update & fix notebook with recent changes (#276)

Author: VincentAuriau

notebooks/auxiliary_tools/assortment_example.ipynb

@@ -29,26 +29,18 @@
    "source": [
     "# Install necessary requirements\n",
     "\n",
-    "# If you run this notebook on Google Colab, or in standalone mode, you need to install the required packages.\n",
-    "# Uncomment the following lines:\n",
+    "# If you run this notebook on Google Colab, or in standalone mode, you need to install\n",
+    "# the required packages. Just uncomment the following lines:\n",
     "\n",
     "# !pip install choice-learn\n",
     "\n",
-    "# If you run the notebook within the GitHub repository, you need to run the following lines, that can skipped otherwise:\n",
+    "# If you run the notebook within the GitHub repository, you need to run the following lines,\n",
+    "# that can skipped otherwise:\n",
     "import os\n",
     "import sys\n",
     "\n",
-    "sys.path.append(\"../../\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing the right base libraries\n",
-    "import os\n",
+    "sys.path.append(\"../../\")\n",
+    "\n",
     "# Remove GPU use\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
     "\n",
@@ -277,36 +269,44 @@
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
+    "\n",
     "from choice_learn.models.base_model import ChoiceModel\n",
     "\n",
     "\n",
     "class TaFengMNL(ChoiceModel):\n",
     "    \"\"\"Custom model for the TaFeng dataset.\"\"\"\n",
     "\n",
     "    def __init__(self, **kwargs):\n",
-    "        \"\"\"Instantiation of our custom model.\"\"\"\n",
+    "        \"\"\"Instantiate of our custom model.\"\"\"\n",
     "        # Standard inheritance stuff\n",
     "        super().__init__(**kwargs)\n",
-    "\n",
-    "        # Instantiation of base utilties weights\n",
-    "        # We have 25 items in the dataset making 25 weights\n",
-    "        self.base_utilities = tf.Variable(\n",
-    "                            tf.random_normal_initializer(0.0, 0.02, seed=42)(shape=(1, 25))\n",
-    "                        )\n",
-    "        # Instantiation of price elasticities weights\n",
-    "        # We have 3 age categories making 3 weights\n",
-    "        self.price_elasticities = tf.Variable(\n",
-    "                            tf.random_normal_initializer(0.0, 0.02, seed=42)(shape=(1, 3))\n",
-    "                        )\n",
-    "        # Don't forget to add the weights to be optimized in self.weights !\n",
-    "        self.trainable_weights = [self.base_utilities, self.price_elasticities]\n",
+    "        # Directly initialize weights in _trainable_weights\n",
+    "        self._trainable_weights = [\n",
+    "            tf.Variable(tf.random_normal_initializer(0.0, 0.02, seed=42)(shape=(1, 25))),\n",
+    "            tf.Variable(tf.random_normal_initializer(0.0, 0.02, seed=42)(shape=(1, 3)))\n",
+    "        ]\n",
+    "\n",
+    "    @property\n",
+    "    def trainable_weights(self):\n",
+    "        \"\"\"Return all the models trainable weights.\"\"\"\n",
+    "        return self._trainable_weights\n",
+    "\n",
+    "    @property\n",
+    "    def base_utilities(self):\n",
+    "        \"\"\"Return itemwise utilities.\"\"\"\n",
+    "        return self._trainable_weights[0]\n",
+    "\n",
+    "    @property\n",
+    "    def price_elasticities(self):\n",
+    "        \"\"\"Return the price elasticities.\"\"\"\n",
+    "        return self._trainable_weights[1]\n",
     "\n",
     "    def compute_batch_utility(self,\n",
     "                              shared_features_by_choice,\n",
     "                              items_features_by_choice,\n",
     "                              available_items_by_choice,\n",
     "                              choices):\n",
-    "        \"\"\"Method that defines how the model computes the utility of a product.\n",
+    "        \"\"\"Define how the model computes the utility of a product.\n",
     "\n",
     "        Parameters\n",
     "        ----------\n",
@@ -323,7 +323,7 @@
     "            Choices\n",
     "            Shape must be (n_choices, )\n",
     "\n",
-    "        Returns:\n",
+    "        Return:\n",
     "        --------\n",
     "        np.ndarray\n",
     "            Utility of each product for each choice.\n",
@@ -375,7 +375,7 @@
     }
    ],
    "source": [
-    "model = TaFengMNL(optimizer=\"lbfgs\", epochs=1000, tolerance=1e-4)\n",
+    "model = TaFengMNL(optimizer=\"lbfgs\", epochs=1000, lbfgs_tolerance=1e-4)\n",
     "history = model.fit(dataset, verbose=1)"
    ]
   },