Merge a54360e into 543754d

ffx/core/__init__.py

@@ -12,6 +12,7 @@
 from sklearn.linear_model import ElasticNet
 
 from .approach import Approach
+from .bases import OperatorBase, ProductBase, SimpleBase
 from .constants import (
     CONSIDER_DENOM,
     CONSIDER_EXPON,
@@ -210,166 +211,15 @@ def complexity(self):
         # We have a leading constant, then for each base we have a
         # coefficient, a multiply, and a plus, plus the complexity of
         # the base itself.
-        num_complexity = 1 + sum(3 + b.complexity() for b in self.bases_n)
+        num_complexity = 1 + sum(3 + b.complexity for b in self.bases_n)
         if self.bases_d:
-            denom_complexity = 1 + sum(3 + b.complexity() for b in self.bases_d)
+            denom_complexity = 1 + sum(3 + b.complexity for b in self.bases_d)
             # add 1 for the division
             return num_complexity + 1 + denom_complexity
         else:
             return num_complexity
 
 
-class SimpleBase:
-    """e.g. x4^2"""
-
-    def __init__(self, var, exponent):
-        self.var = var
-        self.exponent = exponent
-
-    def simulate(self, X):
-        """
-        @arguments
-          X -- 2d array of [sample_i][var_i] : float
-        @return
-          y -- 1d array of [sample_i] : float
-        """
-        return X[:, self.var] ** self.exponent
-
-    def __str__(self):
-        if self.exponent == 1:
-            return 'x%d' % self.var
-        else:
-            return 'x%d^%g' % (self.var, self.exponent)
-
-    def complexity(self):
-        if self.exponent == 1:
-            return 1
-        else:
-            return 3
-
-
-class OperatorBase:
-    """e.g. log(x4^2)"""
-
-    def __init__(self, simple_base, nonlin_op, thr=INF):
-        """
-        @arguments
-          simple_base -- SimpleBase
-          nonlin_op -- one of OPS
-          thr -- None or float -- depends on nonlin_op
-        """
-        self.simple_base = simple_base
-        self.nonlin_op = nonlin_op
-        self.thr = thr
-
-    def simulate(self, X):
-        """
-        @arguments
-          X -- 2d array of [sample_i][var_i] : float
-        @return
-          y -- 1d array of [sample_i] : float
-        """
-        op = self.nonlin_op
-        ok = True
-        y_lin = self.simple_base.simulate(X)
-
-        if op == OP_ABS:
-            ya = numpy.abs(y_lin)
-        elif op == OP_MAX0:
-            ya = numpy.clip(y_lin, 0.0, INF)
-        elif op == OP_MIN0:
-            ya = numpy.clip(y_lin, -INF, 0.0)
-        elif op == OP_LOG10:
-            # safeguard against: log() on values <= 0.0
-            mn, mx = min(y_lin), max(y_lin)
-            if mn <= 0.0 or numpy.isnan(mn) or mx == INF or numpy.isnan(mx):
-                ok = False
-            else:
-                ya = numpy.log10(y_lin)
-        elif op == OP_GTH:
-            ya = numpy.clip(self.thr - y_lin, 0.0, INF)
-        elif op == OP_LTH:
-            ya = numpy.clip(y_lin - self.thr, 0.0, INF)
-        else:
-            raise 'Unknown op %d' % op
-
-        if ok:  # could always do ** exp, but faster ways if exp is 0,1
-            y = ya
-        else:
-            y = INF * numpy.ones(X.shape[0], dtype=float)
-        return y
-
-    def __str__(self):
-        op = self.nonlin_op
-        simple_s = str(self.simple_base)
-        if op == OP_ABS:
-            return 'abs(%s)' % simple_s
-        elif op == OP_MAX0:
-            return 'max(0, %s)' % simple_s
-        elif op == OP_MIN0:
-            return 'min(0, %s)' % simple_s
-        elif op == OP_LOG10:
-            return 'log10(%s)' % simple_s
-        elif op == OP_GTH:
-            return 'max(0,%s-%s)' % (coefStr(self.thr), simple_s)
-        elif op == OP_LTH:
-            return ('max(0,%s-%s)' % (simple_s, coefStr(self.thr))).replace('--', '+')
-        else:
-            raise 'Unknown op %d' % op
-
-    def complexity(self):
-        """Return an integer measure of model complexity. It's intended to
-        measure the number of nodes in the GP tree corresponding to
-        the model. We assume the GP language includes: +, -, *, /,
-        MAX0, MIN0, LOG10 but not GTH, LTH.  Thus, MAX0(x) returns the
-        value max(0, x) but contributes only 1 + complexity(x) to the
-        complexity count. GTH(thr, x) returns the value max(0, thr-x)
-        but because it would be implemented in GP as MAX0(thr-x) it contributes
-        3 + complexity(x) to the count."""
-
-        op = self.nonlin_op
-        if op == OP_ABS:
-            return 1 + self.simple_base.complexity()
-        elif op == OP_MAX0:
-            return 1 + self.simple_base.complexity()
-        elif op == OP_MIN0:
-            return 1 + self.simple_base.complexity()
-        elif op == OP_LOG10:
-            return 1 + self.simple_base.complexity()
-        elif op == OP_GTH:
-            return 3 + self.simple_base.complexity()
-        elif op == OP_LTH:
-            return 3 + self.simple_base.complexity()
-        else:
-            raise 'Unknown op %d' % op
-
-
-class ProductBase:
-
-    """e.g. x2^2 * log(x1^3)"""
-
-    def __init__(self, base1, base2):
-        self.base1 = base1
-        self.base2 = base2
-
-    def simulate(self, X):
-        """
-        @arguments
-          X -- 2d array of [sample_i][var_i] : float
-        @return
-          y -- 1d array of [sample_i] : float
-        """
-        yhat1 = self.base1.simulate(X)
-        yhat2 = self.base2.simulate(X)
-        return yhat1 * yhat2
-
-    def __str__(self):
-        return '%s * %s' % (self.base1, self.base2)
-
-    def complexity(self):
-        return 1 + self.base1.complexity() + self.base2.complexity()
-
-
 class ConstantModel(RegressorMixin):
     """e.g. 3.2"""
 

ffx/core/bases.py

@@ -0,0 +1,158 @@
+import abc
+
+import numpy as np
+import scipy
+import six
+
+from .constants import INF, OP_ABS, OP_GTH, OP_LOG10, OP_LTH, OP_MAX0, OP_MIN0
+from .utils import coef_str
+
+
+class Base(six.with_metaclass(abc.ABCMeta)):
+    @abc.abstractmethod
+    def simulate(self, X):
+        pass
+
+    @abc.abstractproperty
+    def complexity(self):
+        '''Return an integer measure of model complexity. It's intended to
+        measure the number of nodes in the GP tree corresponding to
+        the model. We assume the GP language includes: +, -, *, /,
+        MAX0, MIN0, LOG10 but not GTH, LTH.  Thus, MAX0(x) returns the
+        value max(0, x) but contributes only 1 + complexity(x) to the
+        complexity count. GTH(thr, x) returns the value max(0, thr-x)
+        but because it would be implemented in GP as MAX0(thr-x) it contributes
+        3 + complexity(x) to the count.'''
+
+
+class SimpleBase(Base):
+    '''e.g. x4^2'''
+
+    def __init__(self, var, exponent):
+        self.var = var
+        self.exponent = exponent
+
+    def simulate(self, X):
+        '''
+        @arguments
+          X -- 2d array of [sample_i][var_i] : float
+        @return
+          y -- 1d array of [sample_i] : float
+        '''
+        return X[:, self.var] ** self.exponent
+
+    def __str__(self):
+        if self.exponent == 1:
+            return 'x%d' % self.var
+        else:
+            return 'x%d^%g' % (self.var, self.exponent)
+
+    @property
+    def complexity(self):
+        return 1 if self.exponent == 1 else 3
+
+
+class OperatorBase(Base):
+    '''e.g. log(x4^2)'''
+
+    def __init__(self, simple_base, nonlin_op, thr=INF):
+        '''
+        @arguments
+          simple_base -- SimpleBase
+          nonlin_op -- one of OPS
+          thr -- None or float -- depends on nonlin_op
+        '''
+        self.simple_base = simple_base
+        self.nonlin_op = nonlin_op
+        self.thr = thr
+
+    def simulate(self, X):
+        '''
+        @arguments
+          X -- 2d array of [sample_i][var_i] : float
+        @return
+          y -- 1d array of [sample_i] : float
+        '''
+        op = self.nonlin_op
+        ok = True
+        y_lin = self.simple_base.simulate(X)
+
+        if op == OP_ABS:
+            ya = np.abs(y_lin)
+        elif op == OP_MAX0:
+            ya = np.clip(y_lin, 0.0, INF)
+        elif op == OP_MIN0:
+            ya = np.clip(y_lin, -INF, 0.0)
+        elif op == OP_LOG10:
+            # safeguard against: log() on values <= 0.0
+            mn, mx = min(y_lin), max(y_lin)
+            if mn <= 0.0 or scipy.isnan(mn) or mx == INF or scipy.isnan(mx):
+                ok = False
+            else:
+                ya = np.log10(y_lin)
+        elif op == OP_GTH:
+            ya = np.clip(self.thr - y_lin, 0.0, INF)
+        elif op == OP_LTH:
+            ya = np.clip(y_lin - self.thr, 0.0, INF)
+        else:
+            raise 'Unknown op %d' % op
+
+        if ok:  # could always do ** exp, but faster ways if exp is 0,1
+            y = ya
+        else:
+            y = INF * np.ones(X.shape[0], dtype=float)
+        return y
+
+    def __str__(self):
+        op = self.nonlin_op
+        simple_s = str(self.simple_base)
+        if op == OP_ABS:
+            return 'abs(%s)' % simple_s
+        elif op == OP_MAX0:
+            return 'max(0, %s)' % simple_s
+        elif op == OP_MIN0:
+            return 'min(0, %s)' % simple_s
+        elif op == OP_LOG10:
+            return 'log10(%s)' % simple_s
+        elif op == OP_GTH:
+            return 'max(0,%s-%s)' % (coef_str(self.thr), simple_s)
+        elif op == OP_LTH:
+            return ('max(0,%s-%s)' % (simple_s, coef_str(self.thr))).replace('--', '+')
+        else:
+            raise 'Unknown op %d' % op
+
+    @property
+    def complexity(self):
+        op = self.nonlin_op
+        if op in [OP_ABS, OP_MAX0, OP_MIN0, OP_LOG10]:
+            return 1 + self.simple_base.complexity
+        elif op in [OP_GTH, OP_LTH]:
+            return 3 + self.simple_base.complexity
+        else:
+            raise 'Unknown op %d' % op
+
+
+class ProductBase(Base):
+    '''e.g. x2^2 * log(x1^3)'''
+
+    def __init__(self, base1, base2):
+        self.base1 = base1
+        self.base2 = base2
+
+    def simulate(self, X):
+        '''
+        @arguments
+          X -- 2d array of [sample_i][var_i] : float
+        @return
+          y -- 1d array of [sample_i] : float
+        '''
+        yhat1 = self.base1.simulate(X)
+        yhat2 = self.base2.simulate(X)
+        return yhat1 * yhat2
+
+    def __str__(self):
+        return '%s * %s' % (self.base1, self.base2)
+
+    @property
+    def complexity(self):
+        return 1 + self.base1.complexity + self.base2.complexity

ffx/core/utils.py

@@ -0,0 +1,27 @@
+import numpy as np
+
+
+def coef_str(x):
+    '''Gracefully print a number to 3 significant digits.  See _testcoef_str in
+    unit tests'''
+    if x == 0.0:
+        s = '0'
+    elif np.abs(x) < 1e-4:
+        s = ('%.2e' % x).replace('e-0', 'e-')
+    elif np.abs(x) < 1e-3:
+        s = '%.6f' % x
+    elif np.abs(x) < 1e-2:
+        s = '%.5f' % x
+    elif np.abs(x) < 1e-1:
+        s = '%.4f' % x
+    elif np.abs(x) < 1e0:
+        s = '%.3f' % x
+    elif np.abs(x) < 1e1:
+        s = '%.2f' % x
+    elif np.abs(x) < 1e2:
+        s = '%.1f' % x
+    elif np.abs(x) < 1e4:
+        s = '%.0f' % x
+    else:
+        s = ('%.2e' % x).replace('e+0', 'e')
+    return s