Notebook fixes (#333)

* Fix predict_batch import errors * Import os to avoid NameError * Update ipython kernel name to default * Fix import path to scale_by_instance * Clarify the installation of wilds library before executing the first cell * Update import path to avoid ModuleNotFoundError * Change flag DOWNLOAD=True to ensure the notebook can run, update attributes * Fix typo, update attribues * Remove unused predict_batch import * Update predict_batch calls
SeldonIO · Sep 15, 2021 · c664bbe · c664bbe
1 parent 55beb90
commit c664bbe
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Showing 13 changed files with 74 additions and 128 deletions.
diff --git a/examples/ad_ae_cifar10.ipynb b/examples/ad_ae_cifar10.ipynb
@@ -51,15 +51,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:fbprophet:Importing plotly failed. Interactive plots will not work.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -72,7 +64,7 @@
     "\n",
     "from alibi_detect.ad import AdversarialAE\n",
     "from alibi_detect.utils.fetching import fetch_detector, fetch_tf_model\n",
-    "from alibi_detect.utils.prediction import predict_batch\n",
+    "from alibi_detect.utils.tensorflow.prediction import predict_batch\n",
     "from alibi_detect.utils.saving import save_detector, load_detector\n",
     "from alibi_detect.datasets import fetch_attack, fetch_cifar10c, corruption_types_cifar10c"
    ]
@@ -293,7 +285,7 @@
     }
    ],
    "source": [
-    "y_pred = predict_batch(clf, X_test, batch_size=32, return_class=True)\n",
+    "y_pred = predict_batch(X_test, clf, batch_size=32).argmax(axis=1)\n",
     "acc_y_pred = accuracy(y_test, y_pred)\n",
     "print('Accuracy: {:.4f}'.format(acc_y_pred))"
    ]
@@ -353,8 +345,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred_cw = predict_batch(clf, X_test_cw, batch_size=32, return_class=True)\n",
-    "y_pred_slide = predict_batch(clf, X_test_slide, batch_size=32, return_class=True)"
+    "y_pred_cw = predict_batch(X_test_cw, clf, batch_size=32).argmax(axis=1)\n",
+    "y_pred_slide = predict_batch(X_test_slide, clf, batch_size=32).argmax(axis=1)"
    ]
   },
   {
@@ -554,8 +546,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_recon_cw = predict_batch(ad.ae, X_test_cw, batch_size=32)\n",
-    "X_recon_slide = predict_batch(ad.ae, X_test_slide, batch_size=32)"
+    "X_recon_cw = predict_batch(X_test_cw, ad.ae, batch_size=32)\n",
+    "X_recon_slide = predict_batch(X_test_slide, ad.ae, batch_size=32)"
    ]
   },
   {
@@ -564,8 +556,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_recon_cw = predict_batch(clf, X_recon_cw, batch_size=32, return_class=True)\n",
-    "y_recon_slide = predict_batch(clf, X_recon_slide, batch_size=32, return_class=True)"
+    "y_recon_cw = predict_batch(X_recon_cw, clf, batch_size=32).argmax(axis=1)\n",
+    "y_recon_slide = predict_batch(X_recon_slide, clf, batch_size=32).argmax(axis=1)"
    ]
   },
   {
@@ -840,7 +832,7 @@
     }
    ],
    "source": [
-    "y_pred_mix = predict_batch(clf, X_mix, batch_size=32, return_class=True)\n",
+    "y_pred_mix = predict_batch(X_mix, clf, batch_size=32).argmax(axis=1)\n",
     "acc_y_pred_mix = accuracy(y_mix, y_pred_mix)\n",
     "print('Accuracy {:.4f}'.format(acc_y_pred_mix))"
    ]
@@ -989,12 +981,12 @@
    ],
    "source": [
     "# reconstructed adversarial instances\n",
-    "X_recon_cw_t = predict_batch(ad_t.ae, X_test_cw, batch_size=32)\n",
-    "X_recon_slide_t = predict_batch(ad_t.ae, X_test_slide, batch_size=32)\n",
+    "X_recon_cw_t = predict_batch(X_test_cw, ad_t.ae, batch_size=32)\n",
+    "X_recon_slide_t = predict_batch(X_test_slide, ad_t.ae, batch_size=32)\n",
     "\n",
     "# make predictions on reconstructed instances and compute accuracy\n",
-    "y_recon_cw_t = predict_batch(clf, X_recon_cw_t, batch_size=32, return_class=True)\n",
-    "y_recon_slide_t = predict_batch(clf, X_recon_slide_t, batch_size=32, return_class=True)\n",
+    "y_recon_cw_t = predict_batch(X_recon_cw_t, clf, batch_size=32).argmax(axis=1)\n",
+    "y_recon_slide_t = predict_batch(X_recon_slide_t, clf, batch_size=32).argmax(axis=1)\n",
     "acc_y_recon_cw_t = accuracy(y_test, y_recon_cw_t)\n",
     "acc_y_recon_slide_t = accuracy(y_test, y_recon_slide_t)\n",
     "print('Accuracy after C&W attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_cw, acc_y_recon_cw_t))\n",
@@ -1284,7 +1276,7 @@
     "    X_corr, mean_test, std_test = scale_by_instance(X_corr)\n",
     "    \n",
     "    print('Make predictions on corrupted dataset...')\n",
-    "    y_pred_corr = predict_batch(clf, X_corr, batch_size=32, return_class=True)\n",
+    "    y_pred_corr = predict_batch(X_corr, clf, batch_size=32).argmax(axis=1)\n",
     "    \n",
     "    print('Compute adversarial scores on corrupted dataset...')\n",
     "    score_corr = ad_t.score(X_corr, batch_size=32)\n",
@@ -1401,7 +1393,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

diff --git a/examples/cd_distillation_cifar10.ipynb b/examples/cd_distillation_cifar10.ipynb
@@ -30,17 +30,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Importing plotly failed. Interactive plots will not work.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -52,7 +44,7 @@
     "\n",
     "from alibi_detect.models.tensorflow.resnet import scale_by_instance\n",
     "from alibi_detect.utils.fetching import fetch_tf_model, fetch_detector\n",
-    "from alibi_detect.utils.prediction import predict_batch\n",
+    "from alibi_detect.utils.tensorflow.prediction import predict_batch\n",
     "from alibi_detect.utils.saving import save_detector\n",
     "from alibi_detect.datasets import fetch_cifar10c, corruption_types_cifar10c"
    ]
@@ -500,7 +492,7 @@
     "    4: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
     "    5: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
     "}\n",
-    "y_pred = predict_batch(clf, X_test, batch_size=256, return_class=True)\n",
+    "y_pred = predict_batch(X_test, clf, batch_size=256).argmax(axis=1)\n",
     "score_x = ad.score(X_test, batch_size=256)\n",
     "\n",
     "for s in severities:\n",
@@ -511,7 +503,7 @@
     "    X_corr = scale_by_instance(X_corr)\n",
     "    \n",
     "    print('Make predictions on corrupted dataset...')\n",
-    "    y_pred_corr = predict_batch(clf, X_corr, batch_size=1000, return_class=True)\n",
+    "    y_pred_corr = predict_batch(X_corr, clf, batch_size=1000).argmax(axis=1)\n",
     "    \n",
     "    print('Compute adversarial scores on corrupted dataset...')\n",
     "    score_corr = ad.score(X_corr, batch_size=256)\n",
@@ -780,7 +772,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

diff --git a/examples/cd_ks_cifar10.ipynb b/examples/cd_ks_cifar10.ipynb
@@ -36,7 +36,7 @@
     "import tensorflow as tf\n",
     "\n",
     "from alibi_detect.cd import KSDrift\n",
-    "from alibi_detect.models.resnet import scale_by_instance\n",
+    "from alibi_detect.models.tensorflow.resnet import scale_by_instance\n",
     "from alibi_detect.utils.fetching import fetch_tf_model, fetch_detector\n",
     "from alibi_detect.utils.saving import save_detector, load_detector\n",
     "from alibi_detect.datasets import fetch_cifar10c, corruption_types_cifar10c"
@@ -1327,14 +1327,23 @@
    "hash": "ffba93b5284319fb7a107c8eacae647f441487dcc7e0323a4c0d3feb66ea8c5e"
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 64-bit ('.venv': venv)",
+   "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",
-   "version": ""
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}