"""Force plots."""
import numpy as np
import shap
from shap import initjs
from evalml.model_understanding.prediction_explanations import explain_predictions
from evalml.utils import jupyter_check
[docs]def graph_force_plot(pipeline, rows_to_explain, training_data, y, matplotlib=False):
"""Function to generate force plots for the desired rows of the training data.
Args:
pipeline (PipelineBase): The pipeline to generate the force plot for.
rows_to_explain (list[int]): A list of the indices indicating which of the rows of
the training_data to explain.
training_data (pandas.DataFrame): The data used to train the pipeline.
y (pandas.Series): The target data for the pipeline.
matplotlib (bool): flag to display the force plot using matplotlib (outside of jupyter)
Defaults to False.
Returns:
list[dict[shap.AdditiveForceVisualizer]]: The same as force_plot(), but with an additional
key in each dictionary for the plot.
"""
def gen_force_plot(shap_values, training_data, expected_value, matplotlib):
"""Helper function to generate a single force plot."""
shap_plot = shap.force_plot(
expected_value,
np.array(shap_values),
training_data,
matplotlib=matplotlib,
)
return shap_plot
if jupyter_check():
initjs()
shap_plots = force_plot(pipeline, rows_to_explain, training_data, y)
for ix, row in enumerate(shap_plots):
row_id = rows_to_explain[ix]
for cls in row:
cls_dict = row[cls]
cls_dict["plot"] = gen_force_plot(
cls_dict["shap_values"],
training_data[cls_dict["feature_names"]].iloc[row_id],
cls_dict["expected_value"],
matplotlib=matplotlib,
)
return shap_plots
[docs]def force_plot(pipeline, rows_to_explain, training_data, y):
"""Function to generate the data required to build a force plot.
Args:
pipeline (PipelineBase): The pipeline to generate the force plot for.
rows_to_explain (list[int]): A list of the indices of the training_data to explain.
training_data (pandas.DataFrame): The data used to train the pipeline.
y (pandas.Series): The target data.
Returns:
list[dict]: list of dictionaries where each dict contains force plot data. Each dictionary
entry represents the explanations for a single row.
For single row binary force plots:
[{'malignant': {'expected_value': 0.37,
'feature_names': ['worst concave points', 'worst perimeter', 'worst radius'],
'shap_values': [0.09, 0.09, 0.08],
'plot': AdditiveForceVisualizer}]
For two row binary force plots:
[{'malignant': {'expected_value': 0.37,
'feature_names': ['worst concave points', 'worst perimeter', 'worst radius'],
'shap_values': [0.09, 0.09, 0.08],
'plot': AdditiveForceVisualizer},
{'malignant': {'expected_value': 0.29,
'feature_names': ['worst concave points', 'worst perimeter', 'worst radius'],
'shap_values': [0.05, 0.03, 0.02],
'plot': AdditiveForceVisualizer}]
Raises:
TypeError: If rows_to_explain is not a list.
TypeError: If all values in rows_to_explain aren't integers.
"""
if not isinstance(rows_to_explain, list):
raise TypeError(
"rows_to_explain should be provided as a list of row index integers!",
)
if not all([isinstance(x, int) for x in rows_to_explain]):
raise TypeError("rows_to_explain should only contain integers!")
explanations = []
prediction_explanations = explain_predictions(
pipeline,
training_data,
y,
rows_to_explain,
top_k_features=len(training_data.columns),
include_explainer_values=True,
output_format="dict",
)
row_explanations = prediction_explanations["explanations"]
for row_explanation in row_explanations:
row_exp = row_explanation["explanations"]
row_exp_dict = {}
for cls_exp in row_exp:
cls = (
cls_exp["class_name"]
if cls_exp["class_name"] is not None
else "regression"
)
expected_value = cls_exp["expected_value"]
feature_names = cls_exp["feature_names"]
shap_values = cls_exp["quantitative_explanation"]
row_exp_dict[cls] = {
"expected_value": expected_value,
"feature_names": feature_names,
"shap_values": shap_values,
}
explanations.append(row_exp_dict)
return explanations
