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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"font-width:bold; font-size: 3rem; color:#2656a3;\">**Msc. BDS Module - Data Engineering and Machine Learning Operations in Business (MLOPs)** </span> <span style=\"font-width:bold; font-size: 3rem; color:#333;\">- Part 01: Feature Backfill</span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The project uses [Hopsworks](https://www.hopsworks.ai) as the platform to store features in the **Hopworks Feature Store** and save a trained model in **Hopworks Model Registry**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style='color:#2656a3'> 🗒️ The notebook is divided into the following sections:\n",
"1. Loading the data and process features.\n",
"2. Connecting to Hopsworks Feature Store.\n",
"3. Creating feature groups and uploading them to the feature store."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style='color:#2656a3'> ⚙️ Import of Libraries and Packages\n",
"\n",
"We start by accessing the folder we have created that holds the functions (incl. live API calls and data preprocessing) we need for electricity prices, weather measures, and the Danish calendar. Then, we proceed to import some of the necessary libraries and warnings to avoid unnecessary distractions and keep output clean."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/camillahannesbo/Documents/AAU/Master - BDS/2. semester/Data Engineering and Machine learning operations in Business/MLOPs-Assignment-\n",
"/Users/camillahannesbo/Documents/AAU/Master - BDS/2. semester/Data Engineering and Machine learning operations in Business/MLOPs-Assignment-/notebooks\n"
]
}
],
"source": [
"# First we go one back in our directory to access the folder with our functions\n",
"%cd ..\n",
"\n",
"# Now we import the functions from the features folder\n",
"# This is the functions we have created to generate features for electricity prices, weather measures, and the danish calendar\n",
"from features import electricity_prices, weather_measures, calendar\n",
"\n",
"# We go back into the notebooks folder\n",
"%cd notebooks"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Importing pandas for data handling\n",
"import pandas as pd\n",
"\n",
"# Ignore warnings\n",
"import warnings \n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#2656a3;\"> 💽 Loading the Historical Data\n",
"\n",
"The data used comes from the following different sources:\n",
"\n",
"- Hourly electricity prices in Denmark per day on price area DK1 from [Energinet](https://www.energidataservice.dk). Located in the folder folder `features/electricity_prices`.\n",
"- Different meteorological observations based on Aalborg Denmark from [Open Meteo](https://www.open-meteo.com). Located in the folder `features/weather_measures`.\n",
"- Weather Forecast based on Aalborg Denmark from [Open Meteo](https://www.open-meteo.com). Located in the folder `features/weather_measures`. (This data is used later to parse new real-time weather data)\n",
"- Danish calendar that categorizes dates into types based on whether it is a weekday or not. This file is made manually by the group and is located in the folder `data` inside this repository.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#2656a3;\">💸 Electricity Prices per day from Energinet\n",
"The first dataset we load is hourly electricity prices per day from Energinet/Dataservice."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Fetching historical electricity prices for area DK1 from January 1, 2022\n",
"# Note: The end date is currently left out to retrieve data up to the day before present date \n",
"# Today is not included in the data as it is not historical data\n",
"electricity_df = electricity_prices.electricity_prices(\n",
" historical=True, \n",
" area=[\"DK1\"], \n",
" start='2022-01-01'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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" timestamp datetime date hour dk1_spotpricedkk_kwh\n",
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"1 1640998800000 2022-01-01 01:00:00 2022-01-01 1 0.30735\n",
"2 1641002400000 2022-01-01 02:00:00 2022-01-01 2 0.32141\n",
"3 1641006000000 2022-01-01 03:00:00 2022-01-01 3 0.33806\n",
"4 1641009600000 2022-01-01 04:00:00 2022-01-01 4 0.28013"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the first 5 rows of the electricity dataframe\n",
"electricity_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"text/plain": [
" timestamp datetime date hour \\\n",
"20536 1714935600000 2024-05-05 19:00:00 2024-05-05 19 \n",
"20537 1714939200000 2024-05-05 20:00:00 2024-05-05 20 \n",
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"20540 1714950000000 2024-05-05 23:00:00 2024-05-05 23 \n",
"\n",
" dk1_spotpricedkk_kwh \n",
"20536 0.71783 \n",
"20537 0.83478 \n",
"20538 0.80204 \n",
"20539 0.73647 \n",
"20540 0.66136 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the last 5 rows of the electricity dataframe\n",
"electricity_df.tail(5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 20541 entries, 0 to 20540\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 timestamp 20541 non-null int64 \n",
" 1 datetime 20541 non-null datetime64[ns]\n",
" 2 date 20541 non-null object \n",
" 3 hour 20541 non-null int64 \n",
" 4 dk1_spotpricedkk_kwh 20541 non-null float64 \n",
"dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
"memory usage: 802.5+ KB\n"
]
}
],
"source": [
"# Show the information for the electricity dataframe\n",
"electricity_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#2656a3;\"> 🌤 Weather measurements from Open Meteo\n",
"Next weather measurements from Open Meteo is fetched."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#2656a3;\"> 🕰️ Historical Weather Measures"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Fetching historical weather measurements from January 1, 2022\n",
"# Note: The end date is currently left out to retrieve data up to the day before present date \n",
"# Today is not included in the data as it is not historical data\n",
"historical_weather_df = weather_measures.historical_weather_measures(\n",
" historical=True, \n",
" start = '2022-01-01'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" <td>3.0</td>\n",
" <td>100.0</td>\n",
" <td>10.6</td>\n",
" <td>23.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp datetime date hour temperature_2m \\\n",
"0 1640995200000 2022-01-01 00:00:00 2022-01-01 0 6.7 \n",
"1 1640998800000 2022-01-01 01:00:00 2022-01-01 1 6.6 \n",
"2 1641002400000 2022-01-01 02:00:00 2022-01-01 2 6.7 \n",
"3 1641006000000 2022-01-01 03:00:00 2022-01-01 3 6.7 \n",
"4 1641009600000 2022-01-01 04:00:00 2022-01-01 4 6.7 \n",
"\n",
" relative_humidity_2m precipitation rain snowfall weather_code \\\n",
"0 100.0 0.0 0.0 0.0 3.0 \n",
"1 100.0 0.0 0.0 0.0 3.0 \n",
"2 99.0 0.0 0.0 0.0 3.0 \n",
"3 100.0 0.0 0.0 0.0 3.0 \n",
"4 99.0 0.0 0.0 0.0 3.0 \n",
"\n",
" cloud_cover wind_speed_10m wind_gusts_10m \n",
"0 100.0 16.2 36.0 \n",
"1 100.0 16.2 30.2 \n",
"2 100.0 15.5 30.6 \n",
"3 100.0 12.7 28.8 \n",
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]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the first 5 rows of the weather dataframe\n",
"historical_weather_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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" <td>2024-05-04</td>\n",
" <td>22</td>\n",
" <td>10.1</td>\n",
" <td>100.0</td>\n",
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" <td>0.0</td>\n",
" <td>3.0</td>\n",
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" <td>2.4</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
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" <th>20519</th>\n",
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" <td>2024-05-04 23:00:00</td>\n",
" <td>2024-05-04</td>\n",
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],
"text/plain": [
" timestamp datetime date hour temperature_2m \\\n",
"20515 1714849200000 2024-05-04 19:00:00 2024-05-04 19 12.2 \n",
"20516 1714852800000 2024-05-04 20:00:00 2024-05-04 20 11.4 \n",
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"20519 1714863600000 2024-05-04 23:00:00 2024-05-04 23 9.9 \n",
"\n",
" relative_humidity_2m precipitation rain snowfall weather_code \\\n",
"20515 88.0 0.0 0.0 0.0 3.0 \n",
"20516 92.0 0.0 0.0 0.0 2.0 \n",
"20517 96.0 0.0 0.0 0.0 2.0 \n",
"20518 100.0 0.0 0.0 0.0 3.0 \n",
"20519 100.0 0.0 0.0 0.0 3.0 \n",
"\n",
" cloud_cover wind_speed_10m wind_gusts_10m \n",
"20515 100.0 1.6 4.3 \n",
"20516 70.0 1.5 2.2 \n",
"20517 64.0 0.4 2.5 \n",
"20518 100.0 2.4 3.2 \n",
"20519 100.0 2.9 4.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the last 5 rows of the weather dataframe\n",
"historical_weather_df.tail(5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 20520 entries, 0 to 20519\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 timestamp 20520 non-null int64 \n",
" 1 datetime 20520 non-null datetime64[ns]\n",
" 2 date 20520 non-null object \n",
" 3 hour 20520 non-null int64 \n",
" 4 temperature_2m 20520 non-null float64 \n",
" 5 relative_humidity_2m 20520 non-null float64 \n",
" 6 precipitation 20520 non-null float64 \n",
" 7 rain 20520 non-null float64 \n",
" 8 snowfall 20520 non-null float64 \n",
" 9 weather_code 20520 non-null float64 \n",
" 10 cloud_cover 20520 non-null float64 \n",
" 11 wind_speed_10m 20520 non-null float64 \n",
" 12 wind_gusts_10m 20520 non-null float64 \n",
"dtypes: datetime64[ns](1), float64(9), int64(2), object(1)\n",
"memory usage: 2.2+ MB\n"
]
}
],
"source": [
"# Show the information for the weather dataframe\n",
"historical_weather_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#2656a3;\"> 🌈 Forecast Weather Measures\n",
"Weather Forecast from Open Meteo is now being fetched. This data is used in the `2_feature_pipeline` to parse in new real-time weather data."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Fetching weather forecast measures for the next 5 days\n",
"weather_forecast_df = weather_measures.forecast_weather_measures(\n",
" forecast_length=5\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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" <td>3.0</td>\n",
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" <td>14.0</td>\n",
" <td>24.8</td>\n",
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" <th>3</th>\n",
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" <td>2024-05-06 03:00:00</td>\n",
" <td>2024-05-06</td>\n",
" <td>3</td>\n",
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" <td>91.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>100.0</td>\n",
" <td>13.0</td>\n",
" <td>23.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1714968000000</td>\n",
" <td>2024-05-06 04:00:00</td>\n",
" <td>2024-05-06</td>\n",
" <td>4</td>\n",
" <td>9.6</td>\n",
" <td>92.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>100.0</td>\n",
" <td>14.0</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp datetime date hour temperature_2m \\\n",
"0 1714953600000 2024-05-06 00:00:00 2024-05-06 0 9.6 \n",
"1 1714957200000 2024-05-06 01:00:00 2024-05-06 1 9.7 \n",
"2 1714960800000 2024-05-06 02:00:00 2024-05-06 2 9.5 \n",
"3 1714964400000 2024-05-06 03:00:00 2024-05-06 3 9.5 \n",
"4 1714968000000 2024-05-06 04:00:00 2024-05-06 4 9.6 \n",
"\n",
" relative_humidity_2m precipitation rain snowfall weather_code \\\n",
"0 93.0 0.2 0.2 0.0 51.0 \n",
"1 93.0 0.0 0.0 0.0 3.0 \n",
"2 91.0 0.0 0.0 0.0 3.0 \n",
"3 91.0 0.0 0.0 0.0 3.0 \n",
"4 92.0 0.0 0.0 0.0 3.0 \n",
"\n",
" cloud_cover wind_speed_10m wind_gusts_10m \n",
"0 100.0 14.4 24.8 \n",
"1 100.0 14.0 24.8 \n",
"2 100.0 14.0 24.8 \n",
"3 100.0 13.0 23.4 \n",
"4 100.0 14.0 24.1 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the first 5 rows of the weather forecast dataframe\n",
"weather_forecast_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>115</th>\n",
" <td>1715367600000</td>\n",
" <td>2024-05-10 19:00:00</td>\n",
" <td>2024-05-10</td>\n",
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" <td>0.0</td>\n",
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" <td>5.2</td>\n",
" <td>13.0</td>\n",
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" <tr>\n",
" <th>116</th>\n",
" <td>1715371200000</td>\n",
" <td>2024-05-10 20:00:00</td>\n",
" <td>2024-05-10</td>\n",
" <td>20</td>\n",
" <td>10.5</td>\n",
" <td>71.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>88.0</td>\n",
" <td>3.4</td>\n",
" <td>8.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
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" <td>2024-05-10 21:00:00</td>\n",
" <td>2024-05-10</td>\n",
" <td>21</td>\n",
" <td>9.5</td>\n",
" <td>74.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>87.0</td>\n",
" <td>2.5</td>\n",
" <td>4.3</td>\n",
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" <td>1715378400000</td>\n",
" <td>2024-05-10 22:00:00</td>\n",
" <td>2024-05-10</td>\n",
" <td>22</td>\n",
" <td>8.6</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>4.3</td>\n",
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" <td>2024-05-10 23:00:00</td>\n",
" <td>2024-05-10</td>\n",
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" <td>7.8</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>96.0</td>\n",
" <td>2.5</td>\n",
" <td>4.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp datetime date hour temperature_2m \\\n",
"115 1715367600000 2024-05-10 19:00:00 2024-05-10 19 11.5 \n",
"116 1715371200000 2024-05-10 20:00:00 2024-05-10 20 10.5 \n",
"117 1715374800000 2024-05-10 21:00:00 2024-05-10 21 9.5 \n",
"118 1715378400000 2024-05-10 22:00:00 2024-05-10 22 8.6 \n",
"119 1715382000000 2024-05-10 23:00:00 2024-05-10 23 7.8 \n",
"\n",
" relative_humidity_2m precipitation rain snowfall weather_code \\\n",
"115 68.0 0.0 0.0 0.0 3.0 \n",
"116 71.0 0.0 0.0 0.0 3.0 \n",
"117 74.0 0.0 0.0 0.0 3.0 \n",
"118 78.0 0.0 0.0 0.0 3.0 \n",
"119 81.0 0.0 0.0 0.0 3.0 \n",
"\n",
" cloud_cover wind_speed_10m wind_gusts_10m \n",
"115 89.0 5.2 13.0 \n",
"116 88.0 3.4 8.6 \n",
"117 87.0 2.5 4.3 \n",
"118 91.0 2.6 4.3 \n",
"119 96.0 2.5 4.3 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the last 5 rows of the weather forecast dataframe\n",
"weather_forecast_df.tail(5)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 120 entries, 0 to 119\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 timestamp 120 non-null int64 \n",
" 1 datetime 120 non-null datetime64[ns]\n",
" 2 date 120 non-null object \n",
" 3 hour 120 non-null int64 \n",
" 4 temperature_2m 120 non-null float64 \n",
" 5 relative_humidity_2m 120 non-null float64 \n",
" 6 precipitation 120 non-null float64 \n",
" 7 rain 120 non-null float64 \n",
" 8 snowfall 120 non-null float64 \n",
" 9 weather_code 120 non-null float64 \n",
" 10 cloud_cover 120 non-null float64 \n",
" 11 wind_speed_10m 120 non-null float64 \n",
" 12 wind_gusts_10m 120 non-null float64 \n",
"dtypes: datetime64[ns](1), float64(9), int64(2), object(1)\n",
"memory usage: 12.3+ KB\n"
]
}
],
"source": [
"# Show the information for the weather weather forecast dataframe\n",
"weather_forecast_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#2656a3;\"> 🗓️ Calendar of Danish workdays and holidays \n",
"Lastly, the calendar data is being loaded in. The calendar data includes a `workday` attribute indicating whether the date is a workday or not. This column has been encoded from categorical variables into numerical form in the folder `features/weather_measures`. `1` indicating a workday and `0` indicating a non-workday. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Fetching the Danish calendar from January 1, 2022 to December 31, 2024 \n",
"calender_df = calendar.dk_calendar()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>date</th>\n",
" <th>dayofweek</th>\n",
" <th>day</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>workday</th>\n",
" </tr>\n",
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" <td>1</td>\n",
" <td>2022</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2022-01-03</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2022</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2022-01-04</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2022</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2022-01-05</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2022</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date dayofweek day month year workday\n",
"0 2022-01-01 5 1 1 2022 0\n",
"1 2022-01-02 6 2 1 2022 0\n",
"2 2022-01-03 0 3 1 2022 1\n",
"3 2022-01-04 1 4 1 2022 1\n",
"4 2022-01-05 2 5 1 2022 1"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the first 5 rows of the calendar dataframe\n",
"calender_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>day</th>\n",
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" <th>workday</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1091</th>\n",
" <td>2024-12-27</td>\n",
" <td>4</td>\n",
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" <td>12</td>\n",
" <td>2024</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1092</th>\n",
" <td>2024-12-28</td>\n",
" <td>5</td>\n",
" <td>28</td>\n",
" <td>12</td>\n",
" <td>2024</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1093</th>\n",
" <td>2024-12-29</td>\n",
" <td>6</td>\n",
" <td>29</td>\n",
" <td>12</td>\n",
" <td>2024</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1094</th>\n",
" <td>2024-12-30</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>12</td>\n",
" <td>2024</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1095</th>\n",
" <td>2024-12-31</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>12</td>\n",
" <td>2024</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date dayofweek day month year workday\n",
"1091 2024-12-27 4 27 12 2024 1\n",
"1092 2024-12-28 5 28 12 2024 0\n",
"1093 2024-12-29 6 29 12 2024 0\n",
"1094 2024-12-30 0 30 12 2024 1\n",
"1095 2024-12-31 1 31 12 2024 1"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the last 5 rows of the calendar dataframe\n",
"calender_df.tail(5)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1096 entries, 0 to 1095\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 1096 non-null object\n",
" 1 dayofweek 1096 non-null int64 \n",
" 2 day 1096 non-null int64 \n",
" 3 month 1096 non-null int64 \n",
" 4 year 1096 non-null int64 \n",
" 5 workday 1096 non-null int64 \n",
"dtypes: int64(5), object(1)\n",
"memory usage: 51.5+ KB\n"
]
}
],
"source": [
"# Show the information for the calendar dataframe\n",
"calender_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#2656a3;\"> 📡 Connecting to Hopsworks Feature Store\n",
"\n",
"We connect to Hopsworks Feature Store so we can access and create feature groups."
]
},
{
"cell_type": "code",
"execution_count": 19,
"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/550040\n",
"Connected. Call `.close()` to terminate connection gracefully.\n"
]
}
],
"source": [
"# Importing the hopsworks module for interacting with the Hopsworks platform\n",
"import hopsworks\n",
"\n",
"# Logging into the Hopsworks project\n",
"project = hopsworks.login()\n",
"\n",
"# Getting the feature store from the project\n",
"fs = project.get_feature_store()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#2656a3;\"> 🪄 Creating Feature Groups\n",
"A feature group can be seen as a collection of conceptually related features. In this case we create feature groups for the \n",
"- eletricity price data,\n",
"- weather data,\n",
"- calendar data.\n",
"\n",
"We specify a `primary_key` as `date` and `timestamp`, so we are able to join them when we create a dataset for training later in part `3_training_pipeline`.\n",
"We define a name and a short describtion of the feature group's contents and a version number. \n",
"\n",
"`event_time` is specifyed as `timestamp`. If event_time is set the feature group can be used for point-in-time joins.\n",
"\n",
"We've set `online_enabled` to `True` to enable accessing the feature group through the Online API for a Feature View."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Creating the feature group for the electricity prices\n",
"electricity_fg = fs.get_or_create_feature_group(\n",
" name=\"electricity_prices\",\n",
" version=1,\n",
" description=\"Electricity prices from Energidata API\",\n",
" primary_key=[\"date\",\"timestamp\"], \n",
" online_enabled=True,\n",
" event_time=\"timestamp\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have now outlined metadata for the feature group. Data hasn't been stored yet, and there's no schema defined. To store data persistently for the feature group, we populate it with its associated data using the `insert` function."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Group created successfully, explore it at \n",
"https://c.app.hopsworks.ai:443/p/550040/fs/545863/fg/787801\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Uploading Dataframe: 100.00% |██████████| Rows 20541/20541 | Elapsed Time: 00:08 | Remaining Time: 00:00\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Launching job: electricity_prices_1_offline_fg_materialization\n",
"Job started successfully, you can follow the progress at \n",
"https://c.app.hopsworks.ai/p/550040/jobs/named/electricity_prices_1_offline_fg_materialization/executions\n"
]
},
{
"data": {
"text/plain": [
"(<hsfs.core.job.Job at 0x12fc0d450>, None)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inserting the electricity_df into the feature group named electricity_fg\n",
"electricity_fg.insert(electricity_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We make a descriptions for each feature we put into the feature group. In this way we are adding more information and documentation to the user."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# List of descriptions for electricity features\n",
"electricity_feature_descriptions = [\n",
" {\"name\": \"timestamp\", \"description\": \"Timestamp of the event time\"},\n",
" {\"name\": \"date\", \"description\": \"Date of the electricity measurement\"},\n",
" {\"name\": \"datetime\", \"description\": \"Date and time of the electricity measurement\"},\n",
" {\"name\": \"hour\", \"description\": \"Hour of the day\"},\n",
" {\"name\": \"dk1_spotpricedkk_kwh\", \"description\": \"Spot price in DKK per KWH\"}, \n",
"]\n",
"\n",
"# Updating feature descriptions\n",
"for desc in electricity_feature_descriptions: \n",
" electricity_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We replicate the process for both the `weather_fg` and `danish_holidays_fg` by establishing feature groups and inserting the dataframes into their respective feature groups."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Creating the feature group for the weather data\n",
"weather_fg = fs.get_or_create_feature_group(\n",
" name=\"weather_measurements\",\n",
" version=1,\n",
" description=\"Weather measurements from Open Meteo API\",\n",
" primary_key=[\"date\", \"timestamp\"], \n",
" online_enabled=True,\n",
" event_time=\"timestamp\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Group created successfully, explore it at \n",
"https://c.app.hopsworks.ai:443/p/550040/fs/545863/fg/786783\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Uploading Dataframe: 100.00% |██████████| Rows 20520/20520 | Elapsed Time: 00:08 | Remaining Time: 00:00\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Launching job: weather_measurements_1_offline_fg_materialization\n",
"Job started successfully, you can follow the progress at \n",
"https://c.app.hopsworks.ai/p/550040/jobs/named/weather_measurements_1_offline_fg_materialization/executions\n"
]
},
{
"data": {
"text/plain": [
"(<hsfs.core.job.Job at 0x12fbb3b50>, None)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inserting the weather_df into the feature group named weather_fg\n",
"weather_fg.insert(historical_weather_df)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# List of descriptions for weather features\n",
"weather_feature_descriptions = [\n",
" {\"name\": \"timestamp\", \"description\": \"Timestamp for the weather measurement\"},\n",
" {\"name\": \"date\", \"description\": \"Date of the weather measurement\"},\n",
" {\"name\": \"datetime\", \"description\": \"Date and time of the weather measurement\"},\n",
" {\"name\": \"hour\", \"description\": \"Hour of the day\"},\n",
" {\"name\": \"temperature_2m\", \"description\": \"Temperature at 2m above ground\"},\n",
" {\"name\": \"relative_humidity_2m\", \"description\": \"Relative humidity at 2m above ground\"},\n",
" {\"name\": \"precipitation\", \"description\": \"Precipitation\"},\n",
" {\"name\": \"rain\", \"description\": \"Rain\"},\n",
" {\"name\": \"snowfall\", \"description\": \"Snowfall\"}, \n",
" {\"name\": \"weather_code\", \"description\": \"Weather code\"}, \n",
" {\"name\": \"cloud_cover\", \"description\": \"Cloud cover\"}, \n",
" {\"name\": \"wind_speed_10m\", \"description\": \"Wind speed at 10m above ground\"}, \n",
" {\"name\": \"wind_gusts_10m\", \"description\": \"Wind gusts at 10m above ground\"}, \n",
"]\n",
"\n",
"# Updating feature descriptions\n",
"for desc in weather_feature_descriptions: \n",
" weather_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# Creating the feature group for the danish calendar\n",
"danish_calendar_fg = fs.get_or_create_feature_group(\n",
" name=\"dk_calendar\",\n",
" version=1,\n",
" description=\"Danish calendar\",\n",
" primary_key=[\"date\"],\n",
" online_enabled=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Group created successfully, explore it at \n",
"https://c.app.hopsworks.ai:443/p/550040/fs/545863/fg/786784\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Uploading Dataframe: 100.00% |██████████| Rows 1096/1096 | Elapsed Time: 00:05 | Remaining Time: 00:00\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Launching job: dk_calendar_1_offline_fg_materialization\n",
"Job started successfully, you can follow the progress at \n",
"https://c.app.hopsworks.ai/p/550040/jobs/named/dk_calendar_1_offline_fg_materialization/executions\n"
]
},
{
"data": {
"text/plain": [
"(<hsfs.core.job.Job at 0x12fe04690>, None)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inserting the calendar_df into the feature group named danish_calendar_fg\n",
"danish_calendar_fg.insert(calender_df)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# List of descriptions for danish_calendar features\n",
"danish_calendar_feature_descriptions = [\n",
" {\"name\": \"date\", \"description\": \"Date in the calendar\"},\n",
" {\"name\": \"day\", \"description\": \"Day number of the week. Monday is 0 and Sunday is 6\"},\n",
" {\"name\": \"month\", \"description\": \"Month number of the year\"},\n",
" {\"name\": \"workday\", \"description\": \"Workday or not a workday. Workday is 1 and not a workday is 0\"},\n",
"]\n",
"\n",
"# Updating feature descriptions\n",
"for desc in danish_calendar_feature_descriptions: \n",
" danish_calendar_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## <span style=\"color:#2656a3;\">⏭️ **Next:** Part 02: Feature Pipeline </span>\n",
"\n",
"Next we will generate new data for the Feature Groups."
]
}
],
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"display_name": "bds-streamlit",
"language": "python",
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|