Source code for scvi.model.base._base_model

import inspect
import logging
import os
import warnings
from abc import ABCMeta, abstractmethod
from typing import Dict, Optional, Sequence, Type, Union
from uuid import uuid4

import numpy as np
import pyro
import rich
import torch
from anndata import AnnData

from scvi import settings
from import AnnDataManager
from import registry_from_setup_dict
from import _assign_adata_uuid
from scvi.dataloaders import AnnDataLoader
from scvi.model._utils import parse_use_gpu_arg
from scvi.model.base._utils import _load_legacy_saved_files
from scvi.module.base import PyroBaseModuleClass
from scvi.utils import setup_anndata_dsp

from ._utils import _initialize_model, _load_saved_files, _validate_var_names

logger = logging.getLogger(__name__)

_UNTRAINED_WARNING_MESSAGE = "Trying to query inferred values from an untrained model. Please train the model first."

_SETUP_INPUTS_EXCLUDED_PARAMS = {"adata", "kwargs"}

class BaseModelMetaClass(ABCMeta):
    Metaclass for :class:`~scvi.model.base.BaseModelClass`.

    Constructs model class-specific mappings for :class:`` instances.
    ``cls._setup_adata_manager_store`` maps from AnnData object UUIDs to :class:`` instances.
    This mapping is populated everytime ``cls.setup_anndata()`` is called.
    ``cls._per_isntance_manager_store`` maps from model instance UUIDs to AnnData UUID::class:`` mappings.
    These :class:`` instances are tied to a single model instance and populated either
    during model initialization or after running ``self._validate_anndata()``.

    def __init__(cls, name, bases, dct):
        cls._setup_adata_manager_store: Dict[
            str, Type[AnnDataManager]
        ] = dict()  # Maps adata id to AnnDataManager instances.
        cls._per_instance_manager_store: Dict[
            str, Dict[str, Type[AnnDataManager]]
        ] = dict()  # Maps model instance id to AnnDataManager mappings.
        super().__init__(name, bases, dct)

[docs]class BaseModelClass(metaclass=BaseModelMetaClass): """Abstract class for scvi-tools models.""" def __init__(self, adata: Optional[AnnData] = None): = str(uuid4()) # Used for cls._manager_store keys. if adata is not None: self._adata = adata self._adata_manager = self._get_most_recent_anndata_manager( adata, required=True ) self._register_manager_for_instance(self.adata_manager) # Suffix registry instance variable with _ to include it when saving the model. self.registry_ = self._adata_manager.registry self.summary_stats = self._adata_manager.summary_stats self.is_trained_ = False self._model_summary_string = "" self.train_indices_ = None self.test_indices_ = None self.validation_indices_ = None self.history_ = None self._data_loader_cls = AnnDataLoader @property def adata(self) -> AnnData: """Data attached to model instance.""" return self._adata @adata.setter def adata(self, adata: AnnData): if adata is None: raise ValueError("adata cannot be None.") self._validate_anndata(adata) self._adata = adata self._adata_manager = self.get_anndata_manager(adata, required=True) self.registry_ = self._adata_manager.registry self.summary_stats = self._adata_manager.summary_stats @property def adata_manager(self) -> AnnDataManager: """Manager instance associated with self.adata.""" return self._adata_manager
[docs] def to_device(self, device: Union[str, int]): """ Move model to device. Parameters ---------- device Device to move model to. Options: 'cpu' for CPU, integer GPU index (eg. 0), or 'cuda:X' where X is the GPU index (eg. 'cuda:0'). See torch.device for more info. Examples -------- >>> adata = >>> model = scvi.model.SCVI(adata) >>> model.to_device('cpu') # moves model to CPU >>> model.to_device('cuda:0') # moves model to GPU 0 >>> model.to_device(0) # also moves model to GPU 0 """ my_device = torch.device(device)
@property def device(self) -> str: """The current device that the module's params are on.""" return self.module.device @staticmethod def _get_setup_method_args(**setup_locals) -> dict: """ Returns a dictionary organizing the arguments used to call ``setup_anndata``. Must be called with ``**locals()`` at the start of the ``setup_anndata`` method to avoid the inclusion of any extraneous variables. """ cls = setup_locals.pop("cls") model_name = cls.__name__ setup_args = dict() for k, v in setup_locals.items(): if k not in _SETUP_INPUTS_EXCLUDED_PARAMS: setup_args[k] = v return {_MODEL_NAME_KEY: model_name, _SETUP_ARGS_KEY: setup_args}
[docs] @classmethod def register_manager(cls, adata_manager: AnnDataManager): """ Registers an :class:`` instance with this model class. Stores the :class:`` reference in a class-specific manager store. Intended for use in the ``setup_anndata()`` class method followed up by retrieval of the :class:`` via the ``_get_most_recent_anndata_manager()`` method in the model init method. Notes ----- Subsequent calls to this method with an :class:`` instance referring to the same underlying AnnData object will overwrite the reference to previous :class:``. """ adata_id = adata_manager.adata_uuid cls._setup_adata_manager_store[adata_id] = adata_manager
def _register_manager_for_instance(self, adata_manager: AnnDataManager): """ Registers an :class:`` instance with this model instance. Creates a model-instance specific mapping in ``cls._per_instance_manager_store`` for this :class:`` instance. """ if not in self._per_instance_manager_store: self._per_instance_manager_store[] = dict() adata_id = adata_manager.adata_uuid instance_manager_store = self._per_instance_manager_store[] instance_manager_store[adata_id] = adata_manager @classmethod def _get_most_recent_anndata_manager( cls, adata: AnnData, required: bool = False ) -> Optional[AnnDataManager]: """ Retrieves the :class:`` for a given AnnData object specific to this model class. Checks for the most recent :class:`` created for the given AnnData object via ``setup_anndata()`` on model initialization. Unlike :meth:`scvi.model.base.BaseModelClass.get_anndata_manager`, this method is not model instance specific and can be called before a model is fully initialized. Parameters ---------- adata AnnData object to find manager instance for. required If True, errors on missing manager. Otherwise, returns None when manager is missing. """ if _SCVI_UUID_KEY not in adata.uns: if required: raise ValueError( f"Please set up your AnnData with {cls.__name__}.setup_anndata first." ) return None adata_id = adata.uns[_SCVI_UUID_KEY] if adata_id not in cls._setup_adata_manager_store: if required: raise ValueError( f"Please set up your AnnData with {cls.__name__}.setup_anndata first. " "It appears the AnnData object has been setup with a different model." ) return None adata_manager = cls._setup_adata_manager_store[adata_id] if adata_manager.adata is not adata: raise ValueError( "The provided AnnData object does not match the AnnData object " "previously provided for setup. Did you make a copy?" ) return adata_manager
[docs] def get_anndata_manager( self, adata: AnnData, required: bool = False ) -> Optional[AnnDataManager]: """ Retrieves the :class:`` for a given AnnData object specific to this model instance. Requires ```` has been set. Checks for an :class:`` specific to this model instance. Parameters ---------- adata AnnData object to find manager instance for. required If True, errors on missing manager. Otherwise, returns None when manager is missing. """ cls = self.__class__ if _SCVI_UUID_KEY not in adata.uns: if required: raise ValueError( f"Please set up your AnnData with {cls.__name__}.setup_anndata first." ) return None adata_id = adata.uns[_SCVI_UUID_KEY] if not in cls._per_instance_manager_store: if required: raise AssertionError( "Unable to find instance specific manager store. " "The model has likely not been initialized with an AnnData object." ) return None elif adata_id not in cls._per_instance_manager_store[]: if required: raise AssertionError( "Please call ``self._validate_anndata`` on this AnnData object." ) return None adata_manager = cls._per_instance_manager_store[][adata_id] if adata_manager.adata is not adata: "AnnData object appears to be a copy. Attempting to transfer setup." ) _assign_adata_uuid(adata, overwrite=True) adata_manager = self.adata_manager.transfer_fields(adata) self._register_manager_for_instance(adata_manager) return adata_manager
[docs] def get_from_registry( self, adata: AnnData, registry_key: str, ) -> np.ndarray: """ Returns the object in AnnData associated with the key in the data registry. AnnData object should be registered with the model prior to calling this function via the ``self._validate_anndata`` method. Parameters ---------- registry_key key of object to get from data registry. adata AnnData to pull data from. Returns ------- The requested data as a NumPy array. """ adata_manager = self.get_anndata_manager(adata) if adata_manager is None: raise AssertionError( "AnnData not registered with model. Call `self._validate_anndata` " "prior to calling this function." ) return adata_manager.get_from_registry(registry_key)
def _make_data_loader( self, adata: AnnData, indices: Optional[Sequence[int]] = None, batch_size: Optional[int] = None, shuffle: bool = False, data_loader_class=None, **data_loader_kwargs, ): """ Create a AnnDataLoader object for data iteration. Parameters ---------- adata AnnData object with equivalent structure to initial AnnData. indices Indices of cells in adata to use. If `None`, all cells are used. batch_size Minibatch size for data loading into model. Defaults to `scvi.settings.batch_size`. shuffle Whether observations are shuffled each iteration though data_loader_class Class to use for data loader data_loader_kwargs Kwargs to the class-specific data loader class """ adata_manager = self.get_anndata_manager(adata) if adata_manager is None: raise AssertionError( "AnnDataManager not found. Call `self._validate_anndata` prior to calling this function." ) adata = adata_manager.adata if batch_size is None: batch_size = settings.batch_size if indices is None: indices = np.arange(adata.n_obs) if data_loader_class is None: data_loader_class = self._data_loader_cls if "num_workers" not in data_loader_kwargs: data_loader_kwargs.update({"num_workers": settings.dl_num_workers}) dl = data_loader_class( adata_manager, shuffle=shuffle, indices=indices, batch_size=batch_size, **data_loader_kwargs, ) return dl def _validate_anndata( self, adata: Optional[AnnData] = None, copy_if_view: bool = True ) -> AnnData: """Validate anndata has been properly registered, transfer if necessary.""" if adata is None: adata = self.adata if adata.is_view: if copy_if_view:"Received view of anndata, making copy.") adata._init_as_actual(adata.copy()) # Reassign AnnData UUID to produce a separate AnnDataManager. _assign_adata_uuid(adata, overwrite=True) else: raise ValueError("Please run `adata = adata.copy()`") adata_manager = self.get_anndata_manager(adata) if adata_manager is None: "Input AnnData not setup with scvi-tools. " + "attempting to transfer AnnData setup" ) self._register_manager_for_instance( self.adata_manager.transfer_fields(adata) ) else: # Case where correct AnnDataManager is found, replay registration as necessary. adata_manager.validate() return adata def _check_if_trained( self, warn: bool = True, message: str = _UNTRAINED_WARNING_MESSAGE ): """ Check if the model is trained. If not trained and `warn` is True, raise a warning, else raise a RuntimeError. """ if not self.is_trained_: if warn: warnings.warn(message) else: raise RuntimeError(message) @property def is_trained(self) -> bool: """Whether the model has been trained.""" return self.is_trained_ @property def test_indices(self) -> np.ndarray: """Observations that are in test set.""" return self.test_indices_ @property def train_indices(self) -> np.ndarray: """Observations that are in train set.""" return self.train_indices_ @property def validation_indices(self) -> np.ndarray: """Observations that are in validation set.""" return self.validation_indices_ @train_indices.setter def train_indices(self, value): self.train_indices_ = value @test_indices.setter def test_indices(self, value): self.test_indices_ = value @validation_indices.setter def validation_indices(self, value): self.validation_indices_ = value @is_trained.setter def is_trained(self, value): self.is_trained_ = value @property def history(self): """Returns computed metrics during training.""" return self.history_ def _get_user_attributes(self): """Returns all the self attributes defined in a model class, e.g., self.is_trained_.""" attributes = inspect.getmembers(self, lambda a: not (inspect.isroutine(a))) attributes = [ a for a in attributes if not (a[0].startswith("__") and a[0].endswith("__")) ] attributes = [a for a in attributes if not a[0].startswith("_abc_")] return attributes def _get_init_params(self, locals): """ Returns the model init signature with associated passed in values. Ignores the initial AnnData. """ init = self.__init__ sig = inspect.signature(init) parameters = sig.parameters.values() init_params = [ for p in parameters] all_params = {p: locals[p] for p in locals if p in init_params} all_params = { k: v for (k, v) in all_params.items() if not isinstance(v, AnnData) } # not very efficient but is explicit # seperates variable params (**kwargs) from non variable params into two dicts non_var_params = [ for p in parameters if p.kind != p.VAR_KEYWORD] non_var_params = {k: v for (k, v) in all_params.items() if k in non_var_params} var_params = [ for p in parameters if p.kind == p.VAR_KEYWORD] var_params = {k: v for (k, v) in all_params.items() if k in var_params} user_params = {"kwargs": var_params, "non_kwargs": non_var_params} return user_params
[docs] @abstractmethod def train(self): """Trains the model."""
[docs] def save( self, dir_path: str, prefix: Optional[str] = None, overwrite: bool = False, save_anndata: bool = False, **anndata_write_kwargs, ): """ Save the state of the model. Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0. Parameters ---------- dir_path Path to a directory. prefix Prefix to prepend to saved file names. overwrite Overwrite existing data or not. If `False` and directory already exists at `dir_path`, error will be raised. save_anndata If True, also saves the anndata anndata_write_kwargs Kwargs for :meth:`~anndata.AnnData.write` """ if not os.path.exists(dir_path) or overwrite: os.makedirs(dir_path, exist_ok=overwrite) else: raise ValueError( "{} already exists. Please provide an unexisting directory for saving.".format( dir_path ) ) file_name_prefix = prefix or "" if save_anndata: self.adata.write( os.path.join(dir_path, f"{file_name_prefix}adata.h5ad"), **anndata_write_kwargs, ) model_save_path = os.path.join(dir_path, f"{file_name_prefix}") # save the model state dict and the trainer state dict only model_state_dict = self.module.state_dict() var_names = self.adata.var_names.astype(str) var_names = var_names.to_numpy() # get all the user attributes user_attributes = self._get_user_attributes() # only save the public attributes with _ at the very end user_attributes = {a[0]: a[1] for a in user_attributes if a[0][-1] == "_"} dict( model_state_dict=model_state_dict, var_names=var_names, attr_dict=user_attributes, ), model_save_path, )
[docs] @classmethod def load( cls, dir_path: str, adata: Optional[AnnData] = None, use_gpu: Optional[Union[str, int, bool]] = None, prefix: Optional[str] = None, backup_url: Optional[str] = None, ): """ Instantiate a model from the saved output. Parameters ---------- dir_path Path to saved outputs. adata AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved `scvi` setup dictionary. If None, will check for and load anndata saved with the model. use_gpu Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False). prefix Prefix of saved file names. backup_url URL to retrieve saved outputs from if not present on disk. Returns ------- Model with loaded state dictionaries. Examples -------- >>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save >>> model.get_.... """ load_adata = adata is None use_gpu, device = parse_use_gpu_arg(use_gpu) (attr_dict, var_names, model_state_dict, new_adata,) = _load_saved_files( dir_path, load_adata, map_location=device, prefix=prefix, backup_url=backup_url, ) adata = new_adata if new_adata is not None else adata _validate_var_names(adata, var_names) registry = attr_dict.pop("registry_") if _MODEL_NAME_KEY in registry and registry[_MODEL_NAME_KEY] != cls.__name__: raise ValueError( "It appears you are loading a model from a different class." ) if _SETUP_ARGS_KEY not in registry: raise ValueError( "Saved model does not contain original setup inputs. " "Cannot load the original setup." ) # Calling ``setup_anndata`` method with the original arguments passed into # the saved model. This enables simple backwards compatibility in the case of # newly introduced fields or parameters. cls.setup_anndata(adata, source_registry=registry, **registry[_SETUP_ARGS_KEY]) model = _initialize_model(cls, adata, attr_dict) # some Pyro modules with AutoGuides may need one training step try: model.module.load_state_dict(model_state_dict) except RuntimeError as err: if isinstance(model.module, PyroBaseModuleClass): old_history = model.history_.copy()"Preparing underlying module for load") model.train(max_steps=1) model.history_ = old_history pyro.clear_param_store() model.module.load_state_dict(model_state_dict) else: raise err model.to_device(device) model.module.eval() model._validate_anndata(adata) return model
[docs] @classmethod def convert_legacy_save( cls, dir_path: str, output_dir_path: str, overwrite: bool = False, prefix: Optional[str] = None, ) -> None: """ Converts a legacy saved model (<v0.15.0) to the updated save format. Parameters ---------- dir_path Path to directory where legacy model is saved. output_dir_path Path to save converted save files. overwrite Overwrite existing data or not. If ``False`` and directory already exists at ``output_dir_path``, error will be raised. prefix Prefix of saved file names. """ if not os.path.exists(output_dir_path) or overwrite: os.makedirs(output_dir_path, exist_ok=overwrite) else: raise ValueError( f"{output_dir_path} already exists. Please provide an unexisting directory for saving." ) file_name_prefix = prefix or "" model_state_dict, var_names, attr_dict, _ = _load_legacy_saved_files( dir_path, file_name_prefix, load_adata=False ) if "scvi_setup_dict_" in attr_dict: scvi_setup_dict = attr_dict.pop("scvi_setup_dict_") unlabeled_category_key = "unlabeled_category_" unlabeled_category = attr_dict.get(unlabeled_category_key, None) attr_dict["registry_"] = registry_from_setup_dict( cls, scvi_setup_dict, unlabeled_category=unlabeled_category, ) model_save_path = os.path.join(output_dir_path, f"{file_name_prefix}") dict( model_state_dict=model_state_dict, var_names=var_names, attr_dict=attr_dict, ), model_save_path, )
def __repr__( self, ): summary_string = self._model_summary_string summary_string += "\nTraining status: {}".format( "Trained" if self.is_trained_ else "Not Trained" ) rich.print(summary_string) return ""
[docs] @classmethod @abstractmethod @setup_anndata_dsp.dedent def setup_anndata( cls, adata: AnnData, *args, **kwargs, ): """ %(summary)s. Each model class deriving from this class provides parameters to this method according to its needs. To operate correctly with the model initialization, the implementation must call :meth:`~scvi.model.base.BaseModelClass.register_manager` on a model-specific instance of :class:``. """
[docs] @staticmethod def view_setup_args(dir_path: str, prefix: Optional[str] = None) -> None: """ Print args used to setup a saved model. Parameters ---------- dir_path Path to saved outputs. prefix Prefix of saved file names. """ attr_dict = _load_saved_files(dir_path, False, prefix=prefix)[0] # Legacy support for old setup dict format. if "scvi_setup_dict_" in attr_dict: raise NotImplementedError( "Viewing setup args for pre v0.15.0 models is unsupported. " "Update your save files with ``convert_legacy_save`` first." ) registry = attr_dict.pop("registry_") AnnDataManager.view_setup_method_args(registry)
[docs] def view_anndata_setup( self, adata: Optional[AnnData] = None, hide_state_registries: bool = False ) -> None: """ Print summary of the setup for the initial AnnData or a given AnnData object. Parameters ---------- adata AnnData object setup with ``setup_anndata`` or :meth:``. hide_state_registries If True, prints a shortened summary without details of each state registry. """ if adata is None: adata = self.adata try: adata_manager = self.get_anndata_manager(adata, required=True) except ValueError: raise ValueError( f"Given AnnData not setup with {self.__class__.__name__}. " "Cannot view setup summary." ) adata_manager.view_registry(hide_state_registries=hide_state_registries)