Source code for selene_sdk.evaluate_model

This module provides the EvaluateModel class.
import logging
import os
import warnings

import numpy as np
import torch
import torch.nn as nn
from sklearn.metrics import average_precision_score, roc_auc_score

from .sequences import Genome
from .utils import (PerformanceMetrics, _is_lua_trained_model,
                    initialize_logger, load_model_from_state_dict)

logger = logging.getLogger("selene")

[docs]class EvaluateModel(object): """ Evaluate model on a test set of sequences with known targets. Parameters ---------- model : torch.nn.Module The model architecture. criterion : torch.nn._Loss The loss function that was optimized during training. data_sampler : selene_sdk.samplers.Sampler Used to retrieve samples from the test set for evaluation. features : list(str) List of distinct features the model predicts. trained_model_path : str Path to the trained model file, saved using ``. output_dir : str The output directory in which to save model evaluation and logs. batch_size : int, optional Default is 64. Specify the batch size to process examples. Should be a power of 2. n_test_samples : int or None, optional Default is `None`. Use `n_test_samples` if you want to limit the number of samples on which you evaluate your model. If you are using a sampler of type `selene_sdk.samplers.OnlineSampler`, by default it will draw 640000 samples if `n_test_samples` is `None`. report_gt_feature_n_positives : int, optional Default is 10. In the final test set, each class/feature must have more than `report_gt_feature_n_positives` positive samples in order to be considered in the test performance computation. The output file that states each class' performance will report 'NA' for classes that do not have enough positive samples. use_cuda : bool, optional Default is `False`. Specify whether a CUDA-enabled GPU is available for torch to use during training. data_parallel : bool, optional Default is `False`. Specify whether multiple GPUs are available for torch to use during training. use_features_ord : list(str) or None, optional Default is None. Specify an ordered list of features for which to run the evaluation. The features in this list must be identical to or a subset of `features`, and in the order you want the resulting `test_targets.npz` and `test_predictions.npz` to be saved. If using a FileSampler or H5DataLoader for the evaluation, you can pass in a dataset with the targets matrix only containing these features, but note that this subsetted targets matrix MUST be ordered the same way as `features`, and the predictions and targets .npz output will be reordered according to `use_features_ord`. Attributes ---------- model : torch.nn.Module The trained model. criterion : torch.nn._Loss The model was trained using this loss function. sampler : selene_sdk.samplers.Sampler The example generator. features : list(str) List of distinct features the model predicts. batch_size : int The batch size to process examples. Should be a power of 2. use_cuda : bool If `True`, use a CUDA-enabled GPU. If `False`, use the CPU. data_parallel : bool Whether to use multiple GPUs or not. metrics : dict A dictionary that maps metric names (`str`) to metric functions. By default, this contains `"roc_auc"`, which maps to `sklearn.metrics.roc_auc_score`, and `"average_precision"`, which maps to `sklearn.metrics.average_precision_score`. """ def __init__(self, model, criterion, data_sampler, features, trained_model_path, output_dir, batch_size=64, n_test_samples=None, report_gt_feature_n_positives=10, use_cuda=False, data_parallel=False, use_features_ord=None, metrics=dict(roc_auc=roc_auc_score, average_precision=average_precision_score)): self.criterion = criterion trained_model = torch.load( trained_model_path, map_location=lambda storage, location: storage) if "state_dict" in trained_model: self.model = load_model_from_state_dict( trained_model["state_dict"], model) else: self.model = load_model_from_state_dict( trained_model, model) self.model.eval() self.sampler = data_sampler self.output_dir = output_dir os.makedirs(output_dir, exist_ok=True) self.features = np.array(features) self._use_ixs = list(range(len(features))) if use_features_ord is not None: feature_ixs = {f: ix for (ix, f) in enumerate(features)} self._use_ixs = [] for f in use_features_ord: if f in feature_ixs: self._use_ixs.append(feature_ixs[f]) else: warnings.warn(("Feature {0} in `use_features_ord` " "does not match any features in the list " "`features` and will be skipped.").format(f)) self._write_features_ordered_to_file() initialize_logger( os.path.join(self.output_dir, "{0}.log".format( __name__)), verbosity=2) self.data_parallel = data_parallel if self.data_parallel: self.model = nn.DataParallel(model) logger.debug("Wrapped model in DataParallel") self.use_cuda = use_cuda if self.use_cuda: self.model.cuda() self.batch_size = batch_size self._metrics = PerformanceMetrics( self._get_feature_from_index, report_gt_feature_n_positives=report_gt_feature_n_positives, metrics=metrics) self._test_data, self._all_test_targets = \ self.sampler.get_data_and_targets(self.batch_size, n_test_samples) self._use_testmat_ixs = self._use_ixs[:] # if the targets shape is the same as the subsetted features, # reindex based on the subsetted list if self._all_test_targets.shape[1] == len(self._use_ixs): subset_features = {self.features[ix]: i for (i, ix) in enumerate(sorted(self._use_ixs))} self._use_testmat_ixs = [ subset_features[f] for f in self.features[self._use_ixs]] self._all_test_targets = self._all_test_targets[ :, self._use_testmat_ixs] # save the targets dataset now np.savez_compressed( os.path.join(self.output_dir, "test_targets.npz"), data=self._all_test_targets) # reset Genome base ordering when applicable. if (hasattr(self.sampler, "reference_sequence") and isinstance(self.sampler.reference_sequence, Genome)): if _is_lua_trained_model(model): Genome.update_bases_order(['A', 'G', 'C', 'T']) else: Genome.update_bases_order(['A', 'C', 'G', 'T']) def _write_features_ordered_to_file(self): """ Write the feature ordering specified by `use_features_ord` after matching it with the `features` list from the class initialization parameters. """ fp = os.path.join(self.output_dir, 'use_features_ord.txt') with open(fp, 'w+') as file_handle: for f in self.features[self._use_ixs]: file_handle.write('{0}\n'.format(f)) def _get_feature_from_index(self, index): """ Gets the feature at an index in the features list. Parameters ---------- index : int Returns ------- str The name of the feature/target at the specified index. """ return self.features[self._use_ixs][index]
[docs] def evaluate(self): """ Passes all samples retrieved from the sampler to the model in batches and returns the predictions. Also reports the model's performance on these examples. Returns ------- dict A dictionary, where keys are the features and the values are each a dict of the performance metrics (currently ROC AUC and AUPR) reported for each feature the model predicts. """ batch_losses = [] all_predictions = [] for (inputs, targets) in self._test_data: inputs = torch.Tensor(inputs) targets = torch.Tensor(targets[:, self._use_testmat_ixs]) if self.use_cuda: inputs = inputs.cuda() targets = targets.cuda() with torch.no_grad(): predictions = None if _is_lua_trained_model(self.model): predictions = self.model.forward( inputs.transpose(1, 2).contiguous().unsqueeze_(2)) else: predictions = self.model.forward( inputs.transpose(1, 2)) predictions = predictions[:, self._use_ixs] loss = self.criterion(predictions, targets) all_predictions.append( batch_losses.append(loss.item()) all_predictions = np.vstack(all_predictions) average_scores = self._metrics.update( all_predictions, self._all_test_targets) self._metrics.visualize( all_predictions, self._all_test_targets, self.output_dir) np.savez_compressed( os.path.join(self.output_dir, "test_predictions.npz"), data=all_predictions) loss = np.average(batch_losses)"test loss: {0}".format(loss)) for name, score in average_scores.items():"test {0}: {1}".format(name, score)) test_performance = os.path.join( self.output_dir, "test_performance.txt") feature_scores_dict = self._metrics.write_feature_scores_to_file( test_performance) return feature_scores_dict