# RecTools
[](https://pypi.org/project/rectools)
[](https://pypi.org/project/rectools)
[](https://rectools.readthedocs.io)
[](https://github.com/MobileTeleSystems/RecTools/blob/main/LICENSE)
[](https://app.codecov.io/gh/MobileTeleSystems/RecTools)
[](https://github.com/MobileTeleSystems/RecTools/actions/workflows/test.yml?query=branch%3Amain++)
[](https://github.com/MobileTeleSystems/RecTools/graphs/contributors)
[](https://pepy.tech/project/rectools)
[](https://t.me/RecTools_Support)
<p align="center">
<a href="https://rectools.readthedocs.io/en/stable/">Documentation</a> |
<a href="https://github.com/MobileTeleSystems/RecTools/tree/main/examples">Examples</a> |
<a href="https://github.com/MobileTeleSystems/RecTools/tree/main/examples/tutorials">Tutorials</a> |
<a href="https://github.com/MobileTeleSystems/RecTools/blob/main/CONTRIBUTING.rst">Contributing</a> |
<a href="https://github.com/MobileTeleSystems/RecTools/releases">Releases</a> |
<a href="https://github.com/orgs/MobileTeleSystems/projects/1">Developers Board</a>
</p>
RecTools is an easy-to-use Python library which makes the process of building recommender systems easier and
faster than ever before.
## ✨ Highlights: HSTU model released! ✨
**HSTU arhictecture from ["Actions speak louder then words..."](https://arxiv.org/abs/2402.17152) is now available in RecTools as `HSTUModel`:**
- Fully compatible with our `fit` / `recommend` paradigm and require NO special data processing
- Supports context-aware recommendations in case Relative Time Bias is enabled
- Supports all loss options, item embedding options, category features utilization and other common modular functionality of RecTools transformer models
- In [HSTU tutorial](examples/tutorials/transformers_HSTU_tutorial.ipynb) we show that original metrics reported for HSTU on public Movielens datasets may actually be **underestimated**
- Configurable, customizable, callback-friendly, checkpoints-included, logs-out-of-the-box, custom-validation-ready, multi-gpu-compatible! See [Transformers Advanced Training User Guide](examples/tutorials/transformers_advanced_training_guide.ipynb) and [Transformers Customization Guide](examples/tutorials/transformers_customization_guide.ipynb)
Plase note that we always compare the quality of our implementations to academic papers results. [Public benchmarks for transformer models SASRec and BERT4Rec](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) show that RecTools implementations achieve highest scores on multiple datasets compared to other published results.
## Get started
Prepare data with
```shell
wget https://files.grouplens.org/datasets/movielens/ml-1m.zip
unzip ml-1m.zip
```
```python
import pandas as pd
from rectools import Columns
from rectools.dataset import Dataset
from rectools.models import SASRecModel
# Read the data
ratings = pd.read_csv(
"ml-1m/ratings.dat",
sep="::",
engine="python", # Because of 2-chars separators
header=None,
names=[Columns.User, Columns.Item, Columns.Weight, Columns.Datetime],
)
# Create dataset
dataset = Dataset.construct(ratings)
# Fit model
model = SASRecModel(n_factors=64, epochs=100, loss="sampled_softmax")
model.fit(dataset)
# Make recommendations
recos = model.recommend(
users=ratings[Columns.User].unique(),
dataset=dataset,
k=10,
filter_viewed=True,
)
```
## Installation
RecTools is on PyPI, so you can use `pip` to install it.
```
pip install rectools
```
The default version doesn't contain all the dependencies, because some of them are needed only for specific functionality. Available user extensions are the following:
- `lightfm`: adds wrapper for LightFM model,
- `torch`: adds models based on neural nets,
- `visuals`: adds visualization tools,
- `nmslib`: adds fast ANN recommenders.
Install extension:
```
pip install rectools[extension-name]
```
Install all extensions:
```
pip install rectools[all]
```
## Recommender Models
The table below lists recommender models that are available in RecTools.
| Model | Type | Description (🎏 for user/item features, 🔆 for warm inference, ❄️ for cold inference support) | Tutorials & Benchmarks |
|---------------------|----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|
| HSTU | Neural Network | `rectools.models.HSTUModel` - Sequential model with unidirectional pointwise aggregated attention mechanism, incorporating relative attention bias from positional and temporal information, introduced in ["Actions speak louder then words..."](https://arxiv.org/pdf/2402.17152), combined with "Shifted Sequence" training objective as in original public benchmarks<br>🎏 | 📓 [HSTU Theory & Practice](examples/tutorials/transformers_HSTU_tutorial.ipynb) <br> 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb)<br> 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) <br> 🚀 [Top performance on public datasets](examples/tutorials/transformers_HSTU_tutorial.ipynb)
| SASRec | Neural Network | `rectools.models.SASRecModel` - Transformer-based sequential model with unidirectional attention mechanism and "Shifted Sequence" training objective <br>🎏 | 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb)<br> 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) <br> 📘 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) <br> 🚀 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |
| BERT4Rec | Neural Network | `rectools.models.BERT4RecModel` - Transformer-based sequential model with bidirectional attention mechanism and "MLM" (masked item) training objective <br>🎏 | 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb)<br> 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) <br> 📘 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) <br> 🚀 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |
| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. <br>🎏 | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS)<br> 🚀 [50% boost to metrics with user & item features](examples/5_benchmark_iALS_with_features.ipynb) |
| [implicit](https://github.com/benfred/implicit) BPR-MF Wrapper | Matrix Factorization | `rectools.models.ImplicitBPRWrapperModel` - Bayesian Personalized Ranking Matrix Factorization algorithm. | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Bayesian-Personalized-Ranking-Matrix-Factorization-(BPR-MF)) |
| [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |
| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses.<br>🎏 🔆 ❄️ | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM)<br>🚀 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |
| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |
| DSSM | Neural Network | `rectools.models.DSSMModel` - Two-tower Neural model that learns user and item embeddings utilising their explicit features and learning on triplet loss.<br>🎏 🔆 | - |
| Popular | Heuristic | `rectools.models.PopularModel` - Classic baseline which computes popularity of items and also accepts params like time window and type of popularity computation.<br>❄️ | - |
| Popular in Category | Heuristic | `rectools.models.PopularInCategoryModel` - Model that computes poularity within category and applies mixing strategy to increase Diversity.<br>❄️ | - |
| Random | Heuristic | `rectools.models.RandomModel` - Simple random algorithm useful to benchmark Novelty, Coverage, etc.<br>❄️ | - |
- All of the models follow the same interface. **No exceptions**
- No need for manual creation of sparse matrixes, torch dataloaders or mapping ids. Preparing data for models is as simple as `dataset = Dataset.construct(interactions_df)`
- Fitting any model is as simple as `model.fit(dataset)`
- For getting recommendations `filter_viewed` and `items_to_recommend` options are available
- For item-to-item recommendations use `recommend_to_items` method
- For feeding user/item features to model just specify dataframes when constructing `Dataset`. [Check our example](examples/4_dataset_with_features.ipynb)
- For warm / cold inference just provide all required ids in `users` or `target_items` parameters of `recommend` or `recommend_to_items` methods and make sure you have features in the dataset for warm users/items. **Nothing else is needed, everything works out of the box.**
- Our models can be initialized from configs and have useful methods like `get_config`, `get_params`, `save`, `load`. Common functions `model_from_config`, `model_from_params` and `load_model` are available. [Check our example](examples/9_model_configs_and_saving.ipynb)
## Extended validation tools
### `calc_metrics` for classification, ranking, "beyond-accuracy", DQ, popularity bias and between-model metrics
[User guide](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/3_metrics.ipynb) | [Documentation](https://rectools.readthedocs.io/en/stable/features.html#metrics)
### `DebiasConfig` for debiased metrics calculation
[User guide](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/8_debiased_metrics.ipynb) | [Documentation](https://rectools.readthedocs.io/en/stable/api/rectools.metrics.debias.DebiasConfig.html)
### `cross_validate` for model metrics comparison
[User guide](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/2_cross_validation.ipynb)
### `VisualApp` for model recommendations comparison
<img src="https://recsysart.ru/images/visual_app.gif" width=500>
[Example](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/7_visualization.ipynb) | [Demo](https://recsysart.ru/voila/) | [Documentation](https://rectools.readthedocs.io/en/stable/api/rectools.visuals.visual_app.VisualApp.html)
### `MetricsApp` for metrics trade-off analysis
<img src="https://recsysart.ru/images/metrics_app.gif" width=600>
[Example](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/2_cross_validation.ipynb) |
[Documentation](https://rectools.readthedocs.io/en/stable/api/rectools.visuals.metrics_app.MetricsApp.html)
## Contribution
[Contributing guide](CONTRIBUTING.rst)
To install all requirements
- you must have `python3` and `poetry` installed
- make sure you have no active virtual environments (deactivate conda `base` if applicable)
- run
```
make install
```
For autoformatting run
```
make format
```
For linters check run
```
make lint
```
For tests run
```
make test
```
For coverage run
```
make coverage
```
To remove virtual environment run
```
make clean
```
## RecTools Team
- [Emiliy Feldman](https://github.com/feldlime) [Maintainer]
- [Daria Tikhonovich](https://github.com/blondered) [Maintainer]
- [Andrey Semenov](https://github.com/In48semenov)
- [Mike Sokolov](https://github.com/mikesokolovv)
- [Maya Spirina](https://github.com/spirinamayya)
- [Grigoriy Gusarov](https://github.com/Gooogr)
- [Aki Ariga](https://github.com/chezou)
- [Nikolay Undalov](https://github.com/nsundalov)
- [Aleksey Kuzin](https://github.com/teodor-r)
Previous contributors: [Ildar Safilo](https://github.com/irsafilo) [ex-Maintainer], [Daniil Potapov](https://github.com/sharthZ23) [ex-Maintainer], [Alexander Butenko](https://github.com/iomallach), [Igor Belkov](https://github.com/OzmundSedler), [Artem Senin](https://github.com/artemseninhse), [Mikhail Khasykov](https://github.com/mkhasykov), [Julia Karamnova](https://github.com/JuliaKup), [Maxim Lukin](https://github.com/groundmax), [Yuri Ulianov](https://github.com/yukeeul), [Egor Kratkov](https://github.com/jegorus), [Azat Sibagatulin](https://github.com/azatnv), [Vadim Vetrov](https://github.com/Waujito)
Raw data
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"maintainer_email": "feldlime@yandex.ru",
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"description": "# RecTools\n\n[](https://pypi.org/project/rectools)\n[](https://pypi.org/project/rectools)\n[](https://rectools.readthedocs.io)\n\n[](https://github.com/MobileTeleSystems/RecTools/blob/main/LICENSE)\n[](https://app.codecov.io/gh/MobileTeleSystems/RecTools)\n[](https://github.com/MobileTeleSystems/RecTools/actions/workflows/test.yml?query=branch%3Amain++)\n\n[](https://github.com/MobileTeleSystems/RecTools/graphs/contributors)\n[](https://pepy.tech/project/rectools)\n[](https://t.me/RecTools_Support)\n\n<p align=\"center\">\n <a href=\"https://rectools.readthedocs.io/en/stable/\">Documentation</a> |\n <a href=\"https://github.com/MobileTeleSystems/RecTools/tree/main/examples\">Examples</a> |\n <a href=\"https://github.com/MobileTeleSystems/RecTools/tree/main/examples/tutorials\">Tutorials</a> |\n <a href=\"https://github.com/MobileTeleSystems/RecTools/blob/main/CONTRIBUTING.rst\">Contributing</a> |\n <a href=\"https://github.com/MobileTeleSystems/RecTools/releases\">Releases</a> |\n <a href=\"https://github.com/orgs/MobileTeleSystems/projects/1\">Developers Board</a>\n</p>\n\nRecTools is an easy-to-use Python library which makes the process of building recommender systems easier and\nfaster than ever before.\n\n## \u2728 Highlights: HSTU model released! \u2728\n\n**HSTU arhictecture from [\"Actions speak louder then words...\"](https://arxiv.org/abs/2402.17152) is now available in RecTools as `HSTUModel`:**\n- Fully compatible with our `fit` / `recommend` paradigm and require NO special data processing\n- Supports context-aware recommendations in case Relative Time Bias is enabled\n- Supports all loss options, item embedding options, category features utilization and other common modular functionality of RecTools transformer models\n- In [HSTU tutorial](examples/tutorials/transformers_HSTU_tutorial.ipynb) we show that original metrics reported for HSTU on public Movielens datasets may actually be **underestimated**\n- Configurable, customizable, callback-friendly, checkpoints-included, logs-out-of-the-box, custom-validation-ready, multi-gpu-compatible! See [Transformers Advanced Training User Guide](examples/tutorials/transformers_advanced_training_guide.ipynb) and [Transformers Customization Guide](examples/tutorials/transformers_customization_guide.ipynb)\n\nPlase note that we always compare the quality of our implementations to academic papers results. [Public benchmarks for transformer models SASRec and BERT4Rec](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) show that RecTools implementations achieve highest scores on multiple datasets compared to other published results.\n\n\n## Get started\n\nPrepare data with\n\n```shell\nwget https://files.grouplens.org/datasets/movielens/ml-1m.zip\nunzip ml-1m.zip\n```\n\n```python\nimport pandas as pd\n \nfrom rectools import Columns\nfrom rectools.dataset import Dataset\nfrom rectools.models import SASRecModel\n\n# Read the data\nratings = pd.read_csv(\n \"ml-1m/ratings.dat\", \n sep=\"::\",\n engine=\"python\", # Because of 2-chars separators\n header=None,\n names=[Columns.User, Columns.Item, Columns.Weight, Columns.Datetime],\n)\n \n# Create dataset\ndataset = Dataset.construct(ratings)\n \n# Fit model\nmodel = SASRecModel(n_factors=64, epochs=100, loss=\"sampled_softmax\")\nmodel.fit(dataset)\n\n# Make recommendations\nrecos = model.recommend(\n users=ratings[Columns.User].unique(),\n dataset=dataset,\n k=10,\n filter_viewed=True,\n)\n```\n\n## Installation\n\nRecTools is on PyPI, so you can use `pip` to install it.\n```\npip install rectools\n```\nThe default version doesn't contain all the dependencies, because some of them are needed only for specific functionality. Available user extensions are the following:\n\n- `lightfm`: adds wrapper for LightFM model,\n- `torch`: adds models based on neural nets,\n- `visuals`: adds visualization tools,\n- `nmslib`: adds fast ANN recommenders.\n\nInstall extension:\n```\npip install rectools[extension-name]\n```\n\nInstall all extensions:\n```\npip install rectools[all]\n```\n\n\n## Recommender Models\nThe table below lists recommender models that are available in RecTools. \n\n| Model | Type | Description (\ud83c\udf8f for user/item features, \ud83d\udd06 for warm inference, \u2744\ufe0f for cold inference support) | Tutorials & Benchmarks |\n|---------------------|----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|\n| HSTU | Neural Network | `rectools.models.HSTUModel` - Sequential model with unidirectional pointwise aggregated attention mechanism, incorporating relative attention bias from positional and temporal information, introduced in [\"Actions speak louder then words...\"](https://arxiv.org/pdf/2402.17152), combined with \"Shifted Sequence\" training objective as in original public benchmarks<br>\ud83c\udf8f | \ud83d\udcd3 [HSTU Theory & Practice](examples/tutorials/transformers_HSTU_tutorial.ipynb) <br> \ud83d\udcd5 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb)<br> \ud83d\udcd7 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) <br> \ud83d\ude80 [Top performance on public datasets](examples/tutorials/transformers_HSTU_tutorial.ipynb)\n| SASRec | Neural Network | `rectools.models.SASRecModel` - Transformer-based sequential model with unidirectional attention mechanism and \"Shifted Sequence\" training objective <br>\ud83c\udf8f | \ud83d\udcd5 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb)<br> \ud83d\udcd7 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) <br> \ud83d\udcd8 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) <br> \ud83d\ude80 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |\n| BERT4Rec | Neural Network | `rectools.models.BERT4RecModel` - Transformer-based sequential model with bidirectional attention mechanism and \"MLM\" (masked item) training objective <br>\ud83c\udf8f | \ud83d\udcd5 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb)<br> \ud83d\udcd7 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) <br> \ud83d\udcd8 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) <br> \ud83d\ude80 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |\n| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. <br>\ud83c\udf8f | \ud83d\udcd9 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS)<br> \ud83d\ude80 [50% boost to metrics with user & item features](examples/5_benchmark_iALS_with_features.ipynb) |\n| [implicit](https://github.com/benfred/implicit) BPR-MF Wrapper | Matrix Factorization | `rectools.models.ImplicitBPRWrapperModel` - Bayesian Personalized Ranking Matrix Factorization algorithm. | \ud83d\udcd9 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Bayesian-Personalized-Ranking-Matrix-Factorization-(BPR-MF)) |\n| [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | \ud83d\udcd9 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |\n| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses.<br>\ud83c\udf8f \ud83d\udd06 \u2744\ufe0f | \ud83d\udcd9 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM)<br>\ud83d\ude80 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|\n| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | \ud83d\udcd9 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |\n| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | \ud83d\udcd9 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |\n| DSSM | Neural Network | `rectools.models.DSSMModel` - Two-tower Neural model that learns user and item embeddings utilising their explicit features and learning on triplet loss.<br>\ud83c\udf8f \ud83d\udd06 | - |\n| Popular | Heuristic | `rectools.models.PopularModel` - Classic baseline which computes popularity of items and also accepts params like time window and type of popularity computation.<br>\u2744\ufe0f | - |\n| Popular in Category | Heuristic | `rectools.models.PopularInCategoryModel` - Model that computes poularity within category and applies mixing strategy to increase Diversity.<br>\u2744\ufe0f | - |\n| Random | Heuristic | `rectools.models.RandomModel` - Simple random algorithm useful to benchmark Novelty, Coverage, etc.<br>\u2744\ufe0f | - |\n\n- All of the models follow the same interface. **No exceptions**\n- No need for manual creation of sparse matrixes, torch dataloaders or mapping ids. Preparing data for models is as simple as `dataset = Dataset.construct(interactions_df)`\n- Fitting any model is as simple as `model.fit(dataset)`\n- For getting recommendations `filter_viewed` and `items_to_recommend` options are available\n- For item-to-item recommendations use `recommend_to_items` method\n- For feeding user/item features to model just specify dataframes when constructing `Dataset`. [Check our example](examples/4_dataset_with_features.ipynb)\n- For warm / cold inference just provide all required ids in `users` or `target_items` parameters of `recommend` or `recommend_to_items` methods and make sure you have features in the dataset for warm users/items. **Nothing else is needed, everything works out of the box.**\n- Our models can be initialized from configs and have useful methods like `get_config`, `get_params`, `save`, `load`. Common functions `model_from_config`, `model_from_params` and `load_model` are available. [Check our example](examples/9_model_configs_and_saving.ipynb)\n\n\n## Extended validation tools\n\n### `calc_metrics` for classification, ranking, \"beyond-accuracy\", DQ, popularity bias and between-model metrics\n\n\n[User guide](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/3_metrics.ipynb) | [Documentation](https://rectools.readthedocs.io/en/stable/features.html#metrics)\n\n\n### `DebiasConfig` for debiased metrics calculation\n\n[User guide](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/8_debiased_metrics.ipynb) | [Documentation](https://rectools.readthedocs.io/en/stable/api/rectools.metrics.debias.DebiasConfig.html)\n\n### `cross_validate` for model metrics comparison\n\n\n[User guide](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/2_cross_validation.ipynb) \n\n\n### `VisualApp` for model recommendations comparison\n\n<img src=\"https://recsysart.ru/images/visual_app.gif\" width=500>\n\n\n[Example](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/7_visualization.ipynb) | [Demo](https://recsysart.ru/voila/) | [Documentation](https://rectools.readthedocs.io/en/stable/api/rectools.visuals.visual_app.VisualApp.html)\n\n\n\n### `MetricsApp` for metrics trade-off analysis\n\n\n<img src=\"https://recsysart.ru/images/metrics_app.gif\" width=600>\n\n[Example](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/2_cross_validation.ipynb) |\n[Documentation](https://rectools.readthedocs.io/en/stable/api/rectools.visuals.metrics_app.MetricsApp.html)\n\n\n## Contribution\n[Contributing guide](CONTRIBUTING.rst)\n\nTo install all requirements\n- you must have `python3` and `poetry` installed\n- make sure you have no active virtual environments (deactivate conda `base` if applicable)\n- run\n```\nmake install\n```\n\n\nFor autoformatting run \n```\nmake format\n```\n\nFor linters check run \n```\nmake lint\n```\n\nFor tests run \n```\nmake test\n```\n\nFor coverage run \n```\nmake coverage\n```\n\nTo remove virtual environment run\n```\nmake clean\n```\n\n## RecTools Team\n\n- [Emiliy Feldman](https://github.com/feldlime) [Maintainer]\n- [Daria Tikhonovich](https://github.com/blondered) [Maintainer]\n- [Andrey Semenov](https://github.com/In48semenov)\n- [Mike Sokolov](https://github.com/mikesokolovv)\n- [Maya Spirina](https://github.com/spirinamayya)\n- [Grigoriy Gusarov](https://github.com/Gooogr)\n- [Aki Ariga](https://github.com/chezou)\n- [Nikolay Undalov](https://github.com/nsundalov)\n- [Aleksey Kuzin](https://github.com/teodor-r)\n\nPrevious contributors: [Ildar Safilo](https://github.com/irsafilo) [ex-Maintainer], [Daniil Potapov](https://github.com/sharthZ23) [ex-Maintainer], [Alexander Butenko](https://github.com/iomallach), [Igor Belkov](https://github.com/OzmundSedler), [Artem Senin](https://github.com/artemseninhse), [Mikhail Khasykov](https://github.com/mkhasykov), [Julia Karamnova](https://github.com/JuliaKup), [Maxim Lukin](https://github.com/groundmax), [Yuri Ulianov](https://github.com/yukeeul), [Egor Kratkov](https://github.com/jegorus), [Azat Sibagatulin](https://github.com/azatnv), [Vadim Vetrov](https://github.com/Waujito)\n\n\n",
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