examples
7526 строк · 291.9 Кб
1{2"cells": [3{4"attachments": {},5"cell_type": "markdown",6"metadata": {},7"source": [8"[](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/recommendation/movie-recommender/xx_train_movie_recommender.ipynb) [](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/recommendation/movie-recommender/xx_train_movie_recommender.ipynb)"9]10},11{12"cell_type": "code",13"execution_count": null,14"metadata": {15"id": "AhVk6WLnOqXs"16},17"outputs": [],18"source": [19"!nvidia-smi"20]21},22{23"cell_type": "code",24"execution_count": 2,25"metadata": {26"colab": {27"base_uri": "https://localhost:8080/"28},29"id": "xg3g40e2DnrS",30"outputId": "c2a5ed97-8ec3-45a8-a84d-ed1702f6c9c6"31},32"outputs": [33{34"name": "stdout",35"output_type": "stream",36"text": [37"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",38"Collecting datasets\n",39" Downloading datasets-2.4.0-py3-none-any.whl (365 kB)\n",40"\u001b[K |████████████████████████████████| 365 kB 4.7 MB/s \n",41"\u001b[?25hRequirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.8.2+zzzcolab20220719082949)\n",42"Collecting huggingface-hub\n",43" Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n",44"\u001b[K |████████████████████████████████| 101 kB 13.1 MB/s \n",45"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n",46"Collecting xxhash\n",47" Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",48"\u001b[K |████████████████████████████████| 212 kB 55.8 MB/s \n",49"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.3.5)\n",50"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.3)\n",51"Requirement already satisfied: fsspec[http]>=2021.11.1 in 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aiohttp->datasets) (22.1.0)\n",114"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (6.0.2)\n",115"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.1)\n",116"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",117"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.2.1)\n",118"Installing collected packages: urllib3, xxhash, responses, multiprocess, huggingface-hub, datasets\n",119" Attempting uninstall: urllib3\n",120" Found existing installation: urllib3 1.24.3\n",121" Uninstalling urllib3-1.24.3:\n",122" Successfully uninstalled urllib3-1.24.3\n",123"Successfully installed datasets-2.4.0 huggingface-hub-0.8.1 multiprocess-0.70.13 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0\n"124]125}126],127"source": [128"!pip install datasets tensorflow huggingface-hub"129]130},131{132"cell_type": "markdown",133"metadata": {134"id": "y1bOPXx8va64"135},136"source": [137"# Load and Prepare Dataset"138]139},140{141"cell_type": "code",142"execution_count": 1,143"metadata": {144"colab": {145"base_uri": "https://localhost:8080/",146"height": 104,147"referenced_widgets": [148"5d4fad1f0c774e8b8b59c7fd5d12d2e0",149"4c54d907905749d193ffea37835ece53",150"ca4f453b819e4ee797b8463f396264d7",151"014c0e57dc3d45f29c037be4e967c786",152"2536a807f78440609df3b98a2c4d301b",153"4be08c9e5ed24d72bd6b2e0233bf36cb",154"3dfeee373bdc457d820799a6f53f7b92",155"448a0f4829524d3895d245310f78901e",156"7f544b0f809e4d18beac3b1ccafd5015",157"fa23fdc4e17c4370ab14255bbd68fcc3",158"d731bf4870da4219a54629bb9015d9a3"159]160},161"id": "oAiKitZRxLNN",162"outputId": "7f8f8a51-032c-4f3d-f2bc-116852c2b9f3"163},164"outputs": [165{166"name": "stderr",167"output_type": "stream",168"text": [169"WARNING:datasets.builder:Using custom data configuration default\n",170"WARNING:datasets.builder:Reusing dataset movielens-recent-ratings (/root/.cache/huggingface/datasets/pinecone___movielens-recent-ratings/default/0.0.0/0b5cf78c3c23d9db1c33d17d7d490a06b45c6d9f00a6691aa005c6fcad1c8b82)\n"171]172},173{174"data": {175"application/vnd.jupyter.widget-view+json": {176"model_id": "5d4fad1f0c774e8b8b59c7fd5d12d2e0",177"version_major": 2,178"version_minor": 0179},180"text/plain": [181" 0%| | 0/2 [00:00<?, ?it/s]"182]183},184"metadata": {},185"output_type": "display_data"186}187],188"source": [189"from datasets import load_dataset\n",190"\n",191"movies = load_dataset('pinecone/movielens-recent-ratings')\n",192"train = movies['train'].to_pandas()\n",193"test = movies['test'].to_pandas()\n",194"\n",195"del movies"196]197},198{199"cell_type": "markdown",200"metadata": {201"id": "-pxufhHbzheG"202},203"source": [204"# Training the Model"205]206},207{208"cell_type": "code",209"execution_count": 2,210"metadata": {211"id": "UZpso4PiKSvZ"212},213"outputs": [],214"source": [215"# ROW_COUNT = train.shape[0]\n",216"EMBEDDING_SIZE = 32\n",217"NUM_USERS = train['user_id'].nunique()\n",218"NUM_MOVIES = train['movie_id'].nunique()"219]220},221{222"cell_type": "code",223"execution_count": 3,224"metadata": {225"colab": {226"base_uri": "https://localhost:8080/"227},228"id": "vCEJnfthwqPn",229"outputId": "74a314d8-9e83-4c89-d2aa-22fb5e56b426"230},231"outputs": [232{233"data": {234"text/plain": [235"(10269, 35710)"236]237},238"execution_count": 3,239"metadata": {},240"output_type": "execute_result"241}242],243"source": [244"NUM_MOVIES, NUM_USERS"245]246},247{248"cell_type": "code",249"execution_count": 4,250"metadata": {251"id": "Oo9JXAb5yhe-"252},253"outputs": [],254"source": [255"from tensorflow import keras\n",256"from tensorflow.keras import layers"257]258},259{260"cell_type": "markdown",261"metadata": {262"id": "6r-s5bir0shD"263},264"source": [265"### User Model"266]267},268{269"cell_type": "code",270"execution_count": 5,271"metadata": {272"colab": {273"base_uri": "https://localhost:8080/"274},275"id": "D3ERlhnIyJdQ",276"outputId": "5b003b59-7c6b-411d-93ee-bdebe28df96f"277},278"outputs": [279{280"name": "stdout",281"output_type": "stream",282"text": [283"Model: \"model\"\n",284"_________________________________________________________________\n",285" Layer (type) Output Shape Param # \n",286"=================================================================\n",287" user_id (InputLayer) [(None, 1)] 0 \n",288" \n",289" user_emb (Embedding) (None, 1, 32) 1142720 \n",290" \n",291"=================================================================\n",292"Total params: 1,142,720\n",293"Trainable params: 1,142,720\n",294"Non-trainable params: 0\n",295"_________________________________________________________________\n"296]297}298],299"source": [300"u_inputs = layers.Input(shape=(1,), name='user_id')\n",301"user_emb = layers.Embedding(output_dim=32, input_dim=NUM_USERS, name='user_emb')(u_inputs)\n",302"user_model = keras.Model(u_inputs, user_emb)\n",303"user_model.summary()"304]305},306{307"cell_type": "markdown",308"metadata": {309"id": "kCnovFvk0v2k"310},311"source": [312"### Movie Model"313]314},315{316"cell_type": "code",317"execution_count": 6,318"metadata": {319"colab": {320"base_uri": "https://localhost:8080/"321},322"id": "0kmimEqHyOmS",323"outputId": "d520ff8b-1eae-40e4-c824-75ca679f8984"324},325"outputs": [326{327"name": "stdout",328"output_type": "stream",329"text": [330"Model: \"model_1\"\n",331"_________________________________________________________________\n",332" Layer (type) Output Shape Param # \n",333"=================================================================\n",334" movie_embedding (InputLayer [(None, 1)] 0 \n",335" ) \n",336" \n",337" movie_emb (Embedding) (None, 1, 32) 328608 \n",338" \n",339"=================================================================\n",340"Total params: 328,608\n",341"Trainable params: 328,608\n",342"Non-trainable params: 0\n",343"_________________________________________________________________\n"344]345}346],347"source": [348"m_inputs = layers.Input(shape=(1,), name='movie_embedding')\n",349"movie_emb = layers.Embedding(output_dim=32, input_dim=NUM_MOVIES, name='movie_emb')(m_inputs)\n",350"movie_model = keras.Model(m_inputs, movie_emb)\n",351"movie_model.summary()"352]353},354{355"cell_type": "markdown",356"metadata": {357"id": "dq1mVb_0LEdL"358},359"source": [360"### Merged Model\n",361"\n",362"We merge the two models via a dot product layer (which is normalized, and so is equivalent to cosine similarity) between the movie embedding and user embedding layers. This allows us to optimize the two models to produce similar movie/user embeddings when the ratings between both are higher."363]364},365{366"cell_type": "code",367"execution_count": 7,368"metadata": {369"id": "VbkhJam9yOo6"370},371"outputs": [],372"source": [373"merged = layers.Dot(name = 'dot_product', normalize = True, axes = 2)([movie_emb, user_emb])"374]375},376{377"cell_type": "code",378"execution_count": 8,379"metadata": {380"colab": {381"base_uri": "https://localhost:8080/"382},383"id": "OgZZ-JLUyOrS",384"outputId": "fc995405-ac58-4aea-d81c-2c71fb7550cb"385},386"outputs": [387{388"name": "stdout",389"output_type": "stream",390"text": [391"Model: \"model_2\"\n",392"__________________________________________________________________________________________________\n",393" Layer (type) Output Shape Param # Connected to \n",394"==================================================================================================\n",395" movie_embedding (InputLayer) [(None, 1)] 0 [] \n",396" \n",397" user_id (InputLayer) [(None, 1)] 0 [] \n",398" \n",399" movie_emb (Embedding) (None, 1, 32) 328608 ['movie_embedding[0][0]'] \n",400" \n",401" user_emb (Embedding) (None, 1, 32) 1142720 ['user_id[0][0]'] \n",402" \n",403" dot_product (Dot) (None, 1, 1) 0 ['movie_emb[0][0]', \n",404" 'user_emb[0][0]'] \n",405" \n",406"==================================================================================================\n",407"Total params: 1,471,328\n",408"Trainable params: 1,471,328\n",409"Non-trainable params: 0\n",410"__________________________________________________________________________________________________\n"411]412},413{414"name": "stderr",415"output_type": "stream",416"text": [417"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",418" super(Adam, self).__init__(name, **kwargs)\n"419]420}421],422"source": [423"adam = keras.optimizers.Adam(lr=0.001)\n",424"model = keras.Model([m_inputs, u_inputs], merged)\n",425"model.compile(optimizer=adam, loss= 'mean_absolute_error')\n",426"model.summary()"427]428},429{430"cell_type": "markdown",431"metadata": {432"id": "3XwkS8hgHM0P"433},434"source": [435"Now we normalize the ratings."436]437},438{439"cell_type": "code",440"execution_count": 9,441"metadata": {442"id": "W_sdoTZ6yOtf"443},444"outputs": [],445"source": [446"norm_rating_train = train.rating/5\n",447"norm_rating_test = test.rating/5"448]449},450{451"cell_type": "markdown",452"metadata": {453"id": "fBWGbnPAHP3S"454},455"source": [456"And train..."457]458},459{460"cell_type": "code",461"execution_count": 10,462"metadata": {463"colab": {464"base_uri": "https://localhost:8080/"465},466"id": "rKUW4acOyOva",467"outputId": "076d061c-f519-45bc-837d-b7425de857e7"468},469"outputs": [470{471"name": "stdout",472"output_type": "stream",473"text": [474"Epoch 1/10\n",475"6825/6825 [==============================] - 31s 4ms/step - loss: 0.2525 - val_loss: 0.1437\n",476"Epoch 2/10\n",477"6825/6825 [==============================] - 27s 4ms/step - loss: 0.1323 - val_loss: 0.1353\n",478"Epoch 3/10\n",479"6825/6825 [==============================] - 29s 4ms/step - loss: 0.1197 - val_loss: 0.1311\n",480"Epoch 4/10\n",481"6825/6825 [==============================] - 28s 4ms/step - loss: 0.1118 - val_loss: 0.1285\n",482"Epoch 5/10\n",483"6825/6825 [==============================] - 27s 4ms/step - loss: 0.1063 - val_loss: 0.1272\n",484"Epoch 6/10\n",485"6825/6825 [==============================] - 29s 4ms/step - loss: 0.1024 - val_loss: 0.1266\n",486"Epoch 7/10\n",487"6825/6825 [==============================] - 27s 4ms/step - loss: 0.0995 - val_loss: 0.1260\n",488"Epoch 8/10\n",489"6825/6825 [==============================] - 29s 4ms/step - loss: 0.0972 - val_loss: 0.1252\n",490"Epoch 9/10\n",491"6825/6825 [==============================] - 28s 4ms/step - loss: 0.0953 - val_loss: 0.1249\n",492"Epoch 10/10\n",493"6825/6825 [==============================] - 27s 4ms/step - loss: 0.0938 - val_loss: 0.1250\n"494]495}496],497"source": [498"history = model.fit([train.movie_id, train.user_id], norm_rating_train, batch_size=128,\n",499" epochs = 10, validation_data = ([test.movie_id, test.user_id], norm_rating_test),\n",500" verbose = 1)"501]502},503{504"cell_type": "markdown",505"metadata": {506"id": "l-BsSAx_DhML"507},508"source": [509"### Training History"510]511},512{513"cell_type": "code",514"execution_count": 11,515"metadata": {516"id": "xaDurccIybmH"517},518"outputs": [],519"source": [520"import matplotlib.pyplot as plt"521]522},523{524"cell_type": "code",525"execution_count": 12,526"metadata": {527"colab": {528"base_uri": "https://localhost:8080/",529"height": 295530},531"id": "nKOeCPxdybrB",532"outputId": "6a31fee7-cbb3-4c2d-9845-6c7904557adb"533},534"outputs": [535{536"data": {537"image/png": 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",538"text/plain": [539"<Figure size 432x288 with 1 Axes>"540]541},542"metadata": {543"needs_background": "light"544},545"output_type": "display_data"546}547],548"source": [549"# Plot history: MAE\n",550"plt.plot(history.history['loss'], label='Training data)')\n",551"plt.plot(history.history['val_loss'], label='Validation data)')\n",552"plt.title('MAE')\n",553"plt.ylabel('MAE value')\n",554"plt.xlabel('No. epoch')\n",555"plt.legend(loc=\"upper left\")\n",556"plt.show()"557]558}559],560"metadata": {561"accelerator": "GPU",562"colab": {563"collapsed_sections": [],564"name": "xx_train_movie_recommender.ipynb",565"provenance": []566},567"gpuClass": "standard",568"kernelspec": {569"display_name": "base",570"language": "python",571"name": "python3"572},573"language_info": {574"codemirror_mode": {575"name": "ipython",576"version": 3577},578"file_extension": ".py",579"mimetype": "text/x-python",580"name": "python",581"nbconvert_exporter": "python",582"pygments_lexer": "ipython3",583"version": "3.8.13 (default, 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