Metadata-Version: 1.2
Name: stickbugml
Version: 1.0.3
Summary: A framework to organize the process of designing supervised machine learning systems
Home-page: https://github.com/aaronduino/stick-bug-ml
Author: Aaron Janse
Author-email: gitduino@gmail.com
License: Apache 2.0
Description: # stick-bug-ml
        
        A framework to ease the burden of organizing code of a supervised machine learning system.
        
        It provides decorators that manage data & pass it between common steps in building a machine learning system, such as:
        - loading the dataset
        - preprocessing
        - feature generation
        - model definition
        
        While doing this, it keeps the global namespace free of clutter such as that from an endless chain of features and models.
        
        In addition, it makes it easy to put new, real life, data through the exact same process that training data goes through.
        
        ## Installation
        Install simply via `pip` (Python 3):
        
        ```bash
        $ pip install stickbugml
        ```
        Dependencies:
        - Python 3
        - sklearn
        - pandas
        - numpy
        
        ## Example
        Note: there is also a great [example for use in Jupyter Notebooks](demo.ipynb)
        
        First, import this library:
        
        ```python
        import stickbugml
        from stickbugml.decorators import dataset, feature, model
        ```
        
        Load your dataset:
        
        ```python
        import seaborn.apionly as sns
        import pandas as pd
        
        @dataset(train_valid_test=(0.6, 0.2, 0.2)) # define your train/test/validation data splits
        def raw_dataset():
            titanic_dataset = sns.load_dataset('titanic')
        
            # Drop NaN rows for simplicity
            titanic_dataset.dropna(inplace=True)
        
            # Extract X and y
            X = titanic_dataset.drop('survived', axis=1)
            y = titanic_dataset['survived']
            return X, y
        
        print(raw_dataset.head()) # yes, this does work! raw_dataset is now a pandas DataFrame
        ```
        
        (Optionally) do some pre-processing:
        
        ```python
        @preprocess
        def preprocessed_dataset(X):
            # Encode categorical columns
            categorical_column_names = [
                    'sex', 'embarked', 'class',
                    'who', 'adult_male', 'deck',
                    'embark_town', 'alive', 'alone']
        
            X = pd.get_dummies(X,
                               columns=categorical_column_names,
                               prefix=categorical_column_names)
        
            return X
        
        print(preprocessed_dataset.head()) # See the first code block for explaination
        ```
        
        Generate some features:
        
        ```python
        from sklearn import decomposition
        import numpy as np
        
        @feature('pca')
        def pca_feature(X):
            pca = decomposition.PCA(n_components=3)
            pca.fit(X)
            pca_out = pca.transform(X)
        
            pca_out = np.transpose(pca_out, (1, 0))
            return pd.DataFrame(pca_out)
        
        # let's preview
        print(pca_feature.head()) # See the first code block for explaination
        
        # you can add more features, btw
        ```
        
        And define your (machine learning) model(s):
        
        ```python
        import xgboost as xgb
        
        @model('xgboost')
        def xgboost_model():
            def define(num_columns):
                return None # xgboost models aren't pre-defined
        
        
            def train(model, params, train, validation):
                params['objective'] = 'binary:logistic' # Static parameters can be defined here
                params['eval_metric'] = 'logloss'
        
                d_train = xgb.DMatrix(train['X'], label=train['y'])
                d_valid = xgb.DMatrix(validation['X'], label=validation['y'])
        
                watchlist = [(d_train, 'train'), (d_valid, 'valid')]
        
                trained_model = xgb.train(params, d_train, 2000, watchlist, early_stopping_rounds=50, verbose_eval=10)
        
                return trained_model
        
            def predict(model, X):
                return model.predict(xgb.DMatrix(X))
        
            return define, train, predict
        ```
        
        Now you can train your model, trying out different parameters if you want:
        
        ```python
        stickbugml.train('xgboost', {
            'max_depth': 7,
            'eta': 0.01
        })
        ```
        
        The library keeps the test data's ground truth values locked away so your models won't train on it.
        After you train your model, have the framework evaluate it for you:
        
        ```python
        logloss_score = stickbugml.evaluate('xgboost')
        print(logloss_score)
        ```
        
        You can add more models and features if so desired.
        
        Since this library is built with reality in mind, you can easily get predictions for new/real-life data:
        
        ```python
        raw_X = pd.read_csv('2018_titanic_manifesto.csv') # It will probably sink, but we don't know who will survive
        processed_X = stickbugml.process(raw_X) # Process the data
        del raw_X # Gotta keep that namespace clean, right?
        
        y = stickbugml.predict('xgboost', processed_X) # Make predictions
        
        print(y)
        ```
        
        ## Contributing & Feedback
        If you have any problems, or would like a new feature, submit an Issue.
        
        If you want to help out, feel free to submit a Pull Request.
        
        ## License
        This project uses the Apache 2.0 License
        
Keywords: stick bug,ml,machine learning,ai,artificial intelligence,framework,organization,organize
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3
