我正在尝试通过在带有决策树分类器的管道中使用时空K均值聚类来执行超参数调整。想法是使用K-Means聚类算法生成聚类距离空间矩阵和聚类标签，然后将其传递给决策树分类器。对于超参数调整，只需将参数用于K-Means算法即可。
我正在使用Python 3.8和sklearn 0.22。
我感兴趣的数据具有3列/属性：“时间”，“ x”和“ y”（x和y是空间坐标）。
代码是：
class ST_KMeans(BaseEstimator, TransformerMixin):
# class ST_KMeans():
"""
Note that K-means clustering algorithm is designed for Euclidean distances.
It may stop converging with other distances, when the mean is no longer a
best estimation for the cluster 'center'.
The 'mean' minimizes squared differences (or, squared Euclidean distance).
If you want a different distance function, you need to replace the mean with
an appropriate center estimation.
Parameters:
k: number of clusters
eps1 : float, default=0.5
The spatial density threshold (maximum spatial distance) between
two points to be considered related.
eps2 : float, default=10
The temporal threshold (maximum temporal distance) between two
points to be considered related.
metric : string default='euclidean'
The used distance metric - more options are
‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’,
‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘jensenshannon’,
‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘rogerstanimoto’, ‘sqeuclidean’,
‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘yule’.
n_jobs : int or None, default=-1
The number of processes to start; -1 means use all processors (BE AWARE)
Attributes:
labels : array, shape = [n_samples]
Cluster labels for the data - noise is defined as -1
"""
def __init__(self, k, eps1 = 0.5, eps2 = 10, metric = 'euclidean', n_jobs = 1):
self.k = k
self.eps1 = eps1
self.eps2 = eps2
# self.min_samples = min_samples
self.metric = metric
self.n_jobs = n_jobs
def fit(self, X):
"""
Apply the ST K-Means algorithm
X : 2D numpy array. The first attribute of the array should be time attribute
as float. The following positions in the array are treated as spatial
coordinates.
The structure should look like this [[time_step1, x, y], [time_step2, x, y]..]
For example 2D dataset:
array([[0,0.45,0.43],
[0,0.54,0.34],...])
Returns:
self
"""
# check if input is correct
X = check_array(X)
# type(X)
# numpy.ndarray
# Check arguments for DBSCAN algo-
if not self.eps1 > 0.0 or not self.eps2 > 0.0:
raise ValueError('eps1, eps2, minPts must be positive')
# Get dimensions of 'X'-
# n - number of rows
# m - number of attributes/columns-
n, m = X.shape
# Compute sqaured form Euclidean Distance Matrix for 'time' and spatial attributes-
time_dist = squareform(pdist(X[:, 0].reshape(n, 1), metric = self.metric))
euc_dist = squareform(pdist(X[:, 1:], metric = self.metric))
'''
Filter the euclidean distance matrix using time distance matrix. The code snippet gets all the
indices of the 'time_dist' matrix in which the time distance is smaller than 'eps2'.
Afterward, for the same indices in the euclidean distance matrix the 'eps1' is doubled which results
in the fact that the indices are not considered during clustering - as they are bigger than 'eps1'.
'''
# filter 'euc_dist' matrix using 'time_dist' matrix-
dist = np.where(time_dist <= self.eps2, euc_dist, 2 * self.eps1)
# Initialize K-Means clustering model-
kmeans_clust_model = KMeans(
n_clusters = self.k, init = 'k-means++',
n_init = 10, max_iter = 300,
precompute_distances = 'auto', algorithm = 'auto')
# Train model-
kmeans_clust_model.fit(dist)
self.labels = kmeans_clust_model.labels_
self.X_transformed = kmeans_clust_model.fit_transform(X)
return self
def transform(self, X):
pass
# Initialize ST-K-Means object-
st_kmeans_algo = ST_KMeans(
k = 5, eps1=0.6,
eps2=9, metric='euclidean',
n_jobs=1
)
# Train on a chunk of dataset-
st_kmeans_algo.fit(data.loc[:500, ['time', 'x', 'y']])
# Get clustered data points labels-
kmeans_labels = st_kmeans_algo.labels
kmeans_labels.shape
# (501,)
# Get labels for points clustered using trained model-
kmeans_transformed = st_kmeans_algo.X_transformed
kmeans_transformed.shape
# (501, 5)
dtc = DecisionTreeClassifier()
dtc.fit(kmeans_transformed, kmeans_labels)
y_pred = dtc.predict(kmeans_transformed)
# Get model performance metrics-
accuracy = accuracy_score(kmeans_labels, y_pred)
precision = precision_score(kmeans_labels, y_pred, average='macro')
recall = recall_score(kmeans_labels, y_pred, average='macro')
print("\nDT model metrics are:")
print("accuracy = {0:.4f}, precision = {1:.4f} & recall = {2:.4f}\n".format(
accuracy, precision, recall
))
# DT model metrics are:
# accuracy = 1.0000, precision = 1.0000 & recall = 1.0000
但是，当我尝试使用sklearn的管道执行超参数调整时：
# Hyper-parameter Tuning:
# Define steps of pipeline-
pipeline_steps = [
('st_kmeans_algo' ,ST_KMeans(k = 5, eps1=0.6, eps2=9, metric='euclidean', n_jobs=1)),
('dtc', DecisionTreeClassifier())
]
# Instantiate a pipeline-
pipeline = Pipeline(pipeline_steps)
# Train pipeline-
pipeline.fit(kmeans_transformed, kmeans_labels)
它给我以下错误：
------------------------------------------------ --------------------------- TypeError跟踪（最近的呼叫最后）89＃火车管道----> 10 pipe.fit（kmeans_transformed，kmeans_labels）
〜/ .local / lib / python3.8 / site-packages / sklearn / pipeline.py in fit（self，X，y，** fit_params）348这个估算器349“”“-> 350 Xt，fit_params = self._fit（X，y，** fit_params）第351章;352 self._log_message（len（self.steps）-1））：
〜/ .local / lib / python3.8 / site-packages / sklearn / pipeline.py in _fit（self，X，y，** fit_params）309 cloned_transformer = clone（变压器）310＃从电流互感器中装入或加载-> 311 X，fit_transformer = fit_transform_one_cached（312 cloned_transformer，X，y，None，313 message_clsname ='管道'，
〜/ .local / lib / python3.8 / site-packages / joblib / memory.py在call（（自己，* args，** kwargs）353354 def call（自身，* args，** kwargs）：-> 355 return self.func（* args，** kwargs）356357 def call_and_shelve（self，* args，** kwargs）：
〜/ .local / lib / python3.8 / site-packages / sklearn / pipeline.py在_fit_transform_one（变压器，X，y，重量，message_clsname，message，** fit_params）_print_elapsed_time 726（消息clsname，消息）：第727章真相大白（四更）-> 728分辨率= Transformer.fit_transform（X，y，** fit_params）729其他：730 res = Translator.fit（X，y，** fit_params）.transform（X）
〜/ .local / lib / python3.8 / site-packages / sklearn / base.py在fit_transform（self，X，y，** fit_params）第572章573＃Arity 2的拟合方法（监督转换）-> 574返回self.fit（X，y，** fit_params）.transform（X）575576
TypeError：fit（）接受2个位置参数，但给出了3个]
ST_KMeans中的fit方法仅接受X作为输入，但在此行：
pipeline.fit(kmeans_transformed, kmeans_labels)
您将X和Y都作为输入传递给管道，管道会尝试使用这两个参数调用管道第一阶段的fit方法，即ST_KKeans，从而导致此错误。为了克服这个问题，只需向y对象的fit方法中添加一个虚拟参数ST_KMeans，如下所示：
def fit(self, X, Y):
附加参数Y不会在方法内部的任何地方使用，它只是保持一致性。
希望这会有所帮助！