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39 lines
1.3 KiB
39 lines
1.3 KiB
from sklearn import linear_model
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import pandas as pd
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from sklearn.metrics import mean_squared_error, r2_score
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def k_days_into_future_regression(X, y, k, n0):
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"""
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linear regression that returns the fitted weights as well as metrics
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:param X: x timeseries dataframe (very clean, no unamed columns), multidimensional rows
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:param y: y timeseries dataframe (very clean, no unamed columns), scalar rows
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:param k: days predicting in advance
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:param n0: ignoring the first n0 days
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:return: intercept, slopes, correlation, mean squared error
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"""
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col = "TimeSinceAugFirst"
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inp = []
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out = []
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for day in y[col][n0 - 1:]:
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prev = day - k
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xprev = X[X[col] == prev].drop(columns=[col]).to_numpy()[0, :]
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yt = y[y[col] == day].drop(columns=[col]).to_numpy()[0, :]
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inp.append(xprev)
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out.append(yt)
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regr = linear_model.LinearRegression()
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regr.fit(inp, out)
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predictions = regr.predict(inp)
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mse = mean_squared_error(out, predictions)/(len(out) - 2)
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r2 = r2_score(out, predictions)
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return regr.intercept_, regr.coef_, r2, mse
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def main():
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fatigueSums = pd.read_csv("fatigue_total_sum.csv")
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workMovingAverage21 = pd.read_csv("21DaySlidingWorkAverage.csv", index_col=0)
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print(k_days_into_future_regression(workMovingAverage21, fatigueSums, 0, 21))
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if __name__ == "__main__":
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main()
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