print np.mean(sklearn.cross_validation.cross_val_score(clf, X, Y, cv = 10))
fns = [amino_acid.hydropathy,
amino_acid.volume,
amino_acid.pK_side_chain,
amino_acid.polarity,
amino_acid.prct_exposed_residues,
amino_acid.hydrophilicity,
amino_acid.accessible_surface_area,
amino_acid.local_flexibility,
amino_acid.accessible_surface_area_folded,
amino_acid.refractivity
]
print "All features, all positions"
X2 = features.transform_rows(X, fns)
print np.mean(sklearn.cross_validation.cross_val_score(clf, X2, Y, cv = 10))
print "Pairwise ratios"
X3 = features.transform_rows(X, fns, pairwise_ratios = True)
print X3.shape
print np.mean(sklearn.cross_validation.cross_val_score(clf, X3, Y, cv = 10))
print "Mean per feature"
X3 = features.transform_rows(X, fns, mean = True)
print np.mean(sklearn.cross_validation.cross_val_score(clf, X3, Y, cv = 10))
print "Positions 4,6,8,9"
X4 = features.transform_rows(X, fns, positions = (4,6,8,9))
print np.mean(sklearn.cross_validation.cross_val_score(clf, X4, Y, cv = 10))
import sklearn.cross_validation
import sklearn.ensemble
n_classifiers = 50
clf = sklearn.ensemble.RandomForestClassifier(n_classifiers)
fns = [amino_acid.hydropathy,
amino_acid.volume,
amino_acid.pK_side_chain,
amino_acid.polarity,
amino_acid.prct_exposed_residues,
amino_acid.hydrophilicity,
amino_acid.accessible_surface_area,
amino_acid.local_flexibility,
amino_acid.accessible_surface_area_folded,
amino_acid.refractivity
]
print "Pairwise ratios"
X2 = features.transform_rows(X, fns, pairwise_ratios = True)
print X2.shape
print np.mean(sklearn.cross_validation.cross_val_score(clf, X2, Y, cv = 10))
print "Mean per feature"
X3 = features.transform_rows(X, fns, mean = True)
print np.mean(sklearn.cross_validation.cross_val_score(clf, X3, Y, cv = 10))
