Metadata-Version: 2.1
Name: colt
Version: 0.7.3
Summary: A configuration utility for Python object.
Home-page: https://github.com/altescy/colt
License: MIT
Keywords: config,python,object
Author: altescy
Author-email: altescy@fastmail.com
Requires-Python: >=3.8,<4.0
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Project-URL: Issues, https://github.com/altescy/colt/issues
Description-Content-Type: text/markdown

colt
===

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## Quick Links

- [Installation](#Installation)
- [Basic Examples](#Examples)
- [kaggle Titanic Example](https://github.com/altescy/colt/tree/master/examples/titanic)

## Introduction

`colt` is a configuration utility for Python objects.
`colt` constructs Python objects from a configuration dict which is convertable into JSON.
(Inspired by [AllenNLP](https://github.com/allenai/allennlp))


## Installation

```
pip install colt
```

## Examples

#### Basic Usage

```python
import typing as tp
import colt

@colt.register("foo")
class Foo:
    def __init__(self, message: str) -> None:
        self.message = message

@colt.register("bar")
class Bar:
    def __init__(self, foos: tp.List[Foo]) -> None:
        self.foos = foos

if __name__ == "__main__":
    config = {
        "@type": "bar",  # specify type name with `@type`
        "foos": [
            {"message": "hello"},  # type of this is inferred from type-hint
            {"message": "world"},
        ]
    }

    bar = colt.build(config)

    assert isinstance(bar, Bar)

    print(" ".join(foo.message for foo in bar.foos))
        # => "hello world"
```

#### `scikit-learn` Configuration

```python
import colt

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

if __name__ == "__main__":
    config = {
        # import types automatically if type name is not registerd
        "@type": "sklearn.ensemble.VotingClassifier",
        "estimators": [
            ("rfc", { "@type": "sklearn.ensemble.RandomForestClassifier",
                      "n_estimators": 10 }),
            ("svc", { "@type": "sklearn.svm.SVC",
                      "gamma": "scale" }),
        ]
    }

    X, y = load_iris(return_X_y=True)
    X_train, X_valid, y_train, y_valid = train_test_split(X, y)

    model = colt.build(config)
    model.fit(X_train, y_train)

    valid_accuracy = model.score(X_valid, y_valid)
    print(f"valid_accuracy: {valid_accuracy}")
```


### `Registrable` Class

By using the `Registrable` class, you can devide namespace into each class.
In a following example, `Foo` and `Bar` have different namespaces.

```python
import colt

class Foo(colt.Registrable):
    pass

class Bar(colt.Registrable):
    pass

@Foo.register("baz")
class FooBaz(Foo):
    pass

@Bar.register("baz")
class BarBaz(Bar):
    pass

@colt.register("my_class")
class MyClass:
    def __init__(self, foo: Foo, bar: Bar):
        self.foo = foo
        self.bar = bar

if __name__ == "__main__":
    config = {
        "@type": "my_class",
        "foo": {"@type": "baz"},
        "bar": {"@type": "baz"}
    }

    obj = colt.build(config)

    assert isinstance(obj.foo, FooBaz)
    assert isinstance(obj.bar, BarBaz)
```

