如何在 python 中使用 kNN 动态时间扭曲

我有一个带有两个标签的时间序列数据集（0 and 1）。我在用动态时间扭曲 (DTW)作为使用 k 最近邻 (kNN) 进行分类的相似性度量，如这两篇精彩的博客文章中所述：

• https://nbviewer.jupyter.org/github/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping/blob/master/K_Nearest_Neighbor_Dynamic_Time_Warping.ipynb https://nbviewer.jupyter.org/github/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping/blob/master/K_Nearest_Neighbor_Dynamic_Time_Warping.ipynb

• http://alexminnaar.com/2014/04/16/Time-Series-Classification-and-Clustering-with-Python.html http://alexminnaar.com/2014/04/16/Time-Series-Classification-and-Clustering-with-Python.html

  Arguments
  ---------
  n_neighbors : int, optional (default = 5)
      Number of neighbors to use by default for KNN
  
  max_warping_window : int, optional (default = infinity)
      Maximum warping window allowed by the DTW dynamic
      programming function
  
  subsample_step : int, optional (default = 1)
      Step size for the timeseries array. By setting subsample_step = 2,
      the timeseries length will be reduced by 50% because every second
      item is skipped. Implemented by x[:, ::subsample_step]
  """
  
  def __init__(self, n_neighbors=5, max_warping_window=10000, subsample_step=1):
      self.n_neighbors = n_neighbors
      self.max_warping_window = max_warping_window
      self.subsample_step = subsample_step
  
  def fit(self, x, l):
      """Fit the model using x as training data and l as class labels
  
      Arguments
      ---------
      x : array of shape [n_samples, n_timepoints]
          Training data set for input into KNN classifer
  
      l : array of shape [n_samples]
          Training labels for input into KNN classifier
      """
  
      self.x = x
      self.l = l
  
  def _dtw_distance(self, ts_a, ts_b, d = lambda x,y: abs(x-y)):
      """Returns the DTW similarity distance between two 2-D
      timeseries numpy arrays.
  
      Arguments
      ---------
      ts_a, ts_b : array of shape [n_samples, n_timepoints]
          Two arrays containing n_samples of timeseries data
          whose DTW distance between each sample of A and B
          will be compared
  
      d : DistanceMetric object (default = abs(x-y))
          the distance measure used for A_i - B_j in the
          DTW dynamic programming function
  
      Returns
      -------
      DTW distance between A and B
      """
  
      # Create cost matrix via broadcasting with large int
      ts_a, ts_b = np.array(ts_a), np.array(ts_b)
      M, N = len(ts_a), len(ts_b)
      cost = sys.maxint * np.ones((M, N))
  
      # Initialize the first row and column
      cost[0, 0] = d(ts_a[0], ts_b[0])
      for i in xrange(1, M):
          cost[i, 0] = cost[i-1, 0] + d(ts_a[i], ts_b[0])
  
      for j in xrange(1, N):
          cost[0, j] = cost[0, j-1] + d(ts_a[0], ts_b[j])
  
      # Populate rest of cost matrix within window
      for i in xrange(1, M):
          for j in xrange(max(1, i - self.max_warping_window),
                          min(N, i + self.max_warping_window)):
              choices = cost[i - 1, j - 1], cost[i, j-1], cost[i-1, j]
              cost[i, j] = min(choices) + d(ts_a[i], ts_b[j])
  
      # Return DTW distance given window 
      return cost[-1, -1]
  
  def _dist_matrix(self, x, y):
      """Computes the M x N distance matrix between the training
      dataset and testing dataset (y) using the DTW distance measure
  
      Arguments
      ---------
      x : array of shape [n_samples, n_timepoints]
  
      y : array of shape [n_samples, n_timepoints]
  
      Returns
      -------
      Distance matrix between each item of x and y with
          shape [training_n_samples, testing_n_samples]
      """
  
      # Compute the distance matrix        
      dm_count = 0
  
      # Compute condensed distance matrix (upper triangle) of pairwise dtw distances
      # when x and y are the same array
      if(np.array_equal(x, y)):
          x_s = np.shape(x)
          dm = np.zeros((x_s[0] * (x_s[0] - 1)) // 2, dtype=np.double)
  
          p = ProgressBar(shape(dm)[0])
  
          for i in xrange(0, x_s[0] - 1):
              for j in xrange(i + 1, x_s[0]):
                  dm[dm_count] = self._dtw_distance(x[i, ::self.subsample_step],
                                                    y[j, ::self.subsample_step])
  
                  dm_count += 1
                  p.animate(dm_count)
  
          # Convert to squareform
          dm = squareform(dm)
          return dm
  
      # Compute full distance matrix of dtw distnces between x and y
      else:
          x_s = np.shape(x)
          y_s = np.shape(y)
          dm = np.zeros((x_s[0], y_s[0])) 
          dm_size = x_s[0]*y_s[0]
  
          p = ProgressBar(dm_size)
  
          for i in xrange(0, x_s[0]):
              for j in xrange(0, y_s[0]):
                  dm[i, j] = self._dtw_distance(x[i, ::self.subsample_step],
                                                y[j, ::self.subsample_step])
                  # Update progress bar
                  dm_count += 1
                  p.animate(dm_count)
  
          return dm
  
  def predict(self, x):
      """Predict the class labels or probability estimates for 
      the provided data
  
      Arguments
      ---------
        x : array of shape [n_samples, n_timepoints]
            Array containing the testing data set to be classified
  
      Returns
      -------
        2 arrays representing:
            (1) the predicted class labels 
            (2) the knn label count probability
      """
  
      dm = self._dist_matrix(x, self.x)
  
      # Identify the k nearest neighbors
      knn_idx = dm.argsort()[:, :self.n_neighbors]
  
      # Identify k nearest labels
      knn_labels = self.l[knn_idx]
  
      # Model Label
      mode_data = mode(knn_labels, axis=1)
      mode_label = mode_data[0]
      mode_proba = mode_data[1]/self.n_neighbors
  
      return mode_label.ravel(), mode_proba.ravel()
  

然而，对于 kNN 分类，这两篇文章使用他们自己的 kNN 算法。

我想使用 sklearn 的选项，例如gridsearchcv在我的分类中。因此，我想知道如何将动态时间规整 (DTW) 与 sklearn kNN 结合使用。

注：我不限于sklearn也很高兴在其他图书馆收到答案

如果需要，我很乐意提供更多详细信息。

您可以对 KNN 使用自定义指标。 因此，您只需要自己实现 DTW（或使用/改编 python 中任何现有的 DTW 实现）[此代码的要点] https://gist.github.com/nikolasrieble/8bd3a83e14c0b2fa66bfa2ddd8828717.

import numpy as np
from scipy.spatial import distance
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report

#toy dataset 
X = np.random.random((100,10))
y = np.random.randint(0,2, (100))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

#custom metric
def DTW(a, b):   
    an = a.size
    bn = b.size
    pointwise_distance = distance.cdist(a.reshape(-1,1),b.reshape(-1,1))
    cumdist = np.matrix(np.ones((an+1,bn+1)) * np.inf)
    cumdist[0,0] = 0

    for ai in range(an):
        for bi in range(bn):
            minimum_cost = np.min([cumdist[ai, bi+1],
                                   cumdist[ai+1, bi],
                                   cumdist[ai, bi]])
            cumdist[ai+1, bi+1] = pointwise_distance[ai,bi] + minimum_cost

    return cumdist[an, bn]

#train
parameters = {'n_neighbors':[2, 4, 8]}
clf = GridSearchCV(KNeighborsClassifier(metric=DTW), parameters, cv=3, verbose=1)
clf.fit(X_train, y_train)



#evaluate
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))

哪个产量

Fitting 3 folds for each of 3 candidates, totalling 9 fits        

[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   29.0s finished

                         precision    recall  f1-score   support

                      0       0.57      0.89      0.70        18
                      1       0.60      0.20      0.30        15

            avg / total       0.58      0.58      0.52        33