Source code for lumin.nn.models.model

import numpy as np
import pandas as pd
from typing import List, Optional, Union, Tuple
from collections import OrderedDict
from fastprogress import master_bar, progress_bar
import warnings
from fastcore.all import is_listy, partialler, Path
from random import shuffle
from functools import partial

import torch
from torch import Tensor
import torch.nn as nn

from .abs_model import AbsModel,FitParams
from .model_builder import ModelBuilder
from import BatchYielder
from ..callbacks.abs_callback import AbsCallback
from ..callbacks.cyclic_callbacks import AbsCyclicCallback
from ..callbacks.pred_handlers import PredHandler
from ..callbacks.monitors import MetricLogger
from import FoldYielder
from ..interpretation.features import get_nn_feat_importance
from ..metrics.eval_metric import EvalMetric
from ...plotting.plot_settings import PlotSettings
from ...utils.misc import to_np, to_device, is_partially

__all__ = ['Model']

[docs]class Model(AbsModel): r''' Wrapper class to handle training and inference of NNs created via a :class:`~lumin.nn.models.model_builder.ModelBuilder`. Note that saved models can be instantiated direcly via :meth:`~lumin.nn.models.model.Model.from_save` classmethod. # TODO: Improve mask description & user-friendlyness, change to indicate that 'masked' inputs are actually the ones which are used Arguments: model_builder: :class:`~lumin.nn.models.model_builder.ModelBuilder` which will construct the network, loss, optimiser, and input mask Examples:: >>> model = Model(model_builder) ''' def __init__(self, model_builder:Optional[ModelBuilder]=None): self.model_builder,self.input_mask = model_builder,None if self.model_builder is not None: self.model, self.opt, self.loss, self.input_mask = self.model_builder.get_model() self.head, self.body, self.tail = self.model[0], self.model[1], self.model[2] self.objective = self.model_builder.objective self.n_out = self.tail.get_out_size() self.parameters = self.model.parameters def __repr__(self) -> str: return f'''Inputs:\n{self.head.n_cont_in} Continuous: {self.head.cont_feats} \n{self.head.n_cat_in} Categorical: {self.head.cat_feats} \n{self.head.n_matrix_in} Matrix elements: {self.head.matrix_feats} \n\nModel:\n{self.model.parameters} \n\nNumber of trainable parameters: {self.get_param_count()} \n\nOptimiser:\n{self.opt} \n\nLoss:\n{self.loss}''' def __getitem__(self, key:Union[int,str]) -> nn.Module: if isinstance(key, int): if key == 0: return self.head if key == 1: return self.body if key == 2: return self.tail raise IndexError(f'Index {key} out of range') if isinstance(key, str): if key == 'head': return self.head if key == 'body': return self.body if key == 'tail': return self.tail raise KeyError(key) raise ValueError(f'Expected string or int, recieved {key} of type {type(key)}')
[docs] @classmethod def from_save(cls, name:str, model_builder:ModelBuilder) -> AbsModel: r''' Instantiated a :class:`~lumin.nn.models.model.Model` and load saved state from file. Arguments: name: name of file containing saved state model_builder: :class:`~lumin.nn.models.model_builder.ModelBuilder` which was used to construct the network Returns: Instantiated :class:`~lumin.nn.models.model.Model` with network weights, optimiser state, and input mask loaded from saved state Examples:: >>> model = Model.from_save('weights/model.h5', model_builder) ''' m = cls(model_builder) m.load(name) return m
[docs] def get_param_count(self, trainable:bool=True) -> int: r''' Return number of parameters in model. Arguments: trainable: if true (default) only count trainable parameters Returns: NUmber of (trainable) parameters in model ''' return sum(p.numel() for p in self.parameters() if p.requires_grad or not trainable)
[docs] def set_input_mask(self, mask:np.ndarray) -> None: r''' Mask input columns by only using input columns whose indeces are listed in mask Arguments: mask: array of column indeces to use from all input columns ''' self.input_mask = mask
def _fit_batch(self, x:Tensor, y:Tensor, w:Tensor) -> None: self.fit_params.x,self.fit_params.y,self.fit_params.w = x,y,w for c in c.on_batch_begin() self.fit_params.y_pred = self.model(self.fit_params.x) if self.fit_params.state != 'test' and self.fit_params.loss_func is not None: if hasattr(self.fit_params.loss_func, 'weights'): self.fit_params.loss_func.weights = self.fit_params.w # Proper weighting required else: self.fit_params.loss_func.weight = self.fit_params.w self.fit_params.loss_val = self.fit_params.loss_func(self.fit_params.y_pred, self.fit_params.y) for c in c.on_forwards_end() if self.fit_params.state == 'train': self.fit_params.opt.zero_grad() for c in c.on_backwards_begin() self.fit_params.loss_val.backward() for c in c.on_backwards_end() self.fit_params.opt.step() for c in c.on_batch_end()
[docs] def fit(self, n_epochs:int, fy:FoldYielder, bs:int, bulk_move:bool=True, train_on_weights:bool=True, trn_idxs:Optional[List[int]]=None, val_idx:Optional[int]=None, cbs:Optional[Union[AbsCallback,List[AbsCallback]]]=None, cb_savepath:Path=Path('train_weights'), model_bar:Optional[master_bar]=None, visible_bar:bool=True) -> List[AbsCallback]: r''' Fit network to training data according to the model's loss and optimiser. Training continues until: - All of the training folds are used n_epoch number of times; - Or a callback triggers training to stop, e.g. :class:`~lumin.nn.callbacks.cyclic_callbacks.OneCycle`, or :class:`~lumin.nn.callbacks.monitors.EarlyStopping` Arguments: n_epochs: number of epochs for which to train fy: :class:`` containing training and validation data bs: Batch size bulk_move: if true, will optimise for speed by using more RAM and VRAM train_on_weights: whether to actually use data weights, if present trn_idxs: Fold indexes in `fy` to use for training. If not set, will use all folds except val_idx val_idx: Fold index in `fy` to use for validation. If not set, will not compute validation losses cbs: list of instantiated callbacks to adjust training. Will be called in order listed. cb_savepath: General save directory for any callbacks which require saving models and other information (accessible from `fit_params`), model_bar: Optional `master_bar` for aligning progress bars, i.e. if training multiple models Returns: List of all callbacks used during training ''' if cbs is None: cbs = [] elif not is_listy(cbs): cbs = [cbs] cyclic_cbs,loss_cbs,metric_log = [],[],None for c in cbs: if isinstance(c, AbsCyclicCallback): cyclic_cbs.append(c) # CBs that might prevent a model from stopping training due to a hyper-param cycle if hasattr(c, "get_loss"): loss_cbs.append(c) # CBs that produce alternative losses that should be considered if isinstance(c, MetricLogger): metric_log = c # CB that logs losses and eval_metrics self.fit_params = FitParams(cbs=cbs, cyclic_cbs=cyclic_cbs, loss_cbs=loss_cbs, metric_log=metric_log, stop=False, n_epochs=n_epochs, fy=fy, val_idx=val_idx, bs=bs, bulk_move=bulk_move, train_on_weights=train_on_weights, cb_savepath=Path(cb_savepath), loss_func=self.loss, opt=self.opt, val_requires_grad=False) self.fit_params.cb_savepath.mkdir(parents=True, exist_ok=True) if is_partially(self.fit_params.loss_func): self.fit_params.loss_func = self.fit_params.loss_func() self.fit_params.partial_by = partialler(BatchYielder, objective=self.objective, use_weights=self.fit_params.train_on_weights, bulk_move=self.fit_params.bulk_move, input_mask=self.input_mask) if trn_idxs is None: trn_idxs = list(range(fy.n_folds)) if val_idx is not None and val_idx in trn_idxs: trn_idxs.remove(val_idx) self.fit_params.trn_idxs,self.fit_params.val_idx = trn_idxs,val_idx if self.fit_params.val_idx is not None: if bulk_move: val_by = self.fit_params.partial_by(**self.fit_params.fy.get_fold(self.fit_params.val_idx), drop_last=False, shuffle=False, bs=self.fit_params.fy.get_data_count(self.fit_params.val_idx) if bulk_move else else: val_by = partial(self.fit_params.partial_by, drop_last=False, shuffle=False, bs=self.fit_params.fy.get_data_count(self.fit_params.val_idx) if bulk_move else trn_by = partial(self.fit_params.partial_by, drop_last=True,, shuffle=True) def fit_epoch() -> None: self.model.train() self.fit_params.state = 'train' self.fit_params.epoch += 1 shuffle(trn_idxs) self.fit_params.trn_idxs = trn_idxs for c in c.on_epoch_begin() for self.fit_params.trn_idx in self.fit_params.trn_idxs: self.fit_params.sub_epoch += 1 = trn_by(**self.fit_params.fy.get_fold(self.fit_params.trn_idx)) for c in c.on_fold_begin() for b in self._fit_batch(*b) for c in c.on_fold_end() if self.fit_params.stop: break for c in c.on_epoch_end() if self.fit_params.val_idx is not None: self.model.eval() with torch.set_grad_enabled(self.fit_params.val_requires_grad): self.fit_params.state = 'valid' for c in c.on_epoch_begin() = val_by if bulk_move else val_by(**self.fit_params.fy.get_fold(self.fit_params.val_idx)) for c in c.on_fold_begin() for b in self._fit_batch(*b) for c in c.on_fold_end() for c in c.on_epoch_end() try: for c in c.set_model(self) for c in c.on_train_begin() for e in progress_bar(range(self.fit_params.n_epochs), parent=model_bar, display=visible_bar): fit_epoch() if self.fit_params.stop: break if self.fit_params.val_idx is not None: del val_by for c in c.on_train_end() finally: self.fit_params = None torch.cuda.empty_cache() return cbs
def _predict_by(self, by:BatchYielder, pred_cb:PredHandler=PredHandler(), cbs:Optional[Union[AbsCallback,List[AbsCallback]]]=None) -> np.ndarray: if cbs is None: cbs = [] elif not is_listy(cbs): cbs = [cbs] cbs.append(pred_cb) self.fit_params = FitParams(cbs=cbs, by=by, state='test', val_requires_grad=False) try: for c in c.set_model(self) self.model.eval() with torch.set_grad_enabled(self.fit_params.val_requires_grad): for c in c.on_pred_begin() for b in self._fit_batch(*b) for c in c.on_pred_end() finally: self.fit_params = None cbs.pop() # Remove pred_cb to avoid mutating cbs torch.cuda.empty_cache() return pred_cb.get_preds() def _predict_array(self, inputs:Union[np.ndarray,pd.DataFrame,Tensor,Tuple], as_np:bool=True, pred_cb:PredHandler=PredHandler(), cbs:Optional[List[AbsCallback]]=None, bs:Optional[int]=None) -> Union[np.ndarray, Tensor]: by = BatchYielder(inputs=inputs, bs=len(inputs) if bs is None else bs, objective=self.objective, shuffle=False, bulk_move=bs is None, input_mask=self.input_mask, drop_last=False) preds = self._predict_by(by, pred_cb=pred_cb, cbs=cbs) if as_np: preds = to_np(preds) if 'multiclass' in self.objective: preds = np.exp(preds) return preds
[docs] def evaluate(self, inputs:Union[np.ndarray,Tensor,Tuple,BatchYielder], targets:Optional[Union[np.ndarray,Tensor]]=None, weights:Optional[Union[np.ndarray,Tensor]]=None, bs:Optional[int]=None) -> float: r''' Compute loss on provided data. Arguments: inputs: input data, or :class:`` with input, target, and weight data targets: targets, not required if :class:`` is passed to inputs weights: Optional weights, not required if :class:``, or no weights should be considered bs: batch size to use. If `None`, will evaluate all data at once Returns: (weighted) loss of model predictions on provided data ''' # TODO: make this work with non-meaned losses if hasattr(self, 'fit_params') and self.fit_params is not None: raise ValueError('Evaluate will overwrite exisiting fit_params for this model. Most likely it is being called during training.') if not isinstance(inputs, BatchYielder): inputs = BatchYielder(inputs=inputs, targets=targets, weights=weights, bs=len(inputs) if bs is None else bs, objective=self.objective, shuffle=False, bulk_move=bs is None, input_mask=self.input_mask, drop_last=False) self.fit_params = FitParams(cbs=[], by=inputs, state='valid', loss_func=self.loss) if is_partially(self.fit_params.loss_func): self.fit_params.loss_func = self.fit_params.loss_func() self.model.eval() loss,cnt = 0,0 try: for b in self._fit_batch(*b) sz = len(b[0]) loss +=*sz cnt += sz finally: self.fit_params = None return loss/cnt
def _predict_folds(self, fy:FoldYielder, pred_name:str='pred', pred_cb:PredHandler=PredHandler(), cbs:Optional[List[AbsCallback]]=None, bs:Optional[int]=None) -> None: for fold_idx in progress_bar(range(len(fy))): if not fy.test_time_aug: pred = self._predict_array(fy.get_fold(fold_idx)['inputs'], pred_cb=pred_cb, cbs=cbs, bs=bs) else: tmpPred = [] for aug in range(fy.aug_mult): tmpPred.append(self._predict_array(fy.get_test_fold(fold_idx, aug)['inputs'], pred_cb=pred_cb, cbs=cbs, bs=bs)) pred = np.mean(tmpPred, axis=0) if self.n_out > 1: fy.save_fold_pred(pred, fold_idx, pred_name=pred_name) else: fy.save_fold_pred(pred[:, 0], fold_idx, pred_name=pred_name)
[docs] def predict(self, inputs:Union[np.ndarray, pd.DataFrame, Tensor, FoldYielder], as_np:bool=True, pred_name:str='pred', pred_cb:PredHandler=PredHandler(), cbs:Optional[List[AbsCallback]]=None, bs:Optional[int]=None) -> Union[np.ndarray, Tensor, None]: r''' Apply model to inputed data and compute predictions. Arguments: inputs: input data as Numpy array, Pandas DataFrame, or tensor on device, or :class:`` interfacing to data as_np: whether to return predictions as Numpy array (otherwise tensor) if inputs are a Numpy array, Pandas DataFrame, or tensor pred_name: name of group to which to save predictions if inputs are a :class:`` pred_cb: :class:`~lumin.nn.callbacks.pred_handlers.PredHandler` callback to determin how predictions are computed. Default simply returns the model predictions. Other uses could be e.g. running argmax on a multiclass classifier cbs: list of any instantiated callbacks to use during prediction bs: if not `None`, will run prediction in batches of specified size to save of memory Returns: if inputs are a Numpy array, Pandas DataFrame, or tensor, will return predicitions as either array or tensor ''' if isinstance(inputs, BatchYielder): return self._predict_by(inputs, pred_cb=pred_cb, cbs=cbs) if not isinstance(inputs, FoldYielder): return self._predict_array(inputs, as_np=as_np, pred_cb=pred_cb, cbs=cbs, bs=bs) self._predict_folds(inputs, pred_name, pred_cb=pred_cb, cbs=cbs, bs=bs)
[docs] def get_weights(self) -> OrderedDict: r''' Get state_dict of weights for network Returns: state_dict of weights for network ''' return self.model.state_dict()
[docs] def set_weights(self, weights:OrderedDict) -> None: r''' Set state_dict of weights for network Arguments: weights: state_dict of weights for network ''' self.model.load_state_dict(weights)
[docs] def get_lr(self) -> float: r''' Get learning rate of optimiser Returns: learning rate of optimiser ''' return self.opt.param_groups[0]['lr']
[docs] def set_lr(self, lr:float) -> None: r''' set learning rate of optimiser Arguments: lr: learning rate of optimiser ''' self.opt.param_groups[0]['lr'] = lr
[docs] def get_mom(self) -> float: r''' Get momentum/beta_1 of optimiser Returns: momentum/beta_1 of optimiser ''' if 'betas' in self.opt.param_groups[0]: return self.opt.param_groups[0]['betas'][0] elif 'momentum' in self.opt.param_groups[0]: return self.opt.param_groups[0]['momentum']
[docs] def set_mom(self, mom:float) -> None: r''' Set momentum/beta_1 of optimiser Arguments: mom: momentum/beta_1 of optimiser ''' if 'betas' in self.opt.param_groups[0]: self.opt.param_groups[0]['betas'] = (mom, self.opt.param_groups[0]['betas'][1]) elif 'momentum' in self.opt.param_groups[0]: self.opt.param_groups[0]['momentum'] = mom
[docs] def save(self, name:str) -> None: r''' Save model, optimiser, and input mask states to file Arguments: name: name of save file '''{'model':self.model.state_dict(), 'opt':self.opt.state_dict(), 'input_mask':self.input_mask}, str(name))
[docs] def load(self, name:str, model_builder:ModelBuilder=None) -> None: r''' Load model, optimiser, and input mask states from file Arguments: name: name of save file model_builder: if :class:`~lumin.nn.models.model.Model` was not initialised with a :class:`~lumin.nn.models.model_builder.ModelBuilder`, you will need to pass one here ''' # TODO: update map location when device choice is changable by user if model_builder is not None: self.model, self.opt, self.loss, self.input_mask = model_builder.get_model() state = torch.load(name, map_location='cuda' if torch.cuda.is_available() else 'cpu') self.model.load_state_dict(state['model']) self.opt.load_state_dict(state['opt']) self.input_mask = state['input_mask'] self.objective = self.model_builder.objective if model_builder is None else model_builder.objective
[docs] def export2onnx(self, name:str, bs:int=1) -> None: r''' Export network to ONNX format. Note that ONNX expects a fixed batch size (bs) which is the number of datapoints your wish to pass through the model concurrently. Arguments: name: filename for exported file bs: batch size for exported models ''' # TODO: Pass FoldYielder to get example dummy input, or account for matrix inputs warnings.warn("""ONNX export of LUMIN models has not been fully explored or sufficiently tested yet. Please use with caution, and report any trouble""") if '.onnx' not in name: name += '.onnx' dummy_input = to_device(torch.rand(bs, self.model_builder.n_cont_in+self.model_builder.cat_embedder.n_cat_in)) torch.onnx.export(self.model, dummy_input, name)
[docs] def export2tfpb(self, name:str, bs:int=1) -> None: r''' Export network to Tensorflow ProtocolBuffer format, via ONNX. Note that ONNX expects a fixed batch size (bs) which is the number of datapoints your wish to pass through the model concurrently. Arguments: name: filename for exported file bs: batch size for exported models ''' import onnx from onnx_tf.backend import prepare warnings.warn("""Tensorflow ProtocolBuffer export of LUMIN models (via ONNX) has not been fully explored or sufficiently tested yet. Please use with caution, and report any trouble""") self.export2onnx(name, bs) m = onnx.load(f'{name}.onnx') tf_rep = prepare(m) tf_rep.export_graph(f'{name}.pb')
[docs] def get_feat_importance(self, fy:FoldYielder, bs:Optional[int]=None, eval_metric:Optional[EvalMetric]=None, savename:Optional[str]=None, settings:PlotSettings=PlotSettings()) -> pd.DataFrame: r''' Call :meth:`~lumin.nn.interpretation.features.get_nn_feat_importance` passing this :class:`~lumin.nn.models.model.Model` and provided arguments Arguments: fy: :class:`` interfacing to data used to train model bs: If set, will evaluate model in batches of data, rather than all at once eval_metric: Optional :class:`~lumin.nn.metric.eval_metric.EvalMetric` to use to quantify performance in place of loss savename: Optional name of file to which to save the plot of feature importances settings: :class:`~lumin.plotting.plot_settings.PlotSettings` class to control figure appearance ''' return get_nn_feat_importance(self, fy=fy, bs=bs, eval_metric=eval_metric, savename=savename, settings=settings)
[docs] def get_out_size(self) -> int: r''' Get number of outputs of model Returns: Number of outputs of model ''' return self.tail.get_out_size()
[docs] def freeze_layers(self) -> None: r''' Make parameters untrainable ''' for p in self.model.parameters(): p.requires_grad = False
[docs] def unfreeze_layers(self) -> None: r''' Make parameters trainable ''' for p in self.model.parameters(): p.requires_grad = True
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