examples/tslime/tslime_univariate_demo.ipynb

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   "id": "48f88759-48eb-4f4f-a896-3b60aa00e003",
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   "source": [
    "# TSLimeExplainer For Univariate\n",
    "\n",
    "Advanced time series models are complex and often hard to interpret. Time Series Local Interpretable Model-agnostic Explainer (TSLime) is a model-agnostic local time series explainer. It explains a time series model behavior using an approximate linear model as surrogate. TSLime approximates model response by evaluating the model over time series samples generated by applying time series perturbation techniques. The explanation produced by TSLime is the weights of the linear model over different time point observations. The relative signed value of the explanation is indicative of model sensitivity at temporal resolution. TSLime uses the recent time context length for the local surrogate model fitting.\n",
    "\n",
    "TSLime supports univariate and multi-variate use cases related to forecasting, time series classification, regression and anomaly detection. This notebook demonstrates usage of TSLimeExplainer for Univariate data using [FordA dataset](http://www.timeseriesclassification.com/description.php?Dataset=FordA)."
   ]
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  {
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   "source": [
    "<a id='notebook_workflow'></a>\n",
    "## Notebook Workflow\n",
    "- [Imports](#imports)\n",
    "- [Load Dataset](#load_dataset)\n",
    "- [Train Classifier](#train_classifier)\n",
    "- [Initialize TSLimeExplainer](#initialize_tslime)\n",
    "- [Compute Explanation](#compute_explanation)\n",
    "- [Plot Explanation](#plot_explanation)"
   ]
  },
  {
   "attachments": {},
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   "id": "21e3e84e-ece2-43bb-a11c-9d7dff21bc13",
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   "source": [
    "<a id='imports'></a>\n",
    "### Imports\n",
    "\n",
    "The example model is a pre-trained keras model and hosted on huggingface hub. So, this notebook requires to install tensorflow 2.4+ and huggingface_hub packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d17862aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95272a35-1111-4140-8a07-c0f15e78145d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "# to suppress unrelated tensorflow warnings while importing aix360.datasets\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
    "\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from huggingface_hub import from_pretrained_keras\n",
    "from aix360.datasets import FordDataset\n",
    "from aix360.algorithms.tsutils.tsframe import tsFrame\n",
    "from aix360.algorithms.tsutils.model_wrappers import Tensor_Based_Classification_Model\n",
    "from aix360.algorithms.tslime.tslime import TSLimeExplainer\n",
    "from huggingface_hub.utils import logging as h_logging\n",
    "\n",
    "# huggingface hub warnings\n",
    "h_logging.set_verbosity_error()\n",
    "\n",
    "# tensorflow warnings\n",
    "logger = tf.get_logger()\n",
    "logger.setLevel(logging.ERROR)\n"
   ]
  },
  {
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   "id": "207c0083-17cc-41ad-aeab-e0fbe033def8",
   "metadata": {},
   "source": [
    "<a id='load_dataset'></a>\n",
    "### Load Dataset\n",
    "\n",
    "The dataset is from [UCR archive](), [timeseriesclassification Datasets](http://www.timeseriesclassification.com/description.php?Dataset=FordA) and was used in IEEE World Congress on Computational Intelligence, 2008. As described in the prior websites, the classification problem is to diagnose whether a certain symptom exists or does not exist in an automotive subsystem. Each case consists of 500 measurements of engine noise and a classification. There are two separate problems. The current notebook uses FordA dataset. Train and test data sets were collected in typical operating conditions, with minimal noise contamination. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e89c0e5-33eb-470b-98e6-b07135171170",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_length = 500\n",
    "x_train, x_test, y_train, y_test = FordDataset().load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ac94776-d8ef-478b-bf16-8027a8e6b25a",
   "metadata": {
    "tags": []
   },
   "outputs": [
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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = pd.DataFrame(x_train[0]).plot(figsize=(15, 3), title=\"FordA - 1 Time Series Reading of 500 Measurements\", legend=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1e50af8b-2ca8-45e4-8a6f-48492ce7a084",
   "metadata": {},
   "source": [
    "<a id='train_classifier'></a>\n",
    "### Download Pre-Trained Model\n",
    "Use a pre-trained model from Keras/HuggingFace.\n",
    "\n",
    "The pre-trained model is from: https://keras.io/examples/timeseries/timeseries_transformer_classification/\n",
    "The Hugging face model url is: https://huggingface.co/keras-io/timeseries_transformer_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4660c52f-b3e8-4848-b2c0-372673dc9495",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8832cb4da3c452095767e4e0f16c216",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = from_pretrained_keras(\"keras-io/timeseries_transformer_classification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cb11700-e7b4-40f7-a5d1-0359515c84a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 500, 1)]     0           []                               \n",
      "                                                                                                  \n",
      " layer_normalization (LayerNorm  (None, 500, 1)      2           ['input_1[0][0]']                \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " multi_head_attention (MultiHea  (None, 500, 1)      7169        ['layer_normalization[0][0]',    \n",
      " dAttention)                                                      'layer_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 500, 1)       0           ['multi_head_attention[0][0]']   \n",
      "                                                                                                  \n",
      " tf.__operators__.add (TFOpLamb  (None, 500, 1)      0           ['dropout[0][0]',                \n",
      " da)                                                              'input_1[0][0]']                \n",
      "                                                                                                  \n",
      " layer_normalization_1 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add[0][0]']   \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)                (None, 500, 4)       8           ['layer_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 500, 4)       0           ['conv1d[0][0]']                 \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)              (None, 500, 1)       5           ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " tf.__operators__.add_1 (TFOpLa  (None, 500, 1)      0           ['conv1d_1[0][0]',               \n",
      " mbda)                                                            'tf.__operators__.add[0][0]']   \n",
      "                                                                                                  \n",
      " layer_normalization_2 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_1[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " multi_head_attention_1 (MultiH  (None, 500, 1)      7169        ['layer_normalization_2[0][0]',  \n",
      " eadAttention)                                                    'layer_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 500, 1)       0           ['multi_head_attention_1[0][0]'] \n",
      "                                                                                                  \n",
      " tf.__operators__.add_2 (TFOpLa  (None, 500, 1)      0           ['dropout_2[0][0]',              \n",
      " mbda)                                                            'tf.__operators__.add_1[0][0]'] \n",
      "                                                                                                  \n",
      " layer_normalization_3 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_2[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)              (None, 500, 4)       8           ['layer_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 500, 4)       0           ['conv1d_2[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_3 (Conv1D)              (None, 500, 1)       5           ['dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      " tf.__operators__.add_3 (TFOpLa  (None, 500, 1)      0           ['conv1d_3[0][0]',               \n",
      " mbda)                                                            'tf.__operators__.add_2[0][0]'] \n",
      "                                                                                                  \n",
      " layer_normalization_4 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_3[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " multi_head_attention_2 (MultiH  (None, 500, 1)      7169        ['layer_normalization_4[0][0]',  \n",
      " eadAttention)                                                    'layer_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 500, 1)       0           ['multi_head_attention_2[0][0]'] \n",
      "                                                                                                  \n",
      " tf.__operators__.add_4 (TFOpLa  (None, 500, 1)      0           ['dropout_4[0][0]',              \n",
      " mbda)                                                            'tf.__operators__.add_3[0][0]'] \n",
      "                                                                                                  \n",
      " layer_normalization_5 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_4[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv1d_4 (Conv1D)              (None, 500, 4)       8           ['layer_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, 500, 4)       0           ['conv1d_4[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_5 (Conv1D)              (None, 500, 1)       5           ['dropout_5[0][0]']              \n",
      "                                                                                                  \n",
      " tf.__operators__.add_5 (TFOpLa  (None, 500, 1)      0           ['conv1d_5[0][0]',               \n",
      " mbda)                                                            'tf.__operators__.add_4[0][0]'] \n",
      "                                                                                                  \n",
      " layer_normalization_6 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_5[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " multi_head_attention_3 (MultiH  (None, 500, 1)      7169        ['layer_normalization_6[0][0]',  \n",
      " eadAttention)                                                    'layer_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)            (None, 500, 1)       0           ['multi_head_attention_3[0][0]'] \n",
      "                                                                                                  \n",
      " tf.__operators__.add_6 (TFOpLa  (None, 500, 1)      0           ['dropout_6[0][0]',              \n",
      " mbda)                                                            'tf.__operators__.add_5[0][0]'] \n",
      "                                                                                                  \n",
      " layer_normalization_7 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_6[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv1d_6 (Conv1D)              (None, 500, 4)       8           ['layer_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " dropout_7 (Dropout)            (None, 500, 4)       0           ['conv1d_6[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_7 (Conv1D)              (None, 500, 1)       5           ['dropout_7[0][0]']              \n",
      "                                                                                                  \n",
      " tf.__operators__.add_7 (TFOpLa  (None, 500, 1)      0           ['conv1d_7[0][0]',               \n",
      " mbda)                                                            'tf.__operators__.add_6[0][0]'] \n",
      "                                                                                                  \n",
      " global_average_pooling1d (Glob  (None, 500)         0           ['tf.__operators__.add_7[0][0]'] \n",
      " alAveragePooling1D)                                                                              \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 128)          64128       ['global_average_pooling1d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dropout_8 (Dropout)            (None, 128)          0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 2)            258         ['dropout_8[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 93,130\n",
      "Trainable params: 93,130\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "803929a3-e7e4-4982-8e76-89c998595c23",
   "metadata": {},
   "source": [
    "As the model inputs are tensor based, we are selecting Tensor_Based_Classification_Model from aix360 to wrap the model. The wrapper manages the input and output shapes between the original model and the explainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98259808-59cd-4647-b948-938a754db0a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class_pos = 0 # to select class probability for explanation\n",
    "binary_model = Tensor_Based_Classification_Model(model, \n",
    "                                                 class_pos=class_pos, \n",
    "                                                 input_length=input_length, \n",
    "                                                 n_features=1)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4badaa80-e5e9-4900-a3bc-7785adc1a400",
   "metadata": {},
   "source": [
    "Model accuracy for selected test samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88d267e8-9825-4f49-91f1-85c9f3ad5e02",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model accuracy for 10 samples: 0.9.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "test_sample_size = 10\n",
    "y_pred = binary_model.predict(x_test[0:test_sample_size,...], verbose=0)\n",
    "\n",
    "print(\"model accuracy for {} samples: {}.\".format(test_sample_size, accuracy_score(y_test[:test_sample_size], y_pred.astype(int))))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "64b6fb0f-c9dd-4243-bd5b-c6c44d8160cd",
   "metadata": {},
   "source": [
    "<a id='initialize_tslime'></a>\n",
    "### Initialize TSLimeExplainer\n",
    "\n",
    "`TSLimeExplainer` requires a model function which is to be explained and input (history) length used for the input model. The explanation is for selected latest window of length `relevant_history`. If `input_length=20` and `relevant_history=10`, explanation is computed for last 10 time points. The default linear model used for explanation is sklearn's LinearRegression. If a different linear interpretable model is to be used, the model can be wrapped with `aix360.algorithms.tslime.surrogate.LinearSurrogateModel` and provided as param `local_interpretable_model`. \n",
    "\n",
    "The explainer uses TSPerturber for generating perturbed/simulated time series data to train the linear interpretable model. Available perturbers are \"block-bootstrap\", \"frequency\", \"moving_average\", \"shift\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4345dff3-2e33-4a44-a548-403679c94e0a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import functools\n",
    "from aix360.algorithms.tsutils.tsperturbers import BlockBootstrapPerturber\n",
    "\n",
    "relevant_history = 100\n",
    "explainer = TSLimeExplainer(\n",
    "    model= functools.partial(binary_model.predict_proba, verbose = 0),\n",
    "    input_length= x_test.shape[1],\n",
    "    relevant_history=relevant_history,\n",
    "    perturbers=[\n",
    "        BlockBootstrapPerturber(window_length=min(10, input_length-1), block_length=20, block_swap=2),\n",
    "    ],\n",
    "    n_perturbations=1000,\n",
    "    random_seed=22,\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d6144697-4b3d-4945-85dc-56e087ccd65c",
   "metadata": {},
   "source": [
    "<a id='compute_explanation'></a>\n",
    "### Compute Explanation\n",
    "\n",
    "Compute lime weights for a given input timeseries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e8e46f4-371d-402b-b886-9c2d08052559",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.240282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.331189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.386596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.383220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.305979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-1.146352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-1.011328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-0.931222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-0.934498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-1.001288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           X_1\n",
       "time          \n",
       "0     1.240282\n",
       "1     1.331189\n",
       "2     1.386596\n",
       "3     1.383220\n",
       "4     1.305979\n",
       "...        ...\n",
       "495  -1.146352\n",
       "496  -1.011328\n",
       "497  -0.931222\n",
       "498  -0.934498\n",
       "499  -1.001288\n",
       "\n",
       "[500 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indx = 3\n",
    "instance = x_test[indx, ...]\n",
    "\n",
    "ts_instance = tsFrame(instance) # tsFrame instance\n",
    "ts_instance.index = pd.to_numeric(ts_instance.index)\n",
    "ts_instance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5a7a9119-878d-437c-9b56-f13edc0831b4",
   "metadata": {},
   "source": [
    "Explanation object `explanation` contains the history weights computed for selected time window (relevant history) and surrogate prediction for this selected time window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80bd35e3-cd19-4999-86c8-5517efd661d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "explanation = explainer.explain_instance(ts_instance)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b531066b-b407-4704-9bbd-6a25ccfba8f2",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='plot_explanation'></a>\n",
    "### Plot Explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a8209e4-b341-474c-a535-3222697046eb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relevant_df = ts_instance[-relevant_history:]\n",
    "\n",
    "figsize = (15,4)\n",
    "plt.figure(figsize=figsize, layout='constrained')\n",
    "plt.plot(relevant_df, label='Input Time Series', marker='o')\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "normalized_weights = (explanation[\"history_weights\"] / np.mean(np.abs(explanation[\"history_weights\"]))).flatten()\n",
    "\n",
    "plt.bar(ts_instance.index[-relevant_history:], normalized_weights, 0.4, label = 'TSLime Weights (Normalized)', color='red')\n",
    "plt.axhline(y=0, color='r', linestyle='-', alpha=0.4)\n",
    "plt.title(\"Time Series Lime Explanation Plot\")\n",
    "plt.legend(bbox_to_anchor=(1.25, 1.0), loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a2cd77b8-8513-4bad-ae9e-e7a88241da4e",
   "metadata": {},
   "source": [
    "The above plot shows the normalized weights for each time point in relevant(selected) history window. Weights are normalized/scaled using mean absolute over all the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be099bdc-17ff-4129-a6f9-dfee9be0dbe8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}