learn

pyrimidine/learn/linear_regression.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+
+
+from pyrimidine import *
+from digit_converter import IntervalConverter
+
+c = IntervalConverter(lb=-60, ub=60)
+class _BinaryChromosome(BinaryChromosome):
+    def decode(self):
+        return c(self)
+
+
+import numpy as np
+import numpy.linalg as LA
+from sklearn.linear_model import LinearRegression
+from ..learn import BaseEstimator
+
+
+class GALinearRegression(BaseEstimator, LinearRegression):
+    '''Linear Regression by GA
+    '''
+
+    @classmethod
+    def create_model(cls, *args, **kwargs):
+        return LinearRegression(*args, **kwargs)
+
+    def _fit(self, X, Y):
+        self.pop.ezolve(n_iter=self.max_iter)
+        model_ = self.pop.solution
+        self.coef_ = model_.coef_
+        self.intercept_ = model_.intercept_
+
+    @classmethod
+    def config(cls, X, Y, n_individuals=10, *args, **kwargs):
+
+        input_dim = X.shape[1]
+        # output_dim = Y.shape[1]
+
+        class MyIndividual(MixedIndividual):
+            params={'sigma':0.02}
+            element_class = FloatChromosome, _BinaryChromosome
+
+            def decode(self):
+                model = cls.create_model(*args, **kwargs)
+                model.coef_ = np.asarray(self[0])
+                model.intercept_ = self[1].decode()
+                return model
+
+            def _fitness(self):
+                model = self.decode()
+                return model.score(X, Y)
+
+        class MyPopulation(HOFPopulation):
+            element_class = MyIndividual
+
+        pop = MyPopulation.random(n_individuals=n_individuals, size=(input_dim, 8))
+
+        return pop

pytest.ini

@@ -1,2 +1,2 @@
 [pytest]
-python_files = tests/test_*.py
+python_files = tests/test_*.py

tests/test_learn.py

@@ -19,6 +19,7 @@ def test_ann():
     s1 = model.score(X, Y)
     assert s0 <= s1
 
+
 def test_lr():
     X = np.array([[0,0], [0,1], [1,0], [1,1]])
     Y = np.array([2,1,0,0])