diff --git a/experiment/meteo_france_data/stations_data/main_station_comparison.py b/experiment/meteo_france_data/stations_data/main_station_comparison.py
index e8dd8bb4544f93101dcfc73e243fc0e58de727dd..0405f624c15f31bc15ee86e034242c1bb76ca331 100644
--- a/experiment/meteo_france_data/stations_data/main_station_comparison.py
+++ b/experiment/meteo_france_data/stations_data/main_station_comparison.py
@@ -2,7 +2,7 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
ALL_ALTITUDES_WITH_20_STATIONS_AT_LEAST
from experiment.meteo_france_data.stations_data.comparison_analysis import ComparisonAnalysis
from experiment.meteo_france_data.stations_data.visualization.comparisons_visualization.comparisons_visualization import \
- ComparisonsVisualization
+ ComparisonsVisualization, path_backup_csv_file
def visualize_all_stations():
@@ -48,13 +48,14 @@ def wrong_example3():
def quick_metric_analysis():
- ComparisonsVisualization.visualize_metric()
+ ComparisonsVisualization.visualize_metric(csv_filepath=path_backup_csv_file)
+ # ComparisonsVisualization.visualize_metric()
if __name__ == '__main__':
# wrong_example3()
# visualize_fast_comparison()
- visualize_all_stations()
- # quick_metric_analysis()
+ # visualize_all_stations()
+ quick_metric_analysis()
# wrong_example2()
# visualize_non_nan_station()
# example()
diff --git a/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py b/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
index 0e5a4914b26e4139e3a3f13fdc2a4fa0e149c0ce..5b82cec797fd4069cf4c80c13eb028ec25a3625b 100644
--- a/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
+++ b/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
@@ -1,3 +1,5 @@
+import os.path as op
+from sklearn.metrics import confusion_matrix
from collections import OrderedDict
from itertools import chain
from typing import Dict, List
@@ -20,12 +22,23 @@ from extreme_estimator.extreme_models.utils import r, safe_run_r_estimator, ro
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from utils import classproperty
-MAE_COLUMN_NAME = 'mean absolute difference'
-DISTANCE_COLUMN_NAME = 'distance'
-path_df_location_to_value_csv_example = r'/home/erwan/Documents/projects/spatiotemporalextremes/experiment/meteo_france_data/stations_data/csv/example.csv'
+path = r'/home/erwan/Documents/projects/spatiotemporalextremes/experiment/meteo_france_data/stations_data/csv'
+path_last_csv_file = op.join(path, 'example.csv')
+path_backup_csv_file = op.join(path, 'example_backup.csv')
+
+
+def get_reanalysis_column_name(station_column_name):
+ return station_column_name + ' ' + REANALYSE_STR
+
+WELL_CLASSIFIED_CNAME = 'well classified'
+DISTANCE_COLUMN_NAME = 'distance'
+TREND_TYPE_CNAME = 'display trend type'
+SAFRAN_TREND_TYPE_CNAME = get_reanalysis_column_name('display trend type')
+MAE_COLUMN_NAME = 'mean absolute difference'
+
class ComparisonsVisualization(VisualizationParameters):
def __init__(self, altitudes=None, keep_only_station_without_nan_values=False, margin=150,
@@ -99,24 +112,39 @@ class ComparisonsVisualization(VisualizationParameters):
return df
@classmethod
- def visualize_metric(cls, df=None):
+ def visualize_metric(cls, df=None, csv_filepath=path_last_csv_file):
# Load or update df value from example file
if df is None:
- df = pd.read_csv(path_df_location_to_value_csv_example, index_col=[0, 1, 2])
+ df = pd.read_csv(csv_filepath, index_col=[0, 1, 2])
else:
- df.to_csv(path_df_location_to_value_csv_example)
-
- # Compute some column like a classication boolean
-
- # Display some score spatially
- df_score = df.groupby([MASSIF_COLUMN_NAME]).mean()
- s_mae = df_score[MAE_COLUMN_NAME]
- massif_name_to_value = s_mae.to_dict()
- AbstractStudy.visualize_study(massif_name_to_value=massif_name_to_value,
+ df.to_csv(csv_filepath)
+
+ if TREND_TYPE_CNAME in df.columns:
+ # Display the confusion matrix
+ print(df[TREND_TYPE_CNAME].values)
+ print(AbstractUnivariateTest.three_main_trend_types())
+ m = confusion_matrix(y_true=df[TREND_TYPE_CNAME].values,
+ y_pred=df[SAFRAN_TREND_TYPE_CNAME].values,
+ labels=AbstractUnivariateTest.three_main_trend_types())
+ print(m)
+
+ # Display the classification score per massif
+ df[WELL_CLASSIFIED_CNAME] = df[TREND_TYPE_CNAME] == df[SAFRAN_TREND_TYPE_CNAME]
+ serie_classificaiton = df.groupby([MASSIF_COLUMN_NAME]).mean()[WELL_CLASSIFIED_CNAME] * 100
+ AbstractStudy.visualize_study(massif_name_to_value=serie_classificaiton.to_dict(),
+ default_color_for_missing_massif='b',
+ cmap=plt.cm.Greens,
+ vmin=0,
+ vmax=100,
+ label='agreement on trend type classification (%)' )
+ # Display the mae score
+ serie_mae = df.groupby([MASSIF_COLUMN_NAME]).mean()[MAE_COLUMN_NAME]
+ AbstractStudy.visualize_study(massif_name_to_value=serie_mae.to_dict(),
default_color_for_missing_massif='b',
cmap=plt.cm.Reds,
- vmin=s_mae.min(),
- vmax=s_mae.max())
+ vmin=0,
+ vmax=65,
+ label='average absolute difference between annual maxima snowfall (mm)')
def _visualize_ax_main(self, plot_function, comparison: ComparisonAnalysis, massif, ax=None, show=False):
if ax is None:
@@ -164,7 +192,7 @@ class ComparisonsVisualization(VisualizationParameters):
if isinstance(plot_ordered_value_dict, dict):
if REANALYSE_STR in i:
- plot_station_ordered_value_dict = OrderedDict([(k + ' ' + REANALYSE_STR, v)
+ plot_station_ordered_value_dict = OrderedDict([(get_reanalysis_column_name(k), v)
for k, v in plot_ordered_value_dict.items()])
else:
ordered_value_dict.update(plot_ordered_value_dict)
@@ -214,7 +242,7 @@ class ComparisonsVisualization(VisualizationParameters):
most_likely_trend_type = trend_test_res[best_idx][0]
display_trend_type = AbstractUnivariateTest.get_display_trend_type(real_trend_type=most_likely_trend_type)
label += "\n {} starting in {}".format(display_trend_type, most_likely_year)
- ordered_dict['display trend type'] = display_trend_type
+ ordered_dict[TREND_TYPE_CNAME] = display_trend_type
ordered_dict['most likely year'] = most_likely_year
# Display the nllh against the starting year
step = 1
diff --git a/experiment/trend_analysis/univariate_test/abstract_univariate_test.py b/experiment/trend_analysis/univariate_test/abstract_univariate_test.py
index bfbe4733cfcc2b00fbc611f7f7c566c8e76e4ef0..d412e7c172dade18295d45a650b55ec17ad32682 100644
--- a/experiment/trend_analysis/univariate_test/abstract_univariate_test.py
+++ b/experiment/trend_analysis/univariate_test/abstract_univariate_test.py
@@ -49,6 +49,10 @@ class AbstractUnivariateTest(object):
return [cls.POSITIVE_TREND, cls.NEGATIVE_TREND,
cls.SIGNIFICATIVE_POSITIVE_TREND, cls.SIGNIFICATIVE_NEGATIVE_TREND, cls.NO_TREND]
+ @classmethod
+ def three_main_trend_types(cls):
+ return [cls.SIGNIFICATIVE_NEGATIVE_TREND, cls.NON_SIGNIFICATIVE_TREND, cls.SIGNIFICATIVE_POSITIVE_TREND]
+
@classmethod
def display_trend_type_to_style(cls):
d = OrderedDict()