updated rf_cross_validate to work with linear_modeling APIs

berkeley-stat159 · Dec 13, 2015 · c40405d · c40405d
1 parent 3b1885a
commit c40405d
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Showing 2 changed files with 18 additions and 13 deletions.
diff --git a/code/stat159lambda/classification/random_forest/rf_cross_validate.py b/code/stat159lambda/classification/random_forest/rf_cross_validate.py
@@ -10,7 +10,7 @@
 from stat159lambda.utils import data_path as dp
 
 
-voxels_sorted_by_t_statistic = lm.get_voxels_by_t_statistic('int-ext', 1)
+voxels_sorted_by_t_statistic = lm.VoxelExtractor(1, 'int-ext').t_stat()
 num_features_values = range(100, NUM_VOXELS/100, 100)
 for num_features in num_features_values:
 	voxel_feature_indices = voxels_sorted_by_t_statistic[:num_features_values]
@@ -31,5 +31,5 @@
 		y_predicted = model.predict(X_cv_test)
 		cv_accuracies.append(accuracy_score(y_predicted, y_cv_test))
 
-	print np.mean(cv_accuracies)
+	print(np.mean(cv_accuracies))
 
diff --git a/code/stat159lambda/linear_modeling/linear_modeling.py b/code/stat159lambda/linear_modeling/linear_modeling.py
@@ -10,7 +10,6 @@
 
 
 class VoxelExtractor:
-
     def __init__(self, subject, interest_col_str, data=None):
         """
         VoxelExtractor generates the t-statistic for each voxel based on a
@@ -28,11 +27,10 @@ def __init__(self, subject, interest_col_str, data=None):
         self.subject = subject
         self.interest_col_str = interest_col_str
         if not data:
-            data_path = dp.get_smoothed_path_2d(self.subject, 4)
-            data = nib.load(data_path).get_data()
-            data = data[:, :, :, NUM_OFFSET_VOLUMES:]
-        # Data is shaped as number of voxels by time
-        self.data = np.reshape(data, (-1, data.shape[-1]))
+            data_path = dp.get_smoothed_2d_path(self.subject, 4)
+            data = np.load(data_path)
+            data = data[:, NUM_OFFSET_VOLUMES:]
+        self.data = data
         self.design = None
         self.B = None
 
@@ -51,7 +49,8 @@ def get_design_matrix(self):
             elif self.interest_col_str == "day-night":
                 interest_col_ind = 0
             else:
-                print("Incorrect interest column name: please use either 'int-ext' or 'day-night'")
+                print(
+                    "Incorrect interest column name: please use either 'int-ext' or 'day-night'")
             interest_col = ss.get_scene_slices()[interest_col_ind]
             n_trs = self.data.shape[-1]
             design = np.ones((n_trs, 3))
@@ -70,9 +69,13 @@ def plot_design_matrix(self):
         if self.design is None:
             self.get_design_matrix()
         design_fig = plt.gcf()
-        plt.imshow(self.design, aspect=0.1, cmap='gray', interpolation='nearest')
+        plt.imshow(self.design,
+                   aspect=0.1,
+                   cmap='gray',
+                   interpolation='nearest')
         plt.xticks([])
-        design_fig_path = '{0}/figures/design_fig_{1}.png'.format(REPO_HOME_PATH, self.interest_col_str)
+        design_fig_path = '{0}/figures/design_fig_{1}.png'.format(
+            REPO_HOME_PATH, self.interest_col_str)
         design_fig.savefig(design_fig_path, dpi=100)
         plt.clf()
 
@@ -112,7 +115,8 @@ def t_stat(self):
         SE[SE == 0] = np.amin(SE[SE != 0])
         t = c.T.dot(beta) / SE
         self.t_values = abs(t[0])
-        self.t_indices = np.array(self.t_values).argsort()[::-1][:self.t_values.size]
+        self.t_indices = np.array(self.t_values).argsort(
+        )[::-1][:self.t_values.size]
         return self.t_indices
 
     def plot_single_voxel(self, voxel_index):
@@ -125,6 +129,7 @@ def plot_single_voxel(self, voxel_index):
         """
         voxel_img = plt.gcf()
         plt.plot(self.data[voxel_index, :])
-        voxel_img_path = '{0}/figures/voxel_{1}.png'.format(REPO_HOME_PATH, voxel_index)
+        voxel_img_path = '{0}/figures/voxel_{1}.png'.format(REPO_HOME_PATH,
+                                                            voxel_index)
         voxel_img.savefig(voxel_img_path, dpi=100)
         plt.clf()