ML之Xgboost：利用Xgboost模型(7f-CrVa+网格搜索调参)对数据集(比马印第安人糖尿病)进行二分类预测

ML之Xgboost：利用Xgboost模型(7f-CrVa+网格搜索调参)对数据集(比马印第安人糖尿病)进行二分类预测

目录

输出结果

设计思路

核心代码

输出结果

设计思路

核心代码

grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold)
grid_result = grid_search.fit(X, Y) 


param_grid = dict(learning_rate=learning_rate)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)

class GridSearchCV(BaseSearchCV):
    """Exhaustive search over specified parameter values for an estimator.
    
    Important members are fit, predict.
    
    GridSearchCV implements a "fit" and a "score" method.
    It also implements "predict", "predict_proba", "decision_function",
    "transform" and "inverse_transform" if they are implemented in the
    estimator used.
    
    The parameters of the estimator used to apply these methods are 
     optimized
    by cross-validated grid-search over a parameter grid.
    
    Read more in the :ref:`User Guide <grid_search>`.
    
    Parameters
    ----------
    estimator : estimator object.
    This is assumed to implement the scikit-learn estimator interface.
    Either estimator needs to provide a ``score`` function,
    or ``scoring`` must be passed.
    
    param_grid : dict or list of dictionaries
    Dictionary with parameters names (string) as keys and lists of
    parameter settings to try as values, or a list of such
    dictionaries, in which case the grids spanned by each dictionary
    in the list are explored. This enables searching over any sequence
    of parameter settings.
    
    scoring : string, callable, list/tuple, dict or None, default: None
    A single string (see :ref:`scoring_parameter`) or a callable
    (see :ref:`scoring`) to evaluate the predictions on the test set.
    
    For evaluating multiple metrics, either give a list of (unique) strings
    or a dict with names as keys and callables as values.
    
    NOTE that when using custom scorers, each scorer should return a 
     single
    value. Metric functions returning a list/array of values can be wrapped
    into multiple scorers that return one value each.
    
    See :ref:`multimetric_grid_search` for an example.
    
    If None, the estimator's default scorer (if available) is used.
    
    fit_params : dict, optional
    Parameters to pass to the fit method.
    
    .. deprecated:: 0.19
    ``fit_params`` as a constructor argument was deprecated in version
    0.19 and will be removed in version 0.21. Pass fit parameters to
    the ``fit`` method instead.
    
    n_jobs : int, default=1
    Number of jobs to run in parallel.
    
    pre_dispatch : int, or string, optional
    Controls the number of jobs that get dispatched during parallel
    execution. Reducing this number can be useful to avoid an
    explosion of memory consumption when more jobs get dispatched
    than CPUs can process. This parameter can be:
    
    - None, in which case all the jobs are immediately
    created and spawned. Use this for lightweight and
    fast-running jobs, to avoid delays due to on-demand
    spawning of the jobs
    
    - An int, giving the exact number of total jobs that are
    spawned
    
    - A string, giving an expression as a function of n_jobs,
    as in '2*n_jobs'
    
    iid : boolean, default=True
    If True, the data is assumed to be identically distributed across
    the folds, and the loss minimized is the total loss per sample,
    and not the mean loss across the folds.
    
    cv : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:
    - None, to use the default 3-fold cross validation,
    - integer, to specify the number of folds in a `(Stratified)KFold`,
    - An object to be used as a cross-validation generator.
    - An iterable yielding train, test splits.
    
    For integer/None inputs, if the estimator is a classifier and ``y`` is
    either binary or multiclass, :class:`StratifiedKFold` is used. In all
    other cases, :class:`KFold` is used.
    
    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.
    
    refit : boolean, or string, default=True
    Refit an estimator using the best found parameters on the whole
    dataset.
    
    For multiple metric evaluation, this needs to be a string denoting the
    scorer is used to find the best parameters for refitting the estimator
    at the end.
    
    The refitted estimator is made available at the ``best_estimator_``
    attribute and permits using ``predict`` directly on this
    ``GridSearchCV`` instance.
    
    Also for multiple metric evaluation, the attributes ``best_index_``,
    ``best_score_`` and ``best_parameters_`` will only be available if
    ``refit`` is set and all of them will be determined w.r.t this specific
    scorer.
    
    See ``scoring`` parameter to know more about multiple metric
    evaluation.
    
    verbose : integer
    Controls the verbosity: the higher, the more messages.
    
    error_score : 'raise' (default) or numeric
    Value to assign to the score if an error occurs in estimator fitting.
    If set to 'raise', the error is raised. If a numeric value is given,
    FitFailedWarning is raised. This parameter does not affect the refit
    step, which will always raise the error.
    
    return_train_score : boolean, optional
    If ``False``, the ``cv_results_`` attribute will not include training
    scores.
    
    Current default is ``'warn'``, which behaves as ``True`` in addition
    to raising a warning when a training score is looked up.
    That default will be changed to ``False`` in 0.21.
    Computing training scores is used to get insights on how different
    parameter settings impact the overfitting/underfitting trade-off.
    However computing the scores on the training set can be 
     computationally
    expensive and is not strictly required to select the parameters that
    yield the best generalization performance.
    
    
    Examples
    --------
    >>> from sklearn import svm, datasets
    >>> from sklearn.model_selection import GridSearchCV
    >>> iris = datasets.load_iris()
    >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
    >>> svc = svm.SVC()
    >>> clf = GridSearchCV(svc, parameters)
    >>> clf.fit(iris.data, iris.target)
    ...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    GridSearchCV(cv=None, error_score=...,
    estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
    decision_function_shape='ovr', degree=..., gamma=...,
    kernel='rbf', max_iter=-1, probability=False,
    random_state=None, shrinking=True, tol=...,
    verbose=False),
    fit_params=None, iid=..., n_jobs=1,
    param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,
    scoring=..., verbose=...)
    >>> sorted(clf.cv_results_.keys())
    ...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
    'mean_train_score', 'param_C', 'param_kernel', 'params',...
    'rank_test_score', 'split0_test_score',...
    'split0_train_score', 'split1_test_score', 'split1_train_score',...
    'split2_test_score', 'split2_train_score',...
    'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]
    
    Attributes
    ----------
    cv_results_ : dict of numpy (masked) ndarrays
    A dict with keys as column headers and values as columns, that can be
    imported into a pandas ``DataFrame``.
    
    For instance the below given table
    
    +------------+-----------+------------+-----------------+---+---------+
    |param_kernel|param_gamma|param_degree|split0_test_score|...
     |rank_t...|
    
     +============+===========+============+========
     =========+===+=========+
    |  'poly'    |     --    |      2     |        0.8      |...|    2    |
    +------------+-----------+------------+-----------------+---+---------+
    |  'poly'    |     --    |      3     |        0.7      |...|    4    |
    +------------+-----------+------------+-----------------+---+---------+
    |  'rbf'     |     0.1   |     --     |        0.8      |...|    3    |
    +------------+-----------+------------+-----------------+---+---------+
    |  'rbf'     |     0.2   |     --     |        0.9      |...|    1    |
    +------------+-----------+------------+-----------------+---+---------+
    
    will be represented by a ``cv_results_`` dict of::
    
    {
    'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
    mask = [False False False False]...)
    'param_gamma': masked_array(data = [-- -- 0.1 0.2],
    mask = [ True  True False False]...),
    'param_degree': masked_array(data = [2.0 3.0 -- --],
    mask = [False False  True  True]...),
    'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],
    'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
    'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
    'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
    'rank_test_score'    : [2, 4, 3, 1],
    'split0_train_score' : [0.8, 0.9, 0.7],
    'split1_train_score' : [0.82, 0.5, 0.7],
    'mean_train_score'   : [0.81, 0.7, 0.7],
    'std_train_score'    : [0.03, 0.03, 0.04],
    'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
    'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
    'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
    'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
    'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
    }
    
    NOTE
    
    The key ``'params'`` is used to store a list of parameter
    settings dicts for all the parameter candidates.
    
    The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
    ``std_score_time`` are all in seconds.
    
    For multi-metric evaluation, the scores for all the scorers are
    available in the ``cv_results_`` dict at the keys ending with that
    scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
    above. ('split0_test_precision', 'mean_train_precision' etc.)
    
    best_estimator_ : estimator or dict
    Estimator that was chosen by the search, i.e. estimator
    which gave highest score (or smallest loss if specified)
    on the left out data. Not available if ``refit=False``.
    
    See ``refit`` parameter for more information on allowed values.
    
    best_score_ : float
    Mean cross-validated score of the best_estimator
    
    For multi-metric evaluation, this is present only if ``refit`` is
    specified.
    
    best_params_ : dict
    Parameter setting that gave the best results on the hold out data.
    
    For multi-metric evaluation, this is present only if ``refit`` is
    specified.
    
    best_index_ : int
    The index (of the ``cv_results_`` arrays) which corresponds to the best
    candidate parameter setting.
    
    The dict at ``search.cv_results_['params'][search.best_index_]`` gives
    the parameter setting for the best model, that gives the highest
    mean score (``search.best_score_``).
    
    For multi-metric evaluation, this is present only if ``refit`` is
    specified.
    
    scorer_ : function or a dict
    Scorer function used on the held out data to choose the best
    parameters for the model.
    
    For multi-metric evaluation, this attribute holds the validated
    ``scoring`` dict which maps the scorer key to the scorer callable.
    
    n_splits_ : int
    The number of cross-validation splits (folds/iterations).
    
    Notes
    ------
    The parameters selected are those that maximize the score of the left 
     out
    data, unless an explicit score is passed in which case it is used instead.
    
    If `n_jobs` was set to a value higher than one, the data is copied for 
     each
    point in the grid (and not `n_jobs` times). This is done for efficiency
    reasons if individual jobs take very little time, but may raise errors if
    the dataset is large and not enough memory is available.  A 
     workaround in
    this case is to set `pre_dispatch`. Then, the memory is copied only
    `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
    n_jobs`.
    
    See Also
    ---------
    :class:`ParameterGrid`:
    generates all the combinations of a hyperparameter grid.
    
    :func:`sklearn.model_selection.train_test_split`:
    utility function to split the data into a development set usable
    for fitting a GridSearchCV instance and an evaluation set for
    its final evaluation.
    
    :func:`sklearn.metrics.make_scorer`:
    Make a scorer from a performance metric or loss function.
    
    """
    def __init__(self, estimator, param_grid, scoring=None, 
     fit_params=None, 
        n_jobs=1, iid=True, refit=True, cv=None, verbose=0, 
        pre_dispatch='2*n_jobs', error_score='raise', 
        return_train_score="warn"):
        super(GridSearchCV, self).__init__(estimator=estimator, 
         scoring=scoring, fit_params=fit_params, n_jobs=n_jobs, iid=iid, 
         refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch, 
         error_score=error_score, return_train_score=return_train_score)
        self.param_grid = param_grid
        _check_param_grid(param_grid)
    
    def _get_param_iterator(self):
        """Return ParameterGrid instance for the given param_grid"""
        return ParameterGrid(self.param_grid)

class StratifiedKFold(_BaseKFold):
    """Stratified K-Folds cross-validator
    
    Provides train/test indices to split data in train/test sets.
    
    This cross-validation object is a variation of KFold that returns
    stratified folds. The folds are made by preserving the percentage of
    samples for each class.
    
    Read more in the :ref:`User Guide <cross_validation>`.
    
    Parameters
    ----------
    n_splits : int, default=3
    Number of folds. Must be at least 2.
    
    shuffle : boolean, optional
    Whether to shuffle each stratification of the data before splitting
    into batches.
    
    random_state : int, RandomState instance or None, optional, 
     default=None
    If int, random_state is the seed used by the random number 
     generator;
    If RandomState instance, random_state is the random number 
     generator;
    If None, the random number generator is the RandomState 
     instance used
    by `np.random`. Used when ``shuffle`` == True.
    
    Examples
    --------
    >>> from sklearn.model_selection import StratifiedKFold
    >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
    >>> y = np.array([0, 0, 1, 1])
    >>> skf = StratifiedKFold(n_splits=2)
    >>> skf.get_n_splits(X, y)
    2
    >>> print(skf)  # doctest: +NORMALIZE_WHITESPACE
    StratifiedKFold(n_splits=2, random_state=None, shuffle=False)
    >>> for train_index, test_index in skf.split(X, y):
    ...    print("TRAIN:", train_index, "TEST:", test_index)
    ...    X_train, X_test = X[train_index], X[test_index]
    ...    y_train, y_test = y[train_index], y[test_index]
    TRAIN: [1 3] TEST: [0 2]
    TRAIN: [0 2] TEST: [1 3]
    
    Notes
    -----
    All the folds have size ``trunc(n_samples / n_splits)``, the last one has
    the complementary.
    
    See also
    --------
    RepeatedStratifiedKFold: Repeats Stratified K-Fold n times.
    """
    def __init__(self, n_splits=3, shuffle=False, random_state=None):
        super(StratifiedKFold, self).__init__(n_splits, shuffle, 
         random_state)
    
    def _make_test_folds(self, X, y=None):
        rng = self.random_state
        y = np.asarray(y)
        type_of_target_y = type_of_target(y)
        allowed_target_types = 'binary', 'multiclass'
        if type_of_target_y not in allowed_target_types:
            raise ValueError(
                'Supported target types are: {}. Got {!r} instead.'.format(
                    allowed_target_types, type_of_target_y))
        y = column_or_1d(y)
        n_samples = y.shape[0]
        unique_y, y_inversed = np.unique(y, return_inverse=True)
        y_counts = np.bincount(y_inversed)
        min_groups = np.min(y_counts)
        if np.all(self.n_splits > y_counts):
            raise ValueError(
                "n_splits=%d cannot be greater than the"
                " number of members in each class." % 
                (self.n_splits))
        if self.n_splits > min_groups:
            warnings.warn(("The least populated class in y has only %d"
                    " members, which is too few. The minimum"
                    " number of members in any class cannot"
                    " be less than n_splits=%d." % 
                    (min_groups, self.n_splits)), Warning) # pre-assign each 
                     sample to a test fold index using individual KFold
                    # splitting strategies for each class so as to respect the 
                     balance of
                    # classes
                    # NOTE: Passing the data corresponding to ith class say X
                     [y==class_i]
                    # will break when the data is not 100% stratifiable for all 
                     classes.
                    # So we pass np.zeroes(max(c, n_splits)) as data to the 
                     KFold
        per_cls_cvs = [KFold(self.n_splits, shuffle=self.shuffle, 
         random_state=rng).split(np.zeros(max(count, self.n_splits))) for count 
         in y_counts]
        test_folds = np.zeros(n_samples, dtype=np.int)
        for test_fold_indices, per_cls_splits in enumerate(zip
         (*per_cls_cvs)):
            for cls, (_, test_split) in zip(unique_y, per_cls_splits):
                cls_test_folds = test_folds[y == cls]
                # the test split can be too big because we used
                # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
                # stratifiable for all the classes
                # (we use a warning instead of raising an exception)
                # If this is the case, let's trim it:
                test_split = test_split[test_split < len(cls_test_folds)]
                cls_test_folds[test_split] = test_fold_indices
                test_folds[y == cls] = cls_test_folds
        
        return test_folds
    
    def _iter_test_masks(self, X, y=None, groups=None):
        test_folds = self._make_test_folds(X, y)
        for i in range(self.n_splits):
            yield test_folds == i
    
    def split(self, X, y, groups=None):
        """Generate indices to split data into training and test set.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training data, where n_samples is the number of samples
            and n_features is the number of features.

            Note that providing ``y`` is sufficient to generate the splits and
            hence ``np.zeros(n_samples)`` may be used as a placeholder for
            ``X`` instead of actual training data.

        y : array-like, shape (n_samples,)
            The target variable for supervised learning problems.
            Stratification is done based on the y labels.

        groups : object
            Always ignored, exists for compatibility.

        Returns
        -------
        train : ndarray
            The training set indices for that split.

        test : ndarray
            The testing set indices for that split.

        Notes
        -----
        Randomized CV splitters may return different results for each call 
         of
        split. You can make the results identical by setting 
         ``random_state``
        to an integer.
        """
        y = check_array(y, ensure_2d=False, dtype=None)
        return super(StratifiedKFold, self).split(X, y, groups)