From 990754ac6b01519b52873203fa496e459f4c1097 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Mon, 13 May 2019 11:47:38 +0200
Subject: [PATCH] [COORDINATES] add test_coordinate_sensitivity
---
.../margin_model/parametric_margin_model.py | 1 -
.../coordinates/abstract_coordinates.py | 13 +++++++---
..._bug.py => test_coordinate_sensitivity.py} | 26 +++++++++----------
3 files changed, 21 insertions(+), 19 deletions(-)
rename test/test_experiment/{test_weird_bug.py => test_coordinate_sensitivity.py} (58%)
diff --git a/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
index 7acacf83..feb0f46b 100644
--- a/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
@@ -39,7 +39,6 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
# Enforce a starting point for the temporal trend
if self.transformed_starting_point is not None:
# Compute the indices to modify
- print('transformed starting point', self.transformed_starting_point)
ind_to_modify = df_coordinates_temp.iloc[:, 0] <= self.transformed_starting_point # type: pd.Series
# Assert that some coordinates are selected but not all (at least 20 data should be left for temporal trend)
assert 0 < sum(ind_to_modify) < len(ind_to_modify) - 20
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index 84afada1..a51fe218 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -36,6 +36,7 @@ class AbstractCoordinates(object):
# Coordinates columns
COORDINATES_NAMES = COORDINATE_SPATIAL_NAMES + [COORDINATE_T]
# Coordinate type
+ ALL_COORDINATES_ACCEPTED_TYPES = ['int64', 'float64']
COORDINATE_TYPE = 'float64'
def __init__(self, df: pd.DataFrame, slicer_class: type, s_split_spatial: pd.Series = None,
@@ -46,8 +47,10 @@ class AbstractCoordinates(object):
# Sort coordinates according to a specified order
sorted_coordinates_columns = [c for c in self.COORDINATES_NAMES if c in coordinate_columns]
self.df_all_coordinates = df.loc[:, sorted_coordinates_columns].copy() # type: pd.DataFrame
- # Cast df_all_coordinates to the desired type
- self.df_all_coordinates = self.df_all_coordinates.astype(self.COORDINATE_TYPE)
+ # Check the data type of the coordinate columns
+ accepted_dtypes = ['int']
+ assert len(self.df_all_coordinates.select_dtypes(include=self.ALL_COORDINATES_ACCEPTED_TYPES).columns) \
+ == len(coordinate_columns), 'coordinates columns dtypes should belong to {}'.format(accepted_dtypes)
# Slicing attributes
self.s_split_spatial = s_split_spatial # type: pd.Series
@@ -128,12 +131,14 @@ class AbstractCoordinates(object):
# Normalize
def transform(self, coordinate: np.ndarray) -> np.ndarray:
- return self.transformation.transform_array(coordinate=coordinate)
+ coordinate_float = coordinate.astype(self.COORDINATE_TYPE)
+ return self.transformation.transform_array(coordinate=coordinate_float)
# Split
def df_coordinates(self, split: Split = Split.all) -> pd.DataFrame:
- df_transformed_coordinates = self.transformation.transform_df(df_coord=self.df_all_coordinates)
+ df_all_coordinate_as_float = self.df_all_coordinates.astype(self.COORDINATE_TYPE) # type: pd.DataFrame
+ df_transformed_coordinates = self.transformation.transform_df(df_all_coordinate_as_float)
return df_sliced(df=df_transformed_coordinates, split=split, slicer=self.slicer)
def coordinates_values(self, split: Split = Split.all) -> np.ndarray:
diff --git a/test/test_experiment/test_weird_bug.py b/test/test_experiment/test_coordinate_sensitivity.py
similarity index 58%
rename from test/test_experiment/test_weird_bug.py
rename to test/test_experiment/test_coordinate_sensitivity.py
index 77ad1869..8fcc5363 100644
--- a/test/test_experiment/test_weird_bug.py
+++ b/test/test_experiment/test_coordinate_sensitivity.py
@@ -12,28 +12,26 @@ from utils import get_display_name_from_object_type
class TestCoordinateSensitivity(unittest.TestCase):
+ DISPLAY = False
- def test_weird(self):
- # todo: maybe the code does not like negative coordinates
- # todo: maybe not that the sign of the x coordinate are all negative and the other are all positive, it is easier to find the perfect spatial structure
+ def test_coordinate_normalization_sensitivity(self):
altitudes = [3000]
transformation_classes = [BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization][:]
for transformation_class in transformation_classes:
study_classes = [CrocusSwe]
for study in study_iterator_global(study_classes, altitudes=altitudes, verbose=False):
- print('\n\n')
study_visualizer = StudyVisualizer(study, transformation_class=transformation_class)
study_visualizer.temporal_non_stationarity = True
- print(study_visualizer.coordinates)
- # trend_test = ConditionalIndedendenceLocationTrendTest(study_visualizer.dataset)
- # # years = [1960, 1990]
- # # mu1s = [trend_test.get_mu1(year) for year in years]
- # # print('Stationary')
- # # print(trend_test.get_estimator(trend_test.stationary_margin_model_class, starting_point=None).margin_function_fitted.coef_dict)
- # print('Non Stationary')
- # print(trend_test.get_estimator(trend_test.non_stationary_margin_model_class, starting_point=1960).margin_function_fitted.coef_dict)
- # # print(get_display_name_from_object_type(type(transformation_2D)), 'mu1s: ', mu1s)
- # # self.assertTrue(0.0 not in mu1s)
+ trend_test = ConditionalIndedendenceLocationTrendTest(study_visualizer.dataset)
+ years = [1960, 1990]
+ mu1s = [trend_test.get_mu1(year) for year in years]
+ if self.DISPLAY:
+ print('Stationary')
+ print(trend_test.get_estimator(trend_test.stationary_margin_model_class, starting_point=None).margin_function_fitted.coef_dict)
+ print('Non Stationary')
+ print(trend_test.get_estimator(trend_test.non_stationary_margin_model_class, starting_point=1960).margin_function_fitted.coef_dict)
+ print(get_display_name_from_object_type(transformation_class), 'mu1s: ', mu1s)
+ self.assertTrue(0.0 not in mu1s)
if __name__ == '__main__':
--
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