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"# Implicit Hybrid Model for Movie Recommendation\n",
"> Testing out the features of Collie Recs library on MovieLens-100K. Training Factorization and Hybrid models with Pytorch Lightning.\n",
"\n",
"- toc: true\n",
"- badges: true\n",
"- comments: true\n",
"- categories: [Implicit, PyTorch, PyTorchLightening, Collie, Movie, Factorization, HybridModel, Visualization]\n",
"- author: \"ShopRunner\"\n",
"- image:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UDwkPy1gcA94"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"metadata": {
"id": "P43fS4H27gCt"
},
"source": [
"!pip install -q collie_recs\n",
"!pip install -q git+https://github.com/sparsh-ai/recochef.git"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PD0n8kefAb67"
},
"source": [
"import os\n",
"import joblib\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from collie_recs.interactions import Interactions\n",
"from collie_recs.interactions import ApproximateNegativeSamplingInteractionsDataLoader\n",
"from collie_recs.cross_validation import stratified_split\n",
"from collie_recs.metrics import auc, evaluate_in_batches, mapk, mrr\n",
"from collie_recs.model import CollieTrainer, MatrixFactorizationModel, HybridPretrainedModel\n",
"from collie_recs.movielens import get_recommendation_visualizations\n",
"\n",
"import torch\n",
"from pytorch_lightning.utilities.seed import seed_everything\n",
"\n",
"from recochef.datasets.movielens import MovieLens\n",
"from recochef.preprocessing.encode import label_encode as le\n",
"\n",
"from IPython.display import HTML"
],
"execution_count": 82,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ClWobbREN4VU",
"outputId": "0d5c0bdf-d5ba-4f50-d840-55c240d81326"
},
"source": [
"# this handy PyTorch Lightning function fixes random seeds across all the libraries used here\n",
"seed_everything(22)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Global seed set to 22\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"22"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T0N-kmIrcDZr"
},
"source": [
"## Data Loading"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LEuHGsYgGv-e"
},
"source": [
"data_object = MovieLens()"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "g3lLxQE1G120",
"outputId": "90249953-b9c8-4dc2-a8f9-710fadac69b9"
},
"source": [
"df = data_object.load_interactions()\n",
"df.head()"
],
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" USERID | \n",
" ITEMID | \n",
" RATING | \n",
" TIMESTAMP | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 196 | \n",
" 242 | \n",
" 3.0 | \n",
" 881250949 | \n",
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\n",
" \n",
" | 1 | \n",
" 186 | \n",
" 302 | \n",
" 3.0 | \n",
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\n",
" \n",
" | 2 | \n",
" 22 | \n",
" 377 | \n",
" 1.0 | \n",
" 878887116 | \n",
"
\n",
" \n",
" | 3 | \n",
" 244 | \n",
" 51 | \n",
" 2.0 | \n",
" 880606923 | \n",
"
\n",
" \n",
" | 4 | \n",
" 166 | \n",
" 346 | \n",
" 1.0 | \n",
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"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" USERID | \n",
" ITEMID | \n",
" RATING | \n",
" TIMESTAMP | \n",
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\n",
" \n",
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" 2 | \n",
" 2 | \n",
" 1 | \n",
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\n",
" \n",
" | 3 | \n",
" 3 | \n",
" 3 | \n",
" 1 | \n",
" 876042340 | \n",
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\n",
" \n",
" | 4 | \n",
" 4 | \n",
" 4 | \n",
" 1 | \n",
" 879270459 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" USERID | \n",
" ITEMID | \n",
" RATING | \n",
" TIMESTAMP | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0 | \n",
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\n",
" \n",
" | 1 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 891628467 | \n",
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\n",
" \n",
" | 2 | \n",
" 2 | \n",
" 2 | \n",
" 1 | \n",
" 879781125 | \n",
"
\n",
" \n",
" | 3 | \n",
" 3 | \n",
" 3 | \n",
" 1 | \n",
" 876042340 | \n",
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\n",
" \n",
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" 4 | \n",
" 4 | \n",
" 1 | \n",
" 879270459 | \n",
"
\n",
" \n",
"
\n",
"
User 895:
\n",
" \n",
" \n",
" | \n",
" Willy Wonka and the Chocolate Factory (1971) | \n",
" Mighty Aphrodite (1995) | \n",
" Conspiracy Theory (1997) | \n",
" Sense and Sensibility (1995) | \n",
" Liar Liar (1997) | \n",
" In & Out (1997) | \n",
" Return of the Jedi (1983) | \n",
" Ransom (1996) | \n",
" Emma (1996) | \n",
" Toy Story (1995) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Some loved films: | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BZTllNDU0ZTItYTYxMC00OTI4LThlNDAtZjNiNzdhMWZiYjNmXkEyXkFqcGdeQXVyNzY1NDgwNjQ@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMGM1NzM2ZjktNDM5ZS00YmExLTk5ZmYtNDdkNjdkNTdhZWZkXkEyXkFqcGdeQXVyNjE5MjUyOTM@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMjQ1MTYxMzA4OF5BMl5BanBnXkFtZTgwNjg2NTM1MTI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNzk1MjU3MDQyMl5BMl5BanBnXkFtZTcwNjc1OTM2MQ@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BYjZlYmJjYWYtZDM0NS00YmZlLWIyMTAtMDY5ZTNjZTgwMDhjXkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNDZkMjVhZmItMmIyNC00OWYyLWFmZWQtMzIzZWZhY2QyMjZiXkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BOWZlMjFiYzgtMTUzNC00Y2IzLTk1NTMtZmNhMTczNTk0ODk1XkEyXkFqcGdeQXVyNTAyODkwOQ@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BZTc0ZjNkYTktMmJmOS00OTJlLTg1NWUtMzQ5ZGMxM2NhY2M0L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BN2E1YTUzZDAtODQ2YS00MWNjLWEzMzAtZjgwY2M3ZTcwOTJhXkEyXkFqcGdeQXVyNjE5MjUyOTM@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMDU2ZWJlMjktMTRhMy00ZTA5LWEzNDgtYmNmZTEwZTViZWJkXkEyXkFqcGdeQXVyNDQ2OTk4MzI@.jpg\") | \n",
"
\n",
" \n",
"
\n",
" \n",
" \n",
" | \n",
" Bad Boys (1995) | \n",
" When Harry Met Sally... (1989) | \n",
" Sleeper (1973) | \n",
" Richard III (1995) | \n",
" Cold Comfort Farm (1995) | \n",
" Lawnmower Man, The (1992) | \n",
" Batman Forever (1995) | \n",
" Graduate, The (1967) | \n",
" Independence Day (ID4) (1996) | \n",
" Star Wars (1977) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Recommended films: | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMGE1ZTQ0ZTEtZTEwZS00NWE0LTlmMDUtMTE1ZWJiZTYzZTQ2XkEyXkFqcGdeQXVyNTAyODkwOQ@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMjE0ODEwNjM2NF5BMl5BanBnXkFtZTcwMjU2Mzg3NA@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMTUzMzY1MzI3Nl5BMl5BanBnXkFtZTcwOTM4OTYwNA@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BOWI3NjhhZDItNWQ2NS00Zjg0LWIzMjctNzY0MjRmNzkyYzVmXkEyXkFqcGdeQXVyMTA0MjU0Ng@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BZWI3MTA3NGEtYTg4ZC00MzgwLTliMGQtMjU2MWM5MmVmYThhXkEyXkFqcGdeQXVyNzc5MjA3OA@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BYWZhODdkMTEtMzBmNy00NzU5LWIzYjMtYTkwNWE3YTRjOTZkXkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNDdjYmFiYWEtYzBhZS00YTZkLWFlODgtY2I5MDE0NzZmMDljXkEyXkFqcGdeQXVyMTMxODk2OTU@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNjQ4MzUzYjYtYzk1My00M2Y2LWExMDEtY2E1NTlhMmQ0NGQ3XkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMGQwNDNkMmItYWY1Yy00YTZmLWE5OTAtODU0MGZmMzQ1NDdkXkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNzVlY2MwMjktM2E4OS00Y2Y3LWE3ZjctYzhkZGM3YzA1ZWM2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@.jpg\") | \n",
"
\n",
" \n",
"
-----User 895 has rated 12 films with a 4 or 5
User 895 has rated 8 films with a 1, 2, or 3
% of these films rated 5 or 4 appearing in the first 10 recommendations:0.0%
% of these films rated 1, 2, or 3 appearing in the first 10 recommendations: 0.0%
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DbZ9ufGvghUE"
},
"source": [
"### Save and Load a Standard Model "
]
},
{
"cell_type": "code",
"metadata": {
"id": "WqJbXHXgghUG"
},
"source": [
"# we can save the model with...\n",
"os.makedirs('models', exist_ok=True)\n",
"model.save_model('models/matrix_factorization_model.pth')"
],
"execution_count": 47,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Dz_8miLPghUG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8efcaec9-f018-409b-a01b-e8cc137c8957"
},
"source": [
"# ... and if we wanted to load that model back in, we can do that easily...\n",
"model_loaded_in = MatrixFactorizationModel(load_model_path='models/matrix_factorization_model.pth')\n",
"\n",
"model_loaded_in"
],
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MatrixFactorizationModel(\n",
" (user_biases): ZeroEmbedding(941, 1)\n",
" (item_biases): ZeroEmbedding(1447, 1)\n",
" (user_embeddings): ScaledEmbedding(941, 10)\n",
" (item_embeddings): ScaledEmbedding(1447, 10)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cQpWlCuughUH"
},
"source": [
"Now that we've built our first model and gotten some baseline metrics, we now will be looking at some more advanced features in Collie's ``MatrixFactorizationModel``. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q3Ne8ETsgzLe"
},
"source": [
"## Faster Data Loading Through Approximate Negative Sampling "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8nBu6PZhgzLe"
},
"source": [
"With sufficiently large enough data, verifying that each negative sample is one a user has *not* interacted with becomes expensive. With many items, this can soon become a bottleneck in the training process. \n",
"\n",
"Yet, when we have many items, the chances a user has interacted with most is increasingly rare. Say we have ``1,000,000`` items and we want to sample ``10`` negative items for a user that has positively interacted with ``200`` items. The chance that we accidentally select a positive item in a random sample of ``10`` items is just ``0.2%``. At that point, it might be worth it to forgo the expensive check to assert our negative sample is true, and instead just randomly sample negative items with the hope that most of the time, they will happen to be negative. \n",
"\n",
"This is the theory behind the ``ApproximateNegativeSamplingInteractionsDataLoader``, an alternate DataLoader built into Collie. Let's train a model with this below, noting how similar this procedure looks to that in the previous tutorial. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "MgSijz04gzLf"
},
"source": [
"train_loader = ApproximateNegativeSamplingInteractionsDataLoader(train_interactions, batch_size=1024, shuffle=True)\n",
"val_loader = ApproximateNegativeSamplingInteractionsDataLoader(val_interactions, batch_size=1024, shuffle=False)"
],
"execution_count": 50,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "n9wQLd9-gzLf"
},
"source": [
"model = MatrixFactorizationModel(\n",
" train=train_loader,\n",
" val=val_loader,\n",
" embedding_dim=10,\n",
" lr=1e-2,\n",
")"
],
"execution_count": 51,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 557,
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"b2e0c1050787481f936429c24738e39c"
]
},
"id": "AbYirCSNgzLg",
"outputId": "f04d8302-ad3f-4dd4-bc7f-e39819349e95"
},
"source": [
"trainer = CollieTrainer(model, max_epochs=10, deterministic=True)\n",
"\n",
"trainer.fit(model)\n",
"model.eval()"
],
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"----------------------------------------------------\n",
"0 | user_biases | ZeroEmbedding | 941 \n",
"1 | item_biases | ZeroEmbedding | 1.4 K \n",
"2 | user_embeddings | ScaledEmbedding | 9.4 K \n",
"3 | item_embeddings | ScaledEmbedding | 14.5 K\n",
"4 | dropout | Dropout | 0 \n",
"----------------------------------------------------\n",
"26.3 K Trainable params\n",
"0 Non-trainable params\n",
"26.3 K Total params\n",
"0.105 Total estimated model params size (MB)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Detected GPU. Setting ``gpus`` to 1.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Global seed set to 22\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d0a2f3c7539f47d787665ae5dbd9c708",
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}
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}
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{
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},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch 3: reducing learning rate of group 0 to 1.0000e-03.\n",
"Epoch 3: reducing learning rate of group 0 to 1.0000e-03.\n"
],
"name": "stdout"
},
{
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{
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{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MatrixFactorizationModel(\n",
" (user_biases): ZeroEmbedding(941, 1)\n",
" (item_biases): ZeroEmbedding(1447, 1)\n",
" (user_embeddings): ScaledEmbedding(941, 10)\n",
" (item_embeddings): ScaledEmbedding(1447, 10)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117,
"referenced_widgets": [
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},
"source": [
"mapk_score, mrr_score, auc_score = evaluate_in_batches([mapk, mrr, auc], val_interactions, model)\n",
"\n",
"print(f'MAP@10 Score: {mapk_score}')\n",
"print(f'MRR Score: {mrr_score}')\n",
"print(f'AUC Score: {auc_score}')"
],
"execution_count": 53,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7c34c7be74246acb1a7da702b490029",
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{
"output_type": "stream",
"text": [
"\n",
"MAP@10 Score: 0.027979833367276323\n",
"MRR Score: 0.1703751336709069\n",
"AUC Score: 0.8517987786322347\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qipOCQzxgzLh"
},
"source": [
"We're seeing a small hit on performance and only a marginal improvement in training time compared to the standard ``MatrixFactorizationModel`` model because MovieLens 100K has so few items. ``ApproximateNegativeSamplingInteractionsDataLoader`` is especially recommended for when we have more items in our data and training times need to be optimized. \n",
"\n",
"For more details on this and other DataLoaders in Collie (including those for out-of-memory datasets), check out the [docs](https://collie.readthedocs.io/en/latest/index.html)! "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iGgM-FaegzLi"
},
"source": [
"## Multiple Optimizers "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4xcFspsbgzLi"
},
"source": [
"Training recommendation models at ShopRunner, we have encountered something we call \"the curse of popularity.\" \n",
"\n",
"This is best thought of in the viewpoint of a model optimizer - say we have a user, a positive item, and several negative items that we hope have recommendation scores that score lower than the positive item. As an optimizer, you can either optimize every single embedding dimension (hundreds of parameters) to achieve this, or instead choose to score a quick win by optimizing the bias terms for the items (just add a positive constant to the positive item and a negative constant to each negative item). \n",
"\n",
"While we clearly want to have varied embedding layers that reflect each user and item's taste profiles, some models learn to settle for popularity as a recommendation score proxy by over-optimizing the bias terms, essentially just returning the same set of recommendations for every user. Worst of all, since popular items are... well, popular, **the loss of this model will actually be decent, solidifying the model getting stuck in a local loss minima**. \n",
"\n",
"To counteract this, Collie supports multiple optimizers in a ``MatrixFactorizationModel``. With this, we can have a faster optimizer work to optimize the embedding layers for users and items, and a slower optimizer work to optimize the bias terms. With this, we impel the model to do the work actually coming up with varied, personalized recommendations for users while still taking into account the necessity of the bias (popularity) terms on recommendations. \n",
"\n",
"At ShopRunner, we have seen significantly better metrics and results from this type of model. With Collie, this is simple to do, as shown below. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "GxUMsr61gzLj"
},
"source": [
"model = MatrixFactorizationModel(\n",
" train=train_interactions,\n",
" val=val_interactions,\n",
" embedding_dim=10,\n",
" lr=1e-2,\n",
" bias_lr=1e-1,\n",
" optimizer='adam',\n",
" bias_optimizer='sgd',\n",
")"
],
"execution_count": 54,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 557,
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},
"id": "dQ_tTRfOgzLj",
"outputId": "4d3af437-c62d-4b9b-ddb5-eaf3731556da"
},
"source": [
"trainer = CollieTrainer(model, max_epochs=10, deterministic=True)\n",
"\n",
"trainer.fit(model)\n",
"model.eval()"
],
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"----------------------------------------------------\n",
"0 | user_biases | ZeroEmbedding | 941 \n",
"1 | item_biases | ZeroEmbedding | 1.4 K \n",
"2 | user_embeddings | ScaledEmbedding | 9.4 K \n",
"3 | item_embeddings | ScaledEmbedding | 14.5 K\n",
"4 | dropout | Dropout | 0 \n",
"----------------------------------------------------\n",
"26.3 K Trainable params\n",
"0 Non-trainable params\n",
"26.3 K Total params\n",
"0.105 Total estimated model params size (MB)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Detected GPU. Setting ``gpus`` to 1.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Global seed set to 22\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"Epoch 3: reducing learning rate of group 0 to 1.0000e-03.\n",
"Epoch 3: reducing learning rate of group 0 to 1.0000e-02.\n"
],
"name": "stdout"
},
{
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{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MatrixFactorizationModel(\n",
" (user_biases): ZeroEmbedding(941, 1)\n",
" (item_biases): ZeroEmbedding(1447, 1)\n",
" (user_embeddings): ScaledEmbedding(941, 10)\n",
" (item_embeddings): ScaledEmbedding(1447, 10)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117,
"referenced_widgets": [
"5c2218b13d6345ff91c4d8980b2b2ca0",
"309d24f05b1b4bc5b7d0148795fd5cf4",
"ccfb5dc3eef14f1f8324bc48b3dc0e7f",
"a84e5372c5c24417a08487dd3efcebe8",
"c28c07424b7f45088e73ac25f2bb712a",
"5d01bda6f6354f25bb6dcb15ef4596a0",
"758cba83e9414f60bf87799a71b3cd7f",
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]
},
"id": "ENZSOd1DgzLk",
"outputId": "7b1844fb-b81a-43cb-8a98-5206b87b4506"
},
"source": [
"mapk_score, mrr_score, auc_score = evaluate_in_batches([mapk, mrr, auc], val_interactions, model)\n",
"\n",
"print(f'MAP@10 Score: {mapk_score}')\n",
"print(f'MRR Score: {mrr_score}')\n",
"print(f'AUC Score: {auc_score}')"
],
"execution_count": 56,
"outputs": [
{
"output_type": "display_data",
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"application/vnd.jupyter.widget-view+json": {
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{
"output_type": "stream",
"text": [
"\n",
"MAP@10 Score: 0.03243186201880122\n",
"MRR Score: 0.19819369246580287\n",
"AUC Score: 0.8617710409716284\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "blXcVpg3gzLk"
},
"source": [
"Again, we're not seeing as much performance increase here compared to the standard model because MovieLens 100K has so few items. For a more dramatic difference, try training this model on a larger dataset, such as MovieLens 10M, adjusting the architecture-specific hyperparameters, or train longer. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1Dt0IsWJgzLk"
},
"source": [
"## Item-Item Similarity "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sqQJpyKVgzLl"
},
"source": [
"While we've trained every model thus far to work for member-item recommendations (given a *member*, recommend *items* - think of this best as \"Personalized recommendations for you\"), we also have access to item-item recommendations for free (given a seed *item*, recommend similar *items* - think of this more like \"People who interacted with this item also interacted with...\"). \n",
"\n",
"With Collie, accessing this is simple! "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 343
},
"id": "RRMouFweUOhw",
"outputId": "02e6281a-f64e-43c4-a151-84601d91ab50"
},
"source": [
"df_item = data_object.load_items()\n",
"df_item.head()"
],
"execution_count": 68,
"outputs": [
{
"output_type": "execute_result",
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" | 401 | \n",
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\n",
" \n",
" | 199 | \n",
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" NaN | \n",
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\n",
" \n",
"
\n",
"
User 895:
\n",
" \n",
" \n",
" | \n",
" Willy Wonka and the Chocolate Factory (1971) | \n",
" Mighty Aphrodite (1995) | \n",
" Conspiracy Theory (1997) | \n",
" Sense and Sensibility (1995) | \n",
" Liar Liar (1997) | \n",
" In & Out (1997) | \n",
" Return of the Jedi (1983) | \n",
" Ransom (1996) | \n",
" Emma (1996) | \n",
" Toy Story (1995) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Some loved films: | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
" \n",
" \n",
" | \n",
" Cold Comfort Farm (1995) | \n",
" Apartment, The (1960) | \n",
" Blown Away (1994) | \n",
" Star Wars (1977) | \n",
" Star Trek: First Contact (1996) | \n",
" Sex, Lies, and Videotape (1989) | \n",
" Big Squeeze, The (1996) | \n",
" Client, The (1994) | \n",
" Jerry Maguire (1996) | \n",
" Ghost and the Darkness, The (1996) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Recommended films: | \n",
" | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNDkwMTY4MDIwM15BMl5BanBnXkFtZTcwODc4MDk2Nw@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMmYxYzA1YjYtYTNlNS00YzY4LWFlMDMtZjA2MzA2YWJlM2Q0XkEyXkFqcGdeQXVyNDk3NzU2MTQ@.jpg\") | \n",
" | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BYTllZjRkY2QtYTJlMy00ZTMxLWE0YWQtMWMwYzY2YTM3YzRjXkEyXkFqcGdeQXVyNTAyODkwOQ@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNDllYWVkOTQtZjRlMC00NWFjLWI0OGEtOWY4YzU4ZjMxYzg3XkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMjAxMDUwMjQ3OV5BMl5BanBnXkFtZTcwMTI0ODAyMQ@@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNGNjZWYwZGYtZWIxZC00OTYyLTllYTMtZWNiNmQ0YWUzMjkxXkEyXkFqcGdeQXVyMTQxNzMzNDI@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BYTM0ZWNmZTUtOTVkZS00MTZiLTg3M2QtZjA0Y2RmOWM1NWEyXkEyXkFqcGdeQXVyNjU0OTQ0OTY@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNWQ4NDRiMWItNGI5Yi00N2U1LTlkMGQtM2VjMzdkZTU0YzYyXkEyXkFqcGdeQXVyNTc1NTQxODI@.jpg\") | \n",
"
\n",
" \n",
"
-----User 895 has rated 12 films with a 4 or 5
User 895 has rated 8 films with a 1, 2, or 3
% of these films rated 5 or 4 appearing in the first 10 recommendations:10.0%
% of these films rated 1, 2, or 3 appearing in the first 10 recommendations: 10.0%
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZoIh0oHfg9AQ"
},
"source": [
"Partial credit loss is useful when we want an easy way to boost performance of any implicit model architecture, hybrid or not. When tuned properly, partial credit loss more fairly penalizes the model for more egregious mistakes and relaxes the loss applied when items are more similar. \n",
"\n",
"Of course, the loss function isn't the only place we can incorporate this metadata - we can also directly use this in the model (and even use a hybrid model combined with partial credit loss). Next, we will train a hybrid Collie model! "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DZpMvb3-bidk"
},
"source": [
"## Hybrid Factorization Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Laxa0vh1hE3o"
},
"source": [
"### Train a ``MatrixFactorizationModel`` "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fj3tJg-1hE3o"
},
"source": [
"The first step towards training a Collie Hybrid model is to train a regular ``MatrixFactorizationModel`` to generate rich user and item embeddings. We'll use these embeddings in a ``HybridPretrainedModel`` a bit later. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "m75xWkQLhE3o"
},
"source": [
"model = MatrixFactorizationModel(\n",
" train=train_interactions,\n",
" val=val_interactions,\n",
" embedding_dim=30,\n",
" lr=1e-2,\n",
")"
],
"execution_count": 83,
"outputs": []
},
{
"cell_type": "code",
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"base_uri": "https://localhost:8080/",
"height": 438,
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},
"id": "bjh0jE0yhE3p",
"outputId": "34e09142-f66f-4d32-9052-5e41d3e7d166"
},
"source": [
"trainer = CollieTrainer(model=model, max_epochs=10, deterministic=True)\n",
"\n",
"trainer.fit(model)"
],
"execution_count": 84,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"----------------------------------------------------\n",
"0 | user_biases | ZeroEmbedding | 941 \n",
"1 | item_biases | ZeroEmbedding | 1.4 K \n",
"2 | user_embeddings | ScaledEmbedding | 28.2 K\n",
"3 | item_embeddings | ScaledEmbedding | 43.4 K\n",
"4 | dropout | Dropout | 0 \n",
"----------------------------------------------------\n",
"74.0 K Trainable params\n",
"0 Non-trainable params\n",
"74.0 K Total params\n",
"0.296 Total estimated model params size (MB)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Detected GPU. Setting ``gpus`` to 1.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "62986af6e0394e969bb8942274ba6784",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Global seed set to 22\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1ccac573c5c4c79b4357c14f99a08b6",
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch 3: reducing learning rate of group 0 to 1.0000e-03.\n",
"Epoch 3: reducing learning rate of group 0 to 1.0000e-03.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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},
"metadata": {
"tags": []
}
},
{
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"data": {
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},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e2eb1edad7748ee92249d555840d612",
"version_minor": 0,
"version_major": 2
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"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51757d5291844c6981c0258a245ad5be",
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d1a11fbd7c3a4dadbdee14b744937c30",
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"version_major": 2
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"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c7fec0dc1e8648ca869c65caefa05751",
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117,
"referenced_widgets": [
"9b69ca9af02e4dcead166064746dfcb2",
"1748fb86e5824e739a39350e4777a4eb",
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"outputId": "1424a753-c468-48c2-dfc9-3b2118195955"
},
"source": [
"mapk_score, mrr_score, auc_score = evaluate_in_batches([mapk, mrr, auc], val_interactions, model)\n",
"\n",
"print(f'Standard MAP@10 Score: {mapk_score}')\n",
"print(f'Standard MRR Score: {mrr_score}')\n",
"print(f'Standard AUC Score: {auc_score}')"
],
"execution_count": 85,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b69ca9af02e4dcead166064746dfcb2",
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"version_major": 2
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"HBox(children=(FloatProgress(value=0.0, max=28.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"Standard MAP@10 Score: 0.024415062120220127\n",
"Standard MRR Score: 0.1551878337645617\n",
"Standard AUC Score: 0.8575152364604943\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CRq29RVfhE3q"
},
"source": [
"### Train a ``HybridPretrainedModel`` "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lFh1LEcChE3q"
},
"source": [
"With our trained ``model`` above, we can now use these embeddings and additional side data directly in a hybrid model. The architecture essentially takes our user embedding, item embedding, and item metadata for each user-item interaction, concatenates them, and sends it through a simple feedforward network to output a recommendation score. \n",
"\n",
"We can initially freeze the user and item embeddings from our previously-trained ``model``, train for a few epochs only optimizing our newly-added linear layers, and then train a model with everything unfrozen at a lower learning rate. We will show this process below. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "27SwhWuMYNkQ"
},
"source": [
"HybridPretrainedModel??"
],
"execution_count": 87,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RPgUTdR1hE3r"
},
"source": [
"# we will apply a linear layer to the metadata with ``metadata_layers_dims`` and\n",
"# a linear layer to the combined embeddings and metadata data with ``combined_layers_dims``\n",
"hybrid_model = HybridPretrainedModel(\n",
" train=train_interactions,\n",
" val=val_interactions,\n",
" item_metadata=metadata_df,\n",
" trained_model=model,\n",
" metadata_layers_dims=[8],\n",
" combined_layers_dims=[16],\n",
" lr=1e-2,\n",
" freeze_embeddings=True,\n",
")"
],
"execution_count": 90,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 438,
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},
"id": "vyyUg5ilhE3r",
"outputId": "77ffdf06-0964-4a7d-f491-a83d53d3e210"
},
"source": [
"hybrid_trainer = CollieTrainer(model=hybrid_model, max_epochs=10, deterministic=True)\n",
"\n",
"hybrid_trainer.fit(hybrid_model)"
],
"execution_count": 91,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"--------------------------------------------------------------\n",
"0 | _trained_model | MatrixFactorizationModel | 74.0 K\n",
"1 | embeddings | Sequential | 71.6 K\n",
"2 | dropout | Dropout | 0 \n",
"3 | metadata_layer_0 | Linear | 160 \n",
"4 | combined_layer_0 | Linear | 1.1 K \n",
"5 | combined_layer_1 | Linear | 17 \n",
"--------------------------------------------------------------\n",
"75.3 K Trainable params\n",
"71.6 K Non-trainable params\n",
"146 K Total params\n",
"0.588 Total estimated model params size (MB)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Detected GPU. Setting ``gpus`` to 1.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"text": [
"Global seed set to 22\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
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},
{
"output_type": "stream",
"text": [
"Epoch 10: reducing learning rate of group 0 to 1.0000e-03.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117,
"referenced_widgets": [
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},
"source": [
"mapk_score, mrr_score, auc_score = evaluate_in_batches([mapk, mrr, auc], val_interactions, hybrid_model)\n",
"\n",
"print(f'Hybrid MAP@10 Score: {mapk_score}')\n",
"print(f'Hybrid MRR Score: {mrr_score}')\n",
"print(f'Hybrid AUC Score: {auc_score}')"
],
"execution_count": 92,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n",
"Hybrid MAP@10 Score: 0.02650305521043056\n",
"Hybrid MRR Score: 0.15837650977843062\n",
"Hybrid AUC Score: 0.780685132170672\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EEw83cTUhE3s"
},
"source": [
"hybrid_model_unfrozen = HybridPretrainedModel(\n",
" train=train_interactions,\n",
" val=val_interactions,\n",
" item_metadata=metadata_df,\n",
" trained_model=model,\n",
" metadata_layers_dims=[8],\n",
" combined_layers_dims=[16],\n",
" lr=1e-4,\n",
" freeze_embeddings=False,\n",
")\n",
"\n",
"hybrid_model.unfreeze_embeddings()\n",
"hybrid_model_unfrozen.load_from_hybrid_model(hybrid_model)"
],
"execution_count": 93,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472,
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},
"id": "yiA-EylqhE3t",
"outputId": "aefcb665-c88d-43ab-eb7a-322cbc58a262"
},
"source": [
"hybrid_trainer_unfrozen = CollieTrainer(model=hybrid_model_unfrozen, max_epochs=10, deterministic=True)\n",
"\n",
"hybrid_trainer_unfrozen.fit(hybrid_model_unfrozen)"
],
"execution_count": 94,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"--------------------------------------------------------------\n",
"0 | _trained_model | MatrixFactorizationModel | 74.0 K\n",
"1 | embeddings | Sequential | 71.6 K\n",
"2 | dropout | Dropout | 0 \n",
"3 | metadata_layer_0 | Linear | 160 \n",
"4 | combined_layer_0 | Linear | 1.1 K \n",
"5 | combined_layer_1 | Linear | 17 \n",
"--------------------------------------------------------------\n",
"75.3 K Trainable params\n",
"71.6 K Non-trainable params\n",
"146 K Total params\n",
"0.588 Total estimated model params size (MB)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Detected GPU. Setting ``gpus`` to 1.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Global seed set to 22\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
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},
{
"output_type": "stream",
"text": [
"Epoch 3: reducing learning rate of group 0 to 1.0000e-03.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"text": [
"Epoch 7: reducing learning rate of group 0 to 1.0000e-04.\n"
],
"name": "stdout"
},
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"text": [
"Epoch 9: reducing learning rate of group 0 to 1.0000e-05.\n"
],
"name": "stdout"
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"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2Txbqf3fbqvD"
},
"source": [
"### Evaluate the Model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117,
"referenced_widgets": [
"0d9f2c4528634f63a26527a755b33773",
"6d196fda01ee4b63a7731b169c2f36b7",
"20d9caea7c744c09a8efd05793d2c6db",
"1760eeca2a084b1c8e57a9989139cdf6",
"8ccaa38836fd4fa588723ba2516f635b",
"113a8ae2c462494abf3ca97f65e51d06",
"c0282a49c81f4dce83322f9734eb0efd",
"9848dcb0b0e048c88f9c6243dc5ede33"
]
},
"id": "sof4rqMbhE3u",
"outputId": "a344f1bb-0627-4e46-968e-f89bc81a5224"
},
"source": [
"mapk_score, mrr_score, auc_score = evaluate_in_batches([mapk, mrr, auc],\n",
" val_interactions,\n",
" hybrid_model_unfrozen)\n",
"\n",
"print(f'Hybrid Unfrozen MAP@10 Score: {mapk_score}')\n",
"print(f'Hybrid Unfrozen MRR Score: {mrr_score}')\n",
"print(f'Hybrid Unfrozen AUC Score: {auc_score}')"
],
"execution_count": 95,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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]
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}
},
{
"output_type": "stream",
"text": [
"\n",
"Hybrid Unfrozen MAP@10 Score: 0.02789580198163252\n",
"Hybrid Unfrozen MRR Score: 0.17139103232628614\n",
"Hybrid Unfrozen AUC Score: 0.8118089364191508\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0FzTQc6WbtJA"
},
"source": [
"### Inference"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EwM1pkf_hE3v",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "650e4d07-8566-484c-c7b7-543e7c7428a9"
},
"source": [
"user_id = np.random.randint(10, train_interactions.num_users)\n",
"\n",
"display(\n",
" HTML(\n",
" get_recommendation_visualizations(\n",
" model=hybrid_model_unfrozen,\n",
" user_id=user_id,\n",
" filter_films=True,\n",
" shuffle=True,\n",
" detailed=True,\n",
" )\n",
" )\n",
")"
],
"execution_count": 96,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"User 895:
\n",
" \n",
" \n",
" | \n",
" Willy Wonka and the Chocolate Factory (1971) | \n",
" Mighty Aphrodite (1995) | \n",
" Conspiracy Theory (1997) | \n",
" Sense and Sensibility (1995) | \n",
" Liar Liar (1997) | \n",
" In & Out (1997) | \n",
" Return of the Jedi (1983) | \n",
" Ransom (1996) | \n",
" Emma (1996) | \n",
" Toy Story (1995) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Some loved films: | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
" \n",
" \n",
" | \n",
" Jerry Maguire (1996) | \n",
" Bad Boys (1995) | \n",
" Blown Away (1994) | \n",
" Santa Clause, The (1994) | \n",
" Tin Cup (1996) | \n",
" Graduate, The (1967) | \n",
" Cold Comfort Farm (1995) | \n",
" Princess Bride, The (1987) | \n",
" Private Benjamin (1980) | \n",
" True Romance (1993) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Recommended films: | \n",
" | \n",
" | \n",
" | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMTZlNzk1MjItYjJlYy00MTAxLWJkNjEtZmNiNmVlNjQ4NDE5XkEyXkFqcGdeQXVyMzI0NDc4ODY@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BNDQxNzY1MzItZmU1Mi00MWI3LTk4ZjYtOTBlM2RmYTk1MTJjXkEyXkFqcGdeQXVyNDk3NzU2MTQ@.jpg\") | \n",
" | \n",
" | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMGM4M2Q5N2MtNThkZS00NTc1LTk1NTItNWEyZjJjNDRmNDk5XkEyXkFqcGdeQXVyMjA0MDQ0Mjc@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BMjIyNjkxMjUyMF5BMl5BanBnXkFtZTgwMzU0MjEyMDE@.jpg\") | \n",
" ![](\"https://m.media-amazon.com/images/M/MV5BOWJhMjJjMDItODQxYS00ODIyLWJhYzAtNTliMDhkZjM0YTA1XkEyXkFqcGdeQXVyNzkwMjQ5NzM@.jpg\") | \n",
"
\n",
" \n",
"
-----User 895 has rated 12 films with a 4 or 5
User 895 has rated 8 films with a 1, 2, or 3
% of these films rated 5 or 4 appearing in the first 10 recommendations:0.0%
% of these films rated 1, 2, or 3 appearing in the first 10 recommendations: 10.0%
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fNwj-u-AhE3w"
},
"source": [
"The metrics and results look great, and we should only see a larger difference compared to a standard model as our data becomes more nuanced and complex (such as with MovieLens 10M data). \n",
"\n",
"If we're happy with this model, we can go ahead and save it for later! "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xYmFQZEhhE3w"
},
"source": [
"### Save and Load a Hybrid Model "
]
},
{
"cell_type": "code",
"metadata": {
"id": "2ZDlfmAVhE3w"
},
"source": [
"# we can save the model with...\n",
"os.makedirs('models', exist_ok=True)\n",
"hybrid_model_unfrozen.save_model('models/hybrid_model_unfrozen')"
],
"execution_count": 97,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qW3kPpenhE3x",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "da7c6d72-d7ca-4913-98fc-e85691a771f4"
},
"source": [
"# ... and if we wanted to load that model back in, we can do that easily...\n",
"hybrid_model_loaded_in = HybridPretrainedModel(load_model_path='models/hybrid_model_unfrozen')\n",
"\n",
"\n",
"hybrid_model_loaded_in"
],
"execution_count": 98,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"HybridPretrainedModel(\n",
" (embeddings): Sequential(\n",
" (0): ScaledEmbedding(941, 30)\n",
" (1): ScaledEmbedding(1447, 30)\n",
" )\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (metadata_layer_0): Linear(in_features=19, out_features=8, bias=True)\n",
" (combined_layer_0): Linear(in_features=68, out_features=16, bias=True)\n",
" (combined_layer_1): Linear(in_features=16, out_features=1, bias=True)\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 98
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1rDDCMkehE3x"
},
"source": [
"Thus far, we keep our focus only on the implicit feedback based matrix factorization model on small movielens dataset. In future, we will be expanding this MVP in the following directions:\n",
"1. Large scale industrial datasets - Yoochoose, Trivago\n",
"2. Other available models in [this](https://github.com/ShopRunner/collie_recs/tree/main/collie_recs/model) repo\n",
"3. Really liked the poster carousel. Put it in dash/streamlit app."
]
}
]
}