Delete any reference to Feilong Ma #streisandeffect

.gitignore

@@ -93,3 +93,4 @@ venv.bak/
 
 # Rope project settings
 .ropeproject
+examples/plot_srm_alignment.py

examples/plot_int_alignment.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
 """
-Hyperalignment-base prediction using Feilong Ma's IndividualNeuralTuning Model.
+Hyperalignment-base prediction using the IndividualNeuralTuning Model.
 See article : https://doi.org/10.1162/imag_a_00032
 
 ==========================
@@ -113,11 +113,13 @@
 
 
 train_index = range(53)
-model = IndividualizedNeuralTuning(n_jobs=10, alignment_method="parcelation")
+model = IndividualizedNeuralTuning(
+    n_jobs=10, alignment_method="parcelation", n_components=20
+)
 
-if False:  # Use Parcellation
+if True:  # Use Parcellation
     parcels = compute_parcels(
-        niimg=template_train[0], mask=masker, n_parcels=1000, n_jobs=5
+        niimg=template_train[0], mask=masker, n_parcels=200, n_jobs=5
     )
     model.fit(np.array(masked_imgs)[:, train_index, :], parcels=parcels, verbose=False)
 else:
@@ -144,7 +146,7 @@
 # 0, to 53, and then the PA contrasts from 53 to 106.
 #
 test_index = range(53, 106)
-if False:
+if True:
     model.fit(np.array(masked_imgs)[:, test_index, :], parcels=parcels, verbose=False)
     test_stimulus = np.copy(model.shared_response)
 
@@ -185,14 +187,12 @@
 # made from group average and from template with the real PA contrasts of sub-07
 
 average_score = masker.inverse_transform(
-    np.abs(score_voxelwise(target_test, prediction_from_average, masker, loss="corr"))
+    score_voxelwise(target_test, prediction_from_average, masker, loss="corr")
 )
 
 # I choose abs value in reference to the work we did with the INT
 template_score = masker.inverse_transform(
-    np.abs(
-        score_voxelwise(target_test, prediction_from_template[0], masker, loss="corr")
-    )
+    score_voxelwise(target_test, prediction_from_template[0], masker, loss="corr")
 )
 
 
@@ -205,11 +205,11 @@
 from nilearn import plotting
 
 baseline_display = plotting.plot_stat_map(
-    average_score, display_mode="z", vmax=1, cut_coords=[-15, -5], cmap="hot"
+    average_score, display_mode="z", vmax=1, cut_coords=[-15, -5]
 )
 baseline_display.title("Group average correlation wt ground truth")
 display = plotting.plot_stat_map(
-    template_score, display_mode="z", cut_coords=[-15, -5], vmax=1, cmap="hot"
+    template_score, display_mode="z", cut_coords=[-15, -5], vmax=1
 )
 display.title("Hyperalignment-based prediction correlation wt ground truth")
 

fmralign/alignment_methods.py

@@ -548,7 +548,7 @@ def _tuning_estimator(shared_response, target):
             array-like: The estimated tuning weights.
 
         """
-        return np.linalg.pinv(shared_response).dot(target).astype(np.float32)
+        return np.linalg.pinv(shared_response) @ target
 
     @staticmethod
     def _stimulus_estimator(full_signal, n_t, n_s, latent_dim=None):

fmralign/generate_data.py

@@ -77,11 +77,18 @@ def generate_dummy_signal(
         S_train = np.sqrt(Sigma)[:, None] * rng.randn(n_t, latent_dim)
         S_test = np.sqrt(Sigma)[:, None] * rng.randn(n_t, latent_dim)
 
+    elif generative_method == "multiviewica":
+        S_train = np.random.laplace(size=(n_t, latent_dim))
+        S_test = np.random.laplace(size=(n_t, latent_dim))
+
+    else:
+        raise ValueError("Unknown generative method")
+
     # Generate indiivdual spatial components
     data_train, data_test = [], []
     Ts = []
     for _ in range(n_s):
-        if generative_method == "custom":
+        if generative_method == "custom" or generative_method == "multiviewica":
             W = T_mean + T_std * np.random.randn(latent_dim, n_v)
         else:
             W = projection(rng.randn(latent_dim, n_v))

fmralign/hyperalignment/individualized_neural_tuning.py

@@ -10,7 +10,7 @@ class IndividualizedNeuralTuning(BaseINT):
     Wrapper for the IndividualTuningModel class to be used in fmralign with Niimg objects.
     Preprocessing and searchlight/parcellation alignment are done without any user input.
 
-    Method of alignment based on the Individualized Neural Tuning model, by Feilong Ma et al. (2023).
+    Method of alignment based on the Individualized Neural Tuning model.
     It uses searchlight/parcelation alignment to denoise the data, and then computes the stimulus response matrix.
     See article : https://doi.org/10.1162/imag_a_00032
     """

fmralign/hyperalignment/local_template.py

@@ -1,7 +1,6 @@
 """ Local template computation functions. Those functions are part of the warp hyperalignment
 introducted by Feilong Ma et al. 2023. The functions are adapted from the original code and
 adapted for more general regionations approches.
-Authors: Feilong Ma (Haxby lab, Dartmouth College), Denis Fouchard (MIND, INRIA Saclay).
 """
 
 

fmralign/hyperalignment/toy_experiment.py

@@ -23,12 +23,12 @@
 
 
 n_s = 10
-n_t = 200
+n_t = 100
 n_v = 200
 S_std = 1
 T_std = 1
 SNR = 100
-latent_dim = 6  # if None, latent_dim = n_t
+latent_dim = None  # if None, latent_dim = n_t
 decomposition_method = None  # if None, SVD is used
 
 
@@ -50,9 +50,18 @@
     latent_dim=latent_dim,
     SNR=SNR,
     seed=0,
+    generative_method="fastsrm",
 )
 
-searchlights, dists = generate_dummy_searchlights(n_searchlights=12, n_v=n_v, radius=5)
+SEARCHLIGHT = False
+
+if SEARCHLIGHT:
+    searchlights, dists = generate_dummy_searchlights(
+        n_searchlights=12, n_v=n_v, radius=5
+    )
+else:
+    searchlights = [np.arange(n_v)]
+    dists = [np.zeros((n_v,))]
 
 #############################################################################
 # Test INT on the two parts of the data (ie different runs of the experiment)
@@ -139,6 +148,9 @@
 
 # Stimulus matrix
 
+print("stimulus_pred_run_1.shape", stimulus_pred_run_1.shape)
+print("stimulus_run_1.shape", stimulus_run_1.shape)
+
 correlation_stimulus_true_est_first_part = stimulus_correlation(
     stimulus_pred_run_1.T, stimulus_run_1.T
 )