In [19]:
import json
import numpy as np
import os, sys
sys.path.append('../classification')
import classify_nested
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import SelectKBest
from importlib import reload
In [20]:
reload(classify_nested)
X = np.random.randn(10*150).reshape(10,150)
y = np.array([np.random.random_integers(2)-1 for i in range(10)])
print(X)
print(y)
print(classify_nested.customScore(X,y))
print(f_classif(X,y))
[[-0.21691158 0.90048644 0.16600168 ..., -0.19508829 -0.22549076
2.31706733]
[-1.8505463 -1.21364341 0.88501355 ..., -1.44516826 -0.32793582
-1.09191327]
[ 0.25348016 -0.70214711 0.97341693 ..., 0.02497137 1.90734062
0.13467117]
...,
[-1.85888995 0.83504587 2.00605694 ..., -0.11576481 -0.14075465
0.10064678]
[ 0.29062942 0.185243 0.10799425 ..., -0.19272408 -0.65237792
-1.65459477]
[ 0.69825641 -0.21716935 -1.71415687 ..., 0.72428301 -1.25100438
0.63857934]]
[0 1 0 1 1 1 1 0 1 1]
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/usr/lib/python3.6/site-packages/ipykernel/__main__.py:3: DeprecationWarning: This function is deprecated. Please call randint(1, 2 + 1) instead
app.launch_new_instance()
In [11]:
selector = SelectKBest(k=2)
selector.fit(X,y)
selector.transform(X)
Out[11]:
array([[ 0.5744472 , 1.28897927],
[ 0.36782748, -0.35009266],
[-0.17340258, 0.33533762],
[ 1.6599005 , 2.36536355],
[ 0.27636816, 0.62948091],
[-0.86466352, -0.03451785],
[-0.5960366 , 0.32873435],
[-0.12540493, -1.18958388],
[-0.32582336, -0.92904409],
[ 1.42539249, 0.72388889]])
In [15]:
selector = SelectKBest(classify_nested.customScore, k=2)
selector.fit(X,y)
selector.transform(X)
Out[15]:
<function classify_nested.customScore>
In [ ]:
Content source: tayebzaidi/HonorsThesisTZ
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