rename outofbag_bootstrap (#266)

docs/mkdocs.yml

@@ -53,11 +53,11 @@ pages:
     - user_guide/data/wine_data.md
   - evaluate:
     - user_guide/evaluate/bootstrap.md
+    - user_guide/evaluate/BootstrapOutOufBag.md
     - user_guide/evaluate/confusion_matrix.md
     - user_guide/evaluate/lift_score.md
     - user_guide/evaluate/mcnemar_table.md
     - user_guide/evaluate/mcnemar.md
-    - user_guide/evaluate/OutOufBagBootstrap.md
     - user_guide/evaluate/permutation_test.md
     - user_guide/evaluate/scoring.md
   - feature_extraction:

docs/sources/CHANGELOG.md

@@ -20,7 +20,7 @@ The CHANGELOG for the current development version is available at
 - Added a `loadings_` attribute to `PrincipalComponentAnalysis` to compute the factor loadings of the features on the principal components. [#251](https://github.com/rasbt/mlxtend/pull/251)
 - Allow grid search over classifiers/regressors in ensemble and stacking estimators [#259](https://github.com/rasbt/mlxtend/pull/259)
 - New `make_multiplexer_dataset` function that creates a dataset generated by a n-bit Boolean multiplexer for evaluating supervised learning algorithms [#263](https://github.com/rasbt/mlxtend/pull/263)
-- Added a new `OutOfBagBootstrap` class, an implementation of the out-of-bag bootstrap to evaluate supervised learning algorithms [#265](https://github.com/rasbt/mlxtend/pull/265)
+- Added a new `BootstrapOutOfBag` class, an implementation of the out-of-bag bootstrap to evaluate supervised learning algorithms [#265](https://github.com/rasbt/mlxtend/pull/265)
 
 ##### Changes
 

docs/sources/USER_GUIDE_INDEX.md

@@ -25,11 +25,11 @@
 
 ## `evaluate`
 - [bootstrap](user_guide/evaluate/bootstrap.md)
+- [BootstrapOutOfBag](user_guide/evaluate/BootstrapOutOfBag.md)
 - [confusion_matrix](user_guide/evaluate/confusion_matrix.md)
 - [lift_score](user_guide/evaluate/lift_score.md)
 - [mcnemar_table](user_guide/evaluate/mcnemar_table.md)
 - [mcnemar](user_guide/evaluate/mcnemar.md)
-- [OutOufBagBootstrap](user_guide/evaluate/OutOfBagBootstrap.md)
 - [permutation_test](user_guide/evaluate/permutation_test.md)
 - [scoring](user_guide/evaluate/scoring.md)
 

docs/sources/user_guide/evaluate/OutOfBagBootstrap.ipynb → docs/sources/user_guide/evaluate/BootstrapOutOfBag.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# OutOfBagBootstrap"
+    "# BootstrapOutOfBag"
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> `from mlxtend.evaluate import OutOfBagBootstrap`    "
+    "> `from mlxtend.evaluate import BootstrapOutOfBag`    "
    ]
   },
   {
@@ -35,7 +35,7 @@
     "Originally, the bootstrap method aims to determine the statistical properties of an estimator when the underlying distribution was unknown and additional samples are not available. Now, in order to exploit this method for the evaluation of predictive models, such as hypotheses for classification and regression, we may prefer a slightly different approach to bootstrapping using the so-called Out-Of-Bag (OOB) or Leave-One-Out Bootstrap (LOOB) technique. Here, we use out-of-bag samples as test sets for evaluation instead of evaluating the model on the training data. Out-of-bag samples are the unique sets of instances that are not used for model fitting as shown in the figure below [1].\n",
     "\n",
     "\n",
-    "![](OutOfBagBootstrap_files/bootrap_concept.png)\n",
+    "![](BootstrapOutOfBag_files/bootrap_concept.png)\n",
     "\n",
     "\n",
     "The figure above illustrates how three random bootstrap samples drawn from an exemplary ten-sample dataset ($X_1,X_2, ..., X_{10}$) and their out-of-bag sample for testing may look like. In practice, Bradley Efron and Robert Tibshirani recommend drawing 50 to 200 bootstrap samples as being sufficient for reliable estimates [2]."
@@ -62,7 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `OutOfBagBootstrap` class mimics the behavior of scikit-learn's cross-validation classes, e.g., `KFold`:"
+    "The `BootstrapOutOfBag` class mimics the behavior of scikit-learn's cross-validation classes, e.g., `KFold`:"
    ]
   },
   {
@@ -81,11 +81,11 @@
     }
    ],
    "source": [
-    "from mlxtend.evaluate import OutOfBagBootstrap\n",
+    "from mlxtend.evaluate import BootstrapOutOfBag\n",
     "import numpy as np\n",
     "\n",
     "\n",
-    "oob = OutOfBagBootstrap(n_splits=3)\n",
+    "oob = BootstrapOutOfBag(n_splits=3)\n",
     "for train, test in oob.split(np.array([1, 2, 3, 4, 5])):\n",
     "    print(train, test)"
    ]
@@ -94,7 +94,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Consequently, we can use `OutOfBagBootstrap` objects via the `cross_val_score` method:"
+    "Consequently, we can use `BootstrapOutOfBag` objects via the `cross_val_score` method:"
    ]
   },
   {
@@ -137,7 +137,7 @@
     }
    ],
    "source": [
-    "print(cross_val_score(lr, X, y, cv=OutOfBagBootstrap(n_splits=3, random_seed=456)))"
+    "print(cross_val_score(lr, X, y, cv=BootstrapOutOfBag(n_splits=3, random_seed=456)))"
    ]
   },
   {
@@ -162,7 +162,7 @@
    ],
    "source": [
     "print('Mean accuracy: %.1f%%' % np.mean(100*cross_val_score(\n",
-    "    lr, X, y, cv=OutOfBagBootstrap(n_splits=200, random_seed=456))))"
+    "    lr, X, y, cv=BootstrapOutOfBag(n_splits=200, random_seed=456))))"
    ]
   },
   {
@@ -206,7 +206,7 @@
     }
    ],
    "source": [
-    "accuracies = cross_val_score(lr, X, y, cv=OutOfBagBootstrap(n_splits=1000, random_seed=456))\n",
+    "accuracies = cross_val_score(lr, X, y, cv=BootstrapOutOfBag(n_splits=1000, random_seed=456))\n",
     "mean = np.mean(accuracies)\n",
     "\n",
     "lower = np.percentile(accuracies, 2.5)\n",
@@ -243,9 +243,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "## OutOfBagBootstrap\n",
+      "## BootstrapOutOfBag\n",
       "\n",
-      "*OutOfBagBootstrap(n_splits=200, random_seed=None)*\n",
+      "*BootstrapOutOfBag(n_splits=200, random_seed=None)*\n",
       "\n",
       "**Parameters**\n",
       "\n",
@@ -328,7 +328,7 @@
     }
    ],
    "source": [
-    "with open('../../api_modules/mlxtend.evaluate/OutOfBagBootstrap.md', 'r') as f:\n",
+    "with open('../../api_modules/mlxtend.evaluate/BootstrapOutOfBag.md', 'r') as f:\n",
     "    s = f.read() \n",
     "print(s)"
    ]

mlxtend/evaluate/__init__.py

@@ -10,10 +10,10 @@
 from .mcnemar import mcnemar_table
 from .mcnemar import mcnemar
 from .bootstrap import bootstrap
-from .outofbag_bootstrap import OutOfBagBootstrap
+from .bootstrap_outofbag import BootstrapOutOfBag
 from .permutation import permutation_test
 
 
 __all__ = ["scoring", "confusion_matrix",
            "mcnemar_table", "mcnemar", "lift_score",
-           "bootstrap", "permutation_test", "OutOfBagBootstrap"]
+           "bootstrap", "permutation_test", "BootstrapOutOfBag"]

mlxtend/evaluate/outofbag_bootstrap.py → mlxtend/evaluate/bootstrap_outofbag.py

@@ -9,7 +9,7 @@
 import numpy as np
 
 
-class OutOfBagBootstrap(object):
+class BootstrapOutOfBag(object):
     """
     Parameters
     ----------

mlxtend/evaluate/tests/test_outofbag_bootstrap.py → mlxtend/evaluate/tests/test_bootstrap_outofbag.py

@@ -5,18 +5,18 @@
 # License: BSD 3 clause
 
 import numpy as np
-from mlxtend.evaluate import OutOfBagBootstrap
+from mlxtend.evaluate import BootstrapOutOfBag
 from mlxtend.utils import assert_raises
 
 
 def test_defaults():
-    oob = OutOfBagBootstrap()
+    oob = BootstrapOutOfBag()
     results = list(oob.split(np.array([1, 2, 3, 4, 5])))
     assert len(results) == 200
 
 
 def test_splits():
-    oob = OutOfBagBootstrap(n_splits=3, random_seed=123)
+    oob = BootstrapOutOfBag(n_splits=3, random_seed=123)
     results = list(oob.split(np.array([1, 2, 3, 4, 5])))
     assert len(results) == 3
     assert np.array_equal(results[0][0], np.array([2, 4, 2, 1, 3]))
@@ -28,10 +28,10 @@ def test_splits():
 def test_invalid_splits():
     assert_raises(ValueError,
                   'Number of splits must be greater than 1.',
-                  OutOfBagBootstrap,
+                  BootstrapOutOfBag,
                   0)
 
 
 def test_get_n_splits():
-    oob = OutOfBagBootstrap(n_splits=3, random_seed=123)
+    oob = BootstrapOutOfBag(n_splits=3, random_seed=123)
     assert oob.n_splits == 3