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102 lines
3.6 KiB
102 lines
3.6 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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from matplotlib import pyplot as plt
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import numpy as np
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from sklearn.preprocessing import PolynomialFeatures
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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()
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if xprev.shape[0] != 1:
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continue
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else:
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xprev = xprev[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 standard_lr(x, y):
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x = x.reshape(-1, 1)
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y = y.reshape(-1, 1)
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regr = linear_model.LinearRegression()
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regr.fit(x, y)
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predictions = regr.predict(x)
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mse = mean_squared_error(y, predictions) / (len(y) - 2)
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r2 = r2_score(y, predictions)
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return regr.intercept_, regr.coef_, r2, mse
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def poly_regression(x, y, degree):
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# Polynomial regression with nth degree, gives back rmse and r2
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polynomial_features = PolynomialFeatures(degree=degree)
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x_poly = polynomial_features.fist_transform(x)
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model = linear_model.LinearRegression()
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model.fit(x_poly, y)
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y_poly_pred = model.predict(x_poly)
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rmse = np.sqrt(mean_squared_error(y, y_poly_pred))
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r2 = r2_score(y, y_poly_pred)
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return rmse, r2
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def run_all_linears():
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# Reads in the neccessary csv file
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df = pd.read_csv('data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
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regr = linear_model.LinearRegression()
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for i in range(4, 11):
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for j in range(1, 11 - i):
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mat = df[[df.columns[i], df.columns[i + j]]].values
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regr.intercept_, regr.coef_, r2, mse = standard_lr(mat[:, 0], mat[:, 1])
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plt.figure(figsize=(6, 6))
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plt.xlabel(df.columns[i])
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plt.ylabel(df.columns[i + j])
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plt.title('r2: ' + str(r2))
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plt.scatter(mat[:, 0], mat[:, 1])
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plt.savefig('wellness_linear_regressions/' + df.columns[i] + '_vs_' + df.columns[i + j] + '.png')
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plt.close()
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def run_all_polynomials():
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# Reads in the neccessary csv file
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df = pd.read_csv('data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
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regr = linear_model.LinearRegression()
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for i in range(4, 11):
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for j in range(1, 11 - i):
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mat = df[[df.columns[i], df.columns[i + j]]].values
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for d in range(2, 5):
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rmse, r2 = poly_regression(mat[:, 0], mat[:, 1], d)
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plt.figure(figsize=(6, 6))
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plt.xlabel(df.columns[i])
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plt.ylabel(df.columns[i + j])
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plt.title('r2: ' + str(r2) + 'degree: ' + str(d))
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plt.scatter(mat[:, 0], mat[:, 1])
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plt.savefig('wellness_poly_regressions/' + df.columns[i] + '_vs_' + df.columns[i + j] + '_' + str(d) + '_degree.png')
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print(df.columns[i] + '_vs_' + df.columns[i + j] + '_degree_' + str(d) + '_r2=' + str(r2) + '_rmse=' + str(rmse))
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plt.close()
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run_all_linears()
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run_all_polynomials()
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