MSLR_10k.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Step 1:\n",
        " Install tensorflow ranking "
      ],
      "metadata": {
        "id": "QB3nyRFFxwGo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urcWsYHwk-U2",
        "outputId": "244dad8f-3dab-45c4-82e0-b1d0b837355c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 141 kB 8.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 511.7 MB 6.2 kB/s \n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 42.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 438 kB 39.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 42.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.5 MB 8.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q tensorflow-ranking\n",
        "!pip install -q --upgrade tfds-nightly"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 2:\n",
        " Import tensorflow ranking and tensorflow datasets."
      ],
      "metadata": {
        "id": "yuBMmjPlyeXO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_ranking as tfr"
      ],
      "metadata": {
        "id": "1x17yVP4lK8B"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 3: \n",
        "Importing Dataset\n"
      ],
      "metadata": {
        "id": "cNOOaYa0zO1d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load MSLR-WEB10K dataset from TFDS.\n",
        "ds = tfds.load(\"mslr_web/10k_fold1\", split=\"train\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "4bbb5aba-ff52-4a74-d120-3dcb658afd20"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset 1.15 GiB (download: 1.15 GiB, generated: 381.58 MiB, total: 1.52 GiB) to /root/tensorflow_datasets/mslr_web/10k_fold1/1.0.0...\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
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        {
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            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
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        {
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          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/6000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/mslr_web/10k_fold1/1.0.0.incompleteEKL74A/mslr_web-train.tfrecord*...:   0…"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating vali examples...:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/mslr_web/10k_fold1/1.0.0.incompleteEKL74A/mslr_web-vali.tfrecord*...:   0%…"
            ],
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        {
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            "text/plain": [
              "Generating test examples...:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
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        {
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              "Shuffling /root/tensorflow_datasets/mslr_web/10k_fold1/1.0.0.incompleteEKL74A/mslr_web-test.tfrecord*...:   0%…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset mslr_web downloaded and prepared to /root/tensorflow_datasets/mslr_web/10k_fold1/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 4: \n",
        "Adding mask tensor"
      ],
      "metadata": {
        "id": "Kfr-zL3Zzn9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a mask tensor.\n",
        "ds = ds.map(lambda feature_map:{\"_mask\": tf.ones_like(feature_map[\"label\"], dtype=tf.bool),    **feature_map})"
      ],
      "metadata": {
        "id": "7n-2e-W5lSyb"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 5: \n",
        "Shuffle data & create padded batches\n"
      ],
      "metadata": {
        "id": "IFlAS87Hzy_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shuffle data and create padded batches for queries with different lengths.\n",
        "# Padded items will have False on `_mask`.\n",
        "ds = ds.shuffle(buffer_size=1000).padded_batch(batch_size=32)"
      ],
      "metadata": {
        "id": "M34wNgjmlXci"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 6: \n",
        "Create Tuples"
      ],
      "metadata": {
        "id": "nsF9H_Iqz-G6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create (features, labels) tuples from data and set -1 label for masked items.\n",
        "ds = ds.map (lambda \n",
        "feature_map:(feature_map, tf.where(feature_map[\"_mask\"], feature_map.pop(\"label\"),-1.)))"
      ],
      "metadata": {
        "id": "DS6QYFxtmBp_"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 7: \n",
        "Creating a model with Keras Functional API"
      ],
      "metadata": {
        "id": "GRa8vmB10GfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a model with Keras Functional API.\n",
        "inputs = {    \n",
        "    name: tf.keras.Input(shape=(None,1), dtype=tf.float32, name=name)\n",
        "for name in ds.element_spec[0]\n",
        "if name != \"_mask\"}\n",
        "norm_inputs = [tf.keras.layers.BatchNormalization()(x)for x in inputs.values()]\n",
        "x = tf.concat(norm_inputs, axis=-1)\n",
        "for layer_width in[128,64,32]:  \n",
        "  x = tf.keras.layers.Dense(units=layer_width)(x)  \n",
        "  x = tf.keras.layers.Activation(activation=tf.nn.relu)(x)\n",
        "  scores = tf.squeeze(tf.keras.layers.Dense(units=1)(x), axis=-1)"
      ],
      "metadata": {
        "id": "fbaq1UaxmCgn"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 8: \n",
        "Creating & Compiling & Training the models"
      ],
      "metadata": {
        "id": "nylJeni20Oqy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create, compile and train the model.\n",
        "model = tf.keras.Model(inputs=inputs, outputs=scores)\n",
        "model.compile(    \n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),    \n",
        "    loss=tfr.keras.losses.SoftmaxLoss(),    \n",
        "    metrics=tfr.keras.metrics.get(\"ndcg\", topn=5, name=\"NDCG@5\"))\n",
        "model.fit(ds, epochs=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNixlLh_mGtx",
        "outputId": "2a8cd82c-6a7e-4a7c-8b91-c7edbbb6e921"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:566: UserWarning: Input dict contained keys ['_mask'] which did not match any model input. They will be ignored by the model.\n",
            "  inputs = self._flatten_to_reference_inputs(inputs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "188/188 [==============================] - 55s 151ms/step - loss: 398.2370 - NDCG@5: 0.3442\n",
            "Epoch 2/3\n",
            "188/188 [==============================] - 30s 151ms/step - loss: 396.1655 - NDCG@5: 0.3854\n",
            "Epoch 3/3\n",
            "188/188 [==============================] - 31s 156ms/step - loss: 395.8911 - NDCG@5: 0.3962\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efde1714e50>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}