From 08ca4b8843fc9ddfe55d8442017ddca42b2ad736 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 25 Feb 2021 11:29:21 +0100
Subject: [PATCH] [refactor] remove anything related to total_aic for the
second paper.
---
.../altitudes_fit/main_altitudes_studies.py | 31 +++----
...es_visualizer_for_non_stationary_models.py | 9 +-
.../one_fold_analysis/one_fold_fit.py | 77 ++--------------
.../one_fold_analysis/plot_total_aic.py | 90 -------------------
.../altitudes_fit/plot_mean_and_std.py | 8 --
.../__init__.py | 0
.../main_gap_altitudes_studies.py | 0
7 files changed, 25 insertions(+), 190 deletions(-)
delete mode 100644 projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py
delete mode 100644 projects/altitude_spatial_model/altitudes_fit/plot_mean_and_std.py
create mode 100644 projects/archive/gap_between_my_safran2019_and_safran_2019/__init__.py
rename projects/{altitude_spatial_model/altitudes_fit => archive/gap_between_my_safran2019_and_safran_2019}/main_gap_altitudes_studies.py (100%)
diff --git a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
index e938e386..2209091f 100644
--- a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
+++ b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
@@ -2,35 +2,34 @@ import datetime
import time
from typing import List
-import matplotlib
+import matplotlib as mpl
+mpl.rcParams['text.usetex'] = True
+mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
+
+import matplotlib
matplotlib.use('Agg')
-from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf import SafranSnowfall2019, \
- SafranSnowfall2020
from projects.altitude_spatial_model.altitudes_fit.plots.plot_histogram_altitude_studies import \
plot_shoe_plot_changes_against_altitude, plot_histogram_all_trends_against_altitudes, \
- plot_shoe_plot_ratio_interval_size_against_altitude, plot_histogram_all_models_against_altitudes
+ plot_histogram_all_models_against_altitudes
from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
AbstractExtractEurocodeReturnLevel
-import matplotlib as mpl
+
from extreme_fit.model.utils import set_seed_for_test
from projects.altitude_spatial_model.altitudes_fit.plots.plot_coherence_curves import plot_coherence_curves
-mpl.rcParams['text.usetex'] = True
-mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
+
from projects.altitude_spatial_model.altitudes_fit.utils_altitude_studies_visualizer import load_visualizer_list
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitude_group import altitudes_for_groups
-from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.plot_total_aic import plot_individual_aic
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days, \
- SafranSnowfall5Days, SafranSnowfall7Days, SafranDateFirstSnowfall, SafranPrecipitation1Day, \
- SafranPrecipitation3Days, SafranSnowfallCenterOnDay1dayMeanRate, SafranSnowfallNotCenterOnDay1day
+ SafranSnowfall5Days, SafranSnowfall7Days
from extreme_data.meteo_france_data.scm_models_data.utils import Season
@@ -96,13 +95,11 @@ def plot_visualizer(massif_names, visualizer):
# visualizer.studies.plot_maxima_time_series(massif_names)
# visualizer.studies.plot_maxima_time_series(['Vanoise'])
- # Plot the results for the model that minimizes the individual aic
- plot_individual_aic(visualizer)
-
- # Plot the results for the model that minimizes the total aic
- # plot_total_aic(model_classes, visualizer)
- pass
-
+ visualizer.plot_shape_map()
+ visualizer.plot_moments()
+ visualizer.plot_qqplots()
+ # for std in [True, False]:
+ # visualizer.studies.plot_mean_maxima_against_altitude(std=std)
if __name__ == '__main__':
main()
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
index 9c3b7f9f..ffe76956 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
@@ -191,7 +191,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
if self.temporal_covariate_for_fit is AnomalyTemperatureTemporalCovariate else ' in {}'
str_for_last_year = str_for_last_year.format(self.first_one_fold_fit.covariate_after, **d_temperature)
moment = moment.replace('moment', '{}{}'.format(OneFoldFit.get_moment_str(order=order), str_for_last_year))
- plot_name = '{}{} '.format(OneFoldFit.folder_for_plots, moment)
+ plot_name = '{} '.format(moment)
if 'change' in method_name:
plot_name = plot_name.replace(str_for_last_year, '')
@@ -264,7 +264,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
moment = ' '.join(method_name.split('_'))
moment = moment.replace('moment',
'{} in {}'.format(OneFoldFit.get_moment_str(order=order), OneFoldFit.last_year))
- plot_name = '{}Model {} annual maxima of {}'.format(OneFoldFit.folder_for_plots, moment,
+ plot_name = 'Model {} annual maxima of {}'.format(moment,
SCM_STUDY_CLASS_TO_ABBREVIATION[self.studies.study_class])
ax.set_ylabel('{} ({})'.format(plot_name, self.study.variable_unit), fontsize=15)
ax.set_xlabel('altitudes', fontsize=15)
@@ -321,8 +321,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
negative_and_positive_values = (max(values) > 0) and (min(values) < 0)
raise NotImplementedError
self.plot_map(massif_name_to_value=massif_name_to_best_coef,
- label='{}Coef/{} plot for {} {}'.format(OneFoldFit.folder_for_plots,
- coef_name,
+ label='Coef/{} plot for {} {}'.format(coef_name,
SCM_STUDY_CLASS_TO_ABBREVIATION[
type(self.study)],
self.study.variable_unit),
@@ -387,7 +386,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
ax.set_ylabel('{} of {} in {} ({})'.format(ylabel, SCM_STUDY_CLASS_TO_ABBREVIATION[type(self.study)],
massif_name.replace('_', ' '), self.study.variable_unit))
peak_year_folder = 'Peak year ' + ylabel
- plot_name = '{}{}/Peak year for {}'.format(OneFoldFit.folder_for_plots, peak_year_folder,
+ plot_name = '{}/Peak year for {}'.format(peak_year_folder,
massif_name.replace('_', ''))
self.studies.show_or_save_to_file(plot_name=plot_name, show=self.show, no_title=True, tight_layout=True)
plt.close()
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
index a0451c44..525e969e 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py
@@ -43,7 +43,6 @@ from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observat
class OneFoldFit(object):
SIGNIFICANCE_LEVEL = 0.05
- best_estimator_minimizes_total_aic = False
return_period = 100
quantile_level = 1 - (1 / return_period)
nb_years = 60
@@ -75,21 +74,12 @@ class OneFoldFit(object):
# Compute sorted estimators indirectly
_ = self.has_at_least_one_valid_model
- # Best estimator definition
- self.best_estimator_class_for_total_aic = None
- # Cached object
- self._folder_to_goodness = {}
-
def fitted_linear_margin_estimator(self, model_class, dataset):
return fitted_linear_margin_estimator_short(model_class=model_class,
dataset=dataset,
fit_method=self.fit_method,
temporal_covariate_for_fit=self.temporal_covariate_for_fit)
- @classproperty
- def folder_for_plots(cls):
- return 'Total aic/' if cls.best_estimator_minimizes_total_aic else ''
-
@classmethod
def get_moment_str(cls, order):
if order == 1:
@@ -159,7 +149,7 @@ class OneFoldFit(object):
s = ' between +${}^o\mathrm{C}$ and +${}^o\mathrm{C}$' if self.temporal_covariate_for_fit is AnomalyTemperatureTemporalCovariate \
else ' between {} and {}'
s = s.format(self.covariate_before, self.covariate_after,
- **d_temperature)
+ **d_temperature)
return s
def relative_changes_of_moment(self, altitudes, order=1):
@@ -184,7 +174,7 @@ class OneFoldFit(object):
for e in estimators:
coordinate_values_for_the_fit = e.coordinates_for_nllh(Split.all)
coordinate_values_for_the_result = [np.array([self.altitude_group.reference_altitude, c])
- for c in self._covariate_before_and_after]
+ for c in self._covariate_before_and_after]
coordinate_values_to_check = list(coordinate_values_for_the_fit) + coordinate_values_for_the_result
has_undefined_parameters = False
for coordinate in coordinate_values_to_check:
@@ -218,8 +208,6 @@ class OneFoldFit(object):
if self.only_models_that_pass_goodness_of_fit_test:
return [e for e in self.sorted_estimators
if self.goodness_of_fit_test(e)
- # and self.sensitivity_of_fit_test_top_maxima(e)
- # and self.sensitivity_of_fit_test_last_years(e)
]
else:
if not self.remove_physically_implausible_models:
@@ -236,16 +224,12 @@ class OneFoldFit(object):
@property
def best_estimator(self):
- if self.best_estimator_minimizes_total_aic and self.best_estimator_class_for_total_aic is not None:
- return self.model_class_to_estimator[self.best_estimator_class_for_total_aic]
+ if self.has_at_least_one_valid_model:
+ best_estimator = self.sorted_estimators_with_stationary[0]
+ return best_estimator
else:
- # With stationary
- if self.has_at_least_one_valid_model:
- best_estimator = self.sorted_estimators_with_stationary[0]
- return best_estimator
- else:
- raise ValueError('This object should not have been called because '
- 'has_at_least_one_valid_model={}'.format(self.has_at_least_one_valid_model))
+ raise ValueError('This object should not have been called because '
+ 'has_at_least_one_valid_model={}'.format(self.has_at_least_one_valid_model))
@property
def best_margin_model(self):
@@ -322,52 +306,6 @@ class OneFoldFit(object):
# Bootstrap based significance
return self.cached_results_from_bootstrap[0]
- # @property
- # def goodness_of_fit_anderson_test(self):
- # if self.folder_for_plots in self._folder_to_goodness:
- # return self._folder_to_goodness[self.folder_for_plots]
- # else:
- # estimator = self.best_estimator
- # goodness_of_fit_anderson_test = self.goodness_of_fit_test(estimator)
- # if not goodness_of_fit_anderson_test:
- # print('{} with {} does not pass the anderson test for model {}'.format(self.massif_name,
- # self.folder_for_plots,
- # type(self.best_margin_model)))
- # self._folder_to_goodness[self.folder_for_plots] = goodness_of_fit_anderson_test
- # return goodness_of_fit_anderson_test
-
- def sensitivity_of_fit_test_top_maxima(self, estimator: LinearMarginEstimator):
- # Build the dataset without the maxima for each altitude
- new_dataset = AbstractDataset.remove_top_maxima(self.dataset.observations,
- self.dataset.coordinates)
- # Fit the new estimator
- new_estimator = fitted_linear_margin_estimator_short(model_class=type(estimator.margin_model),
- dataset=new_dataset,
- fit_method=self.fit_method,
- temporal_covariate_for_fit=self.temporal_covariate_for_fit)
- # Compare sign of change
- has_not_opposite_sign = self.sign_of_change(estimator.function_from_fit) * self.sign_of_change(
- new_estimator.function_from_fit) >= 0
- return has_not_opposite_sign
-
- def sensitivity_of_fit_test_last_years(self, estimator: LinearMarginEstimator):
- # Build the dataset without the maxima for each altitude
- new_dataset = AbstractDataset.remove_last_maxima(self.dataset.observations,
- self.dataset.coordinates,
- nb_years=10)
- # Fit the new estimator
- model_class = type(estimator.margin_model)
- new_estimator = fitted_linear_margin_estimator_short(model_class=model_class,
- dataset=new_dataset,
- fit_method=self.fit_method,
- temporal_covariate_for_fit=self.temporal_covariate_for_fit)
- # Compare sign of change
- has_not_opposite_sign = self.sign_of_change(estimator.function_from_fit) * self.sign_of_change(
- new_estimator.function_from_fit) >= 0
- # if not has_not_opposite_sign:
- # print('Last years', self.massif_name, model_class, self.sign_of_change(estimator), self.sign_of_change(new_estimator))
- return has_not_opposite_sign
-
def sign_of_change(self, function_from_fit):
return_levels = []
for temporal_covariate in self._covariate_before_and_after:
@@ -445,7 +383,6 @@ class OneFoldFit(object):
return is_significant, altitude_and_year_to_return_level_mean_estimate, altitude_and_year_to_return_level_confidence_interval
-
@property
def bootstrap_fitted_functions_from_fit(self):
print('nb of bootstrap for confidence interval=', AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP)
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py
deleted file mode 100644
index 2a50df32..00000000
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-import numpy as np
-
-from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitudes_studies_visualizer_for_non_stationary_models import \
- AltitudesStudiesVisualizerForNonStationaryModels
-
-mpl.rcParams['text.usetex'] = True
-mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
-
-from extreme_data.meteo_france_data.scm_models_data.visualization.utils import create_adjusted_axes
-from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.one_fold_fit import OneFoldFit
-from projects.exceeding_snow_loads.utils import dpi_paper1_figure
-
-
-def plots(visualizer: AltitudesStudiesVisualizerForNonStationaryModels):
- visualizer.plot_shape_map()
- visualizer.plot_moments()
- visualizer.plot_qqplots()
- # for std in [True, False]:
- # visualizer.studies.plot_mean_maxima_against_altitude(std=std)
-
-
-def plot_individual_aic(visualizer):
- OneFoldFit.best_estimator_minimizes_total_aic = False
- plots(visualizer)
-
-
-def plot_total_aic(model_classes, visualizer):
- # Compute the mean AIC for each model_class
- OneFoldFit.best_estimator_minimizes_total_aic = True
- model_class_to_total_aic = {model_class: 0 for model_class in model_classes}
- model_class_to_name_str = {}
- # model_class_to_aic_scores = {model_class: [] for model_class in model_classes}
- # model_class_to_n = {model_class: [] for model_class in model_classes}
- for one_fold_fit in visualizer.massif_name_to_one_fold_fit.values():
- for model_class, estimator in one_fold_fit.model_class_to_estimator_with_finite_aic.items():
- model_class_to_total_aic[model_class] += estimator.aic()
- model_class_to_name_str[model_class] = estimator.margin_model.name_str
- # model_class_to_aic_scores[model_class].append(estimator.aic())
- # model_class_to_n[model_class].append(estimator.n())
- # model_class_to_mean_aic_score = {model_class: np.array(aic_scores).sum() / np.array(model_class_to_n[model_class]).sum()
- # for model_class, aic_scores in model_class_to_aic_scores.items()}
- # print(model_class_to_mean_aic_score)
- sorted_model_class = sorted(model_classes, key=lambda m: model_class_to_total_aic[m])
- sorted_scores = [model_class_to_total_aic[model_class] for model_class in sorted_model_class]
- sorted_labels = [model_class_to_name_str[model_class] for model_class in sorted_model_class]
- print(sorted_model_class)
- print(sorted_scores)
- print(sorted_labels)
- best_model_class_for_total_aic = sorted_model_class[0]
- for one_fold_fit in visualizer.massif_name_to_one_fold_fit.values():
- one_fold_fit.best_estimator_class_for_total_aic = best_model_class_for_total_aic
- # Plot the ranking of the model based on their total aic
- plot_total_aic_repartition(visualizer, sorted_labels, sorted_scores)
- # Plot the results for the model that minimizes the mean aic
- plots(visualizer)
-
-
-def plot_total_aic_repartition(visualizer, labels, scores):
- """
- Plot a single trend curves
- :return:
- """
- scores = np.array(scores)
- ax = create_adjusted_axes(1, 1)
-
- # parameters
- width = 3
- size = 30
- legend_fontsize = 30
- labelsize = 10
- linewidth = 3
- x = [2 * width * (i + 1) for i in range(len(labels))]
- ax.bar(x, scores, width=width, color='grey', edgecolor='grey', label='Total Aic',
- linewidth=linewidth)
- ax.legend(loc='upper right', prop={'size': size})
- ax.set_ylabel(' Total AIC score \n '
- 'i.e. sum of AIC score for all massifs ', fontsize=legend_fontsize)
- ax.set_xlabel('Models', fontsize=legend_fontsize)
- ax.tick_params(axis='both', which='major', labelsize=labelsize)
- ax.set_xticks(x)
- ax.set_ylim([scores.min(), scores.max()])
- ax.yaxis.grid()
- ax.set_xticklabels(labels)
-
- # Save plot
- visualizer.plot_name = 'Total AIC ranking'
- visualizer.show_or_save_to_file(no_title=True, dpi=dpi_paper1_figure)
- plt.close()
diff --git a/projects/altitude_spatial_model/altitudes_fit/plot_mean_and_std.py b/projects/altitude_spatial_model/altitudes_fit/plot_mean_and_std.py
deleted file mode 100644
index 20bc2fc7..00000000
--- a/projects/altitude_spatial_model/altitudes_fit/plot_mean_and_std.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
-from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
-
-if __name__ == '__main__':
- studies = AltitudesStudies(study_class=SafranSnowfall1Day,
- altitudes=[600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600])
- for std in [True, False]:
- studies.plot_mean_maxima_against_altitude(std=std)
\ No newline at end of file
diff --git a/projects/archive/gap_between_my_safran2019_and_safran_2019/__init__.py b/projects/archive/gap_between_my_safran2019_and_safran_2019/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/projects/altitude_spatial_model/altitudes_fit/main_gap_altitudes_studies.py b/projects/archive/gap_between_my_safran2019_and_safran_2019/main_gap_altitudes_studies.py
similarity index 100%
rename from projects/altitude_spatial_model/altitudes_fit/main_gap_altitudes_studies.py
rename to projects/archive/gap_between_my_safran2019_and_safran_2019/main_gap_altitudes_studies.py
--
GitLab