Adjusted the notebooks to show how one can use the 'load_movielens100k' function in the library

examples/notebooks/19_wide_and_deep_for_recsys_pt1.ipynb

@@ -23,7 +23,9 @@
     "import warnings\n",
     "\n",
     "import pandas as pd\n",
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from pytorch_widedeep.datasets import load_movielens100k"
    ]
   },
   {
@@ -43,44 +45,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_data_path = Path(\"~/ml_projects/wide_deep_learning_for_recsys/ml-100k\")\n",
-    "\n",
     "save_path = Path(\"prepared_data\")\n",
     "if not save_path.exists():\n",
     "    save_path.mkdir(parents=True, exist_ok=True)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "929a9712",
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5de7a941",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Let's first start by loading the interactions, user and item data"
+    "data, users, items = load_movielens100k(as_frame=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "38de36ff",
+   "execution_count": 5,
+   "id": "7a288aee",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the Ratings/Interaction (triplets (user, item, rating) plus timestamp)\n",
-    "data = pd.read_csv(raw_data_path / \"u.data\", sep=\"\\t\", header=None)\n",
-    "data.columns = [\"user_id\", \"movie_id\", \"rating\", \"timestamp\"]\n",
-    "\n",
-    "# Load the User features\n",
-    "users = pd.read_csv(raw_data_path / \"u.user\", sep=\"|\", encoding=\"latin-1\", header=None)\n",
-    "users.columns = [\"user_id\", \"age\", \"gender\", \"occupation\", \"zip_code\"]\n",
-    "\n",
-    "# Load the Item features\n",
-    "items = pd.read_csv(raw_data_path / \"u.item\", sep=\"|\", encoding=\"latin-1\", header=None)\n",
-    "items.columns = [\n",
-    "    \"movie_id\",\n",
-    "    \"movie_title\",\n",
-    "    \"release_date\",\n",
-    "    \"video_release_date\",\n",
-    "    \"IMDb_URL\",\n",
+    "# Alternatively, as specified in the docs: 'The last 19 fields are the genres' so:\n",
+    "# list_of_genres = items.columns.tolist()[-19:]\n",
+    "list_of_genres = [\n",
     "    \"unknown\",\n",
     "    \"Action\",\n",
     "    \"Adventure\",\n",
@@ -103,9 +92,17 @@
     "]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "929a9712",
+   "metadata": {},
+   "source": [
+    "Let's first start by loading the interactions, user and item data"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "f4c09273",
    "metadata": {},
    "outputs": [
@@ -185,7 +182,7 @@
        "4      166       346       1  886397596"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "18c3faa0",
    "metadata": {},
    "outputs": [
@@ -282,7 +279,7 @@
        "4        5   33      F       other    15213"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "1dbad7b1",
    "metadata": {},
    "outputs": [
@@ -499,55 +496,13 @@
        "[5 rows x 24 columns]"
       ]
      },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "items.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7b1ce069",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['unknown',\n",
-       " 'Action',\n",
-       " 'Adventure',\n",
-       " 'Animation',\n",
-       " \"Children's\",\n",
-       " 'Comedy',\n",
-       " 'Crime',\n",
-       " 'Documentary',\n",
-       " 'Drama',\n",
-       " 'Fantasy',\n",
-       " 'Film-Noir',\n",
-       " 'Horror',\n",
-       " 'Musical',\n",
-       " 'Mystery',\n",
-       " 'Romance',\n",
-       " 'Sci-Fi',\n",
-       " 'Thriller',\n",
-       " 'War',\n",
-       " 'Western']"
-      ]
-     },
      "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "list_of_genres = pd.read_csv(\n",
-    "    raw_data_path / \"u.genre\", sep=\"|\", header=None, usecols=[0]\n",
-    ")[0].tolist()\n",
-    "list_of_genres"
+    "items.head()"
    ]
   },
   {
@@ -1899,16 +1854,7 @@
    "execution_count": 29,
    "id": "68555183",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/javierrodriguezzaurin/Projects/pytorch-widedeep/pytorch_widedeep/preprocessing/tab_preprocessor.py:309: UserWarning: Continuous columns will not be normalised\n",
-      "  warnings.warn(\"Continuous columns will not be normalised\")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_train_tab = tab_preprocessor.fit_transform(X_train.fillna(0))\n",
     "X_test_tab = tab_preprocessor.transform(X_test.fillna(0))"
@@ -2291,16 +2237,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "epoch 1: 100%|██████████████████████████████| 149/149 [00:16<00:00,  9.08it/s, loss=6.66]\n",
-      "valid: 100%|██████████████████████████████████| 38/38 [00:01<00:00, 23.53it/s, loss=6.61]\n",
-      "epoch 2: 100%|██████████████████████████████| 149/149 [00:16<00:00,  9.11it/s, loss=5.99]\n",
-      "valid: 100%|██████████████████████████████████| 38/38 [00:01<00:00, 23.07it/s, loss=6.55]\n",
-      "epoch 3: 100%|██████████████████████████████| 149/149 [00:16<00:00,  9.11it/s, loss=5.67]\n",
-      "valid: 100%|██████████████████████████████████| 38/38 [00:01<00:00, 22.89it/s, loss=6.55]\n",
-      "epoch 4: 100%|██████████████████████████████| 149/149 [00:16<00:00,  8.81it/s, loss=5.43]\n",
-      "valid: 100%|██████████████████████████████████| 38/38 [00:01<00:00, 21.43it/s, loss=6.57]\n",
-      "epoch 5: 100%|██████████████████████████████| 149/149 [00:16<00:00,  8.79it/s, loss=5.24]\n",
-      "valid: 100%|███████████████████████████████████| 38/38 [00:01<00:00, 22.39it/s, loss=6.6]\n"
+      "epoch 1: 100%|█████████████████████████████████████████████████████████████████████████| 149/149 [00:16<00:00,  8.82it/s, loss=6.66]\n",
+      "valid: 100%|█████████████████████████████████████████████████████████████████████████████| 38/38 [00:01<00:00, 21.14it/s, loss=6.61]\n",
+      "epoch 2: 100%|█████████████████████████████████████████████████████████████████████████| 149/149 [00:17<00:00,  8.53it/s, loss=5.98]\n",
+      "valid: 100%|█████████████████████████████████████████████████████████████████████████████| 38/38 [00:01<00:00, 23.20it/s, loss=6.53]\n",
+      "epoch 3: 100%|█████████████████████████████████████████████████████████████████████████| 149/149 [00:17<00:00,  8.61it/s, loss=5.66]\n",
+      "valid: 100%|█████████████████████████████████████████████████████████████████████████████| 38/38 [00:01<00:00, 23.16it/s, loss=6.54]\n",
+      "epoch 4: 100%|█████████████████████████████████████████████████████████████████████████| 149/149 [00:17<00:00,  8.76it/s, loss=5.43]\n",
+      "valid: 100%|█████████████████████████████████████████████████████████████████████████████| 38/38 [00:01<00:00, 22.03it/s, loss=6.56]\n",
+      "epoch 5: 100%|█████████████████████████████████████████████████████████████████████████| 149/149 [00:17<00:00,  8.28it/s, loss=5.23]\n",
+      "valid: 100%|█████████████████████████████████████████████████████████████████████████████| 38/38 [00:01<00:00, 22.60it/s, loss=6.59]\n"
      ]
     }
    ],