evaluators.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from paddle.trainer.config_parser import *
from default_decorators import *

__all__ = [
    "evaluator_base", "classification_error_evaluator", "auc_evaluator",
    "pnpair_evaluator", "precision_recall_evaluator", "ctc_error_evaluator",
    "chunk_evaluator", "sum_evaluator", "column_sum_evaluator",
    "value_printer_evaluator", "gradient_printer_evaluator",
    "maxid_printer_evaluator", "maxframe_printer_evaluator",
    "seqtext_printer_evaluator", "classification_error_printer_evaluator"
]


class EvaluatorAttribute(object):
    FOR_CLASSIFICATION = 1
    FOR_REGRESSION = 1 << 1
    FOR_RANK = 1 << 2
    FOR_PRINT = 1 << 3
    FOR_UTILS = 1 << 4

    KEYS = [
        "for_classification", "for_regression", "for_rank", "for_print",
        "for_utils"
    ]

    @staticmethod
    def to_key(idx):
        tmp = 1
        for i in xrange(0, len(EvaluatorAttribute.KEYS)):
            if idx == tmp:
                return EvaluatorAttribute.KEYS[i]
            else:
                tmp = (tmp << 1)


def evaluator(*attrs):
    def impl(method):
        for attr in attrs:
            setattr(method, EvaluatorAttribute.to_key(attr), True)
        method.is_evaluator = True
        return method

    return impl


def evaluator_base(
        input,
        type,
        label=None,
        weight=None,
        name=None,
        chunk_scheme=None,
        num_chunk_types=None,
        classification_threshold=None,
        positive_label=None,
        dict_file=None,
        result_file=None,
        num_results=None,
        delimited=None,
        excluded_chunk_types=None, ):
    """
    Evaluator will evaluate the network status while training/testing.

    User can use evaluator by classify/regression job. For example.

    ..  code-block:: python

        classify(prediction, output, evaluator=classification_error_evaluator)

    And user could define evaluator separately as follow.

    ..  code-block:: python

        classification_error_evaluator("ErrorRate", prediction, label)

    The evaluator often contains a name parameter. It will also be printed when
    evaluating network. The printed information may look like the following.

    ..  code-block:: text

         Batch=200 samples=20000 AvgCost=0.679655 CurrentCost=0.662179 Eval:
         classification_error_evaluator=0.4486
         CurrentEval: ErrorRate=0.3964

    :param input: Input layers, a object of LayerOutput or a list of
                  LayerOutput.
    :type input: list|LayerOutput
    :param label: An input layer containing the ground truth label.
    :type label: LayerOutput|None
    :param weight: An input layer which is a weight for each sample.
                   Each evaluator may calculate differently to use this weight.
    :type weight: LayerOutput.
    """
    # inputs type assertions.
    assert classification_threshold is None or isinstance(
        classification_threshold, float)
    assert positive_label is None or isinstance(positive_label, int)
    assert num_results is None or isinstance(num_results, int)

    if not isinstance(input, list):
        input = [input]

    if label:
        input.append(label)
    if weight:
        input.append(weight)

    Evaluator(
        name=name,
        type=type,
        inputs=[i.name for i in input],
        chunk_scheme=chunk_scheme,
        num_chunk_types=num_chunk_types,
        classification_threshold=classification_threshold,
        positive_label=positive_label,
        dict_file=dict_file,
        result_file=result_file,
        delimited=delimited,
        excluded_chunk_types=excluded_chunk_types, )


@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def classification_error_evaluator(input,
                                   label,
                                   name=None,
                                   weight=None,
                                   threshold=None):
    """
    Classification Error Evaluator. It will print error rate for classification.

    The classification error is:

    ..  math::

        classification\\_error = \\frac{NumOfWrongPredicts}{NumOfAllSamples}

    The simple usage is:

    .. code-block:: python

       eval =  classification_error_evaluator(input=prob,label=lbl)

    :param name: Evaluator name.
    :type name: basestring
    :param input: Input Layer name. The output prediction of network.
    :type input: LayerOutput
    :param label: Label layer name.
    :type label: basestring
    :param weight: Weight Layer name. It should be a matrix with size
                  [sample_num, 1]. And will just multiply to NumOfWrongPredicts
                  and NumOfAllSamples. So, the elements of weight are all one,
                  then means not set weight. The larger weight it is, the more
                  important this sample is.
    :type weight: LayerOutput
    :param threshold: The classification threshold.
    :type threshold: float
    :return: None.
    """

    evaluator_base(
        name=name,
        type="classification_error",
        input=input,
        label=label,
        weight=weight,
        classification_threshold=threshold, )


@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def auc_evaluator(
        input,
        label,
        name=None,
        weight=None, ):
    """
    Auc Evaluator which adapts to binary classification.

    The simple usage:

    .. code-block:: python

       eval = auc_evaluator(input, label)

    :param name: Evaluator name.
    :type name: None|basestring
    :param input: Input Layer name. The output prediction of network.
    :type input: LayerOutput
    :param label: Label layer name.
    :type label: None|basestring
    :param weight: Weight Layer name. It should be a matrix with size
                  [sample_num, 1].
    :type weight: LayerOutput
    """
    evaluator_base(
        name=name,
        type="last-column-auc",
        input=input,
        label=label,
        weight=weight)


@evaluator(EvaluatorAttribute.FOR_RANK)
@wrap_name_default()
def pnpair_evaluator(
        input,
        label,
        info,
        name=None,
        weight=None, ):
    """
    Positive-negative pair rate Evaluator which adapts to rank task like
    learning to rank. This evaluator must contain at least three layers.

    The simple usage:

    .. code-block:: python

       eval = pnpair_evaluator(input, info, label)

    :param name: Evaluator name.
    :type name: None|basestring
    :param input: Input Layer name. The output prediction of network.
    :type input: LayerOutput
    :param label: Label layer name.
    :type label: LayerOutput
    :param info: Label layer name. (TODO, explaination)
    :type info: LayerOutput
    :param weight: Weight Layer name. It should be a matrix with size
                  [sample_num, 1]. (TODO, explaination)
    :type weight: LayerOutput
    """
    evaluator_base(
        name=name,
        type="pnpair",
        input=input,
        label=label,
        info=info,
        weight=weight)


@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def precision_recall_evaluator(
        input,
        label,
        positive_label=None,
        weight=None,
        name=None, ):
    """
    An Evaluator to calculate precision and recall, F1-score.
    It is adapt to the task with multiple labels.

    - If positive_label=-1, it will print the average precision, recall,
      F1-score of all labels.

    - If use specify positive_label, it will print the precision, recall,
      F1-score of this label.

    The simple usage:

    .. code-block:: python

       eval = precision_recall_evaluator(input, label)

    :param name: Evaluator name.
    :type name: None|basestring
    :param input: Input Layer name. The output prediction of network.
    :type input: LayerOutput
    :param label: Label layer name.
    :type label: LayerOutput
    :param positive_label: The input label layer.
    :type positive_label: LayerOutput.
    :param weight: Weight Layer name. It should be a matrix with size
                  [sample_num, 1]. (TODO, explaination)
    :type weight: LayerOutput
    """
    evaluator_base(
        name=name,
        type="precision_recall",
        input=input,
        label=label,
        positive_label=positive_label,
        weight=weight)


@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def ctc_error_evaluator(
        input,
        label,
        name=None, ):
    """
    This evaluator is to calculate sequence-to-sequence edit distance.

    The simple usage is :

    .. code-block:: python

       eval = ctc_error_evaluator(input=input, label=lbl)

    :param name: Evaluator name.
    :type name: None|basestring
    :param input: Input Layer. Should be the same as the input for ctc_layer.
    :type input: LayerOutput
    :param label: input label, which is a data_layer. Should be the same as the
                  label for ctc_layer
    :type label: LayerOutput
    """
    evaluator_base(
        name=name, type="ctc_edit_distance", input=input, label=label)


@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def chunk_evaluator(
        input,
        label,
        chunk_scheme,
        num_chunk_types,
        name=None,
        excluded_chunk_types=None, ):
    """
    Chunk evaluator is used to evaluate segment labelling accuracy for a
    sequence. It calculates the chunk detection F1 score.

    A chunk is correctly detected if its beginning, end and type are correct.
    Other chunk type is ignored.

    For each label in the label sequence, we have:

    .. code-block:: python

       tagType = label % numTagType
       chunkType = label / numTagType
       otherChunkType = numChunkTypes

    The total number of different labels is numTagType*numChunkTypes+1.
    We support 4 labelling scheme.
    The tag type for each of the scheme is shown as follows:

    .. code-block:: python

       Scheme Begin Inside End   Single
       plain  0     -      -     -
       IOB    0     1      -     -
       IOE    -     0      1     -
       IOBES  0     1      2     3

    'plain' means the whole chunk must contain exactly the same chunk label.

    The simple usage is:

    .. code-block:: python

       eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)

    :param input: The input layers.
    :type input: LayerOutput
    :param label: An input layer containing the ground truth label.
    :type label: LayerOutput
    :param chunk_scheme: The labelling schemes support 4 types. It is one of
                         "IOB", "IOE", "IOBES", "plain". It is required.
    :type chunk_scheme: basestring
    :param num_chunk_types: number of chunk types other than "other"
    :param name: The Evaluator name, it is optional.
    :type name: basename|None
    :param excluded_chunk_types: chunks of these types are not considered
    :type excluded_chunk_types: list of integer|None
    """
    evaluator_base(
        name=name,
        type="chunk",
        input=input,
        label=label,
        chunk_scheme=chunk_scheme,
        num_chunk_types=num_chunk_types,
        excluded_chunk_types=excluded_chunk_types, )


@evaluator(EvaluatorAttribute.FOR_UTILS)
@wrap_name_default()
def sum_evaluator(
        input,
        name=None,
        weight=None, ):
    """
    An Evaluator to sum the result of input.

    The simple usage:

    .. code-block:: python

       eval = sum_evaluator(input)

    :param name: Evaluator name.
    :type name: None|basestring
    :param input: Input Layer name.
    :type input: LayerOutput
    :param weight: Weight Layer name. It should be a matrix with size
                  [sample_num, 1]. (TODO, explaination)
    :type weight: LayerOutput
    """
    evaluator_base(name=name, type="sum", input=input, weight=weight)


@evaluator(EvaluatorAttribute.FOR_UTILS)
@wrap_name_default()
def column_sum_evaluator(
        input,
        name=None,
        weight=None, ):
    """
    This Evaluator is used to sum the last column of input.

    The simple usage is:

    .. code-block:: python

       eval = column_sum_evaluator(input, label)

    :param name: Evaluator name.
    :type name: None|basestring
    :param input: Input Layer name.
    :type input: LayerOutput
    """
    evaluator_base(
        name=name, type="last-column-sum", input=input, weight=weight)


"""
The following are printer Evaluators which are usually used to
print the result, like value or gradient of input layers, the
results generated in machine translation, the classification error etc.
"""


@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def value_printer_evaluator(
        input,
        name=None, ):
    """
    This Evaluator is used to print the values of input layers. It contains
    one or more input layers.

    The simple usage is:

    .. code-block:: python

       eval = value_printer_evaluator(input)

    :param input: One or more input layers.
    :type input: LayerOutput|list
    :param name: Evaluator name.
    :type name: None|basestring
    """
    evaluator_base(name=name, type="value_printer", input=input)


@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def gradient_printer_evaluator(
        input,
        name=None, ):
    """
    This Evaluator is used to print the gradient of input layers. It contains
    one or more input layers.

    The simple usage is:

    .. code-block:: python

       eval = gradient_printer_evaluator(input)

    :param input: One or more input layers.
    :type input: LayerOutput|list
    :param name: Evaluator name.
    :type name: None|basestring
    """
    evaluator_base(name=name, type="gradient_printer", input=input)

@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def maxid_printer_evaluator(
        input,
        num_results=None,
        name=None, ):
    """
    This Evaluator is used to print maximum top k values and their indexes
    of each row of input layers. It contains one or more input layers.
    k is specified by num_results.

    The simple usage is:

    .. code-block:: python

       eval = maxid_printer_evaluator(input)

    :param input: Input Layer name.
    :type input: LayerOutput|list
    :param num_results: This number is used to specify the top k numbers.
                        It is 1 by default.
    :type num_results: int.
    :param name: Evaluator name.
    :type name: None|basestring
    """
    evaluator_base(
        name=name, type="max_id_printer", input=input, num_results=num_results)


@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def maxframe_printer_evaluator(
        input,
        num_results=None,
        name=None, ):
    """
    This Evaluator is used to print the top k frames of each input layers.
    The input layers should contain sequences info or sequences type.
    k is specified by num_results.
    It contains one or more input layers.

    Note:
        The width of each frame is 1.

    The simple usage is:

    .. code-block:: python

       eval = maxframe_printer_evaluator(input)

    :param input: Input Layer name.
    :type input: LayerOutput|list
    :param name: Evaluator name.
    :type name: None|basestring
    """
    evaluator_base(
        name=name,
        type="max_frame_printer",
        input=input,
        num_results=num_results)


@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def seqtext_printer_evaluator(
        input,
        result_file,
        id_input=None,
        dict_file=None,
        delimited=None,
        name=None, ):
    """
    Sequence text printer will print text according to index matrix and a
    dictionary. There can be multiple input to this layer:

    1. If there is no id_input, the input must be a matrix containing
    the sequence of indices;

    2. If there is id_input, it should be ids, and interpreted as sample ids.

    The output format will be:

    1. sequence without sub-sequence, and there is probability.

    .. code-block:: python

         id \t prob space_seperated_tokens_from_dictionary_according_to_seq

    2. sequence without sub-sequence, and there is not probability.

    .. code-block:: python

         id \t space_seperated_tokens_from_dictionary_according_to_seq

    3. sequence with sub-sequence, and there is not probability.

    .. code-block:: python

         id \t space_seperated_tokens_from_dictionary_according_to_sub_seq
         \t \t space_seperated_tokens_from_dictionary_according_to_sub_seq
         ...

    Typically SequenceTextPrinter layer takes output of maxid or RecurrentGroup
    with maxid (when generating) as an input.

    The simple usage is:

    .. code-block:: python

       eval = seqtext_printer_evaluator(input=maxid_layer,
                                        id_input=sample_id,
                                        dict_file=dict_file,
                                        result_file=result_file)

    :param input: Input Layer name.
    :type input: LayerOutput|list
    :param result_file: Path of the file to store the generated results.
    :type result_file: basestring
    :param id_input: Index of the input sequence, and the specified index will
                     be prited in the gereated results. This an optional
                     parameter.
    :type id_input: LayerOutput
    :param dict_file: Path of dictionary. This is an optional parameter.
                      Every line is a word in the dictionary with
                      (line number - 1) as the word index.
                      If this parameter is set to None, or to an empty string,
                      only word index are printed in the generated results.
    :type dict_file: basestring
    :param delimited: Whether to use space to separate output tokens.
                Default is True. No space is added if set to False.
    :type delimited: bool
    :param name: Evaluator name.
    :type name: None|basestring
    :return: The seq_text_printer that prints the generated sequence to a file.
    :rtype: evaluator
    """
    assert isinstance(result_file, basestring)
    if id_input is None:
        inputs = [input]
    else:
        inputs = [id_input, input]
        input.parents.append(id_input)

    evaluator_base(
        name=name,
        type="seq_text_printer",
        input=inputs,
        dict_file=dict_file,
        result_file=result_file,
        delimited=delimited)


@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def classification_error_printer_evaluator(
        input,
        label,
        threshold=0.5,
        name=None, ):
    """
    This Evaluator is used to print the classification error of each sample.

    The simple usage is:

    .. code-block:: python

       eval = classification_error_printer_evaluator(input)

    :param input: Input layer.
    :type input: LayerOutput
    :param label: Input label layer.
    :type label: LayerOutput
    :param name: Evaluator name.
    :type name: None|basestring
    """
    evaluator_base(
        name=name,
        type="classification_error_printer",
        input=input,
        label=label,
        classification_threshold=threshold)