Metadata-Version: 2.0
Name: destimator
Version: 0.0.4.dev0
Summary: A metadata-saving proxy for scikit-learn etimators.
Home-page: https://github.com/rainforestapp/destimator
Author: Maciej Gryka
Author-email: maciej@rainforestqa.com
License: MIT
Keywords: scikit-learn,machine-learning,data,science
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: requests (>=2.8.1)
Requires-Dist: numpy (>=1.10.1)
Requires-Dist: scipy (>=0.16.0)
Requires-Dist: scikit-learn (>=0.17)
Provides-Extra: dev
Requires-Dist: check-manifesttwine; extra == 'dev'
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Requires-Dist: flake8; extra == 'test'
Requires-Dist: pytest-cov; extra == 'test'
Requires-Dist: tox; extra == 'test'

destimator
==========

destimator makes it easy to store trained `scikit-learn` estimators together with their metadata (training data, package versions, performance numbers etc.). This makes it much safer to store already-trained classifiers/regressors and allows for better reproducibility (see [this talk](https://www.youtube.com/watch?v=7KnfGDajDQw) by [Alex Gaynor](https://alexgaynor.net/) for some rationale).

Specifically, the `DescribedEstimator` class proxies most calls to the original `Estimator` it is wrapping, but also contains the following information:
- training and test (validation) data (`features_train`, `labels_train`, `features_test`, `labels_test`)
- creation date (`created_at`)
- feature names (`feature_names`)
- performance numbers on the test set (`precision`, `recall`, `fscore`, `support` via [sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html))
- distribution info (`distribution_info`; python distribution and versions of all installed packages)
- VCS hash (`vcs_hash`, if used inside a git repository, otherwise and empty string).

An instantiated `DescribedEstimator` can be easily serialized using the `.save()` method and deserialized using either `.from_file()` or `.from_url()`. Did you ever want to store your models in S3? Now it's easy!

`DescribedEstimator` can be used as follows:

```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_recall_fscore_support

from destimator import DescribedEstimator


# get some data
iris = load_iris()
features = iris.data
labels = iris.target
features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.1)

# train an estimator as usual (in this case a RandomForestClassifier)
clf = RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=10, random_state=0)
clf.fit(features_train, labels_train)

# wrap the estimator in the DescribedEstimator class giving it all the training and test (validation) data
dclf = DescribedEstimator(
    clf,
    features_train,
    labels_train,
    features_test,
    labels_test,
    iris.feature_names
)
```

Now you can use the classifier as usual:
```python
print(dclf.predict(features_test))
> [2 1 2 2 0 1 0 2 2 1 2 0 2 1 2]
```

and you can also access a bunch of other properties, such as the training data you supplied:
```python
print(dclf.feature_names)
> ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

print(dclf.features_test)
> [[ 6.3  2.8  5.1  1.5]
   [ 5.6  3.   4.5  1.5]
   [ 6.7  3.1  5.6  2.4]
   [ 6.   2.7  5.1  1.6]
   [ 4.9  3.1  1.5  0.1]
   [ 6.2  2.2  4.5  1.5]
   [ 4.7  3.2  1.6  0.2]
   [ 6.9  3.1  5.1  2.3]
   [ 7.7  2.6  6.9  2.3]
   [ 5.8  2.6  4.   1.2]
   [ 7.2  3.   5.8  1.6]
   [ 5.4  3.7  1.5  0.2]
   [ 7.2  3.2  6.   1.8]
   [ 6.3  3.3  4.7  1.6]
   [ 6.8  3.2  5.9  2.3]]

print(dclf.labels_test)
> [2 1 2 1 0 1 0 2 2 1 2 0 2 1 2]
```
the performance numbers:
```python
print('precision: %s' % (dclf.precision))
> precision: [1.0, 1.0, 0.875]

print('recall:    %s' % (dclf.recall))
> recall:    [1.0, 0.8, 1.0]

print('fscore:    %s' % (dclf.fscore))
> fscore:    [1.0, 0.888888888888889, 0.9333333333333333]

print('support:   %s' % (dclf.support))
> support:   [3, 5, 7]
```

or information about the Python distribution used for training:
```python
from pprint import pprint
pprint(dclf.distribution_info)

> {'packages': ['appnope==0.1.0',
                'decorator==4.0.4',
                'destimator==0.0.0.dev3',
                'gnureadline==6.3.3',
                'ipykernel==4.2.1',
                'ipython-genutils==0.1.0',
                'ipython==4.0.1',
                'ipywidgets==4.1.1',
                'jinja2==2.8',
                'jsonschema==2.5.1',
                'jupyter-client==4.1.1',
                'jupyter-console==4.0.3',
                'jupyter-core==4.0.6',
                'jupyter==1.0.0',
                'markupsafe==0.23',
                'mistune==0.7.1',
                'nbconvert==4.1.0',
                'nbformat==4.0.1',
                'notebook==4.0.6',
                'numpy==1.10.1',
                'path.py==8.1.2',
                'pexpect==4.0.1',
                'pickleshare==0.5',
                'pip==7.1.2',
                'ptyprocess==0.5',
                'pygments==2.0.2',
                'pyzmq==15.1.0',
                'qtconsole==4.1.1',
                'requests==2.8.1',
                'scikit-learn==0.17',
                'scipy==0.16.1',
                'setuptools==18.2',
                'simplegeneric==0.8.1',
                'terminado==0.5',
                'tornado==4.3',
                'traitlets==4.0.0',
                'wheel==0.24.0'],
   'python': '3.5.0 (default, Sep 14 2015, 02:37:27) \n'
             '[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]'}
```


