Staging to master to add NCF hyperparameter tuning

notebooks/02_model/ncf_deep_dive.ipynb

@@ -30,13 +30,31 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda/envs/recommenders_gpu/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/anaconda/envs/recommenders_gpu/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/anaconda/envs/recommenders_gpu/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/anaconda/envs/recommenders_gpu/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/anaconda/envs/recommenders_gpu/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/anaconda/envs/recommenders_gpu/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "System version: 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) \n",
+      "System version: 3.6.10 |Anaconda, Inc.| (default, Mar 25 2020, 23:51:54) \n",
       "[GCC 7.3.0]\n",
-      "Pandas version: 0.24.2\n",
+      "Pandas version: 0.25.3\n",
       "Tensorflow version: 1.12.0\n"
      ]
     }
@@ -206,7 +224,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "4.93MB [00:00, 18.4MB/s]                          \n"
+      "100%|██████████| 4.81k/4.81k [00:00<00:00, 10.6kKB/s]\n"
      ]
     },
     {
@@ -380,7 +398,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took 662.0558905601501 seconds for training.\n"
+      "Took 663.2377220259996 seconds for training.\n"
      ]
     }
    ],
@@ -443,43 +461,43 @@
        "      <th>0</th>\n",
        "      <td>1.0</td>\n",
        "      <td>149.0</td>\n",
-       "      <td>0.150561</td>\n",
+       "      <td>0.029076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1.0</td>\n",
        "      <td>88.0</td>\n",
-       "      <td>0.307006</td>\n",
+       "      <td>0.632505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1.0</td>\n",
        "      <td>101.0</td>\n",
-       "      <td>0.346554</td>\n",
+       "      <td>0.492521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1.0</td>\n",
        "      <td>110.0</td>\n",
-       "      <td>0.185647</td>\n",
+       "      <td>0.081617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1.0</td>\n",
        "      <td>103.0</td>\n",
-       "      <td>0.002877</td>\n",
+       "      <td>0.004513</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "   userID  itemID  prediction\n",
-       "0     1.0   149.0    0.150561\n",
-       "1     1.0    88.0    0.307006\n",
-       "2     1.0   101.0    0.346554\n",
-       "3     1.0   110.0    0.185647\n",
-       "4     1.0   103.0    0.002877"
+       "0     1.0   149.0    0.029076\n",
+       "1     1.0    88.0    0.632505\n",
+       "2     1.0   101.0    0.492521\n",
+       "3     1.0   110.0    0.081617\n",
+       "4     1.0   103.0    0.004513"
       ]
      },
      "execution_count": 8,
@@ -514,7 +532,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took 2.801203966140747 seconds for prediction.\n"
+      "Took 3.0358772649997263 seconds for prediction.\n"
      ]
     }
    ],
@@ -546,10 +564,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MAP:\t0.044980\n",
-      "NDCG:\t0.189388\n",
-      "Precision@K:\t0.170626\n",
-      "Recall@K:\t0.094332\n"
+      "MAP:\t0.046273\n",
+      "NDCG:\t0.190750\n",
+      "Precision@K:\t0.173277\n",
+      "Recall@K:\t0.096688\n"
      ]
     }
    ],
@@ -592,8 +610,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "HR:\t0.489403\n",
-      "NDCG:\t0.385408\n"
+      "HR:\t0.488564\n",
+      "NDCG:\t0.383339\n"
      ]
     }
    ],
@@ -674,7 +692,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took 514.4518468379974 seconds for training.\n"
+      "Took 513.9623115730001 seconds for training.\n"
      ]
     }
    ],
@@ -717,7 +735,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took 565.3551139831543 seconds for training.\n"
+      "Took 566.8783325639997 seconds for training.\n"
      ]
     }
    ],
@@ -778,7 +796,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took 660.8611929416656 seconds for training.\n"
+      "Took 655.1110815689999 seconds for training.\n"
      ]
     }
    ],
@@ -807,7 +825,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took 2.8479833602905273 seconds for prediction.\n"
+      "Took 3.083744528999887 seconds for prediction.\n"
      ]
     }
    ],
@@ -839,10 +857,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MAP:\t0.041600\n",
-      "NDCG:\t0.174057\n",
-      "Precision@K:\t0.160657\n",
-      "Recall@K:\t0.091654\n"
+      "MAP:\t0.043232\n",
+      "NDCG:\t0.181301\n",
+      "Precision@K:\t0.165111\n",
+      "Recall@K:\t0.094437\n"
      ]
     }
    ],
@@ -919,9 +937,9 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "reco_gpu",
+   "display_name": "Python (recommenders_gpu)",
    "language": "python",
-   "name": "reco_gpu"
+   "name": "recommenders_gpu"
   },
   "language_info": {
    "codemirror_mode": {
@@ -933,9 +951,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

notebooks/04_model_select_and_optimize/README.md

@@ -8,6 +8,7 @@ In this directory, notebooks are provided to demonstrate how to tune and optimiz
 | [azureml_hyperdrive_wide_and_deep](azureml_hyperdrive_wide_and_deep.ipynb) | Quickstart tutorial on utilizing [Azure Machine Learning service](https://azure.microsoft.com/en-us/services/machine-learning-service/) for hyperparameter tuning of wide-and-deep model.
 | [azureml_hyperdrive_surprise_svd](azureml_hyperdrive_surprise_svd.ipynb) | Quickstart tutorial on utilizing [Azure Machine Learning service](https://azure.microsoft.com/en-us/services/machine-learning-service/) for hyperparameter tuning of the matrix factorization method SVD from [Surprise library](https://surprise.readthedocs.io/en/stable/).
 | [nni_surprise_svd](nni_surprise_svd.ipynb) | Quickstart tutorial on utilizing the [Neural Network Intelligence toolkit](https://github.com/Microsoft/nni) for hyperparameter tuning of the matrix factorization method SVD from [Surprise library](https://surprise.readthedocs.io/en/stable/).
+| [nni_ncf](nni_ncf.ipynb) | Quickstart tutorial on utilizing the [Neural Network Intelligence toolkit](https://github.com/Microsoft/nni) as a tool to tune the [NCF model](../02_model/ncf_deep_dive.ipynb) and [SVD model](../02_model/surprise_svd_deep_dive.ipynb) and compare their performance against one another
 
 ### Prerequisites
 To run the examples running on the Azure Machine Learning service, the [`azureml-sdk`](https://pypi.org/project/azureml-sdk/) is required. The AzureML Python SDK is already installed after setting up the conda environments from this repository (see [SETUP.md](../../SETUP.md)).