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Apply isort and ruff code style

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This commit is contained in:
Michael Osthege
2024-10-03 11:05:44 +02:00
committed by Andreas
parent 05a3c1a5bb
commit a4d178d250
23 changed files with 1787 additions and 866 deletions
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158
modules/class_load_corrector.py
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@@ -1,14 +1,11 @@
import json
import os
import sys
from datetime import datetime, timedelta, timezone
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, r2_score
import mariadb
# from sklearn.model_selection import train_test_split, GridSearchCV
# from sklearn.ensemble import GradientBoostingRegressor
# from xgboost import XGBRegressor
@@ -22,6 +19,7 @@ import mariadb
# Add the parent directory to sys.path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import *
from modules.class_load import *
@@ -38,117 +36,159 @@ class LoadPredictionAdjuster:
def _remove_outliers(self, data, threshold=2):
# Calculate the Z-Score of the 'Last' data
data['Z-Score'] = np.abs((data['Last'] - data['Last'].mean()) / data['Last'].std())
data["Z-Score"] = np.abs(
(data["Last"] - data["Last"].mean()) / data["Last"].std()
)
# Filter the data based on the threshold
filtered_data = data[data['Z-Score'] < threshold]
return filtered_data.drop(columns=['Z-Score'])
filtered_data = data[data["Z-Score"] < threshold]
return filtered_data.drop(columns=["Z-Score"])
def _merge_data(self):
# Convert the time column in both DataFrames to datetime
self.predicted_data['time'] = pd.to_datetime(self.predicted_data['time'])
self.measured_data['time'] = pd.to_datetime(self.measured_data['time'])
self.predicted_data["time"] = pd.to_datetime(self.predicted_data["time"])
self.measured_data["time"] = pd.to_datetime(self.measured_data["time"])
# Ensure both time columns have the same timezone
if self.measured_data['time'].dt.tz is None:
self.measured_data['time'] = self.measured_data['time'].dt.tz_localize('UTC')
if self.measured_data["time"].dt.tz is None:
self.measured_data["time"] = self.measured_data["time"].dt.tz_localize(
"UTC"
)
self.predicted_data['time'] = (
self.predicted_data['time'].dt.tz_localize('UTC')
.dt.tz_convert('Europe/Berlin')
self.predicted_data["time"] = (
self.predicted_data["time"]
.dt.tz_localize("UTC")
.dt.tz_convert("Europe/Berlin")
)
self.measured_data["time"] = self.measured_data["time"].dt.tz_convert(
"Europe/Berlin"
)
self.measured_data['time'] = self.measured_data['time'].dt.tz_convert('Europe/Berlin')
# Optionally: Remove timezone information if only working locally
self.predicted_data['time'] = self.predicted_data['time'].dt.tz_localize(None)
self.measured_data['time'] = self.measured_data['time'].dt.tz_localize(None)
self.predicted_data["time"] = self.predicted_data["time"].dt.tz_localize(None)
self.measured_data["time"] = self.measured_data["time"].dt.tz_localize(None)
# Now you can perform the merge
merged_data = pd.merge(self.measured_data, self.predicted_data, on='time', how='inner')
merged_data = pd.merge(
self.measured_data, self.predicted_data, on="time", how="inner"
)
print(merged_data)
merged_data['Hour'] = merged_data['time'].dt.hour
merged_data['DayOfWeek'] = merged_data['time'].dt.dayofweek
merged_data["Hour"] = merged_data["time"].dt.hour
merged_data["DayOfWeek"] = merged_data["time"].dt.dayofweek
return merged_data
def calculate_weighted_mean(self, train_period_weeks=9, test_period_weeks=1):
self.merged_data = self._remove_outliers(self.merged_data)
train_end_date = self.merged_data['time'].max() - pd.Timedelta(weeks=test_period_weeks)
train_end_date = self.merged_data["time"].max() - pd.Timedelta(
weeks=test_period_weeks
)
train_start_date = train_end_date - pd.Timedelta(weeks=train_period_weeks)
test_start_date = train_end_date + pd.Timedelta(hours=1)
test_end_date = test_start_date + pd.Timedelta(weeks=test_period_weeks) - pd.Timedelta(hours=1)
test_end_date = (
test_start_date
+ pd.Timedelta(weeks=test_period_weeks)
- pd.Timedelta(hours=1)
)
self.train_data = self.merged_data[
(self.merged_data['time'] >= train_start_date) &
(self.merged_data['time'] <= train_end_date)
(self.merged_data["time"] >= train_start_date)
& (self.merged_data["time"] <= train_end_date)
]
self.test_data = self.merged_data[
(self.merged_data['time'] >= test_start_date) &
(self.merged_data['time'] <= test_end_date)
(self.merged_data["time"] >= test_start_date)
& (self.merged_data["time"] <= test_end_date)
]
self.train_data['Difference'] = self.train_data['Last'] - self.train_data['Last Pred']
self.train_data["Difference"] = (
self.train_data["Last"] - self.train_data["Last Pred"]
)
weekdays_train_data = self.train_data[self.train_data['DayOfWeek'] < 5]
weekends_train_data = self.train_data[self.train_data['DayOfWeek'] >= 5]
weekdays_train_data = self.train_data[self.train_data["DayOfWeek"] < 5]
weekends_train_data = self.train_data[self.train_data["DayOfWeek"] >= 5]
self.weekday_diff = weekdays_train_data.groupby('Hour').apply(self._weighted_mean_diff).dropna()
self.weekend_diff = weekends_train_data.groupby('Hour').apply(self._weighted_mean_diff).dropna()
self.weekday_diff = (
weekdays_train_data.groupby("Hour").apply(self._weighted_mean_diff).dropna()
)
self.weekend_diff = (
weekends_train_data.groupby("Hour").apply(self._weighted_mean_diff).dropna()
)
def _weighted_mean_diff(self, data):
train_end_date = self.train_data['time'].max()
weights = 1 / (train_end_date - data['time']).dt.days.replace(0, np.nan)
weighted_mean = (data['Difference'] * weights).sum() / weights.sum()
train_end_date = self.train_data["time"].max()
weights = 1 / (train_end_date - data["time"]).dt.days.replace(0, np.nan)
weighted_mean = (data["Difference"] * weights).sum() / weights.sum()
return weighted_mean
def adjust_predictions(self):
self.train_data['Adjusted Pred'] = self.train_data.apply(self._adjust_row, axis=1)
self.test_data['Adjusted Pred'] = self.test_data.apply(self._adjust_row, axis=1)
self.train_data["Adjusted Pred"] = self.train_data.apply(
self._adjust_row, axis=1
)
self.test_data["Adjusted Pred"] = self.test_data.apply(self._adjust_row, axis=1)
def _adjust_row(self, row):
if row['DayOfWeek'] < 5:
return row['Last Pred'] + self.weekday_diff.get(row['Hour'], 0)
if row["DayOfWeek"] < 5:
return row["Last Pred"] + self.weekday_diff.get(row["Hour"], 0)
else:
return row['Last Pred'] + self.weekend_diff.get(row['Hour'], 0)
return row["Last Pred"] + self.weekend_diff.get(row["Hour"], 0)
def plot_results(self):
self._plot_data(self.train_data, 'Training')
self._plot_data(self.test_data, 'Testing')
self._plot_data(self.train_data, "Training")
self._plot_data(self.test_data, "Testing")
def _plot_data(self, data, data_type):
plt.figure(figsize=(14, 7))
plt.plot(data['time'], data['Last'], label=f'Actual Last - {data_type}', color='blue')
plt.plot(data['time'], data['Last Pred'], label=f'Predicted Last - {data_type}', color='red', linestyle='--')
plt.plot(data['time'], data['Adjusted Pred'], label=f'Adjusted Predicted Last - {data_type}', color='green', linestyle=':')
plt.xlabel('Time')
plt.ylabel('Load')
plt.title(f'Actual vs Predicted vs Adjusted Predicted Load ({data_type} Data)')
plt.plot(
data["time"], data["Last"], label=f"Actual Last - {data_type}", color="blue"
)
plt.plot(
data["time"],
data["Last Pred"],
label=f"Predicted Last - {data_type}",
color="red",
linestyle="--",
)
plt.plot(
data["time"],
data["Adjusted Pred"],
label=f"Adjusted Predicted Last - {data_type}",
color="green",
linestyle=":",
)
plt.xlabel("Time")
plt.ylabel("Load")
plt.title(f"Actual vs Predicted vs Adjusted Predicted Load ({data_type} Data)")
plt.legend()
plt.grid(True)
plt.show()
def evaluate_model(self):
mse = mean_squared_error(self.test_data['Last'], self.test_data['Adjusted Pred'])
r2 = r2_score(self.test_data['Last'], self.test_data['Adjusted Pred'])
print(f'Mean Squared Error: {mse}')
print(f'R-squared: {r2}')
mse = mean_squared_error(
self.test_data["Last"], self.test_data["Adjusted Pred"]
)
r2 = r2_score(self.test_data["Last"], self.test_data["Adjusted Pred"])
print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")
def predict_next_hours(self, hours_ahead):
last_date = self.merged_data['time'].max()
future_dates = [last_date + pd.Timedelta(hours=i) for i in range(1, hours_ahead + 1)]
future_df = pd.DataFrame({'time': future_dates})
future_df['Hour'] = future_df['time'].dt.hour
future_df['DayOfWeek'] = future_df['time'].dt.dayofweek
future_df['Last Pred'] = future_df['time'].apply(self._forecast_next_hours)
future_df['Adjusted Pred'] = future_df.apply(self._adjust_row, axis=1)
last_date = self.merged_data["time"].max()
future_dates = [
last_date + pd.Timedelta(hours=i) for i in range(1, hours_ahead + 1)
]
future_df = pd.DataFrame({"time": future_dates})
future_df["Hour"] = future_df["time"].dt.hour
future_df["DayOfWeek"] = future_df["time"].dt.dayofweek
future_df["Last Pred"] = future_df["time"].apply(self._forecast_next_hours)
future_df["Adjusted Pred"] = future_df.apply(self._adjust_row, axis=1)
return future_df
def _forecast_next_hours(self, timestamp):
date_str = timestamp.strftime('%Y-%m-%d')
date_str = timestamp.strftime("%Y-%m-%d")
hour = timestamp.hour
daily_forecast = self.load_forecast.get_daily_stats(date_str)
return daily_forecast[0][hour] if hour < len(daily_forecast[0]) else np.nan
# if __name__ == '__main__':
# estimator = LastEstimator()
# start_date = "2024-06-01"