Set up library configuration with setup.py and minor changes to source

.gitignore

@@ -1,4 +1,7 @@
 *.pyc
 .project
 .pydevproject
-__pycache__/
+__pycache__/
+*.egg
+*.egg-info/
+build/

highdimensional_boundary/__init__.py

@@ -0,0 +1,2 @@
+from .decisionboundaryplot import DBPlot
+__all__ = ['DBPlot',]

decisionboundaryplot.py → highdimensional_boundary/decisionboundaryplot.py

@@ -9,7 +9,7 @@
 import nlopt
 import random
 from scipy.spatial.distance import euclidean, squareform, pdist
-from utils import minimum_spanning_tree, polar_to_cartesian
+from .utils import minimum_spanning_tree, polar_to_cartesian
 from sklearn.model_selection import GridSearchCV
 from sklearn.svm import SVC
 from sklearn.metrics import accuracy_score, f1_score
@@ -270,6 +270,7 @@ def fit(self, X, y, training_indices=None):
             print(
                 "Failed to find initial decision boundary. Retrying... If this keeps happening, increasing the acceptance threshold might help. Also, make sure the classifier is able to find a point with 0.5 prediction probability (usually requires an even number of estimators/neighbors/etc)."
             )
+            self.acceptance_threshold += 0.03
             return self.fit(X, y, training_indices)
 
         # step 3. look for decision boundary points between already known db
@@ -376,6 +377,7 @@ def plot(
         background_resolution=100,
         scatter_size_scale=1.0,
         legend=True,
+        annotate=None,
     ):
 
         """Plots the dataset and the identified decision boundary in 2D.
@@ -408,6 +410,8 @@ def plot(
         legend : boolean, optional (default=False)
             Whether to display a legend
 
+        annotate : float, optional (default=None)
+            If not None, it specifies the percentage of points to annotate as a float: range from 0 to 1.0 
         Returns
         -------
         plt : The matplotlib.pyplot or axis object which has been passed in, after
@@ -493,14 +497,10 @@ def plot(
         )
 
         # label data points with their indices
-        for i in range(len(self.X2d)):
-            plt.text(
-                self.X2d[i, 0] + (self.X2d_xmax - self.X2d_xmin) * 0.5e-2,
-                self.X2d[i, 1] + (self.X2d_ymax - self.X2d_ymin) * 0.5e-2,
-                str(i),
-                size=8,
-            )
-
+        if annotate:
+            step = int(len(self.X2d)*annotate) + 1
+            for i, txt in enumerate(range(self.X2d.shape[0])[:step:]):    
+                plt.text(self.X2d[i, 0], self.X2d[i, 1], txt, fontsize='xx-small')
         if legend:
             plt.legend(
                 [

requirements.txt

@@ -1,5 +1,5 @@
-matplotlib==3.0.2
-scipy==1.1.0
-numpy==1.15.4
+matplotlib
+scipy
+numpy
 nlopt==2.6.1
-scikit_learn==0.21.2
+scikit_learn

setup.py

@@ -0,0 +1,19 @@
+from setuptools import setup, find_packages
+from os.path import dirname, join, realpath
+from textwrap import dedent
+
+PROJECT_ROOT = dirname(realpath(__file__))
+REQUIREMENTS_FILE = join(PROJECT_ROOT, "requirements.txt")
+
+with open(REQUIREMENTS_FILE) as f:
+    install_reqs = f.read().splitlines()
+
+install_reqs.append("setuptools")
+
+setup(
+    name='highdimensional_boundary',
+    version='1.0.0',
+    author='Tamas Madl',
+    packages=find_packages(),
+    install_requires=install_reqs,
+)