Create olscode.py

doc/src/week37/programs/olscode.py

@@ -0,0 +1,47 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import KFold
+from sklearn.linear_model import Ridge, LinearRegression
+from sklearn.model_selection import cross_val_score
+from sklearn.preprocessing import PolynomialFeatures
+
+# A seed just to ensure that the random numbers are the same for every run.
+# Useful for eventual debugging.
+np.random.seed(3155)
+
+# Generate the data.
+nsamples = 10000
+x = np.random.randn(nsamples)
+y = 3*x**2 + np.random.randn(nsamples)
+
+## Cross-validation on Ridge regression using KFold only
+
+# Decide degree on polynomial to fit
+poly = PolynomialFeatures(degree = 6)
+
+
+# Initialize a KFold instance
+k = 10
+kfold = KFold(n_splits = k)
+
+# Perform the cross-validation to estimate MSE using OLS
+scores_KFold = np.zeros((k))
+model = LinearRegression() 
+j = 0
+for train_inds, test_inds in kfold.split(x):
+    xtrain = x[train_inds]
+    ytrain = y[train_inds]
+    xtest = x[test_inds]
+    ytest = y[test_inds]
+    Xtrain = poly.fit_transform(xtrain[:, np.newaxis])
+    model.fit(Xtrain, ytrain[:, np.newaxis])
+    Xtest = poly.fit_transform(xtest[:, np.newaxis])
+    ypred = model.predict(Xtest)
+    scores_KFold[j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)
+    print(f"Score for each fold:{scores_KFold[j]}")
+    j += 1
+
+
+estimated_mse_KFold = np.mean(scores_KFold)
+print(f"Average OLS score:{estimated_mse_KFold}")
+