Data Science Utils

A toolkit for day-to-day DS tasks such as using custom transformers or estimators.

Found a bug or have a feature request? Open an issue!

Usage

First, import the library:

import pier_ds_utils as ds

Transformers

CustomDiscreteCategorizer

discrete_categorizer = ds.transformer.CustomDiscreteCategorizer(
        column="input_col_name",
        categories=[["my_category_value_1", "my_category_value_2"], ["my_category_value_3"]],
        labels=["label_1", "label_2"],
        default_value="a-default-value",
        output_column="output_col_name",
    )

CustomIntervalCategorizer

interval_categorizer = ds.transformer.CustomIntervalCategorizer(
    column="price",
    intervals=[(6700000, sys.maxsize)],
    labels=["gt_67k"],
    default_value="lt_67k",
    output_column="cat_price",
)

CustomIntervalCategorizerByCategory

interval_categorizer_by_category = ds.transformer.CustomIntervalCategorizerByCategory(
    category_column: "category",
    interval_categorizers: {
        "category_1": CustomIntervalCategorizer(
            column="price",
            intervals=[(6700000, sys.maxsize)],
            labels=["gt_67k"],
            default_value="lt_67k",
            output_column="cat_price",
        ),
        "category_2": CustomIntervalCategorizer(
            column="price",
            intervals=[(0, 1000000)],
            labels=["lt_1M"],
            default_value="gt_1M",
            output_column="cat_price",
        ),
    },
    output_column = "cat_price",
)

LogTransformer

log_transformer = ds.transformer.LogTransformer()

BoundariesTransformer

boundaries_transformer = ds.transformer.BoundariesTransformer(
    lower_bound=0,
    upper_bound=1000000,
)

Estimators

glm_wrapper = ds.estimator.GLMWrapper(...)
predict_proba_selector = ds.estimator.PredictProbaSelector(...)

Predictors

predictor = ds.predictor.StaticGLM(...)

Example usage:

from pier_ds_utils.predictor import StaticGLM
import pandas as pd

glm = StaticGLM(
    coefficients_map={"feature1": 0.5, "feature2": 1.5},  # required
    constant=2.0,  # optional
    os_factor=1.0,  # optional
)

df = pd.DataFrame({"feature1": [1, 2], "feature2": [3, 4]})

# The predict is equivalent to:
# y = (0.5 * feature1 + 1.5 * feature2 + constant) * os_factor
print(glm.predict(df))  # Output: [7. 9.]

Installation

pip install pier-ds-utils

# or

poetry add pier-ds-utils

For a specific version:

pip install pier-ds-utils@_version_

# or

poetry add pier-ds-utils@_version_

Contributing

Contributions are welcome! Please read the contributing guidelines first.