more specific name for the base package (shapelets -> shapelets_lts)

README.md

@@ -13,7 +13,7 @@ pip install .
 ```
 ## Usage ##
 ```python
-from shapelets.classification import LtsShapeletClassifier
+from shapelets_lts.classification import LtsShapeletClassifier
 # create an LtsShapeletClassifier instance
 classifier = LtsShapeletClassifier(
     K=20, 

example.py

@@ -4,32 +4,46 @@
 
 from sklearn.metrics import confusion_matrix, classification_report
 
-from shapelets.classification import LtsShapeletClassifier
-from shapelets.util import ucr_dataset_loader
+from shapelets_lts.classification import LtsShapeletClassifier
+from shapelets_lts.util import ucr_dataset_loader
 
 """
-This example expects a dataset in the "UCR Time Series Classification
-Archive" format. UCR page (http://www.cs.ucr.edu/~eamonn/time_series_data/) 
+This example uses dataset from the UCR archive "UCR Time Series Classification
+Archive" format.  
 
-Follow the instructions at the top of the page UCR page to download the 
-UCR dataset.
+- Follow the instruction on the UCR page 
+(http://www.cs.ucr.edu/~eamonn/time_series_data/) to download the dataset. You 
+need to be patient! :) 
+- Update the vars below to point to the correct dataset location in your  
+machine.
 
-Update the vars below to point to the correct dataset location in your machine.
+Otherwise update _load_train_test_datasets() below to return your own dataset.
 """
 
-ucr_dataset_base_folder = expanduser('~/UCR_TS_Archive_2015')
+ucr_dataset_base_folder = expanduser('~/ws/data/UCR_TS_Archive_2015/')
 ucr_dataset_name = 'Gun_Point'
 
 
-def _evaluate_LtcShapeletClassifier(base_folder, dataset_name):
+def _load_train_test_datasets():
+    """
+    :return: numpy arrays, train_data, train_labels, test_data, test_labels
+        train_data and test_data shape is: (n_samples, n_features)
+        train_labels and test_labels shape is: (n_samples)
+    """
+    return ucr_dataset_loader.load_dataset(
+        dataset_name=ucr_dataset_name,
+        dataset_folder=ucr_dataset_base_folder
+    )
+
+
+def _evaluate_LtcShapeletClassifier():
     # load the data
-    train_data, train_label, test_data, test_label = (
-        ucr_dataset_loader.load_dataset(dataset_name, base_folder)
+    train_data, train_labels, test_data, test_labels = (
+        _load_train_test_datasets()
     )
 
     # create a classifier (the parameter values as per Table1 for the GunPoint
-    # dataset ). 200 epocs yielded 0.99
-    #  accuracy
+    # dataset ). 200 epochs yielded 0.99 accuracy
     Q = train_data.shape[1]
     K = int(0.15 * Q)
     L_min = int(0.2 * Q)
@@ -45,13 +59,13 @@ def _evaluate_LtcShapeletClassifier(base_folder, dataset_name):
     )
 
     # train the classifier
-    classifier.fit(train_data, train_label)
+    classifier.fit(train_data, train_labels)
 
     # evaluate on test data
     prediction = classifier.predict(test_data)
-    print(classification_report(test_label, prediction))
-    print(confusion_matrix(test_label, prediction))
+    print(classification_report(test_labels, prediction))
+    print(confusion_matrix(test_labels, prediction))
 
 
 if __name__ == '__main__':
-    _evaluate_LtcShapeletClassifier(ucr_dataset_base_folder, ucr_dataset_name)
+    _evaluate_LtcShapeletClassifier()

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='shapelets-lts',
-    version='0.2.2.dev3',
+    version='0.2.2.dev4',
     install_requires=[
         'numpy',
         'scipy',

shapelets/classification/shapelet_models.py → shapelets_lts/classification/shapelet_models.py

@@ -6,13 +6,13 @@
 import numpy as np
 from sklearn.base import BaseEstimator
 
-from shapelets.network import AggregationLayer
-from shapelets.network import CrossEntropyLossLayer
-from shapelets.network import LinearLayer
-from shapelets.network import Network
-from shapelets.network import SigmoidLayer
-from shapelets.network import SoftMinLayer
-from shapelets.util import utils
+from shapelets_lts.network import AggregationLayer
+from shapelets_lts.network import CrossEntropyLossLayer
+from shapelets_lts.network import LinearLayer
+from shapelets_lts.network import Network
+from shapelets_lts.network import SigmoidLayer
+from shapelets_lts.network import SoftMinLayer
+from shapelets_lts.util import utils
 
 """
 This class implements the sklearn estimator interface, so sklearn tools like GridsearchCV can be used

shapelets/network/sigmoid_layer.py → shapelets_lts/network/sigmoid_layer.py

@@ -1,4 +1,4 @@
-from shapelets.util import utils
+from shapelets_lts.util import utils
 
 
 class SigmoidLayer:

shapelets/tests/test_aggregation_layer.py → shapelets_lts/tests/test_aggregation_layer.py

@@ -1,7 +1,7 @@
 import numpy as np
 
-from shapelets.network import AggregationLayer
-from shapelets.util import utils, soft_min_layer_factory
+from shapelets_lts.network import AggregationLayer
+from shapelets_lts.util import utils, soft_min_layer_factory
 
 
 def test_forward():

shapelets/tests/test_cross_entropy_loss_layer.py → shapelets_lts/tests/test_cross_entropy_loss_layer.py

@@ -1,7 +1,7 @@
 import numpy as np
 
-from shapelets.network import CrossEntropyLossLayer
-from shapelets.util import utils
+from shapelets_lts.network import CrossEntropyLossLayer
+from shapelets_lts.util import utils
 
 
 def test_forward():

shapelets/tests/test_linear_layer.py → shapelets_lts/tests/test_linear_layer.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-from shapelets.network import LinearLayer
-from shapelets.util import utils
+from shapelets_lts.network import LinearLayer
+from shapelets_lts.util import utils
 
 
 def test_fc_layer_initialization():

shapelets/tests/test_sigmoid_layer.py → shapelets_lts/tests/test_sigmoid_layer.py

@@ -2,8 +2,8 @@
 
 import numpy as np
 
-from shapelets.network import SigmoidLayer
-from shapelets.util import utils
+from shapelets_lts.network import SigmoidLayer
+from shapelets_lts.util import utils
 
 
 def test_forward():

shapelets/tests/test_soft_min_layer.py → shapelets_lts/tests/test_soft_min_layer.py

@@ -1,7 +1,7 @@
 import numpy as np
 
-from shapelets.network import SoftMinLayer
-from shapelets.util import utils
+from shapelets_lts.network import SoftMinLayer
+from shapelets_lts.util import utils
 
 
 def test_forward():

shapelets/tests/test_utils.py → shapelets_lts/tests/test_utils.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from shapelets.util import utils
+from shapelets_lts.util import utils
 
 
 def test_get_centroids():

shapelets/util/soft_min_layer_factory.py → shapelets_lts/util/soft_min_layer_factory.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from shapelets.network.soft_min_layer import SoftMinLayer
+from shapelets_lts.network.soft_min_layer import SoftMinLayer
 
 
 def create_soft_min_layers(sizes):