Metadata-Version: 2.1
Name: DaMa ML
Version: 1.0a1
Summary: A framework for data management and is used to do data science and machine learning's pipelines
Home-page: https://github.com/elaeon/dama_ml
Author: Alejandro Martínez
Author-email: mara80@gmail.com
License: Apache
Description: 
        .. image:: https://travis-ci.org/elaeon/dama_ml.svg?branch=master
            :target: https://travis-ci.org/elaeon/dama_ml
        
        .. image:: https://api.codacy.com/project/badge/Grade/0ab998e72f4f4e31b3dc7b3c9921374a
            :target: https://www.codacy.com/app/elaeon/dama_ml?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=elaeon/dama_ml&amp;utm_campaign=Badge_Grade
        
        
        Overview
        =====================================
        
        Dama ML is a framework for data management and is used to do data science and machine learning's pipelines, also dama-ml try to unify diverse data sources like csv, sql db, hdf5, zarr, etc, and also unify machine learning frameworks (sklearn, Keras, LigthGBM, etc) with a simplify interface.
        
        For more detail read the docs_.
        
        .. _docs: https://elaeon.github.io/dama_ml/
        
        
        Warning
        ---------------
            Although, the API is stable this work is in alpha steps and there are methods that have limited functionality or aren't implemented.
        
        
        Installation
        =====================
        
        .. code-block:: bash
        
            git clone https://github.com/elaeon/dama_ml.git
            pip install dama_ml/
        
        or
        
        .. code-block:: bash
        
            pip install DaMa-ML
        
        
        You can install the python dependences with pip, but we strongly recommend install the dependences with conda and conda forge.
        
        .. code-block:: bash
        
            conda config --add channels conda-forge
            conda create -n new_environment --file dama_ml/requirements.txt
            conda activate new_environment
            pip install DaMa-ML
        
        
        Quick start
        ==================
        
        Configure the data paths where all data will be saved. This can be done with help of dama_ml cli tools.
        
        .. code-block:: python
        
            $ dama-cli config --edit
        
        This will display a nano editor where you can edit data_path, models_path, code_path, class_path and metadata_path.
        
        * data_path is where all datasets are saved.
        * models_path is where all files from your models are saved.
        * code_path is the repository of code. (In development)
        * metadata_path is where the metadata database is saved.
        
        Building a dataset
        
        .. code-block:: python
        
            from dama.data.ds import Data
            from dama.drivers.core import Zarr, HDF5
            import numpy as np
        
            array_0 = np.random.rand(100, 1)
            array_1 = np.random.rand(100,)
            array_2 = np.random.rand(100, 3)
            array_3 = np.random.rand(100, 6)
            array_4 = (np.random.rand(100)*100).astype(int)
            array_5 = np.random.rand(100).astype(str)
            with Data(name=name, driver=Zarr(mode="w")) as data:
                data.from_data({"x": array_0, "y": array_1, "z": array_2, "a": array_3, "b": array_4, "c": array_5})
        
        
        We can use a regression model, in this case we use RandomForestRegressor
        
        .. code-block:: python
        
            from dama.reg.extended.w_sklearn import RandomForestRegressor
            from dama.utils.model_selection import CV
        
            data.driver.mode = "r"  # we changed mode "w" to "r" to not overwrite the data previously saved
            with data, Data(name="test_from_hash", driver=HDF5(mode="w")) as ds:
                cv = CV(group_data="x", group_target="y", train_size=.7, valid_size=.1)  # cross validation class
                stc = cv.apply(data)
                ds.from_data(stc, from_ds_hash=data.hash)
                reg = RandomForestRegressor()
                model_params = dict(n_estimators=25, min_samples_split=2)
                reg.train(ds, num_steps=1, data_train_group="train_x", target_train_group='train_y',
                          data_test_group="test_x", target_test_group='test_y', model_params=model_params,
                          data_validation_group="validation_x", target_validation_group="validation_y")
                reg.save(name="test_model", model_version="1")
        
        Using RandomForestRegressor to do predictions is like this:
        
        .. code-block:: python
        
            with RandomForestRegressor.load(model_name="test_model", model_version="1") as reg:
                for pred in reg.predict(data):
                    prediction = pred.batch.to_ndarray()
        
        
        CLI
        ==============
        dama-ml has a CLI where you can view your datasets and models.
        For example
        
        .. code-block:: bash
        
            dama-cli datasets
        
        Return a table of datasets previously saved.
        
        .. code-block:: python
        
            Using metadata ..../metadata/metadata.sqlite3
            Total 2 / 2
        
            hash                    name            driver    group name    size       num groups  datetime UTC
            ---------------------  --------------  --------  ------------  --------  ------------  -------------------
            sha1.3124d5f16eb0e...  test_from_hash  HDF5      s/n           9.12 KB              6  2019-02-27 19:39:00
            sha1.e832f56e33491...  reg0            Zarr      s/n           23.68 KB             6  2019-02-27 19:39:00
        
        
        .. code-block:: bash
        
            dama-cli models
        
        .. code-block:: bash
        
            Total 3 / 3
            from_ds                       name      group_name    model                                                version     score name        score
            -------------------------  ----------  ------------  -------------------------------------------------  ---------  ---------------  ----------
            sha1.d8ff5a342d2d7229...  test_model  s/n           dama.reg.extended.w_sklearn.RandomForestRegressor          1  mse               0.162365
            sha1.d8ff5a342d2d7229...  test_model  s/n           dama.reg.extended.w_sklearn.RandomForestRegressor          1  msle              0.0741331
            sha1.d8ff5a342d2d7229...  test_model  s/n           dama.reg.extended.w_sklearn.RandomForestRegressor          1  gini_normalized  -0.307407
        
        
        You can use "--help" for view more options.
        
Keywords: data python management machine learning science
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Provides-Extra: docs
Provides-Extra: test
