XGBoost

XGBoost 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from fastai.imports import *
import xgboost as xgb
from sklearn.metrics import mean_squared_error
import zipfile,kaggle
import os
color_pal = sns.color_palette()
plt.style.use('fivethirtyeight')
from nbdevAuto.functions import * 
import nbdevAuto.functions
name = 'hourly-energy-consumption'
path = Path(f'Data/{name}')
user = 'robikscube'
kaggle_dataset_download(user = user, 
                         name = name)
df = pd.read_csv(f'{path}/PJME_hourly.csv')
df = df.set_index('Datetime')
df.index = pd.to_datetime(df.index)
df.plot(style='.',
        figsize=(15, 5),
        color=color_pal[0],
        title='PJME Energy Use in MW')
plt.show()

Train / Test Split

train = df.loc[df.index < '01-01-2015']
test = df.loc[df.index >= '01-01-2015']

fig, ax = plt.subplots(figsize=(15, 5))
train.plot(ax=ax, label='Training Set', title='Data Train/Test Split')
test.plot(ax=ax, label='Test Set')
ax.axvline('01-01-2015', color='black', ls='--')
ax.legend(['Training Set', 'Test Set'])
plt.show()

df.loc[(df.index > '01-01-2010') & (df.index < '01-08-2010')] \
    .plot(figsize=(15, 5), title='Week Of Data')
plt.show()

Feature Creation

def create_features(df):
    """
    Create time series features based on time series index.
    """
    df = df.copy()
    df['hour'] = df.index.hour
    df['dayofweek'] = df.index.dayofweek
    df['quarter'] = df.index.quarter
    df['month'] = df.index.month
    df['year'] = df.index.year
    df['dayofyear'] = df.index.dayofyear
    df['dayofmonth'] = df.index.day
    df['weekofyear'] = df.index.isocalendar().week
    return df

df = create_features(df)

Visualize our Feature / Target Relationship

fig, ax = plt.subplots(figsize=(10, 8))
sns.boxplot(data=df, x='hour', y='PJME_MW')
ax.set_title('MW by Hour')
plt.show()

fig, ax = plt.subplots(figsize=(10, 8))
sns.boxplot(data=df, x='month', y='PJME_MW', palette='Blues')
ax.set_title('MW by Month')
plt.show()
/tmp/ipykernel_109462/958582662.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=df, x='month', y='PJME_MW', palette='Blues')

Create our Model

train = create_features(train)
test = create_features(test)

FEATURES = ['dayofyear', 'hour', 'dayofweek', 'quarter', 'month', 'year']
TARGET = 'PJME_MW'

X_train = train[FEATURES]
y_train = train[TARGET]

X_test = test[FEATURES]
y_test = test[TARGET]
reg = xgb.XGBRegressor(base_score=0.5, booster='gbtree',    
                       n_estimators=1000,
                       early_stopping_rounds=50,
                       objective='reg:linear',
                       max_depth=3,
                       learning_rate=0.01)
reg.fit(X_train, y_train,
        eval_set=[(X_train, y_train), (X_test, y_test)],
        verbose=100)
[0] validation_0-rmse:32605.13970   validation_1-rmse:31657.15729
/home/thekkel/mambaforge/envs/cfast/lib/python3.11/site-packages/xgboost/core.py:160: UserWarning: [17:58:09] WARNING: /workspace/src/objective/regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.
  warnings.warn(smsg, UserWarning)
[100]   validation_0-rmse:12584.35462   validation_1-rmse:11747.28803
[200]   validation_0-rmse:5837.33066    validation_1-rmse:5363.58554
[300]   validation_0-rmse:3923.28511    validation_1-rmse:4020.48045
[400]   validation_0-rmse:3447.54638    validation_1-rmse:3860.60088
[500]   validation_0-rmse:3288.19208    validation_1-rmse:3816.37862
[600]   validation_0-rmse:3206.55619    validation_1-rmse:3779.04119
[700]   validation_0-rmse:3153.61368    validation_1-rmse:3754.45684
[800]   validation_0-rmse:3114.34038    validation_1-rmse:3738.38209
[900]   validation_0-rmse:3084.39550    validation_1-rmse:3730.01893
[989]   validation_0-rmse:3059.85847    validation_1-rmse:3727.94591
XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=0.01, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=3, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=None,
             num_parallel_tree=None, objective='reg:linear', ...)
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Feature Importance

fi = pd.DataFrame(data=reg.feature_importances_,
             index=reg.feature_names_in_,
             columns=['importance'])
fi.sort_values('importance').plot(kind='barh', title='Feature Importance')
plt.show()

Forecast on Test

test['prediction'] = reg.predict(X_test)
df = df.merge(test[['prediction']], how='left', left_index=True, right_index=True)
ax = df[['PJME_MW']].plot(figsize=(15, 5))
df['prediction'].plot(ax=ax, style='.')
plt.legend(['Truth Data', 'Predictions'])
ax.set_title('Raw Dat and Prediction')
plt.show()

ax = df.loc[(df.index > '04-01-2018') & (df.index < '04-18-2018')]['PJME_MW'] \
    .plot(figsize=(15, 5), title='Week Of Data')
df.loc[(df.index > '04-01-2018') & (df.index < '04-18-2018')]['prediction'] \
    .plot(style='.')
plt.legend(['Truth Data','Prediction'])
plt.show()

Score (RMSE)

score = np.sqrt(mean_squared_error(test['PJME_MW'], test['prediction']))
print(f'RMSE Score on Test set: {score:0.2f}')
RMSE Score on Test set: 3726.80

Calculate Error

  • Look at the worst and best predicted days
test['error'] = np.abs(test[TARGET] - test['prediction'])
test['date'] = test.index.date
test.groupby(['date'])['error'].mean().sort_values(ascending=False).head(10)
date
2016-08-13    12879.484619
2016-08-14    12772.887207
2015-02-20    11186.031494
2016-09-09    10966.513102
2016-09-10    10889.102214
2018-01-06    10642.975830
2016-08-12    10041.172689
2015-02-21     9988.168783
2015-02-16     9900.809326
2018-01-07     9852.571370
Name: error, dtype: float64

Next Steps

  • More robust cross validation
  • Add more features (weather forecast, holidays)
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