def main(submit=0):
X_all_df,y = prepare.Prepare_0(model=4).load(preproc=0, update=False)
names = list(X_all_df.columns)
#X_all_df,y = X_all_df.iloc[:500,:],y[:300]
lentrain = len(y)
Xtrain_df = X_all_df.iloc[:lentrain,:]
clf1 = lm.LogisticRegression(penalty='l2', dual=True, tol=0.0001,
C=1, fit_intercept=True, intercept_scaling=1.0,
class_weight=None, random_state=random_state)
clf2 = RandomForestClassifier(n_estimators=200, max_depth=24,
n_jobs=-1, random_state=random_state, verbose=0)
clf3 = GradientBoostingClassifier(n_estimators=42, max_depth=24,
random_state=random_state, verbose=2, subsample=0.9)
clf4 = svm.SVC(probability=True)
clf5 = KNeighborsClassifier(n_neighbors=5)
clf6 = SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,
fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',
loss='hinge', n_iter=50, n_jobs=1, penalty='elasticnet', power_t=0.5,
random_state=random_state, rho=None, shuffle=False, verbose=0,
warm_start=False)
clf = clf1
if 0:
clf = RFECVp(clf,clf, step=4, cv=4, scoring="roc_auc", verbose=2)
rd = Pipeline([
("trans", Transformer(names=names)),
#("scaler",StandardScaler()),
("scaler",StandardScaler(with_mean=False)),
#("selector", SelectPercentile(chi2, percentile=50)),
#("selector", SelectPercentile(f_classif, percentile=50)),
#("selector", lm.RandomizedLogisticRegression(C=1, random_state=random_state, verbose=1)),
#("pca", PCA(n_components='mle')),
#("pca", PCA(n_components=500)),
#("svd", TruncatedSVD(n_components=50, random_state=random_state )),
#("lasso",svm.LinearSVC(C=0.5, penalty="l1", dual=False)),
("est", clf)
])
if not submit:
cv_run(rd, Xtrain_df, y)
return
else:
print "Prepare submission.."
print "training on full data"
rd.fit(Xtrain_df,y)
Xtest_df = X_all_df.iloc[lentrain:,:]
pred = rd.predict_proba(Xtest_df)[:,1]
import submit
submit.do_submit(pred)
def main(submit=0):
Xall_df,y = prepare.Prepare_0().load(preproc=0, update=False)
#Xall_df,y = Xall_df.iloc[:500,:],y[:300]
lentrain = len(y)
Xtrain_df = Xall_df.iloc[:lentrain,:]
clf1 = lm.LogisticRegression(penalty='l2', dual=True, tol=0.0001,
C=1, fit_intercept=True, intercept_scaling=1.0,
class_weight=None, random_state=random_state)
clf2 = RandomForestClassifier(n_estimators=200, max_depth=24,
n_jobs=-1, random_state=random_state, verbose=0)
clf3 = GradientBoostingClassifier(n_estimators=42, max_depth=24,
random_state=random_state, verbose=2, subsample=0.9)
clf4 = svm.SVC(probability=True)
clf5 = KNeighborsClassifier(n_neighbors=5)
clf6 = SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,
fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',
loss='hinge', n_iter=50, n_jobs=1, penalty='elasticnet', power_t=0.5,
random_state=random_state, rho=None, shuffle=False, verbose=0,
warm_start=False)
clf = clf1
if 0:
selector = RFECVp(clf,clf, step=10, cv=4, scoring="roc_auc", verbose=2)
selector = selector.fit( Transformer().fit_transform(Xtrain_df, y), y)
clf = selector
rd = Pipeline([
("trans", Transformer()),
#("selector", SelectPercentile(chi2, percentile=90)),
#("selector", SelectPercentile(f_classif, percentile=50)),
#("selector", lm.RandomizedLogisticRegression(C=1, random_state=random_state, verbose=1)),
#("pca", PCA(n_components='mle')),
#("pca", PCA(n_components=500)),
#("svd", TruncatedSVD(n_components=200, random_state=random_state )),
#("lasso",svm.LinearSVC(C=0.5, penalty="l1", dual=False)),
("est", clf)
])
if not submit:
cv_run(rd, Xtrain_df, y)
return
else:
print "Prepare submission.."
print "training on full data"
rd.fit(Xtrain_df,y)
Xtest_df = Xall_df.iloc[lentrain:,:]
pred = rd.predict_proba(Xtest_df)[:,1]
import submit
submit.do_submit(pred)
def main(n_iter, n_folds, smodels, n_jobs=None, stack=0, use_vote=0, gnrl='KNC',
modsel=0, rfe=0, psearch=0,
starter=0, verbose=0, submit=0):
y, colnames, n_train, n_test, n_all = prepare.Prepare_0().load_y_colnames()
X_all = np.arange(n_all)
models = []
for m in smodels.split('+'):
models.append( eval('Model%02d()'%int(m)) )
#models = (Model02(),Model12(),Model10(),) # ***
logger.debug("models:%s", models)
X = X_all[:n_train]
logger.info('Find params for models')
for model in models:
model.set_params(**find_params(model, X, y, scoring='roc_auc', n_iter=n_iter,
n_jobs=n_jobs, random_state=random_state+1, psearch=psearch)
)
rd = ModelStack(models,gnrl=gnrl,stack=stack, use_vote=use_vote,
modsel=modsel,rfe=rfe)
if starter:
logger.info('Starters start')
rd.starter()
if psearch > 1 and len(models)==1: # update current model best score
y_pred, scores = cv_run(rd, X, y, n_folds=n_folds, n_iter=n_iter,
n_jobs=n_jobs, random_state=random_state+2)
update_params_best_score(models[0], np.mean(scores))
return
elif not submit:
logger.debug('Cross validation starts')
y_pred, scores = cv_run(rd, X, y, n_folds=n_folds, n_iter=n_iter,
n_jobs=n_jobs, random_state=random_state)
prepare.Prepare_0().dump_ypred_residuals(y,y_pred)
if verbose > 1:
plot_errors(X,y,y_pred)
if stack:
logger.info("Mean Coefs: %s", rd.mean_coefs())
return
else:
logger.info("Prepare submission..")
logger.info("training on full data")
rd.fit(X_all[:n_train],y)
Xtest = X_all[n_train:]
pred = rd.predict_proba(Xtest)[:,1]
import submit
submit.do_submit(pred)
def main(submit=0):
y, colnames, n_train, n_test, n_all = prepare.Prepare_0().load_y_colnames()
X_all = np.arange(n_all)
X = X_all[:n_train]
rd = Model14()
rd.starter()
if not submit:
cv_run(rd, X, y)
return
else:
print "Prepare submission.."
print "training on full data"
rd.fit(X_all[:n_train],y)
Xtest = X_all[n_train:]
pred = rd.predict_proba(Xtest)[:,1]
import submit
submit.do_submit(pred)
def main(submit=0):
y, colnames, n_train, n_test, n_all = prepare.Prepare_0().load_y_colnames()
X_all = np.arange(n_all)
X = X_all[:n_train]
rd = Model09()
rd.starter()
if not submit:
cv_run(rd, X, y)
return
else:
print "Prepare submission.."
print "training on full data"
rd.fit(X_all[:n_train], y)
Xtest = X_all[n_train:]
pred = rd.predict_proba(Xtest)[:, 1]
import submit
submit.do_submit(pred)