From 9d2635b495c86220cbb031fa1b7b3038c172162c Mon Sep 17 00:00:00 2001
From: Tyler Thomas <36181311+tylerjthomas9@users.noreply.github.com>
Date: Fri, 13 Nov 2020 12:21:44 -0600
Subject: [PATCH] Fix Stochastic Gradient Descent Example

The example that is currently in the docs does not run. dtype, penalty, lrate, loss are not defined. This new version sets the default values for the parameters of cumlSGD, and copies Mini Batch SGD Regression's dtype for pred_data['col1'], pred_data['col2']. When running this example, I also got slightly different values for the output, so these were also updated.
---
 python/cuml/solvers/sgd.pyx | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/python/cuml/solvers/sgd.pyx b/python/cuml/solvers/sgd.pyx
index 091fada5ed..8c968b2d1b 100644
--- a/python/cuml/solvers/sgd.pyx
+++ b/python/cuml/solvers/sgd.pyx
@@ -137,15 +137,15 @@ class SGD(Base):
         import cudf
         from cuml.solvers import SGD as cumlSGD
         X = cudf.DataFrame()
-        X['col1'] = np.array([1,1,2,2], dtype = np.float32)
-        X['col2'] = np.array([1,2,2,3], dtype = np.float32)
+        X['col1'] = np.array([1,1,2,2], dtype=np.float32)
+        X['col2'] = np.array([1,2,2,3], dtype=np.float32)
         y = cudf.Series(np.array([1, 1, 2, 2], dtype=np.float32))
         pred_data = cudf.DataFrame()
-        pred_data['col1'] = np.asarray([3, 2], dtype=dtype)
-        pred_data['col2'] = np.asarray([5, 5], dtype=dtype)
-        cu_sgd = cumlSGD(learning_rate=lrate, eta0=0.005, epochs=2000,
+        pred_data['col1'] = np.asarray([3, 2], dtype=np.float32)
+        pred_data['col2'] = np.asarray([5, 5], dtype=np.float32)
+        cu_sgd = cumlSGD(learning_rate='constant', eta0=0.005, epochs=2000,
                         fit_intercept=True, batch_size=2,
-                        tol=0.0, penalty=penalty, loss=loss)
+                        tol=0.0, penalty='none', loss='squared_loss')
         cu_sgd.fit(X, y)
         cu_pred = cu_sgd.predict(pred_data).to_array()
         print(" cuML intercept : ", cu_sgd.intercept_)
@@ -156,11 +156,11 @@ class SGD(Base):
 
     .. code-block:: python
 
-        cuML intercept :  0.004561662673950195
-        cuML coef :  0      0.9834546
-                    1    0.010128272
-                   dtype: float32
-        cuML predictions :  [3.0055666 2.0221121]
+        cuML intercept :  0.0041877031326293945
+        cuML coef :  0      0.984174
+                     1      0.009776
+                    dtype: float32
+        cuML predictions :  [3.005588  2.0214138]
 
 
     Parameters