Peer into the future of a data science project
Foreshadow is an automatic pipeline generation tool that makes creating, iterating, and evaluating machine learning pipelines a fast and intuitive experience allowing data scientists to spend more time on data science and less time on code.
$ pip install foreshadow
Read the documentation to set up the project from source.
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.
Great, you now have a working Foreshaow installation! Keep reading to learn how to export, modify and construct pipelines of your own.
- Automatic Feature Engineering
- Automatic Model Selection
- Rapid Pipeline Development / Iteration
- Automatic Parameter Optimization
- Ease of Extensibility
- Scikit-Learn Compatible
Foreshadow supports python 3.6+
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