Contents
Shparkley is a PySpark implementation of Shapley values which uses a monte-carlo approximation algorithm.
Given a dataset and machine learning model, Shparkley can compute Shapley values for all features for a feature vector. Shparkley also handles training weights and is model-agnostic.
pip install shparkley
You must have Apache Spark installed on your machine/cluster.
from typing import List
from sklearn.base import ClassifierMixin
from affirm.model_interpretation.shparkley.estimator_interface import OrderedSet, ShparkleyModel
from affirm.model_interpretation.shparkley.spark_shapley import compute_shapley_for_sample
class MyShparkleyModel(ShparkleyModel):
"""
You need to wrap your model with this interface (by subclassing ShparkleyModel)
"""
def __init__(self, model: ClassifierMixin, required_features: OrderedSet):
self._model = model
self._required_features = required_features
def predict(self, feature_matrix: List[OrderedDict]) -> List[float]:
"""
Generates one prediction per row, taking in a list of ordered dictionaries (one per row).
"""
pd_df = pd.DataFrame.from_dict(feature_matrix)
preds = self._model.predict_proba(pd_df)[:, 1]
return preds
def _get_required_features(self) -> OrderedSet:
"""
An ordered set of feature column names
"""
return self._required_features
row = dataset.filter(dataset.row_id == 'xxxx').rdd.first()
shparkley_wrapped_model = MyShparkleyModel(my_model)
# You need to sample your dataset based on convergence criteria.
# More samples results in more accurate shapley values.
# Repartitioning and caching the sampled dataframe will speed up computation.
sampled_df = training_df.sample(0.1, True).repartition(75).cache()
shapley_scores_by_feature = compute_shapley_for_sample(
df=sampled_df,
model=shparkley_wrapped_model,
row_to_investigate=row,
weight_col_name='training_weight_column_name'
)