"""Human Readable Pipeline Explanations."""
import pandas as pd
import evalml
from evalml.model_family import ModelFamily
from evalml.model_understanding import calculate_permutation_importance
from evalml.utils import get_logger, infer_feature_types
[docs]def readable_explanation(
pipeline,
X=None,
y=None,
importance_method="permutation",
max_features=5,
min_importance_threshold=0.05,
objective="auto",
):
"""Outputs a human-readable explanation of trained pipeline behavior.
Args:
pipeline (PipelineBase): The pipeline to explain.
X (pd.DataFrame): If importance_method is permutation, the holdout X data to compute importance with. Ignored otherwise.
y (pd.Series): The holdout y data, used to obtain the name of the target class. If importance_method is permutation, used to compute importance with.
importance_method (str): The method of determining feature importance. One of ["permutation", "feature"]. Defaults to "permutation".
max_features (int): The maximum number of influential features to include in an explanation. This does not affect the number of detrimental features reported. Defaults to 5.
min_importance_threshold (float): The minimum percent of total importance a single feature can have to be considered important. Defaults to 0.05.
objective (str, ObjectiveBase): If importance_method is permutation, the objective to compute importance with. Ignored otherwise, defaults to "auto".
Raises:
ValueError: if any arguments passed in are invalid or the pipeline is not fitted.
"""
logger = get_logger(f"{__name__}.explain")
if not pipeline._is_fitted:
raise ValueError(
"Pipelines must be fitted in order to run feature explanations.",
)
if min_importance_threshold >= 1 or min_importance_threshold < 0:
raise ValueError(
f"The minimum importance threshold must be a percentage value in the range [0, 1), not {min_importance_threshold}.",
)
if importance_method == "permutation":
if objective == "auto":
objective = evalml.automl.get_default_primary_search_objective(
pipeline.problem_type,
)
if X is None or y is None:
raise ValueError(
"X and y are required parameters for explaining pipelines with permutation importance.",
)
X = infer_feature_types(X)
y = infer_feature_types(y)
imp_df = calculate_permutation_importance(pipeline, X, y, objective)
elif importance_method == "feature":
objective = None
imp_df = pipeline.feature_importance
else:
raise ValueError(f"Unknown importance method {importance_method}.")
linear_importance = False
if (
pipeline.estimator.model_family == ModelFamily.LINEAR_MODEL
and importance_method == "feature"
):
linear_importance = True
(
most_important_features,
somewhat_important_features,
detrimental_features,
) = get_influential_features(
imp_df,
max_features,
min_importance_threshold,
linear_importance,
)
target = y if y is None else y.name
explanation = _fill_template(
pipeline.estimator,
target,
objective,
most_important_features,
somewhat_important_features,
detrimental_features,
)
logger.info(explanation)
[docs]def get_influential_features(
imp_df,
max_features=5,
min_importance_threshold=0.05,
linear_importance=False,
):
"""Finds the most influential features as well as any detrimental features from a dataframe of feature importances.
Args:
imp_df (pd.DataFrame): DataFrame containing feature names and associated importances.
max_features (int): The maximum number of features to include in an explanation. Defaults to 5.
min_importance_threshold (float): The minimum percent of total importance a single feature can have to be considered important. Defaults to 0.05.
linear_importance (bool): When True, negative feature importances are not considered detrimental. Defaults to False.
Returns:
(list, list, list): Lists of feature names corresponding to heavily influential, somewhat influential, and detrimental features, respectively.
"""
heavy_importance_threshold = max(0.2, min_importance_threshold + 0.1)
# Separate negative and positive features, if situation calls
if linear_importance:
pos_imp_df = imp_df
pos_imp_df["importance"] = abs(pos_imp_df["importance"])
neg_imp_df = pd.DataFrame({"feature": [], "importance": []})
else:
neg_imp_df = imp_df[imp_df["importance"] < 0]
pos_imp_df = imp_df[imp_df["importance"] >= 0]
# Normalize the positive features to sum to 1
pos_imp_df["importance"] = pos_imp_df["importance"] / sum(pos_imp_df["importance"])
num_feats = min(len(pos_imp_df), max_features)
imp_features = pos_imp_df[:num_feats]
heavy_importance = imp_features[
imp_features["importance"] >= heavy_importance_threshold
]
somewhat_importance = imp_features[
imp_features["importance"] < heavy_importance_threshold
]
return (
list(heavy_importance["feature"]),
list(
somewhat_importance[
somewhat_importance["importance"] >= min_importance_threshold
]["feature"],
),
list(neg_imp_df["feature"]),
)
def _fill_template(
estimator,
target,
objective,
most_important,
somewhat_important,
detrimental_feats,
):
# Get the objective to a printable string
if objective is not None:
if isinstance(objective, evalml.objectives.ObjectiveBase):
objective = objective.name
if objective != "R2": # Remove any title case if necessary
objective = objective.lower()
# Beginning of description
objective_str = f" as measured by {objective}" if objective is not None else ""
beginning = (
f"{estimator}: The output{objective_str}"
if target is None
else f"{estimator}: The prediction of {target}{objective_str}"
)
def enumerate_features(feature_list):
text = "" if len(feature_list) == 2 else ","
for i in range(1, len(feature_list)):
if i == len(feature_list) - 1:
text = text + f" and {feature_list[i]}"
else:
text = text + f" {feature_list[i]},"
return text
# Heavily influential description
heavy = ""
if len(most_important) > 0:
heavy = f" is heavily influenced by {most_important[0]}"
if len(most_important) > 1:
heavy = heavy + enumerate_features(most_important)
if len(somewhat_important) > 0:
heavy = heavy + ", and"
# Somewhat influential description
somewhat = ""
if len(somewhat_important) > 0:
somewhat = f" is somewhat influenced by {somewhat_important[0]}"
if len(somewhat_important) > 1:
somewhat = somewhat + enumerate_features(somewhat_important)
# Neither!
neither = "."
if not (len(heavy) or len(somewhat)):
neither = " is not strongly influenced by any single feature. Lower the `min_importance_threshold` to see more."
# Detrimental Description
detrimental = ""
if len(detrimental_feats) > 0:
if len(detrimental_feats) == 1:
detrimental = f"\nThe feature {detrimental_feats[0]}"
tag = "this feature."
else:
detrimental = f"\nThe features {detrimental_feats[0]}"
detrimental = detrimental + enumerate_features(detrimental_feats)
tag = "these features."
detrimental = (
detrimental
+ " detracted from model performance. We suggest removing "
+ tag
)
return beginning + heavy + somewhat + neither + detrimental
