Fix no copy_X problem (#108)

* knn_svm

* Revert "knn_svm"

This reverts commit 6272ea5.

* test copy

* remove temp config

* default parametrs

* same problem on lasso

Author: dmitrii-kriukov

sklearn_bench/elasticnet.py

@@ -28,7 +28,7 @@ def main():
     # Create our regression object
     regr = ElasticNet(fit_intercept=params.fit_intercept, l1_ratio=params.l1_ratio,
                       alpha=params.alpha, tol=params.tol,
-                      max_iter=params.maxiter, copy_X=False)
+                      max_iter=params.maxiter)
     # Time fit
     fit_time, _ = bench.measure_function_time(regr.fit, X_train, y_train, params=params)
 
@@ -63,7 +63,7 @@ def main():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='scikit-learn elastic-net regression '
                                      'benchmark')
-    parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=False,
+    parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=True,
                         action='store_false',
                         help="Don't fit intercept (assume data already centered)")
     parser.add_argument('--alpha', dest='alpha', type=float, default=1.0,

sklearn_bench/lasso.py

@@ -27,7 +27,7 @@ def main():
 
     # Create our regression object
     regr = Lasso(fit_intercept=params.fit_intercept, alpha=params.alpha,
-                 tol=params.tol, max_iter=params.maxiter, copy_X=False)
+                 tol=params.tol, max_iter=params.maxiter)
 
     # Time fit
     fit_time, _ = bench.measure_function_time(regr.fit, X_train, y_train, params=params)
@@ -63,7 +63,7 @@ def main():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='scikit-learn lasso regression '
                                      'benchmark')
-    parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=False,
+    parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=True,
                         action='store_false',
                         help="Don't fit intercept (assume data already centered)")
     parser.add_argument('--alpha', dest='alpha', type=float, default=1.0,

sklearn_bench/linear.py

@@ -27,7 +27,7 @@ def main():
 
     # Create our regression object
     regr = LinearRegression(fit_intercept=params.fit_intercept,
-                            n_jobs=params.n_jobs, copy_X=False)
+                            n_jobs=params.n_jobs)
 
     # Time fit
     fit_time, _ = bench.measure_function_time(regr.fit, X_train, y_train, params=params)