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Melihemin/movie-recommender.ipynb

Created April 24, 2021 08:00
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movie-recommender.ipynb
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movie-recommender.ipynb
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"\n",
"from typing import Dict, Text\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"import tensorflow_recommenders as tfrs"
],
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"movies = tfds.load(\"movielens/100k-movies\", split=\"train\")"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset movielens/100k-ratings/0.1.0 (download: 4.70 MiB, generated: 32.41 MiB, total: 37.10 MiB) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.0...\u001b[0m\n"
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"\n",
"\n",
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"\n",
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"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/movielens/100k-ratings/0.1.0.incompleteS0O2TI/movielens-train.tfrecord\n"
],
"name": "stdout"
},
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"\u001b[1mDataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n",
"\r\u001b[1mDownloading and preparing dataset movielens/100k-movies/0.1.0 (download: 4.70 MiB, generated: 150.35 KiB, total: 4.84 MiB) to /root/tensorflow_datasets/movielens/100k-movies/0.1.0...\u001b[0m\n"
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"\rShuffling and writing examples to /root/tensorflow_datasets/movielens/100k-movies/0.1.0.incompleteDZ9D9N/movielens-train.tfrecord\n"
],
"name": "stdout"
},
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"\u001b[1mDataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-movies/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n",
"\r"
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},
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},
"source": [
"for x in ratings.take(1).as_numpy_iterator():\n",
" pprint.pprint(x)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"{'bucketized_user_age': 45.0,\n",
" 'movie_genres': array([7]),\n",
" 'movie_id': b'357',\n",
" 'movie_title': b\"One Flew Over the Cuckoo's Nest (1975)\",\n",
" 'raw_user_age': 46.0,\n",
" 'timestamp': 879024327,\n",
" 'user_gender': True,\n",
" 'user_id': b'138',\n",
" 'user_occupation_label': 4,\n",
" 'user_occupation_text': b'doctor',\n",
" 'user_rating': 4.0,\n",
" 'user_zip_code': b'53211'}\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "L3SOqH132P_F",
"outputId": "c3ddfed0-d3df-4d18-fbb1-1cd48984bd94"
},
"source": [
"for x in movies.take(1).as_numpy_iterator():\n",
" pprint.pprint(x)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"{'movie_genres': array([4]),\n",
" 'movie_id': b'1681',\n",
" 'movie_title': b'You So Crazy (1994)'}\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "U1x9reVF2P8p"
},
"source": [
"ratings = ratings.map(lambda x: {\n",
" \"movie_title\": x[\"movie_title\"],\n",
" \"user_id\": x[\"user_id\"],\n",
"})\n",
"movies = movies.map(lambda x: x[\"movie_title\"])"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "L4kK_LJE2P01"
},
"source": [
"tf.random.set_seed(42)\n",
"shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)\n",
"\n",
"train = shuffled.take(80_000)\n",
"test = shuffled.skip(80_000).take(20_000)"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VwPQYvua3jbk",
"outputId": "c49c7bef-4d34-4b7b-b94d-c2c3c3480556"
},
"source": [
"movie_titles = movies.batch(1_000)\n",
"user_ids = ratings.batch(1_000_000).map(lambda x: x[\"user_id\"])\n",
"\n",
"unique_movie_titles = np.unique(np.concatenate(list(movie_titles)))\n",
"unique_user_ids = np.unique(np.concatenate(list(user_ids)))\n",
"\n",
"unique_movie_titles[:10]"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([b\"'Til There Was You (1997)\", b'1-900 (1994)',\n",
" b'101 Dalmatians (1996)', b'12 Angry Men (1957)', b'187 (1997)',\n",
" b'2 Days in the Valley (1996)',\n",
" b'20,000 Leagues Under the Sea (1954)',\n",
" b'2001: A Space Odyssey (1968)',\n",
" b'3 Ninjas: High Noon At Mega Mountain (1998)',\n",
" b'39 Steps, The (1935)'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hurJdGU73jS2"
},
"source": [
"embedding_dimension = 32"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "huvis2nq3jOx"
},
"source": [
"user_model = tf.keras.Sequential([\n",
" tf.keras.layers.experimental.preprocessing.StringLookup(\n",
" vocabulary=unique_user_ids, mask_token=None),\n",
" # We add an additional embedding to account for unknown tokens.\n",
" tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)\n",
"])"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "e2SqZ1413jML"
},
"source": [
"movie_model = tf.keras.Sequential([\n",
" tf.keras.layers.experimental.preprocessing.StringLookup(\n",
" vocabulary=unique_movie_titles, mask_token=None),\n",
" tf.keras.layers.Embedding(len(unique_movie_titles) + 1, embedding_dimension)\n",
"])"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bCzmvymL4fzw"
},
"source": [
"metrics = tfrs.metrics.FactorizedTopK(\n",
" candidates=movies.batch(128).map(movie_model)\n",
")"
],
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5GNtLHmt4fxv"
},
"source": [
"task = tfrs.tasks.Retrieval(\n",
" metrics=metrics\n",
")"
],
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5JmXV1Wn4fuK"
},
"source": [
"task = tfrs.tasks.Retrieval(\n",
" metrics=metrics\n",
")"
],
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Grftgm5b4frQ"
},
"source": [
"class MovielensModel(tfrs.Model):\n",
"\n",
" def __init__(self, user_model, movie_model):\n",
" super().__init__()\n",
" self.movie_model: tf.keras.Model = movie_model\n",
" self.user_model: tf.keras.Model = user_model\n",
" self.task: tf.keras.layers.Layer = task\n",
"\n",
" def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n",
" # We pick out the user features and pass them into the user model.\n",
" user_embeddings = self.user_model(features[\"user_id\"])\n",
" # And pick out the movie features and pass them into the movie model,\n",
" # getting embeddings back.\n",
" positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n",
"\n",
" # The task computes the loss and the metrics.\n",
" return self.task(user_embeddings, positive_movie_embeddings)"
],
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RwFFNS2U4fhB"
},
"source": [
"class NoBaseClassMovielensModel(tf.keras.Model):\n",
"\n",
" def __init__(self, user_model, movie_model):\n",
" super().__init__()\n",
" self.movie_model: tf.keras.Model = movie_model\n",
" self.user_model: tf.keras.Model = user_model\n",
" self.task: tf.keras.layers.Layer = task\n",
"\n",
" def train_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n",
"\n",
" # Set up a gradient tape to record gradients.\n",
" with tf.GradientTape() as tape:\n",
"\n",
" # Loss computation.\n",
" user_embeddings = self.user_model(features[\"user_id\"])\n",
" positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n",
" loss = self.task(user_embeddings, positive_movie_embeddings)\n",
"\n",
" # Handle regularization losses as well.\n",
" regularization_loss = sum(self.losses)\n",
"\n",
" total_loss = loss + regularization_loss\n",
"\n",
" gradients = tape.gradient(total_loss, self.trainable_variables)\n",
" self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n",
"\n",
" metrics = {metric.name: metric.result() for metric in self.metrics}\n",
" metrics[\"loss\"] = loss\n",
" metrics[\"regularization_loss\"] = regularization_loss\n",
" metrics[\"total_loss\"] = total_loss\n",
"\n",
" return metrics\n",
"\n",
" def test_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n",
"\n",
" # Loss computation.\n",
" user_embeddings = self.user_model(features[\"user_id\"])\n",
" positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n",
" loss = self.task(user_embeddings, positive_movie_embeddings)\n",
"\n",
" # Handle regularization losses as well.\n",
" regularization_loss = sum(self.losses)\n",
"\n",
" total_loss = loss + regularization_loss\n",
"\n",
" metrics = {metric.name: metric.result() for metric in self.metrics}\n",
" metrics[\"loss\"] = loss\n",
" metrics[\"regularization_loss\"] = regularization_loss\n",
" metrics[\"total_loss\"] = total_loss\n",
"\n",
" return metrics"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "08EAV25i4nDA"
},
"source": [
"model = MovielensModel(user_model, movie_model)\n",
"model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))"
],
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_CWnABRm4m4q"
},
"source": [
"cached_train = train.shuffle(100_000).batch(8192).cache()\n",
"cached_test = test.batch(4096).cache()"
],
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UskbOnJj4o7k",
"outputId": "3d9c2d25-3fde-4f80-c426-4878fd8d3a18"
},
"source": [
"model.fit(cached_train, epochs=3)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"10/10 [==============================] - 27s 2s/step - factorized_top_k/top_1_categorical_accuracy: 8.7500e-05 - factorized_top_k/top_5_categorical_accuracy: 0.0017 - factorized_top_k/top_10_categorical_accuracy: 0.0056 - factorized_top_k/top_50_categorical_accuracy: 0.0554 - factorized_top_k/top_100_categorical_accuracy: 0.1242 - loss: 69885.1072 - regularization_loss: 0.0000e+00 - total_loss: 69885.1072\n",
"Epoch 2/3\n",
"10/10 [==============================] - 24s 2s/step - factorized_top_k/top_1_categorical_accuracy: 0.0012 - factorized_top_k/top_5_categorical_accuracy: 0.0125 - factorized_top_k/top_10_categorical_accuracy: 0.0284 - factorized_top_k/top_50_categorical_accuracy: 0.1484 - factorized_top_k/top_100_categorical_accuracy: 0.2727 - loss: 67523.3707 - regularization_loss: 0.0000e+00 - total_loss: 67523.3707\n",
"Epoch 3/3\n",
"10/10 [==============================] - 24s 2s/step - factorized_top_k/top_1_categorical_accuracy: 0.0019 - factorized_top_k/top_5_categorical_accuracy: 0.0190 - factorized_top_k/top_10_categorical_accuracy: 0.0410 - factorized_top_k/top_50_categorical_accuracy: 0.1802 - factorized_top_k/top_100_categorical_accuracy: 0.3082 - loss: 66302.9609 - regularization_loss: 0.0000e+00 - total_loss: 66302.9609\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fbac66935d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3y5ntLUB4o4z",
"outputId": "52c4ca7c-9502-4b37-8403-942a5c5a4df5"
},
"source": [
"model.evaluate(cached_test, return_dict=True)"
],
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"text": [
"5/5 [==============================] - 6s 991ms/step - factorized_top_k/top_1_categorical_accuracy: 7.0000e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0096 - factorized_top_k/top_10_categorical_accuracy: 0.0222 - factorized_top_k/top_50_categorical_accuracy: 0.1248 - factorized_top_k/top_100_categorical_accuracy: 0.2327 - loss: 31079.0635 - regularization_loss: 0.0000e+00 - total_loss: 31079.0635\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'factorized_top_k/top_100_categorical_accuracy': 0.23270000517368317,\n",
" 'factorized_top_k/top_10_categorical_accuracy': 0.02215000055730343,\n",
" 'factorized_top_k/top_1_categorical_accuracy': 0.000699999975040555,\n",
" 'factorized_top_k/top_50_categorical_accuracy': 0.12475000321865082,\n",
" 'factorized_top_k/top_5_categorical_accuracy': 0.009600000455975533,\n",
" 'loss': 28244.771484375,\n",
" 'regularization_loss': 0,\n",
" 'total_loss': 28244.771484375}"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sDlag3Hv4o0Y",
"outputId": "f57bd6b3-838c-43ca-dfc5-3feffa78bafe"
},
"source": [
"# Create a model that takes in raw query features, and\n",
"index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)\n",
"# recommends movies out of the entire movies dataset.\n",
"index.index(movies.batch(100).map(model.movie_model), movies)\n",
"\n",
"# Get recommendations.\n",
"_, titles = index(tf.constant([\"16\"]))\n",
"print(f\"Recommendations for user: {titles[0, :3]}\")"
],
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": [
"Recommendations for user: [b'Murder in the First (1995)' b'Shawshank Redemption, The (1994)'\n",
" b'Bronx Tale, A (1993)']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LGURe73K4or9",
"outputId": "4c7f1f43-4f1c-443d-cdb1-4752792606d6"
},
"source": [
"# Export the query model.\n",
"with tempfile.TemporaryDirectory() as tmp:\n",
" path = os.path.join(tmp, \"model\")\n",
"\n",
" # Save the index.\n",
" index.save(path)\n",
"\n",
" # Load it back; can also be done in TensorFlow Serving.\n",
" loaded = tf.keras.models.load_model(path)\n",
"\n",
" # Pass a user id in, get top predicted movie titles back.\n",
" scores, titles = loaded([\"42\"])\n",
"\n",
" print(f\"Recommendations: {titles[0][:3]}\")"
],
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading.\n",
"WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/tmpwnokz3cl/model/assets\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/tmpwnokz3cl/model/assets\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Recommendations: [b'Bridges of Madison County, The (1995)'\n",
" b'Father of the Bride Part II (1995)' b'Rudy (1993)']\n"
],
"name": "stdout"
}
]
}
]
}
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