etfLIN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afb5d31a-296a-4fb1-90ac-f9a2136e27a4",
   "metadata": {},
   "source": [
    "##### used conda env giza from ll laptop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78186bb5-98ce-471c-93d3-ca19386ca744",
   "metadata": {},
   "source": [
    "## Create and Train an Linear regression Model\n",
    "### We'll start by creating a simple XGBoost model using Scikit-Learn and train it on diabetes dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae27a3e5-d5d6-487d-83f8-d1f61f3a5f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "# import matplotlib.pyplot as plt\n",
    "# import seaborn as sns\n",
    "# import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b60fabc3-dc33-4f66-9350-d5611479bb30",
   "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>Date</th>\n",
       "      <th>Closing Price</th>\n",
       "      <th>IBIT</th>\n",
       "      <th>FBTC</th>\n",
       "      <th>BITB</th>\n",
       "      <th>ARKB</th>\n",
       "      <th>BTCO</th>\n",
       "      <th>EZBC</th>\n",
       "      <th>BRRR</th>\n",
       "      <th>HODL</th>\n",
       "      <th>BTCW</th>\n",
       "      <th>GBTC</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-01-2024</td>\n",
       "      <td>46368.58594</td>\n",
       "      <td>111.7</td>\n",
       "      <td>227.0</td>\n",
       "      <td>237.9</td>\n",
       "      <td>65.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>50.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-95.1</td>\n",
       "      <td>655.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12-01-2024</td>\n",
       "      <td>42853.16797</td>\n",
       "      <td>386.0</td>\n",
       "      <td>195.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-484.1</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16-01-2024</td>\n",
       "      <td>43154.94531</td>\n",
       "      <td>212.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>50.2</td>\n",
       "      <td>122.3</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-594.4</td>\n",
       "      <td>-52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17-01-2024</td>\n",
       "      <td>42742.65234</td>\n",
       "      <td>371.4</td>\n",
       "      <td>358.1</td>\n",
       "      <td>68.2</td>\n",
       "      <td>50.3</td>\n",
       "      <td>57.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-460.6</td>\n",
       "      <td>453.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18-01-2024</td>\n",
       "      <td>41262.05859</td>\n",
       "      <td>145.5</td>\n",
       "      <td>177.9</td>\n",
       "      <td>20.1</td>\n",
       "      <td>41.8</td>\n",
       "      <td>58.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-582.3</td>\n",
       "      <td>-126.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19-01-2024</td>\n",
       "      <td>41618.40625</td>\n",
       "      <td>201.5</td>\n",
       "      <td>222.3</td>\n",
       "      <td>56.7</td>\n",
       "      <td>62.6</td>\n",
       "      <td>63.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-590.4</td>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22-01-2024</td>\n",
       "      <td>39507.36719</td>\n",
       "      <td>260.6</td>\n",
       "      <td>158.7</td>\n",
       "      <td>41.6</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-640.5</td>\n",
       "      <td>-87.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23-01-2024</td>\n",
       "      <td>39845.55078</td>\n",
       "      <td>160.1</td>\n",
       "      <td>157.7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-515.3</td>\n",
       "      <td>-106.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24-01-2024</td>\n",
       "      <td>40077.07422</td>\n",
       "      <td>66.2</td>\n",
       "      <td>125.7</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-429.3</td>\n",
       "      <td>-158.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25-01-2024</td>\n",
       "      <td>39933.80859</td>\n",
       "      <td>170.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-394.1</td>\n",
       "      <td>-79.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Closing Price   IBIT   FBTC   BITB   ARKB  BTCO  EZBC  BRRR  \\\n",
       "0  11-01-2024    46368.58594  111.7  227.0  237.9   65.3  17.4  50.1  29.4   \n",
       "1  12-01-2024    42853.16797  386.0  195.3   17.4   39.8  28.4   0.0  20.2   \n",
       "2  16-01-2024    43154.94531  212.7  102.0   50.2  122.3  31.9   0.0  15.3   \n",
       "3  17-01-2024    42742.65234  371.4  358.1   68.2   50.3  57.6   1.2   1.2   \n",
       "4  18-01-2024    41262.05859  145.5  177.9   20.1   41.8  58.8   0.0   9.3   \n",
       "5  19-01-2024    41618.40625  201.5  222.3   56.7   62.6  63.4   0.0  10.4   \n",
       "6  22-01-2024    39507.36719  260.6  158.7   41.6   65.0   5.6   4.7   9.7   \n",
       "7  23-01-2024    39845.55078  160.1  157.7   26.3   61.8   0.0   1.1   0.0   \n",
       "8  24-01-2024    40077.07422   66.2  125.7   19.1   24.9  19.9   1.2   9.1   \n",
       "9  25-01-2024    39933.80859  170.7  101.0   20.0   16.1   0.0   0.0   6.5   \n",
       "\n",
       "   HODL  BTCW   GBTC  Total  \n",
       "0  10.6   1.0  -95.1  655.3  \n",
       "1   0.0   0.0 -484.1  203.0  \n",
       "2   7.3   0.0 -594.4  -52.7  \n",
       "3   4.8   1.6 -460.6  453.8  \n",
       "4   2.3   0.0 -582.3 -126.6  \n",
       "5  14.2   2.9 -590.4   43.6  \n",
       "6   6.8   0.4 -640.5  -87.4  \n",
       "7   2.2   0.0 -515.3 -106.1  \n",
       "8   4.5   0.4 -429.3 -158.3  \n",
       "9   0.0   0.0 -394.1  -79.8  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('BETF Final.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdfb88e1-3d85-4e4c-b1a5-5f47d3d0693a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Closing Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18f4920c-9306-4bf8-947d-d419a3138232",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = df[['IBIT', 'FBTC', 'BITB', 'ARKB', 'BTCO',\n",
    "#        'EZBC', 'BRRR', 'HODL', 'BTCW',\n",
    "#        'GBTC', ]]\n",
    "X = df[['IBIT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d07ae79b-fd40-48b2-8318-53174ee59eaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a23d5292-6a9b-41b8-b1d8-297b19cae50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=410)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9df3f7dc-c1b6-428e-aa11-0baa65a1f40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7536532e-c34c-4815-8c06-24e53ee22d5c",
   "metadata": {},
   "outputs": [
    {
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       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
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       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4c2d8e4-5b51-47f5-b421-898d5142250c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59425.33942015, 59394.44762914, 59443.63455851, 59268.88099644,\n",
       "       58988.55548841, 58877.78492389, 59257.28407813, 59463.42929838,\n",
       "       59481.52448987, 59191.60153221, 59481.52448987, 59481.52448987,\n",
       "       58918.7740317 , 59481.52448987, 59237.38936483, 59327.26548171,\n",
       "       59481.52448987, 59387.84938252, 59375.75259704, 59145.31383241,\n",
       "       59448.23333646, 59392.5481339 , 59321.46702256, 59370.45400506,\n",
       "       59481.52448987, 59280.07802101, 59446.03392092, 59289.47552378,\n",
       "       59461.82972344, 59136.21624995])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = lm.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0354598e-637d-4ce3-95ac-dc09db3bf15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'IBIT': [120]\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40ed2362-5316-41e2-8f42-c08935e70287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59361.55636946])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = lm.predict(new_data)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33594c4-df2e-4b52-b33e-2cd30c5bd37e",
   "metadata": {},
   "source": [
    "# Convert the Model to ONNX Format\n",
    "### Giza supports ONNX models so you'll need to convert the model to ONNX format. After the model is trained, you can convert it to ONNX format using the skl2onnx library."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574318fe-864a-4f93-9653-aa36aefda062",
   "metadata": {},
   "source": [
    "#### installng from conda env gave problems seems to work from here itself in Jupyter lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ff7a591-b8e5-427e-8cf0-27324210d7a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: skl2onnx in c:\\users\\chuck.raghavan\\appdata\\local\\anaconda3\\envs\\giza3\\lib\\site-packages (1.17.0)\n",
      "Requirement already satisfied: onnx>=1.2.1 in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from skl2onnx) (1.16.1)\n",
      "Requirement already satisfied: scikit-learn>=1.1 in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from skl2onnx) (1.4.2)\n",
      "Requirement already satisfied: onnxconverter-common>=1.7.0 in c:\\users\\chuck.raghavan\\appdata\\local\\anaconda3\\envs\\giza3\\lib\\site-packages (from skl2onnx) (1.14.0)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from onnx>=1.2.1->skl2onnx) (1.26.4)\n",
      "Requirement already satisfied: protobuf>=3.20.2 in c:\\users\\chuck.raghavan\\appdata\\local\\anaconda3\\envs\\giza3\\lib\\site-packages (from onnx>=1.2.1->skl2onnx) (3.20.2)\n",
      "Requirement already satisfied: packaging in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from onnxconverter-common>=1.7.0->skl2onnx) (23.2)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn>=1.1->skl2onnx) (1.13.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn>=1.1->skl2onnx) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\chuck.raghavan\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn>=1.1->skl2onnx) (3.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install skl2onnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e05dfd42-24e1-46ec-9e71-6eb6bc6fd3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skl2onnx import convert_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "866b8757-4045-4a57-9fd0-84351c614623",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skl2onnx.common.data_types import FloatTensorType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a453d17-3dea-4f04-ba5a-0e7c572e58a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the initial types for the ONNX model\n",
    "initial_type = [('float_input', FloatTensorType([None, X_train.shape[1]]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "697e38a2-4b10-4b44-939b-d8cb24661870",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the scikit-learn model to ONNX\n",
    "onnx_model = convert_sklearn(lm, initial_types=initial_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c4a0bb-d0ed-4fb4-9b83-42f447bd7df5",
   "metadata": {},
   "source": [
    "## Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d44f8290-9be5-4c6a-8b6e-0c01883affc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the ONNX model to a file - will create file - \"linear_regression.onnx\"\n",
    "with open(\"linear_regression3.onnx\", \"wb\") as f:\n",
    "    f.write(onnx_model.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abe18478-37c7-4b40-b549-8e8dd3324c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from giza.zkcook import serialize_model\n",
    "# serialize_model(lm, \"etf_lin1.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becd66e2-de26-4358-adcc-fc058a6698e8",
   "metadata": {},
   "source": [
    "## Transpile your model to Orion Cairo\n",
    "### We will use Giza-CLI to transpile our saved model to Orion Cairo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c49d75-4ff7-4b3b-b4f3-8fe09c06dd34",
   "metadata": {},
   "source": [
    "$ giza transpile linear_regression.onnx --output-path verifiable_lr\n",
    "[giza][2024-06-04 22:45:09.893] No model id provided, checking if model exists ✅\n",
    "[giza][2024-06-04 22:45:09.906] Model name is: linear_regression\n",
    "[giza][2024-06-04 22:45:10.610] Model Created with id -> 702! ✅\n",
    "[giza][2024-06-04 22:45:11.331] Version Created with id -> 1! ✅\n",
    "[giza][2024-06-04 22:45:11.344] Sending model for transpilation ✅\n",
    "[giza][2024-06-04 22:45:44.311] Transpilation is fully compatible. Version compiled and Sierra is saved at Giza ✅\n",
    "[giza][2024-06-04 22:45:45.120] Downloading model ✅\n",
    "[giza][2024-06-04 22:45:45.148] model saved at: verifiable_lr\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68316b00-2a32-4fbf-93d0-9083925841df",
   "metadata": {},
   "source": [
    "$ giza transpile linear_regression2.onnx --output-path verifiable_lr2\n",
    "[giza][2024-06-05 00:25:02.804] No model id provided, checking if model exists ✅\n",
    "[giza][2024-06-05 00:25:02.817] Model name is: linear_regression2\n",
    "[giza][2024-06-05 00:25:03.650] Model Created with id -> 703! ✅\n",
    "[giza][2024-06-05 00:25:04.245] Version Created with id -> 1! ✅\n",
    "[giza][2024-06-05 00:25:04.257] Sending model for transpilation ✅\n",
    "[giza][2024-06-05 00:25:46.040] Transpilation is fully compatible. Version compiled and Sierra is saved at Giza ✅\n",
    "[giza][2024-06-05 00:25:47.065] Downloading model ✅\n",
    "[giza][2024-06-05 00:25:47.106] model saved at: verifiable_lr2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baadb278-4cee-4a4b-80f0-b865f107b309",
   "metadata": {},
   "source": [
    "$ giza transpile linear_regression3.onnx --output-path verifiable_lr3\n",
    "[giza][2024-06-05 17:09:23.247] No model id provided, checking if model exists ✅\n",
    "[giza][2024-06-05 17:09:23.258] Model name is: linear_regression3\n",
    "[giza][2024-06-05 17:09:23.729] Model Created with id -> 722! ✅\n",
    "[giza][2024-06-05 17:09:24.636] Version Created with id -> 1! ✅\n",
    "[giza][2024-06-05 17:09:24.642] Sending model for transpilation ✅\n",
    "[giza][2024-06-05 17:09:56.529] Transpilation is fully compatible. Version compiled and Sierra is saved at Giza ✅\n",
    "[giza][2024-06-05 17:09:58.492] Downloading model ✅\n",
    "[giza][2024-06-05 17:09:58.523] model saved at: verifiable_lr3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a75ec157-a235-4645-95e7-1bf97b6ff220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[giza][2024-06-05 17:07:43.804] Retrieving information about me!\n",
      "{\n",
      "  \"username\": \"giza1\",\n",
      "  \"email\": \"rchak1@aol.com\",\n",
      "  \"is_active\": true\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!giza users me"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd00d799-97cb-44a2-bde5-03a433b5f507",
   "metadata": {},
   "source": [
    "## Deploy an inference endpoint\n",
    "### Now that our model is transpiled to Cairo we can deploy an endpoint to run verifiable inferences. We will use Giza CLI again to run and deploy an endpoint. Ensure to replace model-id and version-id with your ids provided during transpilation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f989d383-1aaf-4bf1-b6be-8fbb63e61e1c",
   "metadata": {},
   "source": [
    "$ giza endpoints deploy --model-id 702 --version-id 1\n",
    "▰▱▱▱▱▱▱ Creating endpoint!\n",
    "[giza][2024-06-04 22:47:24.658] Endpoint is successful ✅\n",
    "[giza][2024-06-04 22:47:24.670] Endpoint created with id -> 271 ✅\n",
    "[giza][2024-06-04 22:47:24.672] Endpoint created with endpoint URL: https://endpoint-giza1-702-1-c662a00e-7i3yxzspbq-ew.a.run.app 🎉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eec121c-3898-456c-a8e3-82218582c3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "$ giza endpoints deploy --model-id 703 --version-id 1\n",
    "▰▰▰▰▰▰▰ Creating endpoint!\n",
    "[giza][2024-06-05 00:26:55.605] Endpoint is successful ✅\n",
    "[giza][2024-06-05 00:26:55.615] Endpoint created with id -> 273 ✅\n",
    "[giza][2024-06-05 00:26:55.617] Endpoint created with endpoint URL: https://endpoint-giza1-703-1-c901199c-7i3yxzspbq-ew.a.run.app 🎉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "571a8fb4-9a7e-402c-b628-ffa59dffb732",
   "metadata": {},
   "outputs": [],
   "source": [
    "$ giza endpoints deploy --size XL --model-id 703 --version-id 1\n",
    "▰▱▱▱▱▱▱ Creating endpoint!\n",
    "[giza][2024-06-05 00:31:26.543] Endpoint is successful ✅\n",
    "[giza][2024-06-05 00:31:26.554] Endpoint created with id -> 274 ✅\n",
    "[giza][2024-06-05 00:31:26.557] Endpoint created with endpoint URL: https://endpoint-giza1-703-1-1dfb2243-7i3yxzspbq-ew.a.run.app 🎉"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b5908c-191e-4c44-9909-2bfb4b6a3cc7",
   "metadata": {},
   "source": [
    "$ giza endpoints deploy --size XL --model-id 722 --version-id 1\n",
    "▰▰▰▰▰▱▱ Creating endpoint!\n",
    "[giza][2024-06-05 17:15:20.811] Endpoint is successful ✅\n",
    "[giza][2024-06-05 17:15:20.818] Endpoint created with id -> 282 ✅\n",
    "[giza][2024-06-05 17:15:20.821] Endpoint created with endpoint URL: https://endpoint-giza1-722-1-355e8ee2-7i3yxzspbq-ew.a.run.app 🎉\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8e6bac9-3052-40e2-85c8-6b89a8367ec8",
   "metadata": {},
   "source": [
    "### Create and Agent using the CLI # 1 -- size issue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10fc7376-6f3e-497d-b38b-d1e120cc8ea9",
   "metadata": {},
   "source": [
    "$ giza agents create --model-id 690 --version-id 1 --name etfXGB --description etfXGB\n",
    "[giza][2024-06-03 19:11:50.498] Creating agent ✅\n",
    "[giza][2024-06-03 19:11:50.506] Using model id and version id to create agent\n",
    "[giza][2024-06-03 19:11:50.780] Select an existing account to create the agent.\n",
    "[giza][2024-06-03 19:11:50.787] Available accounts are:\n",
    "┌──────────┐\n",
    "│ Accounts │\n",
    "├──────────┤\n",
    "│  giza1   │\n",
    "└──────────┘\n",
    "Enter the account name: giza1\n",
    "{\n",
    "  \"id\": 34,\n",
    "  \"name\": \"etfXGB\",\n",
    "  \"description\": \"etfXGB\",\n",
    "  \"parameters\": {\n",
    "    \"model_id\": 690,\n",
    "    \"version_id\": 1,\n",
    "    \"endpoint_id\": 261,\n",
    "    \"account\": \"giza1\"\n",
    "  },\n",
    "  \"created_date\": \"2024-06-04T02:11:57.598948\",\n",
    "  \"last_update\": \"2024-06-04T02:11:57.598948\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710aff90-46e0-48f5-8549-9c80dce0bbba",
   "metadata": {},
   "source": [
    "### Create and Agent using the CLI # 2 \n",
    "create again after size is M"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5c314b-51f7-4b45-9008-0ee2d70c5bda",
   "metadata": {},
   "source": [
    "$ giza agents create --model-id 690 --version-id 1 --name etfXGB --description etfXGB\n",
    "[giza][2024-06-04 19:06:18.403] Creating agent ✅\n",
    "[giza][2024-06-04 19:06:18.411] Using model id and version id to create agent\n",
    "[giza][2024-06-04 19:06:18.672] Select an existing account to create the agent.\n",
    "[giza][2024-06-04 19:06:18.676] Available accounts are:\n",
    "┌──────────┐\n",
    "│ Accounts │\n",
    "├──────────┤\n",
    "│  giza1   │\n",
    "└──────────┘\n",
    "Enter the account name: giza1\n",
    "{\n",
    "  \"id\": 37,\n",
    "  \"name\": \"etfXGB\",\n",
    "  \"description\": \"etfXGB\",\n",
    "  \"parameters\": {\n",
    "    \"model_id\": 690,\n",
    "    \"version_id\": 1,\n",
    "    \"endpoint_id\": 269,\n",
    "    \"account\": \"giza1\"\n",
    "  },\n",
    "  \"created_date\": \"2024-06-05T02:06:25.417540\",\n",
    "  \"last_update\": \"2024-06-05T02:06:25.417540\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3668bd-efa5-4586-b30c-f8a9252b6329",
   "metadata": {},
   "source": [
    "### Create and Agent using the CLI # 2 \n",
    "create again after size is XL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21fa10f-8494-4330-8f1f-6d0e178effc0",
   "metadata": {},
   "source": [
    "$ giza agents create --model-id 690 --version-id 1 --endpoint-id 270 --name etfXGB2 --description etfXGB2\n",
    "[giza][2024-06-04 19:24:46.361] Creating agent ✅\n",
    "[giza][2024-06-04 19:24:46.370] Using endpoint id to create agent, retrieving model id and version id\n",
    "[giza][2024-06-04 19:24:46.626] Select an existing account to create the agent.\n",
    "[giza][2024-06-04 19:24:46.629] Available accounts are:\n",
    "┌──────────┐\n",
    "│ Accounts │\n",
    "├──────────┤\n",
    "│  giza1   │\n",
    "└──────────┘\n",
    "Enter the account name: giza1\n",
    "{\n",
    "  \"id\": 40,\n",
    "  \"name\": \"etfXGB2\",\n",
    "  \"description\": \"etfXGB2\",\n",
    "  \"parameters\": {\n",
    "    \"model_id\": 690,\n",
    "    \"version_id\": 1,\n",
    "    \"endpoint_id\": 270,\n",
    "    \"account\": \"giza1\"\n",
    "  },\n",
    "  \"created_date\": \"2024-06-05T02:24:51.304557\",\n",
    "  \"last_update\": \"2024-06-05T02:24:51.304557\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1420f8-0274-4e46-9e01-84cc76bd609d",
   "metadata": {},
   "source": [
    "$ giza agents create --model-id 703 --version-id 1 --endpoint-id 274 --name etfLIN1 --description etfLIN1\n",
    "[giza][2024-06-05 00:48:43.649] Creating agent ✅\n",
    "[giza][2024-06-05 00:48:43.669] Using endpoint id to create agent, retrieving model id and version id\n",
    "[giza][2024-06-05 00:48:43.948] Select an existing account to create the agent.\n",
    "[giza][2024-06-05 00:48:43.957] Available accounts are:\n",
    "┌──────────┐\n",
    "│ Accounts │\n",
    "├──────────┤\n",
    "│  giza1   │\n",
    "└──────────┘\n",
    "Enter the account name: giza1\n",
    "{\n",
    "  \"id\": 41,\n",
    "  \"name\": \"etfLIN1\",\n",
    "  \"description\": \"etfLIN1\",\n",
    "  \"parameters\": {\n",
    "    \"model_id\": 703,\n",
    "    \"version_id\": 1,\n",
    "    \"endpoint_id\": 274,\n",
    "    \"account\": \"giza1\"\n",
    "  },\n",
    "  \"created_date\": \"2024-06-05T07:48:47.721178\",\n",
    "  \"last_update\": \"2024-06-05T07:48:47.721178\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c9b26a-59c9-42b7-98f7-426e49f4bb22",
   "metadata": {},
   "source": [
    "$ giza agents create --model-id 722 --version-id 1 --endpoint-id 282 --name etfLIN3 --description etfLIN3\n",
    "[giza][2024-06-05 17:16:54.636] Creating agent ✅\n",
    "[giza][2024-06-05 17:16:54.645] Using endpoint id to create agent, retrieving model id and version id\n",
    "[giza][2024-06-05 17:16:55.235] Select an existing account to create the agent.\n",
    "[giza][2024-06-05 17:16:55.238] Available accounts are:\n",
    "┌──────────┐\n",
    "│ Accounts │\n",
    "├──────────┤\n",
    "│  giza1   │\n",
    "└──────────┘\n",
    "Enter the account name: giza1\n",
    "{\n",
    "  \"id\": 49,\n",
    "  \"name\": \"etfLIN3\",\n",
    "  \"description\": \"etfLIN3\",\n",
    "  \"parameters\": {\n",
    "    \"model_id\": 722,\n",
    "    \"version_id\": 1,\n",
    "    \"endpoint_id\": 282,\n",
    "    \"account\": \"giza1\"\n",
    "  },\n",
    "  \"created_date\": \"2024-06-06T00:17:00.359062\",\n",
    "  \"last_update\": \"2024-06-06T00:17:00.359062\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ebae06-8a57-40e1-b9d5-990936f896e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c39550c-a76f-4f8c-ab1d-69166b17eebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_data = pd.DataFrame({\n",
    "#     'IBIT': [120],\n",
    "#     'FBTC': [175],\n",
    "#     'BITB': [5],\n",
    "#     'ARKB': [50],\n",
    "#     'BTCO': [1],\n",
    "#     'EZBC': [2],\n",
    "#     'BRRR': [3],\n",
    "#     'HODL': [4],\n",
    "#     'BTCW': [5],\n",
    "#     'GBTC': [-20]\n",
    "# })\n",
    "\n",
    "new_data = pd.DataFrame({\n",
    "    'IBIT': [120]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0519c27-e968-4636-9176-6d9827b7c7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_data = pd.DataFrame({\n",
    "#     'IBIT': [120],\n",
    "#     'FBTC': [175],\n",
    "#     'BITB': [5],\n",
    "#     'ARKB': [50],\n",
    "#     'BTCO': [1],\n",
    "#     'EZBC': [2],\n",
    "#     'BRRR': [3],\n",
    "#     'HODL': [4],\n",
    "#     'BTCW': [5],\n",
    "#     'GBTC': [-20]\n",
    "# })\n",
    "\n",
    "# Predicting the expenses for the new data\n",
    "predicted_btc_closing_price = lm.predict(new_data)\n",
    "\n",
    "print(predicted_btc_closing_price )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de4e4b6a-eb2d-45cc-a4f1-400ea3481a99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "464c2266-4832-46c1-bfcb-417d907e87f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "377dcf19-aff5-456a-9776-bb30dcc7f954",
   "metadata": {},
   "source": [
    "## Run a verifiable inference\n",
    "##### To streamline verifiable inference, you might consider using the endpoint URL obtained after transpilation. However, this approach requires manual serialization of the input for the Cairo program and handling the deserialization process. To make this process more user-friendly and keep you within a Python environment, we've introduced a Python SDK designed to facilitate the creation of ML workflows and execution of verifiable predictions. When you initiate a prediction, our system automatically retrieves the endpoint URL you deployed earlier, converts your input into Cairo-compatible format, executes the prediction, and then converts the output back into a numpy object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7288c29a-0dd0-4214-bb16-29d8f2980bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "!giza users me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66247cbc-4dd0-4a93-80a8-13172668783e",
   "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>IBIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IBIT\n",
       "0   120"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'IBIT': [120]\n",
    "})\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1777f348-1b85-43c1-b3e4-2cc85ed02042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[120.]], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input = new_data.astype(np.float32).values\n",
    "input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5dbf104f-5868-4084-a496-321ae40e4aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Starting deserialization process...\n",
      "✅ Deserialization completed! 🎉\n",
      "Predicted value for input is 59361.556396484375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[59361.55639648]]), '80bae11a43574e0cbf43f53d064aabeb')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from giza.agents.model import GizaModel\n",
    "\n",
    "MODEL_ID = 722  # Update with your model ID\n",
    "VERSION_ID = 1  # Update with your version ID\n",
    "\n",
    "def prediction(input, model_id, version_id):\n",
    "    model = GizaModel(id=model_id, version=version_id)\n",
    "\n",
    "    (result, proof_id) = model.predict(\n",
    "        input_feed={'input': input}, verifiable=True\n",
    "    )\n",
    "\n",
    "    return result, proof_id\n",
    "\n",
    "def execution():\n",
    "    # The input data type should match the model's expected input\n",
    "    # input = np.array([[5.5]]).astype(np.float32)\n",
    "    new_data = pd.DataFrame({\n",
    "    'IBIT': [120]\n",
    "})\n",
    "    # Convert the DataFrame to a NumPy array and ensure it is of type float32\n",
    "    input = new_data.astype(np.float32).values\n",
    "\n",
    "\n",
    "    (result, proof_id) = prediction(input, MODEL_ID, VERSION_ID)\n",
    "    # (result, proof_id) = prediction(new_data, MODEL_ID, VERSION_ID)\n",
    "\n",
    "    print(\n",
    "        # f\"Predicted value for input {input.flatten()[0]} is {result[0].flatten()[0]}\")\n",
    "        f\"Predicted value for input is {result[0].flatten()[0]}\")\n",
    "\n",
    "    return result, proof_id\n",
    "\n",
    "\n",
    "execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d55f279-3629-4615-8ebf-d6547e73e49a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b44f1d7-6254-4380-aaa4-ecd141fc17e7",
   "metadata": {},
   "source": [
    "## Download the proof\n",
    "#### Initiating a verifiable inference sets off a proving job on our server, sparing you the complexities of installing and configuring the prover yourself. Upon completion, you can download your proof.\n",
    "\n",
    "First, let's check the status of the proving job to ensure that it has been completed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25f8d23-0d83-412a-9e3b-fca989972f0a",
   "metadata": {},
   "source": [
    "$ giza endpoints get-proof --endpoint-id 234 --proof-id \"10c164e6c2364ab6b5491702127979a6\"\n",
    "[giza][2024-05-30 00:40:39.691] Getting proof from endpoint 234 ✅\n",
    "{\n",
    "  \"id\": 967,\n",
    "  \"job_id\": 1121,\n",
    "  \"metrics\": {\n",
    "    \"proving_time\": 17.249508\n",
    "  },\n",
    "  \"created_date\": \"2024-05-30T07:33:12.532659\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f427ceb-61d7-4ffc-ba4f-15d4dad1ec2c",
   "metadata": {},
   "source": [
    "Once the proof is ready, you can download it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c280b5-8b29-4a7d-8b0e-a5b90e408852",
   "metadata": {},
   "source": [
    "$ giza endpoints download-proof --endpoint-id 234 --proof-id \"10c164e6c2364ab6b5491702127979a6\" --output-path zk_xgboost.proof\n",
    "[giza][2024-05-30 00:51:52.048] Getting proof from endpoint 234 ✅\n",
    "[giza][2024-05-30 00:51:53.800] Proof downloaded to zk_xgboost.proof ✅\n",
    "(giza3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36fb812-b3ef-4c4e-a142-c3c9263be989",
   "metadata": {},
   "source": [
    "## Verify the proof\n",
    "#### Finally, you can verify the proof."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139ac5f2-0bd8-40d7-88c3-75e162febfb8",
   "metadata": {},
   "source": [
    "$ giza verify --proof-id 967\n",
    "[giza][2024-05-30 00:56:05.847] Verifying proof...\n",
    "[giza][2024-05-30 00:56:07.140] Verification result: True\n",
    "[giza][2024-05-30 00:56:07.145] Verification time: 0.454667226\n",
    "(giza3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8859806a-01d8-40d9-a445-cbe3c8a733fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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