def linearfunction(x,y,name='linear rating'):
datadf = pd.DataFrame.from_dict({'x':x,'y':y}).dropna() ## put x and y in a dataframe so you can drop ones that don't match up
datadf = datadf[datadf>=0].dropna() ##verify data is valid (not inf)
regression = pd.ols(y=datadf['y'],x=datadf['x'])
pearson = pearson_r(datadf['x'],datadf['y'])[0]
spearman = spearman_r(datadf['x'],datadf['y'])[0]
coeffdf = pd.DataFrame({'a':[regression.beta[1]],'b':[regression.beta[0]],'r2':[regression.r2],'rmse':[regression.rmse],'pearson':[pearson],'spearman':[spearman]},index=[name])
return coeffdf
def linearfunction(x,y,name='linear rating'):
datadf = pd.DataFrame.from_dict({'x':x,'y':y}).dropna() ## put x and y in a dataframe so you can drop ones that don't match up
datadf = datadf[datadf>=0].dropna() ##verify data is valid (not inf)
regression = pd.ols(y=datadf['y'],x=datadf['x'])
pearson = pearson_r(datadf['x'],datadf['y'])[0]
spearman = spearman_r(datadf['x'],datadf['y'])[0]
coeffdf = pd.DataFrame({'a':[regression.beta[1]],'b':[regression.beta[0]],'r2':[regression.r2],'rmse':[regression.rmse],'pearson':[pearson],'spearman':[spearman]},index=[name])
return coeffdf
def powerfunction(x,y,name='power rating',pvalue=0.01):
## put x and y in a dataframe so you can drop ones that don't match up
datadf = pd.DataFrame.from_dict({'x':x,'y':y}).dropna().apply(np.log10)
datadf = datadf[datadf>=-10] ##verify data is valid (not inf)
regression = pd.ols(y=datadf['y'],x=datadf['x'])
if pearson_r(datadf['x'],datadf['y'])[1] < pvalue:
pearson = pearson_r(datadf['x'],datadf['y'])[0]
else:
pearson = np.nan
if spearman_r(datadf['x'],datadf['y'])[1] < pvalue:
spearman = spearman_r(datadf['x'],datadf['y'])[0]
else:
spearman = np.nan
coeffdf = pd.DataFrame({'a':[10**regression.beta[1]],'b':[regression.beta[0]],
'r2':[regression.r2],'rmse':[regression.rmse],'pearson':[pearson],'spearman':[spearman]},
index=[name])
return coeffdf
def calculate_correlation(x: List[float], y: List[float]) -> Tuple[float, float, float]: r, p = pearson_r(x, y) return r, pow(r, 2), p
