Fitting Generalized Regression Neural Network with Python

December 9, 2015
By

(This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers)

In [1]: # LOAD PACKAGES

In [2]: import pandas as pd

In [3]: import numpy as np

In [4]: from sklearn import preprocessing as pp

In [5]: from sklearn import cross_validation as cv

In [6]: from neupy.algorithms import GRNN as grnn

In [7]: from neupy.functions import mse

In [8]: # DATA PROCESSING

In [9]: df = pd.read_table("csdata.txt")

In [10]: y = df.ix[:, 0]

In [11]: y.describe()
Out[11]:
count    4421.000000
mean        0.090832
std         0.193872
min         0.000000
25%         0.000000
50%         0.000000
75%         0.011689
max         0.998372
Name: LEV_LT3, dtype: float64

In [12]: x = df.ix[:, 1:df.shape[1]]

In [13]: st_x = pp.scale(x)

In [14]: st_x.mean(axis = 0)
Out[14]:
array([  1.88343648e-17,   5.76080438e-17,  -1.76540780e-16,
        -7.71455583e-17,  -3.80705294e-17,   3.79409490e-15,
         4.99487355e-17,  -2.97100804e-15,   3.93261537e-15,
        -8.70310886e-16,  -1.30728071e-15])

In [15]: st_x.std(axis = 0)
Out[15]: array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])

In [16]: x_train, x_test, y_train, y_test = cv.train_test_split(st_x, y, train_size = 0.7, random_state = 2015)

In [17]: # TRAIN THE NEURAL NETWORK

In [18]: def try_std(x):
   ....:       nn = grnn(std = x, verbose = False)
   ....:       nn.train(x_train, y_train)
   ....:       y_pred = nn.predict(x_test)
   ....:       print mse(y_pred, y_test)
   ....:

In [19]: # TEST A LIST OF VALUES FOR THE TUNING PARAMETER

In [20]: for x in np.linspace(0.5, 1.5, 11):
   ....:       print x
   ....:       try_std(x)
   ....:
0.5
0.034597892756
0.6
0.0331189699098
0.7
0.0323384657283
0.8
0.0319580849146
0.9
0.0318001764256
1.0
0.031751821704
1.1
0.031766356369
1.2
0.03183082142
1.3
0.0319348198865
1.4
0.0320623872248
1.5
0.03219800235

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