Make StackingCVRegressor capable of replacing regressors in GridSearchCV

docs/sources/CHANGELOG.md

@@ -17,13 +17,15 @@ The CHANGELOG for the current development version is available at
 
 ##### New Features
 
-- Adds multiprocessing support to `StackingCVClassifier`. ([#512](https://github.com/rasbt/mlxtend/pull/512) via [Qiang Gu](https://github.com/qiaguhttps://github.com/qiagu))
+- Adds multiprocessing support to `StackingCVRegressor`. ([#512](https://github.com/rasbt/mlxtend/pull/512) via [Qiang Gu](https://github.com/qiaguhttps://github.com/qiagu))
+-  Now, the `StackingCVRegressor` also enables grid search over the `regressors` and even a single base regressor. When there are level-mixed parameters, `GridSearchCV` will try to replace hyperparameters in a top-down order (see the [documentation](http://rasbt.github.io/mlxtend/user_guide/regressor/StackingCVRegressor/) for examples details). ([#515](https://github.com/rasbt/mlxtend/pull/512) via [Qiang Gu](https://github.com/qiaguhttps://github.com/qiagu))
 - Adds a `verbose` parameter to `apriori` to show the current iteration number as well as the itemset size currently being sampled. ([#519](https://github.com/rasbt/mlxtend/pull/519)
 - Adds an optional `class_name` parameter to the confusion matrix function to display class names on the axis as tick marks. ([#487](https://github.com/rasbt/mlxtend/pull/487) via [sandpiturtle](https://github.com/qiaguhttps://github.com/sandpiturtle))
 
 ##### Changes
 
--
+- Due to new features, restructuring, and better scikit-learn support (for `GridSearchCV`, etc.) the `StackingCVRegressor`'s meta regressor is now being accessed via `'meta_regressor__*` in the parameter grid. E.g., if a `RandomForestRegressor` as meta- egressor was previously tuned via `'randomforestregressor__n_estimators'`, this has now changed to `'meta_regressor__n_estimators'`. ([#515](https://github.com/rasbt/mlxtend/pull/512) via [Qiang Gu](https://github.com/qiaguhttps://github.com/qiagu))
+
 
 ##### Bug Fixes
 

docs/sources/user_guide/regressor/StackingCVRegressor.ipynb

@@ -84,10 +84,10 @@
      "text": [
       "5-fold cross validation scores:\n",
       "\n",
-      "R^2 Score: 0.45 (+/- 0.29) [SVM]\n",
+      "R^2 Score: 0.46 (+/- 0.29) [SVM]\n",
       "R^2 Score: 0.43 (+/- 0.14) [Lasso]\n",
-      "R^2 Score: 0.52 (+/- 0.28) [Random Forest]\n",
-      "R^2 Score: 0.58 (+/- 0.24) [StackingCVRegressor]\n"
+      "R^2 Score: 0.53 (+/- 0.28) [Random Forest]\n",
+      "R^2 Score: 0.58 (+/- 0.23) [StackingCVRegressor]\n"
      ]
     }
    ],
@@ -138,10 +138,10 @@
      "text": [
       "5-fold cross validation scores:\n",
       "\n",
-      "Neg. MSE Score: -33.69 (+/- 22.36) [SVM]\n",
+      "Neg. MSE Score: -33.34 (+/- 22.36) [SVM]\n",
       "Neg. MSE Score: -35.53 (+/- 16.99) [Lasso]\n",
-      "Neg. MSE Score: -27.32 (+/- 16.62) [Random Forest]\n",
-      "Neg. MSE Score: -25.64 (+/- 18.11) [StackingCVRegressor]\n"
+      "Neg. MSE Score: -27.25 (+/- 16.76) [Random Forest]\n",
+      "Neg. MSE Score: -25.56 (+/- 18.22) [StackingCVRegressor]\n"
      ]
     }
    ],
@@ -177,19 +177,27 @@
    "source": [
     "In this second example we demonstrate how `StackingCVRegressor` works in combination with `GridSearchCV`. The stack still allows tuning hyper parameters of the base and meta models!\n",
     "\n",
-    "To set up a parameter grid for scikit-learn's `GridSearch`, we simply provide the estimator's names in the parameter grid -- in the special case of the meta-regressor, we append the `'meta-'` prefix.\n"
+    "For instance, we can use `estimator.get_params().keys()` to get a full list of tunable parameters.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/guq/miniconda3/envs/python3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
+      "  DeprecationWarning)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best: 0.673590 using {'lasso__alpha': 0.4, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.3}\n"
+      "Best: 0.674237 using {'lasso__alpha': 1.6, 'meta_regressor__n_estimators': 100, 'ridge__alpha': 0.2}\n"
      ]
     }
    ],
@@ -203,8 +211,8 @@
     "\n",
     "X, y = load_boston(return_X_y=True)\n",
     "\n",
-    "ridge = Ridge()\n",
-    "lasso = Lasso()\n",
+    "ridge = Ridge(random_state=RANDOM_SEED)\n",
+    "lasso = Lasso(random_state=RANDOM_SEED)\n",
     "rf = RandomForestRegressor(random_state=RANDOM_SEED)\n",
     "\n",
     "# The StackingCVRegressor uses scikit-learn's check_cv\n",
@@ -224,7 +232,7 @@
     "    param_grid={\n",
     "        'lasso__alpha': [x/5.0 for x in range(1, 10)],\n",
     "        'ridge__alpha': [x/20.0 for x in range(1, 10)],\n",
-    "        'meta-randomforestregressor__n_estimators': [10, 100]\n",
+    "        'meta_regressor__n_estimators': [10, 100]\n",
     "    }, \n",
     "    cv=5,\n",
     "    refit=True\n",
@@ -244,20 +252,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.622 +/- 0.10 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.05}\n",
-      "0.649 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.1}\n",
-      "0.650 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.15}\n",
-      "0.667 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.2}\n",
-      "0.629 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.25}\n",
-      "0.663 +/- 0.08 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.3}\n",
-      "0.633 +/- 0.08 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.35}\n",
-      "0.637 +/- 0.08 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.4}\n",
-      "0.649 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.45}\n",
-      "0.653 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 100, 'ridge__alpha': 0.05}\n",
-      "0.648 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 100, 'ridge__alpha': 0.1}\n",
-      "0.645 +/- 0.09 {'lasso__alpha': 0.2, 'meta-randomforestregressor__n_estimators': 100, 'ridge__alpha': 0.15}\n",
+      "0.616 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.05}\n",
+      "0.656 +/- 0.08 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.1}\n",
+      "0.653 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.15}\n",
+      "0.669 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.2}\n",
+      "0.632 +/- 0.08 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.25}\n",
+      "0.664 +/- 0.08 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.3}\n",
+      "0.632 +/- 0.08 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.35}\n",
+      "0.642 +/- 0.08 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.4}\n",
+      "0.653 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 10, 'ridge__alpha': 0.45}\n",
+      "0.657 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 100, 'ridge__alpha': 0.05}\n",
+      "0.650 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 100, 'ridge__alpha': 0.1}\n",
+      "0.648 +/- 0.09 {'lasso__alpha': 0.2, 'meta_regressor__n_estimators': 100, 'ridge__alpha': 0.15}\n",
       "...\n",
-      "Best parameters: {'lasso__alpha': 0.4, 'meta-randomforestregressor__n_estimators': 10, 'ridge__alpha': 0.3}\n",
+      "Best parameters: {'lasso__alpha': 1.6, 'meta_regressor__n_estimators': 100, 'ridge__alpha': 0.2}\n",
       "Accuracy: 0.67\n"
      ]
     }
@@ -284,12 +292,12 @@
    "source": [
     "**Note**\n",
     "\n",
-    "The `StackingCVRegressor` also enables grid search over the `regressors` argument. However, due to the current implementation of `GridSearchCV` in scikit-learn, it is not possible to search over both, different regressors and regressor parameters at the same time. For instance, while the following parameter dictionary works\n",
+    "The `StackingCVRegressor` also enables grid search over the `regressors` and even a single base regressor. When there are level-mixed hyperparameters, `GridSearchCV` will try to replace hyperparameters in a top-down order, i.e., `regressors` -> single base regressor -> regressor hyperparameter. For instance, given a hyperparameter grid such as\n",
     "\n",
     "    params = {'randomforestregressor__n_estimators': [1, 100],\n",
     "    'regressors': [(regr1, regr1, regr1), (regr2, regr3)]}\n",
     "    \n",
-    "it will use the instance settings of `regr1`, `regr2`, and `regr3` and not overwrite it with the `'n_estimators'` settings from `'randomforestregressor__n_estimators': [1, 100]`."
+    "it will first use the instance settings of either `(regr1, regr2, regr3)` or `(regr2, regr3)` . Then it will replace the `'n_estimators'` settings for a matching regressor based on `'randomforestregressor__n_estimators': [1, 100]`."
    ]
   },
   {
@@ -605,7 +613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.1"
   },
   "toc": {
    "nav_menu": {},

mlxtend/regressor/stacking_cv_regression.py

@@ -14,10 +14,9 @@
 # License: BSD 3 clause
 
 from ..externals.estimator_checks import check_is_fitted
-from ..externals import six
 from ..externals.name_estimators import _name_estimators
+from ..utils.base_compostion import _BaseXComposition
 from scipy import sparse
-from sklearn.base import BaseEstimator
 from sklearn.base import RegressorMixin
 from sklearn.base import TransformerMixin
 from sklearn.base import clone
@@ -27,7 +26,7 @@
 import numpy as np
 
 
-class StackingCVRegressor(BaseEstimator, RegressorMixin, TransformerMixin):
+class StackingCVRegressor(_BaseXComposition, RegressorMixin, TransformerMixin):
     """A 'Stacking Cross-Validation' regressor for scikit-learn estimators.
 
     New in mlxtend v0.7.0
@@ -123,12 +122,6 @@ def __init__(self, regressors, meta_regressor, cv=5,
 
         self.regressors = regressors
         self.meta_regressor = meta_regressor
-        self.named_regressors = {key: value for
-                                 key, value in
-                                 _name_estimators(regressors)}
-        self.named_meta_regressor = {'meta-%s' % key: value for
-                                     key, value in
-                                     _name_estimators([meta_regressor])}
         self.cv = cv
         self.shuffle = shuffle
         self.n_jobs = n_jobs
@@ -273,25 +266,29 @@ def predict_meta_features(self, X):
         check_is_fitted(self, 'regr_')
         return np.column_stack([regr.predict(X) for regr in self.regr_])
 
+    @property
+    def named_regressors(self):
+        """
+        Returns
+        -------
+        List of named estimator tuples, like [('svc', SVC(...))]
+        """
+        return _name_estimators(self.regressors)
+
     def get_params(self, deep=True):
         #
         # Return estimator parameter names for GridSearch support.
         #
-        if not deep:
-            return super(StackingCVRegressor, self).get_params(deep=False)
-        else:
-            out = self.named_regressors.copy()
-            for name, step in six.iteritems(self.named_regressors):
-                for key, value in six.iteritems(step.get_params(deep=True)):
-                    out['%s__%s' % (name, key)] = value
+        return self._get_params('named_regressors', deep=deep)
 
-            out.update(self.named_meta_regressor.copy())
-            for name, step in six.iteritems(self.named_meta_regressor):
-                for key, value in six.iteritems(step.get_params(deep=True)):
-                    out['%s__%s' % (name, key)] = value
+    def set_params(self, **params):
+        """Set the parameters of this estimator.
 
-            for key, value in six.iteritems(super(StackingCVRegressor,
-                                            self).get_params(deep=False)):
-                out['%s' % key] = value
+        Valid parameter keys can be listed with ``get_params()``.
 
-            return out
+        Returns
+        -------
+        self
+        """
+        self._set_params('regressors', 'named_regressors', **params)
+        return self

mlxtend/regressor/tests/test_stacking_cv_regression.py

@@ -98,7 +98,7 @@ def test_gridsearch_numerate_regr():
     params = {'ridge-1__alpha': [0.01, 1.0],
               'ridge-2__alpha': [0.01, 1.0],
               'svr__C': [0.01, 1.0],
-              'meta-svr__C': [0.01, 1.0],
+              'meta_regressor__C': [0.01, 1.0],
               'use_features_in_secondary': [True, False]}
 
     grid = GridSearchCV(estimator=stack,
@@ -122,7 +122,6 @@ def test_get_params():
     got = sorted(list({s.split('__')[0] for s in stregr.get_params().keys()}))
     expect = ['cv',
               'linearregression',
-              'meta-svr',
               'meta_regressor',
               'n_jobs',
               'pre_dispatch',
@@ -332,3 +331,34 @@ def test_weight_unsupported_with_no_weight():
     stack = StackingCVRegressor(regressors=[svr_lin, lr, ridge],
                                 meta_regressor=lasso)
     stack.fit(X1, y).predict(X1)
+
+
+def test_gridsearch_replace_mix():
+    svr_lin = SVR(kernel='linear', gamma='auto')
+    ridge = Ridge(random_state=1)
+    svr_rbf = SVR(kernel='rbf', gamma='auto')
+    lr = LinearRegression()
+    lasso = Lasso(random_state=1)
+    stack = StackingCVRegressor(regressors=[svr_lin, lasso, ridge],
+                                meta_regressor=svr_rbf,
+                                shuffle=False)
+
+    params = {'regressors': [[svr_lin, lr]],
+              'linearregression': [None, lasso, ridge],
+              'svr__kernel': ['poly']}
+
+    grid = GridSearchCV(estimator=stack,
+                        param_grid=params,
+                        cv=KFold(5, shuffle=True, random_state=42),
+                        iid=False,
+                        refit=True,
+                        verbose=0)
+    grid = grid.fit(X1, y)
+
+    got1 = round(grid.best_score_, 2)
+    got2 = len(grid.best_params_['regressors'])
+    got3 = grid.best_params_['regressors'][0].kernel
+
+    assert got1 == 0.73, got1
+    assert got2 == 2, got2
+    assert got3 == 'poly', got3

mlxtend/utils/base_compostion.py

@@ -0,0 +1,40 @@
+"""Utilties to handle estimator list"""
+
+from ..externals import six
+from sklearn.utils.metaestimators import _BaseComposition
+
+
+class _BaseXComposition(_BaseComposition):
+    """
+    parameter handler for list of estimators
+    """
+    def _set_params(self, attr, named_attr, **params):
+        # Ordered parameter replacement
+        # 1. root parameter
+        if attr in params:
+            setattr(self, attr, params.pop(attr))
+
+        # 2. single estimator replacement
+        items = getattr(self, named_attr)
+        names = []
+        if items:
+            names, estimators = zip(*items)
+            estimators = list(estimators)
+        for name in list(six.iterkeys(params)):
+            if '__' not in name and name in names:
+                # replace single estimator and re-build the
+                # root estimators list
+                for i, est_name in enumerate(names):
+                    if est_name == name:
+                        new_val = params.pop(name)
+                        if new_val is None:
+                            del estimators[i]
+                        else:
+                            estimators[i] = new_val
+                        break
+                # replace the root estimators
+                setattr(self, attr, estimators)
+
+        # 3. estimator parameters and other initialisation arguments
+        super(_BaseXComposition, self).set_params(**params)
+        return self