I am trying to perform hyperparameter tuning for Spatio-Temporal K-Means clustering by using it in a pipeline with a Decision Tree classifier. The idea is to use K-Means clustering algorithm to generate cluster-distance space matrix and clustered labels which will be then passed to Decision Tree classifier. For hyperparameter tuning, just use parameters for K-Means algorithm.
I am using Python 3.8 and sklearn 0.22.
The data I am interested is having 3 columns/attributes: 'time', 'x' and 'y' (x and y are spatial coordinates).
The code is:
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, Y = None):
"""
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-
self.kmeans_clust_model = KMeans(
n_clusters = self.k, init = 'k-means++',
n_init = 10, max_iter = 300,
precompute_distances = 'auto', algorithm = 'auto')
# Train model-
self.kmeans_clust_model.fit(dist)
self.labels = self.kmeans_clust_model.labels_
self.X_transformed = self.kmeans_clust_model.fit_transform(X)
return self
def transform(self, X):
if not isinstance(X, np.ndarray):
# Convert to numpy array-
X = X.values
# 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 'euc_dist' matrix using 'time_dist' matrix-
dist = np.where(time_dist <= self.eps2, euc_dist, 2 * self.eps1)
# return self.kmeans_clust_model.transform(X)
return self.kmeans_clust_model.transform(dist)
# Initialize ST-K-Means object-
st_kmeans_algo = ST_KMeans(
k = 5, eps1=0.6,
eps2=9, metric='euclidean',
n_jobs=1
)
Y = np.zeros(shape = (501,))
# Train on a chunk of dataset-
st_kmeans_algo.fit(data.loc[:500, ['time', 'x', 'y']], 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 = st_kmeans_algo.transform(data.loc[:500, ['time', 'x', 'y']])
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
# 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)
kmeans_transformed.shape, kmeans_labels.shape
# ((501, 5), (501,))
# Train pipeline-
pipeline.fit(kmeans_transformed, kmeans_labels)
# Specify parameters to be hyper-parameter tuned-
params = [
{
'st_kmeans_algo__k': [3, 5, 7]
}
]
# Initialize GridSearchCV object-
grid_cv = GridSearchCV(estimator=pipeline, param_grid=params, cv = 2)
# Train GridSearch on computed data from above-
grid_cv.fit(kmeans_transformed, kmeans_labels)
The 'grid_cv.fit()' call gives the following error:
ValueError Traceback (most recent call last) <ipython-input-38-e62a8e6db82e> in <module>
5
6 # Train GridSearch on computed data from above-
----> 7 grid_cv.fit(kmeans_transformed, kmeans_labels)
~/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
708 return results
709
--> 710 self._run_search(evaluate_candidates)
711
712 # For multi-metric evaluation, store the best_index_, best_params_ and
~/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates) 1149 def
_run_search(self, evaluate_candidates): 1150 """Search all candidates in param_grid"""
-> 1151 evaluate_candidates(ParameterGrid(self.param_grid)) 1152 1153
~/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params)
680 n_splits, n_candidates, n_candidates * n_splits))
681
--> 682 out = parallel(delayed(_fit_and_score)(clone(base_estimator),
683 X, y,
684 train=train, test=test,
~/.local/lib/python3.8/site-packages/joblib/parallel.py in
__call__(self, iterable) 1002 # remaining jobs. 1003 self._iterating = False
-> 1004 if self.dispatch_one_batch(iterator): 1005 self._iterating = self._original_iterator is not None 1006
~/.local/lib/python3.8/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
833 return False
834 else:
--> 835 self._dispatch(tasks)
836 return True
837
~/.local/lib/python3.8/site-packages/joblib/parallel.py in
_dispatch(self, batch)
752 with self._lock:
753 job_idx = len(self._jobs)
--> 754 job = self._backend.apply_async(batch, callback=cb)
755 # A job can complete so quickly than its callback is
756 # called before we get here, causing self._jobs to
~/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
207 def apply_async(self, func, callback=None):
208 """Schedule a func to be run"""
--> 209 result = ImmediateResult(func)
210 if callback:
211 callback(result)
~/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py in
__init__(self, batch)
588 # Don't delay the application, to avoid keeping the input
589 # arguments in memory
--> 590 self.results = batch()
591
592 def get(self):
~/.local/lib/python3.8/site-packages/joblib/parallel.py in
__call__(self)
253 # change the default number of processes to -1
254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 255 return [func(*args, **kwargs)
256 for func, args, kwargs in self.items]
257
~/.local/lib/python3.8/site-packages/joblib/parallel.py in <listcomp>(.0)
253 # change the default number of processes to -1
254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 255 return [func(*args, **kwargs)
256 for func, args, kwargs in self.items]
257
~/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
542 else:
543 fit_time = time.time() - start_time
--> 544 test_scores = _score(estimator, X_test, y_test, scorer)
545 score_time = time.time() - start_time - fit_time
546 if return_train_score:
~/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py in _score(estimator, X_test, y_test, scorer)
589 scores = scorer(estimator, X_test)
590 else:
--> 591 scores = scorer(estimator, X_test, y_test)
592
593 error_msg = ("scoring must return a number, got %s (%s) "
~/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py in
__call__(self, estimator, *args, **kwargs)
87 *args, **kwargs)
88 else:
---> 89 score = scorer(estimator, *args, **kwargs)
90 scores[name] = score
91 return scores
~/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py in
_passthrough_scorer(estimator, *args, **kwargs)
369 def _passthrough_scorer(estimator, *args, **kwargs):
370 """Function that wraps estimator.score"""
--> 371 return estimator.score(*args, **kwargs)
372
373
~/.local/lib/python3.8/site-packages/sklearn/utils/metaestimators.py in <lambda>(*args, **kwargs)
114
115 # lambda, but not partial, allows help() to work with update_wrapper
--> 116 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
117 # update the docstring of the returned function
118 update_wrapper(out, self.fn)
~/.local/lib/python3.8/site-packages/sklearn/pipeline.py in score(self, X, y, sample_weight)
617 if sample_weight is not None:
618 score_params['sample_weight'] = sample_weight
--> 619 return self.steps[-1][-1].score(Xt, y, **score_params)
620
621 @property
~/.local/lib/python3.8/site-packages/sklearn/base.py in score(self, X, y, sample_weight)
367 """
368 from .metrics import accuracy_score
--> 369 return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
370
371
~/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py in accuracy_score(y_true, y_pred, normalize, sample_weight)
183
184 # Compute accuracy for each possible representation
--> 185 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
186 check_consistent_length(y_true, y_pred, sample_weight)
187 if y_type.startswith('multilabel'):
~/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
78 y_pred : array or indicator matrix
79 """
---> 80 check_consistent_length(y_true, y_pred)
81 type_true = type_of_target(y_true)
82 type_pred = type_of_target(y_pred)
~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
209 uniques = np.unique(lengths)
210 if len(uniques) > 1:
--> 211 raise ValueError("Found input variables with inconsistent numbers of"
212 " samples: %r" % [int(l) for l in lengths])
213
ValueError: Found input variables with inconsistent numbers of samples: [251, 250]
The different dimensions/shapes are:
kmeans_transformed.shape, kmeans_labels.shape, data.loc[:500, ['time', 'x', 'y']].shape
# ((501, 5), (501,), (501, 3))
I don't get it how the error arrives at the "samples: [251, 25]" ?
What's going wrong?
250 and 251 are respectively the shapes of your train and validation in GridSearchCV
look at your custom estimator...
def transform(self, X):
return self.X_transformed
the original transform method doesn't apply any sort of operation it simply returns the train data. we need an estimator that is able to transform the new data (in sour case the validation inside gridsearch) in a flexible way. change the transform method in this way
def transform(self, X):
return self.kmeans_clust_model.transform(X)