foreshadow 0.2.1

Installing Foreshadow

$ pip install foreshadow

Read the documentation to set up the project from source.

Getting Started

To get started with foreshadow, install the package using pip install. This will also install the dependencies. Now create a simple python script that uses all the defaults with Foreshadow.

First import foreshadow

import foreshadow as fs

Also import sklearn, pandas, and numpy for the demo

import pandas as pd

from sklearn.datasets import boston_housing
from sklearn.model_selection import train_test_split

Now load in the boston housing dataset from sklearn into pandas dataframes. This is a common dataset for testing machine learning models and comes built in to scikit-learn.

boston = load_boston()
bostonX_df = pd.DataFrame(boston.data, columns=boston.feature_names)
bostony_df = pd.DataFrame(boston.target, columns=['target'])

Next, exactly as if working with an sklearn estimator, perform a train test split on the data and pass the train data into the fit function of a new Foreshadow object

X_train, X_test, y_train, y_test = train_test_split(bostonX_df,
   bostony_df, test_size=0.2)
shadow = fs.Foreshadow()
shadow.fit(X_train, y_train)

Now fs is a fit Foreshadow object for which all feature engineering has been performed and the estimator has been trained and optimized. It is now possible to utilize this exactly as a fit sklearn estimator to make predictions.

shadow.score(X_test, y_test)

Great, you now have a working Foreshaow installation! Keep reading to learn how to export, modify and construct pipelines of your own.

Key Features

• Automatic Feature Engineering

• Automatic Model Selection

• Rapid Pipeline Development / Iteration

• Automatic Parameter Optimization

• Ease of Extensibility

• Scikit-Learn Compatible

Foreshadow supports python 3.6+

Documentation

Read the docs!