Added TemplateClassifer

Author: vighneshbirodkar

examples/plot_classifier.py

@@ -0,0 +1,30 @@
+"""
+============================
+Plotting Template Classifier
+============================
+
+An example plot of TemplateClassifier
+"""
+import numpy as np
+from skltemplate import TemplateClassifier
+from matplotlib import pyplot as plt
+
+
+X = [[0, 0], [1, 1]]
+y = [0, 1]
+clf = TemplateClassifier()
+clf.fit(X, y)
+
+rng = np.random.RandomState(13)
+X_test = rng.rand(500, 2)
+y_pred = clf.predict(X_test)
+
+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)
+ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=30)
+
+
+plt.legend([ax0, ax1], ['Class 0', 'Class 1'])
+plt.show()

skltemplate/__init__.py

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

skltemplate/template.py

@@ -1,12 +1,16 @@
 """
 This is a module to be used as a reference for building other modules
 """
-from sklearn.base import BaseEstimator
-from sklearn.utils import check_X_y, check_array
+import numpy as np
+from sklearn.base import BaseEstimator, ClassifierMixin
+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
+
 
 class TemplateEstimator(BaseEstimator):
     """ A template estimator to be used as a reference implementation .
-    
+
     Parameters
     ----------
     demo_param : str, optional
@@ -21,7 +25,7 @@ def fit(self, X, y):
         Parameters
         ----------
         X : array-like or sparse matrix of shape = [n_samples, n_features]
-            The training input samples. 
+            The training input samples.
         y : array-like, shape = [n_samples] or [n_samples, n_outputs]
             The target values (class labels in classification, real numbers in
             regression).
@@ -47,4 +51,63 @@ def predict(self, X):
             Returns :math:`x^2` where :math:`x` is the first column of `X`.
         """
         X = check_array(X)
-        return X[:, 0]**2
+        return X[:, 0]**2
+
+
+class TemplateClassifier(BaseEstimator, ClassifierMixin):
+    """ An example for a classifier which implements a 1-NN algorithm.
+
+    Parameters
+    ----------
+    demo_param : str, optional
+        A parameter used for demonstation of how to pass and store paramters.
+    """
+    def __init__(self, demo_param='demo'):
+        self.demo_param = demo_param
+
+    def fit(self, X, y):
+        """A reference implementation of a fitting function for a classifier.
+
+        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.
+        """
+        # Check that X and y have correct shape
+        X, y = check_X_y(X, y)
+        # Store the classes seen durind fit
+        self.classes_ = unique_labels(y)
+
+        self.X_ = X
+        self.y_ = y
+        # Return the classifier
+        return self
+
+    def predict(self, X):
+        """ A reference implementation of a prediction for a classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape = [n_samples, n_features]
+            The input samples.
+
+        Returns
+        -------
+        y : array of int of shape = [n_samples]
+            The label for each sample is the label of the closest sample
+            seen udring fit.
+        """
+        # Check is fit had been called
+        check_is_fitted(self, ['X_', 'y_'])
+
+        # Input validation
+        X = check_array(X)
+
+        closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
+        return self.y_[closest]

skltemplate/tests/test_common.py

@@ -1,5 +1,10 @@
 from sklearn.utils.estimator_checks import check_estimator
-from skltemplate import TemplateEstimator
+from skltemplate import TemplateEstimator, TemplateClassifier
 
-def test_common():
+
+def test_estimator():
     return check_estimator(TemplateEstimator)
+
+
+def test_classifier():
+    return check_estimator(TemplateClassifier)