2c PM - gaussian and benchmark

eXascaleInfolab · qnater · Jan 9, 2025 · Jan 7, 2025 · Jan 8, 2025 · Jan 8, 2025
commit 82165ce236f778d01b9e1e9fd4b4130c912a3acb
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
diff --git a/imputegap/recovery/__pycache__/manager.cpython-312.pyc b/imputegap/recovery/__pycache__/manager.cpython-312.pyc
diff --git a/imputegap/runner_contamination.py b/imputegap/runner_contamination.py
@@ -9,8 +9,7 @@
 ts_1.normalize(normalizer="min_max")
 
 # 3. contamination of the data with MCAR pattern
-#incomp_data = ts_1.Contamination.mcar(ts_1.data, series_rate=0.2, missing_rate=0.2, seed=True)
-incomp_data = ts_1.Contamination.gaussian(ts_1.data, series_rate=0.2, missing_rate=0.6, std_dev=0.9, seed=True)
+incomp_data = ts_1.Contamination.mcar(ts_1.data, series_rate=0.2, missing_rate=0.2, seed=True)
 
 # [OPTIONAL] you can plot your raw data / print the contamination
 ts_1.print(limit_timestamps=12, limit_series=7)

diff --git a/imputegap/runner_imputation.py b/imputegap/runner_imputation.py
@@ -32,4 +32,4 @@
 
 # 6. display the results
 ts_3.print_results(cdrec.metrics, algorithm="cdrec")
-ts_3.plot(input_data=ts_1.data, incomp_data=ts_2.data, recov_data=ts_3.data, subplot=True, save_path="./assets")
+ts_3.plot(input_data=ts_1.data, incomp_data=ts_2.data, recov_data=ts_3.data, max_series=9, subplot=True, save_path="./assets")