def test_gradient_boosting(n_samples=1000):
# Generating some samples correlated with first variable
distance = 0.6
testX, testY = generate_sample(n_samples, 10, distance)
trainX, trainY = generate_sample(n_samples, 10, distance)
# We will try to get uniform distribution along this variable
uniform_variables = ['column0']
n_estimators = 20
loss1 = SimpleKnnLossFunction(uniform_variables)
# loss2 = PairwiseKnnLossFunction(uniform_variables, knn=10)
loss3 = BinomialDevianceLossFunction()
# loss4 = RandomKnnLossFunction(uniform_variables, samples * 2, knn=5, knn_factor=3)
# loss5 = DistanceBasedKnnFunction(uniform_variables, knn=10, distance_dependence=lambda r: numpy.exp(-0.1 * r))
loss6bin = BinFlatnessLossFunction(uniform_variables, ada_coefficient=0.5)
loss7bin = BinFlatnessLossFunction(uniform_variables,
ada_coefficient=0.5,
uniform_label=[0, 1])
loss6knn = KnnFlatnessLossFunction(uniform_variables, ada_coefficient=0.5)
loss7knn = KnnFlatnessLossFunction(uniform_variables,
ada_coefficient=0.5,
uniform_label=[0, 1])
# loss8 = NewFlatnessLossFunction(uniform_variables, ada_coefficient=0.5, uniform_label=1)
# loss9 = NewFlatnessLossFunction(uniform_variables, ada_coefficient=0.5, uniform_label=[0, 1])
for loss in [loss1, loss3, loss6bin, loss7bin, loss6knn, loss7knn]:
result = uGradientBoostingClassifier(loss=loss, min_samples_split=20, max_depth=5, learning_rate=.2,
subsample=0.7, n_estimators=n_estimators, train_variables=None) \
.fit(trainX[:n_samples], trainY[:n_samples]).score(testX, testY)
assert result >= 0.7, "The quality is too poor: %.3f" % result
def test_gradient_boosting(n_samples=1000):
"""
Testing workability of GradientBoosting with different loss function
"""
# Generating some samples correlated with first variable
distance = 0.6
testX, testY = generate_sample(n_samples, 10, distance)
trainX, trainY = generate_sample(n_samples, 10, distance)
# We will try to get uniform distribution along this variable
uniform_features = ['column0']
loss1 = LogLossFunction()
loss2 = AdaLossFunction()
loss3 = CompositeLossFunction()
loss4 = KnnAdaLossFunction(uniform_features=uniform_features,
uniform_label=1)
loss5 = KnnAdaLossFunction(uniform_features=uniform_features,
uniform_label=[0, 1])
loss6bin = BinFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=0)
loss7bin = BinFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=[0, 1])
loss6knn = KnnFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=1)
loss7knn = KnnFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=[0, 1])
for loss in [
loss1, loss2, loss3, loss4, loss5, loss6bin, loss7bin, loss6knn,
loss7knn
]:
clf = UGradientBoostingClassifier(loss=loss, min_samples_split=20, max_depth=5, learning_rate=0.2,
subsample=0.7, n_estimators=25, train_features=None) \
.fit(trainX[:n_samples], trainY[:n_samples])
result = clf.score(testX, testY)
assert result >= 0.7, "The quality is too poor: {} with loss: {}".format(
result, loss)
def flatnessloss(X, y, test):
features = list(X.columns)
features.remove('mass')
loss = BinFlatnessLossFunction(['mass'], n_bins=15, uniform_label=0)
clf = UGradientBoostingClassifier(loss=loss,
n_estimators=300,
subsample=0.7,
max_depth=9,
min_samples_leaf=8,
learning_rate=0.1,
train_features=features,
random_state=11)
arr = np.random.permutation(X.shape[0])
X = X.ix[arr, ]
y = y[arr]
skf = cross_validation.StratifiedKFold(y, n_folds=7)
blend_train = np.zeros(X.shape[0])
prediction = []
blend_test_j = np.zeros((test.shape[0], len(skf)))
for i, (train_index, cv_index) in enumerate(skf):
print "Fold:", i
X_train = X.ix[train_index, ]
y_train = y[train_index]
X_cv = X.ix[cv_index, ]
#y_cv = y[cv_index]
clf.fit(X_train, y_train)
blend_train[cv_index] = clf.predict_proba(X_cv)[:, 1]
blend_test_j[:, i] = clf.predict_proba(test)[:, 1]
prediction = blend_test_j.mean(1)
return prediction
for i in range(100):
print 'shuffling'
train = shuffle(train)
print("Train a Random Forest model")
rf1 = RandomForestClassifier(n_estimators=500,
n_jobs=-1,
criterion="entropy",
max_depth=10,
max_features=6,
min_samples_leaf=2)
rf1.fit(train[features], train["signal"])
print("Train a UGradientBoostingClassifier")
loss = BinFlatnessLossFunction(['mass'], n_bins=15, uniform_label=0)
rf = UGradientBoostingClassifier(loss=loss,
n_estimators=200,
max_depth=6,
learning_rate=0.15,
train_features=features,
subsample=0.7,
random_state=369)
rf.fit(train[features + ['mass']], train['signal'])
print("Train a XGBoost model")
params = {
"objective": "binary:logistic",
"learning_rate": 0.2,
"max_depth": 6,
]
features = list(f for f in df.columns if f not in features_out)
print("Split train/test")
train, test = train_test_split(df, test_size=0.33)
X_train = train[features]
y_train = train['signal']
X_val = test[features]
y_val = test['signal']
loss = BinFlatnessLossFunction(['mass'],
n_bins=15,
uniform_label=0,
fl_coefficient=15,
power=2)
ugbc = UGradientBoostingClassifier(loss=loss,
n_estimators=550,
max_depth=6,
learning_rate=0.15,
train_features=features,
subsample=0.7,
random_state=123)
ugbc.fit(train[features + ['mass']], train['signal'])
pred_raw = ugbc.predict(test[features])
#print(pred_raw)
pred = pd.DataFrame(data={'signal': pred_raw})
#print(pred.head(5))
#accuracy_fn(pred,y_val)
def Model1():
# Model 1 is an ensemble of XGBoost, Random Forest and Uniform Gradient Boosting Classifiers
# which are trained using the stacked data
model = 1 # set the model number for feature engineering
n_folds = 3 # set the number of folders for generating meta-features
n_stack = 15 # number of models used for stacking
train, test, features = utils.LoadData(
model) # load data and obtain the list of features for estimation
# Initialize models for stacking
clf1 = KNeighborsClassifier(n_neighbors=5,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf2 = KNeighborsClassifier(n_neighbors=10,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf3 = KNeighborsClassifier(n_neighbors=20,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf4 = KNeighborsClassifier(n_neighbors=40,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf5 = KNeighborsClassifier(n_neighbors=80,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf6 = KNeighborsClassifier(n_neighbors=160,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf7 = KNeighborsClassifier(n_neighbors=320,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None)
clf8 = LogisticRegression(penalty='l2',
dual=False,
tol=0.0001,
C=5.0,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=101,
solver='lbfgs',
max_iter=200,
multi_class='ovr',
verbose=0)
clf9 = GaussianNB()
clf10 = SVC(C=5.0,
kernel='rbf',
degree=3,
gamma=0.0,
coef0=0.008,
shrinking=True,
probability=True,
tol=0.001,
cache_size=200,
class_weight=None,
verbose=False,
max_iter=-1,
random_state=101)
clf11 = RandomForestClassifier(n_estimators=250,
criterion='gini',
max_depth=6,
min_samples_split=2,
min_samples_leaf=5,
min_weight_fraction_leaf=0.0,
max_features=0.7,
max_leaf_nodes=None,
bootstrap=False,
oob_score=False,
n_jobs=2,
random_state=101,
verbose=0,
warm_start=False,
class_weight=None)
clf12 = ExtraTreesClassifier(n_estimators=250,
criterion='gini',
max_depth=6,
min_samples_split=2,
min_samples_leaf=5,
min_weight_fraction_leaf=0.0,
max_features=0.7,
max_leaf_nodes=None,
bootstrap=False,
oob_score=False,
n_jobs=2,
random_state=101,
verbose=0,
warm_start=False,
class_weight=None)
clf13 = GradientBoostingClassifier(loss='deviance',
learning_rate=0.2,
n_estimators=450,
subsample=0.7,
min_samples_split=2,
min_samples_leaf=5,
min_weight_fraction_leaf=0.0,
max_depth=6,
init=None,
random_state=101,
max_features=None,
verbose=0,
max_leaf_nodes=None,
warm_start=False)
clf14 = SGDClassifier(loss='log',
penalty='l2',
alpha=0.0001,
l1_ratio=0.15,
fit_intercept=True,
n_iter=10,
shuffle=True,
verbose=0,
epsilon=0.1,
n_jobs=2,
random_state=101,
learning_rate='optimal',
eta0=0.0,
power_t=0.5,
class_weight=None,
warm_start=False,
average=False)
clf15 = models.XGBoostClassifier(nthread=2,
eta=.2,
gamma=0,
max_depth=6,
min_child_weight=3,
max_delta_step=0,
subsample=0.7,
colsample_bytree=0.7,
silent=1,
seed=101,
l2_reg=1,
l1_reg=0,
n_estimators=450)
clfs = [
clf1, clf2, clf3, clf4, clf5, clf6, clf7, clf8, clf9, clf10, clf11,
clf12, clf13, clf14, clf15
]
# Construct stacked datasets
train_blend, test_blend, train_probs, test_probs = utils.StackModels(
train[features], test[features], train.signal.values, clfs, n_folds)
# Construct data for uniform boosting
columns = ['p%s ' % (i) for i in range(0, n_stack)]
meta_train = pd.DataFrame(
{columns[i]: train_probs[:, i]
for i in range(0, n_stack)})
meta_test = pd.DataFrame(
{columns[i]: test_probs[:, i]
for i in range(0, n_stack)})
train_ugb = pd.concat([train, meta_train], axis=1)
test_ugb = pd.concat([test, meta_test], axis=1)
features_ugb = features + columns # features used for UGB training (original features + meta-features)
# Initialize models for ensemble
loss = BinFlatnessLossFunction(['mass'],
n_bins=20,
power=1,
fl_coefficient=3,
uniform_label=0)
clf_ugb = UGradientBoostingClassifier(loss=loss,
n_estimators=275,
max_depth=11,
min_samples_leaf=3,
learning_rate=0.03,
train_features=features_ugb,
subsample=0.85,
random_state=101)
clf_xgb = models.XGBoostClassifier(nthread=6,
eta=.0225,
gamma=1.225,
max_depth=11,
min_child_weight=10,
max_delta_step=0,
subsample=0.8,
colsample_bytree=0.3,
silent=1,
seed=101,
l2_reg=1,
l1_reg=0,
n_estimators=1100)
clf_rf = RandomForestClassifier(n_estimators=375,
criterion='gini',
max_depth=10,
min_samples_split=6,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=0.6,
max_leaf_nodes=None,
bootstrap=True,
oob_score=False,
n_jobs=4,
random_state=101,
verbose=0,
warm_start=False,
class_weight=None)
# Train models
print("Training a Uniform Gradient Boosting model")
clf_ugb.fit(train_ugb[features_ugb + ['mass']], train_ugb['signal'])
preds_ugb = clf_ugb.predict_proba(test_ugb[features_ugb])[:, 1]
print("Training a XGBoost model")
clf_xgb.fit(train_blend, train['signal'])
preds_xgb = clf_xgb.predict_proba(test_blend)
print("Training a Random Forest model")
clf_rf.fit(train_blend, train['signal'])
preds_rf = clf_rf.predict_proba(test_blend)[:, 1]
# Compute ensemble predictions
preds = 0.3 * (preds_xgb**(0.65)) * (preds_rf**(0.35)) + 0.7 * preds_ugb
return preds
def stacked_models(train, features, test, in_sample=True):
"""
Build stacked generalization models, set in_sample to False
to predict on test set.
"""
if in_sample:
np.random.seed(1)
new_indices = np.asarray(train.index.copy())
np.random.shuffle(new_indices)
train = train.iloc[new_indices].reset_index(drop=True).copy()
# not used in CV testing..
del test
cutoff = int(new_indices.shape[0] * 0.75)
X_dev = train[:cutoff].reset_index(drop=True).copy()
Y_dev = train[:cutoff]['signal'].reset_index(drop=True).copy()
X_test = train[cutoff:][
train[cutoff:]['min_ANNmuon'] > 0.4].reset_index(drop=True).copy()
Y_test = train[cutoff:][
train[cutoff:]['min_ANNmuon'] > 0.4]['signal'].reset_index(
drop=True).copy()
else:
np.random.seed(1)
new_indices = np.asarray(train.index.copy())
np.random.shuffle(new_indices)
train = train.iloc[new_indices].reset_index(drop=True).copy()
X_dev = train.reset_index(drop=True).copy()
Y_dev = train['signal'].reset_index(drop=True).copy()
X_test = test.reset_index(drop=True).copy()
Y_test = None
n_folds = 5
# put ur parameter tuned CLFs in this list.
clfs = [
RandomForestClassifier(n_estimators=200,
criterion='entropy',
random_state=20,
n_jobs=-1),
RandomForestClassifier(n_estimators=200,
criterion='entropy',
random_state=20,
n_jobs=-1,
max_depth=6),
ExtraTreesClassifier(n_estimators=200,
criterion='entropy',
random_state=50,
n_jobs=-1),
ExtraTreesClassifier(n_estimators=200,
criterion='entropy',
random_state=50,
n_jobs=-1,
max_depth=6),
Pipeline([('scaler', StandardScaler()), ('lr', LogisticRegression())]),
UGradientBoostingClassifier(loss=BinFlatnessLossFunction(
['mass'], n_bins=15, uniform_label=0),
n_estimators=150,
subsample=0.1,
max_depth=6,
min_samples_leaf=10,
learning_rate=0.1,
train_features=features,
random_state=11),
UGradientBoostingClassifier(loss=KnnFlatnessLossFunction(
['mass'], n_neighbours=30, uniform_label=0),
n_estimators=150,
subsample=0.1,
max_depth=6,
min_samples_leaf=10,
learning_rate=0.1,
train_features=features,
random_state=11),
UGradientBoostingClassifier(loss=BinFlatnessLossFunction(
['mass'], n_bins=15, uniform_label=0),
n_estimators=100,
subsample=0.8,
max_depth=6,
min_samples_leaf=10,
learning_rate=0.1,
train_features=features,
random_state=11),
UGradientBoostingClassifier(loss=KnnFlatnessLossFunction(
['mass'], n_neighbours=30, uniform_label=0),
n_estimators=100,
subsample=0.8,
max_depth=6,
min_samples_leaf=10,
learning_rate=0.1,
train_features=features,
random_state=11),
XGBoostClassifier(eval_metric='auc',
objective='binary:logistic',
num_class=2,
nthread=4,
silent=1,
colsample_bytree=0.6,
eta=0.005,
max_depth=6,
min_child_weight=13,
seed=1337,
subsample=0.7),
NN1(len(features)),
NN2(len(features)),
NN3(len(features)),
NN4(len(features))
]
skf = list(StratifiedKFold(Y_dev, n_folds))
# Number of training data x Number of classifiers
blend_train = np.zeros((X_dev.shape[0], len(clfs)))
# Number of testing data x Number of classifiers
blend_test = np.zeros((X_test.shape[0], len(clfs)))
print 'X_test.shape = %s' % (str(X_test.shape))
print 'blend_train.shape = %s' % (str(blend_train.shape))
print 'blend_test.shape = %s' % (str(blend_test.shape))
# For each classifier, we train the number of fold times (=len(skf))
for j, clf in enumerate(clfs):
print 'Training classifier [%s]' % (j)
# Number of testing data x Number of folds , we will take the mean of
# the predictions later
blend_test_j = np.zeros((X_test.shape[0], len(skf)))
for i, (train_index, cv_index) in enumerate(skf):
print 'Fold [%s]' % (i)
# This is the training and validation set
X_train = X_dev.iloc[train_index].copy()
Y_train = Y_dev.iloc[train_index].copy()
X_cv = X_dev.iloc[cv_index].copy()
Y_cv = Y_dev.iloc[cv_index].copy()
# handle the case of hep.ml stuff
if type(clf) == type(UGradientBoostingClassifier()):
clf.fit(X_train[features + ['mass']],
Y_train.values.astype(np.int32))
else:
clf.fit(X_train[features], Y_train.values.astype(np.int32))
# This output will be the basis for our blended classifier to train against,
# which is also the output of our classifiers
blend_train[cv_index, j] = clf.predict_proba(X_cv[features])[:, 1]
blend_test_j[:, i] = clf.predict_proba(X_test[features])[:, 1]
# Take the mean of the predictions of the cross validation set
blend_test[:, j] = blend_test_j.mean(1)
print 'Y_dev.shape = %s' % (Y_dev.shape)
# blend with LR...
bclf = LogisticRegression()
bclf.fit(blend_train, Y_dev)
bclf2 = GradientBoostingClassifier(n_estimators=150,
learning_rate=0.02,
max_depth=4,
subsample=0.9,
verbose=3,
random_state=1337)
bclf2.fit(blend_train, Y_dev)
bclf3 = NeuralNet(
layers=[('input', layers.InputLayer), ('hidden', layers.DenseLayer),
('output', layers.DenseLayer)],
# layer parameters:
input_shape=(None, blend_train.shape[1]),
hidden_num_units=blend_train.shape[1],
output_nonlinearity=nonlinearities.
softmax, # output layer uses identity function
output_num_units=2, # 2 target values
# optimization method:
update=nesterov_momentum,
update_learning_rate=0.01,
update_momentum=0.9,
regression=
False, # flag to indicate we're dealing with regression problem
max_epochs=53, # TRY 50 and 46 epochs!
verbose=1,
eval_size=0.10)
bclf3.fit(blend_train.astype(np.float32), Y_dev.astype(np.int32))
bclf4 = AdaBoostClassifier(n_estimators=400, random_state=88)
bclf4.fit(blend_train, Y_dev)
# Predict now
Y_test_predict = bclf.predict_proba(blend_test)[:, 1]
Y_test_predict2 = bclf2.predict_proba(blend_test)[:, 1]
Y_test_predict3 = bclf3.predict_proba(blend_test.astype(np.float32))[:, 1]
Y_test_predict4 = bclf4.predict_proba(blend_test)[:, 1]
print 'Logit Coefs:', bclf.coef_
if in_sample:
score = evaluation.roc_auc_truncated(Y_test, Y_test_predict)
score2 = evaluation.roc_auc_truncated(Y_test, Y_test_predict2)
score3 = evaluation.roc_auc_truncated(Y_test, blend_test.mean(1))
score4 = evaluation.roc_auc_truncated(
Y_test, scipy_opt(blend_train, Y_dev, blend_test))
score5 = evaluation.roc_auc_truncated(
Y_test, (Y_test_predict + Y_test_predict2) / 2.0)
score6 = evaluation.roc_auc_truncated(Y_test, Y_test_predict3)
score7 = evaluation.roc_auc_truncated(
Y_test, (Y_test_predict + Y_test_predict2 + Y_test_predict3) / 3.0)
score8 = evaluation.roc_auc_truncated(Y_test, Y_test_predict4)
score9 = evaluation.roc_auc_truncated(
Y_test,
(Y_test_predict2 + Y_test_predict3 + Y_test_predict4) / 3.0)
score10 = evaluation.roc_auc_truncated(
Y_test, (Y_test_predict + Y_test_predict2 + Y_test_predict3 +
Y_test_predict4) / 4.0)
print 'LR Score = %s' % (score)
print 'GB Score = %s' % (score2)
print 'MEAN Score = %s' % (score3)
print 'Scipy Score = %s' % (score4)
print 'LR + GB score = %s' % (score5)
print 'ANN Score= %s' % (score6)
print 'LR + GB + ANN Score = %s' % (score7)
print 'ADA Score = %s' % (score8)
print 'GB + ANN + ADA Score = %s' % (score9)
print 'LR + GB + ANN + ADA Score = %s' % (score10)
return blend_train, Y_dev, blend_test, Y_test
# average of ADA, ANN and GBM.
return (Y_test_predict + Y_test_predict2 + Y_test_predict3 +
Y_test_predict4) / 4.0