ML之kNNC：基于iris鶯尾花數據集(PCA處理+三維散點圖可視化)利用kNN算法實現分類預測

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目錄

基于iris鶯尾花數據集(PCA處理+三維散點圖可視化)利用kNN算法實現分類預測

設計思路

輸出結果

核心代碼

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相關文章
ML之kNNC：基于iris鶯尾花數據集(PCA處理+三維散點圖可視化)利用kNN算法實現分類預測
ML之kNNC：基于iris鶯尾花數據集(PCA處理+三維散點圖可視化)利用kNN算法實現分類預測實現

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基于iris鶯尾花數據集(PCA處理+三維散點圖可視化)利用kNN算法實現分類預測

設計思路

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輸出結果

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(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 切分點： 29 label_classes: ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] kNNDIY模型預測，基于原數據： 0.95 kNN模型預測，基于原數據預測： [0.96666667 1. 0.93333333 1. 0.93103448] kNN模型預測，原數據PCA處理后： [1. 0.96 0.95918367]

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核心代碼

class KNeighborsClassifier Found at: sklearn.neighbors._classificationclass 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=5Number 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 neighborhoodare weighted equally.- 'distance' : weight points by the inverse of their distance.in this case, closer neighbors of a query point will have agreater influence than neighbors which are further away.- [callable] : a user-defined function which accepts anarray of distances, and returns an array of the same shapecontaining 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 algorithmbased on the values passed to :meth:`fit` method.Note: fitting on sparse input will override the setting ofthis parameter, using brute force.leaf_size : int, default=30Leaf size passed to BallTree or KDTree. This can affect thespeed of the construction and query, as well as the memoryrequired to store the tree. The optimal value depends on thenature of the problem.p : int, default=2Power parameter for the Minkowski metric. When p = 1, this isequivalent 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 isminkowski, and with p=2 is equivalent to the standard Euclideanmetric. See the documentation of :class:`DistanceMetric` for alist of available metrics.If metric is "precomputed", X is assumed to be a distance matrix andmust 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=NoneAdditional keyword arguments for the metric function.n_jobs : int, default=NoneThe 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 classifiereffective_metric_ : str or callbleThe distance metric used. It will be same as the `metric` parameteror a synonym of it, e.g. 'euclidean' if the `metric` parameter set to'minkowski' and `p` parameter set to 2.effective_metric_params_ : dictAdditional keyword arguments for the metric function. For most metricswill 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_ : boolFalse when `y`'s shape is (n_samples, ) or (n_samples, 1) during fitotherwise 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--------RadiusNeighborsClassifierKNeighborsRegressorRadiusNeighborsRegressorNearestNeighborsNotes-----See :ref:`Nearest Neighbors <neighbors>` in the online documentationfor a discussion of the choice of ``algorithm`` and ``leaf_size``... warning::Regarding the Nearest Neighbors algorithms, if it is found that twoneighbors, neighbor `k+1` and `k`, have identical distancesbut different labels, the results will depend on the ordering of thetraining data.https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm"""@_deprecate_positional_argsdef __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._yif 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_preddef 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_outputsof such arrays if n_outputs > 1.The class probabilities of the input samples. Classes are orderedby lexicographic order."""X = check_array(X, accept_sparse='csr')neigh_dist, neigh_ind = self.kneighbors(X)classes_ = self.classes__y = self._yif 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 rightfor i, idx in enumerate(pred_labels.T): # loop is O(n_neighbors)proba_k[all_rowsidx] += weights[:i]# normalize 'votes' into real [0,1] probabilitiesnormalizer = proba_k.sum(axis=1)[:np.newaxis]normalizer[normalizer == 0.0] = 1.0proba_k /= normalizerprobabilities.append(proba_k)if not self.outputs_2d_:probabilities = probabilities[0]return probabilities

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