chore: update nbs on 0.3.x

* chore: update nbs and minor fix comparison nb
zama-ai · Sep 5, 2022 · 393510e · 393510e
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Showing 16 changed files with 698 additions and 435 deletions.
diff --git a/docs/advanced_examples/ClassifierComparison.ipynb b/docs/advanced_examples/ClassifierComparison.ipynb
diff --git a/docs/advanced_examples/ClientServer.ipynb b/docs/advanced_examples/ClientServer.ipynb
@@ -140,10 +140,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "total 28K\n",
-      "-rw-r--r-- 1 dev_user dev_user  453 Jun 21 09:16 client.zip\n",
-      "-rw-r--r-- 1 dev_user dev_user 5.2K Jun 21 09:16 serialized_processing.json\n",
-      "-rw-r--r-- 1 dev_user dev_user  13K Jun 21 09:16 server.zip\n"
+      "total 60K\r\n",
+      "-rw-r--r-- 1 dev_user dev_user  464 Sep  2 00:09 client.zip\r\n",
+      "-rw-r--r-- 1 dev_user dev_user  43K Sep  2 00:09 serialized_processing.json\r\n",
+      "-rw-r--r-- 1 dev_user dev_user 8.4K Sep  2 00:09 server.zip\r\n"
      ]
     }
    ],
@@ -171,8 +171,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "total 16K\n",
-      "-rw-r--r-- 1 dev_user dev_user 13K Jun 21 09:16 server.zip\n"
+      "total 12K\r\n",
+      "-rw-r--r-- 1 dev_user dev_user 8.4K Sep  2 00:09 server.zip\r\n"
      ]
     }
    ],
@@ -191,9 +191,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "total 12K\n",
-      "-rw-r--r-- 1 dev_user dev_user  453 Jun 21 09:16 client.zip\n",
-      "-rw-r--r-- 1 dev_user dev_user 5.2K Jun 21 09:16 serialized_processing.json\n"
+      "total 48K\r\n",
+      "-rw-r--r-- 1 dev_user dev_user 464 Sep  2 00:09 client.zip\r\n",
+      "-rw-r--r-- 1 dev_user dev_user 43K Sep  2 00:09 serialized_processing.json\r\n"
      ]
     }
    ],
@@ -219,7 +219,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "KeySetCache: miss, regenerating /tmp/tmp61r9hui6/7071120041192343400/0_0\n"
+      "KeySetCache: miss, regenerating /tmp/tmpmomc9x1m/7071120041192343400/0_0\n"
      ]
     }
    ],

diff --git a/docs/advanced_examples/ConvolutionalNeuralNetwork.ipynb b/docs/advanced_examples/ConvolutionalNeuralNetwork.ipynb
diff --git a/docs/advanced_examples/DecisionTreeClassifier.ipynb b/docs/advanced_examples/DecisionTreeClassifier.ipynb
@@ -58,9 +58,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best hyper parameters: {'max_depth': None, 'max_features': None, 'min_samples_leaf': 10, 'min_samples_split': 100}\n",
+      "Best score: 0.9300712036006302\n"
+     ]
+    }
+   ],
    "source": [
     "# Find best hyper parameters with cross validation\n",
     "from sklearn.model_selection import GridSearchCV\n",
@@ -107,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,9 +125,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sklearn average precision score: 0.95\n",
+      "Concrete average precision score: 0.97\n"
+     ]
+    }
+   ],
    "source": [
     "# Compute average precision on test\n",
     "from sklearn.metrics import average_precision_score\n",
@@ -140,9 +158,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of test samples: 691\n",
+      "Number of spams in test samples: 304\n",
+      "True Negative (legit mail well classified) rate: 0.9612403100775194\n",
+      "False Positive (legit mail classified as spam) rate: 0.03875968992248062\n",
+      "False Negative (spam mail classified as legit) rate: 0.14473684210526316\n",
+      "True Positive (spam well classified) rate: 0.8552631578947368\n"
+     ]
+    }
+   ],
    "source": [
     "# Show the confusion matrix on x_test\n",
     "from sklearn.metrics import confusion_matrix\n",
@@ -173,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,18 +221,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating a key for an 6-bit circuit\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Generating a key for an {circuit.graph.maximum_integer_bit_width()}-bit circuit\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key generation time: 253.61 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "time_begin = time.time()\n",
     "circuit.client.keygen(force=False)\n",
@@ -210,9 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution time: 0.08 seconds per sample\n"
+     ]
+    }
+   ],
    "source": [
     "# Predict in FHE for a few examples\n",
     "time_begin = time.time()\n",
@@ -222,9 +277,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction FHE:     [0 0 0 1 0 1 0 0 0 0]\n",
+      "Prediction sklearn: [0 0 0 1 0 1 0 0 0 0]\n"
+     ]
+    }
+   ],
    "source": [
     "# Check prediction FHE vs sklearn\n",
     "print(f\"Prediction FHE:     {y_pred_fhe}\")\n",
@@ -233,9 +297,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10/10 predictions are similar between the FHE model and the clear sklearn model.\n"
+     ]
+    }
+   ],
    "source": [
     "print(\n",
     "    f\"{numpy.sum(y_pred_fhe==y_pred[:10])}/\"\n",

diff --git a/docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb b/docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb
diff --git a/docs/advanced_examples/GLMComparison.ipynb b/docs/advanced_examples/GLMComparison.ipynb
diff --git a/docs/advanced_examples/KaggleTitanic.ipynb b/docs/advanced_examples/KaggleTitanic.ipynb
@@ -580,7 +580,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best hyperparameters found in 2.10s : {'learning_rate': 1, 'max_depth': 4, 'n_estimators': 4}\n"
+      "Best hyperparameters found in 6.97s : {'learning_rate': 1, 'max_depth': 4, 'n_estimators': 4}\n"
      ]
     }
    ],
@@ -629,7 +629,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best hyperparameters found in 50.34s : {'learning_rate': 0.1, 'max_depth': 4, 'n_bits': 2, 'n_estimators': 4}\n"
+      "Best hyperparameters found in 77.28s : {'learning_rate': 0.1, 'max_depth': 4, 'n_bits': 2, 'n_estimators': 4}\n"
      ]
     }
    ],
@@ -674,16 +674,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████| 418/418 [17:00<00:00,  2.44s/it]"
+      "100%|████████████████████████████████████████████████████████████████████████| 418/418 [14:31<00:00,  2.08s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Key generation time: 20.20s\n",
-      "Total execution time for 418 inferences: 1020.74s\n",
-      "Execution time per inference in FHE: 2.44s\n"
+      "Key generation time: 53.37s\n",
+      "Total execution time for 418 inferences: 871.32s\n",
+      "Execution time per inference in FHE: 2.08s\n"
      ]
     },
     {

diff --git a/docs/advanced_examples/LinearRegression.ipynb b/docs/advanced_examples/LinearRegression.ipynb
diff --git a/docs/advanced_examples/LogisticRegression.ipynb b/docs/advanced_examples/LogisticRegression.ipynb
diff --git a/docs/advanced_examples/PoissonRegression.ipynb b/docs/advanced_examples/PoissonRegression.ipynb
diff --git a/docs/advanced_examples/QuantizationAwareTraining.ipynb b/docs/advanced_examples/QuantizationAwareTraining.ipynb