def test_variant_B_labels(self):
# reference
model = LogisticRegression(random_state=0)
S_train_1 = cross_val_predict(model, X_train, y=y_train,
cv=n_folds, n_jobs=1, verbose=0,
method='predict').reshape(-1, 1)
model = model.fit(X_train, y_train)
S_test_1 = model.predict(X_test).reshape(-1, 1)
# fit then transform
estimators = [('logit', LogisticRegression(random_state=0))]
stack = StackingTransformer(estimators, regression=False,
n_folds=n_folds, shuffle=False,
variant='B', random_state=0,
stratified=True, verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
# compare
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
def test_custom_metric_and_scores_proba(self):
model = LogisticRegression(random_state=0)
scorer = make_scorer(roc_auc_score_universal, needs_proba=True)
scores_1 = cross_val_score(model, X_train, y=y_train,
cv=n_folds, scoring=scorer,
n_jobs=1, verbose=0)
# fit then transform
estimators = [('logit', LogisticRegression(random_state=0))]
stack = StackingTransformer(estimators, regression=False,
n_folds=n_folds, shuffle=False,
variant='B', random_state=0,
stratified=True, needs_proba=True,
metric=roc_auc_score_universal, verbose=0)
stack = stack.fit(X_train, y_train)
scores_2 = stack.scores_[0].copy()
# fit_transform
# also check refitting already fitted transformer
_ = stack.fit_transform(X_train, y_train)
scores_3 = stack.scores_[0].copy()
assert_array_equal(scores_1, scores_2)
assert_array_equal(scores_1, scores_3)
# mean and std
mean_1 = np.mean(scores_1)
std_1 = np.std(scores_1)
mean_2 = stack.mean_std_[0][1]
std_2 = stack.mean_std_[0][2]
assert_equal(mean_1, mean_2)
assert_equal(std_1, std_2)
def test_variant_B_default_classifier_proba(self):
# reference
model = DummyClassifier(strategy='constant', constant=1)
S_train_1 = cross_val_predict(model, X_train, y=y_train,
cv=n_folds, n_jobs=1, verbose=0,
method='predict_proba')
model = model.fit(X_train, y_train)
S_test_1 = model.predict_proba(X_test)
# fit then transform
stack = StackingTransformer(estimators=None, regression=False,
n_folds=n_folds, shuffle=False,
variant='B', random_state=0,
needs_proba=True, verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
# compare
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
def test_variant_A_proba_shuffle_random_state(self):
S_test_1 = np.zeros((X_test.shape[0], n_classes))
S_test_temp = np.zeros((X_test.shape[0], n_folds * n_classes))
# Using StratifiedKFold because by defauld cross_val_predict uses StratifiedKFold
kf = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=0)
for fold_counter, (tr_index,
te_index) in enumerate(kf.split(X_train, y_train)):
# Split data and target
X_tr = X_train[tr_index]
y_tr = y_train[tr_index]
# X_te = X_train[te_index]
# y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = model.fit(X_tr, y_tr)
col_slice_fold = slice(fold_counter * n_classes,
fold_counter * n_classes + n_classes)
S_test_temp[:, col_slice_fold] = model.predict_proba(X_test)
for class_id in range(n_classes):
S_test_1[:, class_id] = np.mean(S_test_temp[:,
class_id::n_classes],
axis=1)
model = LogisticRegression(random_state=0)
# !!! Important. Here we pass CV-generator ``cv=kf`` not number of folds
S_train_1 = cross_val_predict(model,
X_train,
y=y_train,
cv=kf,
n_jobs=1,
verbose=0,
method='predict_proba')
# fit then transform
estimators = [('logit', LogisticRegression(random_state=0))]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=True,
variant='A',
random_state=0,
stratified=True,
needs_proba=True,
verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
# compare
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
def test_variant_B_2_estimators_proba(self):
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
S_train_1_e1 = cross_val_predict(model,
X_train,
y=y_train,
cv=n_folds,
n_jobs=1,
verbose=0,
method='predict_proba')
model = model.fit(X_train, y_train)
S_test_1_e1 = model.predict_proba(X_test)
model = GaussianNB()
S_train_1_e2 = cross_val_predict(model,
X_train,
y=y_train,
cv=n_folds,
n_jobs=1,
verbose=0,
method='predict_proba')
model = model.fit(X_train, y_train)
S_test_1_e2 = model.predict_proba(X_test)
S_train_1 = np.c_[S_train_1_e1, S_train_1_e2]
S_test_1 = np.c_[S_test_1_e1, S_test_1_e2]
# fit then transform
estimators = [('logit',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')),
('bayes', GaussianNB())]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='B',
random_state=0,
stratified=True,
needs_proba=True,
verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
def test_variant_A_labels(self):
S_test_temp = np.zeros((X_test.shape[0], n_folds))
# Using StratifiedKFold because by defauld cross_val_predict uses StratifiedKFold
kf = StratifiedKFold(n_splits=n_folds, shuffle=False, random_state=0)
for fold_counter, (tr_index,
te_index) in enumerate(kf.split(X_train, y_train)):
# Split data and target
X_tr = X_train[tr_index]
y_tr = y_train[tr_index]
# X_te = X_train[te_index]
# y_te = y_train[te_index]
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
model = model.fit(X_tr, y_tr)
S_test_temp[:, fold_counter] = model.predict(X_test)
S_test_1 = st.mode(S_test_temp, axis=1)[0]
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
S_train_1 = cross_val_predict(model,
X_train,
y=y_train,
cv=n_folds,
n_jobs=1,
verbose=0,
method='predict').reshape(-1, 1)
# fit then transform
estimators = [('logit',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr'))]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='A',
random_state=0,
stratified=True,
verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
# compare
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
def stack_predict(self, df, holdout, pipes, amount=2):
X, y = self.split_x_y(df)
X_test, y_test = self.split_x_y(holdout)
pipe = Pipeline(self.top_pipeline(pipes).steps[:-1])
X = pipe.fit_transform(X)
X_test = pipe.transform(X_test)
estimators = []
for i in range(amount):
estimators.append((str(i), self.top_pipeline(pipes,
i).steps[-1][1]))
regression = False
if self.METRIC in [
"explained_variance",
"neg_mean_absolute_error",
"neg_mean_squared_error",
"neg_mean_squared_log_error",
"neg_median_absolute_error",
"r2",
]:
regression = True
stack = StackingTransformer(estimators, regression)
stack.fit(X, y)
S_train = stack.transform(X)
S_test = stack.transform(X_test)
final_estimator = estimators[0][1]
final_estimator.fit(S_train, y)
return final_estimator, y_test, final_estimator.predict(S_test)
def test_custom_metric_and_scores_labels(self):
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
scorer = make_scorer(zero_one_loss)
scores_1 = cross_val_score(model,
X_train,
y=y_train,
cv=n_folds,
scoring=scorer,
n_jobs=1,
verbose=0)
# fit then transform
estimators = [('logit',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr'))]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='B',
random_state=0,
stratified=True,
metric=zero_one_loss,
verbose=0)
stack = stack.fit(X_train, y_train)
scores_2 = stack.scores_[0].copy()
# fit_transform
# also check refitting already fitted transformer
_ = stack.fit_transform(X_train, y_train)
scores_3 = stack.scores_[0].copy()
assert_array_equal(scores_1, scores_2)
assert_array_equal(scores_1, scores_3)
# mean and std
mean_1 = np.mean(scores_1)
std_1 = np.std(scores_1)
mean_2 = stack.mean_std_[0][1]
std_2 = stack.mean_std_[0][2]
assert_equal(mean_1, mean_2)
assert_equal(std_1, std_2)
def test_variant_A_2_estimators_proba(self):
# Estimator 1
S_test_1_e1 = np.zeros((X_test.shape[0], n_classes))
S_test_temp_e1 = np.zeros((X_test.shape[0], n_folds * n_classes))
# Using StratifiedKFold because by defauld cross_val_predict uses StratifiedKFold
kf = StratifiedKFold(n_splits=n_folds, shuffle=False, random_state=0)
for fold_counter, (tr_index,
te_index) in enumerate(kf.split(X_train, y_train)):
# Split data and target
X_tr = X_train[tr_index]
y_tr = y_train[tr_index]
# X_te = X_train[te_index]
# y_te = y_train[te_index]
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
model = model.fit(X_tr, y_tr)
col_slice_fold = slice(fold_counter * n_classes,
fold_counter * n_classes + n_classes)
S_test_temp_e1[:, col_slice_fold] = model.predict_proba(X_test)
for class_id in range(n_classes):
S_test_1_e1[:, class_id] = np.mean(
S_test_temp_e1[:, class_id::n_classes], axis=1)
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
S_train_1_e1 = cross_val_predict(model,
X_train,
y=y_train,
cv=n_folds,
n_jobs=1,
verbose=0,
method='predict_proba')
# Estimator 2
S_test_1_e2 = np.zeros((X_test.shape[0], n_classes))
S_test_temp_e2 = np.zeros((X_test.shape[0], n_folds * n_classes))
# Using StratifiedKFold because by defauld cross_val_predict uses StratifiedKFold
kf = StratifiedKFold(n_splits=n_folds, shuffle=False, random_state=0)
for fold_counter, (tr_index,
te_index) in enumerate(kf.split(X_train, y_train)):
# Split data and target
X_tr = X_train[tr_index]
y_tr = y_train[tr_index]
# X_te = X_train[te_index]
# y_te = y_train[te_index]
model = GaussianNB()
model = model.fit(X_tr, y_tr)
col_slice_fold = slice(fold_counter * n_classes,
fold_counter * n_classes + n_classes)
S_test_temp_e2[:, col_slice_fold] = model.predict_proba(X_test)
for class_id in range(n_classes):
S_test_1_e2[:, class_id] = np.mean(
S_test_temp_e2[:, class_id::n_classes], axis=1)
model = GaussianNB()
S_train_1_e2 = cross_val_predict(model,
X_train,
y=y_train,
cv=n_folds,
n_jobs=1,
verbose=0,
method='predict_proba')
S_train_1 = np.c_[S_train_1_e1, S_train_1_e2]
S_test_1 = np.c_[S_test_1_e1, S_test_1_e2]
# fit then transform
estimators = [('logit',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')),
('bayes', GaussianNB())]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='A',
random_state=0,
stratified=True,
needs_proba=True,
verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
# compare
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
def test_variant_B_verbose(self):
model = LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr')
S_train_1 = cross_val_predict(model,
X_train,
y=y_train,
cv=n_folds,
n_jobs=1,
verbose=0,
method='predict').reshape(-1, 1)
model = model.fit(X_train, y_train)
S_test_1 = model.predict(X_test).reshape(-1, 1)
# verbose=0
# fit then transform
estimators = [('lr',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr'))]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='B',
random_state=0,
stratified=True,
verbose=0)
stack = stack.fit(X_train, y_train)
S_train_2 = stack.transform(X_train)
S_test_2 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_3 = stack.fit_transform(X_train, y_train)
S_test_3 = stack.transform(X_test)
# verbose=1
# fit then transform
estimators = [('lr',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr'))]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='B',
random_state=0,
stratified=True,
verbose=1)
stack = stack.fit(X_train, y_train)
S_train_4 = stack.transform(X_train)
S_test_4 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_5 = stack.fit_transform(X_train, y_train)
S_test_5 = stack.transform(X_test)
# verbose=2
# fit then transform
estimators = [('lr',
LogisticRegression(random_state=0,
solver='liblinear',
multi_class='ovr'))]
stack = StackingTransformer(estimators,
regression=False,
n_folds=n_folds,
shuffle=False,
variant='B',
random_state=0,
stratified=True,
verbose=2)
stack = stack.fit(X_train, y_train)
S_train_6 = stack.transform(X_train)
S_test_6 = stack.transform(X_test)
# fit_transform
# also check refitting already fitted transformer
S_train_7 = stack.fit_transform(X_train, y_train)
S_test_7 = stack.transform(X_test)
assert_array_equal(S_train_1, S_train_2)
assert_array_equal(S_test_1, S_test_2)
assert_array_equal(S_train_1, S_train_3)
assert_array_equal(S_test_1, S_test_3)
assert_array_equal(S_train_1, S_train_4)
assert_array_equal(S_test_1, S_test_4)
assert_array_equal(S_train_1, S_train_5)
assert_array_equal(S_test_1, S_test_5)
assert_array_equal(S_train_1, S_train_6)
assert_array_equal(S_test_1, S_test_6)
assert_array_equal(S_train_1, S_train_7)
assert_array_equal(S_test_1, S_test_7)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=0, test_size=0.1)
X, Y = X_train, Y_train
# Perform Feature ranking with recursive feature elimination (5 features only)
selector = RFE(LinearSVC(random_state=0, class_weight='balanced', tol=1.0, max_iter=1000), 5, step=3)
X, X_test = selector.fit_transform(X, Y), selector.transform(X_test)
print(X.shape)
print(selector)
print(X_test)
# Features Selected by above step
#['Clusterin_Apo_J', 'Cystatin_C', 'FAS', 'NrCAM', 'tau']
# Perform Stacking
models = [('xgb', XGBClassifier(random_state=0)), ('svc', LinearSVC(random_state=0, class_weight='balanced', tol=1.0, max_iter=1000))]
stack = StackingTransformer(models, regression=False, verbose=0)
stack = stack.fit(X, Y)
pickle.dump(stack, open('stacker.pkl','wb'))
X, X_test = np.concatenate((X, stack.transform(X)), axis=1), np.concatenate((X_test, stack.transform(X_test)), axis=1)
# Let's test the effect of different threshold values and record it
# in order to take the best threshold valuess
threshold, final_scores = [-0.8], list()
for i in range(21):
scores, roc_auc = list(), 0
kfolds = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
for train, test in kfolds.split(X, Y):
x_train, x_test = X[train], X[test]
y_train, y_test = Y[train], Y[test]
model = LinearSVC(random_state=0, tol=1.0, class_weight='balanced')
model.fit(x_train, y_train)
# define estimator for stacking transformer
estimator = [('voting', voting_models), ('bag_knn', bag_knn),
('bag_dt', bag_dt), ('ada', ada), ('gb', gb),
('xgboost', xgboost)]
stack = StackingTransformer(estimator,
regression=False,
needs_proba=False,
variant='A',
metric=metrics.accuracy_score,
n_folds=4,
stratified=True,
shuffle=True,
random_state=0,
verbose=2)
stack = stack.fit(X_train, y_train)
filename = 'stack.sav'
pickle.dump(stack, open(filename, 'wb'))
# stacked feature
S_train = stack.transform(X_train)
S_test = stack.transform(X_test)
# Create the parameter grid
params = {
'eta': [0.1, 0.2, 0.3, 0.4, 0.5],
'max_depth': [4, 5, 6, 7],
'n_estimators': [500, 1000, 1500, 2000]
}
gbm = xgb.XGBClassifier(gamma=0.9,
def cv(num_splits, X_train, Y):
# Define the type of cross-validation
kf, scores = StratifiedKFold(n_splits=num_splits, random_state=0), list()
# Perform CV
for train_index, test_index in kf.split(X_train, Y):
# Splitting into train and test
x_train, y_train, x_train1 = X_train[train_index], Y[
train_index], X_train1[train_index]
x_test, y_test, x_test1 = X_train[test_index], Y[test_index], X_train1[
test_index]
# Define base estimators for stacking
estimators = [('lgbm',
LGBMClassifier(random_state=0,
n_estimators=520,
learning_rate=0.1,
num_leaves=31,
is_unbalance=True)),
('rf',
RandomForestClassifier(random_state=0,
max_depth=10,
class_weight={
0: 0.2,
1: 0.8
},
n_estimators=500,
max_features=None,
n_jobs=4))]
# Perform stacking
stack = StackingTransformer(estimators,
regression=False,
verbose=2,
needs_proba=True,
stratified=True,
shuffle=True)
stack = stack.fit(x_train, y_train)
# Get the stacked features
S_train = stack.transform(x_train)
S_test = stack.transform(x_test)
# Also take the weighted average of the stacked features as another feature
S_train_av, S_test_av = np.zeros(
(len(S_train), 2), dtype=np.float32), np.zeros((len(S_test), 2),
dtype=np.float32)
for index, vals in enumerate(S_train):
S_train_av[index, 0] = (vals[0] * 0.7) + (vals[2] * 0.3)
S_train_av[index, 1] = (vals[1] * 0.7) + (vals[3] * 0.3)
for index, vals in enumerate(S_test):
S_test_av[index, 0] = (vals[0] * 0.7) + (vals[2] * 0.3)
S_test_av[index, 1] = (vals[1] * 0.7) + (vals[3] * 0.3)
# Define the final estimator
model = XGBClassifier(random_state=0,
n_jobs=4,
max_depth=4,
scale_pos_weight=2.5,
n_estimators=200,
learning_rate=0.1,
gamma=1)
model.fit(np.concatenate((S_train, S_train_av, x_train1), axis=1),
y_train)
preds4 = model.predict_proba(
np.concatenate((S_test, S_test_av, x_test1), axis=1))
# Now perform random under-sampling on the data
rus = RandomUnderSampler(random_state=0, sampling_strategy=0.3)
x_train, y_train_ = rus.fit_resample(x_train, y_train)
# Get predictions from models on this majority class under-sampled dataset
model1 = LGBMClassifier(random_state=0,
n_estimators=100,
learning_rate=0.1,
num_leaves=31,
categorical_feature=[8, 9, 10, 11, 12, 13, 14])
model2 = RandomForestClassifier(random_state=0,
max_depth=13,
n_estimators=100,
max_features=None,
n_jobs=4,
class_weight={
0: 0.4,
1: 0.6
})
model1.fit(x_train, y_train_), model2.fit(x_train, y_train_)
preds1, preds2 = model1.predict_proba(x_test), model2.predict_proba(
x_test)
# Get weighted average predictions
preds3 = list()
for a, b in zip(preds1, preds2):
preds3.append([(0.7 * a[0]) + (0.3 * b[0]),
(0.7 * a[1]) + (0.3 * b[1])])
# Finally, perform weighted average prediction of stacked ensemble and weighted average ensemble
preds = list()
for a, b in zip(preds3, preds4):
preds.append([(0.5 * a[0]) + (0.5 * b[0]),
(0.5 * a[1]) + (0.5 * b[1])])
preds = np.array(preds)
preds = np.argmax(preds, axis=1)
# Check out the score
scores.append(f1_score(y_test, preds))
print("Score: ", scores[-1])
print("Average Score: ", sum(scores) / len(scores))
def final_submission(X_train, Y, X_test):
# Define base estimators for stacking
estimators = [('lgbm',
LGBMClassifier(random_state=0,
n_estimators=520,
learning_rate=0.1,
num_leaves=31,
is_unbalance=True)),
('rf',
RandomForestClassifier(random_state=0,
max_depth=10,
class_weight={
0: 0.2,
1: 0.8
},
n_estimators=500,
max_features=None,
n_jobs=4))]
# Perform stacking
stack = StackingTransformer(estimators,
regression=False,
verbose=2,
needs_proba=True,
stratified=True,
shuffle=True)
stack = stack.fit(X_train, Y)
# Get the stacked features
S_train = stack.transform(X_train)
S_test = stack.transform(X_test)
# Also take the weighted average of the stacked features as another feature
S_train_av, S_test_av = np.zeros(
(len(S_train), 2), dtype=np.float32), np.zeros((len(S_test), 2),
dtype=np.float32)
for index, vals in enumerate(S_train):
S_train_av[index, 0] = (vals[0] * 0.7) + (vals[2] * 0.3)
S_train_av[index, 1] = (vals[1] * 0.7) + (vals[3] * 0.3)
for index, vals in enumerate(S_test):
S_test_av[index, 0] = (vals[0] * 0.7) + (vals[2] * 0.3)
S_test_av[index, 1] = (vals[1] * 0.7) + (vals[3] * 0.3)
# Define the final estimator
model = XGBClassifier(random_state=0,
n_jobs=4,
max_depth=4,
scale_pos_weight=2.5,
n_estimators=200,
learning_rate=0.1,
gamma=1)
model.fit(np.concatenate((S_train, S_train_av, X_train1), axis=1), Y)
preds4 = model.predict_proba(
np.concatenate((S_test, S_test_av, X_test1), axis=1))
# Now perform random under-sampling on the data
rus = RandomUnderSampler(random_state=0, sampling_strategy=0.3)
X_train, Y_ = rus.fit_resample(X_train, Y)
# Get predictions from models on this majority class under-sampled dataset
model1 = LGBMClassifier(random_state=0,
n_estimators=100,
learning_rate=0.1,
num_leaves=31,
categorical_feature=[8, 9, 10, 11, 12, 13, 14])
model2 = RandomForestClassifier(random_state=0,
max_depth=13,
n_estimators=100,
max_features=None,
n_jobs=4,
class_weight={
0: 0.4,
1: 0.6
})
model1.fit(X_train, Y_), model2.fit(X_train, Y_)
preds1, preds2 = model1.predict_proba(X_test), model2.predict_proba(X_test)
# Get weighted average predictions
preds3 = list()
for a, b in zip(preds1, preds2):
preds3.append([(0.7 * a[0]) + (0.3 * b[0]),
(0.7 * a[1]) + (0.3 * b[1])])
# Finally, perform weighted average prediction of stacked ensemble and weighted average ensemble
preds = list()
for a, b in zip(preds3, preds4):
preds.append([(0.5 * a[0]) + (0.5 * b[0]),
(0.5 * a[1]) + (0.5 * b[1])])
preds = np.array(preds)
preds = np.argmax(preds, axis=1)
# Make the submission!
fp = open("submit.csv", "w")
fp.write("labels\n")
for pred in preds:
fp.write(str(pred) + "\n")
fp.close()
def lvl2_xgb_vsrandomsearch(rawdf, results_dir, pp_choice, param_dir, passthrough, final_pp_choice=None):
x_train = rawdf.iloc[:, :-1]
y_train = rawdf.iloc[:, -1]
model_store = ['rf', 'et', 'xgb']
model_object = {
'xgb': XGBRegressor(),
'rf': RandomForestRegressor(),
'et': ExtraTreesRegressor()
}
with open(param_dir, 'rb') as f:
model_results = pickle.load(f)
model_results = {k: pd.DataFrame(v).sort_values('mean_test_score', ascending=False) for k, v in
model_results.items()}
model_object = {k: model_object[k].set_params(**{kk.split('__')[1]: vv for kk, vv in v.loc[0, 'params'].items()})
for k, v in model_results.items()}
preprocess_pipeline = pp_selector(pp_choice)
lvl1_pipeline = [(model_name,model_object[model_name]) for model_name in model_store]
stack = StackingTransformer(estimators=lvl1_pipeline, # base estimators
regression=True, # regression task (if you need
# classification - set to False)
variant='A', # oof for train set, predict test
# set in each fold and find mean
metric=rmsle, # metric: callable
n_folds=5, # number of folds
shuffle=True, # shuffle the data
random_state=0, # ensure reproducibility
verbose=0)
stack.fit(preprocess_pipeline.fit_transform(x_train), y_train)
s_train = stack.transform(preprocess_pipeline.fit_transform(x_train))
if passthrough:
final_est = Pipeline([
('final_preprocess', final_est_pipeline(feature_names=x_train.columns.tolist(),
preprocess_pipeline=pp_selector(final_pp_choice),
no_of_lvl1=len(lvl1_pipeline))),
#('debugger', DebuggerTransformer(info='final')),
('final_est', XGBRegressor())
])
est_name = 'final_est__'
train = np.concatenate((s_train, x_train.values), axis=1)
else:
final_est = XGBRegressor()
est_name = ''
train = s_train
final_estimator_params = {f'{est_name}n_estimators': scipy.stats.randint(150, 1000),
f'{est_name}learning_rate': scipy.stats.uniform(0.01, 0.59),
f'{est_name}subsample': scipy.stats.uniform(0.3, 0.6),
f'{est_name}max_depth': scipy.stats.randint(1, 16),
f'{est_name}colsample_bytree': scipy.stats.uniform(0.5, 0.4),
f'{est_name}min_child_weight': [1, 2, 3, 4],
f'{est_name}gamma': scipy.stats.expon(scale=0.05),
}
est = RandomizedSearchCV(final_est,
param_distributions=final_estimator_params,
cv=5,
n_iter=100,
scoring=make_scorer(rmsle, greater_is_better=False),
verbose=1,
n_jobs=-1)
est.fit(train, y_train)
score = {'lvl2ptvs_xgb': est.cv_results_}
results_dir = create_results_directory(results_dir)
with open(f'{results_dir}/results_store.pkl', 'wb') as f:
pickle.dump(score, f)