notebooks/AReM/AReM_TF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a85f1b2",
   "metadata": {},
   "source": [
    "<h1 style=\"color:#3da1da;font-size: 300%;\" id=\"timeshap tutorial\" align=\"center\"  >TimeSHAP Tutorial - TensorFlow - AReM dataset</h1><p>&nbsp;\n",
    "\n",
    "<a id='top_cell'></a>\n",
    "\n",
    "## Table of contents\n",
    "1. [Data Processing](#1.-Data-Processing)\n",
    "  1. [Data Loading](#1.1-Data-Loading)\n",
    "  2. [Data Treatment](#1.2-Data-Treatment)\n",
    "2. [Model](#2.-Model)\n",
    "  1. [Model Definition](#2.1-Model-Definition)\n",
    "  2. [Model Training](#2.2-Model-Training)\n",
    "3. [TimeSHAP](#3.-TimeSHAP)\n",
    "  1. [Local Explanations](#3.1-Local-Explanations)\n",
    "  2. [Global Explanations](#3.2-Global-Explanations)\n",
    "  3. [Individual Plots](#3.3-Individual-Plots)\n",
    "    \n",
    "\n",
    "# TimeSHAP\n",
    "\n",
    "TimeSHAP is a model-agnostic, recurrent explainer that builds upon KernelSHAP and extends it to the sequential domain. \n",
    "\n",
    "TimeSHAP computes local event/timestamp- feature-, and cell-level attributions. \n",
    "    \n",
    "Aditionally TimeSHAP also computes global event- and feature-level explanations.\n",
    "    \n",
    "As sequences can be arbitrarily long, TimeSHAP also implements a pruning algorithm based on Shapley Values, \n",
    "that finds a subset of consecutive, recent events that contribute the most to the decision."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8deb3c97",
   "metadata": {},
   "source": [
    "---\n",
    "# 1. Data-Processing\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1937fda2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db6e1187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from timeshap import __version__\n",
    "__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbfbdd16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c758a7b",
   "metadata": {},
   "source": [
    "## 1.1 Data-Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66dcf4eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sitting/dataset5.csv ------ (480, 7)\n",
      "sitting/dataset11.csv ------ (480, 7)\n",
      "sitting/dataset3.csv ------ (480, 7)\n",
      "sitting/dataset10.csv ------ (480, 7)\n",
      "sitting/dataset12.csv ------ (480, 7)\n",
      "sitting/dataset4.csv ------ (480, 7)\n",
      "sitting/dataset13.csv ------ (480, 7)\n",
      "sitting/dataset9.csv ------ (480, 7)\n",
      "sitting/dataset2.csv ------ (480, 7)\n",
      "sitting/dataset15.csv ------ (480, 7)\n",
      "sitting/dataset1.csv ------ (480, 7)\n",
      "sitting/dataset14.csv ------ (480, 7)\n",
      "sitting/dataset6.csv ------ (480, 7)\n",
      "sitting/dataset7.csv ------ (480, 7)\n",
      "standing/dataset5.csv ------ (480, 7)\n",
      "standing/dataset11.csv ------ (480, 7)\n",
      "standing/dataset3.csv ------ (480, 7)\n",
      "standing/dataset8.csv ------ (480, 7)\n",
      "standing/dataset10.csv ------ (480, 7)\n",
      "standing/dataset12.csv ------ (480, 7)\n",
      "standing/dataset4.csv ------ (480, 7)\n",
      "standing/dataset13.csv ------ (480, 7)\n",
      "standing/dataset9.csv ------ (480, 7)\n",
      "standing/dataset2.csv ------ (480, 7)\n",
      "standing/dataset15.csv ------ (480, 7)\n",
      "standing/dataset1.csv ------ (480, 7)\n",
      "standing/dataset14.csv ------ (480, 7)\n",
      "standing/dataset6.csv ------ (480, 7)\n",
      "standing/dataset7.csv ------ (480, 7)\n",
      "walking/dataset5.csv ------ (480, 7)\n",
      "walking/dataset11.csv ------ (480, 7)\n",
      "walking/dataset3.csv ------ (480, 7)\n",
      "walking/dataset8.csv ------ (480, 7)\n",
      "walking/dataset10.csv ------ (480, 7)\n",
      "walking/dataset12.csv ------ (480, 7)\n",
      "walking/dataset4.csv ------ (480, 7)\n",
      "walking/dataset13.csv ------ (480, 7)\n",
      "walking/dataset9.csv ------ (480, 7)\n",
      "walking/dataset2.csv ------ (480, 7)\n",
      "walking/dataset15.csv ------ (480, 7)\n",
      "walking/dataset1.csv ------ (480, 7)\n",
      "walking/dataset14.csv ------ (480, 7)\n",
      "walking/dataset6.csv ------ (480, 7)\n",
      "walking/dataset7.csv ------ (480, 7)\n",
      "cycling/dataset5.csv ------ (480, 7)\n",
      "cycling/dataset11.csv ------ (480, 7)\n",
      "cycling/dataset3.csv ------ (480, 7)\n",
      "cycling/dataset8.csv ------ (480, 7)\n",
      "cycling/dataset10.csv ------ (480, 7)\n",
      "cycling/dataset12.csv ------ (480, 7)\n",
      "cycling/dataset4.csv ------ (480, 7)\n",
      "cycling/dataset13.csv ------ (480, 7)\n",
      "cycling/dataset9.csv ------ (480, 7)\n",
      "cycling/dataset2.csv ------ (480, 7)\n",
      "cycling/dataset15.csv ------ (480, 7)\n",
      "cycling/dataset1.csv ------ (480, 7)\n",
      "cycling/dataset14.csv ------ (480, 7)\n",
      "cycling/dataset6.csv ------ (480, 7)\n",
      "cycling/dataset7.csv ------ (480, 7)\n",
      "lying/dataset5.csv ------ (480, 7)\n",
      "lying/dataset11.csv ------ (480, 7)\n",
      "lying/dataset3.csv ------ (480, 7)\n",
      "lying/dataset8.csv ------ (480, 7)\n",
      "lying/dataset10.csv ------ (480, 7)\n",
      "lying/dataset12.csv ------ (480, 7)\n",
      "lying/dataset4.csv ------ (480, 7)\n",
      "lying/dataset13.csv ------ (480, 7)\n",
      "lying/dataset9.csv ------ (480, 7)\n",
      "lying/dataset2.csv ------ (480, 7)\n",
      "lying/dataset15.csv ------ (480, 7)\n",
      "lying/dataset1.csv ------ (480, 7)\n",
      "lying/dataset14.csv ------ (480, 7)\n",
      "lying/dataset6.csv ------ (480, 7)\n",
      "lying/dataset7.csv ------ (480, 7)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "\n",
    "data_directories = next(os.walk(\"./AReM\"))[1]\n",
    "\n",
    "all_csvs = []\n",
    "for folder in data_directories:\n",
    "    if folder in ['bending1', 'bending2']:\n",
    "        continue\n",
    "    folder_csvs = next(os.walk(f\"./AReM/{folder}\"))[2]\n",
    "    for data_csv in folder_csvs:\n",
    "        if data_csv == 'dataset8.csv' and folder == 'sitting':\n",
    "            # this dataset only has 479 instances\n",
    "            # it is possible to use it, but would require padding logic\n",
    "            continue\n",
    "        loaded_data = pd.read_csv(f\"./AReM/{folder}/{data_csv}\", skiprows=4)\n",
    "        print(f\"{folder}/{data_csv} ------ {loaded_data.shape}\")\n",
    "        \n",
    "        csv_id = re.findall(r'\\d+', data_csv)[0]\n",
    "        loaded_data['id'] = csv_id\n",
    "        loaded_data['all_id'] = f\"{folder}_{csv_id}\"\n",
    "        loaded_data['activity'] = folder\n",
    "        all_csvs.append(loaded_data)\n",
    "all_data = pd.concat(all_csvs)\n",
    "raw_model_features = ['avg_rss12', 'var_rss12', 'avg_rss13', 'var_rss13', 'avg_rss23', 'var_rss23']\n",
    "all_data.columns = ['timestamp', 'avg_rss12', 'var_rss12', 'avg_rss13', 'var_rss13', 'avg_rss23', 'var_rss23', 'id', 'all_id', 'activity']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aea2406",
   "metadata": {},
   "source": [
    "## 1.2 Data Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db62bc15",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Separate in train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3df1a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose ids to use for test\n",
    "ids_for_test = np.random.choice(all_data['id'].unique(), size = 4, replace=False)\n",
    "\n",
    "d_train =  all_data[~all_data['id'].isin(ids_for_test)]\n",
    "d_test = all_data[all_data['id'].isin(ids_for_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fcabe53",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35520, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f422360",
   "metadata": {},
   "outputs": [],
   "source": [
    "###  Normalize Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c801cff",
   "metadata": {},
   "source": [
    "###  Normalize Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d627aeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NumericalNormalizer:\n",
    "    def __init__(self, fields: list):\n",
    "        self.metrics = {}\n",
    "        self.fields = fields\n",
    "\n",
    "    def fit(self, df: pd.DataFrame ) -> list:\n",
    "        means = df[self.fields].mean()\n",
    "        std = df[self.fields].std()\n",
    "        for field in self.fields:\n",
    "            field_mean = means[field]\n",
    "            field_stddev = std[field]\n",
    "            self.metrics[field] = {'mean': field_mean, 'std': field_stddev}\n",
    "\n",
    "    def transform(self, df: pd.DataFrame) -> pd.DataFrame:\n",
    "        # Transform to zero-mean and unit variance.\n",
    "        for field in self.fields:\n",
    "            f_mean = self.metrics[field]['mean']\n",
    "            f_stddev = self.metrics[field]['std']\n",
    "            # OUTLIER CLIPPING to [avg-3*std, avg+3*avg]\n",
    "            df[field] = df[field].apply(lambda x: f_mean - 3 * f_stddev if x < f_mean - 3 * f_stddev else x)\n",
    "            df[field] = df[field].apply(lambda x: f_mean + 3 * f_stddev if x > f_mean + 3 * f_stddev else x)\n",
    "            if f_stddev > 1e-5:\n",
    "                df[f'p_{field}_normalized'] = df[field].apply(lambda x: ((x - f_mean)/f_stddev))\n",
    "            else:\n",
    "                df[f'p_{field}_normalized'] = df[field].apply(lambda x: x * 0)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a622fbed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#all features are numerical\n",
    "normalizor = NumericalNormalizer(raw_model_features)\n",
    "normalizor.fit(d_train)\n",
    "d_train_normalized = normalizor.transform(d_train)\n",
    "d_test_normalized = normalizor.transform(d_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ed80b8",
   "metadata": {},
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3be4cdd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_features = [f\"p_{x}_normalized\" for x in raw_model_features]\n",
    "time_feat = 'timestamp'\n",
    "label_feat = 'activity'\n",
    "sequence_id_feat = 'all_id'\n",
    "\n",
    "plot_feats = {\n",
    "    'p_avg_rss12_normalized': \"Mean Chest <-> Right Ankle\",\n",
    "    'p_var_rss12_normalized': \"STD Chest <-> Right Ankle\",\n",
    "    'p_avg_rss13_normalized': \"Mean Chest <-> Left Ankle\",\n",
    "    'p_var_rss13_normalized': \"STD Chest <-> Left Ankle\",\n",
    "    'p_avg_rss23_normalized': \"Mean Right Ankle <-> Left Ankle\",\n",
    "    'p_var_rss23_normalized': \"STD Right Ankle <-> Left Ankle\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e08290",
   "metadata": {},
   "source": [
    "### Transform the dataset from multi-label to binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1cba387",
   "metadata": {},
   "outputs": [],
   "source": [
    "# possible activities ['cycling', 'lying', 'sitting', 'standing', 'walking']\n",
    "#Select the activity to predict\n",
    "chosen_activity = 'cycling'\n",
    "\n",
    "d_train_normalized['label'] = d_train_normalized['activity'].apply(lambda x: int(x == chosen_activity))\n",
    "d_test_normalized['label'] = d_test_normalized['activity'].apply(lambda x: int(x == chosen_activity))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d821f9ca",
   "metadata": {},
   "source": [
    "This example notebook requires TensorFlow!\n",
    "\n",
    "Install it if you haven't already:\n",
    "```\n",
    "!pip install tensorflow\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35cf4835",
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_numpy(df, model_feats, label_feat, group_by_feat, timestamp_Feat):\n",
    "    sequence_length = len(df[timestamp_Feat].unique())\n",
    "\n",
    "    data_tensor = np.zeros(\n",
    "        (len(df[group_by_feat].unique()), sequence_length, len(model_feats)))\n",
    "    labels_tensor = np.zeros((len(df[group_by_feat].unique()), 1))\n",
    "\n",
    "    for i, name in enumerate(df[group_by_feat].unique()):\n",
    "        name_data = df[df[group_by_feat] == name]\n",
    "        sorted_data = name_data.sort_values(timestamp_Feat)\n",
    "\n",
    "        data_x = sorted_data[model_feats].values\n",
    "        labels = sorted_data[label_feat].values\n",
    "        assert labels.sum() == 0 or labels.sum() == len(labels)\n",
    "        data_tensor[i, :, :] = data_x\n",
    "        labels_tensor[i, :] = labels[0]\n",
    "    return data_tensor, labels_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5b4ab48",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = df_to_numpy(d_train_normalized, model_features, 'label', sequence_id_feat, time_feat)\n",
    "\n",
    "X_test, y_test = df_to_numpy(d_test_normalized, model_features, 'label', sequence_id_feat, time_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c4aa88",
   "metadata": {},
   "source": [
    "___\n",
    "# 2. Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15684c2c",
   "metadata": {},
   "source": [
    "This example notebook requires Tensorflow!\n",
    "\n",
    "Install it if you haven't already:\n",
    "```\n",
    "!pip install tensorflow\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992d6c20",
   "metadata": {},
   "source": [
    "## 2.1 Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b7b9cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "inputs = tf.keras.layers.Input(shape=(None, 6))\n",
    "lstm1 = tf.keras.layers.LSTM(64)(inputs)\n",
    "ff1 = tf.keras.layers.Dense(64, activation='relu')(lstm1)\n",
    "ff2 = tf.keras.layers.Dense(1, activation='sigmoid')(ff1)\n",
    "model = tf.keras.models.Model(inputs=inputs, outputs=ff2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a01e96a",
   "metadata": {},
   "source": [
    "## 2.2 Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "abb35ac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 [==============================] - 10s 10s/step - loss: 0.7023 - val_loss: 0.6826\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.6758 - val_loss: 0.6597\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 1s 686ms/step - loss: 0.6522 - val_loss: 0.6384\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 1s 737ms/step - loss: 0.6303 - val_loss: 0.6176\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 1s 596ms/step - loss: 0.6091 - val_loss: 0.5970\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 1s 631ms/step - loss: 0.5888 - val_loss: 0.5765\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 1s 852ms/step - loss: 0.5684 - val_loss: 0.5556\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 1s 801ms/step - loss: 0.5476 - val_loss: 0.5338\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 1s 605ms/step - loss: 0.5262 - val_loss: 0.5109\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 1s 643ms/step - loss: 0.5041 - val_loss: 0.4871\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 1s 663ms/step - loss: 0.4807 - val_loss: 0.4622\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 1s 674ms/step - loss: 0.4562 - val_loss: 0.4356\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 1s 554ms/step - loss: 0.4305 - val_loss: 0.4070\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 1s 849ms/step - loss: 0.4031 - val_loss: 0.3766\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 1s 785ms/step - loss: 0.3745 - val_loss: 0.3451\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 1s 736ms/step - loss: 0.3448 - val_loss: 0.3132\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 1s 672ms/step - loss: 0.3148 - val_loss: 0.2817\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 1s 709ms/step - loss: 0.2850 - val_loss: 0.2515\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 1s 718ms/step - loss: 0.2566 - val_loss: 0.2237\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 1s 694ms/step - loss: 0.2299 - val_loss: 0.1981\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f3720111f50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              optimizer=tf.keras.optimizers.Adam(0.001))\n",
    "\n",
    "model.fit(X_train, y_train, epochs=20, batch_size=55, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf04894",
   "metadata": {},
   "source": [
    "---\n",
    "# 3. TimeSHAP\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d33a0da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeshap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9212121",
   "metadata": {},
   "source": [
    "### Model entry point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "396cb0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x: model.predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66a417b",
   "metadata": {},
   "source": [
    "### Baseline event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8d7584d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeshap.utils import calc_avg_event\n",
    "average_event = calc_avg_event(d_train_normalized, numerical_feats=model_features, categorical_feats=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f549aa7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_avg_rss12_normalized</th>\n",
       "      <th>p_var_rss12_normalized</th>\n",
       "      <th>p_avg_rss13_normalized</th>\n",
       "      <th>p_var_rss13_normalized</th>\n",
       "      <th>p_avg_rss23_normalized</th>\n",
       "      <th>p_var_rss23_normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.095791</td>\n",
       "      <td>-0.531224</td>\n",
       "      <td>0.178574</td>\n",
       "      <td>-0.416398</td>\n",
       "      <td>0.129324</td>\n",
       "      <td>-0.392722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_avg_rss12_normalized  p_var_rss12_normalized  p_avg_rss13_normalized  \\\n",
       "0                0.095791               -0.531224                0.178574   \n",
       "\n",
       "   p_var_rss13_normalized  p_avg_rss23_normalized  p_var_rss23_normalized  \n",
       "0               -0.416398                0.129324               -0.392722  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_event"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c59d861",
   "metadata": {},
   "source": [
    "### Baseline event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75a68af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeshap.utils import calc_avg_sequence\n",
    "average_sequence = calc_avg_sequence(d_train_normalized, numerical_feats=model_features, categorical_feats=[],model_features=model_features, entity_col=sequence_id_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f61c4b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000e+00, 4.0875e+01, 5.0000e-01, 1.4625e+01, 1.0500e+00,\n",
       "        1.5250e+01],\n",
       "       [2.5000e+02, 4.0585e+01, 6.0500e-01, 1.4875e+01, 7.9000e-01,\n",
       "        1.4875e+01],\n",
       "       [5.0000e+02, 3.9835e+01, 7.5500e-01, 1.4125e+01, 8.7000e-01,\n",
       "        1.7000e+01],\n",
       "       ...,\n",
       "       [1.1925e+05, 3.9625e+01, 6.6000e-01, 1.4400e+01, 9.7000e-01,\n",
       "        1.4165e+01],\n",
       "       [1.1950e+05, 3.9500e+01, 5.0000e-01, 1.2710e+01, 1.2050e+00,\n",
       "        1.5000e+01],\n",
       "       [1.1975e+05, 3.9625e+01, 6.6000e-01, 1.4000e+01, 1.0150e+00,\n",
       "        1.4750e+01]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b707fa7",
   "metadata": {},
   "source": [
    "### Average score over baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b254545e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeshap.utils import get_avg_score_with_avg_event\n",
    "avg_score_over_len = get_avg_score_with_avg_event(f, average_event, top=480)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873b1de2",
   "metadata": {},
   "source": [
    "## 3.1 Local Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee86089f",
   "metadata": {},
   "source": [
    "### Select sequences to explain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df877e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_sequence_id = f\"cycling_{np.random.choice(ids_for_test)}\"\n",
    "pos_x_pd = d_test_normalized[d_test_normalized['all_id'] == positive_sequence_id]\n",
    "\n",
    "# select model features only\n",
    "pos_x_data = pos_x_pd[model_features]\n",
    "# convert the instance to numpy so TimeSHAP receives it\n",
    "pos_x_data = np.expand_dims(pos_x_data.to_numpy().copy(), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc37382",
   "metadata": {},
   "source": [
    "### Local Report on positive instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be908ef3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming all features are model features\n"
     ]
    },
    {
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\"rounded_str\": \"0.181\"}, {\"Event\": \"Other Events\", \"Feature\": \"Other Features\", \"Shapley Value\": 0.09529791619806038, \"rounded\": 0.095, \"rounded_str\": \"0.095\"}, {\"Event\": \"Event -4\", \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.026702670717523358, \"rounded\": 0.027, \"rounded_str\": \"0.027\"}, {\"Event\": \"Event -8\", \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.016357692780475885, \"rounded\": 0.016, \"rounded_str\": \"0.016\"}, {\"Event\": \"Event -8\", \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.011905748858338283, \"rounded\": 0.012, \"rounded_str\": \"0.012\"}, {\"Event\": \"Event -4\", \"Feature\": \"Other Features\", \"Shapley Value\": 0.0051559989532779515, \"rounded\": 0.005, \"rounded_str\": \"0.005\"}, {\"Event\": \"Event -4\", \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.0025903202593329233, \"rounded\": 0.003, \"rounded_str\": \"0.003\"}, {\"Event\": \"Event -8\", \"Feature\": \"Other Features\", \"Shapley Value\": 0.0006207454535695489, \"rounded\": 0.001, \"rounded_str\": \"0.001\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from timeshap.explainer import local_report\n",
    "\n",
    "pruning_dict = {'tol': 0.025}\n",
    "event_dict = {'rs': 42, 'nsamples': 32000}\n",
    "feature_dict = {'rs': 42, 'nsamples': 32000, 'feature_names': model_features, 'plot_features': plot_feats}\n",
    "cell_dict = {'rs': 42, 'nsamples': 32000, 'top_x_feats': 2, 'top_x_events': 2}\n",
    "local_report(f, pos_x_data, pruning_dict, event_dict, feature_dict, cell_dict=cell_dict, entity_uuid=positive_sequence_id, entity_col='all_id', baseline=average_event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f773fa6f",
   "metadata": {},
   "source": [
    "## 3.2 Global Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f02c0b",
   "metadata": {},
   "source": [
    "### Explain all \n",
    "\n",
    "TimeSHAP offers methods to explain all instances and save as CSV.\n",
    "This allows for global explanations and local plots with no calculation delay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f790f55b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating pruning algorithm\n",
      "Calculating event data\n",
      "Calculating feat data\n",
      "Calculating pruning indexes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tolerance</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.05</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.57735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.075</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Pruning</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tolerance   Mean      Std\n",
       "0        0.05   24.5  0.57735\n",
       "1       0.075   21.0  0.00000\n",
       "2  No Pruning  480.0  0.00000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from timeshap.explainer import global_report\n",
    "\n",
    "pos_dataset = d_test_normalized[d_test_normalized['label'] == 1]\n",
    "schema = schema = list(pos_dataset.columns)\n",
    "pruning_dict = {'tol': [0.05, 0.075], 'path': 'outputs/prun_all_tf.csv'}\n",
    "event_dict = {'path': 'outputs/event_all_tf.csv', 'rs': 42, 'nsamples': 32000}\n",
    "feature_dict = {'path': 'outputs/feature_all_tf.csv', 'rs': 42, 'nsamples': 32000, 'feature_names': model_features, 'plot_features': plot_feats,}\n",
    "prun_stats, global_plot = global_report(f, pos_dataset, pruning_dict, event_dict, feature_dict, average_event, model_features, schema, sequence_id_feat, time_feat, )\n",
    "prun_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4859ec88",
   "metadata": {},
   "outputs": [
    {
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       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"axis\": {\"domain\": true, \"domainColor\": \"#666F89\", \"grid\": false, \"gridCap\": \"round\", \"gridColor\": \"#B3B7C4\", \"gridDash\": [2, 4], \"gridWidth\": 0.5, \"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelPadding\": 2, \"tickColor\": \"#666F89\", \"tickOpacity\": 0.5, \"tickSize\": 4, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12}, \"axisBand\": {\"domain\": true, \"labelPadding\": 4, \"ticks\": false}, \"axisY\": {\"domain\": false, \"titleAlign\": \"left\", \"titleAngle\": 0, \"titleX\": -20, \"titleY\": -10}, \"legend\": {\"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelFontSize\": 12, \"symbolSize\": 40, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12, \"titlePadding\": 4}, \"arc\": {\"stroke\": \"#FFFFFF\", \"strokeWidth\": 1}, \"bar\": {\"fill\": \"#2770EB\", \"stroke\": 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Ankle\", \"type\": \"Mean\"}, {\"Shapley Value\": 0.007144150208859418, \"Feature\": \"STD Chest <-> Right Ankle\", \"type\": \"Mean\"}, {\"Shapley Value\": 0.20275280246777155, \"Feature\": \"STD Chest <-> Left Ankle\", \"type\": \"Mean\"}, {\"Shapley Value\": 0.17428368771714806, \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"type\": \"Mean\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2870782f",
   "metadata": {},
   "source": [
    "## 3.3 Individual Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aafb0500",
   "metadata": {},
   "source": [
    "### Local Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f35f03d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeshap.plot import plot_temp_coalition_pruning, plot_event_heatmap, plot_feat_barplot, plot_cell_level\n",
    "from timeshap.explainer import local_pruning, local_event, local_feat, local_cell_level\n",
    "# select model features only\n",
    "pos_x_data = pos_x_pd[model_features]\n",
    "# convert the instance to numpy so TimeSHAP receives it\n",
    "pos_x_data = np.expand_dims(pos_x_data.to_numpy().copy(), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7312cb0",
   "metadata": {},
   "source": [
    "##### Pruning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d72a92fa",
   "metadata": {},
   "outputs": [
    {
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       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pruning_dict = {'tol': 0.025,}\n",
    "coal_plot_data, coal_prun_idx = local_pruning(f, pos_x_data, pruning_dict, average_event, positive_sequence_id, sequence_id_feat, False)\n",
    "# coal_prun_idx is in negative terms\n",
    "pruning_idx = pos_x_data.shape[1] + coal_prun_idx\n",
    "pruning_plot = plot_temp_coalition_pruning(coal_plot_data, coal_prun_idx, plot_limit=40)\n",
    "pruning_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb4260c",
   "metadata": {},
   "source": [
    "##### Event-level explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4ac0cb06",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "alt.LayerChart(...)"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "event_dict = {'rs': 42, 'nsamples': 32000}\n",
    "event_data = local_event(f, pos_x_data, event_dict, positive_sequence_id, sequence_id_feat, average_event, pruning_idx)\n",
    "event_plot = plot_event_heatmap(event_data)\n",
    "event_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "21cc7458",
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      "text/plain": [
       "alt.LayerChart(...)"
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   "source": [
    "event_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5034207",
   "metadata": {},
   "source": [
    "##### Feature-level explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c6776c0",
   "metadata": {},
   "outputs": [
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       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"axis\": {\"domain\": true, \"domainColor\": \"#666F89\", \"grid\": false, \"gridCap\": \"round\", \"gridColor\": \"#B3B7C4\", \"gridDash\": [2, 4], \"gridWidth\": 0.5, \"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelPadding\": 2, \"tickColor\": \"#666F89\", \"tickOpacity\": 0.5, \"tickSize\": 4, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12}, \"axisBand\": {\"domain\": true, \"labelPadding\": 4, \"ticks\": false}, \"axisY\": {\"domain\": false, \"titleAlign\": \"left\", \"titleAngle\": 0, \"titleX\": -20, \"titleY\": -10}, \"legend\": {\"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelFontSize\": 12, \"symbolSize\": 40, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12, \"titlePadding\": 4}, \"arc\": {\"stroke\": \"#FFFFFF\", \"strokeWidth\": 1}, \"bar\": {\"fill\": \"#2770EB\", \"stroke\": null}, \"line\": {\"stroke\": \"#2770EB\", \"strokeWidth\": 2}, \"path\": {\"stroke\": \"#2770EB\", \"strokeWidth\": 0.5}, \"point\": {\"fill\": \"#2770EB\", \"shape\": \"circle\", \"filled\": true}, \"rect\": {\"fill\": \"#2770EB\"}, \"rule\": {\"stroke\": \"#666F89\"}, \"shape\": {\"stroke\": \"#2770EB\"}, \"text\": {\"color\": \"#19274E\", \"font\": \"Roboto, Arial, sans-serif\", \"fontSize\": 12}, \"range\": {\"category\": [\"#2770EB\", \"#77B98A\", \"#DA5252\", \"#FFC400\", \"#A87AEA\", \"#00A39E\", \"#19274E\"], \"diverging\": [\"#006360\", \"#2BB3AE\", \"#96D9D7\", \"#FFFFFF\", \"#EDAAAA\", \"#DA5252\", \"#811D1D\"], \"heatmap\": [\"#18448F\", \"#1B4EA5\", \"#2770EB\", \"#4C88EE\", \"#82ACF3\", \"#A6C4F7\", \"#E9F1FD\"], \"ramp\": [\"#18448F\", \"#1B4EA5\", \"#2770EB\", \"#4C88EE\", \"#82ACF3\", \"#A6C4F7\", \"#E9F1FD\"]}, \"background\": \"#FFFFFF\", \"group\": {\"fill\": \"#FFFFFF\"}, \"header\": {\"labelColor\": \"#19274E\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelFontSize\": 12, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 16}, \"title\": {\"anchor\": \"start\", \"color\": \"#19274E\", \"font\": \"Roboto, Arial, sans-serif\", \"fontSize\": 20, \"fontWeight\": \"bold\", \"offset\": 20, \"subtitleColor\": \"#19274E\", \"subtitleFontSize\": 16}, \"view\": {\"continuousHeight\": 300, \"continuousWidth\": 400, \"stroke\": \"transparent\"}}, \"layer\": [{\"data\": {\"name\": \"data-c4881ee1f636b5305f8345e5f3913d97\"}, \"mark\": {\"type\": \"bar\", \"size\": 15, \"thickness\": 1}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": {\"grid\": true, \"labelFontSize\": 15, \"title\": \"Shapley Value\", \"titleFontSize\": 15}, \"field\": \"Shapley Value\", \"scale\": {\"domain\": [-0.1, 0.4]}}, \"y\": {\"type\": \"nominal\", \"axis\": {\"labelFontSize\": 15, \"title\": \"Feature\", \"titleFontSize\": 15, \"titleX\": -61}, \"field\": \"Feature\", \"sort\": {\"field\": \"sort_col\", \"order\": \"descending\"}}}}, {\"data\": {\"name\": \"data-9c00aa9dc2b1c1a0b4bf9b34ead12925\"}, \"mark\": {\"type\": \"rule\", \"color\": \"#798184\"}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"x\"}}}], \"height\": 225, \"width\": 190, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-c4881ee1f636b5305f8345e5f3913d97\": [{\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"Mean Chest <-> Right Ankle\", \"Shapley Value\": 0.01568724619490769, \"sort_col\": 0.01568724619490769}, {\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"STD Chest <-> Right Ankle\", \"Shapley Value\": 0.0013354555481956965, \"sort_col\": 0.0013354555481956965}, {\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"Mean Chest <-> Left Ankle\", \"Shapley Value\": 0.04191808806998398, \"sort_col\": 0.04191808806998398}, {\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.19718013462566164, \"sort_col\": 0.19718013462566164}, {\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"Mean Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.03199030366681871, \"sort_col\": 0.03199030366681871}, {\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.2433303138329867, \"sort_col\": 0.2433303138329867}, {\"Random seed\": 42, \"NSamples\": 32000, \"Feature\": \"Pruned Events\", \"Shapley Value\": 0.09246885535262905, \"sort_col\": 0.09246885535262905}], \"data-9c00aa9dc2b1c1a0b4bf9b34ead12925\": [{\"x\": 0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_dict = {'rs': 42, 'nsamples': 32000, 'feature_names': model_features, 'plot_features': plot_feats}\n",
    "feature_data = local_feat(f, pos_x_data, feature_dict, positive_sequence_id, sequence_id_feat, average_event, pruning_idx)\n",
    "feature_plot = plot_feat_barplot(feature_data, feature_dict.get('top_feats'), feature_dict.get('plot_features'))\n",
    "feature_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d8f3c6",
   "metadata": {},
   "source": [
    "##### Cell-level explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a13c961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-aaa098ca66974f82aceb379819671370\"></div>\n",
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       "      outputDiv = document.getElementById(\"altair-viz-aaa098ca66974f82aceb379819671370\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"axis\": {\"domain\": true, \"domainColor\": \"#666F89\", \"grid\": false, \"gridCap\": \"round\", \"gridColor\": \"#B3B7C4\", \"gridDash\": [2, 4], \"gridWidth\": 0.5, \"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelPadding\": 2, \"tickColor\": \"#666F89\", \"tickOpacity\": 0.5, \"tickSize\": 4, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12}, \"axisBand\": {\"domain\": true, \"labelPadding\": 4, \"ticks\": false}, \"axisY\": {\"domain\": false, \"titleAlign\": \"left\", \"titleAngle\": 0, \"titleX\": -20, \"titleY\": -10}, \"legend\": {\"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelFontSize\": 12, \"symbolSize\": 40, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12, \"titlePadding\": 4}, \"arc\": {\"stroke\": \"#FFFFFF\", \"strokeWidth\": 1}, \"bar\": {\"fill\": \"#2770EB\", \"stroke\": null}, \"line\": {\"stroke\": \"#2770EB\", \"strokeWidth\": 2}, \"path\": {\"stroke\": \"#2770EB\", \"strokeWidth\": 0.5}, \"point\": {\"fill\": \"#2770EB\", \"shape\": \"circle\", \"filled\": true}, \"rect\": {\"fill\": \"#2770EB\"}, \"rule\": {\"stroke\": \"#666F89\"}, \"shape\": {\"stroke\": \"#2770EB\"}, \"text\": {\"color\": \"#19274E\", \"font\": \"Roboto, Arial, sans-serif\", \"fontSize\": 12}, \"range\": {\"category\": [\"#2770EB\", \"#77B98A\", \"#DA5252\", \"#FFC400\", \"#A87AEA\", \"#00A39E\", \"#19274E\"], \"diverging\": [\"#006360\", \"#2BB3AE\", \"#96D9D7\", \"#FFFFFF\", \"#EDAAAA\", \"#DA5252\", \"#811D1D\"], \"heatmap\": [\"#18448F\", \"#1B4EA5\", \"#2770EB\", \"#4C88EE\", \"#82ACF3\", \"#A6C4F7\", \"#E9F1FD\"], \"ramp\": [\"#18448F\", \"#1B4EA5\", \"#2770EB\", \"#4C88EE\", \"#82ACF3\", \"#A6C4F7\", \"#E9F1FD\"]}, \"background\": \"#FFFFFF\", \"group\": {\"fill\": \"#FFFFFF\"}, \"header\": {\"labelColor\": \"#19274E\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelFontSize\": 12, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 16}, \"title\": {\"anchor\": \"start\", \"color\": \"#19274E\", \"font\": \"Roboto, Arial, sans-serif\", \"fontSize\": 20, \"fontWeight\": \"bold\", \"offset\": 20, \"subtitleColor\": \"#19274E\", \"subtitleFontSize\": 16}, \"view\": {\"continuousHeight\": 300, \"continuousWidth\": 400, \"stroke\": \"transparent\"}}, \"hconcat\": [{\"layer\": [{\"mark\": \"rect\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"rounded\", \"legend\": null, \"scale\": {\"domain\": [-0.5, 0.5], \"range\": [\"#5f8fd6\", \"#99c3fb\", \"#f5f5f5\", \"#ffaa92\", \"#d16f5b\"]}, \"title\": null}, \"x\": {\"type\": \"nominal\", \"axis\": {\"titleFontSize\": 15}, \"field\": \"Event\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labels\": false, \"title\": null}, \"field\": \"Feature\"}}}, {\"mark\": {\"type\": \"text\", \"align\": \"right\", \"baseline\": \"middle\", \"color\": \"#798184\", \"dx\": 18, \"fontSize\": 15}, \"encoding\": {\"text\": {\"type\": \"nominal\", \"field\": \"rounded_str\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": 0, \"labelFontSize\": 15, \"labelOffset\": 24, \"labelPadding\": 30, \"orient\": \"top\", \"title\": null}, \"field\": \"Event\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labels\": false, \"title\": null}, \"field\": \"Feature\"}}}], \"data\": {\"name\": \"data-739224059e7daff62226ed3a17e45e8f\"}, \"height\": 225, \"width\": 66.66666666666667}, {\"layer\": [{\"mark\": \"rect\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"rounded\", \"legend\": {\"gradientLength\": 225, \"gradientThickness\": 10, \"labelFontSize\": 15, \"orient\": \"right\"}, \"scale\": {\"domain\": [-0.5, 0.5], \"range\": [\"#5f8fd6\", \"#99c3fb\", \"#f5f5f5\", \"#ffaa92\", \"#d16f5b\"]}, \"title\": null}, \"x\": {\"type\": \"nominal\", \"axis\": {\"titleFontSize\": 15}, \"field\": \"Event\", \"sort\": [\"Event -8\", \"Event -4\", \"Event -3\"]}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelFontSize\": 15, \"title\": null}, \"field\": \"Feature\", \"sort\": [\"Mean Chest <-> Left Ankle\", \"STD Chest <-> Left Ankle\", \"STD Right Ankle <-> Left Ankle\"]}}}, {\"mark\": {\"type\": \"text\", \"align\": \"right\", \"baseline\": \"middle\", \"color\": \"#798184\", \"dx\": 18, \"fontSize\": 15}, \"encoding\": {\"text\": {\"type\": \"nominal\", \"field\": \"rounded_str\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": 30, \"labelFontSize\": 15, \"orient\": \"top\", \"title\": \"Shapley Value\", \"titleX\": 172, \"titleY\": 245}, \"field\": \"Event\", \"sort\": [\"Event -8\", \"Event -4\", \"Event -3\"]}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelFontSize\": 15, \"title\": null}, \"field\": \"Feature\", \"sort\": [\"Mean Chest <-> Left Ankle\", \"STD Chest <-> Left Ankle\", \"STD Right Ankle <-> Left Ankle\"]}}}], \"data\": {\"name\": \"data-154bfac978c6352e7579874ef58e6451\"}, \"height\": 225, \"width\": 160}], \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-739224059e7daff62226ed3a17e45e8f\": [{\"Event\": [\"Pruned\", \"Events\"], \"Feature\": \"Other features\", \"rounded\": 0.099, \"rounded_str\": \"0.099\"}], \"data-154bfac978c6352e7579874ef58e6451\": [{\"Event\": \"Other Events\", \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.18684749373651932, \"rounded\": 0.187, \"rounded_str\": \"0.187\"}, {\"Event\": \"Other Events\", \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.1570078587210375, \"rounded\": 0.157, \"rounded_str\": \"0.157\"}, {\"Event\": \"Other Events\", \"Feature\": \"Other Features\", \"Shapley Value\": 0.06779807677607813, \"rounded\": 0.068, \"rounded_str\": \"0.068\"}, {\"Event\": \"Event -4\", \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.028004327821945285, \"rounded\": 0.028, \"rounded_str\": \"0.028\"}, {\"Event\": \"Other Events\", \"Feature\": \"Mean Chest <-> Left Ankle\", \"Shapley Value\": 0.02115406386896812, \"rounded\": 0.021, \"rounded_str\": \"0.021\"}, {\"Event\": \"Event -3\", \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.019356709391817994, \"rounded\": 0.019, \"rounded_str\": \"0.019\"}, {\"Event\": \"Event -8\", \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.01620792134460347, \"rounded\": 0.016, \"rounded_str\": \"0.016\"}, {\"Event\": \"Event -8\", \"Feature\": \"STD Right Ankle <-> Left Ankle\", \"Shapley Value\": 0.012667965916633994, \"rounded\": 0.013, \"rounded_str\": \"0.013\"}, {\"Event\": \"Event -3\", \"Feature\": \"Mean Chest <-> Left Ankle\", \"Shapley Value\": 0.00927759552957591, \"rounded\": 0.009, \"rounded_str\": \"0.009\"}, {\"Event\": \"Event -4\", \"Feature\": \"Mean Chest <-> Left Ankle\", \"Shapley Value\": 0.007385068525234351, \"rounded\": 0.007, \"rounded_str\": \"0.007\"}, {\"Event\": \"Event -8\", \"Feature\": \"Mean Chest <-> Left Ankle\", \"Shapley Value\": 0.00523027594551756, \"rounded\": 0.005, \"rounded_str\": \"0.005\"}, {\"Event\": \"Event -3\", \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.002329654623350108, \"rounded\": 0.002, \"rounded_str\": \"0.002\"}, {\"Event\": \"Event -4\", \"Feature\": \"STD Chest <-> Left Ankle\", \"Shapley Value\": 0.002181800465652195, \"rounded\": 0.002, \"rounded_str\": \"0.002\"}, {\"Event\": \"Event -4\", \"Feature\": \"Other Features\", \"Shapley Value\": -0.0017691757968849031, \"rounded\": -0.002, \"rounded_str\": \"-0.002\"}, {\"Event\": \"Event -8\", \"Feature\": \"Other Features\", \"Shapley Value\": -0.004350176350618962, \"rounded\": -0.004, \"rounded_str\": \"-0.004\"}, {\"Event\": \"Event -3\", \"Feature\": \"Other Features\", \"Shapley Value\": -0.004468961144592332, \"rounded\": -0.004, \"rounded_str\": \"-0.004\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_dict = {'rs': 42, 'nsamples': 32000, 'top_x_events': 3, 'top_x_feats': 3}\n",
    "cell_data = local_cell_level(f, pos_x_data, cell_dict, event_data, feature_data, positive_sequence_id, sequence_id_feat, average_event, pruning_idx)\n",
    "feat_names = list(feature_data['Feature'].values)[:-1] # exclude pruned events\n",
    "cell_plot = plot_cell_level(cell_data, feat_names, feature_dict.get('plot_features'))\n",
    "cell_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d78212",
   "metadata": {},
   "source": [
    "### Global Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0b57cf3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeshap.explainer import prune_all, pruning_statistics, event_explain_all, feat_explain_all\n",
    "from timeshap.plot import plot_global_event, plot_global_feat\n",
    "\n",
    "pos_dataset = d_test_normalized[d_test_normalized['label'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6925bb1",
   "metadata": {},
   "source": [
    "##### Pruning statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3f522caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tolerance</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.05</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.57735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.075</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Pruning</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tolerance   Mean      Std\n",
       "0        0.05   24.5  0.57735\n",
       "1       0.075   21.0  0.00000\n",
       "2  No Pruning  480.0  0.00000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pruning_dict = {'tol': [0.05, 0.075], 'path': 'outputs/prun_all_tf.csv'}\n",
    "prun_indexes = prune_all(f, pos_dataset, pruning_dict, average_event, model_features, schema, sequence_id_feat, time_feat)\n",
    "pruning_stats = pruning_statistics(prun_indexes, pruning_dict.get('tol'))\n",
    "pruning_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb518f71",
   "metadata": {},
   "source": [
    "##### Global event-level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5669e063",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<div id=\"altair-viz-4818eb59056f40118138417a6979fdba\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
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       "      outputDiv = document.getElementById(\"altair-viz-4818eb59056f40118138417a6979fdba\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
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       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
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       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"axis\": {\"domain\": true, \"domainColor\": \"#666F89\", \"grid\": false, \"gridCap\": \"round\", \"gridColor\": \"#B3B7C4\", \"gridDash\": [2, 4], \"gridWidth\": 0.5, \"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelPadding\": 2, \"tickColor\": \"#666F89\", \"tickOpacity\": 0.5, \"tickSize\": 4, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12}, \"axisBand\": {\"domain\": true, \"labelPadding\": 4, \"ticks\": false}, \"axisY\": {\"domain\": false, \"titleAlign\": \"left\", \"titleAngle\": 0, \"titleX\": -20, \"titleY\": -10}, \"legend\": {\"labelColor\": \"#666F89\", \"labelFont\": \"Roboto, Arial, sans-serif\", \"labelFontSize\": 12, \"symbolSize\": 40, \"titleColor\": \"#19274E\", \"titleFont\": \"Roboto, Arial, sans-serif\", \"titleFontSize\": 12, \"titlePadding\": 4}, \"arc\": {\"stroke\": \"#FFFFFF\", \"strokeWidth\": 1}, \"bar\": {\"fill\": \"#2770EB\", \"stroke\": 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0.0162211712184281, \"t (event index)\": -14, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.0197584100725227, \"t (event index)\": -15, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.0073289753824446, \"t (event index)\": -16, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.0046645675438778, \"t (event index)\": -17, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.0092104902363273, \"t (event index)\": -18, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.0122516450003229, \"t (event index)\": -19, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.0089790285658669, \"t (event index)\": -20, \"type\": \"Shapley Value\"}, {\"Shapley Value\": 0.008743646415836576, \"t (event index)\": -20, \"type\": \"Mean\"}, {\"Shapley Value\": 0.01409526705456815, \"t (event index)\": -19, \"type\": \"Mean\"}, {\"Shapley Value\": 0.0105768167756167, \"t (event index)\": -18, \"type\": \"Mean\"}, {\"Shapley Value\": 0.011453590797090626, \"t (event index)\": -17, \"type\": \"Mean\"}, {\"Shapley Value\": 0.014743910078106949, \"t (event index)\": -16, \"type\": \"Mean\"}, {\"Shapley Value\": 0.016173167670528323, \"t (event index)\": -15, \"type\": \"Mean\"}, {\"Shapley Value\": 0.0189702353840066, \"t (event index)\": -14, \"type\": \"Mean\"}, {\"Shapley Value\": 0.017701259845897627, \"t (event index)\": -13, \"type\": \"Mean\"}, {\"Shapley Value\": 0.023497845204059276, \"t (event index)\": -12, \"type\": \"Mean\"}, {\"Shapley Value\": 0.011360833679309274, \"t (event index)\": -11, \"type\": \"Mean\"}, {\"Shapley Value\": 0.0222948505985874, \"t (event index)\": -10, \"type\": \"Mean\"}, {\"Shapley Value\": 0.018269197793390424, \"t (event index)\": -9, \"type\": \"Mean\"}, {\"Shapley Value\": 0.02930569361999335, \"t (event index)\": -8, \"type\": \"Mean\"}, {\"Shapley Value\": 0.03160621430474383, \"t (event index)\": -7, \"type\": \"Mean\"}, {\"Shapley Value\": 0.0275878524086424, \"t (event index)\": -6, \"type\": \"Mean\"}, {\"Shapley Value\": 0.027080726693089578, \"t (event index)\": -5, \"type\": \"Mean\"}, {\"Shapley Value\": 0.03867106224608372, \"t (event index)\": -4, \"type\": \"Mean\"}, {\"Shapley Value\": 0.030228376594951097, \"t (event index)\": -3, \"type\": \"Mean\"}, {\"Shapley Value\": 0.026957225278818726, \"t (event index)\": -2, \"type\": \"Mean\"}, {\"Shapley Value\": 0.0254614279575614, \"t (event index)\": -1, \"type\": \"Mean\"}, {\"Shapley Value\": 0.030425491908776803, \"t (event index)\": 0, \"type\": \"Mean\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_dict = {'path': 'outputs/event_all_tf.csv', 'rs': 42, 'nsamples': 32000}\n",
    "event_data = event_explain_all(f, pos_dataset, event_dict, prun_indexes, average_event, model_features, schema, sequence_id_feat, time_feat)\n",
    "event_global_plot = plot_global_event(event_data)\n",
    "event_global_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ee433c",
   "metadata": {},
   "source": [
    "##### Global feature-level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0564654",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_dict = {'path': 'outputs/feature_all_tf.csv', 'rs': 42, 'nsamples': 32000, 'feature_names': model_features, 'plot_features': plot_feats, }\n",
    "feat_data = feat_explain_all(f, pos_dataset, feature_dict, prun_indexes, average_event, model_features, schema, sequence_id_feat, time_feat)\n",
    "feat_global_plot = plot_global_feat(feat_data, **feature_dict)\n",
    "feat_global_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eda9bcb4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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