Simplified to only include enet_path (mlflow#134)

removed lasso calls and cleaned up the code

example/tutorial/train_diabetes.py

@@ -1,15 +1,20 @@
 #
 # train_diabetes.py
 #
-#   MLflow model using ElasticNet (sklearn) and Plots Lasso vs. ElasticNet Descent Paths
+#   MLflow model using ElasticNet (sklearn) and Plots ElasticNet Descent Paths
 #
 #   Uses the sklearn Diabetes dataset to predict diabetes progression using ElasticNet
 #       The predicted "progression" column is a quantitative measure of disease progression one year after baseline
 #       http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html
-#   Combines the above with the Lasso Descent Path Plot
+#   Combines the above with the Lasso Coordinate Descent Path Plot
 #       http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html
 #       Original author: Alexandre Gramfort <alexandre.gramfort@inria.fr>; License: BSD 3 clause
 #
+#  Usage:
+#    python train_diabetes.py 0.01 0.01
+#    python train_diabetes.py 0.01 0.75
+#    python train_diabetes.py 0.01 1.0
+#
 
 import os
 import warnings
@@ -91,69 +96,30 @@ def eval_metrics(actual, pred):
     # Compute paths
     eps = 5e-3  # the smaller it is the longer is the path
 
-    print("Computing regularization path using the lasso...")
-    alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps, fit_intercept=False)
-
-    print("Computing regularization path using the positive lasso...")
-    alphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(X, y, eps, positive=True, fit_intercept=False)
-
-    print("Computing regularization path using the elastic net...")
-    alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8, fit_intercept=False)
-
-    print("Computing regularization path using the positive elastic net...")
-    alphas_positive_enet, coefs_positive_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8, positive=True, fit_intercept=False)
-
+    print("Computing regularization path using the elastic net.")
+    alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=l1_ratio, fit_intercept=False)
 
     # Display results
-    fig1 = plt.figure(1)
+    fig = plt.figure(1)
     ax = plt.gca()
 
     colors = cycle(['b', 'r', 'g', 'c', 'k'])
-    neg_log_alphas_lasso = -np.log10(alphas_lasso)
     neg_log_alphas_enet = -np.log10(alphas_enet)
-    for coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):
-        l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
+    for coef_e, c in zip(coefs_enet, colors):
         l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle='--', c=c)
 
     plt.xlabel('-Log(alpha)')
     plt.ylabel('coefficients')
-    plt.title('Lasso and Elastic-Net Paths')
-    plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left')
-    plt.axis('tight')
-
-    fig2 = plt.figure(2)
-    ax = plt.gca()
-    neg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)
-    for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):
-        l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
-        l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle='--', c=c)
-
-    plt.xlabel('-Log(alpha)')
-    plt.ylabel('coefficients')
-    plt.title('Lasso and positive Lasso')
-    plt.legend((l1[-1], l2[-1]), ('Lasso', 'positive Lasso'), loc='lower left')
+    title = 'ElasticNet Path by alpha for l1_ratio = ' + str(l1_ratio)
+    plt.title(title)
     plt.axis('tight')
 
 
-    fig3 = plt.figure(3)
-    ax = plt.gca()
-    neg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)
-    for (coef_e, coef_pe, c) in zip(coefs_enet, coefs_positive_enet, colors):
-        l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)
-        l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle='--', c=c)
-
-    plt.xlabel('-Log(alpha)')
-    plt.ylabel('coefficients')
-    plt.title('Elastic-Net and positive Elastic-Net')
-    plt.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'), loc='lower left')
-    plt.axis('tight')
-
     # Save figures
-    fig1.savefig("plot1.png")
-    fig2.savefig("plot2.png")
-    fig3.savefig("plot3.png")
+    fig.savefig("ElasticNet-paths.png")
+
+    # Close plot
+    plt.close(fig)
 
     # Log artifacts (output files)
-    mlflow.log_artifact("plot1.png")
-    mlflow.log_artifact("plot2.png")
-    mlflow.log_artifact("plot3.png")
+    mlflow.log_artifact("ElasticNet-paths.png")