code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import collections.abc as container_abcs
import numpy as np
import re
import sys
import torch
from ...dataset import MultiConditionAnnotatedDataset
def print_progress(epoch, logs, n_epochs=10000, only_val_losses=True):
"""Creates Message for '_print_progress_bar'.
Parameters
----------
epoch:... | /scArches-0.5.9.tar.gz/scArches-0.5.9/scarches/trainers/scpoli/_utils.py | 0.702122 | 0.358662 | _utils.py | pypi |
from .trainer import Trainer
import torch
class trVAETrainer(Trainer):
"""ScArches Unsupervised Trainer class. This class contains the implementation of the unsupervised CVAE/TRVAE
Trainer.
Parameters
----------
model: trVAE
Number of input features (i.e. g... | /scArches-0.5.9.tar.gz/scArches-0.5.9/scarches/trainers/trvae/unsupervised.py | 0.92958 | 0.723041 | unsupervised.py | pypi |
import torch
import torch.nn as nn
from collections import defaultdict
import numpy as np
import time
from torch.utils.data import DataLoader
from torch.utils.data import WeightedRandomSampler
from ...utils.monitor import EarlyStopping
from ._utils import make_dataset, custom_collate, print_progress
class Trainer:
... | /scArches-0.5.9.tar.gz/scArches-0.5.9/scarches/trainers/trvae/trainer.py | 0.92382 | 0.524943 | trainer.py | pypi |
import sys
import numpy as np
import re
import torch
import collections.abc as container_abcs
from torch.utils.data import DataLoader
from ...dataset import trVAEDataset
def print_progress(epoch, logs, n_epochs=10000, only_val_losses=True):
"""Creates Message for '_print_progress_bar'.
Parameters
... | /scArches-0.5.9.tar.gz/scArches-0.5.9/scarches/trainers/trvae/_utils.py | 0.716119 | 0.353233 | _utils.py | pypi |
from typing import Optional
import anndata
import logging
import torch
from torch.distributions import Normal
import numpy as np
from scvi import _CONSTANTS
from scarchest.dataset.scvi import ScviDataLoader
from scarchest.models.scvi import totalVI
logger = logging.getLogger(__name__)
def _unpack_tensors(tensors)... | /scArchest-0.0.1-py3-none-any.whl/scarchest/dataset/scvi/total_data_loader.py | 0.958079 | 0.320476 | total_data_loader.py | pypi |
import numpy as np
import logging
from sklearn.neighbors import KNeighborsClassifier
import torch
from scvi.core import unsupervised_clustering_accuracy
from scvi import _CONSTANTS
from scarchest.dataset.scvi import ScviDataLoader
logger = logging.getLogger(__name__)
class AnnotationDataLoader(ScviDataLoader):
... | /scArchest-0.0.1-py3-none-any.whl/scarchest/dataset/scvi/annotation_data_loader.py | 0.849066 | 0.280179 | annotation_data_loader.py | pypi |
import numpy as np
import scanpy as sc
from scarchest.dataset.trvae import AnnotatedDataset
from scarchest.dataset.trvae._utils import label_encoder
class MetaAnnotatedDataset(object):
def __init__(self,
adata: sc.AnnData,
task_key: str,
meta_test_task: str = No... | /scArchest-0.0.1-py3-none-any.whl/scarchest/dataset/mars/meta_anndata.py | 0.545165 | 0.243789 | meta_anndata.py | pypi |
import numpy as np
import torch
from torch.utils.data import Dataset
from scipy import sparse
from .data_handling import remove_sparsity
from ._utils import label_encoder
class AnnotatedDataset(Dataset):
def __init__(self,
adata,
condition_key=None,
condition_en... | /scArchest-0.0.1-py3-none-any.whl/scarchest/dataset/trvae/anndata.py | 0.822082 | 0.316132 | anndata.py | pypi |
import scanpy as sc
from scipy import sparse
def hvg_batch(adata, batch_key=None, target_genes=2000, flavor='cell_ranger', n_bins=20, adataout=False):
"""
Method to select HVGs based on mean dispersions of genes that are highly
variable genes in all batches. Using a the top target_genes per batch by
a... | /scArchest-0.0.1-py3-none-any.whl/scarchest/dataset/trvae/data_handling.py | 0.808823 | 0.525308 | data_handling.py | pypi |
import numpy as np
import torch
from typing import Sequence
from torch.distributions import Normal, Categorical, kl_divergence as kl
from scvi.core.modules.utils import broadcast_labels
from scarchest.models.scvi._base import Decoder, Encoder
from .classifier import Classifier
from .scvi import scVI
class scANVI(s... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/scvi/scanvi.py | 0.911155 | 0.59796 | scanvi.py | pypi |
"""Main module."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal, kl_divergence as kl
from scvi.core._distributions import (
ZeroInflatedNegativeBinomial,
NegativeBinomial,
Poisson,
)
from scvi.core.modules.utils import one_hot
from scarchest.mo... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/scvi/scvi.py | 0.960519 | 0.525978 | scvi.py | pypi |
"""Main module."""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.distributions import Normal, kl_divergence as kl
from typing import Dict, Optional, Tuple, Union, List
from scvi.core._distributions import ZeroInflatedNegativeBinomial, NegativeBinomial
from scvi.core._... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/scvi/totalvi.py | 0.956012 | 0.525795 | totalvi.py | pypi |
from typing import Optional
import torch
import torch.nn as nn
from scarchest.models.trvae._utils import one_hot_encoder
from .activations import ACTIVATIONS
from .losses import mse, kl
import numpy as np
def dense_block(i, in_features, out_features, use_batchnorm, dropout_rate, activation):
model = nn.Sequenti... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/mars/mars.py | 0.94285 | 0.396127 | mars.py | pypi |
import torch
import numpy as np
from functools import partial
from torch.autograd import Variable
from ._utils import partition
def _nan2inf(x):
return torch.where(torch.isnan(x), torch.zeros_like(x) + np.inf, x)
def _nan2zero(x):
return torch.where(torch.isnan(x), torch.zeros_like(x), x)
def kl(mu, logv... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/trvae/losses.py | 0.952386 | 0.853547 | losses.py | pypi |
import torch
import torch.nn as nn
from ._utils import one_hot_encoder
class CondLayers(nn.Module):
def __init__(
self,
n_in: int,
n_out: int,
n_cond: int,
bias: bool = True,
):
super().__init__()
self.n_cond = n_cond
self.ex... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/trvae/modules.py | 0.954276 | 0.410756 | modules.py | pypi |
from typing import Optional
import torch
import torch.nn as nn
from .modules import Encoder, Decoder
from .losses import mse, mmd, zinb, nb, kl
class trVAE(nn.Module):
"""scNet class. This class contains the implementation of Conditional Variational Auto-encoder.
Parameters
----------
inpu... | /scArchest-0.0.1-py3-none-any.whl/scarchest/models/trvae/trvae.py | 0.970219 | 0.704795 | trvae.py | pypi |
import numpy as np
from typing import Union
import torch
import torch.nn as nn
import anndata
from scvi.data import get_from_registry
from scarchest.models import scVI, scANVI
from scarchest.trainers import scVITrainer, scANVITrainer, UnlabelledScanviTrainer
def weight_update_check(old_network, op_network):
for... | /scArchest-0.0.1-py3-none-any.whl/scarchest/surgery/scvi.py | 0.883864 | 0.328893 | scvi.py | pypi |
import numpy as np
from typing import Union
import torch
from scarchest.models.trvae.trvae import trVAE
def trvae_operate(network: trVAE,
data_conditions: Union[list, str],
freeze: bool = True,
freeze_expression: bool = True,
remove_dropout: boo... | /scArchest-0.0.1-py3-none-any.whl/scarchest/surgery/trvae.py | 0.899817 | 0.526647 | trvae.py | pypi |
import numpy as np
from typing import Union
import torch
import scanpy as sc
from scarchest.models import MARS
from scarchest.trainers import MARSTrainer
def mars_operate(network: MARS,
new_adata: sc.AnnData,
new_tasks: Union[list, str],
meta_tasks: list,
... | /scArchest-0.0.1-py3-none-any.whl/scarchest/surgery/mars.py | 0.888475 | 0.432962 | mars.py | pypi |
import numpy as np
class EarlyStopping(object):
def __init__(self,
mode: str = 'min',
early_stopping_metric: str = 'val_loss',
save_best_state_metric: str = 'val_loss',
benchmark: bool = False,
threshold: int = 3,
... | /scArchest-0.0.1-py3-none-any.whl/scarchest/utils/monitor.py | 0.825449 | 0.201971 | monitor.py | pypi |
import numpy as np
from scipy.stats import itemfreq, entropy
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, adjusted_rand_score, normalized_mutual_info_score
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import LabelEncoder
from scarchest.dataset.trvae.data... | /scArchest-0.0.1-py3-none-any.whl/scarchest/metrics/metrics.py | 0.939401 | 0.779028 | metrics.py | pypi |
from scvi.data import get_from_registry
from scarchest.metrics.metrics import entropy_batch_mixing, knn_purity
from scarchest.models import scVI, scANVI
from scarchest.trainers import scVITrainer, scANVITrainer
import numpy as np
import scanpy as sc
import torch
from typing import Union
import anndata
import matplot... | /scArchest-0.0.1-py3-none-any.whl/scarchest/plotting/scvi_eval.py | 0.814791 | 0.278508 | scvi_eval.py | pypi |
import numpy as np
import logging
import torch
from torch.nn import functional as F
from scvi.data._anndata import get_from_registry
from scvi import _CONSTANTS
from scarchest.trainers.scvi import Trainer, scVITrainer
from scarchest.dataset.scvi import AnnotationDataLoader
logger = logging.getLogger(__name__)
clas... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/scvi/annotation.py | 0.901531 | 0.458955 | annotation.py | pypi |
from typing import Union
import anndata
import logging
import torch
from scvi import _CONSTANTS
from scvi.core.modules import Classifier
from scvi.core.modules.utils import one_hot
from scarchest.models.scvi import totalVI
from scarchest.trainers.scvi import scVITrainer
from scarchest.dataset.scvi import TotalDataLoa... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/scvi/total_inference.py | 0.934947 | 0.289862 | total_inference.py | pypi |
import logging
import time
import numpy as np
import torch
import torch.nn as nn
import anndata
from abc import abstractmethod
from collections import defaultdict, OrderedDict
from itertools import cycle
from typing import List
from sklearn.model_selection._split import _validate_shuffle_split
from torch.utils.data.sam... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/scvi/trainer.py | 0.919665 | 0.415017 | trainer.py | pypi |
import time
import torch
from sklearn.cluster import KMeans
from torch.utils.data import DataLoader
from scarchest.dataset.mars import MetaAnnotatedDataset
from scarchest.dataset.trvae import AnnotatedDataset
from scarchest.models import MARS
from ._utils import split_meta_train_tasks, euclidean_dist, print_meta_prog... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/mars/mars_meta_trainer.py | 0.842734 | 0.242301 | mars_meta_trainer.py | pypi |
import time
import scanpy as sc
import torch
from torch.utils.data import DataLoader
from scarchest.models import MARS
from scarchest.trainers.trvae._utils import make_dataset
from ._utils import print_meta_progress
from .meta import MetaTrainer
class MARSPreTrainer(MetaTrainer):
def __init__(self, model: MARS,... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/mars/unsupervised.py | 0.848157 | 0.1779 | unsupervised.py | pypi |
import torch
import numpy as np
from torch.utils.data import DataLoader, SubsetRandomSampler
from scarchest.dataset.mars import MetaAnnotatedDataset
from scarchest.trainers.trvae._utils import _print_progress_bar
def print_meta_progress(epoch, logs, n_epochs=10000):
"""Creates Message for '_print_progress_bar'.
... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/mars/_utils.py | 0.893675 | 0.331241 | _utils.py | pypi |
from abc import abstractmethod
import torch
import torch.nn as nn
from collections import defaultdict
import numpy as np
import time
from torch.utils.data import DataLoader
from torch.utils.data import WeightedRandomSampler
from scarchest.utils.monitor import EarlyStopping
from ._utils import make_dataset, custom_col... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/trvae/trainer.py | 0.816882 | 0.21262 | trainer.py | pypi |
import sys
import numpy as np
import re
import torch
from torch._six import container_abcs
from torch.utils.data import DataLoader, SubsetRandomSampler
from scarchest.dataset.trvae import AnnotatedDataset
def print_progress(epoch, logs, n_epochs=10000):
"""Creates Message for '_print_progress_bar'.
Param... | /scArchest-0.0.1-py3-none-any.whl/scarchest/trainers/trvae/_utils.py | 0.747339 | 0.317903 | _utils.py | pypi |
from typing import Optional
import anndata
import logging
import torch
from torch.distributions import Normal
import numpy as np
from scvi import _CONSTANTS
from scarchest.dataset.scvi import ScviDataLoader
from scarchest.models.scvi import totalVI
logger = logging.getLogger(__name__)
def _unpack_tensors(tensors)... | /scArchest-0.0.1-py3-none-any.whl/scarches/dataset/scvi/total_data_loader.py | 0.958079 | 0.320476 | total_data_loader.py | pypi |
import numpy as np
import logging
from sklearn.neighbors import KNeighborsClassifier
import torch
from scvi.core import unsupervised_clustering_accuracy
from scvi import _CONSTANTS
from scarchest.dataset.scvi import ScviDataLoader
logger = logging.getLogger(__name__)
class AnnotationDataLoader(ScviDataLoader):
... | /scArchest-0.0.1-py3-none-any.whl/scarches/dataset/scvi/annotation_data_loader.py | 0.849066 | 0.280179 | annotation_data_loader.py | pypi |
import numpy as np
import scanpy as sc
from scarchest.dataset.trvae import AnnotatedDataset
from scarchest.dataset.trvae._utils import label_encoder
class MetaAnnotatedDataset(object):
def __init__(self,
adata: sc.AnnData,
task_key: str,
meta_test_task: str = No... | /scArchest-0.0.1-py3-none-any.whl/scarches/dataset/mars/meta_anndata.py | 0.545165 | 0.243789 | meta_anndata.py | pypi |
import numpy as np
import torch
from torch.utils.data import Dataset
from scipy import sparse
from .data_handling import remove_sparsity
from ._utils import label_encoder
class AnnotatedDataset(Dataset):
def __init__(self,
adata,
condition_key=None,
condition_en... | /scArchest-0.0.1-py3-none-any.whl/scarches/dataset/trvae/anndata.py | 0.822082 | 0.316132 | anndata.py | pypi |
import scanpy as sc
from scipy import sparse
def hvg_batch(adata, batch_key=None, target_genes=2000, flavor='cell_ranger', n_bins=20, adataout=False):
"""
Method to select HVGs based on mean dispersions of genes that are highly
variable genes in all batches. Using a the top target_genes per batch by
a... | /scArchest-0.0.1-py3-none-any.whl/scarches/dataset/trvae/data_handling.py | 0.808823 | 0.525308 | data_handling.py | pypi |
import numpy as np
import torch
from typing import Sequence
from torch.distributions import Normal, Categorical, kl_divergence as kl
from scvi.core.modules.utils import broadcast_labels
from scarchest.models.scvi._base import Decoder, Encoder
from .classifier import Classifier
from .scvi import scVI
class scANVI(s... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/scvi/scanvi.py | 0.911155 | 0.59796 | scanvi.py | pypi |
"""Main module."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal, kl_divergence as kl
from scvi.core._distributions import (
ZeroInflatedNegativeBinomial,
NegativeBinomial,
Poisson,
)
from scvi.core.modules.utils import one_hot
from scarchest.mo... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/scvi/scvi.py | 0.960519 | 0.525978 | scvi.py | pypi |
"""Main module."""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.distributions import Normal, kl_divergence as kl
from typing import Dict, Optional, Tuple, Union, List
from scvi.core._distributions import ZeroInflatedNegativeBinomial, NegativeBinomial
from scvi.core._... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/scvi/totalvi.py | 0.956012 | 0.525795 | totalvi.py | pypi |
from typing import Optional
import torch
import torch.nn as nn
from scarchest.models.trvae._utils import one_hot_encoder
from .activations import ACTIVATIONS
from .losses import mse, kl
import numpy as np
def dense_block(i, in_features, out_features, use_batchnorm, dropout_rate, activation):
model = nn.Sequenti... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/mars/mars.py | 0.94285 | 0.396127 | mars.py | pypi |
import torch
import numpy as np
from functools import partial
from torch.autograd import Variable
from ._utils import partition
def _nan2inf(x):
return torch.where(torch.isnan(x), torch.zeros_like(x) + np.inf, x)
def _nan2zero(x):
return torch.where(torch.isnan(x), torch.zeros_like(x), x)
def kl(mu, logv... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/trvae/losses.py | 0.952386 | 0.853547 | losses.py | pypi |
import torch
import torch.nn as nn
from ._utils import one_hot_encoder
class CondLayers(nn.Module):
def __init__(
self,
n_in: int,
n_out: int,
n_cond: int,
bias: bool = True,
):
super().__init__()
self.n_cond = n_cond
self.ex... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/trvae/modules.py | 0.954276 | 0.410756 | modules.py | pypi |
from typing import Optional
import torch
import torch.nn as nn
from .modules import Encoder, Decoder
from .losses import mse, mmd, zinb, nb, kl
class trVAE(nn.Module):
"""scNet class. This class contains the implementation of Conditional Variational Auto-encoder.
Parameters
----------
inpu... | /scArchest-0.0.1-py3-none-any.whl/scarches/models/trvae/trvae.py | 0.970219 | 0.704795 | trvae.py | pypi |
import numpy as np
from typing import Union
import torch
import torch.nn as nn
import anndata
from scvi.data import get_from_registry
from scarchest.models import scVI, scANVI
from scarchest.trainers import scVITrainer, scANVITrainer, UnlabelledScanviTrainer
def weight_update_check(old_network, op_network):
for... | /scArchest-0.0.1-py3-none-any.whl/scarches/surgery/scvi.py | 0.883864 | 0.328893 | scvi.py | pypi |
import numpy as np
from typing import Union
import torch
from scarchest.models.trvae.trvae import trVAE
def trvae_operate(network: trVAE,
data_conditions: Union[list, str],
freeze: bool = True,
freeze_expression: bool = True,
remove_dropout: boo... | /scArchest-0.0.1-py3-none-any.whl/scarches/surgery/trvae.py | 0.899817 | 0.526647 | trvae.py | pypi |
import numpy as np
from typing import Union
import torch
import scanpy as sc
from scarchest.models import MARS
from scarchest.trainers import MARSTrainer
def mars_operate(network: MARS,
new_adata: sc.AnnData,
new_tasks: Union[list, str],
meta_tasks: list,
... | /scArchest-0.0.1-py3-none-any.whl/scarches/surgery/mars.py | 0.888475 | 0.432962 | mars.py | pypi |
import numpy as np
class EarlyStopping(object):
def __init__(self,
mode: str = 'min',
early_stopping_metric: str = 'val_loss',
save_best_state_metric: str = 'val_loss',
benchmark: bool = False,
threshold: int = 3,
... | /scArchest-0.0.1-py3-none-any.whl/scarches/utils/monitor.py | 0.825449 | 0.201971 | monitor.py | pypi |
import numpy as np
from scipy.stats import itemfreq, entropy
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, adjusted_rand_score, normalized_mutual_info_score
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import LabelEncoder
from scarchest.dataset.trvae.data... | /scArchest-0.0.1-py3-none-any.whl/scarches/metrics/metrics.py | 0.939401 | 0.779028 | metrics.py | pypi |
from scvi.data import get_from_registry
from scarchest.metrics.metrics import entropy_batch_mixing, knn_purity
from scarchest.models import scVI, scANVI
from scarchest.trainers import scVITrainer, scANVITrainer
import numpy as np
import scanpy as sc
import torch
from typing import Union
import anndata
import matplot... | /scArchest-0.0.1-py3-none-any.whl/scarches/plotting/scvi_eval.py | 0.814791 | 0.278508 | scvi_eval.py | pypi |
import numpy as np
import logging
import torch
from torch.nn import functional as F
from scvi.data._anndata import get_from_registry
from scvi import _CONSTANTS
from scarchest.trainers.scvi import Trainer, scVITrainer
from scarchest.dataset.scvi import AnnotationDataLoader
logger = logging.getLogger(__name__)
clas... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/scvi/annotation.py | 0.901531 | 0.458955 | annotation.py | pypi |
from typing import Union
import anndata
import logging
import torch
from scvi import _CONSTANTS
from scvi.core.modules import Classifier
from scvi.core.modules.utils import one_hot
from scarchest.models.scvi import totalVI
from scarchest.trainers.scvi import scVITrainer
from scarchest.dataset.scvi import TotalDataLoa... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/scvi/total_inference.py | 0.934947 | 0.289862 | total_inference.py | pypi |
import logging
import time
import numpy as np
import torch
import torch.nn as nn
import anndata
from abc import abstractmethod
from collections import defaultdict, OrderedDict
from itertools import cycle
from typing import List
from sklearn.model_selection._split import _validate_shuffle_split
from torch.utils.data.sam... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/scvi/trainer.py | 0.919665 | 0.415017 | trainer.py | pypi |
import time
import torch
from sklearn.cluster import KMeans
from torch.utils.data import DataLoader
from scarchest.dataset.mars import MetaAnnotatedDataset
from scarchest.dataset.trvae import AnnotatedDataset
from scarchest.models import MARS
from ._utils import split_meta_train_tasks, euclidean_dist, print_meta_prog... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/mars/mars_meta_trainer.py | 0.842734 | 0.242301 | mars_meta_trainer.py | pypi |
import time
import scanpy as sc
import torch
from torch.utils.data import DataLoader
from scarchest.models import MARS
from scarchest.trainers.trvae._utils import make_dataset
from ._utils import print_meta_progress
from .meta import MetaTrainer
class MARSPreTrainer(MetaTrainer):
def __init__(self, model: MARS,... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/mars/unsupervised.py | 0.848157 | 0.1779 | unsupervised.py | pypi |
import torch
import numpy as np
from torch.utils.data import DataLoader, SubsetRandomSampler
from scarchest.dataset.mars import MetaAnnotatedDataset
from scarchest.trainers.trvae._utils import _print_progress_bar
def print_meta_progress(epoch, logs, n_epochs=10000):
"""Creates Message for '_print_progress_bar'.
... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/mars/_utils.py | 0.893675 | 0.331241 | _utils.py | pypi |
from abc import abstractmethod
import torch
import torch.nn as nn
from collections import defaultdict
import numpy as np
import time
from torch.utils.data import DataLoader
from torch.utils.data import WeightedRandomSampler
from scarchest.utils.monitor import EarlyStopping
from ._utils import make_dataset, custom_col... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/trvae/trainer.py | 0.816882 | 0.21262 | trainer.py | pypi |
import sys
import numpy as np
import re
import torch
from torch._six import container_abcs
from torch.utils.data import DataLoader, SubsetRandomSampler
from scarchest.dataset.trvae import AnnotatedDataset
def print_progress(epoch, logs, n_epochs=10000):
"""Creates Message for '_print_progress_bar'.
Param... | /scArchest-0.0.1-py3-none-any.whl/scarches/trainers/trvae/_utils.py | 0.747339 | 0.317903 | _utils.py | pypi |
# scBC
scBC —— a single-cell transcriptome Bayesian biClustering framework.
This document will help you easily go through the scBC model.
## Installation
To install our package, run
```bash
conda install -c conda-forge scvi-tools #pip install scvi-tools
pip install scBC
```
## Quick start
To simply illustra... | /scBC-0.3.0.tar.gz/scBC-0.3.0/README.md | 0.480722 | 0.956022 | README.md | pypi |
# scBayesDeconv
Package which allow the deconvolution of two added random variables using bayesian mixture approaches.
```
Z = X + Y
```
where `X` we call it the autofluorescence,`Y` the deconvolution and`Z` the convolution; which are random variables. If we have a sample of values from distribution `X` and `Z`, the... | /scBayesDeconv-0.1.tar.gz/scBayesDeconv-0.1/README.md | 0.554953 | 0.99406 | README.md | pypi |
import numpy as np
import pandas as pd
def _binarize_discarded(gene: pd.Series, *args):
"""Helper function for the binarization of discarded genes.
Not intended to be called directly. The inclusion of the variadic
argument *args was introduced to avoid type errors when calling it
from scboolseq.core.s... | /scBoolSeq-0.8.3-py3-none-any.whl/scboolseq/binarization.py | 0.87879 | 0.437523 | binarization.py | pypi |
__all__ = ["normalize", "normalise", "log_transform", "log_normalize", "log_normalise"]
import numpy as np
import pandas as pd
from typing import Optional
def normalize(raw_counts: pd.DataFrame, method: Optional[str] = None) -> pd.DataFrame:
"""
Normalize count matrix.
Parameters
----------
raw... | /scBoolSeq-0.8.3-py3-none-any.whl/scboolseq/utils/normalization.py | 0.94843 | 0.499634 | normalization.py | pypi |
from pathlib import (
Path as _Path_,
PosixPath as _PosixPath_,
WindowsPath as _WindowsPath_,
)
import os
import argparse
from time import time
class Timer(object):
"""A simple timer class used to measure execution time,
without all the problems related to using timeit."""
def __init__(self, ... | /scBoolSeq-0.8.3-py3-none-any.whl/scboolseq/utils/customobjs.py | 0.847463 | 0.314327 | customobjs.py | pypi |
# scButterfly: single-cell cross-modality translation via multi-use dual-aligned variational autoencoders
## Installation
It's prefered to create a new environment for scButterfly
```
conda create scButterfly python==3.9
conda activate scButterfly
```
scButterfly is available on PyPI, and could be installed using
... | /scButterfly-0.0.4.tar.gz/scButterfly-0.0.4/README.md | 0.501709 | 0.902136 | README.md | pypi |
import argparse
from scCASE import run
if __name__ == '__main__':
def parse_args():
parser = argparse.ArgumentParser(description='Parameters')
parser.add_argument('--method', type=str, default='scCASE', help='scCASE or scCASER.')
parser.add_argument('--data_path', type=str,default=None, hel... | /scCASE-0.0.4-py3-none-any.whl/scCASE-0.0.4.data/scripts/scCASE.py | 0.473657 | 0.191781 | scCASE.py | pypi |
# scCODA - Compositional analysis of single-cell data
This notebook serves as a tutorial for using the *scCODA* package ([Büttner, Ostner et al., 2021](https://www.nature.com/articles/s41467-021-27150-6)) to analyze changes in cell composition data.
The package is intended to be used with cell composition from sing... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/docs/source/getting_started.ipynb | 0.516839 | 0.976736 | getting_started.ipynb | pypi |
# scCODA - Compositional analysis of single-cell data
This notebook serves as a tutorial for using the *scCODA* package ([Büttner, Ostner et al., 2021](https://www.nature.com/articles/s41467-021-27150-6)) to analyze changes in cell composition data.
The package is intended to be used with cell composition from sing... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/tutorials/getting_started.ipynb | 0.502686 | 0.993944 | getting_started.ipynb | pypi |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm, rcParams
from matplotlib.colors import ListedColormap
from anndata import AnnData
from typing import Optional, Tuple, Collection, Union, List
sns.set_style("ticks")
def stackbar(
y: np.nd... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/sccoda/util/data_visualization.py | 0.895177 | 0.622717 | data_visualization.py | pypi |
import numpy as np
import arviz as az
import pandas as pd
import pickle as pkl
from typing import Optional, Tuple, Collection, Union, List
class CAResultConverter(az.data.io_dict.DictConverter):
"""
Helper class for result conversion into arviz's format
"""
def to_result_data(self, sampling_stats, m... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/sccoda/util/result_classes.py | 0.89069 | 0.580233 | result_classes.py | pypi |
import numpy as np
import anndata as ad
import pandas as pd
from scipy.special import softmax
from anndata import AnnData
from typing import Optional, Tuple, Collection, Union, List
def generate_case_control(
cases: int = 1,
K: int = 5,
n_total: int = 1000,
n_samples: List[any] = [5, ... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/sccoda/util/data_generation.py | 0.898003 | 0.752808 | data_generation.py | pypi |
import numpy as np
import patsy as pt
from anndata import AnnData
from sccoda.model import scCODA_model as dm
from typing import Union, Optional
class CompositionalAnalysis:
"""
Initializer class for scCODA models. This class is called when performing compositional analysis with scCODA.
Usage: model = C... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/sccoda/util/comp_ana.py | 0.93811 | 0.724432 | comp_ana.py | pypi |
import pandas as pd
import anndata as ad
import os
import numpy as np
from anndata import AnnData
from typing import Optional, Tuple, Collection, Union, List
def read_anndata_one_sample(
adata: AnnData,
cell_type_identifier: str,
covariate_key: Optional[str] = None
) -> Tuple[np.ndarray, dict... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/sccoda/util/cell_composition_data.py | 0.924048 | 0.683314 | cell_composition_data.py | pypi |
import numpy as np
import time
import warnings
import tensorflow as tf
import tensorflow_probability as tfp
from sccoda.util import result_classes as res
from typing import Optional, Tuple, Collection, Union, List
tfd = tfp.distributions
tfb = tfp.bijectors
class CompositionalModel:
"""
Dynamical framework... | /scCODA-0.1.9.tar.gz/scCODA-0.1.9/sccoda/model/scCODA_model.py | 0.943777 | 0.72526 | scCODA_model.py | pypi |
# How to use scConnect to build and analyze a connectivity graph
scConnect integrate into a Scanpy analysis pipeline by utilizing the AnnData objects. This tutorial will not cover usage of scanpy, but tutorials for this can be found [here](https://scanpy.readthedocs.io/en/latest/tutorials.html).
We will cover four as... | /scConnect-1.0.2.tar.gz/scConnect-1.0.2/tutorial/Connecting brain regions.ipynb | 0.444565 | 0.986954 | Connecting brain regions.ipynb | pypi |
=========
Work flow
=========
+++++++++++++++++++++
Building the database
+++++++++++++++++++++
The current version of scConnect have version 2019-5 of the latest ligand, receptor and interaction data from `guide to pharmacology`__ allready compiled.
Should you find the need to change to an older database version, dow... | /scConnect-1.0.2.tar.gz/scConnect-1.0.2/docs/source/Tutorials.rst | 0.857127 | 0.65466 | Tutorials.rst | pypi |
# scETM: single-cell Embedded Topic Model
A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.
The full description of scETM and its application on published single cell RNA-seq datasets are available [here](https://www.biorxiv.org/content/10.1101/2021.01.13.42... | /scETM-0.5.1a0.tar.gz/scETM-0.5.1a0/README.md | 0.900426 | 0.960025 | README.md | pypi |
import numpy as np
import torch
from sklearn.manifold import TSNE
import torch.nn as nn
class tSNE(nn.Module):
def __init__(self, data, n_components=2, *, perplexity=30.0, early_exaggeration=12.0,
learning_rate=200.0, n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07,
... | /Dim_reduction/TSNE.py | 0.909594 | 0.240412 | TSNE.py | pypi |
import warnings
import itertools
import numpy as np
import pandas as pd
import statsmodels.api as sm
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from math import sqrt
import torch
import torch.nn as nn
import numpy as np
from tqdm import tqdm
import numpy as np
imp... | /Dim_reduction/VAE.py | 0.841044 | 0.368974 | VAE.py | pypi |
# scHiCTools
### Summary
A computational toolbox for analyzing single cell Hi-C (high-throughput sequencing for 3C) data which includes functions for:
1. Load single-cell HiC datasets
2. Smoothing the contact maps with linear convolution, random walk or network enhancing
3. Calculating embeddings for single cell HiC d... | /scHiCTools-0.0.3.tar.gz/scHiCTools-0.0.3/README.md | 0.899257 | 0.938969 | README.md | pypi |
# Author: Vlad Niculae
# Lars Buitinck
# Mathieu Blondel <mathieu@mblondel.org>
# Tom Dupre la Tour
# License: BSD 3 clause
from math import sqrt
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from datetime import datetime
from sklearn.base import BaseEstimator, Tr... | /scOpen-1.0.1.tar.gz/scOpen-1.0.1/scopen/MF.py | 0.901176 | 0.601828 | MF.py | pypi |
# scRFE
```
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accur... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFEv1.4.2.ipynb | 0.787114 | 0.846451 | scRFEv1.4.2.ipynb | pypi |
# Visualization: Venn Diagram
```
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/venn-diagram.ipynb | 0.508544 | 0.665143 | venn-diagram.ipynb | pypi |
# scRFE
```
# madeline editting 06/22
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from s... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFEjun24.ipynb | 0.728652 | 0.868994 | scRFEjun24.ipynb | pypi |
# # scRFE
# In[154]:
# madeline editting 06/22
# In[186]:
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection impo... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFEjun24.py | 0.67694 | 0.606469 | scRFEjun24.py | pypi |
# # scRFE
# In[3]:
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics imp... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFEv1.4.2.py | 0.7478 | 0.643721 | scRFEv1.4.2.py | pypi |
# scRFEjun19
```
# AnnDatasubset
# Angela sent to Madeline on June 22
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selecti... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFE_jun19_FROMANGELA.ipynb | 0.580233 | 0.803482 | scRFE_jun19_FROMANGELA.ipynb | pypi |
# Sorting Results
```
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy_... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/SortingResults.ipynb | 0.417509 | 0.525673 | SortingResults.ipynb | pypi |
# # scRFE
# In[3]:
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics impo... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFEV142.py | 0.811415 | 0.555496 | scRFEV142.py | pypi |
# # scRFE Tutorial
#
# Here we present an example of how to use scRFE. We analyze the Limb Muscle Facs data from the Tabula-Muris-Senis dataset that is available on Figshare. We split the data by age.
# More features were selected than ideal in this model, because we used a very small number of estimators and a low... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFE-tutorial.py | 0.509764 | 0.759805 | scRFE-tutorial.py | pypi |
# scRFE
```
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accur... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/scRFEdefaultParams.ipynb | 0.760562 | 0.885483 | scRFEdefaultParams.ipynb | pypi |
# Visualization: Venn Diagram
```
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/.ipynb_checkpoints/venn-diagram-checkpoint.ipynb | 0.508544 | 0.665143 | venn-diagram-checkpoint.ipynb | pypi |
```
# testing scRFE
from scRFE import scRFE
from scRFE import scRFEimplot
from scRFE.scRFE import makeOneForest
import numpy as np
import pandas as pd
from anndata import read_h5ad
adata = read_h5ad('/Users/madelinepark/Downloads/Liver_droplet.h5ad')
madeForest = makeOneForest(dataMatrix=adata, classOfInterest='age... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/.ipynb_checkpoints/scRFE-Copy1-checkpoint.ipynb | 0.624064 | 0.739822 | scRFE-Copy1-checkpoint.ipynb | pypi |
# Sorting Results
```
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy_... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/.ipynb_checkpoints/SortingResults-checkpoint.ipynb | 0.417509 | 0.525673 | SortingResults-checkpoint.ipynb | pypi |
# scRFE
```
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accur... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/practiceScripts/.ipynb_checkpoints/scRFEv1.4.2-checkpoint.ipynb | 0.787114 | 0.846451 | scRFEv1.4.2-checkpoint.ipynb | pypi |
## Matrix plots July 16
```
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy_score... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/visualization/matrixPlotsJul16.ipynb | 0.596668 | 0.5526 | matrixPlotsJul16.ipynb | pypi |
# scRFE
```
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accur... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/.ipynb_checkpoints/scRFEdefaultParams-checkpoint.ipynb | 0.760562 | 0.885483 | scRFEdefaultParams-checkpoint.ipynb | pypi |
# scRFE
```
# madeline editting 06/22
# Imports
import numpy as np
import pandas as pd
import scanpy as sc
import random
from anndata import read_h5ad
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from s... | /scRFE-1.5.6.tar.gz/scRFE-1.5.6/scripts/.ipynb_checkpoints/scRFEjun24-checkpoint.ipynb | 0.728652 | 0.868994 | scRFEjun24-checkpoint.ipynb | pypi |
from time import time
import os
import torch
from torch_geometric.data import InMemoryDataset, Data
from collections import defaultdict
import episcanpy.api as epi
import scanpy as sc
import numpy as np
import pandas as pd
import anndata as ad
from anndata import AnnData
from typing import Optional, Mapping, List, Un... | /scReGAT-0.0.4.tar.gz/scReGAT-0.0.4/scregat/data_process_2.py | 0.867485 | 0.419767 | data_process_2.py | pypi |
from time import time
import os
import torch
from torch_geometric.data import InMemoryDataset, Data
from collections import defaultdict
import episcanpy.api as epi
import scanpy as sc
import numpy as np
import pandas as pd
from pandas import DataFrame
import anndata as ad
from anndata import AnnData
from typing impor... | /scReGAT-0.0.4.tar.gz/scReGAT-0.0.4/scregat/data_process.py | 0.813127 | 0.342544 | data_process.py | pypi |
import os
import numpy as np
import torch
import torch.nn as nn
import random
def seed_all(seed_value, cuda_deterministic=False):
"""
设置所有的随机种子
"""
random.seed(seed_value)
os.environ['PYTHONHASHSEED'] = str(seed_value)
np.random.seed(seed_value)
torch.manual_seed(seed_value)
if torch.cu... | /scSTEM-0.0.2-py3-none-any.whl/STEM/utils.py | 0.801392 | 0.336372 | utils.py | pypi |
import torch
from torch.utils.data import DataLoader, Dataset
from torch import nn, optim
from torch.nn import functional as F
import torch.optim.lr_scheduler as lr_scheduler
import os
import numpy as np
import time
from .utils import *
import scipy
class STData(Dataset):
def __init__(self,data,coord):
sel... | /scSTEM-0.0.2-py3-none-any.whl/STEM/model.py | 0.846101 | 0.339499 | model.py | pypi |
import tensorflow as tf
def _variable_with_weight_decay(name, shape, stddev, wd):
"""
Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: nam... | /scScope_cpu-0.1.5.tar.gz/scScope_cpu-0.1.5/scscope/ops.py | 0.844409 | 0.578865 | ops.py | pypi |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.