From 873cb9a42de3332f234a1c2ae6d1c20cb867356f Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 9 May 2019 17:31:01 +0200
Subject: [PATCH] [SCM][NON STATIONARITY] add test_margin_temporal. fix issue
in spatio_temporal_coordinates
---
.../main_study_visualizer.py | 6 +-
.../coordinates/abstract_coordinates.py | 2 +-
.../abstract_spatio_temporal_coordinates.py | 4 +-
.../test_SCM_study.py | 3 +-
.../test_meteo_france_SCM_study/__init__.py | 0
.../test_safran_gev.py | 31 -------
.../test_safran/test_safran_visualizer.py | 29 ------
test/test_experiment/test_simulation.py | 14 ---
test/test_experiment/test_split_curve.py | 43 ---------
.../test_margin_temporal.py | 93 +++++++++++++++++++
...emporal_margin.py => test_gev_temporal.py} | 29 ++++--
.../test_coordinates.py | 3 +
12 files changed, 125 insertions(+), 132 deletions(-)
rename test/test_experiment/{test_meteo_france_SCM_study => }/test_SCM_study.py (97%)
delete mode 100644 test/test_experiment/test_meteo_france_SCM_study/__init__.py
delete mode 100644 test/test_experiment/test_meteo_france_SCM_study/test_safran_gev.py
delete mode 100644 test/test_experiment/test_safran/test_safran_visualizer.py
delete mode 100644 test/test_experiment/test_simulation.py
delete mode 100644 test/test_experiment/test_split_curve.py
create mode 100644 test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
rename test/test_extreme_estimator/test_margin_fits/test_gev/{test_gev_temporal_margin.py => test_gev_temporal.py} (79%)
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
index 358ff9ef..24c9c8c0 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
@@ -111,9 +111,9 @@ def complete_analysis(only_first_one=False):
def trend_analysis():
- save_to_file = False
- only_first_one = True
- altitudes = [300, 1200, 2100, 3000][3:]
+ save_to_file = True
+ only_first_one = False
+ altitudes = [300, 1200, 2100, 3000][:]
study_classes = [CrocusSwe, CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature]
for study in study_iterator_global(study_classes, only_first_one=only_first_one, altitudes=altitudes):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file)
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index 95229f9f..cff19947 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -84,7 +84,7 @@ class AbstractCoordinates(object):
@classmethod
def from_df_and_slicer(cls, df: pd.DataFrame, slicer_class: type, train_split_ratio: float = None):
# All the index should be unique
- assert len(set(df.index)) == len(df)
+ assert len(set(df.index)) == len(df), 'df indices are not unique'
# Create a spatial split
s_split_spatial = s_split_from_df(df, cls.COORDINATE_X, cls.SPATIAL_SPLIT, train_split_ratio, True)
diff --git a/spatio_temporal_dataset/coordinates/spatio_temporal_coordinates/abstract_spatio_temporal_coordinates.py b/spatio_temporal_dataset/coordinates/spatio_temporal_coordinates/abstract_spatio_temporal_coordinates.py
index c0ff737e..485368ec 100644
--- a/spatio_temporal_dataset/coordinates/spatio_temporal_coordinates/abstract_spatio_temporal_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/spatio_temporal_coordinates/abstract_spatio_temporal_coordinates.py
@@ -23,8 +23,8 @@ class AbstractSpatioTemporalCoordinates(AbstractCoordinates):
for t in range(nb_steps):
df_time_step = df_spatial.copy()
df_time_step[cls.COORDINATE_T] = start + t
- index_suffix = index_type(t + len(df_spatial))
- time_step_index = [i + index_suffix for i in df_spatial.index] if t > 0 else df_spatial.index
+ index_suffix = index_type(t * len(df_spatial))
+ time_step_index = [i + index_suffix for i in df_spatial.index]
df_time_step.index = time_step_index
df_time_steps.append(df_time_step)
df_time_steps = pd.concat(df_time_steps)
diff --git a/test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py b/test/test_experiment/test_SCM_study.py
similarity index 97%
rename from test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py
rename to test/test_experiment/test_SCM_study.py
index 3686f766..10ebe4f7 100644
--- a/test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py
+++ b/test/test_experiment/test_SCM_study.py
@@ -10,10 +10,11 @@ from experiment.meteo_france_SCM_study.safran.safran import SafranSnowfall, Exte
from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
from test.test_utils import load_scm_studies
+
class TestSCMAllStudy(unittest.TestCase):
def test_extended_run(self):
- for study_class in [ExtendedSafranSnowfall, ExtendedCrocusSwe]:
+ for study_class in [ExtendedSafranSnowfall]:
for study in study_iterator(study_class, only_first_one=True, both_altitude=False, verbose=False):
study_visualizer = StudyVisualizer(study, show=False, save_to_file=False)
study_visualizer.visualize_all_mean_and_max_graphs()
diff --git a/test/test_experiment/test_meteo_france_SCM_study/__init__.py b/test/test_experiment/test_meteo_france_SCM_study/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/test/test_experiment/test_meteo_france_SCM_study/test_safran_gev.py b/test/test_experiment/test_meteo_france_SCM_study/test_safran_gev.py
deleted file mode 100644
index 03430b13..00000000
--- a/test/test_experiment/test_meteo_france_SCM_study/test_safran_gev.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import unittest
-import os
-import os.path as op
-from collections import OrderedDict
-from typing import List, Dict
-
-import matplotlib.pyplot as plt
-import pandas as pd
-from netCDF4 import Dataset
-
-from experiment.meteo_france_SCM_study.abstract_variable import AbstractVariable
-from experiment.meteo_france_SCM_study.massif import safran_massif_names_from_datasets
-from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
- AbstractSpatialCoordinates
-from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
-from utils import get_full_path, cached_property
-#
-# from test.test_utils import load_safran_objects
-#
-#
-# class TestFullEstimators(unittest.TestCase):
-#
-# def test_gev_mle_per_massif(self):
-# safran_1800_one_day = load_safran_objects()[0]
-# df = safran_1800_one_day.df_gev_mle_each_massif
-# self.assertAlmostEqual(df.values.sum(), 1131.4551665871832)
-#
-#
-# if __name__ == '__main__':
-# unittest.main()
diff --git a/test/test_experiment/test_safran/test_safran_visualizer.py b/test/test_experiment/test_safran/test_safran_visualizer.py
deleted file mode 100644
index 6f94d5c4..00000000
--- a/test/test_experiment/test_safran/test_safran_visualizer.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# import unittest
-#
-# from experiment.meteo_france_SCM_study.safran import Safran
-# from experiment.meteo_france_SCM_study.safran_visualizer import SafranVisualizer
-#
-#
-# class TestSafranVisualizer(unittest.TestCase):
-# DISPLAY = False
-#
-# def setUp(self):
-# super().setUp()
-# self.safran = Safran(1800, 1)
-# self.safran_visualizer = SafranVisualizer(self.safran, show=self.DISPLAY)
-#
-# def tearDown(self) -> None:
-# self.assertTrue(True)
-#
-# def test_safran_smooth_margin_estimator(self):
-# self.safran_visualizer.visualize_smooth_margin_fit()
-#
-# def test_safran_independent_margin_fits(self):
-# self.safran_visualizer.visualize_independent_margin_fits()
-#
-# def test_safran_full_estimator(self):
-# self.safran_visualizer.visualize_full_fit()
-#
-#
-# if __name__ == '__main__':
-# unittest.main()
diff --git a/test/test_experiment/test_simulation.py b/test/test_experiment/test_simulation.py
deleted file mode 100644
index fb21c521..00000000
--- a/test/test_experiment/test_simulation.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# import unittest
-#
-# from experiment.simulation.lin_space_simulation import LinSpaceSimulation
-#
-#
-# class TestSimulation(unittest.TestCase):
-# DISPLAY = False
-#
-# def test_lin_space(self):
-# s = LinSpaceSimulation()
-# s.fit(show=self.DISPLAY)
-#
-# if __name__ == '__main__':
-# unittest.main()
diff --git a/test/test_experiment/test_split_curve.py b/test/test_experiment/test_split_curve.py
deleted file mode 100644
index e112d4a0..00000000
--- a/test/test_experiment/test_split_curve.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# import unittest
-#
-#
-# from experiment.simulation.abstract_simulation import AbstractSimulation
-# from extreme_estimator.extreme_models.margin_model.smooth_margin_model import ConstantMarginModel, \
-# LinearAllParametersAllDimsMarginModel
-# 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 TestSplitCurve(unittest.TestCase):
-# DISPLAY = False
-#
-# class SplitCurveFastForTest(AbstractSimulation):
-#
-# def __init__(self, nb_fit: int = 1):
-# super().__init__(nb_fit)
-# self.nb_points = 50
-# self.nb_obs = 60
-# self.coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=self.nb_points, train_split_ratio=0.8)
-# # MarginModel Linear with respect to the shape (from 0.01 to 0.02)
-# params_sample = {
-# (GevParams.GEV_LOC, 0): 10,
-# (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 load_dataset(self):
-# return FullSimulatedDataset.from_double_sampling(nb_obs=self.nb_obs, margin_model=self.margin_model,
-# coordinates=self.coordinates,
-# max_stable_model=self.max_stable_model)
-#
-# def test_split_curve(self):
-# s = self.SplitCurveFastForTest(nb_fit=2)
-# s.fit(show=self.DISPLAY)
-#
-#
-# if __name__ == '__main__':
-# unittest.main()
diff --git a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
new file mode 100644
index 00000000..606d52b9
--- /dev/null
+++ b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
@@ -0,0 +1,93 @@
+import unittest
+
+import numpy as np
+import pandas as pd
+
+from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
+from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel
+from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
+ NonStationaryStationModel
+from extreme_estimator.extreme_models.utils import r, set_seed_r
+from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
+from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
+ AbstractTemporalCoordinates
+from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from spatio_temporal_dataset.dataset.simulation_dataset import MarginDataset
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+ AbstractSpatioTemporalObservations
+from test.test_utils import load_test_spatiotemporal_coordinates, load_smooth_margin_models
+
+
+class TestMarginTemporal(unittest.TestCase):
+
+ def setUp(self) -> None:
+ self.nb_points = 2
+ self.nb_steps = 5
+ self.nb_obs = 1
+ # Load some 2D spatial coordinates
+ self.coordinates = load_test_spatiotemporal_coordinates(nb_steps=self.nb_steps, nb_points=self.nb_points)[1]
+ self.start_year = 2
+ smooth_margin_models = LinearNonStationaryLocationMarginModel(coordinates=self.coordinates,
+ starting_point=self.start_year)
+ self.dataset = MarginDataset.from_sampling(nb_obs=self.nb_obs,
+ margin_model=smooth_margin_models,
+ coordinates=self.coordinates)
+
+ def test_loading_dataset(self):
+ self.assertTrue(True)
+
+ # def test_gev_temporal_margin_fit_stationary(self):
+ # # Create estimator
+ # margin_model = StationaryStationModel(self.coordinates)
+ # estimator = LinearMarginEstimator(self.dataset, margin_model)
+ # estimator.fit()
+ # ref = {'loc': 0.0219, 'scale': 1.0347, 'shape': 0.8295}
+ # for year in range(1, 3):
+ # mle_params_estimated = estimator.margin_function_fitted.get_gev_params(np.array([year])).to_dict()
+ # for key in ref.keys():
+ # self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
+ #
+ # def test_gev_temporal_margin_fit_nonstationary(self):
+ # # Create estimator
+ # margin_model = NonStationaryStationModel(self.coordinates)
+ # estimator = LinearMarginEstimator(self.dataset, margin_model)
+ # estimator.fit()
+ # self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
+ # # Checks that parameters returned are indeed different
+ # mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
+ # mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
+ # self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
+ #
+ # def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
+ # # Create estimator
+ # estimator = self.fit_non_stationary_estimator(starting_point=3)
+ # self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
+ # # Checks starting point parameter are well passed
+ # self.assertEqual(3, estimator.margin_function_fitted.starting_point)
+ # # Checks that parameters returned are indeed different
+ # mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
+ # mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
+ # self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
+ # mle_params_estimated_year5 = estimator.margin_function_fitted.get_gev_params(np.array([5])).to_dict()
+ # self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
+ #
+ # def fit_non_stationary_estimator(self, starting_point):
+ # margin_model = NonStationaryStationModel(self.coordinates, starting_point=starting_point + self.start_year)
+ # estimator = LinearMarginEstimator(self.dataset, margin_model)
+ # estimator.fit()
+ # return estimator
+ #
+ # def test_two_different_starting_points(self):
+ # # Create two different estimators
+ # estimator1 = self.fit_non_stationary_estimator(starting_point=3)
+ # estimator2 = self.fit_non_stationary_estimator(starting_point=28)
+ # mu1_estimator1 = estimator1.margin_function_fitted.mu1_temporal_trend
+ # mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ # print(mu1_estimator1, mu1_estimator2)
+ # self.assertNotEqual(mu1_estimator1, mu1_estimator2)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_margin.py b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
similarity index 79%
rename from test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_margin.py
rename to test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
index 176d0a03..6977e111 100644
--- a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal_margin.py
+++ b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
@@ -17,7 +17,7 @@ from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_tempor
AbstractSpatioTemporalObservations
-class TestGevTemporalMargin(unittest.TestCase):
+class TestGevTemporal(unittest.TestCase):
def setUp(self) -> None:
set_seed_r()
@@ -28,7 +28,8 @@ class TestGevTemporalMargin(unittest.TestCase):
start_loc = 0; start_scale = 1; start_shape = 1
""")
# Compute the stationary temporal margin with isMev
- df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: range(50)})
+ self.start_year = 0
+ df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: range(self.start_year, self.start_year + 50)})
self.coordinates = AbstractTemporalCoordinates.from_df(df)
df2 = pd.DataFrame(data=np.array(r['x_gev']), index=df.index)
observations = AbstractSpatioTemporalObservations(df_maxima_gev=df2)
@@ -50,7 +51,7 @@ class TestGevTemporalMargin(unittest.TestCase):
margin_model = NonStationaryStationModel(self.coordinates)
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
- self.assertNotEqual(estimator.result_from_fit.margin_coef_dict['tempCoeffLoc1'], 0.0)
+ self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
# Checks that parameters returned are indeed different
mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
@@ -58,10 +59,8 @@ class TestGevTemporalMargin(unittest.TestCase):
def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
# Create estimator
- margin_model = NonStationaryStationModel(self.coordinates, starting_point=3)
- estimator = LinearMarginEstimator(self.dataset, margin_model)
- estimator.fit()
- self.assertNotEqual(estimator.result_from_fit.margin_coef_dict['tempCoeffLoc1'], 0.0)
+ estimator = self.fit_non_stationary_estimator(starting_point=3)
+ self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
# Checks starting point parameter are well passed
self.assertEqual(3, estimator.margin_function_fitted.starting_point)
# Checks that parameters returned are indeed different
@@ -70,7 +69,21 @@ class TestGevTemporalMargin(unittest.TestCase):
self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
mle_params_estimated_year5 = estimator.margin_function_fitted.get_gev_params(np.array([5])).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
- # todo: create same test with a starting value, to check if the starting value is taken into account in the margin_function_fitted
+
+ def fit_non_stationary_estimator(self, starting_point):
+ margin_model = NonStationaryStationModel(self.coordinates, starting_point=starting_point + self.start_year)
+ estimator = LinearMarginEstimator(self.dataset, margin_model)
+ estimator.fit()
+ return estimator
+
+ def test_two_different_starting_points(self):
+ # Create two different estimators
+ estimator1 = self.fit_non_stationary_estimator(starting_point=3)
+ estimator2 = self.fit_non_stationary_estimator(starting_point=28)
+ mu1_estimator1 = estimator1.margin_function_fitted.mu1_temporal_trend
+ mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ print(mu1_estimator1, mu1_estimator2)
+ self.assertNotEqual(mu1_estimator1, mu1_estimator2)
if __name__ == '__main__':
diff --git a/test/test_spatio_temporal_dataset/test_coordinates.py b/test/test_spatio_temporal_dataset/test_coordinates.py
index 0fbe5007..a2c0b7b4 100644
--- a/test/test_spatio_temporal_dataset/test_coordinates.py
+++ b/test/test_spatio_temporal_dataset/test_coordinates.py
@@ -65,6 +65,9 @@ class SpatioTemporalCoordinates(unittest.TestCase):
# the uniqueness of each spatio temporal index is not garanteed by the current algo
# it will work in classical cases, and raise an assert when uniqueness is needed (when using a slicer)
index1 = pd.Series(spatial_coordinates.spatial_index())
+ # Add the suffix to the index1
+ suffix = '0' if isinstance(df_spatial.index[0], str) else 0
+ index1 += suffix
index2 = pd.Series(coordinates.spatial_index())
ind = index1 != index2 # type: pd.Series
self.assertEqual(sum(ind), 0, msg="spatial_coordinates:\n{} \n!= spatio_temporal_coordinates \n{}".
--
GitLab