stickbugml 1.0.4

Installation

Install simply via pip (Python 3):

$ pip install stickbugml

Dependencies: - Python 3 - sklearn - pandas - numpy

Example

Note: there is also a great example for use in Jupyter Notebooks

First, import this library:

import stickbugml
from stickbugml.decorators import dataset, feature, model

Load your dataset:

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:

@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:

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):

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:

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:

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:

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