You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
43 lines
1.4 KiB
43 lines
1.4 KiB
from sklearn import linear_model
|
|
import pandas as pd
|
|
from sklearn.metrics import mean_squared_error, r2_score
|
|
|
|
|
|
def k_days_into_future_regression(X, y, k, n0):
|
|
"""
|
|
linear regression that returns the fitted weights as well as metrics
|
|
:param X: x timeseries dataframe (very clean, no unamed columns), multidimensional rows
|
|
:param y: y timeseries dataframe (very clean, no unamed columns), scalar rows
|
|
:param k: days predicting in advance
|
|
:param n0: ignoring the first n0 days
|
|
:return: intercept, slopes, correlation, mean squared error
|
|
"""
|
|
col = "TimeSinceAugFirst"
|
|
inp = []
|
|
out = []
|
|
for day in y[col][n0 - 1:]:
|
|
prev = day - k
|
|
xprev = X[X[col] == prev].drop(columns=[col]).to_numpy()
|
|
if xprev.shape[0] != 1:
|
|
continue
|
|
else:
|
|
xprev = xprev[0, :]
|
|
yt = y[y[col] == day].drop(columns=[col]).to_numpy()[0, :]
|
|
inp.append(xprev)
|
|
out.append(yt)
|
|
regr = linear_model.LinearRegression()
|
|
regr.fit(inp, out)
|
|
predictions = regr.predict(inp)
|
|
mse = mean_squared_error(out, predictions)/(len(out) - 2)
|
|
r2 = r2_score(out, predictions)
|
|
return regr.intercept_, regr.coef_, r2, mse
|
|
|
|
|
|
def main():
|
|
fatigueSums = pd.read_csv("fatigue_total_sum.csv")
|
|
performance = pd.read_csv("../data_preparation/cleaned/expSmoothWorkAndFatigueData.csv", index_col=0).drop(columns=["totalWork", "averageWorkLoad", "smoothedFatigueData"])
|
|
print(k_days_into_future_regression(fatigueSums, performance, 0, 1))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|