remove jaxlib

Author: thierrymoudiki

examples/elasticnet2regressor.py

@@ -24,6 +24,7 @@
     print("Elapsed: ", time()-start)
 
     print(f"RMSE for {datasets_names[i]} : {root_mean_squared_error(preds, y_test)}")
+    print("regr.beta_", regr.beta_)
 
     regr = ns.ElasticNet2Regressor(solver="cd", type_loss='quantile')
 
@@ -33,6 +34,7 @@
     print("Elapsed: ", time()-start)
 
     print(f"RMSE for {datasets_names[i]} : {root_mean_squared_error(preds, y_test)}")
+    print("regr.beta_", regr.beta_)
 
     # Example 1: Adam optimizer (Optax)
     regr = ns.ElasticNet2Regressor(
@@ -47,6 +49,7 @@
     preds = regr.predict(X_test)
     print(f"Adam - RMSE for {datasets_names[i]}: {root_mean_squared_error(preds, y_test)}")
     print(f"Elapsed: {time() - start:.2f}s\n")
+    print("regr.beta_", regr.beta_)
 
     # Example 2: SGD with momentum (Optax)
     regr = ns.ElasticNet2Regressor(
@@ -60,5 +63,6 @@
     regr.fit(X_train, y_train)
     preds = regr.predict(X_test)
     print(f"SGD (Quantile) - RMSE for {datasets_names[i]}: {root_mean_squared_error(preds, y_test)}")
-    print(f"Elapsed: {time() - start:.2f}s\n")    
+    print(f"Elapsed: {time() - start:.2f}s\n")   
+    print("regr.beta_", regr.beta_) 
 

nnetsauce/base/base.py

@@ -283,7 +283,7 @@ def encode_clusters(self, X=None, predict=False, scaler=None, **kwargs):  #
                 **kwargs
             )
 
-            if self.cluster_encode == True:
+            if self.cluster_encode:
                 return mo.one_hot_encode(X_clustered, self.n_clusters).astype(
                     np.float16
                 )
@@ -663,7 +663,7 @@ def cook_test_set(self, X, **kwargs):
                     else self.nn_scaler_.transform(X[:, self.index_col_])
                 )
                 Phi_X = self.create_layer(scaled_X, self.W_)
-                if self.direct_link == True:
+                if self.direct_link:
                     return self.scaler_.transform(
                         mo.cbind(scaled_X, Phi_X, backend=self.backend)
                     )
@@ -687,7 +687,7 @@ def cook_test_set(self, X, **kwargs):
         if self.n_hidden_features > 0:  # if hidden layer
             scaled_X = self.nn_scaler_.transform(augmented_X)
             Phi_X = self.create_layer(scaled_X, self.W_)
-            if self.direct_link == True:
+            if self.direct_link:
                 return self.scaler_.transform(
                     mo.cbind(augmented_X, Phi_X, backend=self.backend)
                 )

nnetsauce/utils/matrixops.py

@@ -81,9 +81,13 @@ def cbind(x, y, backend="cpu"):
 
 
 # center... response
-def center_response(y):
-    y_mean = np.mean(y)
-    return y_mean, (y - y_mean)
+def center_response(y, method="mean"):
+    if method == "mean":
+        y_mean = np.mean(y)
+        return y_mean, (y - y_mean)
+    elif method == "median":
+        y_median = np.median(y)
+        return y_median, (y - y_median)
 
 
 # cluster the covariates

requirements.txt

@@ -1,5 +1,4 @@
 jax 
-jaxlib
 joblib
 matplotlib
 numpy

setup.py

@@ -3,7 +3,7 @@
 from codecs import open
 from os import path
 
-__version__ = '0.38.0'
+__version__ = '0.38.1'
 
 # get the dependencies and installs
 here = path.abspath(path.dirname(__file__))