Cleared metrics

multiview_platform/mono_multi_view_classifiers/metrics/accuracy_score.py

@@ -25,11 +25,7 @@ def score(y_true, y_pred, multiclass=False, **kwargs):
 
     Returns:
     Weighted accuracy score for y_true, y_pred"""
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    score = metric(y_true, y_pred, sample_weight=sample_weight)
+    score = metric(y_true, y_pred, **kwargs)
     return score
 
 
@@ -39,19 +35,10 @@ def get_scorer(**kwargs):
 
     Returns:
     A weighted sklearn scorer for accuracy"""
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
     return make_scorer(metric, greater_is_better=True,
-                       sample_weight=sample_weight)
+                       **kwargs)
 
 
 def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    config_string = "Accuracy score using " + str(
-        sample_weight) + " as sample_weights (higher is better)"
+    config_string = "Accuracy score using {}, (higher is better)".format(kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/f1_score.py

@@ -14,75 +14,15 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, multiclass=True, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        if multiclass:
-            average = "micro"
-        else:
-            average = "micro"
-
-    score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels,
-                   pos_label=pos_label, average=average)
-
+def score(y_true, y_pred, multiclass=True, average='micro',  **kwargs):
+    score = metric(y_true, y_pred, average=average, **kwargs)
     return score
 
 
-def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except:
-        average = "micro"
-    return make_scorer(metric, greater_is_better=True,
-                       sample_weight=sample_weight, labels=labels,
-                       pos_label=pos_label, average=average)
+def get_scorer(average="micro", **kwargs):
+    return make_scorer(metric, greater_is_better=True, average=average, **kwargs)
 
 
-def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        average = "micro"
-    config_string = "F1 score using " + str(
-        sample_weight) + " as sample_weights, " + str(
-        labels) + " as labels, " + str(
-        pos_label) \
-                   + " as pos_label, " + average + " as average (higher is better)"
+def get_config(average="micro", **kwargs, ):
+    config_string = "F1 score using average: {}, {} (higher is better)".format(average, kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/fbeta_score.py

@@ -10,86 +10,16 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, multiclass=True, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        beta = kwargs["1"]
-    except Exception:
-        beta = 10.0
-    try:
-        labels = kwargs["2"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["3"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["4"]
-    except Exception:
-        if multiclass:
-            average = "micro"
-        else:
-            average = "binary"
-    score = metric(y_true, y_pred, beta, sample_weight=sample_weight,
-                   labels=labels, pos_label=pos_label,
-                   average=average)
+def score(y_true, y_pred, beta=2.0, average="micro", **kwargs):
+    score = metric(y_true, y_pred, beta=beta, average=average, **kwargs)
     return score
 
 
-def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        beta = kwargs["1"]
-    except Exception:
-        beta = 1.0
-    try:
-        labels = kwargs["2"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["3"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["4"]
-    except Exception:
-        average = "micro"
+def get_scorer(beta=2.0, average="micro", **kwargs):
     return make_scorer(metric, greater_is_better=True, beta=beta,
-                       sample_weight=sample_weight, labels=labels,
-                       pos_label=pos_label, average=average)
+                       average=average, **kwargs)
 
 
-def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        beta = kwargs["1"]
-    except Exception:
-        beta = 1.0
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        average = "binary"
-    config_string = "F-beta score using " + str(
-        sample_weight) + " as sample_weights, " + str(
-        labels) + " as labels, " + str(pos_label) \
-                   + " as pos_label, " + average + " as average, " + str(
-        beta) + " as beta (higher is better)"
+def get_config(beta=2.0, average="micro", **kwargs):
+    config_string = "F-beta score using beta: {}, average: {}, {} (higher is better)".format(beta, average, kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/hamming_loss.py

@@ -10,27 +10,14 @@ __status__ = "Prototype"  # Production, Development, Prototype
 
 
 def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        classes = kwargs["0"]
-    except Exception:
-        classes = None
-    score = metric(y_true, y_pred)
+    score = metric(y_true, y_pred, **kwargs)
     return score
 
 
 def get_scorer(**kwargs):
-    try:
-        classes = kwargs["0"]
-    except Exception:
-        classes = None
-    return make_scorer(metric, greater_is_better=False, classes=classes)
+    return make_scorer(metric, greater_is_better=False, **kwargs)
 
 
 def get_config(**kwargs):
-    try:
-        classes = kwargs["0"]
-    except Exception:
-        classes = None
-    config_string = "Hamming loss using " + str(
-        classes) + " as classes (lower is better)"
+    config_string = "Hamming loss using {} (lower is better)".format(kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/jaccard_similarity_score.py

@@ -10,28 +10,15 @@ __status__ = "Prototype"  # Production, Development, Prototype
 
 
 def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    score = metric(y_true, y_pred, sample_weight=sample_weight)
+    score = metric(y_true, y_pred, **kwargs)
     return score
 
 
 def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
     return make_scorer(metric, greater_is_better=True,
-                       sample_weight=sample_weight)
+                       **kwargs)
 
 
 def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    config_string = "Jaccard_similarity score using " + str(
-        sample_weight) + " as sample_weights (higher is better)"
+    config_string = "Jaccard_similarity score using {} (higher is better)".format(kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/log_loss.py

@@ -10,41 +10,15 @@ __status__ = "Prototype"  # Production, Development, Prototype
 
 
 def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        eps = kwargs["1"]
-    except Exception:
-        eps = 1e-15
-    score = metric(y_true, y_pred, sample_weight=sample_weight, eps=eps)
+    score = metric(y_true, y_pred, **kwargs)
     return score
 
 
 def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        eps = kwargs["1"]
-    except Exception:
-        eps = 1e-15
     return make_scorer(metric, greater_is_better=False,
-                       sample_weight=sample_weight, eps=eps)
+                       **kwargs)
 
 
 def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        eps = kwargs["1"]
-    except Exception:
-        eps = 1e-15
-    config_string = "Log loss using " + str(
-        sample_weight) + " as sample_weights, " + str(
-        eps) + " as eps (lower is better)"
+    config_string = "Log loss using {} (lower is better)".format(kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/precision_score.py

@@ -7,73 +7,16 @@ warnings.warn("the precision_score module  is deprecated", DeprecationWarning,
 __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
-def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        if multiclass:
-            average = "micro"
-        else:
-            average = "binary"
-    score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels,
-                   pos_label=pos_label, average=average)
+def score(y_true, y_pred, average='micro', multiclass=False, **kwargs):
+    score = metric(y_true, y_pred, average=average, **kwargs)
     return score
 
 
-def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        average = "binary"
+def get_scorer(average='micro', **kwargs):
     return make_scorer(metric, greater_is_better=True,
-                       sample_weight=sample_weight, labels=labels,
-                       pos_label=pos_label,
-                       average=average)
+                       average=average, **kwargs)
 
 
-def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except:
-        sample_weight = None
-        try:
-            labels = kwargs["1"]
-        except Exception:
-            labels = None
-        try:
-            pos_label = kwargs["2"]
-        except Exception:
-            pos_label = 1
-        try:
-            average = kwargs["3"]
-        except:
-            average = "binary"
-        config_string = "Precision score using " + str(
-            sample_weight) + " as sample_weights, " + str(
-            labels) + " as labels, " + str(pos_label) \
-                       + " as pos_label, " + average + " as average (higher is better)"
+def get_config(average='micro', **kwargs):
+    config_string = "Precision score using average: {}, {} (higher is better)".format(average, kwargs)
     return config_string

multiview_platform/mono_multi_view_classifiers/metrics/recall_score.py

@@ -9,74 +9,16 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        if multiclass:
-            average = "micro"
-        else:
-            average = "binary"
-    score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels,
-                   pos_label=pos_label, average=average)
+def score(y_true, y_pred, average='micro', **kwargs):
+    score = metric(y_true, y_pred, average=average, **kwargs)
     return score
 
 
-def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        average = "binary"
+def get_scorer(average='micro', **kwargs):
     return make_scorer(metric, greater_is_better=True,
-                       sample_weight=sample_weight, labels=labels,
-                       pos_label=pos_label,
-                       average=average)
+                       average=average, **kwargs)
 
 
-def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    try:
-        labels = kwargs["1"]
-    except Exception:
-        labels = None
-    try:
-        pos_label = kwargs["2"]
-    except Exception:
-        pos_label = 1
-    try:
-        average = kwargs["3"]
-    except Exception:
-        average = "binary"
-    configString = "Recall score using " + str(
-        sample_weight) + " as sample_weights, " + str(
-        labels) + " as labels, " + str(pos_label) \
-                   + " as pos_label, " + average + "as average (higher is " \
-                                                   "better) "
+def get_config(average="micro", **kwargs):
+    configString = "Recall score using average: {}, {} (higher is better)".format(average, kwargs)
     return configString

multiview_platform/mono_multi_view_classifiers/metrics/roc_auc_score.py

@@ -11,17 +11,6 @@ __status__ = "Prototype"  # Production, Development, Prototype
 
 
 def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception :
-        sample_weight = None
-    try:
-        average = kwargs["1"]
-    except Exception:
-        if multiclass:
-            average = "micro"
-        else:
-            average = None
     if multiclass:
         mlb = MultiLabelBinarizer()
         y_true = mlb.fit_transform([(label) for label in y_true])

multiview_platform/mono_multi_view_classifiers/metrics/zero_one_loss.py

@@ -11,28 +11,15 @@ __status__ = "Prototype"  # Production, Development, Prototype
 
 
 def score(y_true, y_pred, multiclass=False, **kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    score = metric(y_true, y_pred, sample_weight=sample_weight)
+    score = metric(y_true, y_pred, **kwargs)
     return score
 
 
 def get_scorer(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
     return make_scorer(metric, greater_is_better=False,
-                       sample_weight=sample_weight)
+                       **kwargs)
 
 
 def get_config(**kwargs):
-    try:
-        sample_weight = kwargs["0"]
-    except Exception:
-        sample_weight = None
-    configString = "Zero_one loss using " + str(
-        sample_weight) + " as sample_weights (lower is better)"
+    configString = "Zero_one loss using {} (lower is better)".format(kwargs)
     return configString

multiview_platform/tests/test_mono_view/test_MonoviewUtils.py

@@ -14,8 +14,7 @@ class Test_genTestFoldsPreds(unittest.TestCase):
         cls.random_state = np.random.RandomState(42)
         cls.X_train = cls.random_state.random_sample((31, 10))
         cls.y_train = np.ones(31, dtype=int)
-        cls.KFolds = StratifiedKFold(n_splits=3, random_state=cls.random_state,
-                                     shuffle=True)
+        cls.KFolds = StratifiedKFold(n_splits=3,)
 
         cls.estimator = DecisionTreeClassifier(max_depth=1)
 
@@ -30,5 +29,5 @@ class Test_genTestFoldsPreds(unittest.TestCase):
                                                           cls.estimator)
         cls.assertEqual(testFoldsPreds.shape, (3, 10))
         np.testing.assert_array_equal(testFoldsPreds[0], np.array(
-            [ 1,  1,  1,  1, -1, -1,  1, -1,  1,  1]))
+            [ 1,  1, -1, -1,  1,  1, -1,  1, -1,  1]))