|
|
- from sklearn import linear_model
- from sklearn.preprocessing import PolynomialFeatures
- import numpy as np
- 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 standard_lr(x, y):
- # Standard linear regression formula, gives back params and r2
- regr = linear_model.LinearRegression()
- regr.fit(x, y)
- predictions = regr.predict(x)
- mse = mean_squared_error(y, predictions) / (len(y) - 2)
- r2 = r2_score(y, predictions)
- return regr.intercept_, regr.coef_, r2, mse
-
-
- def poly_regression(x, y, degree):
- # Polynomial regression with nth degree, gives back rmse and r2
- polynomial_features = PolynomialFeatures(degree=degree)
- x_poly = polynomial_features.fit_transform(x)
-
- model = linear_model.LinearRegression()
- model.fit(x_poly, y)
- y_poly_pred = model.predict(x_poly)
-
- rmse = np.sqrt(mean_squared_error(y, y_poly_pred))
- r2 = r2_score(y, y_poly_pred)
- return rmse, r2
-
-
- def main():
- player = pd.read_csv("../data_preparation/cleaned/personal.csv", index_col=0)
- player = player[player['playerID'] == 1]
- x = player[['fatigueNorm', 'day']]
- y = player['sorenessNorm']
- print(standard_lr(x, y))
- print(poly_regression(x, y, 5))
-
-
- if __name__ == "__main__":
- main()
|
