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.
76 lines
2.5 KiB
76 lines
2.5 KiB
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
|
|
|
|
|
|
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 run_all_linears():
|
|
|
|
# Reads in the neccessary csv file
|
|
df = pd.read_csv('data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
|
|
regr = linear_model.LinearRegression()
|
|
for i in range(4, 11):
|
|
for j in range(1, 11 - i):
|
|
mat = df[[df.columns[i], df.columns[i + j]]].values
|
|
regr.intercept_, regr.coef_, r2, mse = standard_lr(mat[:, 0], mat[:, 1])
|
|
plt.figure(figsize=(6, 6))
|
|
plt.xlabel(df.columns[i])
|
|
plt.ylabel(df.columns[i + j])
|
|
plt.title('r2: ' + str(r2))
|
|
plt.scatter(mat[:, 0], mat[:, 1])
|
|
plt.savefig('wellness_linear_regressions/' + df.columns[i] + '_vs_' + df.columns[i + j] + '.png')
|
|
plt.close()
|
|
|
|
|
|
def run_all_polynomials():
|
|
# Reads in the neccessary csv file
|
|
df = pd.read_csv('data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
|
|
regr = linear_model.LinearRegression()
|
|
for i in range(4, 11):
|
|
for j in range(1, 11 - i):
|
|
mat = df[[df.columns[i], df.columns[i + j]]].values
|
|
|
|
|
|
run_all_linears()
|