Added TemplateTransformer

Author: vighneshbirodkar

examples/plot_classifier.py

@@ -3,7 +3,7 @@
 Plotting Template Classifier
 ============================
 
-An example plot of TemplateClassifier
+An example plot of :class:`skltemplate.template.TemplateClassifier`
 """
 import numpy as np
 from skltemplate import TemplateClassifier
@@ -22,7 +22,7 @@
 X_0 = X_test[y_pred == 0]
 X_1 = X_test[y_pred == 1]
 
-ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='darkorange', s=30)
+ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='crimson', s=30)
 ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=30)
 
 

examples/plot_template.py

@@ -3,7 +3,7 @@
 Plotting Template Estimator
 ===========================
 
-An example plot of TemplateEstimator
+An example plot of :class:`skltemplate.template.TemplateEstimator`
 """
 import numpy as np
 from skltemplate import TemplateEstimator
@@ -14,4 +14,4 @@
 estimator = TemplateEstimator()
 estimator.fit(X, y)
 plt.plot(estimator.predict(X))
-plt.show()
+plt.show()

examples/plot_transformer.py

@@ -0,0 +1,21 @@
+"""
+=============================
+Plotting Template Transformer
+=============================
+
+An example plot of :class:`skltemplate.template.TemplateTransformer`
+"""
+import numpy as np
+from skltemplate import TemplateTransformer
+from matplotlib import pyplot as plt
+
+X = np.arange(50).reshape(-1, 1)
+estimator = TemplateTransformer()
+X_transformed = estimator.fit_transform(X)
+
+plt.plot(X.flatten()/X.max(), label='Original Data')
+plt.plot(X_transformed.flatten()/X_transformed.max(), label='Transformed Data')
+plt.title('Scaled plots of original and transformed data')
+
+plt.legend(loc='best')
+plt.show()

skltemplate/__init__.py

@@ -1,4 +1,6 @@
-from .template import TemplateEstimator, TemplateClassifier
+from .template import (TemplateEstimator, TemplateClassifier,
+                       TemplateTransformer)
 from . import template
 
-__all__ = ['TemplateEstimator', 'TemplateClassifier', 'template']
+__all__ = ['TemplateEstimator', 'TemplateClassifier',
+           'TemplateTransformer', 'template']

skltemplate/template.py

@@ -2,7 +2,7 @@
 This is a module to be used as a reference for building other modules
 """
 import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
 from sklearn.utils.multiclass import unique_labels
 from sklearn.metrics import euclidean_distances
@@ -55,12 +55,19 @@ def predict(self, X):
 
 
 class TemplateClassifier(BaseEstimator, ClassifierMixin):
-    """ An example for a classifier which implements a 1-NN algorithm.
+    """ An example classifier which implements a 1-NN algorithm.
 
     Parameters
     ----------
     demo_param : str, optional
         A parameter used for demonstation of how to pass and store paramters.
+
+    Attributes
+    ----------
+    X_ : array, shape = [n_samples, n_features]
+        The input passed during :meth:`fit`
+    y_ : array, shape = [n_samples]
+        The labels passed during :meth:`fit`
     """
     def __init__(self, demo_param='demo'):
         self.demo_param = demo_param
@@ -70,7 +77,7 @@ def fit(self, X, y):
 
         Parameters
         ----------
-        X : array-like or sparse matrix of shape = [n_samples, n_features]
+        X : array-like, shape = [n_samples, n_features]
             The training input samples.
         y : array-like, shape = [n_samples]
             The target values. An array of int.
@@ -111,3 +118,68 @@ def predict(self, X):
 
         closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
         return self.y_[closest]
+
+
+class TemplateTransformer(BaseEstimator, TransformerMixin):
+    """ An example transformer that returns the element-wise square root..
+
+    Parameters
+    ----------
+    demo_param : str, optional
+        A parameter used for demonstation of how to pass and store paramters.
+
+    Attributes
+    ----------
+    input_shape : tuple
+        The shape the data passed to :meth:`fit`
+    """
+    def __init__(self, demo_param='demo'):
+        self.demo_param = demo_param
+
+    def fit(self, X, y=None):
+        """A reference implementation of a fitting function for a transformer.
+
+        Parameters
+        ----------
+        X : array-like or sparse matrix of shape = [n_samples, n_features]
+            The training input samples.
+        y : array-like, shape = [n_samples]
+            The target values. An array of int.
+        Returns
+        -------
+        self : object
+            Returns self.
+        """
+        X = check_array(X)
+
+        self.input_shape_ = X.shape
+
+        # Return the classifier
+        return self
+
+    def transform(self, X):
+        """ A reference implementation of a transform function.
+
+        Parameters
+        ----------
+        X : array-like of shape = [n_samples, n_features]
+            The input samples.
+
+        Returns
+        -------
+        X_transformed : array of int of shape = [n_samples, n_features]
+            The array containing the element-wise square roots of the values
+            in `X`
+        """
+        # Check is fit had been called
+        check_is_fitted(self, ['input_shape_'])
+
+        # Input validation
+        X = check_array(X)
+
+        # Check that the input is of the same shape as the one passed
+        # during fit.
+        if X.shape != self.input_shape_:
+            raise ValueError('Shape of input is different from what was seen'
+                             'in `fit`')
+        return np.sqrt(X)

skltemplate/tests/test_common.py

@@ -1,5 +1,6 @@
 from sklearn.utils.estimator_checks import check_estimator
-from skltemplate import TemplateEstimator, TemplateClassifier
+from skltemplate import (TemplateEstimator, TemplateClassifier,
+                         TemplateTransformer)
 
 
 def test_estimator():
@@ -8,3 +9,7 @@ def test_estimator():
 
 def test_classifier():
     return check_estimator(TemplateClassifier)
+
+
+def test_transformer():
+    return check_estimator(TemplateTransformer)