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import datetime
import pandas as pd
import hopsworks
import os
import datetime
from xgboost import XGBRegressor
import pandas as pd
import hopsworks
import os
os.environ['HOPSWORKS_PROJECT'] = os.getenv('HOPSWORKS_PROJECT')
os.environ['HOPSWORKS_API_KEY'] = os.getenv('HOPSWORKS_API_KEY')
project = hopsworks.login()
fs = project.get_feature_store()
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
retrieved_model = mr.get_model(
name="air_quality_xgboost_model",
version=1,
)
saved_model_dir = retrieved_model.download()
def get_merged_dataframe():
# Get data
monitor_fg = fs.get_or_create_feature_group(
name='aq_predictions',
description='Air Quality prediction monitoring',
version=1,
primary_key=['city','street','date','days_before_forecast_day'],
event_time="date"
)
air_quality_fg = fs.get_feature_group(
name='air_quality',
version=1,
)
weather_fg = fs.get_feature_group(
name='weather',
version=1,
)
retrieved_xgboost_model = XGBRegressor()
retrieved_xgboost_model.load_model(saved_model_dir + "/model.json")
selected_features = air_quality_fg.select_all(['pm25', 'past_air_quality']).join(weather_fg.select(['temperature_2m_mean', 'precipitation_sum', 'wind_speed_10m_max', 'wind_direction_10m_dominant']), on=['city'])
selected_features = selected_features.read()
selected_features['date'] = pd.to_datetime(selected_features['date'], utc=True).dt.tz_convert(None).astype('datetime64[ns]')
predicted_data = monitor_fg.read()
predicted_data = predicted_data[['date','predicted_pm25']]
predicted_data['date'] = predicted_data['date'].dt.tz_convert(None).astype('datetime64[ns]')
predicted_data = predicted_data.sort_values(by=['date'], ascending=True).reset_index(drop=True)
#get historical predicted pm25
selected_features['predicted_pm25'] = retrieved_xgboost_model.predict(selected_features[['past_air_quality','temperature_2m_mean', 'precipitation_sum', 'wind_speed_10m_max', 'wind_direction_10m_dominant']])
#merge data
selected_features = selected_features[['date', 'pm25', 'predicted_pm25']]
combined_df = pd.merge(selected_features, predicted_data,on='date', how='outer')
combined_df['date'] = pd.to_datetime(combined_df['date'], utc=True).dt.tz_convert(None).astype('datetime64[ns]')
# Combine the predicted_pm25_x and predicted_pm25_y columns into one
combined_df['predicted_pm25'] = combined_df['predicted_pm25_x'].combine_first(combined_df['predicted_pm25_y'])
# Drop the individual columns after merging
combined_df = combined_df.drop(columns=['predicted_pm25_x', 'predicted_pm25_y'])
print(get_merged_dataframe()) |