Adding Time Series Lime Explainer

.github/workflows/Build.yml

@@ -287,13 +287,17 @@ jobs:
       - name: Step 6 - Test TSSaliencyExplainer
         run: python ./tests/tssaliency/test_tssaliency.py
 
+      # tslime deps are already satisfied.
+      - name: Step 7 - Test TSLimeExplainer
+        run: python ./tests/tslime/test_tslime.py
+
   build-imd-on-py38-310:
     # The type of runner that the job will run on
     runs-on: "${{ matrix.os }}"
     strategy:
       fail-fast: false
       matrix:
-#        os: [ubuntu-18.04, ubuntu-latest, macos-latest, windows-latest]
+        #        os: [ubuntu-18.04, ubuntu-latest, macos-latest, windows-latest]
         os: [ubuntu-20.04]
         python-version: ["3.10"]
 

aix360/algorithms/tslime/__init__.py

@@ -0,0 +1 @@
+from .tslime import TSLimeExplainer

aix360/algorithms/tslime/surrogate.py

@@ -0,0 +1,50 @@
+from abc import abstractmethod
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+
+class LinearSurrogateModel:
+    """Linear Interpretable Surrogate Model Wrapper."""
+
+    def __init__(self, model):
+        self.model = model
+
+    def fit(self, *args, **kwargs):
+        self.model.fit(*args, **kwargs)
+
+    def predict(self, *args, **kwargs):
+        return self.model.predict(*args, **kwargs)
+
+    @abstractmethod
+    def get_weights(self):
+        pass
+
+
+class LinearRegressionSurrogate(LinearSurrogateModel):
+    """Linear Interpretable Surrogate Model using LinearRegression from Scikit-Learn."""
+
+    def __init__(self):
+        super(LinearRegressionSurrogate, self).__init__(LinearRegression())
+
+    def get_weights(self):
+        return self.model.coef_
+
+
+def linear_surrogate_weights(
+    x_perturbations: np.ndarray,
+    y_perturbations: np.ndarray,
+    surrogate: LinearSurrogateModel = None,
+):
+    """Function to compute weights from a linear interpretable model
+    using provided time series pertubations."""
+
+    if surrogate is None:
+        surrogate = LinearRegressionSurrogate()
+
+    surrogate.fit(
+        x_perturbations.reshape(x_perturbations.shape[0], -1),
+        y_perturbations.reshape(y_perturbations.shape[0], -1),
+    )
+
+    # retrieve weights
+    return surrogate, surrogate.get_weights()

aix360/algorithms/tslime/tslime.py

@@ -0,0 +1,217 @@
+import warnings
+import numpy as np
+import pandas as pd
+from typing import Union, List, Callable
+from aix360.algorithms.tslbbe import TSLocalBBExplainer
+from aix360.algorithms.tsutils.tsframe import tsFrame, to_np_array
+from aix360.algorithms.tslime.surrogate import (
+    linear_surrogate_weights,
+    LinearSurrogateModel,
+)
+from aix360.algorithms.tsutils.tsperturbers.perturbed_data_generator import (
+    PerturbedDataGenerator,
+)
+from aix360.algorithms.tsutils.tsperturbers.tsperturber import (
+    TSPerturber,
+    BlockSelector,
+)
+
+
+class TSLimeExplainer(TSLocalBBExplainer):
+    """Time Series Local Interpretable Model-agnostic Explainer (TSLime) is a model-agnostic local time series
+    explainer. LIME (Locally interpretable Model agnostic explainer) is a popular algorithm for local
+    explanation. LIME explains the model behavior by approximating the model response with linear models.
+    LIME algorithm specifically assumes tabular data format, where each row is a data point, and columns
+    are features. A generalization of LIME algorithm for image data uses super pixel based perturbation.
+    TSLIME generalizes LIME algorithm for time series context.
+
+    TSLIME uses time series perturbation methods to produce a local input perturbation, and linear model
+    surrogate which best approximates the model response. TSLime produces an interpretable explanation.
+    The explanation weights produced by the TSLime explanation indicates model local sensitivity.
+
+    References:
+        .. [##] Marco Tulio Ribeiro et al. '"Why Should I Trust You?": Explaining the Predictions of Any Classifier'
+            https://arxiv.org/abs/1602.04938
+    """
+
+    def __init__(
+        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,
+    ):
+        """Initializer for TSLimeExplainer
+
+        Args:
+            model (Callable): Callable object produces a prediction as numpy array
+                for a given input as numpy array.
+            input_length (int): Input (history) length used for input model.
+            n_perturbations (int): Number of perturbed instance for TSExplanation. Defaults to 25.
+            relevant_history (int): Interested window size for explanations. The explanation is
+                computed for selected latest window of length `relevant_history`. If `input_length=20`
+                and `relevant_history=10`, explanation is computed for last 10 time points. If None,
+                relevant_history is set to input_length. Defaults to None.
+            perturbers (List[TSPerturber, dict]): data perturbation algorithm specification by TSPerturber
+                instance or dict. Allowed values for "type" key in dictionary are block-bootstrap, frequency,
+                moving-average, shift. Block-bootstrap split the time series into contiguous
+                chunks called blocks, for each block noise is estimated and noise is exchanged
+                and added to the signal between randomly selected blocks. Moving-average perturbation
+                maintains the moving mean of the time series data with the specified window length,
+                but add perturbed noise with similar distribution as the data. Frequency
+                perturber performs FFT on the noise, and removes random high frequency
+                components from the noise estimates. Number of frequencies to be removed
+                is specified by the truncate_frequencies argument. Shift perturber adds
+                random upward or downward shift in the data value over time continuous
+                blocks. If not provided default perturber is block-bootstrap. Defaults to None.
+            local_interpretable_model (LinearSurrogateModel): Local interpretable model, a surrogate that
+                is to be trained on the given input time series neighborhood. This model is used to provide
+                local weights for each time point in the selected timeseries. If None, sklearn's Linear Regression
+                model, aix360.algorithms.tslime.surrogate.LinearRegressionSurrogate is used. Defaults to None.
+            random_seed (int): random seed to get consistent results. Refer to numpy random state.
+                Defaults to None.
+        """
+        self.model = model
+
+        if perturbers is None:
+            perturbers = [
+                dict(type="block-bootstrap"),
+            ]
+
+        block_selector = BlockSelector(start=-input_length, end=None)
+        perturber = PerturbedDataGenerator(
+            perturber_engines=perturbers,
+            block_selector=block_selector,
+        )
+        self._parameters = dict()
+
+        # Input Specification
+        self.input_length = input_length
+
+        # Surrogate training params
+        self.local_interpretable_model = local_interpretable_model
+        self.n_perturbations = n_perturbations
+        self.perturber = perturber
+
+        # Explanation params
+        if relevant_history is None:
+            relevant_history = input_length
+
+        self.relevant_history = relevant_history
+        self.random_seed = random_seed
+
+    def get_params(self):
+        return self._parameters.copy()
+
+    def set_params(self, *argv, **kwargs):
+        self._parameters.update(kwargs)
+        return self
+
+    def _ts_perturb(self, x):
+        # create perturbations
+        x_perturbations = None
+        y_perturbations = None
+
+        x_perturbations, _ = self.perturber.fit_transform(
+            x, None, n=self.n_perturbations
+        )
+
+        x_perturbations = np.asarray(x_perturbations).astype("float")
+        return x_perturbations
+
+    def _batch_predict(self, x_perturbations):
+        f_predict_samples = None
+
+        try:
+            f_predict_samples = self.model(x_perturbations)
+        except Exception as ex:
+            warnings.warn(
+                "Batch scoring failed with error: {}. Scoring sequentially...".format(
+                    ex
+                )
+            )
+            f_predict_samples = [
+                self.model(x_perturbations[i]) for i in range(x_perturbations.shape[0])
+            ]
+            f_predict_samples = np.array(f_predict_samples)
+
+        return f_predict_samples
+
+    def explain_instance(self, ts: tsFrame, **explain_params):
+        """Explain the prediction made by the time series model at a certain point in time
+        (**local explanation**).
+
+        Args
+            ts (tsFrame): Input time series signal in ``tsFrame`` format. This can
+                be generated using :py:mod:`aix360.algorithms.tsframe.tsFrame`.
+                A ``tsFrame`` is a pandas ``DataFrame`` indexed by ``Timestamp`` objects
+                (that is ``DatetimeIndex``). Each column corresponds to an input feature.
+            explain_params: Arbitrary explainer parameters.
+
+        Returns:
+            explanation (Union[List[Dict], Dict]): Dictionary with keys: input_data, history_weights,
+                model_prediction, surrogate_prediction, x_perturbations, y_perturbations.
+        """
+        return super(TSLimeExplainer, self).explain_instance(
+            ts=ts, ts_related=None, **explain_params
+        )
+
+    def _explain_instance(
+        self,
+        ts: tsFrame,
+        **explain_params,
+    ):
+        # for consistent results. Is it possible here?
+        np.random.seed(self.random_seed)
+
+        ### input validation
+        if ts.shape[0] < self.input_length:
+            raise ValueError(
+                "Error: expecting input length {} but found {}.".format(
+                    self.input_length, ts.shape[0]
+                )
+            )
+        xc = ts[-self.input_length :]
+        xc = to_np_array(xc)
+
+        ### generate time series perturbations
+        x_perturbations = self._ts_perturb(x=xc)
+
+        ### generate y
+        y_perturbations = self._batch_predict(x_perturbations)
+        if y_perturbations is None:
+            raise Exception(
+                "Model prediction could not be computed for gradient samples."
+            )
+
+        y_perturbations = np.asarray(y_perturbations).astype("float")
+
+        ### select k time points - relevant_history
+        x_perturbations = x_perturbations[
+            :, -self.relevant_history :
+        ]  # consider only k time points
+
+        xc_relevant = xc[-self.relevant_history :, :].reshape(1, -1)
+
+        ### compute weights using a linear model
+        surrogate, history_weights = linear_surrogate_weights(
+            surrogate=self.local_interpretable_model,
+            x_perturbations=x_perturbations,
+            y_perturbations=y_perturbations,
+        )
+
+        model_prediction = self._batch_predict(xc)
+
+        surrogate_prediction = surrogate.predict(xc_relevant)
+        explanation = {
+            "input_data": ts,
+            "model_prediction": model_prediction,
+            "surrogate_prediction": surrogate_prediction,
+            "history_weights": history_weights.reshape(self.relevant_history, -1),
+            "x_perturbations": x_perturbations,
+            "y_perturbations": y_perturbations,
+        }
+        return explanation

aix360/algorithms/tssaliency/tssaliency.py

@@ -115,7 +115,7 @@ def explain_instance(self, ts: tsFrame, **explain_params):
 
     def _explain_instance(
         self,
-        ts: Union["tsFrame", np.ndarray],
+        ts: tsFrame,
         **explain_params,
     ):
         # fix seed for consistent results

aix360/algorithms/tsutils/tsperturbers/block_bootstrap_perturber.py

@@ -68,7 +68,7 @@ def _transform(
         block_swap = self._parameters.get("block_swap")
 
         x_res = [self._residual.copy() for _ in range(n_perturbations)]
-        margin = self._residual.shape[0] - block_length
+        margin = self._residual.shape[0] - block_length + 1
         for _ in range(block_swap):
             if block_selector is None:
                 from_point = np.random.randint(

aix360/algorithms/tsutils/tsperturbers/perturber_utils.py

@@ -25,11 +25,11 @@ def ts_rolling_mean(
     if isinstance(ts, np.ndarray):
         if len(ts.shape) == 1:
             ts = ts.reshape(-1, 1)
-        ts = ts.astype("float32")
+        ts = ts.astype("float")
         n_obs, n_vars = ts.shape
         den = np.convolve(
-            np.ones(n_obs), np.ones(window_size, dtype="float32"), "same"
-        ).astype("float32")
+            np.ones(n_obs), np.ones(window_size, dtype="float"), "same"
+        ).astype("float")
         df = np.asarray(
             [
                 np.convolve(ts[:, i], np.ones(window_size), "same") / den
@@ -51,8 +51,8 @@ def ts_split_mean_residual(
     format.
 
     Args:
-        ts (Union[tsFrame, numpy ndarray]): input time series as dataframe or numpy array
-        window_size (int): numer of observation for averaging.
+        ts (Union[tsFrame, numpy ndarray]): input time series as tsFrame or numpy array
+        window_size (int): number of observations for averaging.
 
     Returns:
         tuple (Union[Tuple[numpy ndarray, numpy ndarray], Tuple[tsFrame, tsFrame]]): depending

aix360/datasets/sunspots_dataset.py

@@ -14,10 +14,12 @@ class SunspotDataset:
 
     References:
         .. [#1] Andrews, D. F. and Herzberg, A. M., "Data: A Collection of Problems from
-        Many Fields for the Student and Research Worker,"
-        New York: Springer-Verlag, 1985.
+            Many Fields for the Student and Research Worker,"
+            New York: Springer-Verlag, 1985.
         .. [#2] https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/sunspots.html
         .. [#3] https://r-data.pmagunia.com/dataset/r-dataset-package-datasets-sunspots
+        .. [#4] Avishek Pal, PKS Prakash, "Practical Time Series Analysis"
+            https://github.com/PacktPublishing/Practical-Time-Series-Analysis/
 
     """
 
@@ -30,16 +32,17 @@ def __init__(self):
         self.data_file = os.path.realpath(
             os.path.join(self.data_folder, "sunspots.csv")
         )
-        sunspots_url = (
-            "https://r-data.pmagunia.com/system/files/datasets/dataset-61024.csv"
-        )
+        sunspots_url = "https://raw.githubusercontent.com/PacktPublishing/Practical-Time-Series-Analysis/master/Data%20Files/monthly-sunspot-number-zurich-17.csv"
 
         if not os.path.exists(self.data_file):
             response = requests.get(sunspots_url)
-            data = pd.read_csv(StringIO(response.text))
-            data["time"] = pd.to_datetime(
-                data["time"].apply(self._convert_to_date), format="%Y-%m"
+            data = pd.read_csv(
+                StringIO(response.text),
+                skiprows=0,
+                nrows=2820,
             )
+            data.columns = ["time", "sunspots"]
+            data["time"] = pd.to_datetime(data["time"], format="%Y-%m")
 
             data.to_csv(self.data_file, index=False)
 

docs/tslbbe.rst

@@ -12,3 +12,9 @@ Time Series Saliency (TSSaliency) Explainer
 
 .. autoclass:: aix360.algorithms.tssaliency.tssaliency.TSSaliencyExplainer
    :members:
+
+Time Series Local Interpretable Model-agnostic Explainer (TSLime)
+-------------------------------------------------------------------------
+
+.. autoclass:: aix360.algorithms.tslime.tslime.TSLimeExplainer
+   :members:

examples/README.md

@@ -39,3 +39,7 @@ the user through the various steps of the notebook.
 - [TSSaliencyExplainer using FordA dataset](./tssaliency/tssaliency_univariate_demo.ipynb)[[on nbviewer](https://nbviewer.org/github/Trusted-AI/AIX360/blob/master/examples/tssaliency/tssaliency_univariate_demo.ipynb)]
 
 - [TSSaliencyExplainer using Climate dataset](./tssaliency/tssaliency_multivariate_demo.ipynb)[[on nbviewer](https://nbviewer.org/github/Trusted-AI/AIX360/blob/master/examples/tssaliency/tssaliency_multivariate_demo.ipynb)]
+
+- [TSLimeExplainer using FordA dataset](./tslime/tslime_univariate_demo.ipynb)[[on nbviewer](https://nbviewer.org/github/Trusted-AI/AIX360/blob/master/examples/tslime/tslime_univariate_demo.ipynb)]
+
+- [TSLimeExplainer using Climate dataset](./tslime/tslime_multivariate_demo.ipynb)[[on nbviewer](https://nbviewer.org/github/Trusted-AI/AIX360/blob/master/examples/tslime/tslime_multivariate_demo.ipynb)]