r-squared.py

from sklearn.metrics import r2_score
# create columns of 14 day smoothed data
start_R_calc = 192 # index value for Aug 1 2020
end_R_calc = 316 # index for last 'real' data day at time of writing
df['deathIncrease-14daysmoothed'] = df['deathIncrease'].rolling(window=rolling_avg).mean()
df['predicted_Death-14daysmoothed'] = df['predicted_death'].rolling(window=rolling_avg).mean()
model = df[start_R_calc:end_R_calc]['deathIncrease-14daysmoothed']
prediction = df[start_R_calc:end_R_calc]['predicted_Death-14daysmoothed']

R2 = r2_score(model,prediction)
print(f"R^2 Analysis start date is {df.loc[start_R_calc,'date']:%m %d %Y}")
print(f"R^2 value is {R2:.2f}")