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

@@ -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