def SVC_fill(feature, df, metric='auc', test_size=.3):
'''
###Support Vector Inference
* Using [SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)
function from [Scikit Learn Package](http://scikit-learn.org/stable/)
* Features are the binarized differential expression vectors
* Have high change in expression from tumor to normal
* Thresholded at 1 standard deviation over the mean to reduce overfitting
* Parameters are fit using cross validation, optimizing for AUC score
* I try linear, RBF, and polynomial kernels under a variety of parameters
* The best model in cross validation is fit on the entire dataset
* Missing values are filled in based on the model prediction
'''
gg = df.apply(lambda s: to_quants(s, std=1) > 0, axis=1)
mat = gg
pats = gg.columns.intersection(feature.index)
X = mat.ix[:, pats].T.as_matrix()
Y = (feature)
Y = np.array(Y.ix[pats])
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size=test_size, random_state=5796503)
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [0, .1, .05, .01, 1e-3, 1e-4, 1e-5],
'C': [.1, 1, 10, 100, 1000], 'class_weight': ['auto']},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000], 'class_weight': ['auto']},
{'kernel': ['poly'], 'C': [1, 10, 100, 1000], 'class_weight': ['auto']}]
if metric == 'auc':
metric = auc_score
elif metric == 'precision':
metric = precision_score
clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=metric)
clf.fit(X_train, y_train, cv=5);
best = clf.best_estimator_
auc = clf.score(X, Y)
mat_all = gg.ix[mat.index].T.as_matrix()
inferred = best.predict(mat_all)
inferred = pd.Series(inferred, index=gg.columns)
fun = pd.Series(best.decision_function(mat_all)[:, 0], mat.columns)
f = feature.copy()
f = f.ix[inferred.index]
f[f.isnull()] = inferred[f.isnull()]
filled_feature = f.astype(float)
return {'auc': auc, 'model': best, 'decision_function': fun,
'inferred_values': inferred, 'filled_feature': filled_feature}
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data), lty=1, col=ls, lwd=4, cex=1.25,
xlab='Years to Event', ylab='Survival');
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data),
lty=1,
col=ls,
lwd=4,
cex=1.25,
xlab='Years to Event',
ylab='Survival')
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
def SVC_fill_old(feature, df):
gg = df.apply(lambda s: to_quants(s, std=1) > 0)
diff = screen_feature(feature, chi2_cont_test, gg)
dd = diff[diff.p < .05]
pats = gg.columns.intersection(feature.index)
mat = gg.ix[dd.index]
X = mat.ix[:, pats].T.as_matrix()
Y = (feature)
Y = np.array(Y.ix[pats])
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size=0.35, random_state=5796543)
params = [{'kernel': ['rbf'], 'gamma': [0, .1, .05, .01, 1e-3, 1e-4, 1e-5],
'C': [.1, 1, 10, 100, 1000], 'class_weight': ['auto']},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000],
'class_weight': ['auto']},
{'kernel': ['poly'], 'C': [1, 10, 100, 1000],
'class_weight': ['auto']}]
clf = GridSearchCV(SVC(C=1), params, score_func=auc_score)
clf.fit(X_train, y_train, cv=5);
best = clf.best_estimator_
auc = clf.score(X, Y)
mat_all = gg.ix[mat.index].T.as_matrix()
inferred = best.predict(mat_all)
inferred = pd.Series(inferred, index=gg.columns)
fun = pd.Series(best.decision_function(mat_all)[:, 0], mat.columns)
f = feature.copy()
f = f.ix[inferred.index]
f[f.isnull()] = inferred[f.isnull()]
filled_feature = f.astype(float)
return {'auc': auc, 'model': best, 'decision_function': fun,
'inferred_values': inferred, 'filled_feature': filled_feature}
def process_feature(feature, q, std):
if (get_vec_type(feature) == 'real') and (len(feature.unique()) > 10):
feature = to_quants(feature, q=q, std=std, labels=True)
return feature