Evaluator

CHANGELOG.md

@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added an `Evaluator` class to make comparing source, target, and predictions easier.
 - Added a way to resize the vocabulary in the T5 module
 - Added an argument `reinit_modules` to `cached_transformers.get()` that allows you to re-initialize the pretrained weights of a transformer model, using layer indices or regex strings.
 - Added attribute `_should_validate_this_epoch` to `GradientDescentTrainer` that controls whether validation is run at the end of each epoch.

allennlp/commands/evaluate.py

@@ -7,17 +7,17 @@
 import argparse
 import json
 import logging
-from typing import Any, Dict
-
+from pathlib import Path
+from os import PathLike
+from typing import Union, Dict, Any, Optional
 from copy import deepcopy
 
-
 from allennlp.commands.subcommand import Subcommand
 from allennlp.common import logging as common_logging
 from allennlp.common.util import prepare_environment
 from allennlp.data import DataLoader
 from allennlp.models.archival import load_archive
-from allennlp.training.util import evaluate
+from allennlp.evaluation import Evaluator
 
 logger = logging.getLogger(__name__)
 
@@ -35,28 +35,23 @@ def add_subparser(self, parser: argparse._SubParsersAction) -> argparse.Argument
         subparser.add_argument(
             "input_file",
             type=str,
-            help=(
-                "path to the file containing the evaluation data"
-                ' (for mutiple files, put "," between filenames e.g., input1.txt,input2.txt)'
-            ),
+            help="path to the file containing the evaluation data (for mutiple "
+            "files, put between filenames e.g., input1.txt,input2.txt)",
         )
 
         subparser.add_argument(
             "--output-file",
             type=str,
-            help=(
-                "optional path to write the metrics to as JSON"
-                ' (for mutiple files, put "," between filenames e.g., output1.txt,output2.txt)'
-            ),
+            help="optional path to write the metrics to as JSON (for mutiple "
+            "files, put  between filenames e.g., output1.txt,output2.txt)",
         )
 
         subparser.add_argument(
             "--predictions-output-file",
             type=str,
-            help=(
-                "optional path to write the predictions to as JSON lines"
-                ' (for mutiple files, put "," between filenames e.g., output1.jsonl,output2.jsonl)'
-            ),
+            help="optional path to write the predictions to as JSON lines "
+            "(for mutiple files, put  between filenames e.g., "
+            "output1.jsonl,output2.jsonl)",
         )
 
         subparser.add_argument(
@@ -116,13 +111,126 @@ def add_subparser(self, parser: argparse._SubParsersAction) -> argparse.Argument
             help="outputs tqdm status on separate lines and slows tqdm refresh rate",
         )
 
+        subparser.add_argument(
+            "--auto-names",
+            default="NONE",
+            help="Automatically create output names for each evaluation file. "
+            "`NONE` will not automatically generate a file name for the "
+            "neither the metrics nor the predictions. In this case you will"
+            " need to pas in both `metrics_output_file` and `predictions_output_file`. "
+            "`METRICS` will only automatically create a file name for the"
+            " metrics file. `PREDS` will only automatically create a file"
+            " name for the predictions outputs. `ALL` will create a "
+            "filename for both the metrics and the predictions.",
+            choices=["NONE", "METRICS", "PREDS", "ALL"],
+        )
+
         subparser.set_defaults(func=evaluate_from_args)
 
         return subparser
 
 
 def evaluate_from_args(args: argparse.Namespace) -> Dict[str, Any]:
-    common_logging.FILE_FRIENDLY_LOGGING = args.file_friendly_logging
+    return evaluate_from_archive(
+        archive_file=args.archive_file,
+        input_file=args.input_file,
+        metrics_output_file=args.output_file,
+        predictions_output_file=args.predictions_output_file,
+        batch_size=args.batch_size,
+        cmd_overrides=args.overrides,
+        cuda_device=args.cuda_device,
+        embedding_sources_mapping=args.embedding_sources_mapping,
+        extend_vocab=args.extend_vocab,
+        weights_file=args.weights_file,
+        file_friendly_logging=args.file_friendly_logging,
+        batch_weight_key=args.batch_weight_key,
+        auto_names=args.auto_names,
+    )
+
+
+def evaluate_from_archive(
+    archive_file: Union[str, PathLike],
+    input_file: str,
+    metrics_output_file: Optional[str] = None,
+    predictions_output_file: Optional[str] = None,
+    batch_size: Optional[int] = None,
+    cmd_overrides: Union[str, Dict[str, Any]] = "",
+    cuda_device: int = -1,
+    embedding_sources_mapping: str = None,
+    extend_vocab: bool = False,
+    weights_file: str = None,
+    file_friendly_logging: bool = False,
+    batch_weight_key: str = None,
+    auto_names: str = "NONE",
+) -> Dict[str, Any]:
+    """
+
+    # Parameters
+
+    archive_file: `Union[str, PathLike]`
+        Path to an archived trained model.
+
+    input_file: `str`
+        path to the file containing the evaluation data (for multiple files,
+         put ":" between filenames e.g., input1.txt:input2.txt)
+
+    metrics_output_file: `str`, optional (default=`None`)
+        optional path to write the metrics to as JSON (for multiple files, put
+         ":" between filenames e.g., output1.txt:output2.txt)
+
+    predictions_output_file: `str`, optional (default=`None`)
+        "optional path to write the predictions to (for multiple files, put ":"
+         between filenames e.g., output1.jsonl:output2.jsonl)
+
+    batch_size: `int`, optional (default=`None`)
+        If non-empty, the batch size to use during evaluation.
+
+    cmd_overrides: `str`, optional (default=`""`)
+        a json(net) structure used to override the experiment configuration,
+         e.g., '{\"iterator.batch_size\": 16}'.  Nested parameters can be
+          specified either with nested dictionaries or with dot syntax.
+
+    cuda_device: `int`, optional (default=`-1`)
+        id of GPU to use (if any)
+
+    embedding_sources_mapping: `str`, optional (default=`None`)
+        a JSON dict defining mapping from embedding module path to embedding
+        pretrained-file used during training. If not passed, and embedding
+        needs to be extended, we will try to use the original file paths used
+        during training. If they are not available we will use random vectors
+        for embedding extension.
+
+    extend_vocab: `bool`, optional (default=`False`)
+        if specified, we will use the instances in your new dataset to extend
+        your vocabulary. If pretrained-file was used to initialize embedding
+        layers, you may also need to pass --embedding-sources-mapping.
+
+    weights_file:`str`, optional (default=`None`)
+        A path that overrides which weights file to use
+
+    file_friendly_logging : `bool`, optional (default=`False`)
+        If `True`, we add newlines to tqdm output, even on an interactive terminal, and we slow
+        down tqdm's output to only once every 10 seconds.
+
+    batch_weight_key: `str`, optional (default=`None`)
+        If non-empty, name of metric used to weight the loss on a per-batch basis.
+
+    auto_names:`str`, optional (default=`"NONE"`)
+        Automatically create output names for each evaluation file.`NONE` will
+        not automatically generate a file name for the neither the metrics nor
+        the predictions. In this case you will need to pas in both
+        `metrics_output_file` and `predictions_output_file`. `METRICS` will only
+        automatically create a file name for the metrics file. `PREDS` will only
+        automatically create a file name for the predictions outputs. `ALL`
+        will create a filename for both the metrics and the predictions.
+
+    # Returns
+
+    all_metrics: `Dict[str, Any]`
+        The metrics from every evaluation file passed.
+
+    """
+    common_logging.FILE_FRIENDLY_LOGGING = file_friendly_logging
 
     # Disable some of the more verbose logging statements
     logging.getLogger("allennlp.common.params").disabled = True
@@ -131,77 +239,124 @@ def evaluate_from_args(args: argparse.Namespace) -> Dict[str, Any]:
 
     # Load from archive
     archive = load_archive(
-        args.archive_file,
-        weights_file=args.weights_file,
-        cuda_device=args.cuda_device,
-        overrides=args.overrides,
+        archive_file,
+        weights_file=weights_file,
+        cuda_device=cuda_device,
+        overrides=cmd_overrides,
     )
     config = deepcopy(archive.config)
     prepare_environment(config)
     model = archive.model
     model.eval()
 
+    # Load the evaluator from the config key `Evaluator`
+    evaluator_params = config.pop("evaluation", {})
+    evaluator_params["cuda_device"] = cuda_device
+    evaluator = Evaluator.from_params(evaluator_params)
+
     # Load the evaluation data
     dataset_reader = archive.validation_dataset_reader
 
     # split files
-    evaluation_data_path_list = args.input_file.split(",")
-    if args.output_file is not None:
-        output_file_list = args.output_file.split(",")
-        assert len(output_file_list) == len(
-            evaluation_data_path_list
-        ), "The number of `output_file` paths must be equal to the number of datasets being evaluated."
-    if args.predictions_output_file is not None:
-        predictions_output_file_list = args.predictions_output_file.split(",")
-        assert len(predictions_output_file_list) == len(evaluation_data_path_list), (
-            "The number of `predictions_output_file` paths must be equal"
-            + "to the number of datasets being evaluated. "
-        )
+    evaluation_data_path_list = input_file.split(",")
+
+    # TODO(gabeorlanski): Is it safe to always default to .outputs and .preds?
+    # TODO(gabeorlanski): Add in way to save to specific output directory
+    if metrics_output_file is not None:
+        if auto_names == "METRICS" or auto_names == "ALL":
+            logger.warning(
+                f"Passed output_files will be ignored, auto_names is" f" set to {auto_names}"
+            )
+
+            # Keep the path of the parent otherwise it will write to the CWD
+            output_file_list = [
+                p.parent.joinpath(f"{p.stem}.outputs") for p in map(Path, evaluation_data_path_list)
+            ]
+        else:
+            output_file_list = metrics_output_file.split(",")  # type: ignore
+            assert len(output_file_list) == len(evaluation_data_path_list), (
+                "The number of `metrics_output_file` paths must be equal to the number "
+                "of datasets being evaluated."
+            )
+    if predictions_output_file is not None:
+        if auto_names == "PREDS" or auto_names == "ALL":
+            logger.warning(
+                f"Passed predictions files will be ignored, auto_names is" f" set to {auto_names}"
+            )
+
+            # Keep the path of the parent otherwise it will write to the CWD
+            predictions_output_file_list = [
+                p.parent.joinpath(f"{p.stem}.preds") for p in map(Path, evaluation_data_path_list)
+            ]
+        else:
+            predictions_output_file_list = predictions_output_file.split(",")  # type: ignore
+            assert len(predictions_output_file_list) == len(evaluation_data_path_list), (
+                "The number of `predictions_output_file` paths must be equal"
+                + "to the number of datasets being evaluated. "
+            )
 
     # output file
     output_file_path = None
     predictions_output_file_path = None
 
     # embedding sources
-    if args.extend_vocab:
+    if extend_vocab:
         logger.info("Vocabulary is being extended with embedding sources.")
         embedding_sources = (
-            json.loads(args.embedding_sources_mapping) if args.embedding_sources_mapping else {}
+            json.loads(embedding_sources_mapping) if embedding_sources_mapping else {}
         )
 
+    all_metrics = {}
     for index in range(len(evaluation_data_path_list)):
         config = deepcopy(archive.config)
         evaluation_data_path = evaluation_data_path_list[index]
-        if args.output_file is not None:
+
+        # Get the eval file name so we can save each metric by file name in the
+        # output dictionary.
+        eval_file_name = Path(evaluation_data_path).stem
+
+        if metrics_output_file is not None:
+            # noinspection PyUnboundLocalVariable
             output_file_path = output_file_list[index]
-        if args.predictions_output_file is not None:
+
+        if predictions_output_file is not None:
+            # noinspection PyUnboundLocalVariable
             predictions_output_file_path = predictions_output_file_list[index]
 
         logger.info("Reading evaluation data from %s", evaluation_data_path)
         data_loader_params = config.get("validation_data_loader", None)
         if data_loader_params is None:
             data_loader_params = config.get("data_loader")
-        if args.batch_size:
-            data_loader_params["batch_size"] = args.batch_size
+        if batch_size:
+            data_loader_params["batch_size"] = batch_size
         data_loader = DataLoader.from_params(
             params=data_loader_params, reader=dataset_reader, data_path=evaluation_data_path
         )
 
-        if args.extend_vocab:
+        if extend_vocab:
             logger.info("Vocabulary is being extended with test instances.")
             model.vocab.extend_from_instances(instances=data_loader.iter_instances())
+            # noinspection PyUnboundLocalVariable
             model.extend_embedder_vocab(embedding_sources)
 
         data_loader.index_with(model.vocab)
 
-        metrics = evaluate(
+        metrics = evaluator(
             model,
             data_loader,
-            args.cuda_device,
-            args.batch_weight_key,
-            output_file=output_file_path,
+            batch_weight_key,
+            metrics_output_file=output_file_path,
             predictions_output_file=predictions_output_file_path,
         )
+
+        # Add the metric prefixed by the file it came from.
+        for name, value in metrics.items():
+            if len(evaluation_data_path_list) > 1:
+                key = f"{eval_file_name}_"
+            else:
+                key = ""
+            all_metrics[f"{key}{name}"] = value
+
     logger.info("Finished evaluating.")
 
-    return metrics
+    return all_metrics

allennlp/commands/train.py

@@ -238,6 +238,11 @@ def train_model(
     training_util.create_serialization_dir(params, serialization_dir, recover, force)
     params.to_file(os.path.join(serialization_dir, CONFIG_NAME))
 
+    # Change Author: Gabe Orlanski
+    # Placeholder for the time being to make sure no errors are raised b/c of
+    # the evaluator.
+    params.pop("evaluation", None)
+
     meta = Meta.new()
     meta.to_file(os.path.join(serialization_dir, META_NAME))
 

allennlp/common/testing/test_case.py

@@ -32,7 +32,6 @@ def setup_method(self):
         logging.getLogger("allennlp.modules.token_embedders.embedding").setLevel(logging.INFO)
         logging.getLogger("urllib3.connectionpool").disabled = True
         log_pytorch_version_info()
-
         self.TEST_DIR = pathlib.Path(TEST_DIR)
 
         os.makedirs(self.TEST_DIR, exist_ok=True)

allennlp/evaluation/__init__.py

@@ -0,0 +1,2 @@
+from allennlp.evaluation.evaluator import Evaluator, SimpleEvaluator
+from allennlp.evaluation.serializers.serializers import Serializer