tgsmith61591 / skoot

A package for data science practitioners. This library implements a number of helpful, common data transformations with a scikit-learn friendly interface in an effort to expedite the modeling process.

skoot

Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas. Its objective is to expedite data munging and pre-processing tasks that can tend to take up so much of data science practitioners' time. See the documentation for more info.

Note that skoot is the preferred alternative to the now deprecated skutil library

Two minutes to model-readiness

Real world data is nasty. Most data scientists spend the majority of their time tackling data cleansing tasks. With skoot, we can automate away so much of the bespoke hacking solutions that consume data scientists' time.

In this example, we'll examine a common dataset (the adult dataset from the UCI machine learning repo) that requires significant pre-processing.

from skoot.datasets import load_adult_df
from skoot.feature_selection import FeatureFilter
from skoot.decomposition import SelectivePCA
from skoot.preprocessing import DummyEncoder
from skoot.utils.dataframe import get_numeric_columns
from skoot.utils.dataframe import get_categorical_columns
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# load the dataset with the skoot-native loader & split it
adult = load_adult_df(tgt_name="target")
y = adult.pop("target")
X_train, X_test, y_train, y_test = train_test_split(
    adult, y, random_state=42, test_size=0.2)
    
# get numeric and categorical feature names
num_cols = get_numeric_columns(X_train).columns
obj_cols = get_categorical_columns(X_train).columns

# remove the education-num from the num_cols since we're going to remove it
num_cols = num_cols[~(num_cols == "education-num")]
    
# build a pipeline
pipe = Pipeline([
    # drop out the ordinal level that's otherwise equal to "education"
    ("dropper", FeatureFilter(cols=["education-num"])),
    
    # decompose the numeric features with PCA
    ("pca", SelectivePCA(cols=num_cols)),
    
    # dummy encode the categorical features
    ("dummy", DummyEncoder(cols=obj_cols, handle_unknown="ignore")),
    
    # and a simple classifier class
    ("clf", RandomForestClassifier(n_estimators=100, random_state=42))
])

pipe.fit(X_train, y_train)

# produce predictions
preds = pipe.predict(X_test)
print("Test accuracy: %.3f" % accuracy_score(y_test, preds))

For more tutorials, check out the documentation.