Add plot and as_dataframe (#174)

- Move tests
- Add tslime extra dependency

Author: ruivieira

pyproject.toml

@@ -49,7 +49,7 @@ dev = [
     "xgboost==1.4.2"
 ]
 extras = [
-    "aix360 [default,tsice,tssaliency] @ https://github.com/Trusted-AI/AIX360/archive/refs/heads/master.zip"
+    "aix360 [default,tsice,tslime,tssaliency] @ https://github.com/Trusted-AI/AIX360/archive/refs/heads/master.zip"
 ]
 
 [project.urls]

src/trustyai/explainers/extras/tslime.py

@@ -0,0 +1,95 @@
+"""
+Wrapper module for TSLIME from aix360.
+Original at https://github.com/Trusted-AI/AIX360/
+"""
+
+from typing import Callable, List, Union
+
+import pandas as pd
+import numpy as np
+from aix360.algorithms.tslime import TSLimeExplainer as TSLimeExplainerAIX
+from aix360.algorithms.tslime.surrogate import LinearSurrogateModel
+from pandas.io.formats.style import Styler
+import matplotlib.pyplot as plt
+
+from trustyai.explainers.explanation_results import ExplanationResults
+from trustyai.utils.extras.timeseries import TSPerturber
+
+
+class TSSLIMEResults(ExplanationResults):
+    """Wraps TSLimeExplainer results. This object is returned by the :class:`~TSLimeExplainer`,
+    and provides a variety of methods to visualize and interact with the explanation.
+    """
+
+    def __init__(self, explanation):
+        self.explanation = explanation
+
+    def as_dataframe(self) -> pd.DataFrame:
+        """Returns the weights as a pandas dataframe."""
+        return pd.DataFrame(self.explanation["history_weights"])
+
+    def as_html(self) -> Styler:
+        """Returns the explanation as an HTML table."""
+        dataframe = self.as_dataframe()
+        return dataframe.style
+
+    def plot(self):
+        """Plot TSLime explanation for the time-series instance. Based on
+        https://github.com/Trusted-AI/AIX360/blob/master/examples/tslime/tslime_univariate_demo.ipynb"""
+        relevant_history = self.explanation["history_weights"].shape[0]
+        input_data = self.explanation["input_data"]
+        relevant_df = input_data[-relevant_history:]
+
+        plt.figure(layout="constrained")
+        plt.plot(relevant_df, label="Input Time Series", marker="o")
+        plt.gca().invert_yaxis()
+
+        normalized_weights = (
+            self.explanation["history_weights"]
+            / np.mean(np.abs(self.explanation["history_weights"]))
+        ).flatten()
+
+        plt.bar(
+            input_data.index[-relevant_history:],
+            normalized_weights,
+            0.4,
+            label="TSLime Weights (Normalized)",
+            color="red",
+        )
+        plt.axhline(y=0, color="r", linestyle="-", alpha=0.4)
+        plt.title("Time Series Lime Explanation Plot")
+        plt.legend(bbox_to_anchor=(1.25, 1.0), loc="upper right")
+        plt.show()
+
+
+class TSLimeExplainer(TSLimeExplainerAIX):
+    """
+    Wrapper for TSLimeExplainer from aix360.
+    """
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        model: Callable,
+        input_length: int,
+        n_perturbations: int = 2000,
+        relevant_history: int = None,
+        perturbers: List[Union[TSPerturber, dict]] = None,
+        local_interpretable_model: LinearSurrogateModel = None,
+        random_seed: int = None,
+    ):
+        super().__init__(
+            model=model,
+            input_length=input_length,
+            n_perturbations=n_perturbations,
+            relevant_history=relevant_history,
+            perturbers=perturbers,
+            local_interpretable_model=local_interpretable_model,
+            random_seed=random_seed,
+        )
+
+    def explain(self, inputs, outputs=None, **kwargs) -> TSSLIMEResults:
+        """
+        Explain the model's prediction on X.
+        """
+        _explanation = super().explain_instance(inputs, y=outputs, **kwargs)
+        return TSSLIMEResults(_explanation)

src/trustyai/utils/extras/models.py

@@ -0,0 +1,2 @@
+"""AIX360 model wrappers"""
+from aix360.algorithms.tsutils.model_wrappers import *  # pylint: disable=wildcard-import,unused-wildcard-import

tests/extras/tsice/test_tsice.py renamed to tests/extras/test_tsice.py

@@ -0,0 +1,101 @@
+import os
+import unittest
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from trustyai.utils.extras.timeseries import tsFrame
+from aix360.datasets import SunspotDataset
+from trustyai.explainers.extras.tslime import TSLimeExplainer
+from trustyai.utils.extras.timeseries import BlockBootstrapPerturber
+
+
+# transform a time series dataset into a supervised learning dataset
+# below sample forecaster is from: https://machinelearningmastery.com/random-forest-for-time-series-forecasting/
+class RandomForestUniVariateForecaster:
+    def __init__(self, n_past=4, n_future=1, RFparams={"n_estimators": 250}):
+        self.n_past = n_past
+        self.n_future = n_future
+        self.model = RandomForestRegressor(**RFparams)
+
+    def fit(self, X):
+        train = self._series_to_supervised(X, n_in=self.n_past, n_out=self.n_future)
+        trainX, trainy = train[:, : -self.n_future], train[:, -self.n_future:]
+        self.model = self.model.fit(trainX, trainy)
+        return self
+
+    def _series_to_supervised(self, data, n_in=1, n_out=1, dropnan=True):
+        n_vars = 1 if type(data) is list else data.shape[1]
+        df = pd.DataFrame(data)
+        cols = list()
+
+        # input sequence (t-n, ... t-1)
+        for i in range(n_in, 0, -1):
+            cols.append(df.shift(i))
+        # forecast sequence (t, t+1, ... t+n)
+        for i in range(0, n_out):
+            cols.append(df.shift(-i))
+        # put it all together
+        agg = pd.concat(cols, axis=1)
+        # drop rows with NaN values
+        if dropnan:
+            agg.dropna(inplace=True)
+        return agg.values
+
+    def predict(self, X):
+        row = X[-self.n_past:].flatten()
+        y_pred = self.model.predict(np.asarray([row]))
+        return y_pred
+
+
+class TestTSLimeExplainer(unittest.TestCase):
+    def setUp(self):
+        # load data
+        df, schema = SunspotDataset().load_data()
+        ts = tsFrame(
+            df, timestamp_column=schema["timestamp"], columns=schema["targets"]
+        )
+
+        (self.ts_train, self.ts_test) = train_test_split(
+            ts, shuffle=False, stratify=None, test_size=0.15, train_size=None
+        )
+
+    def test_tslime(self):
+        # load model
+        input_length = 24
+        forecast_horizon = 4
+        forecaster = RandomForestUniVariateForecaster(
+            n_past=input_length, n_future=forecast_horizon
+        )
+
+        forecaster.fit(self.ts_train.iloc[-200:])
+
+        # initialize/fit explainer
+
+        relevant_history = 12
+        explainer = TSLimeExplainer(
+            model=forecaster.predict,
+            input_length=input_length,
+            relevant_history=relevant_history,
+            perturbers=[
+                BlockBootstrapPerturber(
+                    window_length=min(4, input_length - 1), block_length=2, block_swap=2
+                ),
+            ],
+            n_perturbations=10,
+            random_seed=22,
+        )
+
+        # compute explanations
+        test_window = self.ts_test.iloc[:input_length]
+        explanation = explainer.explain(test_window)
+
+        # validate explanation structure
+        self.assertIn("input_data", explanation.explanation)
+        self.assertIn("history_weights", explanation.explanation)
+        self.assertIn("x_perturbations", explanation.explanation)
+        self.assertIn("y_perturbations", explanation.explanation)
+        self.assertIn("model_prediction", explanation.explanation)
+        self.assertIn("surrogate_prediction", explanation.explanation)
+
+        self.assertEqual(explanation.explanation["history_weights"].shape[0], relevant_history)