def __init__(
self,
Dist=Normal,
Score=LogScore,
Base=default_tree_learner,
natural_gradient=True,
n_estimators=500,
learning_rate=0.01,
minibatch_frac=1.0,
col_sample=1.0,
verbose=True,
verbose_eval=100,
tol=1e-4,
random_state=None,
):
self.Dist = Dist
self.Score = Score
self.Base = Base
self.Manifold = manifold(Score, Dist)
self.natural_gradient = natural_gradient
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.minibatch_frac = minibatch_frac
self.col_sample = col_sample
self.verbose = verbose
self.verbose_eval = verbose_eval
self.init_params = None
self.base_models = []
self.scalings = []
self.col_idxs = []
self.tol = tol
self.random_state = check_random_state(random_state)
self.best_val_loss_itr = None
def __setstate__(self, state_dict):
# Recreate the object which could not be pickled
state_dict["Dist"] = SurvivalDistnClass(state_dict["_basedist"])
del state_dict["_basedist"]
state_dict["Manifold"] = manifold(state_dict["Score"],
state_dict["Dist"])
self.__dict__ = state_dict
def __setstate__(self, state_dict):
# Recreate the object which could not be pickled
name_to_dist_dict = {"Categorical": k_categorical, "MVN": MultivariateNormal}
if "K" in state_dict.keys():
state_dict["Dist"] = name_to_dist_dict[state_dict["Dist_name"]](
state_dict["K"]
)
state_dict["Manifold"] = manifold(state_dict["Score"], state_dict["Dist"])
self.__dict__ = state_dict
def test_dists_grad(dist_score_pair: DistScore):
# Set seed as this test involves randomness
# All errors around 1e-5 mark
np.random.seed(9)
dist, score = dist_score_pair
params = np.random.rand(dist.n_params, 1)
manifold_test = manifold(score, dist)
grad_err = estimate_grad_err(params, manifold_test)
assert grad_err < 1e-3
def test_dists_metric(dist_score_pair: DistScore, seed: int):
# Set seed as this test involves randomness
# Note if this test fails on a new distribution
# There is a chance it is due to randomness
np.random.seed(seed)
dist, score = dist_score_pair
params = np.random.rand(dist.n_params, 1)
manifold_test = manifold(LogScore, dist)
FI_err = estimate_metric_err(params, manifold_test)
assert FI_err < 1e-1
def __init__(
self,
Dist=Normal,
Score=LogScore,
Base=default_tree_learner,
natural_gradient=True,
n_estimators=500,
learning_rate=0.01,
minibatch_frac=1.0,
col_sample=1.0,
verbose=True,
verbose_eval=100,
tol=1e-4,
random_state=None,
validation_fraction=0.1,
early_stopping_rounds=None,
):
self.Dist = Dist
self.Score = Score
self.Base = Base
self.Manifold = manifold(Score, Dist)
self.natural_gradient = natural_gradient
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.minibatch_frac = minibatch_frac
self.col_sample = col_sample
self.verbose = verbose
self.verbose_eval = verbose_eval
self.init_params = None
self.n_features = None
self.base_models = []
self.scalings = []
self.col_idxs = []
self.tol = tol
self.random_state = check_random_state(random_state)
self.best_val_loss_itr = None
self.validation_fraction = validation_fraction
self.early_stopping_rounds = early_stopping_rounds
if hasattr(self.Dist, "multi_output"):
self.multi_output = self.Dist.multi_output
else:
self.multi_output = False
import itertools
import matplotlib as mpl
import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm
from ngboost.distns import Normal
from ngboost.manifold import manifold
from ngboost.scores import LogScore
if __name__ == "__main__":
rvs = np.random.randn(500)
nll_fn = (lambda p: manifold(LogScore, Normal)
(np.array(p)[:, np.newaxis]).score(rvs).mean())
fisher_fn = lambda p: manifold(LogScore, Normal)(np.array(p)[:, np.newaxis]
).metric()
grad_fn = (lambda p: manifold(LogScore, Normal)
(np.array(p)[:, np.newaxis]).d_score(rvs).mean(axis=0))
loc = np.linspace(-3, 3, 20)
scale = np.linspace(-0.5, 2, 20)
loc, scale = np.meshgrid(loc, scale)
grads_fisher_x = np.zeros((20, 20))
grads_fisher_y = np.zeros((20, 20))
grads_x = np.zeros((20, 20))
grads_y = np.zeros((20, 20))
nlls = np.zeros((20, 20))
import matplotlib as mpl
import numpy as np
import scipy as sp
import scipy.stats
from matplotlib import pyplot as plt
from tqdm import tqdm
from ngboost.distns import Normal
from ngboost.manifold import manifold
from ngboost.scores import CRPScore
if __name__ == "__main__":
rvs = np.random.randn(500)
crps_fn = (lambda p: manifold(CRPScore, Normal)
(np.array(p)[:, np.newaxis]).score(rvs).mean())
metric_fn = lambda p: manifold(CRPScore, Normal)(np.array(p)[:, np.newaxis]
).metric()
grad_fn = (lambda p: manifold(CRPScore, Normal)
(np.array(p)[:, np.newaxis]).d_score(rvs).mean(axis=0))
loc = np.linspace(-3, 3, 20)
scale = np.linspace(-0.5, 2, 20)
loc, scale = np.meshgrid(loc, scale)
grads_metric_x = np.zeros((20, 20))
grads_metric_y = np.zeros((20, 20))
grads_x = np.zeros((20, 20))
grads_y = np.zeros((20, 20))
crps = np.zeros((20, 20))
def __setstate__(self, state_dict):
if "K" in state_dict.keys():
state_dict["Dist"] = k_categorical(state_dict["K"])
state_dict["Manifold"] = manifold(state_dict["Score"], state_dict["Dist"])
self.__dict__ = state_dict
