New plots

bsi_zoo/__init__.py

@@ -16,4 +16,4 @@
 # Dev branch marker is: 'X.Y.devN' where N is an integer.
 #
 
-__version__ = "1.1a1"
+__version__ = 1.1

bsi_zoo/estimators.py

@@ -139,7 +139,7 @@ def _compute_eloreta_kernel(L, *, lambda2, n_orient, whitener, loose=1.0, max_it
     )
     G_R_Gt = _this_normalize_R(G, R, G_3)
     # extra = " (this make take a while)" if n_orient == 3 else ""
-    for kk in range(max_iter):
+    for _ in range(max_iter):
         # 1. Compute inverse of the weights (stabilized) and C
         s, u = eigh(G_R_Gt)
         s = abs(s)
@@ -171,7 +171,7 @@ def _compute_eloreta_kernel(L, *, lambda2, n_orient, whitener, loose=1.0, max_it
         if delta < eps:
             break
     else:
-        warnings.warn("eLORETA weight fitting did not converge (>= %s)" % eps)
+        warnings.warn("eLORETA weight fitting did not converge (>= %s)" % eps, UserWarning, stacklevel=2)
     del G_R_Gt
     G /= source_std  # undo our biasing
     G_3 = _get_G_3(G, n_orient)
@@ -412,6 +412,7 @@ def __init__(self, solver, alpha, cov_type, cov, n_orient, extra_params={}):
         self.cov_type = cov_type
         self.n_orient = n_orient
         self.extra_params = extra_params
+        self.classes_ = np.array([0, 0])
 
     def fit(self, L, y):
         self.L_ = L
@@ -716,8 +717,8 @@ def eloreta(L, y, cov=1, alpha=1 / 9, n_orient=1):
     K = _compute_eloreta_kernel(L, lambda2=alpha, n_orient=n_orient, whitener=whitener)
     x = K @ y  # get the source time courses with simple dot product
 
-    if n_orient > 1:
-        x = x.reshape((-1, n_orient, x.shape[1]))
+    # if n_orient > 1:
+        # x = x.reshape((-1, n_orient, x.shape[1]))
     return x
 
 

bsi_zoo/run_benchmark.ipynb

@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":75,"metadata":{},"outputs":[],"source":["import os\n","import numpy as np\n","import pandas as pd\n","\n","path = \"/home/anuja/tj/bdsg/BSI-Zoo/bsi_zoo/data/free4\"\n","files = os.listdir(path)\n","benchmark_files_free =  [i for i in files if 'spatialCV' not in i]\n","benchmark_files_free_with_spatial_cv = [i for i in files if 'spatialCV' in i]\n","\n","\n","eloreta_path = \"/home/anuja/tj/bdsg/BSI-Zoo/bsi_zoo/data/eloreta\"\n","files = os.listdir(eloreta_path)\n","benchmark_files_eloreta =  [i for i in files if 'benchmark' not in i]\n","\n","dfs_free = [pd.read_pickle(f'{path}/{file}') for file in benchmark_files_free]\n","df_results_free = pd.concat(dfs_free)\n","df_results_free['alpha'] = 1-df_results_free['alpha'].astype(float)\n","\n","\n","\n","dfs_free_with_spatial_cv = [pd.read_pickle(f'{path}/{file}') for file in benchmark_files_free_with_spatial_cv]\n","eloreta_dfs = [pd.read_pickle(f'{eloreta_path}/{file}') for file in benchmark_files_eloreta]\n","df_results_free_with_spatial_cv = pd.concat(dfs_free_with_spatial_cv)\n","df_results_eloreta = pd.concat(eloreta_dfs)\n","\n","# join with eloreta\n","df_results_free_with_spatial_cv = pd.concat([df_results_eloreta, df_results_free_with_spatial_cv])\n","df_results_free_with_spatial_cv['alpha'] = 1-df_results_free_with_spatial_cv['alpha'].astype(float)"]},{"cell_type":"code","execution_count":92,"metadata":{},"outputs":[],"source":["# save df_results_free as .mat file\n","import scipy.io\n","Output = {}\n","\n","scipy.io.savemat('free_spatialCV.mat', df_results_free_with_spatial_cv.to_dict('list'))"]},{"cell_type":"code","execution_count":95,"metadata":{},"outputs":[{"data":{"text/plain":["dict_keys(['__header__', '__version__', '__globals__', 'estimator', 'euclidean_distance', 'mse', 'emd', 'f1', 'reconstructed_noise', 'alpha', 'cov_type', 'n_sensors', 'n_sources', 'n_times', 'nnz', 'orientation_type', 'path_to_leadfield', 'extra_params', 'estimator__alpha', 'estimator__alpha_cv', 'error'])"]},"execution_count":95,"metadata":{},"output_type":"execute_result"}],"source":["# load .mat file /home/anuja/tj/bdsg/BSI-Zoo/bsi_zoo/data/sent to stefan/fixed.mat\n","import scipy.io\n","\n","d = scipy.io.loadmat('/home/anuja/tj/bdsg/BSI-Zoo/bsi_zoo/data/sent to stefan/free_spatialCV.mat')\n","d.keys()"]},{"cell_type":"code","execution_count":94,"metadata":{},"outputs":[{"data":{"text/plain":["dict_keys(['__header__', '__version__', '__globals__', 'Unnamed: 0', 'estimator', 'euclidean_distance', 'mse', 'emd', 'f1', 'reconstructed_noise', 'alpha', 'cov_type', 'n_times', 'nnz', 'orientation_type', 'path_to_leadfield', 'extra_params', 'estimator__alpha', 'estimator__alpha_cv', 'error'])"]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["d = scipy.io.loadmat('/home/anuja/tj/bdsg/BSI-Zoo/bsi_zoo/data/sent to stefan/fixed_spatialCV.mat')\n","d.keys()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":2}

bsi_zoo/run_benchmark.py

@@ -18,10 +18,11 @@
 from bsi_zoo.metrics import euclidean_distance, mse, emd, f1, reconstructed_noise
 from bsi_zoo.config import get_leadfield_path
 
-n_jobs = 30
-nruns = 10
-spatial_cv = [False, True]
-subjects = ["CC120166", "CC120313", "CC120264", "CC120313", "CC120309"]
+n_jobs = 12
+nruns = 1
+spatial_cv = [True]
+# True]
+subjects = ["CC120313", "CC120309", "CC120166", "CC120264"]
 metrics = [
     euclidean_distance,
     mse,
@@ -41,77 +42,88 @@
 #     0.13572088,
 #     0.2096144,
 # ]  # logspaced
-estimator_alphas = np.logspace(0, -2, 20)[1:]
+estimator_alphas_I = np.logspace(0, -2, 20)[1:]
+estimator_alphas_II = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
 memory = Memory(".")
 
 for do_spatial_cv in spatial_cv:
     for subject in subjects:
-        """Fixed orientation parameters for the benchmark"""
-
-        orientation_type = "fixed"
-        data_args_I = {
-            # "n_sensors": [50],
-            "n_times": [10],
-            # "n_sources": [200],
-            "nnz": nnzs,
-            "cov_type": ["diag"],
-            "path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
-            "orientation_type": [orientation_type],
-            "alpha": alpha_SNR,  # this is actually SNR
-        }
-
-        data_args_II = {
-            # "n_sensors": [50],
-            "n_times": [10],
-            # "n_sources": [200],
-            "nnz": nnzs,
-            "cov_type": ["full"],
-            "path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
-            "orientation_type": [orientation_type],
-            "alpha": alpha_SNR,  # this is actually SNR
-        }
-
-        estimators = [
-            (fake_solver, data_args_I, {"alpha": estimator_alphas}, {}),
-            (eloreta, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_L1, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_L2, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_sqrt, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
-            (iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
-            # (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 1}),
-            (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 2}),
-            # (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 3}),
-        ]
-
-        df_results = []
-        for estimator, data_args, estimator_args, estimator_extra_params in estimators:
-            benchmark = Benchmark(
-                estimator,
-                subject,
-                metrics,
-                data_args,
-                estimator_args,
-                random_state=42,
-                memory=memory,
-                n_jobs=n_jobs,
-                do_spatial_cv=do_spatial_cv,
-                estimator_extra_params=estimator_extra_params,
-            )
-            results = benchmark.run(nruns=nruns)
-            df_results.append(results)
-
-        df_results = pd.concat(df_results, axis=0)
-
-        data_path = Path("bsi_zoo/data")
-        data_path.mkdir(exist_ok=True)
-        if do_spatial_cv:
-            FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
-        else:
-            FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
-        df_results.to_pickle(data_path / FILE_NAME)
-
-        print(df_results)
+
+        # """Fixed orientation parameters for the benchmark"""
+
+        # orientation_type = "fixed"
+        # data_args_I = {
+        #     # "n_sensors": [50],
+        #     "n_times": [10],
+        #     # "n_sources": [200],
+        #     "nnz": nnzs,
+        #     "cov_type": ["diag"],
+        #     "path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
+        #     "orientation_type": [orientation_type],
+        #     "alpha": alpha_SNR,  # this is actually SNR
+        # }
+
+        # data_args_II = {
+        #     # "n_sensors": [50],
+        #     "n_times": [10],
+        #     # "n_sources": [200],
+        #     "nnz": nnzs,
+        #     "cov_type": ["full"],
+        #     "path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
+        #     "orientation_type": [orientation_type],
+        #     "alpha": alpha_SNR,  # this is actually SNR
+        # }
+
+        # estimators = [
+        #     # (fake_solver, data_args_I, {"alpha": estimator_alphas_I}, {}),
+        #     (eloreta, data_args_I, {"alpha": estimator_alphas_II}, {}),
+        #     # (iterative_L1, data_args_I, {"alpha": estimator_alphas_I}, {}),
+        #     # (iterative_L2, data_args_I, {"alpha": estimator_alphas_I}, {}),
+        #     # (iterative_sqrt, data_args_I, {"alpha": estimator_alphas_I}, {}),
+        #     # (iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
+        #     # (iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
+        #     # (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 1}),
+        #     (gamma_map, data_args_II, {"alpha": estimator_alphas_II}, {"update_mode": 2}),
+        #     # (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 3}),
+        # ]
+
+        # df_results = []
+        # for estimator, data_args, estimator_args, estimator_extra_params in estimators:
+        #     benchmark = Benchmark(
+        #         estimator,
+        #         subject,
+        #         metrics,
+        #         data_args,
+        #         estimator_args,
+        #         random_state=42,
+        #         memory=memory,
+        #         n_jobs=n_jobs,
+        #         do_spatial_cv=do_spatial_cv,
+        #         estimator_extra_params=estimator_extra_params,
+        #     )
+        #     results = benchmark.run(nruns=nruns)
+        #     df_results.append(results)
+        #      # save results
+        #     data_path = Path("bsi_zoo/data/updated_alpha_grid")
+        #     data_path.mkdir(exist_ok=True)
+        #     if do_spatial_cv:
+        #         FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
+        #     else:
+        #         FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
+        #     results.to_pickle(data_path / FILE_NAME)
+
+
+        # df_results = pd.concat(df_results, axis=0)
+
+        # data_path = Path("bsi_zoo/data/ramen")
+        # data_path.mkdir(exist_ok=True)
+        # if do_spatial_cv:
+        #     FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
+        # else:
+        #     FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
+        # df_results.to_pickle(data_path / FILE_NAME)
+
+        # print(df_results)
 
         """ Free orientation parameters for the benchmark """
 
@@ -139,16 +151,16 @@
         }
 
         estimators = [
-            (fake_solver, data_args_I, {"alpha": estimator_alphas}, {}),
-            (eloreta, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_L1, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_L2, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_sqrt, data_args_I, {"alpha": estimator_alphas}, {}),
-            (iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
-            (iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
-            # (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 1}),
-            (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 2}),
-            # (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 3}),
+            # (fake_solver, data_args_I, {"alpha": estimator_alphas_I}, {}),
+            (eloreta, data_args_I, {"alpha": estimator_alphas_II}, {}),
+            # (iterative_L1, data_args_I, {"alpha": estimator_alphas_I}, {}),
+            # (iterative_L2, data_args_I, {"alpha": estimator_alphas_I}, {}),
+            # (iterative_sqrt, data_args_I, {"alpha": estimator_alphas_I}, {}),
+            # (iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
+            # (iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
+            # (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 1}),
+            # (gamma_map, data_args_II, {"alpha": estimator_alphas_II}, {"update_mode": 2}),
+            # (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 3}),
         ]
 
         df_results = []
@@ -167,10 +179,20 @@
             )
             results = benchmark.run(nruns=nruns)
             df_results.append(results)
+            # save results
+            data_path = Path("bsi_zoo/data/eloreta")
+            data_path.mkdir(exist_ok=True)
+
+            if do_spatial_cv:
+                FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
+            else:
+                FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
+            results.to_pickle(data_path / FILE_NAME)
+            print(results)
 
         df_results = pd.concat(df_results, axis=0)
 
-        data_path = Path("bsi_zoo/data")
+        data_path = Path("bsi_zoo/data/eloreta")
         data_path.mkdir(exist_ok=True)
         if do_spatial_cv:
             FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
@@ -179,3 +201,4 @@
         df_results.to_pickle(data_path / FILE_NAME)
 
         print(df_results)
+