2. adding of eeg dataset with bayesian tests

eXascaleInfolab · Sep 27, 2024 · 390895d · 390895d
1 parent 3b40dbf
commit 390895d
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Showing 9 changed files with 19 additions and 30 deletions.
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
diff --git a/env/default_values.toml b/env/default_values.toml
@@ -10,7 +10,7 @@ gamma = 0.85
 alpha = 7
 
 [iim]
-neighbor = 10
+learning_neighbors = 10
 algorithm_code = "iim 2"
 
 [mrnn]

diff --git a/imputegap/imputation/__pycache__/imputation.cpython-312.pyc b/imputegap/imputation/__pycache__/imputation.cpython-312.pyc
diff --git a/imputegap/imputation/imputation.py b/imputegap/imputation/imputation.py
@@ -32,7 +32,7 @@ def load_parameters(query="default", algorithm="cdrec"):
         if not os.path.exists(filepath):
             filepath = filepath[:1]
 
-        with open(filepath, "r") as file:
+        with open(filepath, "r") as _:
             config = toml.load(filepath)
 
         params = None
@@ -47,9 +47,9 @@ def load_parameters(query="default", algorithm="cdrec"):
             alpha = int(config['stmvl']['alpha'])
             params = (window_size, gamma, alpha)
         elif algorithm == "iim":
-            neighbors = int(config['iim']['neighbor'])
+            learning_neighbors = int(config['iim']['learning_neighbors'])
             algo_code = config['iim']['algorithm_code']
-            params = (neighbors, algo_code)
+            params = (learning_neighbors, algo_code)
         elif algorithm == "mrnn":
             hidden_dim = int(config['mrnn']['hidden_dim'])
             learning_rate = float(config['mrnn']['learning_rate'])

diff --git a/imputegap/optimization/__pycache__/algorithm_parameters.cpython-312.pyc b/imputegap/optimization/__pycache__/algorithm_parameters.cpython-312.pyc
diff --git a/imputegap/optimization/algorithm_parameters.py b/imputegap/optimization/algorithm_parameters.py
@@ -42,7 +42,7 @@
 # Define the parameter names for each algorithm
 PARAM_NAMES = {
     'cdrec': ['rank', 'epsilon', 'iteration'],
-    'iim': ['neighbor'],
+    'iim': ['learning_neighbors'],
     'mrnn': ['hidden_dim', 'learning_rate', 'iterations', 'seq_len' ],
     'stmvl': ['window_size', 'gamma', 'alpha']
 }

diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,4 @@
-numpy==1.13.3
+numpy==1.21.5
 pandas==2.0.3
 matplotlib==3.7.5
 toml==0.10.2

diff --git a/tests/__pycache__/test_opti_bayesian_iim.cpython-312.pyc b/tests/__pycache__/test_opti_bayesian_iim.cpython-312.pyc
diff --git a/tests/test_opti_bayesian_iim.py b/tests/test_opti_bayesian_iim.py
@@ -55,8 +55,7 @@ def test_optimization_bayesian_stmvl(self):
         params = Imputation.load_parameters(query="default", algorithm=algorithm)
         params_optimal = (optimal_params['neighbor'], "iim 2")
 
-        _, metrics_optimal = Imputation.Regression.iim_imputation(ground_truth=gap.ts, contamination=ts_contaminated,
-                                                 params=params_optimal)
+        _, metrics_optimal = Imputation.Regression.iim_imputation(ground_truth=gap.ts, contamination=ts_contaminated, params=params_optimal)
         _, metrics_default = Imputation.Regression.iim_imputation(ground_truth=gap.ts, contamination=ts_contaminated, params=params)
 
         Optimization.save_optimization(optimal_params=optimal_params, algorithm=algorithm+"_test")