ML之kNNC：基于iris莺尾花数据集(PCA处理+三维散点图可视化)利用kNN算法实现分类预测daiding

Bertha 。 2022-12-29 09:46 81阅读 0赞

ML之kNNC：基于iris莺尾花数据集(PCA处理+三维散点图可视化)利用kNN算法实现分类预测

**目录**

基于iris莺尾花数据集(PCA处理+三维散点图可视化)利用kNN算法实现分类预测

设计思路

输出结果

核心代码

--------------------

**相关文章**  
[ML之kNNC：基于iris莺尾花数据集(PCA处理+三维散点图可视化)利用kNN算法实现分类预测][ML_kNNC_iris_PCA_kNN]  
[ML之kNNC：基于iris莺尾花数据集(PCA处理+三维散点图可视化)利用kNN算法实现分类预测实现][ML_kNNC_iris_PCA_kNN 1]

# 基于iris莺尾花数据集(PCA处理+三维散点图可视化)利用kNN算法实现分类预测 #

## 设计思路 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 输出结果 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

![20201128213834194.gif][]

![20201128214113466.gif][]

![20201128214113814.gif][]

(149, 5) 
        5.1  3.5  1.4  0.2  Iris-setosa
    0  4.9  3.0  1.4  0.2  Iris-setosa
    1  4.7  3.2  1.3  0.2  Iris-setosa
    2  4.6  3.1  1.5  0.2  Iris-setosa
    3  5.0  3.6  1.4  0.2  Iris-setosa
    4  5.4  3.9  1.7  0.4  Iris-setosa
    (149, 5) 
        Sepal_Length  Sepal_Width  Petal_Length  Petal_Width            type
    0           4.5          2.3           1.3          0.3     Iris-setosa
    1           6.3          2.5           5.0          1.9  Iris-virginica
    2           5.1          3.4           1.5          0.2     Iris-setosa
    3           6.3          3.3           6.0          2.5  Iris-virginica
    4           6.8          3.2           5.9          2.3  Iris-virginica
    切分点： 29
    label_classes: ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
    kNNDIY模型预测，基于原数据： 0.95
    kNN模型预测，基于原数据预测： [0.96666667 1.         0.93333333 1.         0.93103448]
    kNN模型预测，原数据PCA处理后： [1.         0.96       0.95918367]

## 核心代码 ##

class KNeighborsClassifier Found at: sklearn.neighbors._classification
    
    class KNeighborsClassifier(NeighborsBase, KNeighborsMixin, 
        SupervisedIntegerMixin, ClassifierMixin):
        """Classifier implementing the k-nearest neighbors vote.
        
        Read more in the :ref:`User Guide <classification>`.
        
        Parameters
        ----------
        n_neighbors : int, default=5
        Number of neighbors to use by default for :meth:`kneighbors` queries.
        
        weights : {'uniform', 'distance'} or callable, default='uniform'
        weight function used in prediction.  Possible values:
        
        - 'uniform' : uniform weights.  All points in each neighborhood
        are weighted equally.
        - 'distance' : weight points by the inverse of their distance.
        in this case, closer neighbors of a query point will have a
        greater influence than neighbors which are further away.
        - [callable] : a user-defined function which accepts an
        array of distances, and returns an array of the same shape
        containing the weights.
        
        algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
        Algorithm used to compute the nearest neighbors:
        
        - 'ball_tree' will use :class:`BallTree`
        - 'kd_tree' will use :class:`KDTree`
        - 'brute' will use a brute-force search.
        - 'auto' will attempt to decide the most appropriate algorithm
        based on the values passed to :meth:`fit` method.
        
        Note: fitting on sparse input will override the setting of
        this parameter, using brute force.
        
        leaf_size : int, default=30
        Leaf size passed to BallTree or KDTree.  This can affect the
        speed of the construction and query, as well as the memory
        required to store the tree.  The optimal value depends on the
        nature of the problem.
        
        p : int, default=2
        Power parameter for the Minkowski metric. When p = 1, this is
        equivalent to using manhattan_distance (l1), and euclidean_distance
        (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
        
        metric : str or callable, default='minkowski'
        the distance metric to use for the tree.  The default metric is
        minkowski, and with p=2 is equivalent to the standard Euclidean
        metric. See the documentation of :class:`DistanceMetric` for a
        list of available metrics.
        If metric is "precomputed", X is assumed to be a distance matrix and
        must be square during fit. X may be a :term:`sparse graph`,
        in which case only "nonzero" elements may be considered neighbors.
        
        metric_params : dict, default=None
        Additional keyword arguments for the metric function.
        
        n_jobs : int, default=None
        The number of parallel jobs to run for neighbors search.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.
        Doesn't affect :meth:`fit` method.
        
        Attributes
        ----------
        classes_ : array of shape (n_classes,)
        Class labels known to the classifier
        
        effective_metric_ : str or callble
        The distance metric used. It will be same as the `metric` parameter
        or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
        'minkowski' and `p` parameter set to 2.
        
        effective_metric_params_ : dict
        Additional keyword arguments for the metric function. For most 
         metrics
        will be same with `metric_params` parameter, but may also contain the
        `p` parameter value if the `effective_metric_` attribute is set to
        'minkowski'.
        
        outputs_2d_ : bool
        False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
        otherwise True.
        
        Examples
        --------
        >>> X = [[0], [1], [2], [3]]
        >>> y = [0, 0, 1, 1]
        >>> from sklearn.neighbors import KNeighborsClassifier
        >>> neigh = KNeighborsClassifier(n_neighbors=3)
        >>> neigh.fit(X, y)
        KNeighborsClassifier(...)
        >>> print(neigh.predict([[1.1]]))
        [0]
        >>> print(neigh.predict_proba([[0.9]]))
        [[0.66666667 0.33333333]]
        
        See also
        --------
        RadiusNeighborsClassifier
        KNeighborsRegressor
        RadiusNeighborsRegressor
        NearestNeighbors
        
        Notes
        -----
        See :ref:`Nearest Neighbors <neighbors>` in the online 
         documentation
        for a discussion of the choice of ``algorithm`` and ``leaf_size``.
        
        .. warning::
        
        Regarding the Nearest Neighbors algorithms, if it is found that two
        neighbors, neighbor `k+1` and `k`, have identical distances
        but different labels, the results will depend on the ordering of the
        training data.
        
        https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
        """
        @_deprecate_positional_args
        def __init__(self, n_neighbors=5, 
            *, weights='uniform', algorithm='auto', leaf_size=30, 
            p=2, metric='minkowski', metric_params=None, n_jobs=None, **
            kwargs):
            super().__init__(n_neighbors=n_neighbors, algorithm=algorithm, 
             leaf_size=leaf_size, metric=metric, p=p, metric_params=metric_params, 
             n_jobs=n_jobs, **kwargs)
            self.weights = _check_weights(weights)
        
        def predict(self, X):
            """Predict the class labels for the provided data.
    
            Parameters
            ----------
            X : array-like of shape (n_queries, n_features), \
                    or (n_queries, n_indexed) if metric == 'precomputed'
                Test samples.
    
            Returns
            -------
            y : ndarray of shape (n_queries,) or (n_queries, n_outputs)
                Class labels for each data sample.
            """
            X = check_array(X, accept_sparse='csr')
            neigh_dist, neigh_ind = self.kneighbors(X)
            classes_ = self.classes_
            _y = self._y
            if not self.outputs_2d_:
                _y = self._y.reshape((-1, 1))
                classes_ = [self.classes_]
            n_outputs = len(classes_)
            n_queries = _num_samples(X)
            weights = _get_weights(neigh_dist, self.weights)
            y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].
             dtype)
            for k, classes_k in enumerate(classes_):
                if weights is None:
                    mode, _ = stats.mode(_y[neigh_indk], axis=1)
                else:
                    mode, _ = weighted_mode(_y[neigh_indk], weights, axis=1)
                mode = np.asarray(mode.ravel(), dtype=np.intp)
                y_pred[:k] = classes_k.take(mode)
            
            if not self.outputs_2d_:
                y_pred = y_pred.ravel()
            return y_pred
        
        def predict_proba(self, X):
            """Return probability estimates for the test data X.
    
            Parameters
            ----------
            X : array-like of shape (n_queries, n_features), \
                    or (n_queries, n_indexed) if metric == 'precomputed'
                Test samples.
    
            Returns
            -------
            p : ndarray of shape (n_queries, n_classes), or a list of n_outputs
                of such arrays if n_outputs > 1.
                The class probabilities of the input samples. Classes are ordered
                by lexicographic order.
            """
            X = check_array(X, accept_sparse='csr')
            neigh_dist, neigh_ind = self.kneighbors(X)
            classes_ = self.classes_
            _y = self._y
            if not self.outputs_2d_:
                _y = self._y.reshape((-1, 1))
                classes_ = [self.classes_]
            n_queries = _num_samples(X)
            weights = _get_weights(neigh_dist, self.weights)
            if weights is None:
                weights = np.ones_like(neigh_ind)
            all_rows = np.arange(X.shape[0])
            probabilities = []
            for k, classes_k in enumerate(classes_):
                pred_labels = _y[:k][neigh_ind]
                proba_k = np.zeros((n_queries, classes_k.size))
                # a simple ':' index doesn't work right
                for i, idx in enumerate(pred_labels.T): # loop is O(n_neighbors)
                    proba_k[all_rowsidx] += weights[:i]
                
                # normalize 'votes' into real [0,1] probabilities
                normalizer = proba_k.sum(axis=1)[:np.newaxis]
                normalizer[normalizer == 0.0] = 1.0
                proba_k /= normalizer
                probabilities.append(proba_k)
            
            if not self.outputs_2d_:
                probabilities = probabilities[0]
            return probabilities

[ML_kNNC_iris_PCA_kNN]: https://yunyaniu.blog.csdn.net/article/details/110248996
[ML_kNNC_iris_PCA_kNN 1]: https://yunyaniu.blog.csdn.net/article/details/110249638
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70]: /images/20221120/c244bc27e51045e6b670f070e473e065.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1]: /images/20221120/0e74834f11d842678f3f9be606045bca.png
[20201128213834194.gif]: /images/20221120/fea57bc313c6479d973156746943d032.png
[20201128214113466.gif]: https://img-blog.csdnimg.cn/20201128214113466.gif
[20201128214113814.gif]: https://img-blog.csdnimg.cn/20201128214113814.gif