file stringlengths 6 44 | content stringlengths 38 162k ⌀ |
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__init__.py | null |
__init__.py | """
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__init__.py | """
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input_drift.py | """
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output_drift.py | """
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deploy.py | """
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trainer.py | """
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selector.py | """
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utility.py | """
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drift_analysis.py | """
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data_reader.py | """
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imports.py | """
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transformer.py | """
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functions.py | """
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register.py | """
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__init__.py | """
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load_data.py | """
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input_drift.py | """
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output_drift.py | """
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deploy.py | """
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trainer.py | """
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selector.py | """
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utility.py | """
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drift_analysis.py | """
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transformer.py | """
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register.py | """
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__init__.py | """
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load_data.py | """
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__init__.py | """
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input_drift.py | """
/**
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output_drift.py | """
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deploy.py | """
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trainer.py | """
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selector.py | """
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utility.py | """
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drift_analysis.py | """
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data_reader.py | """
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imports.py | """
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transformer.py | """
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functions.py | """
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register.py | """
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__init__.py | """
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load_data.py | """
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deploy.py | """
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trainer.py | """
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selector.py | """
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utility.py | """
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drift_analysis.py | """
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transformer.py | """
/**
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register.py | """
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__init__.py | """
/**
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load_data.py | """
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__init__.py | """
/**
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__init__.py | '''
*
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baseline.py | import joblib
import pandas as pd
import sys
import math
import time
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error
from sk... |
uq_interface.py | '''
*
* =============================================================================
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aionUQ.py | '''
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__init__.py | '''
*
* =============================================================================
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uq_main.py | '''
*
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__init__.py | '''
*
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associationrules.py | '''
*
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featureReducer.py | '''
*
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featureSelector.py | '''
*
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* remains... |
featureImportance.py | '''
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__init__.py | '''
*
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AION_Gluon_MultiModalPrediction.py | '''
*
* =============================================================================
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__init__.py | '''
*
* =============================================================================
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AION_Gluon_MultiLabelPrediction.py | '''
*
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__init__.py | null |
regression_metrics.py | import numpy as np
from scipy.stats import norm
from sklearn.metrics import mean_squared_error, r2_score
from ..utils.misc import fitted_ucc_w_nullref
def picp(y_true, y_lower, y_upper):
"""
Prediction Interval Coverage Probability (PICP). Computes the fraction of samples for which the grounds truth lies
... |
classification_metrics.py | import numpy as np
import pandas as pd
from scipy.stats import entropy
from sklearn.metrics import roc_auc_score, log_loss, accuracy_score
def entropy_based_uncertainty_decomposition(y_prob_samples):
""" Entropy based decomposition [2]_ of predictive uncertainty into aleatoric and epistemic components.
Refer... |
__init__.py | from .classification_metrics import expected_calibration_error, area_under_risk_rejection_rate_curve, \
compute_classification_metrics, entropy_based_uncertainty_decomposition
from .regression_metrics import picp, mpiw, compute_regression_metrics, plot_uncertainty_distribution, \
plot_uncertainty_by_feature, pl... |
__init__.py | from .uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve
|
uncertainty_characteristics_curve.py | from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import simps, trapz
from sklearn.isotonic import IsotonicRegression
DEFAULT_X_AXIS_NAME = 'excess'
DEFAULT_Y_AXIS_NAME = 'missrate'
class UncertaintyCharacteristicsCurve:
"""
Class with main functions of the Un... |
heteroscedastic_mlp.py | import torch
import torch.nn.functional as F
from uq360.models.noise_models.heteroscedastic_noise_models import GaussianNoise
class GaussianNoiseMLPNet(torch.nn.Module):
def __init__(self, num_features, num_outputs, num_hidden):
super(GaussianNoiseMLPNet, self).__init__()
self.fc = torch.nn.Linear... |
__init__.py | null |
layer_utils.py | """
Contains implementations of various utilities used by Horseshoe Bayesian layers
"""
import numpy as np
import torch
from torch.nn import Parameter
td = torch.distributions
gammaln = torch.lgamma
def diag_gaussian_entropy(log_std, D):
return 0.5 * D * (1.0 + torch.log(2 * np.pi)) + torch.sum(log_std)
def ... |
layers.py | """
Contains implementations of various Bayesian layers
"""
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from uq360.models.bayesian_neural_networks.layer_utils import InvGammaHalfCauchyLayer, InvGammaLayer
td = torch.distributions
def reparam(mu, logvar, do_sample... |
__init__.py | null |
misc.py | import numpy as np
import torch
from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseFixedPrecision
def compute_test_ll(y_test, y_pred_samples, std_y=1.):
"""
Computes test log likelihoods = (1 / Ntest) * \sum_n p(y_n | x_n, D_train)
:param y_test: True y
:param y_pred_samples:... |
horseshoe_mlp.py | from abc import ABC
import numpy as np
import torch
from torch import nn
from uq360.models.bayesian_neural_networks.layers import HorseshoeLayer, BayesianLinearLayer, RegularizedHorseshoeLayer
from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision
import numpy as np
td = torch.di... |
bayesian_mlp.py | from abc import ABC
import torch
from torch import nn
from uq360.models.bayesian_neural_networks.layers import BayesianLinearLayer
from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision
import numpy as np
td = torch.distributions
class BayesianNN(nn.Module, ABC):
"""
Bay... |
__init__.py | null |
homoscedastic_noise_models.py | import math
import numpy as np
import torch
from scipy.special import gammaln
from uq360.models.noise_models.noisemodel import AbstractNoiseModel
from torch.nn import Parameter
td = torch.distributions
def transform(a):
return torch.log(1 + torch.exp(a))
class GaussianNoiseGammaPrecision(torch.nn.Module, Abst... |
heteroscedastic_noise_models.py | import math
import numpy as np
import torch
from scipy.special import gammaln
from uq360.models.noise_models.noisemodel import AbstractNoiseModel
from torch.nn import Parameter
td = torch.distributions
def transform(a):
return torch.log(1 + torch.exp(a))
class GaussianNoise(torch.nn.Module, AbstractNoiseModel... |
noisemodel.py | import abc
import sys
# Ensure compatibility with Python 2/3
if sys.version_info >= (3, 4):
ABC = abc.ABC
else:
ABC = abc.ABCMeta(str('ABC'), (), {})
class AbstractNoiseModel(ABC):
""" Abstract class. All noise models inherit from here.
"""
def __init__(self, *argv, **kwargs):
""" Initia... |
__init__.py | null |
__init__.py | null |
builtinuq.py | import abc
import sys
# Ensure compatibility with Python 2/3
if sys.version_info >= (3, 4):
ABC = abc.ABC
else:
ABC = abc.ABCMeta(str('ABC'), (), {})
class BuiltinUQ(ABC):
""" BuiltinUQ is the base class for any algorithm that has UQ built into it.
"""
def __init__(self, *argv, **kwargs):
... |
__init__.py | null |
posthocuq.py | import abc
import sys
# Ensure compatibility with Python 2/3
if sys.version_info >= (3, 4):
ABC = abc.ABC
else:
ABC = abc.ABCMeta(str('ABC'), (), {})
class PostHocUQ(ABC):
""" PostHocUQ is the base class for any algorithm that quantifies uncertainty of a pre-trained model.
"""
def __init__(self,... |
__init__.py | from .ucc_recalibration import UCCRecalibration
|
ucc_recalibration.py | from collections import namedtuple
from uq360.algorithms.posthocuq import PostHocUQ
from uq360.utils.misc import form_D_for_auucc
from uq360.metrics.uncertainty_characteristics_curve.uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve
class UCCRecalibration(PostHocUQ):
""" Recalibration a re... |
__init__.py | from .classification_calibration import ClassificationCalibration
|
classification_calibration.py | from collections import namedtuple
import numpy as np
from sklearn.calibration import CalibratedClassifierCV
from sklearn.preprocessing import LabelEncoder
from uq360.utils.misc import DummySklearnEstimator
from uq360.algorithms.posthocuq import PostHocUQ
class ClassificationCalibration(PostHocUQ):
"""Post hoc... |
auxiliary_interval_predictor.py | from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from scipy.stats import norm
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
from uq360.algorithms.builtinuq import BuiltinUQ
np.random.seed(42)
torch.manual_seed(42)
class _MLPNet_... |
__init__.py | from .auxiliary_interval_predictor import AuxiliaryIntervalPredictor
|
bnn.py | import copy
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.utils.data as data_utils
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from uq360.algorithms.builtinuq import BuiltinUQ
fr... |
__init__.py | null |
homoscedastic_gaussian_process_regression.py | from collections import namedtuple
import botorch
import gpytorch
import numpy as np
import torch
from botorch.models import SingleTaskGP
from botorch.utils.transforms import normalize
from gpytorch.constraints import GreaterThan
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from uq360... |
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