--- a
+++ b/GP/run_ridge.py
@@ -0,0 +1,42 @@
+from sklearn.linear_model import RidgeCV
+from scipy.stats.stats import pearsonr
+import os
+from matplotlib import pyplot as plt
+
+
+def run_ridge_main(X_tr, y_tr, X_vl, y_val, output, main):
+
+    os.chdir(output)
+
+    if not os.path.exists("ridge"):
+            os.makedirs("ridge")
+            dir = output + "ridge/"
+
+    os.chdir(os.path.join(output, 'ridge/'))
+
+
+    if not os.path.exists("figures"):
+        os.makedirs("figures")
+        dir = output + "ridge/figures/"
+
+    os.chdir(os.path.join(output, 'ridge/figures/'))
+
+    for i in range(0, y_tr.shape[1]):
+
+        ridge = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X_tr, y_tr[:,i])
+        ridge.score(X_tr, y_tr[:, i])
+        y_hat = ridge.predict(X_vl)
+
+
+        # correlation btw predicted and observed
+        corr = pearsonr(y_hat, y_val[:,i])
+        fig = plt.figure()
+        # plot observed vs. predicted targets
+        plt.title('Ridge: Observed vs Predicted Y_trait_' + str(i) + 'cor:' + str(corr[0]))
+        plt.ylabel('Predicted')
+        plt.xlabel('Observed')
+        plt.scatter(y_val[:,i], y_hat, marker='o')
+        fig.savefig("Ridge_Out" + str(i) + '.png', dpi=300)
+        plt.close(fig)
+
+