use whole training set in .632 and .632+ bootstrap

docs/sources/CHANGELOG.md

@@ -27,12 +27,13 @@ The CHANGELOG for the current development version is available at
 - Removes deprecated `res` argument from `plot_decision_regions`. ([#803](https://github.com/rasbt/mlxtend/pull/803))
 - Adds a `title_fontsize` parameter to `plot_learning_curves` for controlling the title font size; also the plot style is now the matplotlib default. ([#818](https://github.com/rasbt/mlxtend/pull/818))
 - Internal change using `'c': 'none'` instead of `'c': ''` in `mlxtend.plotting.plot_decision_regions`'s scatterplot highlights to stay compatible with Matplotlib 3.4 and newer. ([#822](https://github.com/rasbt/mlxtend/pull/822))
-- Adds a `fontcolor_threshold` parameter to the `mlxtend.plotting.plot_confusion_matrix` function as an additional option for determining the font color cut-off manually. ([#825](https://github.com/rasbt/mlxtend/pull/825))
-- The `frequent_patterns.association_rules` now raises a `ValueError` if an empty frequent itemset DataFrame is passed. ([#842](https://github.com/rasbt/mlxtend/pull/842))
+- Adds a `fontcolor_threshold` parameter to the `mlxtend.plotting.plot_confusion_matrix` function as an additional option for determining the font color cut-off manually. ([#827](https://github.com/rasbt/mlxtend/pull/827))
+- The `frequent_patterns.association_rules` now raises a `ValueError` if an empty frequent itemset DataFrame is passed. ([#843](https://github.com/rasbt/mlxtend/pull/843))
+- The .632 and .632+ bootstrap method implemented in the `mlxtend.evaluate.bootstrap_point632_score` function now use the whole training set for the resubstitution weighting term instead of the internal training set that is a new bootstrap sample in each round. ([#844](https://github.com/rasbt/mlxtend/pull/844))
 
 ##### Bug Fixes
 
-- Fixes a typo in the SequentialFeatureSelector documentation ([Issue #835](https://github.com/rasbt/mlxtend/issues/835) via [João Pedro Zanlorensi Cardoso](https://github.com/joaozanlorensi))
+- Fixes a typo in the SequentialFeatureSelector documentation ([#835](https://github.com/rasbt/mlxtend/issues/835) via [João Pedro Zanlorensi Cardoso](https://github.com/joaozanlorensi))
 
 
 ### Version 0.18.0 (11/25/2020)

docs/sources/user_guide/evaluate/bootstrap_point632_score.ipynb

@@ -56,7 +56,7 @@
     "\n",
     "$$\\text{ACC}_{boot} = \\frac{1}{b} \\sum_{i=1}^b \\big(0.632 \\cdot \\text{ACC}_{h, i} + 0.368 \\cdot \\text{ACC}_{r, i}\\big), $$\n",
     "\n",
-    "where $\\text{ACC}_{r, i}$ is the resubstitution accuracy, and $\\text{ACC}_{h, i}$ is the accuracy on the out-of-bag sample.\n",
+    "where $\\text{ACC}_{train}$ is the accuracy computed on the whole training set, and $\\text{ACC}_{h, i}$ is the accuracy on the out-of-bag sample.\n",
     "\n",
     "### .632+ Bootstrap\n",
     "\n",
@@ -71,9 +71,9 @@
     "\n",
     "where *R* is the *relative overfitting rate*\n",
     "\n",
-    "$$R = \\frac{(-1) \\times (\\text{ACC}_{h, i} - \\text{ACC}_{r, i})}{\\gamma - (1 -\\text{ACC}_{h, i})}.$$\n",
+    "$$R = \\frac{(-1) \\times (\\text{ACC}_{h, i} - \\text{ACC}_{train})}{\\gamma - (1 -\\text{ACC}_{h, i})}.$$\n",
     "\n",
-    "(Since we are plugging $\\omega$ into the equation for computing $$ACC_{boot}$$ that we defined above, $$\\text{ACC}_{h, i}$$ and $\\text{ACC}_{r, i}$ still refer to the resubstitution and out-of-bag accuracy estimates in the *i*th bootstrap round, respectively.)\n",
+    "(Since we are plugging $\\omega$ into the equation for computing $$ACC_{boot}$$ that we defined above, $$\\text{ACC}_{h, i}$$ and $\\text{ACC}_{r, i}$ still refer to the out-of-bag accuracy in the *i*th bootstrap round and the whole training set accuracy, respectively.)\n",
     "\n",
     "Further, we need to determine the *no-information rate* $\\gamma$ in order to compute *R*. For instance, we can compute $\\gamma$ by fitting a model to a dataset that contains all possible combinations between samples $x_{i'}$ and target class labels $y_{i}$ — we pretend that the observations and class labels are independent:\n",
     "\n",
@@ -121,8 +121,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 94.36%\n",
-      "95% Confidence interval: [88.46, 98.31]\n"
+      "Accuracy: 94.45%\n",
+      "95% Confidence interval: [87.71, 100.00]\n"
      ]
     }
    ],
@@ -135,7 +135,7 @@
     "iris = datasets.load_iris()\n",
     "X = iris.data\n",
     "y = iris.target\n",
-    "tree = DecisionTreeClassifier(random_state=0)\n",
+    "tree = DecisionTreeClassifier(random_state=123)\n",
     "\n",
     "# Model accuracy\n",
     "scores = bootstrap_point632_score(tree, X, y, method='oob')\n",
@@ -165,8 +165,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 96.57%\n",
-      "95% Confidence interval: [92.37, 98.95]\n"
+      "Accuracy: 96.42%\n",
+      "95% Confidence interval: [92.41, 100.00]\n"
      ]
     }
    ],
@@ -179,7 +179,7 @@
     "iris = datasets.load_iris()\n",
     "X = iris.data\n",
     "y = iris.target\n",
-    "tree = DecisionTreeClassifier(random_state=0)\n",
+    "tree = DecisionTreeClassifier(random_state=123)\n",
     "\n",
     "# Model accuracy\n",
     "scores = bootstrap_point632_score(tree, X, y)\n",
@@ -209,8 +209,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 96.28%\n",
-      "95% Confidence interval: [92.10, 98.90]\n"
+      "Accuracy: 96.29%\n",
+      "95% Confidence interval: [91.86, 98.92]\n"
      ]
     }
    ],
@@ -223,7 +223,7 @@
     "iris = datasets.load_iris()\n",
     "X = iris.data\n",
     "y = iris.target\n",
-    "tree = DecisionTreeClassifier(random_state=0)\n",
+    "tree = DecisionTreeClassifier(random_state=123)\n",
     "\n",
     "# Model accuracy\n",
     "scores = bootstrap_point632_score(tree, X, y, method='.632+')\n",
@@ -255,21 +255,21 @@
      "text": [
       "## bootstrap_point632_score\n",
       "\n",
-      "*bootstrap_point632_score(estimator, X, y, n_splits=200, method='.632', scoring_func=None, random_seed=None, clone_estimator=True)*\n",
+      "*bootstrap_point632_score(estimator, X, y, n_splits=200, method='.632', scoring_func=None, predict_proba=False, random_seed=None, clone_estimator=True)*\n",
       "\n",
       "Implementation of the .632 [1] and .632+ [2] bootstrap\n",
-      "for supervised learning\n",
+      "    for supervised learning\n",
       "\n",
-      "References:\n",
+      "    References:\n",
       "\n",
-      "- [1] Efron, Bradley. 1983. “Estimating the Error Rate\n",
-      "of a Prediction Rule: Improvement on Cross-Validation.”\n",
-      "Journal of the American Statistical Association\n",
-      "78 (382): 316. doi:10.2307/2288636.\n",
-      "- [2] Efron, Bradley, and Robert Tibshirani. 1997.\n",
-      "“Improvements on Cross-Validation: The .632+ Bootstrap Method.”\n",
-      "Journal of the American Statistical Association\n",
-      "92 (438): 548. doi:10.2307/2965703.\n",
+      "    - [1] Efron, Bradley. 1983. \"Estimating the Error Rate\n",
+      "    of a Prediction Rule: Improvement on Cross-Validation.\"\n",
+      "    Journal of the American Statistical Association\n",
+      "    78 (382): 316. doi:10.2307/2288636.\n",
+      "    - [2] Efron, Bradley, and Robert Tibshirani. 1997.\n",
+      "    \"Improvements on Cross-Validation: The .632+ Bootstrap Method.\"\n",
+      "    Journal of the American Statistical Association\n",
+      "    92 (438): 548. doi:10.2307/2965703.\n",
       "\n",
       "**Parameters**\n",
       "\n",
@@ -316,6 +316,19 @@
       "    if the estimator is a regressor.\n",
       "\n",
       "\n",
+      "- `predict_proba` : bool\n",
+      "\n",
+      "    Whether to use the `predict_proba` function for the\n",
+      "    `estimator` argument. This is to be used in conjunction\n",
+      "    with `scoring_func` which takes in probability values\n",
+      "    instead of actual predictions.\n",
+      "    For example, if the scoring_func is\n",
+      "    :meth:`sklearn.metrics.roc_auc_score`, then use\n",
+      "    `predict_proba=True`.\n",
+      "    Note that this requires `estimator` to have\n",
+      "    `predict_proba` method implemented.\n",
+      "\n",
+      "\n",
       "- `random_seed` : int (default=None)\n",
       "\n",
       "    If int, random_seed is the seed used by\n",
@@ -336,7 +349,7 @@
       "\n",
       "**Examples**\n",
       "\n",
-      "\n",
+      "```\n",
       "    >>> from sklearn import datasets, linear_model\n",
       "    >>> from mlxtend.evaluate import bootstrap_point632_score\n",
       "    >>> iris = datasets.load_iris()\n",
@@ -352,8 +365,9 @@
       "    >>> print('95%% Confidence interval: [%.2f, %.2f]' % (lower, upper))\n",
       "    95% Confidence interval: [0.90, 0.98]\n",
       "\n",
-      "For more usage examples, please see\n",
-      "[http://rasbt.github.io/mlxtend/user_guide/evaluate/bootstrap_point632_score/](http://rasbt.github.io/mlxtend/user_guide/evaluate/bootstrap_point632_score/)\n",
+      "    For more usage examples, please see\n",
+      "    http://rasbt.github.io/mlxtend/user_guide/evaluate/bootstrap_point632_score/\n",
+      "```\n",
       "\n",
       "\n"
      ]
@@ -369,7 +383,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -383,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.9.6"
   },
   "toc": {
    "nav_menu": {},

mlxtend/evaluate/bootstrap_point632.py

@@ -185,13 +185,32 @@ def bootstrap_point632_score(estimator, X, y, n_splits=200,
     oob = BootstrapOutOfBag(n_splits=n_splits, random_seed=random_seed)
     scores = np.empty(dtype=np.float, shape=(n_splits,))
     cnt = 0
+
     for train, test in oob.split(X):
         cloned_est.fit(X[train], y[train])
 
         # get the prediction probability
         # for binary class uses the last column
         predicted_test_val = predict_func(X[test])
-        predicted_train_val = predict_func(X[train])
+
+        if method in ('.632', '.632+'):
+            # predictions on the internal training set:
+            # predicted_train_val = predict_func(X[train])
+
+            # compute training error on the whole training set as reported in
+            # the original .632 boostrap paper
+            # in Eq (6.12) in
+            #    "Estimating the Error Rate of a Prediction Rule: Improvement
+            #     on Cross-Validation"
+            #     by B. Efron, 1983, https://doi.org/10.2307/2288636
+            # Also see the discussion at
+            #   https://github.com/rasbt/mlxtend/discussions/828
+            #
+            # This also applies to the .632+ estimate in the paper
+            #    "Improvements on Cross-Validation: The .632+ Bootstrap Method"
+            #    https://www.tandfonline.com/doi/abs/10.1080/01621459.1997.10474007
+            predicted_train_val = predict_func(X)
+
         if predict_proba:
             len_uniq = np.unique(y)
 
@@ -206,13 +225,19 @@ def bootstrap_point632_score(estimator, X, y, n_splits=200,
 
         else:
             test_err = 1 - test_acc
-            train_err = 1 - scoring_func(y[train], predicted_train_val)
+
+            # training error on the whole training set as mentioned in the
+            # previous comment above
+            train_err = 1 - scoring_func(
+                    y, predicted_train_val)
+
             if method == '.632+':
                 gamma = 1 - (no_information_rate(
                     y,
                     cloned_est.predict(X),
                     scoring_func))
-                R = (test_err - train_err) / (gamma - train_err)
+                R = (test_err - train_err) / (
+                    gamma - train_err)
                 weight = 0.632 / (1 - 0.368*R)
 
             else:

mlxtend/evaluate/tests/test_bootstrap_point632.py

@@ -40,7 +40,7 @@ def test_defaults():
     scores = bootstrap_point632_score(lr, X, y, random_seed=123)
     acc = np.mean(scores)
     assert len(scores == 200)
-    assert np.round(acc, 5) == 0.95306, np.round(acc, 5)
+    assert np.round(acc, 5) == 0.95117, np.round(acc, 5)
 
 
 def test_oob():
@@ -58,14 +58,14 @@ def test_632():
                                       method='.632')
     acc = np.mean(scores)
     assert len(scores == 200)
-    assert np.round(acc, 5) == 0.96629, np.round(acc, 5)
+    assert np.round(acc, 5) == 0.95914, np.round(acc, 5)
 
     tree2 = DecisionTreeClassifier(random_state=123, max_depth=1)
     scores = bootstrap_point632_score(tree2, X, y, random_seed=123,
                                       method='.632')
     acc = np.mean(scores)
     assert len(scores == 200)
-    assert np.round(acc, 5) == 0.65512, np.round(acc, 5)
+    assert np.round(acc, 5) == 0.64355, np.round(acc, 5)
 
 
 def test_632plus():
@@ -74,14 +74,14 @@ def test_632plus():
                                       method='.632+')
     acc = np.mean(scores)
     assert len(scores == 200)
-    assert np.round(acc, 5) == 0.9649, np.round(acc, 5)
+    assert np.round(acc, 5) == 0.95855, np.round(acc, 5)
 
     tree2 = DecisionTreeClassifier(random_state=123, max_depth=1)
     scores = bootstrap_point632_score(tree2, X, y, random_seed=123,
                                       method='.632+')
     acc = np.mean(scores)
     assert len(scores == 200)
-    assert np.round(acc, 5) == 0.64831, np.round(acc, 5)
+    assert np.round(acc, 5) == 0.64078, np.round(acc, 5)
 
 
 def test_custom_accuracy():
@@ -95,7 +95,7 @@ def accuracy2(targets, predictions):
                                       scoring_func=accuracy2)
     acc = np.mean(scores)
     assert len(scores == 200)
-    assert np.round(acc, 5) == 0.95306, np.round(acc, 5)
+    assert np.round(acc, 5) == 0.95117, np.round(acc, 5)
 
 
 def test_invalid_splits():