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210 changes: 46 additions & 164 deletions README.md
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<img src=docs/imgs/lightautoml_logo_color.png />

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lightautoml)](https://pypi.org/project/lightautoml/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lightautoml)](https://pypi.org/project/lightautoml)
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![pypi - Downloads](https://img.shields.io/pypi/dm/lightautoml?color=green&label=PyPI%20downloads&logo=pypi&logoColor=green)
[![GitHub License](https://img.shields.io/github/license/sb-ai-lab/LightAutoML)](https://github.com/sb-ai-lab/RePlay/blob/main/LICENSE)
[![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/lightautoml)
<br>
[![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/sb-ai-lab/lightautoml/CI.yml)](https://github.com/sb-ai-lab/lightautoml/actions/workflows/CI.yml?query=branch%3Amain)
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[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
### [Documentation](https://lightautoml.readthedocs.io/) | [Installation](#installation) | [Examples](#resources) | [Telegram chat](https://t.me/joinchat/sp8P7sdAqaU0YmRi) | [Telegram channel](https://t.me/lightautoml)

LightAutoML (LAMA) is an AutoML framework which provides automatic model creation for the following tasks:
- binary classification
- multiclass classification
- multilabel classification
- regression

Current version of the package handles datasets that have independent samples in each row. I.e. **each row is an object with its specific features and target**.
LightAutoML (LAMA) allows you to create machine learning models using just a few lines of code, or build your own custom pipeline using ready blocks. It supports tabular, time series, image and text data.

**Authors**: [Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Anton Vakhrushev](https://kaggle.com/btbpanda), [Dmitry Simakov](https://kaggle.com/simakov), Rinchin Damdinov, Vasilii Bunakov, Alexander Kirilin, Pavel Shvets.


<a name="toc"></a>
# Table of Contents

* [Installation](#installation)
* [Documentation](https://lightautoml.readthedocs.io/)
* [Quick tour](#quicktour)
* [Resources](#examples)
* [Advanced features](#advancedfeatures)
* [Support and feature requests](#support)
* [Contributing to LightAutoML](#contributing)
* [License](#license)

**Documentation** of LightAutoML is available [here](https://lightautoml.readthedocs.io/), you can also [generate](https://github.com/AILab-MLTools/LightAutoML/blob/master/.github/CONTRIBUTING.md#building-documentation) it.


<a name="installation"></a>
# Installation
To install LAMA framework on your machine from PyPI:
```bash
# Base functionality:
pip install -U lightautoml

# For partial installation use corresponding option
# Extra dependecies: [nlp, cv, report] or use 'all' to install all dependecies
pip install -U lightautoml[nlp]
```

Additionally, run following commands to enable pdf report generation:

```bash
# MacOS
brew install cairo pango gdk-pixbuf libffi

# Debian / Ubuntu
sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info

# Fedora
sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2

# Windows
# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows
```
[Back to top](#toc)

<a name="quicktour"></a>
# Quick tour

Let's solve the popular Kaggle Titanic competition below. There are two main ways to solve machine learning problems using LightAutoML:
### Use ready preset for tabular data
```python
import pandas as pd
from sklearn.metrics import f1_score

from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task

df_train = pd.read_csv('../input/titanic/train.csv')
df_test = pd.read_csv('../input/titanic/test.csv')

automl = TabularAutoML(
task = Task(
name = 'binary',
metric = lambda y_true, y_pred: f1_score(y_true, (y_pred > 0.5)*1))
)
oof_pred = automl.fit_predict(
df_train,
roles = {'target': 'Survived', 'drop': ['PassengerId']}
)
test_pred = automl.predict(df_test)

pd.DataFrame({
'PassengerId':df_test.PassengerId,
'Survived': (test_pred.data[:, 0] > 0.5)*1
}).to_csv('submit.csv', index = False)
```
There are two main ways to solve machine learning problems using LightAutoML:
* Ready to use preset
```python
from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task

### LightAutoML as a framework: build your own custom pipeline

```python
import pandas as pd
from sklearn.metrics import f1_score

from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task

df_train = pd.read_csv('../input/titanic/train.csv')
df_test = pd.read_csv('../input/titanic/test.csv')
N_THREADS = 4

reader = PandasToPandasReader(Task("binary"), cv=5, random_state=42)

# create a feature selector
selector = ImportanceCutoffSelector(
LGBSimpleFeatures(),
BoostLGBM(
default_params={'learning_rate': 0.05, 'num_leaves': 64,
'seed': 42, 'num_threads': N_THREADS}
),
ModelBasedImportanceEstimator(),
cutoff=0
)

# build first level pipeline for AutoML
pipeline_lvl1 = MLPipeline([
# first model with hyperparams tuning
(
BoostLGBM(
default_params={'learning_rate': 0.05, 'num_leaves': 128,
'seed': 1, 'num_threads': N_THREADS}
),
OptunaTuner(n_trials=20, timeout=30)
),
# second model without hyperparams tuning
BoostLGBM(
default_params={'learning_rate': 0.025, 'num_leaves': 64,
'seed': 2, 'num_threads': N_THREADS}
)
], pre_selection=selector, features_pipeline=LGBSimpleFeatures(), post_selection=None)

# build second level pipeline for AutoML
pipeline_lvl2 = MLPipeline(
[
BoostLGBM(
default_params={'learning_rate': 0.05, 'num_leaves': 64,
'max_bin': 1024, 'seed': 3, 'num_threads': N_THREADS},
freeze_defaults=True
)
],
pre_selection=None,
features_pipeline=LGBSimpleFeatures(),
post_selection=None
)

# build AutoML pipeline
automl = AutoML(reader, [
[pipeline_lvl1],
[pipeline_lvl2],
],
skip_conn=False
)

# train AutoML and get predictions
oof_pred = automl.fit_predict(df_train, roles = {'target': 'Survived', 'drop': ['PassengerId']})
test_pred = automl.predict(df_test)

pd.DataFrame({
'PassengerId':df_test.PassengerId,
'Survived': (test_pred.data[:, 0] > 0.5)*1
}).to_csv('submit.csv', index = False)
```
automl = TabularAutoML(task = Task(name = 'binary', metric = 'auc'))
oof_preds = automl.fit_predict(train_df, roles = {'target': 'my_target', 'drop': ['column_to_drop']}).data
test_preds = automl.predict(test_df).data
```

LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#Resources) section.
* As a framework</br>
LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#resources) section.

<a name="examples"></a>
<a name="resources"></a>
# Resources

### Kaggle kernel examples of LightAutoML usage:
Expand Down Expand Up @@ -230,6 +84,35 @@ LighAutoML framework has a lot of ready-to-use parts and extensive customization
- (English) [LightAutoML vs Titanic: 80% accuracy in several lines of code (Medium)](https://alexmryzhkov.medium.com/lightautoml-preset-usage-tutorial-2cce7da6f936)
- (English) [Hands-On Python Guide to LightAutoML – An Automatic ML Model Creation Framework (Analytic Indian Mag)](https://analyticsindiamag.com/hands-on-python-guide-to-lama-an-automatic-ml-model-creation-framework/?fbclid=IwAR0f0cVgQWaLI60m1IHMD6VZfmKce0ZXxw-O8VRTdRALsKtty8a-ouJex7g)

<a name="installation"></a>
# Installation
To install LAMA framework on your machine from PyPI:
```bash
# Base functionality:
pip install -U lightautoml

# For partial installation use corresponding option
# Extra dependecies: [nlp, cv, report] or use 'all' to install all dependecies
pip install -U lightautoml[nlp]
```

Additionally, run following commands to enable pdf report generation:

```bash
# MacOS
brew install cairo pango gdk-pixbuf libffi

# Debian / Ubuntu
sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info

# Fedora
sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2

# Windows
# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows
```


<a name="advancedfeatures"></a>
# Advanced features
### GPU and Spark pipelines
Expand All @@ -243,9 +126,8 @@ If you are interested in contributing to LightAutoML, please read the [Contribut

<a name="support"></a>
# Support and feature requests
Seek prompt advice at [Telegram group](https://t.me/lightautoml).

Open bug reports and feature requests on GitHub [issues](https://github.com/AILab-MLTools/LightAutoML/issues).
- Seek prompt advice at [Telegram group](https://t.me/joinchat/sp8P7sdAqaU0YmRi).
- Open bug reports and feature requests on GitHub [issues](https://github.com/AILab-MLTools/LightAutoML/issues).

<a name="license"></a>
# License
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"source": [
"# Tutorial 10: Relational datasets (with star scheme)\n",
"\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/39cb56feae6766464d39dd2349480b97099d2535/imgs/LightAutoML_logo_big.png)\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/39cb56feae6766464d39dd2349480b97099d2535/docs/imgs/lightautoml_logo_color.png)\n",
"\n"
]
},
Expand Down Expand Up @@ -110,7 +110,7 @@
"source": [
"Consider an example of data with a star scheme organization. The dataset contains data on the sale of meals in the restaurant chain, consists of three tables: the main one containing information about completed orders (`train` and `test` parts), and two auxiliary tables containing information about restaurants (`fulfilment_center_info`) and available dishes (`meal_info`). The tables and the scheme of their organization are shown in the image below.\n",
"\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/master/imgs/Star_scheme_tables.png)"
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/master/docs/imgs/Star_scheme_tables.png)"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../imgs/tutorial_11_general_problem_statement.png\" alt=\"Time series general problem statement\"/>"
"<img src=\"../../docs/imgs/tutorial_11_general_problem_statement.png\" alt=\"Time series general problem statement\"/>"
]
},
{
Expand All @@ -475,7 +475,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../imgs/tutorial_11_case_problem_statement.png\" alt=\"Time series case problem statement\"/>"
"<img src=\"../../docs/imgs/tutorial_11_case_problem_statement.png\" alt=\"Time series case problem statement\"/>"
]
},
{
Expand All @@ -489,7 +489,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../imgs/tutorial_11_history_step_params.png\" alt=\"History and step params description\"/>"
"<img src=\"../../docs/imgs/tutorial_11_history_step_params.png\" alt=\"History and step params description\"/>"
]
},
{
Expand All @@ -503,7 +503,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../imgs/tutorial_11_transformers_params.png\" alt=\"Transformers params description\"/>"
"<img src=\"../../docs/imgs/tutorial_11_transformers_params.png\" alt=\"Transformers params description\"/>"
]
},
{
Expand Down
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"id": "35c56a11",
"metadata": {},
"source": [
"<img src=\"../../imgs/LightAutoML_logo_big.png\" alt=\"LightAutoML logo\" style=\"width:100%;\"/>"
"<img src=\"../../docs/imgs/lightautoml_logo_color.png\" alt=\"LightAutoML logo\" style=\"width:100%;\"/>"
]
},
{
Expand Down Expand Up @@ -986,7 +986,7 @@
"\n",
"Let's look at how the LightAutoML model is arranged and what it consists in general.\n",
"\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/master/imgs/tutorial_1_laml_big.png)\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/master/docs/imgs/tutorial_1_laml_big.png)\n",
"\n",
"#### 1.3.1 Reader object\n",
"\n",
Expand All @@ -1008,7 +1008,7 @@
"\n",
"As a result, after analyzing and processing the data, the ```Reader``` object forms and returns a ```LAMA Dataset```. It contains the original data and markup with metainformation. In this dataset it is possible to see the roles defined by the ```Reader``` object, selected features etc. Then ML pipelines are trained on this data. \n",
"\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/master/imgs/tutorial_1_ml_pipeline.png)\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/master/docs/imgs/tutorial_1_ml_pipeline.png)\n",
"\n",
"Each such pipeline is one or more machine learning algorithms that share one post-processing block and one validation scheme. Several such pipelines can be trained in parallel on one dataset, and they form a level. Number of levels can be unlimited as possible. List of all levels of AutoML pipeline is available via ```.levels``` attribute of ```AutoML``` instance. Level predictions can be inputs to other models or ML pipelines (i. e. stacking scheme). As inputs for subsequent levels, it is possible to use the original data by setting ```skip_conn``` argument in ```True``` when initializing preset instance. At the last level, if there are several pipelines, blending is used to build a prediction. \n",
"\n",
Expand Down Expand Up @@ -1036,11 +1036,11 @@
"\n",
"Here is a default AutoML pipeline for binary classification and regression tasks (```TabularAutoML``` preset):\n",
"\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/ac3c1b38873437eb74354fb44e68a449a0200aa6/imgs/tutorial_blackbox_pipeline.png)\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/ac3c1b38873437eb74354fb44e68a449a0200aa6/docs/imgs/tutorial_blackbox_pipeline.png)\n",
"\n",
"Another example:\n",
"\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/ac3c1b38873437eb74354fb44e68a449a0200aa6/imgs/tutorial_1_pipeline.png)\n",
"![](https://raw.githubusercontent.com/sb-ai-lab/LightAutoML/ac3c1b38873437eb74354fb44e68a449a0200aa6/docs/imgs/tutorial_1_pipeline.png)\n",
"\n",
"Let's discuss some of the params we can setup:\n",
"- `task` - the type of the ML task (the only **must have** parameter)\n",
Expand Down
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"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../imgs/LightAutoML_logo_big.png\" alt=\"LightAutoML logo\" style=\"width:100%;\"/>"
"<img src=\"../../docs/imgs/lightautoml_logo_color.png\" alt=\"LightAutoML logo\" style=\"width:100%;\"/>"
]
},
{
Expand All @@ -34,7 +34,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![WB0](../../imgs/tutorial_whitebox_report_1.png)"
"![WB0](../../docs/imgs/tutorial_whitebox_report_1.png)"
]
},
{
Expand All @@ -48,7 +48,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![WB1](../../imgs/tutorial_whitebox_report_2.png)"
"![WB1](../../docs/imgs/tutorial_whitebox_report_2.png)"
]
},
{
Expand All @@ -62,7 +62,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![WB2](../../imgs/tutorial_whitebox_report_3.png)"
"![WB2](../../docs/imgs/tutorial_whitebox_report_3.png)"
]
},
{
Expand All @@ -76,7 +76,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![WB3](../../imgs/tutorial_whitebox_report_4.png)"
"![WB3](../../docs/imgs/tutorial_whitebox_report_4.png)"
]
},
{
Expand Down
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