Merge branch 'packaging'

5 files changed, 37 insertions, 20 deletions

diff --git a/gensvm/core.py b/gensvm/core.py
index bfd5d9a..45d59ad 100644
--- a/gensvm/core.py
+++ b/gensvm/core.py
@@ -16,9 +16,9 @@ from sklearn.exceptions import ConvergenceWarning, FitFailedWarning
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import check_array, check_X_y, check_random_state
 from sklearn.utils.multiclass import type_of_target
-from sklearn.utils.validation import check_is_fitted
 
 from .cython_wrapper import wrapper
+from .util import check_is_fitted
 
 
 def _fit_gensvm(
diff --git a/gensvm/cython_wrapper/wrapper.pyx b/gensvm/cython_wrapper/wrapper.pyx
index 009e70b..3a85e92 100644
--- a/gensvm/cython_wrapper/wrapper.pyx
+++ b/gensvm/cython_wrapper/wrapper.pyx
@@ -88,8 +88,7 @@ def train_wrap(
         raise ValueError(error_repl)
 
     # Do the actual training
-    with nogil:
-        gensvm_train(model, data, seed_model)
+    gensvm_train(model, data, seed_model)
 
     # update the number of variables (this may have changed due to kernel)
     n_var = get_m(model)
@@ -134,12 +133,11 @@ def predict_wrap(
 
     # output vector
     cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions
-    predictions = np.empty((n_test_obs, ), dtype=np.int)
+    predictions = np.empty((n_test_obs, ), dtype=np.int_)
 
     # do the prediction
-    with nogil:
-        gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, 
-                predictions.data)
+    gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, 
+            predictions.data)
 
     return predictions
 
@@ -174,10 +172,9 @@ def predict_kernels_wrap(
     cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions
     predictions = np.empty((n_obs_test, ), dtype=np.int)
 
-    with nogil:
-        gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, 
-                V_cols, n_obs_train, n_obs_test, n_var, n_class, kernel_idx, 
-                gamma, coef, degree, kernel_eigen_cutoff, predictions.data)
+    gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, V_cols, 
+            n_obs_train, n_obs_test, n_var, n_class, kernel_idx, gamma, coef, 
+            degree, kernel_eigen_cutoff, predictions.data)
 
     return predictions
 
@@ -243,9 +240,7 @@ def grid_wrap(
 
     set_queue(queue, n_tasks, tasks)
 
-    with nogil:
-        gensvm_train_q_helper(queue, cv_idx.data, store_predictions, 
-                verbosity)
+    gensvm_train_q_helper(queue, cv_idx.data, store_predictions, verbosity)
 
     cdef np.ndarray[np.int_t, ndim=1, mode='c'] pred
     cdef np.ndarray[np.double_t, ndim=1, mode='c'] dur
@@ -264,12 +259,12 @@ def grid_wrap(
         results['params'].append(candidate_params[ID])
         results['scores'].append(get_task_performance(tasks[ID]))
         if store_predictions:
-            pred = np.zeros((n_obs, ), dtype=np.int)
+            pred = np.zeros((n_obs, ), dtype=np.int_)
             copy_task_predictions(tasks[ID], pred.data, n_obs)
             results['predictions'].append(pred.copy())
         dur = np.zeros((n_folds, ), dtype=np.double)
         copy_task_durations(tasks[ID], dur.data, n_folds)
-        results['durations'].append(dur.copy())
+        results['durations'].append(dur)
 
     gensvm_free_queue(queue)
     free_data(data)
diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py
index b27a347..22125a4 100644
--- a/gensvm/gridsearch.py
+++ b/gensvm/gridsearch.py
@@ -116,7 +116,7 @@ def _wrap_score(y_pred, y_true, scorers, is_multimetric):
             results["score"] = np.nan
     else:
         estimator = _MockEstimator(y_pred)
-        results = _score(estimator, None, y_true, scorers, is_multimetric)
+        results = _score(estimator, None, y_true, scorers)
     score_time = time.time() - start_time
     return results, score_time
 
diff --git a/gensvm/sklearn_util.py b/gensvm/sklearn_util.py
index 182f257..eb8ceb6 100644
--- a/gensvm/sklearn_util.py
+++ b/gensvm/sklearn_util.py
@@ -89,7 +89,9 @@ def _skl_format_cv_results(
             score_time,
         ) = zip(*out)
     else:
-        (test_score_dicts, test_sample_counts, fit_time, score_time) = zip(*out)
+        (test_score_dicts, test_sample_counts, fit_time, score_time) = zip(
+            *out
+        )
 
     # test_score_dicts and train_score dicts are lists of dictionaries and
     # we make them into dict of lists
@@ -160,7 +162,9 @@ def _skl_format_cv_results(
         )
         if return_train_score:
             _store(
-                "train_%s" % scorer_name, train_scores[scorer_name], splits=True
+                "train_%s" % scorer_name,
+                train_scores[scorer_name],
+                splits=True,
             )
 
     return results
@@ -207,7 +211,12 @@ def _skl_check_is_fitted(estimator, method_name, refit):
             "attribute" % (type(estimator).__name__, method_name)
         )
     else:
-        check_is_fitted(estimator, "best_estimator_")
+        if not hasattr(estimator, "best_estimator_"):
+            raise NotFittedError(
+                "This %s instance is not fitted yet. Call "
+                "'fit' with appropriate arguments before using this "
+                "estimator." % type(estimator).__name__
+            )
 
 
 def _skl_grid_score(X, y, scorer_, best_estimator_, refit, multimetric_):
diff --git a/gensvm/util.py b/gensvm/util.py
index 046f3be..40d0eb1 100644
--- a/gensvm/util.py
+++ b/gensvm/util.py
@@ -8,6 +8,7 @@ Utility functions for GenSVM
 
 import numpy as np
 
+from sklearn.exceptions import NotFittedError
 
 def get_ranks(a):
     """
@@ -37,3 +38,15 @@ def get_ranks(a):
     ranks[~np.isnan(orig)] = count[dense - 1] + 1
     ranks[np.isnan(orig)] = np.max(ranks) + 1
     return list(ranks)
+
+
+def check_is_fitted(estimator, attribute):
+    msg = (
+        "This %(name)s instance is not fitted yet. Call 'fit' "
+        "with appropriate arguments before using this estimator."
+    )
+    if not hasattr(estimator, "fit"):
+        raise TypeError("%s is not an estimator instance" % (estimator))
+
+    if not hasattr(estimator, attribute):
+        raise NotFittedError(msg % {"name": type(estimator).__name__})