Cleaned up snippets from notebook version

Author: tirthajyoti

Synthetic_data_generation/Symbolic_regression_classification_generator.py

@@ -1,27 +1,22 @@
-
-# coding: utf-8
-
-# Evaluate a polynomial string
+from sympy import sympify, Symbol
+import numpy as np
 
 def symbolize(s):
     """
     Converts a a string (equation) to a SymPy symbol object
     """
-    from sympy import sympify
     s1=s.replace('.','*')
     s2=s1.replace('^','**')
     s3=sympify(s2)
 
     return(s3)
 
-
 def eval_multinomial(s,vals=None,symbolic_eval=False):
     """
     Evaluates polynomial at vals.
     vals can be simple list, dictionary, or tuple of values.
     vals can also contain symbols instead of real values provided those symbols have been declared before using SymPy
     """
-    from sympy import Symbol
     sym_s=symbolize(s)
     sym_set=sym_s.atoms(Symbol)
     sym_lst=[]
@@ -47,11 +42,10 @@ def eval_multinomial(s,vals=None,symbolic_eval=False):
 
     return result
 
-
-# ### Helper function for flipping binary values of a _ndarray_
-
 def flip(y,p):
-    import numpy as np
+    """
+    Flips random bit (used to make a classification problem haredr)
+    """
     lst=[]
     for i in range(len(y)):
         f=np.random.choice([1,0],p=[p,1-p])
@@ -60,8 +54,6 @@ def flip(y,p):
     return np.array(np.logical_xor(y,lst),dtype=int)
 
 
-# ### Classification sample generation based on a symbolic expression
-
 def gen_classification_symbolic(m=None,n_samples=100,n_features=2,flip_y=0.0):
     """
     Generates classification sample based on a symbolic expression.
@@ -77,9 +69,6 @@ def gen_classification_symbolic(m=None,n_samples=100,n_features=2,flip_y=0.0):
     Returns a numpy ndarray with dimension (n_samples,n_features+1). Last column is the response vector.
     """
 
-    import numpy as np
-    from sympy import Symbol,sympify
-    
     if m==None:
         m=''
         for i in range(1,n_features+1):
@@ -110,8 +99,6 @@ def gen_classification_symbolic(m=None,n_samples=100,n_features=2,flip_y=0.0):
 
     return (x)
 
-# ### Regression sample generation based on a symbolic expression
-
 
 def gen_regression_symbolic(m=None,n_samples=100,n_features=2,noise=0.0,noise_dist='normal'):
     """
@@ -129,9 +116,6 @@ def gen_regression_symbolic(m=None,n_samples=100,n_features=2,noise=0.0,noise_di
     Returns a numpy ndarray with dimension (n_samples,n_features+1). Last column is the response vector.
     """
 
-    import numpy as np
-    from sympy import Symbol,sympify
-    
     if m==None:
         m=''
         for i in range(1,n_features+1):
@@ -173,4 +157,4 @@ def gen_regression_symbolic(m=None,n_samples=100,n_features=2,noise=0.0,noise_di
 
     x=np.hstack((lst_features,evals))
 
-    return (x)
+    return (x)