Added script for automatically running regressions and generating charts

run_all_regressions.py

@@ -0,0 +1,76 @@
+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()