import numpy as np
import sklearn
import sklearn.cross_validation
import sklearn.ensemble
import sklearn.svm
from epitopes import imma2, features
print "Loading data and transforming to toxin features"
imm, non = imma2.load_classes()
X, Y = features.toxin_features(imm, non, substring_length = 2, positional=False)
def run_classifiers(X,Y):
print "Data shape", X.shape
for c in [0.0001, 0.001, 0.01]:#, 0.1, 1, 10]:
svm = sklearn.svm.LinearSVC(C=c)
print "SVM C =", c
print np.mean(sklearn.cross_validation.cross_val_score(svm, X, Y, cv = 10))
n_classifiers = 2000
rf = sklearn.ensemble.RandomForestClassifier(n_classifiers)
print "Random Forest"
print np.mean(sklearn.cross_validation.cross_val_score(rf, X, Y, cv = 10))
"""
Toxin features alone seem to do terribly
"""
run_classifiers(X,Y)
"""
Try reducing the dimensionality
import numpy as np
import sklearn
import sklearn.cross_validation
import sklearn.ensemble
import sklearn.svm
from epitopes import features, imma2
print "Loading data and transforming to toxin features"
imm, non = imma2.load_classes()
X, Y = features.toxin_features(imm, non, substring_length = 3, positional=True)
def run_classifiers(X,Y):
print "Data shape", X.shape
for c in [0.0001, 0.001, 0.01, 0.1, 1]:
svm = sklearn.svm.LinearSVC(C=c)
print "SVM C =", c
print np.mean(sklearn.cross_validation.cross_val_score(svm, X, Y, cv = 10))
n_classifiers = 1000
rf = sklearn.ensemble.RandomForestClassifier(n_classifiers)
print "Random Forest"
print np.mean(sklearn.cross_validation.cross_val_score(rf, X, Y, cv = 10))
"""
Toxin features alone seem to do terribly
"""
run_classifiers(X,Y)