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_scanvi.py
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_scanvi.py
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import logging
import warnings
from copy import deepcopy
from typing import List, Literal, Optional, Sequence, Union
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scvi import REGISTRY_KEYS, settings
from scvi._types import MinifiedDataType
from scvi.data import AnnDataManager
from scvi.data._constants import (
_ADATA_MINIFY_TYPE_UNS_KEY,
_SETUP_ARGS_KEY,
ADATA_MINIFY_TYPE,
)
from scvi.data._utils import _get_adata_minify_type, _is_minified, get_anndata_attribute
from scvi.data.fields import (
BaseAnnDataField,
CategoricalJointObsField,
CategoricalObsField,
LabelsWithUnlabeledObsField,
LayerField,
NumericalJointObsField,
NumericalObsField,
ObsmField,
StringUnsField,
)
from scvi.dataloaders import SemiSupervisedDataSplitter
from scvi.model._utils import _init_library_size, get_max_epochs_heuristic
from scvi.model.utils import get_minified_adata_scrna
from scvi.module import SCANVAE
from scvi.train import SemiSupervisedTrainingPlan, TrainRunner
from scvi.train._callbacks import SubSampleLabels
from scvi.utils import setup_anndata_dsp
from scvi.utils._docstrings import devices_dsp
from ._scvi import SCVI
from .base import ArchesMixin, BaseMinifiedModeModelClass, RNASeqMixin, VAEMixin
_SCANVI_LATENT_QZM = "_scanvi_latent_qzm"
_SCANVI_LATENT_QZV = "_scanvi_latent_qzv"
_SCANVI_OBSERVED_LIB_SIZE = "_scanvi_observed_lib_size"
logger = logging.getLogger(__name__)
class SCANVI(RNASeqMixin, VAEMixin, ArchesMixin, BaseMinifiedModeModelClass):
"""Single-cell annotation using variational inference :cite:p:`Xu21`.
Inspired from M1 + M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf).
Parameters
----------
adata
AnnData object that has been registered via :meth:`~scvi.model.SCANVI.setup_anndata`.
n_hidden
Number of nodes per hidden layer.
n_latent
Dimensionality of the latent space.
n_layers
Number of hidden layers used for encoder and decoder NNs.
dropout_rate
Dropout rate for neural networks.
dispersion
One of the following:
* ``'gene'`` - dispersion parameter of NB is constant per gene across cells
* ``'gene-batch'`` - dispersion can differ between different batches
* ``'gene-label'`` - dispersion can differ between different labels
* ``'gene-cell'`` - dispersion can differ for every gene in every cell
gene_likelihood
One of:
* ``'nb'`` - Negative binomial distribution
* ``'zinb'`` - Zero-inflated negative binomial distribution
* ``'poisson'`` - Poisson distribution
linear_classifier
If `True`, uses a single linear layer for classification instead of a
multi-layer perceptron.
**model_kwargs
Keyword args for :class:`~scvi.module.SCANVAE`
Examples
--------
>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.SCANVI.setup_anndata(adata, batch_key="batch", labels_key="labels")
>>> vae = scvi.model.SCANVI(adata, "Unknown")
>>> vae.train()
>>> adata.obsm["X_scVI"] = vae.get_latent_representation()
>>> adata.obs["pred_label"] = vae.predict()
Notes
-----
See further usage examples in the following tutorials:
1. :doc:`/tutorials/notebooks/scrna/harmonization`
2. :doc:`/tutorials/notebooks/scrna/scarches_scvi_tools`
3. :doc:`/tutorials/notebooks/scrna/seed_labeling`
"""
_module_cls = SCANVAE
_training_plan_cls = SemiSupervisedTrainingPlan
def __init__(
self,
adata: AnnData,
n_hidden: int = 128,
n_latent: int = 10,
n_layers: int = 1,
dropout_rate: float = 0.1,
dispersion: Literal["gene", "gene-batch", "gene-label", "gene-cell"] = "gene",
gene_likelihood: Literal["zinb", "nb", "poisson"] = "zinb",
linear_classifier: bool = False,
**model_kwargs,
):
super().__init__(adata)
scanvae_model_kwargs = dict(model_kwargs)
self._set_indices_and_labels()
# ignores unlabeled catgegory
n_labels = self.summary_stats.n_labels - 1
n_cats_per_cov = (
self.adata_manager.get_state_registry(
REGISTRY_KEYS.CAT_COVS_KEY
).n_cats_per_key
if REGISTRY_KEYS.CAT_COVS_KEY in self.adata_manager.data_registry
else None
)
n_batch = self.summary_stats.n_batch
use_size_factor_key = (
REGISTRY_KEYS.SIZE_FACTOR_KEY in self.adata_manager.data_registry
)
library_log_means, library_log_vars = None, None
if not use_size_factor_key and self.minified_data_type is None:
library_log_means, library_log_vars = _init_library_size(
self.adata_manager, n_batch
)
self.module = self._module_cls(
n_input=self.summary_stats.n_vars,
n_batch=n_batch,
n_labels=n_labels,
n_continuous_cov=self.summary_stats.get("n_extra_continuous_covs", 0),
n_cats_per_cov=n_cats_per_cov,
n_hidden=n_hidden,
n_latent=n_latent,
n_layers=n_layers,
dropout_rate=dropout_rate,
dispersion=dispersion,
gene_likelihood=gene_likelihood,
use_size_factor_key=use_size_factor_key,
library_log_means=library_log_means,
library_log_vars=library_log_vars,
linear_classifier=linear_classifier,
**scanvae_model_kwargs,
)
self.module.minified_data_type = self.minified_data_type
self.unsupervised_history_ = None
self.semisupervised_history_ = None
self._model_summary_string = (
"ScanVI Model with the following params: \nunlabeled_category: {}, n_hidden: {}, n_latent: {}"
", n_layers: {}, dropout_rate: {}, dispersion: {}, gene_likelihood: {}"
).format(
self.unlabeled_category_,
n_hidden,
n_latent,
n_layers,
dropout_rate,
dispersion,
gene_likelihood,
)
self.init_params_ = self._get_init_params(locals())
self.was_pretrained = False
self.n_labels = n_labels
@classmethod
def from_scvi_model(
cls,
scvi_model: SCVI,
unlabeled_category: str,
labels_key: Optional[str] = None,
adata: Optional[AnnData] = None,
**scanvi_kwargs,
):
"""Initialize scanVI model with weights from pretrained :class:`~scvi.model.SCVI` model.
Parameters
----------
scvi_model
Pretrained scvi model
labels_key
key in `adata.obs` for label information. Label categories can not be different if
labels_key was used to setup the SCVI model. If None, uses the `labels_key` used to
setup the SCVI model. If that was None, and error is raised.
unlabeled_category
Value used for unlabeled cells in `labels_key` used to setup AnnData with scvi.
adata
AnnData object that has been registered via :meth:`~scvi.model.SCANVI.setup_anndata`.
scanvi_kwargs
kwargs for scANVI model
"""
scvi_model._check_if_trained(
message="Passed in scvi model hasn't been trained yet."
)
scanvi_kwargs = dict(scanvi_kwargs)
init_params = scvi_model.init_params_
non_kwargs = init_params["non_kwargs"]
kwargs = init_params["kwargs"]
kwargs = {k: v for (i, j) in kwargs.items() for (k, v) in j.items()}
for k, v in {**non_kwargs, **kwargs}.items():
if k in scanvi_kwargs.keys():
warnings.warn(
f"Ignoring param '{k}' as it was already passed in to pretrained "
f"SCVI model with value {v}.",
UserWarning,
stacklevel=settings.warnings_stacklevel,
)
del scanvi_kwargs[k]
if scvi_model.minified_data_type is not None:
raise ValueError(
"We cannot use the given scvi model to initialize scanvi because it has a minified adata."
)
if adata is None:
adata = scvi_model.adata
else:
if _is_minified(adata):
raise ValueError(
"Please provide a non-minified `adata` to initialize scanvi."
)
# validate new anndata against old model
scvi_model._validate_anndata(adata)
scvi_setup_args = deepcopy(scvi_model.adata_manager.registry[_SETUP_ARGS_KEY])
scvi_labels_key = scvi_setup_args["labels_key"]
if labels_key is None and scvi_labels_key is None:
raise ValueError(
"A `labels_key` is necessary as the SCVI model was initialized without one."
)
if scvi_labels_key is None:
scvi_setup_args.update({"labels_key": labels_key})
cls.setup_anndata(
adata,
unlabeled_category=unlabeled_category,
**scvi_setup_args,
)
scanvi_model = cls(adata, **non_kwargs, **kwargs, **scanvi_kwargs)
scvi_state_dict = scvi_model.module.state_dict()
scanvi_model.module.load_state_dict(scvi_state_dict, strict=False)
scanvi_model.was_pretrained = True
return scanvi_model
def _set_indices_and_labels(self):
"""Set indices for labeled and unlabeled cells."""
labels_state_registry = self.adata_manager.get_state_registry(
REGISTRY_KEYS.LABELS_KEY
)
self.original_label_key = labels_state_registry.original_key
self.unlabeled_category_ = labels_state_registry.unlabeled_category
labels = get_anndata_attribute(
self.adata,
self.adata_manager.data_registry.labels.attr_name,
self.original_label_key,
).ravel()
self._label_mapping = labels_state_registry.categorical_mapping
# set unlabeled and labeled indices
self._unlabeled_indices = np.argwhere(
labels == self.unlabeled_category_
).ravel()
self._labeled_indices = np.argwhere(labels != self.unlabeled_category_).ravel()
self._code_to_label = dict(enumerate(self._label_mapping))
def predict(
self,
adata: Optional[AnnData] = None,
indices: Optional[Sequence[int]] = None,
soft: bool = False,
batch_size: Optional[int] = None,
) -> Union[np.ndarray, pd.DataFrame]:
"""Return cell label predictions.
Parameters
----------
adata
AnnData object that has been registered via :meth:`~scvi.model.SCANVI.setup_anndata`.
indices
Return probabilities for each class label.
soft
If True, returns per class probabilities
batch_size
Minibatch size for data loading into model. Defaults to `scvi.settings.batch_size`.
"""
adata = self._validate_anndata(adata)
if indices is None:
indices = np.arange(adata.n_obs)
scdl = self._make_data_loader(
adata=adata,
indices=indices,
batch_size=batch_size,
)
y_pred = []
for _, tensors in enumerate(scdl):
x = tensors[REGISTRY_KEYS.X_KEY]
batch = tensors[REGISTRY_KEYS.BATCH_KEY]
cont_key = REGISTRY_KEYS.CONT_COVS_KEY
cont_covs = tensors[cont_key] if cont_key in tensors.keys() else None
cat_key = REGISTRY_KEYS.CAT_COVS_KEY
cat_covs = tensors[cat_key] if cat_key in tensors.keys() else None
pred = self.module.classify(
x, batch_index=batch, cat_covs=cat_covs, cont_covs=cont_covs
)
if not soft:
pred = pred.argmax(dim=1)
y_pred.append(pred.detach().cpu())
y_pred = torch.cat(y_pred).numpy()
if not soft:
predictions = []
for p in y_pred:
predictions.append(self._code_to_label[p])
return np.array(predictions)
else:
n_labels = len(pred[0])
pred = pd.DataFrame(
y_pred,
columns=self._label_mapping[:n_labels],
index=adata.obs_names[indices],
)
return pred
@devices_dsp.dedent
def train(
self,
max_epochs: Optional[int] = None,
n_samples_per_label: Optional[float] = None,
check_val_every_n_epoch: Optional[int] = None,
train_size: float = 0.9,
validation_size: Optional[float] = None,
shuffle_set_split: bool = True,
batch_size: int = 128,
use_gpu: Optional[Union[str, int, bool]] = None,
accelerator: str = "auto",
devices: Union[int, List[int], str] = "auto",
plan_kwargs: Optional[dict] = None,
**trainer_kwargs,
):
"""Train the model.
Parameters
----------
max_epochs
Number of passes through the dataset for semisupervised training.
n_samples_per_label
Number of subsamples for each label class to sample per epoch. By default, there
is no label subsampling.
check_val_every_n_epoch
Frequency with which metrics are computed on the data for validation set for both
the unsupervised and semisupervised trainers. If you'd like a different frequency for
the semisupervised trainer, set check_val_every_n_epoch in semisupervised_train_kwargs.
train_size
Size of training set in the range [0.0, 1.0].
validation_size
Size of the test set. If `None`, defaults to 1 - `train_size`. If
`train_size + validation_size < 1`, the remaining cells belong to a test set.
shuffle_set_split
Whether to shuffle indices before splitting. If `False`, the val, train, and test set are split in the
sequential order of the data according to `validation_size` and `train_size` percentages.
batch_size
Minibatch size to use during training.
%(param_use_gpu)s
%(param_accelerator)s
%(param_devices)s
plan_kwargs
Keyword args for :class:`~scvi.train.SemiSupervisedTrainingPlan`. Keyword arguments passed to
`train()` will overwrite values present in `plan_kwargs`, when appropriate.
**trainer_kwargs
Other keyword args for :class:`~scvi.train.Trainer`.
"""
if max_epochs is None:
max_epochs = get_max_epochs_heuristic(self.adata.n_obs)
if self.was_pretrained:
max_epochs = int(np.min([10, np.max([2, round(max_epochs / 3.0)])]))
logger.info(f"Training for {max_epochs} epochs.")
plan_kwargs = {} if plan_kwargs is None else plan_kwargs
# if we have labeled cells, we want to subsample labels each epoch
sampler_callback = (
[SubSampleLabels()] if len(self._labeled_indices) != 0 else []
)
data_splitter = SemiSupervisedDataSplitter(
adata_manager=self.adata_manager,
train_size=train_size,
validation_size=validation_size,
shuffle_set_split=shuffle_set_split,
n_samples_per_label=n_samples_per_label,
batch_size=batch_size,
)
training_plan = self._training_plan_cls(
self.module, self.n_labels, **plan_kwargs
)
if "callbacks" in trainer_kwargs.keys():
trainer_kwargs["callbacks"].concatenate(sampler_callback)
else:
trainer_kwargs["callbacks"] = sampler_callback
runner = TrainRunner(
self,
training_plan=training_plan,
data_splitter=data_splitter,
max_epochs=max_epochs,
use_gpu=use_gpu,
accelerator=accelerator,
devices=devices,
check_val_every_n_epoch=check_val_every_n_epoch,
**trainer_kwargs,
)
return runner()
@classmethod
@setup_anndata_dsp.dedent
def setup_anndata(
cls,
adata: AnnData,
labels_key: str,
unlabeled_category: Union[str, int, float],
layer: Optional[str] = None,
batch_key: Optional[str] = None,
size_factor_key: Optional[str] = None,
categorical_covariate_keys: Optional[List[str]] = None,
continuous_covariate_keys: Optional[List[str]] = None,
**kwargs,
):
"""%(summary)s.
Parameters
----------
%(param_adata)s
%(param_labels_key)s
%(param_unlabeled_category)s
%(param_layer)s
%(param_batch_key)s
%(param_size_factor_key)s
%(param_cat_cov_keys)s
%(param_cont_cov_keys)s
"""
setup_method_args = cls._get_setup_method_args(**locals())
anndata_fields = [
LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
CategoricalObsField(REGISTRY_KEYS.BATCH_KEY, batch_key),
LabelsWithUnlabeledObsField(
REGISTRY_KEYS.LABELS_KEY, labels_key, unlabeled_category
),
NumericalObsField(
REGISTRY_KEYS.SIZE_FACTOR_KEY, size_factor_key, required=False
),
CategoricalJointObsField(
REGISTRY_KEYS.CAT_COVS_KEY, categorical_covariate_keys
),
NumericalJointObsField(
REGISTRY_KEYS.CONT_COVS_KEY, continuous_covariate_keys
),
]
# register new fields if the adata is minified
adata_minify_type = _get_adata_minify_type(adata)
if adata_minify_type is not None:
anndata_fields += cls._get_fields_for_adata_minification(adata_minify_type)
adata_manager = AnnDataManager(
fields=anndata_fields, setup_method_args=setup_method_args
)
adata_manager.register_fields(adata, **kwargs)
cls.register_manager(adata_manager)
@staticmethod
def _get_fields_for_adata_minification(
minified_data_type: MinifiedDataType,
) -> List[BaseAnnDataField]:
"""Return the anndata fields required for adata minification of the given minified_data_type."""
if minified_data_type == ADATA_MINIFY_TYPE.LATENT_POSTERIOR:
fields = [
ObsmField(
REGISTRY_KEYS.LATENT_QZM_KEY,
_SCANVI_LATENT_QZM,
),
ObsmField(
REGISTRY_KEYS.LATENT_QZV_KEY,
_SCANVI_LATENT_QZV,
),
NumericalObsField(
REGISTRY_KEYS.OBSERVED_LIB_SIZE,
_SCANVI_OBSERVED_LIB_SIZE,
),
]
else:
raise NotImplementedError(f"Unknown MinifiedDataType: {minified_data_type}")
fields.append(
StringUnsField(
REGISTRY_KEYS.MINIFY_TYPE_KEY,
_ADATA_MINIFY_TYPE_UNS_KEY,
),
)
return fields
def minify_adata(
self,
minified_data_type: MinifiedDataType = ADATA_MINIFY_TYPE.LATENT_POSTERIOR,
use_latent_qzm_key: str = "X_latent_qzm",
use_latent_qzv_key: str = "X_latent_qzv",
):
"""Minifies the model's adata.
Minifies the adata, and registers new anndata fields: latent qzm, latent qzv, adata uns
containing minified-adata type, and library size.
This also sets the appropriate property on the module to indicate that the adata is minified.
Parameters
----------
minified_data_type
How to minify the data. Currently only supports `latent_posterior_parameters`.
If minified_data_type == `latent_posterior_parameters`:
* the original count data is removed (`adata.X`, adata.raw, and any layers)
* the parameters of the latent representation of the original data is stored
* everything else is left untouched
use_latent_qzm_key
Key to use in `adata.obsm` where the latent qzm params are stored
use_latent_qzv_key
Key to use in `adata.obsm` where the latent qzv params are stored
Notes
-----
The modification is not done inplace -- instead the model is assigned a new (minified)
version of the adata.
"""
if minified_data_type != ADATA_MINIFY_TYPE.LATENT_POSTERIOR:
raise NotImplementedError(f"Unknown MinifiedDataType: {minified_data_type}")
if self.module.use_observed_lib_size is False:
raise ValueError(
"Cannot minify the data if `use_observed_lib_size` is False"
)
minified_adata = get_minified_adata_scrna(self.adata, minified_data_type)
minified_adata.obsm[_SCANVI_LATENT_QZM] = self.adata.obsm[use_latent_qzm_key]
minified_adata.obsm[_SCANVI_LATENT_QZV] = self.adata.obsm[use_latent_qzv_key]
counts = self.adata_manager.get_from_registry(REGISTRY_KEYS.X_KEY)
minified_adata.obs[_SCANVI_OBSERVED_LIB_SIZE] = np.squeeze(
np.asarray(counts.sum(axis=1))
)
self._update_adata_and_manager_post_minification(
minified_adata, minified_data_type
)
self.module.minified_data_type = minified_data_type