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- from sklearn import linear_model
- import pandas as pd
- from sklearn.metrics import mean_squared_error, r2_score
- from matplotlib import pyplot as plt
- import numpy as np
- from sklearn.preprocessing import PolynomialFeatures
-
-
- 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):
- x = x.reshape(-1, 1)
- y = y.reshape(-1, 1)
- 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):
-
- # Reshapes the models to be able to run regression on them
- x = x.reshape(-1, 1)
- y = y.reshape(-1, 1)
-
- # 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 run_all_polynomials():
- # Reads in the neccessary csv file
- df = pd.read_csv('data_preparation/cleaned/personal.csv')
- regr = linear_model.LinearRegression()
- print("xVal, yVal, degree, r2, rmse")
- for i in range(3, 14):
- for j in range(1, 14 - i):
- mat = df[[df.columns[i], df.columns[i + j]]].values
- for d in range(1, 6):
- rmse, r2 = poly_regression(mat[:, 0], mat[:, 1], d)
- plt.figure(figsize=(6, 6))
- plt.xlabel(df.columns[i])
- plt.ylabel(df.columns[i + j])
- plt.title('r2: ' + str(r2) + 'degree: ' + str(d))
- plt.scatter(mat[:, 0], mat[:, 1])
- plt.savefig('personal_regression_info/' + df.columns[i] + '_vs_' + df.columns[i + j] + '_' + str(d) + '_degree.png')
- print(df.columns[i] + ', ' + df.columns[i + j] + ', ' + str(d) + ', ' + str(r2) + ', ' + str(rmse))
- plt.close()
-
-
- # run_all_linears()
- run_all_polynomials()
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