def __init__(self, Dist=Normal, Score=MLE(),
Base=default_tree_learner, natural_gradient=False,
n_estimators=100, learning_rate=0.1, minibatch_frac=1.0,
verbose=True, tol=1e-4):
self.Dist = Dist
self.Score = Score
self.Base = Base
self.natural_gradient = natural_gradient
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.minibatch_frac = minibatch_frac
self.verbose = verbose
self.init_params = None
self.base_models = []
self.scalings = []
self.tol = tol
self.loss_fn = lambda P, Y: self.Score(self.Dist(P.T), Y).sum()
self.grad_fn = grad(self.loss_fn)
#self.grad_fn = jit(vmap(grad(self.loss_fn)))
self.hessian_fn = jit(vmap(jacrev(grad(self.loss_fn))))
#self.loss_fn = jit(vmap(self.loss_fn))
self.Score.setup_distn(self.Dist)
if isinstance(self.Score, CRPS_SURV):
self.marginal_score = MLE_SURV()
elif isinstance(self.Score, CRPS):
self.marginal_score = MLE()
else:
self.marginal_score = self.Score
self.marginal_loss = lambda P, Y: self.marginal_score(self.Dist(P), Y)
self.marginal_grad = jit(vmap(grad(self.marginal_loss)))
self.marginal_loss = jit(vmap(self.marginal_loss))
self.matmul_inv_fn = jit(vmap(lambda A, b: np.linalg.solve(A, b)))
def mvnorm_mle(Y, max_iter=1e4, lr=0.5, eps=1e-4):
N = Y.shape[0]
params = np.array([[0, 0, 1, 0, 1]] * N).T
for _ in range(max_iter):
D = MultivariateNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - lr * grad
if np.linalg.norm(grad) < eps:
break
def lognormal_mle(Y, max_iter=1e4, lr=0.05, eps=1e-4, verbose=False):
N = Y.shape[0]
params = np.array([[0, 0]] * N).T
for i in range(int(max_iter)):
if i % 500 == 1 and verbose:
print('Param: ', params[:, :2])
print('Grad: ', grad)
D = LogNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - lr * grad
if np.linalg.norm(grad) < eps:
break
mu = params[0, 0]
sigma = params[1, 0]
return mu, sigma
def objective(trial):
param = {
'learning_rate': trial.suggest_loguniform('learning_rate', 1e-3, 1e0),
'n_estimators': trial.suggest_int('n_estimators', 100, 800),
'minibatch_frac':trial.suggest_discrete_uniform('minibatch_frac', 0.1, 0.9, 0.1),
}
regression_model = NGBRegressor(**param, Base= best_base, Dist=Normal, Score=MLE(), natural_gradient=True, verbose=False)
estimated_y_in_cv = model_selection.cross_val_predict(regression_model, train_x, train_y, cv=fold_number)
r2 = metrics.r2_score(train_y, estimated_y_in_cv)
return 1.0 - r2
def estimate_infcens(Y):
res = {}
params = np.array([[0, 0, 1, 0, 1]] * N).T
for _ in range(100000):
D = MultivariateNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 1 * grad
if np.linalg.norm(grad) < 1e-4:
break
print('Jointly Estimated E:', params[0, 0])
res['joint'] = params[0, 0]
params = np.array([[0, 0]] * N).T
for _ in range(100000):
D = LogNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 0.005 * grad
if np.linalg.norm(grad) < 1e-4:
break
print('Estimate E (assume non-inf):', params[0, 0])
res['lognorm'] = params[0, 0]
return res
def __init__(self, Dist=Normal, Score=MLE(),
Base=default_tree_learner, natural_gradient=False,
n_estimators=500, learning_rate=0.01, minibatch_frac=1.0,
verbose=True, verbose_eval=100, tol=1e-4):
self.Dist = Dist
self.Score = Score
self.Base = Base
self.natural_gradient = natural_gradient
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.minibatch_frac = minibatch_frac
self.verbose = verbose
self.verbose_eval = verbose_eval
self.init_params = None
self.base_models = []
self.scalings = []
self.tol = tol
from ngboost.ngboost import NGBoost
from ngboost.distns import Bernoulli
from ngboost.learners import default_tree_learner
from ngboost.scores import MLE
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
if __name__ == "__main__":
X, Y = load_breast_cancer(True)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
ngb = NGBoost(Base=default_tree_learner,
Dist=Bernoulli,
Score=MLE(),
verbose=True)
ngb.fit(X_train, Y_train)
preds = ngb.pred_dist(X_test)
print("ROC:", roc_auc_score(Y_test, preds.prob))
print("Models")
start = datetime.now().timestamp()
qreg = MLPQuantile()
qreg.fit(X_train_std,y_train)
preds = qreg.predict(X_test_std)
end = datetime.now().timestamp()
results=evaluate((np.exp(preds)-1),(np.exp(y_test)-1).values)
results["duration"]=end-start
save_result([horizon,
"MLP",
results,
1],f"unit_{horizon}",folder)
start = datetime.now().timestamp()
ngb = NGBoost(Base=default_tree_learner, Dist=Normal, Score=MLE(), natural_gradient=True,
verbose=True,n_estimators=1500)
ngb.fit(X_train_std, y_train.values)
Y_dists = ngb.pred_dist(X_test_std)
a=pd.DataFrame()
for i in np.arange(1,100):
a[i]=Y_dists.ppf(i/100)
preds = a.values
end = datetime.now().timestamp()
results=evaluate((np.exp(preds)-1),(np.exp(y_test)-1).values)
results["duration"]=end-start
save_result([horizon,
"NGBOOST",
results,
1],f"unit_{horizon}",folder)
def train_half(self,
params,
train_data,
num_boost_round,
early_stopping_rounds,
verbose,
importance_df,
use_feature_num=None):
print('Model Creating...')
self.data = train_data
if use_feature_num is not None:
self.features = importance_df['feature'][:use_feature_num].tolist()
else:
self.features = self.data.X.columns
self.models = []
assert self.data.phase == 'train', 'Use Train Dataset!'
self.features = [c for c in self.features if c not in ['M']]
self.X_train = self.data.X[self.features]
self.y_train = self.data.y
del self.data
gc.collect()
if self.model_type == 'lgb':
print('LightGBM Model Creating...')
d_half_1 = lgb.Dataset(
self.X_train[:int(self.X_train.shape[0] / 2)],
label=self.y_train[:int(self.X_train.shape[0] / 2)])
d_half_2 = lgb.Dataset(
self.X_train[int(self.X_train.shape[0] / 2):],
label=self.y_train[int(self.X_train.shape[0] / 2):])
print(
"Building model with first half and validating on second half:"
)
model_1 = lgb.train(params,
train_set=d_half_1,
num_boost_round=num_boost_round,
valid_sets=[d_half_1, d_half_2],
verbose_eval=verbose,
early_stopping_rounds=early_stopping_rounds)
self.models.append(model_1)
print('')
print(
"Building model with second half and validating on first half:"
)
model_2 = lgb.train(params,
train_set=d_half_2,
num_boost_round=num_boost_round,
valid_sets=[d_half_2, d_half_1],
verbose_eval=verbose,
early_stopping_rounds=early_stopping_rounds)
self.models.append(model_2)
elif self.model_type == 'cat':
print('CatBoost Model Creating...')
cat_features_index = np.where(self.X_train.dtypes == 'category')[0]
d_half_1 = Pool(self.X_train[:int(self.X_train.shape[0] / 2)],
label=self.y_train[:int(self.X_train.shape[0] /
2)],
cat_features=cat_features_index)
d_half_2 = Pool(self.X_train[int(self.X_train.shape[0] / 2):],
label=self.y_train[int(self.X_train.shape[0] /
2):],
cat_features=cat_features_index)
params['iterations'] = num_boost_round
print(
"Building model with first half and validating on second half:"
)
model_1 = CatBoostRegressor(**params)
model_1.fit(d_half_1,
eval_set=d_half_2,
use_best_model=True,
early_stopping_rounds=early_stopping_rounds,
verbose=verbose)
self.models.append(model_1)
print('')
print(
"Building model with second half and validating on first half:"
)
model_2 = CatBoostRegressor(**params)
model_2.fit(d_half_2,
eval_set=d_half_1,
use_best_model=True,
early_stopping_rounds=early_stopping_rounds,
verbose=verbose)
self.models.append(model_2)
elif self.model_type == 'ng':
print('NGBoost Model Creating...')
print(
"Building model with first half and validating on second half:"
)
self.X_train[np.isnan(self.X_train.astype(np.float32))] = -9999
model_1 = NGBRegressor(
Base=default_tree_learner,
Dist=Normal, # Normal, LogNormal
Score=MLE(),
natural_gradient=True,
verbose=True,
n_estimators=num_boost_round,
verbose_eval=verbose,
learning_rate=0.01,
minibatch_frac=1.0)
model_1.fit(self.X_train[:int(self.X_train.shape[0] / 2)],
self.y_train[:int(self.X_train.shape[0] / 2)],
X_val=self.X_train[int(self.X_train.shape[0] / 2):],
Y_val=self.y_train[int(self.X_train.shape[0] / 2):])
self.models.append(model_1)
print('')
print(
"Building model with second half and validating on first half:"
)
model_2 = NGBRegressor(
Base=default_tree_learner,
Dist=Normal, # Normal, LogNormal
Score=MLE(),
natural_gradient=True,
verbose=True,
n_estimators=num_boost_round,
verbose_eval=verbose,
learning_rate=0.01,
minibatch_frac=1.0)
model_2.fit(self.X_train[int(self.X_train.shape[0] / 2):],
self.y_train[int(self.X_train.shape[0] / 2):],
X_val=self.X_train[:int(self.X_train.shape[0] / 2)],
Y_val=self.y_train[:int(self.X_train.shape[0] / 2)])
self.models.append(model_2)
del self.X_train, self.y_train
gc.collect()
return self.models
def Y_join(T, E):
col_event = 'Event'
col_time = 'Time'
y = np.empty(dtype=[(col_event, np.bool), (col_time, np.float64)],
shape=T.shape[0])
y[col_event] = E
y[col_time] = np.exp(T)
return y
Y = Y_join(T, E)
params = np.array([[0, 0, 1, 0, 1]] * N).T
for _ in range(100000):
D = MultivariateNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 1 * grad
if np.linalg.norm(grad) < 1e-4:
break
print('Jointly Estimated E:', params[0, 0])
params = np.array([[0, 0]] * N).T
for _ in range(100000):
D = LogNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 0.1 * grad
if np.linalg.norm(grad) < 1e-4:
break
