run_r_estimator(function, **parameters):
- try:
- res = function(**parameters) # type:
- except (RRuntimeError, RRuntimeWarning) as e:
- if isinstance(e, RRuntimeError):
- raise Exception('Some R exception have been launched at RunTime: \n {}'.format(e.__repr__()))
- if isinstance(e, RRuntimeWarning):
- print(e.__repr__())
- print('WARNING')
+def safe_run_r_estimator(function, use_start=False, **parameters):
+ # First run without using start value
+ # Then if it crashes, use start value
+ run_successful = False
+ while not run_successful:
+ current_parameter = parameters.copy()
+ if not use_start:
+ current_parameter.pop('start')
+ try:
+ res = function(**current_parameter) # type:
+ run_successful = True
+ except (RRuntimeError, RRuntimeWarning) as e:
+ if not use_start:
+ use_start = True
+ continue
+ elif isinstance(e, RRuntimeError):
+ raise Exception('Some R exception have been launched at RunTime: \n {}'.format(e.__repr__()))
+ if isinstance(e, RRuntimeWarning):
+ print(e.__repr__())
+ print('WARNING')
return res
diff --git a/extreme_estimator/gev_params.py b/extreme_estimator/gev_params.py
index 78a6fa2e3a766971f84dd4be6157d00b9bcfe144..3fe0a213669a8de5d942cef63d552cbda233aa4e 100644
--- a/extreme_estimator/gev_params.py
+++ b/extreme_estimator/gev_params.py
@@ -23,6 +23,7 @@ class GevParams(object):
self.location = loc
self.scale = scale
self.shape = shape
+ # self.scale = max(self.scale, 1e-4)
assert self.scale > 0
@classmethod
diff --git a/spatio_temporal_dataset/slicer/split.py b/spatio_temporal_dataset/slicer/split.py
index 564657e0905957d7a699cb834cad75106859eef2..6558fbdf133b227803f541e9d62ac259d5dac04f 100644
--- a/spatio_temporal_dataset/slicer/split.py
+++ b/spatio_temporal_dataset/slicer/split.py
@@ -19,6 +19,24 @@ class Split(Enum):
test_temporal = 8
+def split_to_display_kwargs(split: Split):
+ marker = None
+ gridsize = 1000
+ if 'train' in split.name:
+ linewidth = 0.5
+ else:
+ linewidth = 2
+ if 'spatiotemporal' in split.name:
+ gridsize = 20
+ if 'spatial' in split.name and 'temporal' in split.name:
+ marker = '*'
+ elif 'spatial' in split.name:
+ marker = '^'
+ else:
+ marker = '>'
+ return {'marker': marker, 'linewidth': linewidth, 'gridsize':gridsize}
+
+
ALL_SPLITS_EXCEPT_ALL = [split for split in Split if split is not Split.all]
SPLIT_NAME = 'split'
@@ -46,7 +64,7 @@ def small_s_split_from_ratio(index: pd.Index, train_split_ratio):
def s_split_from_df(df: pd.DataFrame, column, split_column, train_split_ratio, spatial_split) -> Union[None, pd.Series]:
- df = df.copy() # type: pd.DataFrame
+ df = df.copy() # type: pd.DataFrame
# Extract the index
if train_split_ratio is None:
return None
@@ -69,4 +87,3 @@ def s_split_from_df(df: pd.DataFrame, column, split_column, train_split_ratio, s
s_split.iloc[i] = small_s_split.iloc[i // multiplication_factor]
s_split.index = df.index
return s_split
-
diff --git a/test/test_extreme_estimator/test_estimator/test_full_estimators.py b/test/test_extreme_estimator/test_estimator/test_full_estimators.py
index 25ceec23cec4b27413082c9dbd9ad36498076a78..2925c2736dc6ae9db259458e0224ede8b6f3f71d 100644
--- a/test/test_extreme_estimator/test_estimator/test_full_estimators.py
+++ b/test/test_extreme_estimator/test_estimator/test_full_estimators.py
@@ -20,10 +20,14 @@ class TestFullEstimators(unittest.TestCase):
for coordinates in self.spatial_coordinates:
smooth_margin_models = load_smooth_margin_models(coordinates=coordinates)
for margin_model, max_stable_model in product(smooth_margin_models, self.max_stable_models):
+
dataset = FullSimulatedDataset.from_double_sampling(nb_obs=self.nb_obs, margin_model=margin_model,
coordinates=coordinates,
max_stable_model=max_stable_model)
+ margin_model.use_start_value = True
+ # todo: understand why it is crashing without specifying that (when not using start value was passed by default this test started crashing)
+
for full_estimator in load_test_full_estimators(dataset, margin_model, max_stable_model):
full_estimator.fit()
if self.DISPLAY: