wehub-resource-sync
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
import numpy as np
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import pandas as pd
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from .._explainer import Explainer
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try:
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import lime
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import lime.lime_tabular
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except ImportError:
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pass
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class LimeTabular(Explainer):
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"""Simply wrap of lime.lime_tabular.LimeTabularExplainer into the common shap interface.
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Parameters
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----------
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model : function or iml.Model
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User supplied function that takes a matrix of samples (# samples x # features) and
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computes the output of the model for those samples. The output can be a vector
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(# samples) or a matrix (# samples x # model outputs).
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data : numpy.array
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The background dataset.
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mode : "classification" or "regression"
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Control the mode of LIME tabular.
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"""
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def __init__(self, model, data, mode="classification"):
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self.model = model
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if mode not in ["classification", "regression"]:
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emsg = f"Invalid mode {mode!r}, must be one of 'classification' or 'regression'"
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raise ValueError(emsg)
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self.mode = mode
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if isinstance(data, pd.DataFrame):
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data = data.values
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self.data = data
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self.explainer = lime.lime_tabular.LimeTabularExplainer(data, mode=mode)
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out = self.model(data[0:1])
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if len(out.shape) == 1:
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self.out_dim = 1
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self.flat_out = True
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if mode == "classification":
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def pred(X): # assume that 1d outputs are probabilities
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preds = self.model(X).reshape(-1, 1)
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p0 = 1 - preds
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return np.hstack((p0, preds))
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self.model = pred
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else:
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self.out_dim = self.model(data[0:1]).shape[1]
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self.flat_out = False
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def attributions(self, X, nsamples=5000, num_features=None):
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num_features = X.shape[1] if num_features is None else num_features
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if isinstance(X, pd.DataFrame):
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X = X.values
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out = [np.zeros(X.shape) for j in range(self.out_dim)]
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for i in range(X.shape[0]):
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exp = self.explainer.explain_instance(
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X[i], self.model, labels=range(self.out_dim), num_features=num_features
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)
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for j in range(self.out_dim):
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for k, v in exp.local_exp[j]:
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out[j][i, k] = v
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# because it output two results even for only one model output, and they are negated from what we expect
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if self.mode == "regression":
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for i in range(len(out)):
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out[i] = -out[i]
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return out[0] if self.flat_out else out
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