repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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inFairness | inFairness-main/examples/sentiment-analysis/data.py | import torch
import re
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
sns.set_context(rc={'figure.figsize': (9, 9)}, font_scale=2.)
TOKEN_RE = re.compi... | 9,005 | 38.327511 | 132 | py |
inFairness | inFairness-main/examples/synthetic-data/trainer.py |
class Trainer(object):
"""Main trainer class that orchestrates the entire learning routine
Use this class to start training a model using individual fairness routines
Args:
dataloader (torch.util.data.DataLoader): training data loader
model (inFairness.fairalgo): Individual fairness algori... | 1,516 | 29.959184 | 94 | py |
inFairness | inFairness-main/examples/word-embedding-association-test/utils.py | import numpy as np
from sklearn.preprocessing import normalize
from sklearn.utils.random import sample_without_replacement
from itertools import combinations
try: # SciPy >= 0.19
from scipy.special import comb
except ImportError:
from scipy.misc import comb
def statistics(X, Y, A, B):
score_x = np.m... | 2,492 | 33.150685 | 94 | py |
inFairness | inFairness-main/examples/word-embedding-association-test/data.py | import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.utils.random import sample_without_replacement
from sklearn.decomposition import TruncatedSVD
def load_embedd... | 4,721 | 37.704918 | 116 | py |
inFairness | inFairness-main/tests/postprocessing/test_data_ds.py | import pytest
import torch
import numpy as np
from inFairness.distances import EuclideanDistance
from inFairness.postprocessing.data_ds import PostProcessingDataStore
def test_add_data():
ntries = 10
B, D = 10, 50
distance_x = EuclideanDistance()
data_ds = PostProcessingDataStore(distance_x)
cou... | 1,153 | 22.08 | 70 | py |
inFairness | inFairness-main/tests/postprocessing/test_glif.py | import pytest
import torch
import torch.nn.functional as F
import numpy as np
from inFairness.distances import EuclideanDistance
from inFairness.postprocessing import GraphLaplacianIF
def test_postprocess_incorrectargs():
params = (1.0, 1.0, 100.0, True)
dist_x = EuclideanDistance()
pp = GraphLaplacian... | 1,926 | 29.109375 | 90 | py |
inFairness | inFairness-main/tests/distances/test_common_distances.py | import pytest
import math
import torch
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from inFairness import distances
def test_euclidean_distance():
dist = distances.EuclideanDistance()
X = torch.FloatTensor([[0.0... | 7,261 | 26.507576 | 112 | py |
inFairness | inFairness-main/tests/distances/test_distance_state.py | import pytest
import torch
from inFairness import distances
def test_mahalanobis_dist_state_buffer_set():
dist = distances.MahalanobisDistances()
sigma = torch.rand(size=(10, 10))
dist.fit(sigma)
state_dict = dist.state_dict()
assert "sigma" in state_dict
assert torch.all(state_dict["sigma"... | 4,247 | 26.230769 | 70 | py |
inFairness | inFairness-main/tests/utils/test_datautils.py | import pytest
import numpy as np
from inFairness.utils import datautils
from inFairness.utils.datautils import include_exclude_terms
def test_datapair_generation_1data_random():
# Generate data pairs fewer than possible
data = np.random.random(size=(100, 5, 5))
npairs = 10
pair_idxs = datautils.gene... | 3,817 | 30.04065 | 87 | py |
inFairness | inFairness-main/tests/utils/test_normalized_discounted_cumulative_gain.py | import torch
import inFairness.utils.ndcg as ndcg
def test_normalized_discounted_cumulative_gain():
x = torch.tensor([10, 8.0, 1.0])
assert ndcg.normalized_discounted_cumulative_gain(x) == 1.0
x = torch.tensor([1.,2,3])
assert ndcg.normalized_discounted_cumulative_gain(x) - 0.7397 < 0.01
batch_x = torch.... | 597 | 32.222222 | 89 | py |
inFairness | inFairness-main/tests/utils/test_plackett_luce.py | import torch
from torch.nn.parameter import Parameter
from functorch import vmap
from inFairness.utils import plackett_luce
from inFairness.utils.plackett_luce import PlackettLuce
from inFairness.utils.ndcg import vect_normalized_discounted_cumulative_gain as v_ndcg
vect_gather = vmap(torch.gather, in_dims=(None,Non... | 1,752 | 34.77551 | 100 | py |
inFairness | inFairness-main/tests/utils/test_initializer.py | import pytest
from inFairness.utils.misc import initializer
def test_initializer():
class MyClass:
@initializer
def __init__(self, a, b=1):
pass
x = MyClass(a=1, b=2)
assert x.a == 1 and x.b == 2
x = MyClass(a=1)
assert x.a == 1 and x.b == 1
if __name__ == "__main__"... | 345 | 17.210526 | 45 | py |
inFairness | inFairness-main/tests/auditor/test_senstir_auditor.py | import pytest
import torch
from mock import patch
from inFairness.auditor import SenSTIRAuditor
from inFairness.distances import (
SensitiveSubspaceDistance,
SquaredEuclideanDistance,
)
def mock_torch_rand_like(*size):
return torch.ones_like(*size)
@patch("torch.rand_like", mock_torch_rand_like)
def t... | 2,070 | 30.378788 | 141 | py |
inFairness | inFairness-main/tests/auditor/test_sensei_auditor.py | import pytest
import numpy as np
from mock import patch
import torch
from torch.nn import functional as F
from inFairness.auditor import SenSeIAuditor
def mock_adam_optim(
params, lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False
):
return torch.optim.SGD(params, lr=lr)
def my_dist(s, t):
... | 3,926 | 24.5 | 88 | py |
inFairness | inFairness-main/tests/auditor/test_auditor.py | from re import X
import pytest
import numpy as np
from inFairness.auditor import Auditor
from mock import patch
import torch
from torch.nn import functional as F
def mock_adam_optim(
params, lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False
):
return torch.optim.SGD(params, lr=lr)
def my_dis... | 1,177 | 21.226415 | 88 | py |
inFairness | inFairness-main/tests/auditor/test_sensr_auditor.py | import pytest
import numpy as np
from inFairness.auditor import SenSRAuditor
from mock import patch
import torch
from torch.nn import functional as F
def mock_adam_optim(
params, lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False
):
return torch.optim.SGD(params, lr=lr)
def my_dist(s, t):
... | 3,772 | 25.384615 | 87 | py |
inFairness | inFairness-main/tests/fairalgo/test_sensei.py | import pytest
import numpy as np
from inFairness.auditor import SenSeIAuditor
from inFairness.fairalgo import SenSeI
from mock import patch
import torch
from torch.nn import functional as F
def mock_generate_worst_case_examples(cls, network, x, lambda_param):
return torch.ones_like(x) * -1.0
def mock_dist(s, t... | 1,593 | 24.709677 | 80 | py |
inFairness | inFairness-main/tests/fairalgo/test_senstir.py | import torch
from inFairness.distances import (
SensitiveSubspaceDistance,
SquaredEuclideanDistance,
)
from inFairness.fairalgo import SenSTIR
def generate_test_data(num_batches, queries_per_batch, items_per_query):
num_features = 2
item_data = torch.rand(
num_batches, queries_per_batch, item... | 2,236 | 28.826667 | 110 | py |
inFairness | inFairness-main/tests/fairalgo/test_sensr.py | import pytest
import numpy as np
from inFairness.auditor import SenSRAuditor
from inFairness.fairalgo import SenSR
from mock import patch
import torch
from torch.nn import functional as F
def mock_generate_worst_case_examples(cls, network, x, y, lambda_param):
return torch.ones_like(x) * -1.0
def mock_dist(s, ... | 1,576 | 24.852459 | 83 | py |
inFairness | inFairness-main/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 3,873 | 27.910448 | 79 | py |
inFairness | inFairness-main/inFairness/__init__.py | 0 | 0 | 0 | py | |
inFairness | inFairness-main/inFairness/postprocessing/datainterfaces.py | from typing import Dict
import torch
from dataclasses import dataclass
@dataclass
class PostProcessingObjectiveResponse:
"""Class to store the result from a post-processing algorithm"""
y_solution: torch.Tensor = None
objective: Dict = None
| 256 | 20.416667 | 68 | py |
inFairness | inFairness-main/inFairness/postprocessing/data_ds.py | import torch
from inFairness.postprocessing.distance_ds import DistanceStructure
class PostProcessingDataStore(object):
"""Data strucuture to hold the data used for post-processing
Parameters
-------------
distance_x: inFairness.distances.Distance
Distance metric in the input space
... | 2,843 | 30.955056 | 80 | py |
inFairness | inFairness-main/inFairness/postprocessing/__init__.py | from inFairness.postprocessing.glif import GraphLaplacianIF
from inFairness.postprocessing.base_postprocessing import BasePostProcessing
__all__ = [symb for symb in globals() if not symb.startswith("_")]
| 206 | 33.5 | 76 | py |
inFairness | inFairness-main/inFairness/postprocessing/glif.py | import torch
import numpy as np
from inFairness.utils.postprocessing import (
build_graph_from_dists,
get_laplacian,
laplacian_solve,
)
from inFairness.postprocessing.base_postprocessing import BasePostProcessing
from inFairness.postprocessing.datainterfaces import PostProcessingObjectiveResponse
class G... | 11,440 | 34.977987 | 174 | py |
inFairness | inFairness-main/inFairness/postprocessing/base_postprocessing.py | import torch
from typing import Tuple
from inFairness.postprocessing.data_ds import PostProcessingDataStore
class BasePostProcessing(object):
"""Base class for Post-Processing methods
Parameters
-------------
distance_x: inFairness.distances.Distance
Distance matrix in the input spac... | 2,253 | 28.272727 | 84 | py |
inFairness | inFairness-main/inFairness/postprocessing/distance_ds.py | import torch
class DistanceStructure(object):
"""Data structure to store and track the distance matrix between data points
Parameters
-------------
distance_x: inFairness.distances.Distance
Distance metric in the input space
"""
def __init__(self, distance_x):
self.di... | 1,604 | 28.722222 | 84 | py |
inFairness | inFairness-main/inFairness/distances/wasserstein_distance.py | import torch
from ot import emd2
from inFairness.distances import MahalanobisDistances
class WassersteinDistance(MahalanobisDistances):
"""computes a batched Wasserstein Distance for pairs of sets of items on each batch in the tensors
with dimensions B, N, D and B, M, D where B and D are the batch and featur... | 1,924 | 34.648148 | 136 | py |
inFairness | inFairness-main/inFairness/distances/explore_distance.py | import numpy as np
import torch
from scipy.stats import logistic
from inFairness.utils import datautils
from inFairness.distances.mahalanobis_distance import MahalanobisDistances
class EXPLOREDistance(MahalanobisDistances):
"""Implements the Embedded Xenial Pairs Logistic Regression metric
(EXPLORE) defined ... | 4,287 | 35.338983 | 101 | py |
inFairness | inFairness-main/inFairness/distances/euclidean_dists.py | import torch
from inFairness.distances.distance import Distance
class EuclideanDistance(Distance):
def __init__(self):
super().__init__()
def forward(self, x, y, itemwise_dist=True):
if itemwise_dist:
return torch.cdist(x.unsqueeze(1), y.unsqueeze(1)).reshape(-1, 1)
else... | 2,264 | 30.458333 | 88 | py |
inFairness | inFairness-main/inFairness/distances/logistic_sensitive_subspace.py | from typing import Iterable
import numpy as np
import torch
from sklearn.linear_model import LogisticRegression
from inFairness.distances import SensitiveSubspaceDistance
from inFairness.utils import datautils, validationutils
class LogisticRegSensitiveSubspace(SensitiveSubspaceDistance):
"""Implements the Softm... | 8,395 | 39.757282 | 174 | py |
inFairness | inFairness-main/inFairness/distances/__init__.py | from inFairness.distances.distance import Distance
from inFairness.distances.euclidean_dists import (
EuclideanDistance,
ProtectedEuclideanDistance,
)
from inFairness.distances.sensitive_subspace_dist import (
SVDSensitiveSubspaceDistance,
SensitiveSubspaceDistance,
)
from inFairness.distances.explore_d... | 717 | 27.72 | 66 | py |
inFairness | inFairness-main/inFairness/distances/mahalanobis_distance.py | import torch
import numpy as np
from functorch import vmap
from inFairness.distances.distance import Distance
class MahalanobisDistances(Distance):
"""Base class implementing the Generalized Mahalanobis Distances
Mahalanobis distance between two points X1 and X2 is computed as:
.. math:: \\text{dist}(X... | 5,180 | 30.785276 | 107 | py |
inFairness | inFairness-main/inFairness/distances/sensitive_subspace_dist.py | import numpy as np
import torch
from sklearn.decomposition import TruncatedSVD
from typing import List
from inFairness.distances.mahalanobis_distance import MahalanobisDistances
from inFairness.utils import datautils
class SensitiveSubspaceDistance(MahalanobisDistances):
"""Implements Sensitive Subspace metric b... | 5,490 | 35.852349 | 95 | py |
inFairness | inFairness-main/inFairness/distances/distance.py | from abc import ABCMeta, abstractmethod
from torch import nn
class Distance(nn.Module, metaclass=ABCMeta):
"""
Abstract base class for model distances
"""
def __init__(self):
super().__init__()
def fit(self, **kwargs):
"""
Fits the metric parameters for learnable metrics
... | 1,196 | 28.925 | 179 | py |
inFairness | inFairness-main/inFairness/utils/datautils.py | from typing import Iterable
import torch
import numpy as np
from itertools import product
def generate_data_pairs(n_pairs, datasamples_1, datasamples_2=None, comparator=None):
"""Utility function to generate (in)comparable data pairs given data samples. Use case includes
creating a dataset of comparable and... | 4,492 | 31.092857 | 105 | py |
inFairness | inFairness-main/inFairness/utils/misc.py | from functools import wraps
import inspect
def initializer(func):
"""
Automatically assigns the parameters.
>>> class process:
... @initializer
... def __init__(self, cmd, reachable=False, user='root'):
... pass
>>> p = process('halt', True)
>>> p.cmd, p.reachable, p.u... | 953 | 23.461538 | 75 | py |
inFairness | inFairness-main/inFairness/utils/ndcg.py | import torch
from functorch import vmap
def discounted_cumulative_gain(relevances):
numerator = torch.pow(torch.tensor([2.0]), relevances)
denominator = torch.log2(torch.arange(len(relevances), dtype=torch.float) + 2)
return (numerator / denominator).sum()
def normalized_discounted_cumulative_gain(relev... | 1,822 | 36.204082 | 108 | py |
inFairness | inFairness-main/inFairness/utils/validationutils.py | import numpy as np
def is_tensor_binary(data: np.ndarray):
"""Checks if the data is binary (0/1) or not. Return True if it is binary data
Parameters
--------------
data: np.ndarray
Data to validata if binary or not
Returns
----------
is_binary: bool
True i... | 475 | 21.666667 | 82 | py |
inFairness | inFairness-main/inFairness/utils/plackett_luce.py | """
This file implements Plackett-Luce distribution and is taken from the
following source:
Source: Github PyTorch PR#50362 - Add Plackett-Luce Distribution
URL: https://github.com/pytorch/pytorch/pull/50362/
Author: Jeremy Salwen (https://github.com/jeremysalwen)
"""
from typing import Optional
import... | 5,646 | 39.335714 | 117 | py |
inFairness | inFairness-main/inFairness/utils/__init__.py | 0 | 0 | 0 | py | |
inFairness | inFairness-main/inFairness/utils/params.py | import torch.nn
def freeze_network(network: torch.nn.Module):
"""Freeze network parameters.
:param network: torch.nn.Module
:type network: torch.nn.Module
"""
for p in network.parameters():
p.requires_grad = False
def unfreeze_network(network: torch.nn.Module):
"""Unfreeze network pa... | 475 | 22.8 | 47 | py |
inFairness | inFairness-main/inFairness/utils/postprocessing.py | import torch
def build_graph_from_dists(
dists: torch.Tensor,
scale: float = None,
threshold: float = None,
normalize: bool = False,
):
"""Build the adjacency matrix `W` given distances
Parameters
-------------
dists: torch.Tensor
Distance matrix between data points. S... | 2,900 | 27.441176 | 86 | py |
inFairness | inFairness-main/inFairness/auditor/sensr_auditor.py | import torch
from torch.nn import Parameter
from inFairness.auditor import Auditor
from inFairness.utils.params import freeze_network, unfreeze_network
from inFairness.utils.datautils import get_device
class SenSRAuditor(Auditor):
"""SenSR Auditor implements the functionality to generate worst-case examples
... | 5,871 | 32.175141 | 125 | py |
inFairness | inFairness-main/inFairness/auditor/sensei_auditor.py | import torch
from torch.nn import Parameter
from inFairness.auditor.auditor import Auditor
from inFairness.utils.params import freeze_network, unfreeze_network
from inFairness.utils.datautils import get_device
class SenSeIAuditor(Auditor):
"""SenSeI Auditor implements the functionality to generate worst-case exa... | 5,904 | 33.331395 | 128 | py |
inFairness | inFairness-main/inFairness/auditor/datainterface.py | import torch
from dataclasses import dataclass
@dataclass
class AuditorResponse:
"""Class to store a result from the auditor"""
lossratio_mean: float = None
lossratio_std: float = None
lower_bound: float = None
threshold: float = None
pval: float = None
confidence: float = None
is_mo... | 342 | 19.176471 | 50 | py |
inFairness | inFairness-main/inFairness/auditor/senstir_auditor.py | import torch
from torch.nn.parameter import Parameter
from inFairness.distances import (
WassersteinDistance,
MahalanobisDistances,
)
from inFairness.auditor import Auditor
from inFairness.utils.params import freeze_network, unfreeze_network
class SenSTIRAuditor(Auditor):
"""SenSTIR Auditor generates wo... | 4,386 | 34.096 | 128 | py |
inFairness | inFairness-main/inFairness/auditor/auditor.py | import torch
import numpy as np
from abc import ABCMeta
from scipy.stats import norm
from inFairness.utils.datautils import convert_tensor_to_numpy
from inFairness.auditor.datainterface import AuditorResponse
class Auditor(metaclass=ABCMeta):
"""
Abstract class for model auditors, e.g. Sensei or Sensr
""... | 4,199 | 34.294118 | 85 | py |
inFairness | inFairness-main/inFairness/auditor/__init__.py | from inFairness.auditor.auditor import Auditor
from inFairness.auditor.sensei_auditor import SenSeIAuditor
from inFairness.auditor.sensr_auditor import SenSRAuditor
from inFairness.auditor.senstir_auditor import SenSTIRAuditor
__all__ = [symb for symb in globals() if not symb.startswith("_")]
| 295 | 41.285714 | 66 | py |
inFairness | inFairness-main/inFairness/fairalgo/datainterfaces.py | import torch
from dataclasses import dataclass
@dataclass
class FairModelResponse:
"""Class to store a result from the fairmodel algorithm"""
loss: torch.Tensor = None
y_pred: torch.Tensor = None
| 212 | 16.75 | 62 | py |
inFairness | inFairness-main/inFairness/fairalgo/sensei.py | import torch
from torch import nn
from inFairness.auditor import SenSeIAuditor
from inFairness.fairalgo.datainterfaces import FairModelResponse
from inFairness.utils import datautils
class SenSeI(nn.Module):
"""Implementes the Sensitive Set Invariane (SenSeI) algorithm.
Proposed in `SenSeI: Sensitive Set In... | 3,743 | 27.580153 | 120 | py |
inFairness | inFairness-main/inFairness/fairalgo/__init__.py | from inFairness.fairalgo.sensei import SenSeI
from inFairness.fairalgo.sensr import SenSR
from inFairness.fairalgo.senstir import SenSTIR
from inFairness.fairalgo.datainterfaces import FairModelResponse
__all__ = [symb for symb in globals() if not symb.startswith("_")]
| 271 | 37.857143 | 66 | py |
inFairness | inFairness-main/inFairness/fairalgo/senstir.py | import torch
from torch import nn
from functorch import vmap
from inFairness.auditor import SenSTIRAuditor
from inFairness.distances.mahalanobis_distance import MahalanobisDistances
from inFairness.fairalgo.datainterfaces import FairModelResponse
from inFairness.utils import datautils
from inFairness.utils.plackett_l... | 5,938 | 33.132184 | 97 | py |
inFairness | inFairness-main/inFairness/fairalgo/sensr.py | import torch
from torch import nn
from inFairness.auditor import SenSRAuditor
from inFairness.fairalgo.datainterfaces import FairModelResponse
from inFairness.utils import datautils
class SenSR(nn.Module):
"""Implementes the Sensitive Subspace Robustness (SenSR) algorithm.
Proposed in `Training individually... | 3,534 | 27.055556 | 125 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/KPN.py | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torchsummary import summary
import torchvision.models as models
# KPN基本网路单元
class Basic(nn.Module):
def __init__(self, in_ch, out_ch, g=16, channel_att=False, spatial_att=False):
super(Basic, self).__init__()
... | 12,210 | 39.30033 | 150 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/train_eval_syn.py | import torch
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import argparse
import os, sys, time, shutil
from data_provider import OnTheFlyDataset, _configspec_path
from kpn_data_provider import TrainDataSet, UndosRGBG... | 13,114 | 37.014493 | 144 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/kpn_data_provider.py | import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import os
from PIL import Image
import numpy as np
from skimage.color import rgb2xyz
import inspect
from utils.training_util import read_config
from data_genera... | 10,342 | 37.737828 | 134 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_provider.py | import glob
import inspect
import os
import zlib
from time import time
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.data as data
from PIL import Image
from torch import FloatTensor
from data_generation.pipeline import ImageDegradationPipeline
from utils.image_utils import bayer_c... | 31,377 | 45.076358 | 193 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/generate_dataset.py | import tifffile
import skimage
import numpy as np
import os
import argparse
import glob
import json
from tqdm import tqdm
from sklearn.feature_extraction.image import extract_patches_2d
import torch
from torch.autograd import Variable
from torch import FloatTensor
from data_generation.pipeline import ImageDegradation... | 7,397 | 41.034091 | 149 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/constants.py | import math
import torch
from torch import FloatTensor
XYZ2sRGB = FloatTensor([[ 3.2406, -1.5372, -0.4986],
[-0.9689, 1.8758, 0.0415],
[ 0.0557, -0.2040, 1.0570]])
# http://brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html
ProPhotoRGB2XYZ = FloatTensor([[0.79767... | 16,240 | 44.113889 | 990 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/data_utils.py | """ Utilities functions.
"""
import numbers
import numpy as np
import torch
from torch import FloatTensor
def random_crop(im, num_patches, w, h=None):
h = w if h is None else h
nw = im.size(-1) - w
nh = im.size(-2) - h
if nw < 0 or nh < 0:
raise RuntimeError("Image is to small {} for the desir... | 3,336 | 25.696 | 76 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/image_io.py | """ I/O module
This unit deals with the nitty gritty of reading in DSLR raw camera and
various other formats.
"""
import numpy as np
import rawpy
def read_raw(path, n_bits=None):
with rawpy.imread(path) as raw:
im_ = raw.raw_image_visible.copy()
# subtract black level
im = np.zeros(im_.s... | 1,176 | 29.973684 | 106 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/ahd_demosaicking.py | import numpy as np
import scipy
from scipy.io import savemat
from .constants import RGB2YUV
from scipy.interpolate import interp2d
_RGB2YUV = RGB2YUV.cpu().data.numpy()
def ahd_demosaicking(mosaic, delta=1, sobel_sz=3, avg_sz=3):
"""Demosaicking using AHD algorithm.
No median filtering, assume GRBG for... | 4,292 | 27.058824 | 97 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/denoise_wavelet.py | # coding: utf-8
# Modified from skimage's wavelet.
# https://github.com/scikit-image/scikit-image/blob/f0d48db4c246989182aa01c837d04903bc2330ae/skimage/restoration/_denoise.py
import scipy.stats
import numpy as np
from math import ceil
import warnings
import pywt
import skimage.color as color
from skimage import img_as... | 16,124 | 41.101828 | 124 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/image_processing.py | import torch
import torch.nn as nn
from torch import FloatTensor, IntTensor
# For drawing motion blur kernel.
import numpy as np
import cv2
import scipy
import functools
import math
from .data_utils import mosaick_multiply, expand_to_4d_batch
from .data_utils import python_to_tensor, cuda_like, number_to_list, is_numb... | 60,184 | 36.615625 | 130 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/__init__.py | import sys
import inspect, os
# Need this to import halide. See:
# https://stackoverflow.com/questions/6323860/sibling-package-imports
sys.path.insert(0, os.path.join(
os.path.dirname(inspect.getfile(inspect.currentframe())),
'..'))
sys.path.insert(0, os.path.join(
... | 389 | 34.454545 | 79 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/pipeline.py | import torch.nn as nn
import torch
from . import image_processing
class ImageDegradationPipeline(nn.Module):
def __init__(self, configs):
""" Image Degradation Pipeline.
Args:
configs: list of modules to be implemented and their parameters.
The list should contai... | 1,439 | 34.121951 | 92 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/data_generation/kernel.py | import torch
def gausskern1d(sig, sz=None):
""" 1D Gaussian kernel.
Args:
sz: kernel size.
sig: stdev of the kernel
"""
if sz is None:
sz = int(2*int(sig) + 1)
sz = max(sz, 3)
half_sz = int(sz / 2)
neg_half_sz = half_sz - sz + 1
neg_half_sz = float(neg_half... | 1,285 | 26.956522 | 129 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/utils/image_utils.py | import numpy as np
import torch
def center_crop_tensor(tensor, w, h):
tw = tensor.size(-1)
th = tensor.size(-2)
if tw < w or th < h:
raise RuntimeError("Crop size is larger than image size.")
h0 = int((th - h) / 2)
w0 = int((tw - w) / 2)
h1 = h0 + h
w1 = w0 + w
return tensor[..... | 1,497 | 26.740741 | 106 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/utils/__init__.py | import sys
import inspect, os
sys.path.insert(0, os.path.join(
os.path.dirname(inspect.getfile(inspect.currentframe())),
'..'))
sys.path.insert(0, os.path.join(
os.path.dirname(inspect.getfile(inspect.currentframe()))))
| 284 | 30.666667 | 79 | py |
kernel-prediction-networks-PyTorch | kernel-prediction-networks-PyTorch-master/utils/training_util.py | import numpy as np
import glob
import torch
import shutil
import os
import cv2
import numbers
import skimage
from collections import OrderedDict
from configobj import ConfigObj
from validate import Validator
from data_generation.pipeline import ImageDegradationPipeline
class MovingAverage(object):
def __init__(se... | 7,118 | 34.41791 | 105 | py |
Geo-FPT | Geo-FPT-main/interface.py | import numpy as np
import ctypes
from ctypes import *
from numpy.ctypeslib import ndpointer
"define a pointer for 1D arrays"
_doublep = ndpointer(ctypes.c_double, flags='C_CONTIGUOUS')
"define a pointer for 1D arrays INT "
_intp = ndpointer(ctypes.c_int, flags='C_CONTIGUOUS')
"define a pointer for 2D arrays"
_double... | 2,862 | 31.908046 | 154 | py |
prospector | prospector-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import re
import glob
#import subprocess
try:
from setuptools import setup
setup
except ImportError:
from distutils.core import setup
setup
#githash = subprocess.check_output(["git", "log", "--format=%h"], universal_newlines=True).spli... | 1,173 | 25.088889 | 105 | py |
prospector | prospector-master/prospect/__init__.py | try:
from ._version import __version__, __githash__
except(ImportError):
pass
from . import models
from . import fitting
from . import io
from . import sources
from . import utils
from . import likelihood
from .utils import prospect_args
| 247 | 18.076923 | 50 | py |
prospector | prospector-master/prospect/fitting/nested.py | import sys, time
import numpy as np
from numpy.random import normal, multivariate_normal
from six.moves import range
try:
import nestle
except(ImportError):
pass
try:
import dynesty
from dynesty.utils import *
from dynesty.dynamicsampler import _kld_error
except(ImportError):
pass
__all__ =... | 6,784 | 41.142857 | 91 | py |
prospector | prospector-master/prospect/fitting/fitting.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""fitting.py -- Default posterior probability function and high-level fitting
methods for prospector
"""
import time
from functools import partial as argfix
import numpy as np
from scipy.optimize import minimize, least_squares
from .minimizer import minimize_wrapper,... | 19,201 | 35.575238 | 93 | py |
prospector | prospector-master/prospect/fitting/minimizer.py | import warnings
import numpy as np
from numpy.random import normal, multivariate_normal
__all__ = ["minimize_wrapper", "minimizer_ball", "reinitialize"]
class minimize_wrapper(object):
"""This is a hack to make the minimization function pickleable (for MPI)
even though it requires many arguments. Ripped of... | 3,482 | 34.907216 | 80 | py |
prospector | prospector-master/prospect/fitting/__init__.py | from .ensemble import run_emcee_sampler, restart_emcee_sampler
from .minimizer import reinitialize
from .nested import run_dynesty_sampler
from .fitting import fit_model, lnprobfn, run_minimize
__all__ = ["fit_model", "lnprobfn",
# below should all be removed/deprecated
"run_emcee_sampler", "rest... | 417 | 37 | 62 | py |
prospector | prospector-master/prospect/fitting/ensemble.py | import sys
import numpy as np
from numpy.random import normal, multivariate_normal
try:
import emcee
EMCEE_VERSION = emcee.__version__.split('.')[0]
except(ImportError):
pass
from .convergence import convergence_check
__all__ = ["run_emcee_sampler", "restart_emcee_sampler",
"reinitialize_ball"... | 20,408 | 38.399614 | 96 | py |
prospector | prospector-master/prospect/fitting/convergence.py | import numpy as np
__all__ = ["convergence_check", "make_kl_bins", "kl_divergence",
"find_subsequence"]
def find_subsequence(subseq, seq):
"""If subsequence exists in sequence, return True. otherwise return False.
can be modified to return the appropriate index (useful to test WHERE a
chain is... | 4,582 | 37.512605 | 97 | py |
prospector | prospector-master/prospect/io/read_results.py | import sys, os
from copy import deepcopy
import warnings
import pickle, json
import numpy as np
try:
import h5py
except:
pass
try:
from sedpy.observate import load_filters
except:
pass
"""Convenience functions for reading and reconstructing results from a fitting
run, including reconstruction of the m... | 20,576 | 33.525168 | 95 | py |
prospector | prospector-master/prospect/io/write_results.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
""" write_results.py - Methods for writing prospector ingredients and outputs
to HDF5 files as well as to pickles.
"""
import os, time, warnings
import pickle, json, base64
import numpy as np
try:
import h5py
_has_h5py_ = True
except(ImportError):
_has_h5py_ =... | 15,472 | 33.006593 | 95 | py |
prospector | prospector-master/prospect/io/__init__.py | from . import write_results
from . import read_results
__all__ = ["write_results", "read_results"]
| 100 | 19.2 | 43 | py |
prospector | prospector-master/prospect/models/sedmodel.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""sedmodel.py - classes and methods for storing parameters and predicting
observed spectra and photometry from them, given a Source object.
"""
import numpy as np
import os
from numpy.polynomial.chebyshev import chebval, chebvander
from .parameters import ProspectorParam... | 34,356 | 40.89878 | 105 | py |
prospector | prospector-master/prospect/models/priors.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""priors.py -- This module contains various objects to be used as priors.
When called these return the ln-prior-probability, and they can also be used to
construct prior transforms (for nested sampling) and can be sampled from.
"""
import numpy as np
import scipy.stats
... | 13,637 | 26.330661 | 106 | py |
prospector | prospector-master/prospect/models/parameters.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""parameters.py -- This module contains the prospector base class for
models, ProspectorParams. This class is responsible for maintaining model
parameter state, converting between parameter dictionaries and vectors,
and computing parameter dependencies and prior probabil... | 15,162 | 36.07335 | 90 | py |
prospector | prospector-master/prospect/models/model_setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, os, getopt, json, warnings
from copy import deepcopy
import numpy as np
from . import parameters
from ..utils.obsutils import fix_obs
"""This module has methods to take a .py file containing run parameters, model
parameters and other info and return a run_para... | 7,298 | 29.668067 | 87 | py |
prospector | prospector-master/prospect/models/templates.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""templates.py -- A set of predefined "base" prospector model specifications
that can be used as a starting point and then combined or altered.
"""
from copy import deepcopy
import numpy as np
from . import priors
from . import transforms
__all__ = ["TemplateLibrary",
... | 25,141 | 40.284072 | 130 | py |
prospector | prospector-master/prospect/models/__init__.py | """This module includes objects that store parameter specfications and
efficiently convert between parameter dictionaries and parameter vectors
necessary for fitting algorithms. There are submodules for parameter priors,
common parameter transformations, and pre-defined sets of parameter
specifications.
"""
from .sed... | 470 | 38.25 | 77 | py |
prospector | prospector-master/prospect/models/transforms.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""transforms.py -- This module contains parameter transformations that may be
useful to transform from parameters that are easier to _sample_ in to the
parameters required for building SED models.
They can be used as ``"depends_on"`` entries in parameter specifications.
... | 12,300 | 33.946023 | 116 | py |
prospector | prospector-master/prospect/sources/star_basis.py | from itertools import chain
import numpy as np
from numpy.polynomial.chebyshev import chebval
from scipy.spatial import Delaunay
from ..utils.smoothing import smoothspec
from .constants import lightspeed, lsun, jansky_cgs, to_cgs_at_10pc
try:
from sklearn.neighbors import KDTree
except(ImportError):
from scip... | 24,900 | 39.033762 | 99 | py |
prospector | prospector-master/prospect/sources/dust_basis.py | import numpy as np
try:
from sedpy.observate import getSED
except(ImportError):
pass
__all__ = ["BlackBodyDustBasis"]
# cgs constants
from .constants import lsun, pc, kboltz, hplanck
lightspeed = 29979245800.0
class BlackBodyDustBasis(object):
"""
"""
def __init__(self, **kwargs):
self.... | 3,381 | 31.209524 | 83 | py |
prospector | prospector-master/prospect/sources/constants.py | import numpy as np
try:
from astropy.cosmology import WMAP9 as cosmo
except(ImportError):
cosmo = None
__all__ = ['lsun', 'pc', 'lightspeed', 'ckms',
'jansky_mks', 'jansky_cgs',
'to_cgs_at_10pc', 'loge',
'kboltz', 'hplanck',
'cosmo']
# Useful constants
lsun = 3.846... | 674 | 21.5 | 52 | py |
prospector | prospector-master/prospect/sources/elines.py | wavelength = {
# Balmer Lines
'Ha': 6564.61,
'Hb': 4862.69,
'Hg': 4341.69,
'Hd': 4102.92,
'He': 3971.19,
'H8': 3890.15,
'H9': 3836.48,
'H10': 3798.98,
# Paschen lines
'P9': 923... | 3,122 | 26.394737 | 69 | py |
prospector | prospector-master/prospect/sources/galaxy_basis.py | from itertools import chain
import numpy as np
from copy import deepcopy
from .ssp_basis import SSPBasis
from ..utils.smoothing import smoothspec
from .constants import cosmo, lightspeed, jansky_cgs, to_cgs_at_10pc
try:
import fsps
from sedpy.observate import getSED, vac2air, air2vac
except(ImportError):
... | 10,936 | 37.921708 | 103 | py |
prospector | prospector-master/prospect/sources/ssp_basis.py | from copy import deepcopy
import numpy as np
from numpy.polynomial.chebyshev import chebval
from ..utils.smoothing import smoothspec
from .constants import cosmo, lightspeed, jansky_cgs, to_cgs_at_10pc
try:
import fsps
from sedpy.observate import getSED
except(ImportError):
pass
__all__ = ["SSPBasis", "F... | 15,536 | 38.234848 | 98 | py |
prospector | prospector-master/prospect/sources/boneyard.py | import numpy as np
from scipy.special import expi, gammainc
from .ssp_basis import SSPBasis
__all__ = ["CSPBasis", "StepSFHBasis", "CompositeSFH", "LinearSFHBasis"]
# change base
from .constants import loge
class CSPBasis(object):
"""
A class for composite stellar populations, which can be composed from
... | 18,149 | 36.192623 | 94 | py |
prospector | prospector-master/prospect/sources/__init__.py | from .galaxy_basis import *
from .ssp_basis import *
from .star_basis import *
from .dust_basis import *
from .boneyard import StepSFHBasis
__all__ = ["to_cgs",
"CSPSpecBasis", "MultiComponentCSPBasis",
"FastSSPBasis", "SSPBasis",
"FastStepBasis", "StepSFHBasis",
"StarBasis"... | 371 | 27.615385 | 52 | py |
prospector | prospector-master/prospect/utils/plotting.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
try:
import matplotlib.pyplot as pl
except(ImportError):
pass
__all__ = ["get_best", "get_truths", "get_percentiles", "get_stats",
"posterior_samples", "hist_samples", "joint_pdf", "compute_sigma_level",
"trim_walkers", "fi... | 9,476 | 31.56701 | 95 | py |
prospector | prospector-master/prospect/utils/obsutils.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
""" obsutils.py - Utilities for manipulating observational data, especially
ensuring the the required keys are present in the `obs` dictionary.
"""
import numpy as np
import warnings
np.errstate(invalid='ignore')
__all__ = ["fix_obs", "rectify_obs", "norm_spectrum", "log... | 8,493 | 37.261261 | 92 | py |
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