Updated with feature view

feature_engineering.ipynb

@@ -64,7 +64,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   },
   "orig_nbformat": 4
  },

feature_pipeline.ipynb

@@ -2,18 +2,178 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#from dotenv import load_dotenv\n",
-    "#import os "
+    "from dotenv import load_dotenv\n",
+    "import os "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting great_expectations==0.18.12\n",
+      "  Using cached great_expectations-0.18.12-py3-none-any.whl.metadata (8.9 kB)\n",
+      "Requirement already satisfied: altair<5.0.0,>=4.2.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (4.2.2)\n",
+      "Requirement already satisfied: Click>=7.1.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (8.1.7)\n",
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+      "Requirement already satisfied: cryptography>=3.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (42.0.6)\n",
+      "Requirement already satisfied: Ipython>=7.16.3 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (8.22.2)\n",
+      "Requirement already satisfied: ipywidgets>=7.5.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (8.1.2)\n",
+      "Requirement already satisfied: jinja2>=2.10 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (3.1.3)\n",
+      "Requirement already satisfied: jsonpatch>=1.22 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (1.33)\n",
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+      "Requirement already satisfied: pure-eval in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from stack-data->Ipython>=7.16.3->great_expectations==0.18.12) (0.2.2)\n",
+      "Requirement already satisfied: sniffio>=1.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.1)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (21.2.0)\n",
+      "Requirement already satisfied: httpcore==1.* in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from httpx>=0.25.0->jupyterlab<4.2,>=4.1.1->notebook>=6.4.10->great_expectations==0.18.12) (1.0.5)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from httpcore==1.*->httpx>=0.25.0->jupyterlab<4.2,>=4.1.1->notebook>=6.4.10->great_expectations==0.18.12) (0.14.0)\n",
+      "Requirement already satisfied: debugpy>=1.6.5 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from ipykernel>=6.5.0->jupyterlab<4.2,>=4.1.1->notebook>=6.4.10->great_expectations==0.18.12) (1.6.7)\n",
+      "Requirement already satisfied: nest-asyncio in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from ipykernel>=6.5.0->jupyterlab<4.2,>=4.1.1->notebook>=6.4.10->great_expectations==0.18.12) (1.6.0)\n",
+      "Requirement already satisfied: psutil in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from ipykernel>=6.5.0->jupyterlab<4.2,>=4.1.1->notebook>=6.4.10->great_expectations==0.18.12) (5.9.0)\n",
+      "Requirement already satisfied: python-json-logger>=2.0.4 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (2.0.7)\n",
+      "Requirement already satisfied: pyyaml>=5.3 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (6.0.1)\n",
+      "Requirement already satisfied: rfc3339-validator in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.1.4)\n",
+      "Requirement already satisfied: rfc3986-validator>=0.1.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.1.1)\n",
+      "Requirement already satisfied: beautifulsoup4 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (4.12.3)\n",
+      "Requirement already satisfied: bleach!=5.0.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (6.1.0)\n",
+      "Requirement already satisfied: defusedxml in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.7.1)\n",
+      "Requirement already satisfied: jupyterlab-pygments in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.3.0)\n",
+      "Requirement already satisfied: nbclient>=0.5.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.10.0)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.5.1)\n",
+      "Requirement already satisfied: tinycss2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.0)\n",
+      "Requirement already satisfied: webencodings in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from bleach!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.5.1)\n",
+      "Requirement already satisfied: fqdn in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.5.1)\n",
+      "Requirement already satisfied: isoduration in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (20.11.0)\n",
+      "Requirement already satisfied: uri-template in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.0)\n",
+      "Requirement already satisfied: webcolors>=1.11 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.13)\n",
+      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from beautifulsoup4->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (2.5)\n",
+      "Requirement already satisfied: arrow>=0.15.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.0)\n",
+      "Requirement already satisfied: types-python-dateutil>=2.8.10 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (2.9.0.20240316)\n",
+      "Using cached great_expectations-0.18.12-py3-none-any.whl (5.4 MB)\n",
+      "Installing collected packages: great_expectations\n",
+      "  Attempting uninstall: great_expectations\n",
+      "    Found existing installation: great-expectations 0.18.13\n",
+      "    Uninstalling great-expectations-0.18.13:\n",
+      "      Successfully uninstalled great-expectations-0.18.13\n",
+      "Successfully installed great_expectations-0.18.12\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install great_expectations==0.18.12"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import pandas as pd               # For data manipulation using DataFrames\n",
+    "import numpy as np                # For numerical operations\n",
+    "import matplotlib.pyplot as plt   # For data visualization\n",
+    "import os                         # For operating system-related tasks\n",
+    "import joblib                     # For saving and loading models\n",
+    "import hopsworks                  # For getting access to hopsworks\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Import specific modules from scikit-learn\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder   # For data preprocessing\n",
+    "from sklearn.metrics import accuracy_score                        # For evaluating model accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -21,11 +181,11 @@
      "text": [
       "            1. open  2. high    3. low  4. close    5. volume\n",
       "date                                                         \n",
+      "2024-05-03   182.10   184.78  178.4200    181.19   75491539.0\n",
+      "2024-05-02   182.86   184.60  176.0200    180.01   89148041.0\n",
+      "2024-05-01   182.00   185.86  179.0100    179.99   92829719.0\n",
       "2024-04-30   186.98   190.95  182.8401    183.28  127031787.0\n",
-      "2024-04-29   188.42   198.87  184.5400    194.05  243869678.0\n",
-      "2024-04-26   168.85   172.12  166.3700    168.29  109815725.0\n",
-      "2024-04-25   158.96   170.88  158.3600    170.18  126427521.0\n",
-      "2024-04-24   162.84   167.97  157.5100    162.13  181178020.0\n"
+      "2024-04-29   188.42   198.87  184.5400    194.05  243869678.0\n"
      ]
     }
    ],
@@ -111,101 +271,85 @@
    ],
    "source": [
     "# Define your file path and name\n",
-    "file_path = 'TSLA_stock_price.csv'  # Customize the path and filename\n",
+    "#file_path = 'TSLA_stock_price.csv'  # Customize the path and filename\n",
     "\n",
     "# Save the DataFrame to CSV\n",
-    "data.to_csv(file_path)\n",
+    "#stock_data.to_csv(file_path)\n",
     "\n",
-    "print(f\"Data saved to {file_path}\")\n"
+    "#print(f\"Data saved to {file_path}\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "         date  1. open  2. high  3. low  4. close    5. volume\n",
-      "0  2024-04-29   188.42   198.87  184.54    194.05  243869678.0\n",
-      "1  2024-04-26   168.85   172.12  166.37    168.29  109815725.0\n",
-      "2  2024-04-25   158.96   170.88  158.36    170.18  126427521.0\n",
-      "3  2024-04-24   162.84   167.97  157.51    162.13  181178020.0\n",
-      "4  2024-04-23   143.33   147.26  141.11    144.68  124545104.0\n"
+      "         date  1. open  2. high    3. low  4. close    5. volume ticker\n",
+      "0  2024-05-02   182.86   184.60  176.0200    180.01   89148041.0   TSLA\n",
+      "1  2024-05-01   182.00   185.86  179.0100    179.99   92829719.0   TSLA\n",
+      "2  2024-04-30   186.98   190.95  182.8401    183.28  127031787.0   TSLA\n",
+      "3  2024-04-29   188.42   198.87  184.5400    194.05  243869678.0   TSLA\n",
+      "4  2024-04-26   168.85   172.12  166.3700    168.29  109815725.0   TSLA\n"
      ]
     }
    ],
    "source": [
     "# Load and display the data from CSV to confirm\n",
-    "tsla_df = pd.read_csv(file_path)\n",
-    "print(tsla_df.head())\n"
+    "tsla_df = pd.read_csv('TSLA_stock_price.csv')\n",
+    "print(tsla_df.head())\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected. Call `.close()` to terminate connection gracefully.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "UserWarning: The installed hopsworks client version 3.4.4 may not be compatible with the connected Hopsworks backend version 3.7.1. \n",
-      "To ensure compatibility please install the latest bug fix release matching the minor version of your backend (3.7) by running 'pip install hopsworks==3.7.*'\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Connected. Call `.close()` to terminate connection gracefully.\n",
       "\n",
-      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/549016\n",
+      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/549015\n",
       "Connected. Call `.close()` to terminate connection gracefully.\n"
      ]
     }
    ],
    "source": [
-    "import hopsworks\n",
-    "\n",
-    "project = hopsworks.login()\n",
-    "fs = project.get_feature_store()\n"
+    "api_key = os.environ.get('hopsworks_api')\n",
+    "project = hopsworks.login(api_key_value=api_key)\n",
+    "fs = project.get_feature_store()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#import re "
+    "import re "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#def clean_column_name(name):\n",
+    "def clean_column_name(name):\n",
     "    # Remove all non-letter characters\n",
-    "    #cleaned_name = re.sub(r'[^a-zA-Z]', '', name)\n",
-    "    #return cleaned_name\n"
+    "    cleaned_name = re.sub(r'[^a-zA-Z]', '', name)\n",
+    "    return cleaned_name\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -235,53 +379,59 @@
        "      <th>3. low</th>\n",
        "      <th>4. close</th>\n",
        "      <th>5. volume</th>\n",
+       "      <th>ticker</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>188.42</td>\n",
-       "      <td>198.8700</td>\n",
-       "      <td>184.54</td>\n",
-       "      <td>194.05</td>\n",
-       "      <td>243869678.0</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>182.86</td>\n",
+       "      <td>184.6000</td>\n",
+       "      <td>176.0200</td>\n",
+       "      <td>180.01</td>\n",
+       "      <td>89148041.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2024-04-26</td>\n",
-       "      <td>168.85</td>\n",
-       "      <td>172.1200</td>\n",
-       "      <td>166.37</td>\n",
-       "      <td>168.29</td>\n",
-       "      <td>109815725.0</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>182.00</td>\n",
+       "      <td>185.8600</td>\n",
+       "      <td>179.0100</td>\n",
+       "      <td>179.99</td>\n",
+       "      <td>92829719.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2024-04-25</td>\n",
-       "      <td>158.96</td>\n",
-       "      <td>170.8800</td>\n",
-       "      <td>158.36</td>\n",
-       "      <td>170.18</td>\n",
-       "      <td>126427521.0</td>\n",
+       "      <td>2024-04-30</td>\n",
+       "      <td>186.98</td>\n",
+       "      <td>190.9500</td>\n",
+       "      <td>182.8401</td>\n",
+       "      <td>183.28</td>\n",
+       "      <td>127031787.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2024-04-24</td>\n",
-       "      <td>162.84</td>\n",
-       "      <td>167.9700</td>\n",
-       "      <td>157.51</td>\n",
-       "      <td>162.13</td>\n",
-       "      <td>181178020.0</td>\n",
+       "      <td>2024-04-29</td>\n",
+       "      <td>188.42</td>\n",
+       "      <td>198.8700</td>\n",
+       "      <td>184.5400</td>\n",
+       "      <td>194.05</td>\n",
+       "      <td>243869678.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2024-04-23</td>\n",
-       "      <td>143.33</td>\n",
-       "      <td>147.2600</td>\n",
-       "      <td>141.11</td>\n",
-       "      <td>144.68</td>\n",
-       "      <td>124545104.0</td>\n",
+       "      <td>2024-04-26</td>\n",
+       "      <td>168.85</td>\n",
+       "      <td>172.1200</td>\n",
+       "      <td>166.3700</td>\n",
+       "      <td>168.29</td>\n",
+       "      <td>109815725.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -291,140 +441,177 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3477</th>\n",
+       "      <th>3480</th>\n",
        "      <td>2010-07-06</td>\n",
        "      <td>20.00</td>\n",
        "      <td>20.0000</td>\n",
-       "      <td>15.83</td>\n",
+       "      <td>15.8300</td>\n",
        "      <td>16.11</td>\n",
        "      <td>6866900.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3478</th>\n",
+       "      <th>3481</th>\n",
        "      <td>2010-07-02</td>\n",
        "      <td>23.00</td>\n",
        "      <td>23.1000</td>\n",
-       "      <td>18.71</td>\n",
+       "      <td>18.7100</td>\n",
        "      <td>19.20</td>\n",
        "      <td>5139800.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3479</th>\n",
+       "      <th>3482</th>\n",
        "      <td>2010-07-01</td>\n",
        "      <td>25.00</td>\n",
        "      <td>25.9200</td>\n",
-       "      <td>20.27</td>\n",
+       "      <td>20.2700</td>\n",
        "      <td>21.96</td>\n",
        "      <td>8218800.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3480</th>\n",
+       "      <th>3483</th>\n",
        "      <td>2010-06-30</td>\n",
        "      <td>25.79</td>\n",
        "      <td>30.4192</td>\n",
-       "      <td>23.30</td>\n",
+       "      <td>23.3000</td>\n",
        "      <td>23.83</td>\n",
        "      <td>17187100.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3481</th>\n",
+       "      <th>3484</th>\n",
        "      <td>2010-06-29</td>\n",
        "      <td>19.00</td>\n",
        "      <td>25.0000</td>\n",
-       "      <td>17.54</td>\n",
+       "      <td>17.5400</td>\n",
        "      <td>23.89</td>\n",
        "      <td>18766300.0</td>\n",
+       "      <td>TSLA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>3482 rows × 6 columns</p>\n",
+       "<p>3485 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            date  1. open   2. high  3. low  4. close    5. volume\n",
-       "0     2024-04-29   188.42  198.8700  184.54    194.05  243869678.0\n",
-       "1     2024-04-26   168.85  172.1200  166.37    168.29  109815725.0\n",
-       "2     2024-04-25   158.96  170.8800  158.36    170.18  126427521.0\n",
-       "3     2024-04-24   162.84  167.9700  157.51    162.13  181178020.0\n",
-       "4     2024-04-23   143.33  147.2600  141.11    144.68  124545104.0\n",
-       "...          ...      ...       ...     ...       ...          ...\n",
-       "3477  2010-07-06    20.00   20.0000   15.83     16.11    6866900.0\n",
-       "3478  2010-07-02    23.00   23.1000   18.71     19.20    5139800.0\n",
-       "3479  2010-07-01    25.00   25.9200   20.27     21.96    8218800.0\n",
-       "3480  2010-06-30    25.79   30.4192   23.30     23.83   17187100.0\n",
-       "3481  2010-06-29    19.00   25.0000   17.54     23.89   18766300.0\n",
+       "            date  1. open   2. high    3. low  4. close    5. volume ticker\n",
+       "0     2024-05-02   182.86  184.6000  176.0200    180.01   89148041.0   TSLA\n",
+       "1     2024-05-01   182.00  185.8600  179.0100    179.99   92829719.0   TSLA\n",
+       "2     2024-04-30   186.98  190.9500  182.8401    183.28  127031787.0   TSLA\n",
+       "3     2024-04-29   188.42  198.8700  184.5400    194.05  243869678.0   TSLA\n",
+       "4     2024-04-26   168.85  172.1200  166.3700    168.29  109815725.0   TSLA\n",
+       "...          ...      ...       ...       ...       ...          ...    ...\n",
+       "3480  2010-07-06    20.00   20.0000   15.8300     16.11    6866900.0   TSLA\n",
+       "3481  2010-07-02    23.00   23.1000   18.7100     19.20    5139800.0   TSLA\n",
+       "3482  2010-07-01    25.00   25.9200   20.2700     21.96    8218800.0   TSLA\n",
+       "3483  2010-06-30    25.79   30.4192   23.3000     23.83   17187100.0   TSLA\n",
+       "3484  2010-06-29    19.00   25.0000   17.5400     23.89   18766300.0   TSLA\n",
        "\n",
-       "[3482 rows x 6 columns]"
+       "[3485 rows x 7 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#tsla_df"
+    "tsla_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Assuming 'tsla_df' is your DataFrame\n",
-    "#tsla_df.columns = [clean_column_name(col) for col in tsla_df.columns]\n"
+    "tsla_df.columns = [clean_column_name(col) for col in tsla_df.columns]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['date', 'open', 'high', 'low', 'close', 'volume'], dtype='object')\n"
+      "Index(['date', 'open', 'high', 'low', 'close', 'volume', 'ticker'], dtype='object')\n"
      ]
     }
    ],
    "source": [
-    "#print(tsla_df.columns)\n"
+    "print(tsla_df.columns)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Assuming tsla_df is your pandas DataFrame\n",
+    "# Convert the \"date\" column to timestamp\n",
+    "tsla_df['date'] = pd.to_datetime(tsla_df['date'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-04 20:50:09,340 WARNING: DeprecationWarning: Providing event_time as a single-element list is deprecated and will be dropped in future versions. Provide the feature_name string instead.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Define a feature group\n",
     "tesla_fg = fs.get_or_create_feature_group(\n",
-    "    name=\"tsla_stock\",\n",
+    "    name=\"tesla_stock\",\n",
     "    description=\"Tesla stock dataset from alpha vantage\",\n",
-    "    version=1,\n",
-    "    primary_key=[\"date\"],\n",
-    "    online_enabled=True,\n",
+    "    version=3,\n",
+    "    primary_key=[\"ticker\"],\n",
+    "    event_time=['date'],\n",
+    "    online_enabled=False,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature Group created successfully, explore it at \n",
+      "https://c.app.hopsworks.ai:443/p/549015/fs/544840/fg/782673\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae6a0214d34943cabcdd66d70198ae3a",
+       "model_id": "4971a5b3b0854f14ad95992b88f71a16",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Uploading Dataframe: 0.00% |          | Rows 0/3482 | Elapsed Time: 00:00 | Remaining Time: ?"
+       "Uploading Dataframe: 0.00% |          | Rows 0/3485 | Elapsed Time: 00:00 | Remaining Time: ?"
       ]
      },
      "metadata": {},
@@ -434,18 +621,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Launching job: tsla_stock_1_offline_fg_materialization\n",
+      "Launching job: tesla_stock_3_offline_fg_materialization\n",
       "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/549016/jobs/named/tsla_stock_1_offline_fg_materialization/executions\n"
+      "https://c.app.hopsworks.ai/p/549015/jobs/named/tesla_stock_3_offline_fg_materialization/executions\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x162ac3e50>, None)"
+       "(<hsfs.core.job.Job at 0x21d84b9dc10>, None)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,7 +643,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "news_df = pd.read_csv('news_articles_ema.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "news_df_updated = news_df.drop(columns=['exp_mean_7_days'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "news_df_updated['date'] = pd.to_datetime(news_df_updated['date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-04 20:53:43,335 WARNING: DeprecationWarning: Providing event_time as a single-element list is deprecated and will be dropped in future versions. Provide the feature_name string instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "news_sentiment_fg = fs.get_or_create_feature_group(\n",
+    "    name='news_sentiment_updated',\n",
+    "    description='News sentiment from Polygon',\n",
+    "    version=2,\n",
+    "    primary_key=['ticker'],\n",
+    "    event_time=['date'],\n",
+    "    online_enabled=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -464,18 +703,18 @@
      "output_type": "stream",
      "text": [
       "Feature Group created successfully, explore it at \n",
-      "https://c.app.hopsworks.ai:443/p/549016/fs/544838/fg/766341\n"
+      "https://c.app.hopsworks.ai:443/p/549015/fs/544840/fg/780662\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74f0d70aeb3942c093321c530120434e",
+       "model_id": "868940acc1d74ad987a98daee01a2b75",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Uploading Dataframe: 0.00% |          | Rows 0/712 | Elapsed Time: 00:00 | Remaining Time: ?"
+       "Uploading Dataframe: 0.00% |          | Rows 0/720 | Elapsed Time: 00:00 | Remaining Time: ?"
       ]
      },
      "metadata": {},
@@ -485,35 +724,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Launching job: news_sentiment_1_offline_fg_materialization\n",
+      "Launching job: news_sentiment_updated_2_offline_fg_materialization\n",
       "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/549016/jobs/named/news_sentiment_1_offline_fg_materialization/executions\n"
+      "https://c.app.hopsworks.ai/p/549015/jobs/named/news_sentiment_updated_2_offline_fg_materialization/executions\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x164180710>, None)"
+       "(<hsfs.core.job.Job at 0x21d85223610>, None)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Create feature group for historical news data\n",
-    "news_df = pd.read_csv('news_articles.csv')\n",
-    "\n",
-    "news_sentiment_fg = fs.get_or_create_feature_group(\n",
-    "    name='news_sentiment',\n",
-    "    description='News sentiment from Polygon',\n",
-    "    version=1,\n",
-    "    primary_key=['date'],\n",
-    "    online_enabled=True,\n",
-    ")\n",
-    "\n",
-    "news_sentiment_fg.insert(news_df)"
+    "news_sentiment_fg.insert(news_df_updated)"
    ]
   }
  ],
@@ -533,7 +761,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   },
   "orig_nbformat": 4
  },

feature_pipeline.py

@@ -3,51 +3,64 @@ from dotenv import load_dotenv
 import os 
 
 # %%
-from alpha_vantage.timeseries import TimeSeries
-import pandas as pd
+#!pip install great_expectations==0.18.12
 
-load_dotenv()
+# %%
+# Import necessary libraries
+import pandas as pd               # For data manipulation using DataFrames
+import numpy as np                # For numerical operations
+import matplotlib.pyplot as plt   # For data visualization
+import os                         # For operating system-related tasks
+import joblib                     # For saving and loading models
+import hopsworks                  # For getting access to hopsworks
 
-api_key = os.environ.get('stocks_api') # Replace this with your actual API key
-ts = TimeSeries(key=api_key, output_format='pandas')
 
-# Fetch daily adjusted stock prices; adjust the symbol as needed
-data, meta_data = ts.get_daily(symbol='TSLA', outputsize='full')
 
-print(data.head())
+# Import specific modules from scikit-learn
+from sklearn.preprocessing import StandardScaler, OneHotEncoder   # For data preprocessing
+from sklearn.metrics import accuracy_score                        # For evaluating model accuracy
 
 # %%
+#from alpha_vantage.timeseries import TimeSeries
+#import pandas as pd
 
+#load_dotenv()
+
+#api_key = os.environ.get('stocks_api') # Replace this with your actual API key
+#ts = TimeSeries(key=api_key, output_format='pandas')
+
+# Fetch daily adjusted stock prices; adjust the symbol as needed
+#data, meta_data = ts.get_daily(symbol='TSLA', outputsize='full')
+
+#print(data.head())
 
 # %%
-data.info()
+#data.info()
 
 # %%
-meta_data
+#meta_data
 
 # %%
 # Define your file path and name
-file_path = 'TSLA_stock_price.csv'  # Customize the path and filename
+#file_path = 'TSLA_stock_price.csv'  # Customize the path and filename
 
 # Save the DataFrame to CSV
-data.to_csv(file_path)
+#stock_data.to_csv(file_path)
 
-print(f"Data saved to {file_path}")
+#print(f"Data saved to {file_path}")
 
 
 # %%
 # Load and display the data from CSV to confirm
-tsla_df = pd.read_csv(file_path)
+tsla_df = pd.read_csv('TSLA_stock_price.csv')
 print(tsla_df.head())
-
+    
 
 # %%
-import hopsworks
-
-project = hopsworks.login()
+api_key = os.environ.get('hopsworks_api')
+project = hopsworks.login(api_key_value=api_key)
 fs = project.get_feature_store()
 
-
 # %%
 import re 
 
@@ -70,31 +83,49 @@ tsla_df.columns = [clean_column_name(col) for col in tsla_df.columns]
 print(tsla_df.columns)
 
 
+# %%
+import pandas as pd
+
+# Assuming tsla_df is your pandas DataFrame
+# Convert the "date" column to timestamp
+tsla_df['date'] = pd.to_datetime(tsla_df['date'])
+
+
 # %%
 # Define a feature group
 tesla_fg = fs.get_or_create_feature_group(
-    name="tsla_stock",
+    name="tesla_stock",
     description="Tesla stock dataset from alpha vantage",
-    version=1,
-    primary_key=["date"],
-    online_enabled=True,
+    version=3,
+    primary_key=["ticker"],
+    event_time=['date'],
+    online_enabled=False,
 )
 
 # %%
 tesla_fg.insert(tsla_df, write_options={"wait_for_job" : False})
 
 # %%
-# Create feature group for historical news data
-news_df = pd.read_csv('news_articles.csv')
+news_df = pd.read_csv('news_articles_ema.csv')
+
 
+# %%
+news_df_updated = news_df.drop(columns=['exp_mean_7_days'])
+
+# %%
+news_df_updated['date'] = pd.to_datetime(news_df_updated['date'])
+
+# %%
 news_sentiment_fg = fs.get_or_create_feature_group(
-    name='news_sentiment',
+    name='news_sentiment_updated',
     description='News sentiment from Polygon',
-    version=1,
-    primary_key=['date'],
-    online_enabled=True,
+    version=2,
+    primary_key=['ticker'],
+    event_time=['date'],
+    online_enabled=False,
 )
 
-news_sentiment_fg.insert(news_df)
+# %%
+news_sentiment_fg.insert(news_df_updated)
 
 

feature_view_freddie.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,9 +23,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         date  1. open  2. high    3. low  4. close    5. volume ticker\n",
+      "0  2024-05-02   182.86   184.60  176.0200    180.01   89148041.0   TSLA\n",
+      "1  2024-05-01   182.00   185.86  179.0100    179.99   92829719.0   TSLA\n",
+      "2  2024-04-30   186.98   190.95  182.8401    183.28  127031787.0   TSLA\n",
+      "3  2024-04-29   188.42   198.87  184.5400    194.05  243869678.0   TSLA\n",
+      "4  2024-04-26   168.85   172.12  166.3700    168.29  109815725.0   TSLA\n",
+      "Connected. Call `.close()` to terminate connection gracefully.\n",
+      "\n",
+      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/549015\n",
+      "Connected. Call `.close()` to terminate connection gracefully.\n",
+      "Index(['date', 'open', 'high', 'low', 'close', 'volume', 'ticker'], dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baf52b6f47c74767b5aecb1df6f17c0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading Dataframe: 0.00% |          | Rows 0/3485 | Elapsed Time: 00:00 | Remaining Time: ?"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Launching job: tesla_stock_3_offline_fg_materialization\n",
+      "Job started successfully, you can follow the progress at \n",
+      "https://c.app.hopsworks.ai/p/549015/jobs/named/tesla_stock_3_offline_fg_materialization/executions\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3deb8c10be3a44a2b91c63ffe8762725",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading Dataframe: 0.00% |          | Rows 0/720 | Elapsed Time: 00:00 | Remaining Time: ?"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Launching job: news_sentiment_updated_2_offline_fg_materialization\n",
+      "Job started successfully, you can follow the progress at \n",
+      "https://c.app.hopsworks.ai/p/549015/jobs/named/news_sentiment_updated_2_offline_fg_materialization/executions\n"
+     ]
+    }
+   ],
    "source": [
     "from feature_pipeline import tesla_fg\n",
     "from feature_pipeline import news_sentiment_fg"
@@ -33,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -42,7 +106,7 @@
        "True"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -56,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -79,86 +143,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "def create_stocks_feature_view(fs, version):\n",
     "\n",
-    "    #Loading in the feature groups\n",
-    "    tesla_fg = fs.get_feature_group('tsla_stock', version = 1)\n",
-    "    news_sentiment_fg = fs.get_feature_group('news_sentiment', version = 1)\n",
+    "    # Loading in the feature groups\n",
+    "    tesla_fg = fs.get_feature_group('tesla_stock', version=3)\n",
+    "    news_sentiment_fg = fs.get_feature_group('news_sentiment_updated', version=2)\n",
     "\n",
-    "    ds_query = tesla_fg.select(['date','open', 'high', 'low', 'closed', 'volume'])\\\n",
-    "        .join(news_sentiment_fg.select_except(['time', 'amp_url', 'image_url']))\n",
-    "    \n",
-    "    transformation_functions = {\n",
-    "        'open': fs.get_transformation_function(name='min_max_scaler'),\n",
-    "        'high': fs.get_transformation_function(name='min_max_scaler'),\n",
-    "        'low' : fs.get_transformation_function(name='min_max_scaler'),\n",
-    "        'closed' : fs.get_transformation_function(name='min_max_scaler'),\n",
-    "        'volume' : fs.get_transformation_function(name='min_max_scaler'),\n",
-    "        'sentiment' : fs.get_transformation_function(name='min_max_scaler'),\n",
-    "    }\n",
+    "    # Define the query\n",
+    "    ds_query = tesla_fg.select(['date', 'open', 'ticker'])\\\n",
+    "        .join(news_sentiment_fg.select_except(['ticker', 'time', 'amp_url', 'image_url']))\n",
+    "\n",
+    "    # Create the feature view\n",
+    "    feature_view = fs.create_feature_view(\n",
+    "        name='tsla_stocks_fv',\n",
+    "        query=ds_query,\n",
+    "        labels=['ticker']\n",
+    "    )\n",
     "\n",
-    "    return (fs.create_tesla_feature_view(\n",
-    "        name = 'tsla_stocks_fv',\n",
-    "        query = ds_query,\n",
-    "        labels=['date']\n",
-    "    ), tesla_fg)"
+    "    return feature_view, tesla_fg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[<hsfs.feature_group.FeatureGroup object at 0x000001A5AA2B98D0>]\n",
-      "[<hsfs.feature_group.FeatureGroup object at 0x000001A5A9D90F90>]\n"
-     ]
-    },
     {
      "ename": "RestAPIError",
-     "evalue": "Metadata operation error: (url: https://c.app.hopsworks.ai/hopsworks-api/api/project/549015/featurestores/544840/transformationfunctions). Server response: \nHTTP code: 404, HTTP reason: Not Found, body: b'{\"errorCode\":270046,\"usrMsg\":\"HOPSFS Connector: HOPSFS\",\"errorMsg\":\"HopsFs Connector not found\"}', error code: 270046, error msg: HopsFs Connector not found, user msg: HOPSFS Connector: HOPSFS",
+     "evalue": "Metadata operation error: (url: https://c.app.hopsworks.ai/hopsworks-api/api/project/549015/featurestores/544840/featureview). Server response: \nHTTP code: 404, HTTP reason: Not Found, body: b'{\"errorCode\":270046,\"usrMsg\":\"HOPSFS Connector: fklitte_Training_Datasets\",\"errorMsg\":\"HopsFs Connector not found\"}', error code: 270046, error msg: HopsFs Connector not found, user msg: HOPSFS Connector: fklitte_Training_Datasets",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mRestAPIError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[29], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m----> 2\u001b[0m     feature_view \u001b[38;5;241m=\u001b[39m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_feature_view\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtsla_stocks_fv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m     tesla_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtsla_stock\u001b[39m\u001b[38;5;124m'\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\usage.py:212\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    211\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\usage.py:208\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;66;03m# Call the original method\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\feature_store.py:1661\u001b[0m, in \u001b[0;36mFeatureStore.get_feature_view\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m   1660\u001b[0m     version \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDEFAULT_VERSION\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_engine.py:127\u001b[0m, in \u001b[0;36mFeatureViewEngine.get\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m version:\n\u001b[1;32m--> 127\u001b[0m     fv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_by_name_version\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    128\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattach_transformation_function(fv)\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:113\u001b[0m, in \u001b[0;36mFeatureViewApi.get_by_name_version\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 113\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:101\u001b[0m, in \u001b[0;36mFeatureViewApi.get_by_name_version\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m feature_view\u001b[38;5;241m.\u001b[39mFeatureView\u001b[38;5;241m.\u001b[39mfrom_response_json(\n\u001b[1;32m--> 101\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    102\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_GET\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexpand\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquery\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfeatures\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\n\u001b[0;32m    103\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    104\u001b[0m     )\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RestAPIError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\decorators.py:35\u001b[0m, in \u001b[0;36mconnected.<locals>.if_connected\u001b[1;34m(inst, *args, **kwargs)\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoHopsworksConnectionError\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43minst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\client\\base.py:179\u001b[0m, in \u001b[0;36mClient._send_request\u001b[1;34m(self, method, path_params, query_params, headers, data, stream, files)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mRestAPIError(url, response)\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n",
+      "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m----> 2\u001b[0m     feature_view \u001b[38;5;241m=\u001b[39m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_feature_view\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtsla_stocks_fv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m     tesla_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesla_stock\u001b[39m\u001b[38;5;124m'\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\usage.py:212\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    211\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\usage.py:208\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;66;03m# Call the original method\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\feature_store.py:1661\u001b[0m, in \u001b[0;36mFeatureStore.get_feature_view\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m   1660\u001b[0m     version \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDEFAULT_VERSION\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_engine.py:127\u001b[0m, in \u001b[0;36mFeatureViewEngine.get\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m version:\n\u001b[1;32m--> 127\u001b[0m     fv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_by_name_version\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    128\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattach_transformation_function(fv)\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:113\u001b[0m, in \u001b[0;36mFeatureViewApi.get_by_name_version\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 113\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:101\u001b[0m, in \u001b[0;36mFeatureViewApi.get_by_name_version\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m feature_view\u001b[38;5;241m.\u001b[39mFeatureView\u001b[38;5;241m.\u001b[39mfrom_response_json(\n\u001b[1;32m--> 101\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    102\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_GET\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexpand\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquery\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfeatures\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\n\u001b[0;32m    103\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    104\u001b[0m     )\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RestAPIError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\decorators.py:35\u001b[0m, in \u001b[0;36mconnected.<locals>.if_connected\u001b[1;34m(inst, *args, **kwargs)\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoHopsworksConnectionError\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43minst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\client\\base.py:176\u001b[0m, in \u001b[0;36mClient._send_request\u001b[1;34m(self, method, path_params, query_params, headers, data, stream, files)\u001b[0m\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mRestAPIError(url, response)\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n",
       "\u001b[1;31mRestAPIError\u001b[0m: Metadata operation error: (url: https://c.app.hopsworks.ai/hopsworks-api/api/project/549015/featurestores/544840/featureview/tsla_stocks_fv/version/1). Server response: \nHTTP code: 404, HTTP reason: Not Found, body: b'{\"errorCode\":270181,\"usrMsg\":\"There exists no feature view with the name tsla_stocks_fv and version 1.\",\"errorMsg\":\"Feature view wasn\\'t found.\"}', error code: 270181, error msg: Feature view wasn't found., user msg: There exists no feature view with the name tsla_stocks_fv and version 1.",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001b[1;31mRestAPIError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[29], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m     tesla_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtsla_stock\u001b[39m\u001b[38;5;124m'\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m----> 5\u001b[0m     feature_view, trans_fg \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_stocks_feature_view\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[1;32mIn[28], line 11\u001b[0m, in \u001b[0;36mcreate_stocks_feature_view\u001b[1;34m(fs, version)\u001b[0m\n\u001b[0;32m      5\u001b[0m news_sentiment_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnews_sentiment\u001b[39m\u001b[38;5;124m'\u001b[39m, version \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      7\u001b[0m ds_query \u001b[38;5;241m=\u001b[39m tesla_fg\u001b[38;5;241m.\u001b[39mselect([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopen\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhigh\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlow\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume\u001b[39m\u001b[38;5;124m'\u001b[39m])\\\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;241m.\u001b[39mjoin(news_sentiment_fg\u001b[38;5;241m.\u001b[39mselect_except([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mamp_url\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage_url\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m     10\u001b[0m transformation_functions \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m---> 11\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopen\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_transformation_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmin_max_scaler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m,\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhigh\u001b[39m\u001b[38;5;124m'\u001b[39m: fs\u001b[38;5;241m.\u001b[39mget_transformation_function(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_max_scaler\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m     13\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlow\u001b[39m\u001b[38;5;124m'\u001b[39m : fs\u001b[38;5;241m.\u001b[39mget_transformation_function(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_max_scaler\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m'\u001b[39m : fs\u001b[38;5;241m.\u001b[39mget_transformation_function(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_max_scaler\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume\u001b[39m\u001b[38;5;124m'\u001b[39m : fs\u001b[38;5;241m.\u001b[39mget_transformation_function(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_max_scaler\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m     16\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msentiment\u001b[39m\u001b[38;5;124m'\u001b[39m : fs\u001b[38;5;241m.\u001b[39mget_transformation_function(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_max_scaler\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m     17\u001b[0m }\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (fs\u001b[38;5;241m.\u001b[39mcreate_tesla_feature_view(\n\u001b[0;32m     20\u001b[0m     name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtsla_stocks_fv\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     21\u001b[0m     query \u001b[38;5;241m=\u001b[39m ds_query,\n\u001b[0;32m     22\u001b[0m     labels\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     23\u001b[0m ), tesla_fg)\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\usage.py:212\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    211\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    214\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\usage.py:208\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    205\u001b[0m exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;66;03m# Call the original method\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\feature_store.py:1404\u001b[0m, in \u001b[0;36mFeatureStore.get_transformation_function\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m   1304\u001b[0m \u001b[38;5;129m@usage\u001b[39m\u001b[38;5;241m.\u001b[39mmethod_logger\n\u001b[0;32m   1305\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transformation_function\u001b[39m(\n\u001b[0;32m   1306\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1307\u001b[0m     name: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   1308\u001b[0m     version: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1309\u001b[0m ):\n\u001b[0;32m   1310\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get  transformation function metadata object.\u001b[39;00m\n\u001b[0;32m   1311\u001b[0m \n\u001b[0;32m   1312\u001b[0m \u001b[38;5;124;03m    !!! example \"Get transformation function by name. This will default to version 1\"\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1402\u001b[0m \u001b[38;5;124;03m        `TransformationFunction`: The TransformationFunction metadata object.\u001b[39;00m\n\u001b[0;32m   1403\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1404\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transformation_function_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_transformation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\core\\transformation_function_engine.py:65\u001b[0m, in \u001b[0;36mTransformationFunctionEngine.get_transformation_fn\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transformation_fn\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m     64\u001b[0m     transformation_fn_instances \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m---> 65\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transformation_function_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_transformation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m     )\n\u001b[0;32m     67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transformation_fn_instances[\u001b[38;5;241m0\u001b[39m]\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\core\\transformation_function_api.py:72\u001b[0m, in \u001b[0;36mTransformationFunctionApi.get_transformation_fn\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m version:\n\u001b[0;32m     70\u001b[0m         query_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mversion\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m version\n\u001b[0;32m     71\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transformation_function\u001b[38;5;241m.\u001b[39mTransformationFunction\u001b[38;5;241m.\u001b[39mfrom_response_json(\n\u001b[1;32m---> 72\u001b[0m         \u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m     )\n\u001b[0;32m     74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transformation_function\u001b[38;5;241m.\u001b[39mTransformationFunction\u001b[38;5;241m.\u001b[39mfrom_response_json(\n\u001b[0;32m     76\u001b[0m         _client\u001b[38;5;241m.\u001b[39m_send_request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGET\u001b[39m\u001b[38;5;124m\"\u001b[39m, path_params)\n\u001b[0;32m     77\u001b[0m     )\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\decorators.py:35\u001b[0m, in \u001b[0;36mconnected.<locals>.if_connected\u001b[1;34m(inst, *args, **kwargs)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inst\u001b[38;5;241m.\u001b[39m_connected:\n\u001b[0;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoHopsworksConnectionError\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43minst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod\\.conda\\Lib\\site-packages\\hsfs\\client\\base.py:179\u001b[0m, in \u001b[0;36mClient._send_request\u001b[1;34m(self, method, path_params, query_params, headers, data, stream, files)\u001b[0m\n\u001b[0;32m    176\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session\u001b[38;5;241m.\u001b[39msend(prepped, verify\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verify, stream\u001b[38;5;241m=\u001b[39mstream)\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mRestAPIError(url, response)\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
-      "\u001b[1;31mRestAPIError\u001b[0m: Metadata operation error: (url: https://c.app.hopsworks.ai/hopsworks-api/api/project/549015/featurestores/544840/transformationfunctions). Server response: \nHTTP code: 404, HTTP reason: Not Found, body: b'{\"errorCode\":270046,\"usrMsg\":\"HOPSFS Connector: HOPSFS\",\"errorMsg\":\"HopsFs Connector not found\"}', error code: 270046, error msg: HopsFs Connector not found, user msg: HOPSFS Connector: HOPSFS"
+      "Cell \u001b[1;32mIn[19], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m     tesla_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesla_stock\u001b[39m\u001b[38;5;124m'\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m----> 5\u001b[0m     feature_view, tesla_fg \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_stocks_feature_view\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[18], line 12\u001b[0m, in \u001b[0;36mcreate_stocks_feature_view\u001b[1;34m(fs, version)\u001b[0m\n\u001b[0;32m      8\u001b[0m ds_query \u001b[38;5;241m=\u001b[39m tesla_fg\u001b[38;5;241m.\u001b[39mselect([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopen\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mticker\u001b[39m\u001b[38;5;124m'\u001b[39m])\\\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;241m.\u001b[39mjoin(news_sentiment_fg\u001b[38;5;241m.\u001b[39mselect_except([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mticker\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mamp_url\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage_url\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Create the feature view\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m feature_view \u001b[38;5;241m=\u001b[39m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_feature_view\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtsla_stocks_fv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mds_query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mticker\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m feature_view, tesla_fg\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\usage.py:212\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    211\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    214\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\usage.py:208\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    205\u001b[0m exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;66;03m# Call the original method\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\feature_store.py:1538\u001b[0m, in \u001b[0;36mFeatureStore.create_feature_view\u001b[1;34m(self, name, query, version, description, labels, inference_helper_columns, training_helper_columns, transformation_functions)\u001b[0m\n\u001b[0;32m   1436\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Create a feature view metadata object and saved it to hopsworks.\u001b[39;00m\n\u001b[0;32m   1437\u001b[0m \n\u001b[0;32m   1438\u001b[0m \u001b[38;5;124;03m!!! example\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1524\u001b[0m \u001b[38;5;124;03m    `FeatureView`: The feature view metadata object.\u001b[39;00m\n\u001b[0;32m   1525\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1526\u001b[0m feat_view \u001b[38;5;241m=\u001b[39m feature_view\u001b[38;5;241m.\u001b[39mFeatureView(\n\u001b[0;32m   1527\u001b[0m     name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m   1528\u001b[0m     query\u001b[38;5;241m=\u001b[39mquery,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1536\u001b[0m     transformation_functions\u001b[38;5;241m=\u001b[39mtransformation_functions,\n\u001b[0;32m   1537\u001b[0m )\n\u001b[1;32m-> 1538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeat_view\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_engine.py:113\u001b[0m, in \u001b[0;36mFeatureViewEngine.save\u001b[1;34m(self, feature_view_obj)\u001b[0m\n\u001b[0;32m    104\u001b[0m         feature_view_obj\u001b[38;5;241m.\u001b[39m_features\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m    105\u001b[0m             training_dataset_feature\u001b[38;5;241m.\u001b[39mTrainingDatasetFeature(\n\u001b[0;32m    106\u001b[0m                 name\u001b[38;5;241m=\u001b[39mfeature\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    109\u001b[0m             )\n\u001b[0;32m    110\u001b[0m         )\n\u001b[0;32m    112\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformation_function_engine\u001b[38;5;241m.\u001b[39mattach_transformation_fn(feature_view_obj)\n\u001b[1;32m--> 113\u001b[0m updated_fv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeature_view_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattach_transformation_function(updated_fv)\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature view created successfully, explore it at \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    117\u001b[0m     \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_feature_view_url(updated_fv)\n\u001b[0;32m    118\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:60\u001b[0m, in \u001b[0;36mFeatureViewApi.post\u001b[1;34m(self, feature_view_obj)\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, feature_view_obj):\n\u001b[0;32m     58\u001b[0m     headers \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m     59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m feature_view_obj\u001b[38;5;241m.\u001b[39mupdate_from_response_json(\n\u001b[1;32m---> 60\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     61\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_POST\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     62\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_base_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     63\u001b[0m \u001b[43m            \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     64\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_view_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     65\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\decorators.py:35\u001b[0m, in \u001b[0;36mconnected.<locals>.if_connected\u001b[1;34m(inst, *args, **kwargs)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inst\u001b[38;5;241m.\u001b[39m_connected:\n\u001b[0;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoHopsworksConnectionError\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43minst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\client\\base.py:176\u001b[0m, in \u001b[0;36mClient._send_request\u001b[1;34m(self, method, path_params, query_params, headers, data, stream, files)\u001b[0m\n\u001b[0;32m    171\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retry_token_expired(\n\u001b[0;32m    172\u001b[0m         request, stream, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mTOKEN_EXPIRED_RETRY_INTERVAL, \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    173\u001b[0m     )\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mRestAPIError(url, response)\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n\u001b[0;32m    179\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
+      "\u001b[1;31mRestAPIError\u001b[0m: Metadata operation error: (url: https://c.app.hopsworks.ai/hopsworks-api/api/project/549015/featurestores/544840/featureview). Server response: \nHTTP code: 404, HTTP reason: Not Found, body: b'{\"errorCode\":270046,\"usrMsg\":\"HOPSFS Connector: fklitte_Training_Datasets\",\"errorMsg\":\"HopsFs Connector not found\"}', error code: 270046, error msg: HopsFs Connector not found, user msg: HOPSFS Connector: fklitte_Training_Datasets"
      ]
     }
    ],
    "source": [
     "try:\n",
     "    feature_view = fs.get_feature_view(\"tsla_stocks_fv\", version=1)\n",
-    "    tesla_fg = fs.get_feature_group('tsla_stock', version=1)\n",
+    "    tesla_fg = fs.get_feature_group('tesla_stock', version=3)\n",
     "except:\n",
-    "    feature_view, trans_fg = create_stocks_feature_view(fs, 1)"
+    "    feature_view, tesla_fg = create_stocks_feature_view(fs, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#def create_stocks_feature_view(fs, version):\n",
+    "\n",
+    "    #Loading in the feature groups\n",
+    "#    tesla_fg = fs.get_feature_group('tesla_stock', version = 3)\n",
+    "#    news_sentiment_fg = fs.get_feature_group('news_sentiment_updated', version = 2)\n",
+    "\n",
+    "#    ds_query = tesla_fg.select(['date','open', 'ticker'])\\\n",
+    "#        .join(news_sentiment_fg.select_except(['ticker','time', 'amp_url', 'image_url']))\n",
+    "    \n",
+    "#    return (fs.create_tesla_feature_view(\n",
+    "#        name = 'tsla_stocks_fv',\n",
+    "#        query = ds_query,\n",
+    "#        labels=['ticker']\n",
+    "#    ), tesla_fg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'FeatureStore' object has no attribute 'create_tesla_feature_view'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRestAPIError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m----> 2\u001b[0m     feature_view \u001b[38;5;241m=\u001b[39m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_feature_view\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtsla_stocks_fv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m     tesla_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesla_stock\u001b[39m\u001b[38;5;124m'\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\usage.py:212\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    211\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\usage.py:208\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;66;03m# Call the original method\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\feature_store.py:1661\u001b[0m, in \u001b[0;36mFeatureStore.get_feature_view\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m   1660\u001b[0m     version \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDEFAULT_VERSION\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_engine.py:127\u001b[0m, in \u001b[0;36mFeatureViewEngine.get\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m version:\n\u001b[1;32m--> 127\u001b[0m     fv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_view_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_by_name_version\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    128\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattach_transformation_function(fv)\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:113\u001b[0m, in \u001b[0;36mFeatureViewApi.get_by_name_version\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 113\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\core\\feature_view_api.py:101\u001b[0m, in \u001b[0;36mFeatureViewApi.get_by_name_version\u001b[1;34m(self, name, version)\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m feature_view\u001b[38;5;241m.\u001b[39mFeatureView\u001b[38;5;241m.\u001b[39mfrom_response_json(\n\u001b[1;32m--> 101\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    102\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_GET\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexpand\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquery\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfeatures\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\n\u001b[0;32m    103\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    104\u001b[0m     )\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RestAPIError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\decorators.py:35\u001b[0m, in \u001b[0;36mconnected.<locals>.if_connected\u001b[1;34m(inst, *args, **kwargs)\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NoHopsworksConnectionError\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43minst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\frede\\OneDrive\\Dokumenter\\Master\\MLops\\MLops_mod-2\\.conda\\Lib\\site-packages\\hsfs\\client\\base.py:176\u001b[0m, in \u001b[0;36mClient._send_request\u001b[1;34m(self, method, path_params, query_params, headers, data, stream, files)\u001b[0m\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mRestAPIError(url, response)\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n",
+      "\u001b[1;31mRestAPIError\u001b[0m: Metadata operation error: (url: https://c.app.hopsworks.ai/hopsworks-api/api/project/549015/featurestores/544840/featureview/tsla_stocks_fv/version/1). Server response: \nHTTP code: 404, HTTP reason: Not Found, body: b'{\"errorCode\":270181,\"usrMsg\":\"There exists no feature view with the name tsla_stocks_fv and version 1.\",\"errorMsg\":\"Feature view wasn\\'t found.\"}', error code: 270181, error msg: Feature view wasn't found., user msg: There exists no feature view with the name tsla_stocks_fv and version 1.",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m     tesla_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesla_stock\u001b[39m\u001b[38;5;124m'\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m----> 5\u001b[0m     feature_view, tesla_fg \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_stocks_feature_view\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[14], line 10\u001b[0m, in \u001b[0;36mcreate_stocks_feature_view\u001b[1;34m(fs, version)\u001b[0m\n\u001b[0;32m      5\u001b[0m news_sentiment_fg \u001b[38;5;241m=\u001b[39m fs\u001b[38;5;241m.\u001b[39mget_feature_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnews_sentiment_updated\u001b[39m\u001b[38;5;124m'\u001b[39m, version \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m      7\u001b[0m ds_query \u001b[38;5;241m=\u001b[39m tesla_fg\u001b[38;5;241m.\u001b[39mselect([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopen\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mticker\u001b[39m\u001b[38;5;124m'\u001b[39m])\\\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;241m.\u001b[39mjoin(news_sentiment_fg\u001b[38;5;241m.\u001b[39mselect_except([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mticker\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mamp_url\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage_url\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_tesla_feature_view\u001b[49m(\n\u001b[0;32m     11\u001b[0m     name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtsla_stocks_fv\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     12\u001b[0m     query \u001b[38;5;241m=\u001b[39m ds_query,\n\u001b[0;32m     13\u001b[0m     labels\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mticker\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     14\u001b[0m ), tesla_fg)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'FeatureStore' object has no attribute 'create_tesla_feature_view'"
+     ]
+    }
+   ],
+   "source": [
+    "#try:\n",
+    "#    feature_view = fs.get_feature_view(\"tsla_stocks_fv\", version=1)\n",
+    "#    tesla_fg = fs.get_feature_group('tesla_stock', version=3)\n",
+    "#except:\n",
+    "#    feature_view, tesla_fg = create_stocks_feature_view(fs, 1)"
    ]
   }
  ],

requirements.txt

@@ -16,3 +16,4 @@ python-dotenv
 requests
 alpha_vantage
 textblob
+great_expectations==0.18.12