MNT update notebooks

Author: mvdoc

tutorials/notebooks/movies_3T/00_load_colab.ipynb

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "# !ls 'drive' && echo SUCCESS || echo FAILURE"
+        "# !ls drive/MyDrive/vim-5"
       ]
     },
     {

tutorials/notebooks/movies_3T/02_plot_wordnet_model.ipynb

@@ -644,7 +644,7 @@
       },
       "outputs": [],
       "source": [
-        "del pipeline"
+        "del pipeline, kernel_ridge_cv"
       ]
     }
   ],

tutorials/notebooks/movies_3T/03_plot_hemodynamic_response.ipynb

@@ -307,7 +307,7 @@
       },
       "outputs": [],
       "source": [
-        "del pipeline"
+        "del pipeline, pipeline_no_delay"
       ]
     }
   ],

tutorials/notebooks/movies_3T/merged_for_colab.ipynb

@@ -102,7 +102,7 @@
    },
    "outputs": [],
    "source": [
-    "# !ls 'drive' && echo SUCCESS || echo FAILURE"
+    "# !ls drive/MyDrive/vim-5"
    ]
   },
   {
@@ -1720,7 +1720,7 @@
    },
    "outputs": [],
    "source": [
-    "del pipeline"
+    "del pipeline, kernel_ridge_cv"
    ]
   },
   {
@@ -2221,7 +2221,7 @@
    },
    "outputs": [],
    "source": [
-    "del pipeline"
+    "del pipeline, pipeline_no_delay"
    ]
   },
   {

tutorials/notebooks/movies_4T/02_plot_ridge_model.ipynb

@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Here the data is not loaded in memory, we only take a peak at the data shape.\n\n"
+        "Here the data is not loaded in memory, we only take a peek at the data shape.\n\n"
       ]
     },
     {
@@ -267,7 +267,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compare with a model without delays\n\n# To present an example of model comparison, we define here another model,\nwithout feature delays (i.e. no Delayer). This model is unlikely to perform\nwell, since fMRI responses are delayed in time with respect to the stimulus.\n\n"
+        "## Compare with a model without delays\n\nTo present an example of model comparison, we define here another model,\nwithout feature delays (i.e. no Delayer). This model is unlikely to perform\nwell, since fMRI responses are delayed in time with respect to the stimulus.\n\n"
       ]
     },
     {
@@ -278,7 +278,18 @@
       },
       "outputs": [],
       "source": [
-        "pipeline = make_pipeline(\n    StandardScaler(with_mean=True, with_std=False),\n    KernelRidgeCV(\n        alphas=alphas, cv=cv,\n        solver_params=dict(n_targets_batch=100, n_alphas_batch=2,\n                           n_targets_batch_refit=50),\n        Y_in_cpu=True),\n)\npipeline\n\npipeline.fit(X_train, Y_train)\nscores_nodelay = pipeline.score(X_test, Y_test)\nscores_nodelay = backend.to_numpy(scores_nodelay)"
+        "pipeline = make_pipeline(\n    StandardScaler(with_mean=True, with_std=False),\n    KernelRidgeCV(\n        alphas=alphas, cv=cv,\n        solver_params=dict(n_targets_batch=100, n_alphas_batch=2,\n                           n_targets_batch_refit=50),\n        Y_in_cpu=True),\n)\npipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pipeline.fit(X_train, Y_train)\nscores_nodelay = pipeline.score(X_test, Y_test)\nscores_nodelay = backend.to_numpy(scores_nodelay)"
       ]
     },
     {