8. adding of eeg dataset with bayesian tests

.github/workflows/pytest_contamination.yml

@@ -29,4 +29,6 @@ jobs:
         pip install pytest
 
     - name: Run pytest
-      run: python -m pytest ./tests/test_contamination_mcar.py
+      run:  |
+        python -m pytest ./tests/test_contamination_mcar.py
+        python -m pytest ./tests/test_contamination_mp.py

imputegap/imputation/imputation.py

@@ -12,7 +12,7 @@
 
 class Imputation:
 
-    def load_parameters(query="default", algorithm="cdrec"):
+    def load_parameters(query: str = "default", algorithm: str = "cdrec"):
         """
         Load default values of algorithms
 
@@ -79,7 +79,7 @@ def evaluate_params(ground_truth, contamination, configuration, algorithm="cdrec
             imputation, error_measures = Imputation.MR.cdrec(ground_truth, contamination, (rank, eps, iters))
         elif algorithm == 'iim':
             learning_neighbours = configuration
-            alg_code = "iim " + learning_neighbours
+            alg_code = "iim " + re.sub(r'[\W_]', '', str(learning_neighbours))
             imputation, error_measures = Imputation.Regression.iim_imputation(ground_truth, contamination, (learning_neighbours, alg_code))
         elif algorithm == 'mrnn':
             hidden_dim, learning_rate, iterations, keep_prob, seq_len = configuration

imputegap/runner_optimization.py

@@ -37,7 +37,7 @@ def check_block_size(filename):
     gap.print()
     gap.plot(ts_type="contamination", title="test", save_path="assets", limitation=3, display=False)
 
-    optimal_params, yi = Optimization.Bayesian.bayesian_optimization(ground_truth=gap.ts, contamination=gap.ts_contaminate)
+    optimal_params, yi = Optimization.Bayesian.bayesian_optimization(ground_truth=gap.ts, contamination=gap.ts_contaminate, algorithm="iim")
 
     print("\nOptical Params : ", optimal_params)
     print("\nyi : ", yi, "\n")