Add files via upload

training_pipeline-copy.ipynb

@@ -0,0 +1,1058 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import hopsworks\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from hsml.schema import Schema\n",
+    "from hsml.model_schema import ModelSchema\n",
+    "\n",
+    "\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "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/693399\n",
+      "Connected. Call `.close()` to terminate connection gracefully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "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": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected. Call `.close()` to terminate connection gracefully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import hsfs\n",
+    "\n",
+    "# Connection setup\n",
+    "# Connect to Hopsworks\n",
+    "api_key = os.getenv('hopsworks_api')\n",
+    "connection = hsfs.connection()\n",
+    "fs = connection.get_feature_store()\n",
+    "\n",
+    "# Get feature view\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_view = fs.get_feature_view(\n",
+    "    name='tesla_stocks_fv',\n",
+    "    version=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_start = \"2022-06-22\"\n",
+    "train_end = \"2023-12-31\"\n",
+    "\n",
+    "test_start = '2024-01-01'\n",
+    "test_end = \"2024-05-03\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training dataset job started successfully, you can follow the progress at \n",
+      "https://c.app.hopsworks.ai/p/693399/jobs/named/tesla_stocks_fv_1_create_fv_td_06052024212158/executions\n",
+      "2024-05-06 23:23:21,130 WARNING: VersionWarning: Incremented version to `5`.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(5, <hsfs.core.job.Job at 0x194e21067d0>)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_view.create_train_test_split(\n",
+    "    train_start=train_start,\n",
+    "    train_end=train_end,\n",
+    "    test_start=test_start,\n",
+    "    test_end=test_end,\n",
+    "    data_format='csv',\n",
+    "    coalesce= True,\n",
+    "    statistics_config={'histogram':True,'correlations':True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = feature_view.get_train_test_split(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>ticker</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2022-12-14T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.102207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-02-21T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.155833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-08-17T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.024046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-09-16T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.087306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-08-28T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.024046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2023-02-10T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.155833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>2023-05-08T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.141296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>2022-09-08T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.087306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>2023-07-06T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.119444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>2023-10-27T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.164868</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>383 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date ticker  sentiment\n",
+       "0    2022-12-14T00:00:00.000Z   TSLA   0.102207\n",
+       "1    2023-02-21T00:00:00.000Z   TSLA   0.155833\n",
+       "2    2023-08-17T00:00:00.000Z   TSLA   0.024046\n",
+       "3    2022-09-16T00:00:00.000Z   TSLA   0.087306\n",
+       "4    2023-08-28T00:00:00.000Z   TSLA   0.024046\n",
+       "..                        ...    ...        ...\n",
+       "378  2023-02-10T00:00:00.000Z   TSLA   0.155833\n",
+       "379  2023-05-08T00:00:00.000Z   TSLA   0.141296\n",
+       "380  2022-09-08T00:00:00.000Z   TSLA   0.087306\n",
+       "381  2023-07-06T00:00:00.000Z   TSLA   0.119444\n",
+       "382  2023-10-27T00:00:00.000Z   TSLA   0.164868\n",
+       "\n",
+       "[383 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>ticker</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2024-04-16T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.018769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2024-02-22T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.212963</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2024-02-13T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.099363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2024-01-17T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.099363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2024-02-16T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.099363</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       date ticker  sentiment\n",
+       "0  2024-04-16T00:00:00.000Z   TSLA   0.018769\n",
+       "1  2024-02-22T00:00:00.000Z   TSLA   0.212963\n",
+       "2  2024-02-13T00:00:00.000Z   TSLA   0.099363\n",
+       "3  2024-01-17T00:00:00.000Z   TSLA   0.099363\n",
+       "4  2024-02-16T00:00:00.000Z   TSLA   0.099363"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>ticker</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>2024-05-02T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.001443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>2024-04-02T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.080911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>2024-03-22T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.080911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>2024-01-02T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>-0.122579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>2024-02-26T00:00:00.000Z</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.152764</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        date ticker  sentiment\n",
+       "80  2024-05-02T00:00:00.000Z   TSLA   0.001443\n",
+       "81  2024-04-02T00:00:00.000Z   TSLA   0.080911\n",
+       "82  2024-03-22T00:00:00.000Z   TSLA   0.080911\n",
+       "83  2024-01-02T00:00:00.000Z   TSLA  -0.122579\n",
+       "84  2024-02-26T00:00:00.000Z   TSLA   0.152764"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train['date'] = pd.to_datetime(X_train['date']).dt.date\n",
+    "X_test['date'] = pd.to_datetime(X_test['date']).dt.date\n",
+    "X_train['date'] = pd.to_datetime(X_train['date'])\n",
+    "X_test['date'] = pd.to_datetime(X_test['date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>ticker</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2022-12-14</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.102207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.155833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-08-17</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.024046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-09-16</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.087306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-08-28</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>0.024046</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date ticker  sentiment\n",
+       "0 2022-12-14   TSLA   0.102207\n",
+       "1 2023-02-21   TSLA   0.155833\n",
+       "2 2023-08-17   TSLA   0.024046\n",
+       "3 2022-09-16   TSLA   0.087306\n",
+       "4 2023-08-28   TSLA   0.024046"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the 'ticker' column\n",
+    "tickers = X_train[['ticker']]\n",
+    "\n",
+    "# Initialize OneHotEncoder\n",
+    "encoder = OneHotEncoder()\n",
+    "\n",
+    "# Fit and transform the 'ticker' column\n",
+    "ticker_encoded = encoder.fit_transform(tickers)\n",
+    "\n",
+    "# Convert the encoded column into a DataFrame\n",
+    "ticker_encoded_df = pd.DataFrame(ticker_encoded.toarray(), columns=encoder.get_feature_names_out(['ticker']))\n",
+    "\n",
+    "# Concatenate the encoded DataFrame with the original DataFrame\n",
+    "X_train = pd.concat([X_train, ticker_encoded_df], axis=1)\n",
+    "\n",
+    "# Drop the original 'ticker' column\n",
+    "X_train.drop('ticker', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>ticker_TSLA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2022-12-14</td>\n",
+       "      <td>0.102207</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>0.155833</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-08-17</td>\n",
+       "      <td>0.024046</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-09-16</td>\n",
+       "      <td>0.087306</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-08-28</td>\n",
+       "      <td>0.024046</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  sentiment  ticker_TSLA\n",
+       "0 2022-12-14   0.102207          1.0\n",
+       "1 2023-02-21   0.155833          1.0\n",
+       "2 2023-08-17   0.024046          1.0\n",
+       "3 2022-09-16   0.087306          1.0\n",
+       "4 2023-08-28   0.024046          1.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tickers = X_test[['ticker']]\n",
+    "\n",
+    "# Initialize OneHotEncoder\n",
+    "encoder = OneHotEncoder()\n",
+    "\n",
+    "# Fit and transform the 'ticker' column\n",
+    "ticker_encoded_test = encoder.fit_transform(tickers)\n",
+    "\n",
+    "# Convert the encoded column into a DataFrame\n",
+    "ticker_encoded_df_test = pd.DataFrame(ticker_encoded_test.toarray(), columns=encoder.get_feature_names_out(['ticker']))\n",
+    "\n",
+    "# Concatenate the encoded DataFrame with the original DataFrame\n",
+    "X_test = pd.concat([X_test, ticker_encoded_df_test], axis=1)\n",
+    "\n",
+    "# Drop the original 'ticker' column\n",
+    "X_test.drop('ticker', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "# Fit and transform the 'open' column\n",
+    "y_train['open_scaled'] = scaler.fit_transform(y_train[['open']])\n",
+    "y_train.drop('open', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test['open_scaled'] = scaler.fit_transform(y_test[['open']])\n",
+    "y_test.drop('open', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Input, LSTM, Dense, Dropout\n",
+    "from sklearn.preprocessing import StandardScaler  # Import StandardScaler from scikit-learn\n",
+    "\n",
+    "def create_model(input_shape,\n",
+    "                 LSTM_filters=64,\n",
+    "                 dropout=0.1,\n",
+    "                 recurrent_dropout=0.1,\n",
+    "                 dense_dropout=0.5,\n",
+    "                 activation='relu',\n",
+    "                 depth=1):\n",
+    "\n",
+    "    model = Sequential()\n",
+    "\n",
+    "    # Input layer\n",
+    "    model.add(Input(shape=input_shape))\n",
+    "\n",
+    "    if depth > 1:\n",
+    "        for i in range(1, depth):\n",
+    "            # Recurrent layer\n",
+    "            model.add(LSTM(LSTM_filters, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout))\n",
+    "\n",
+    "    # Recurrent layer\n",
+    "    model.add(LSTM(LSTM_filters, return_sequences=False, dropout=dropout, recurrent_dropout=recurrent_dropout))\n",
+    "\n",
+    "    # Fully connected layer\n",
+    "    if activation == 'relu':\n",
+    "        model.add(Dense(LSTM_filters, activation='relu'))\n",
+    "    elif activation == 'leaky_relu':\n",
+    "        model.add(Dense(LSTM_filters))\n",
+    "        model.add(tf.keras.layers.LeakyReLU(alpha=0.1))\n",
+    "\n",
+    "    # Dropout for regularization\n",
+    "    model.add(Dropout(dense_dropout))\n",
+    "\n",
+    "    # Output layer for predicting one day forward\n",
+    "    model.add(Dense(1, activation='linear'))\n",
+    "\n",
+    "    # Compile the model\n",
+    "    model.compile(optimizer='adam', loss='mse')\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-06 23:23:33,215 WARNING: DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
+      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Assuming X_train['date'] column exists and is in datetime format\n",
+    "X_train['year'] = X_train['date'].dt.year\n",
+    "X_train['month'] = X_train['date'].dt.month\n",
+    "X_train['day'] = X_train['date'].dt.day\n",
+    "\n",
+    "# Drop the original date column\n",
+    "X_train.drop(columns=['date'], inplace=True)\n",
+    "\n",
+    "# Convert dataframe to numpy array\n",
+    "X_train_array = X_train.to_numpy()\n",
+    "\n",
+    "# Reshape the array to have a shape suitable for LSTM\n",
+    "# Assuming each row represents a sample and each column represents a feature\n",
+    "# Reshape to [samples, timesteps, features]\n",
+    "X_train_array = np.expand_dims(X_train_array, axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Convert DataFrame to numpy array\n",
+    "X_train_array = X_train.values\n",
+    "\n",
+    "# Reshape X_train_array to add a time step dimension\n",
+    "X_train_reshaped = X_train_array.reshape(X_train_array.shape[0], 1, X_train_array.shape[1])\n",
+    "\n",
+    "# Assuming X_train_reshaped shape is now (374, 1, 5)\n",
+    "input_shape = X_train_reshaped.shape[1:]\n",
+    "\n",
+    "# Create the model\n",
+    "model = create_model(input_shape=input_shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - loss: 0.5165\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x194ec2a4550>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train_reshaped, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-06 23:23:37,549 WARNING: DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
+      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Assuming X_test['date'] column exists and is in datetime format\n",
+    "X_test['year'] = X_test['date'].dt.year\n",
+    "X_test['month'] = X_test['date'].dt.month\n",
+    "X_test['day'] = X_test['date'].dt.day\n",
+    "\n",
+    "# Drop the original date column\n",
+    "X_test.drop(columns=['date'], inplace=True)\n",
+    "\n",
+    "# Convert dataframe to numpy array\n",
+    "X_test_array = X_test.to_numpy()\n",
+    "\n",
+    "# Reshape the array to have a shape suitable for LSTM\n",
+    "# Assuming each row represents a sample and each column represents a feature\n",
+    "# Reshape to [samples, timesteps, features]\n",
+    "X_test_array = np.expand_dims(X_test_array, axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 237ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected. Call `.close()` to terminate connection gracefully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "mr = project.get_model_registry()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LSTM_model.keras']"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "joblib.dump(model, 'LSTM_model.keras')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'RMSE': 0.40675989895763576}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "import numpy as np\n",
+    "\n",
+    "# Compute RMSE\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "rmse_metrics = {\"RMSE\": rmse}\n",
+    "rmse_metrics\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_schema = Schema(X_train)\n",
+    "output_schema = Schema(y_train)\n",
+    "model_schema = ModelSchema(input_schema, output_schema)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dd08fa9a7c144638a9f5c4600df04fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/6 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a02a72f26d7b433599f80f8b7d3ad72c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading: 0.000%|          | 0/291253 elapsed<00:00 remaining<?"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac84dbdca58f4648b1eb54452812b563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading: 0.000%|          | 0/561 elapsed<00:00 remaining<?"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model created, explore it at https://c.app.hopsworks.ai:443/p/693399/models/stock_pred_model/4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Model(name: 'stock_pred_model', version: 4)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock_pred_model = mr.tensorflow.create_model(\n",
+    "        name=\"stock_pred_model\",\n",
+    "        metrics= rmse_metrics,\n",
+    "        model_schema=model_schema,\n",
+    "        description=\"Stock Market TSLA Predictor from News Sentiment\",\n",
+    "    )\n",
+    "\n",
+    "stock_pred_model.save('LSTM_model.keras')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}