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bob.measure
Commit 5b829ad6 authored by André Anjos's avatar André Anjos 💬
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Fix documentation building (no more warnings)

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    bob/measure/__init__.py
    View file @ 5b829ad6
    ......@@ -180,13 +180,13 @@ def recognition_rate(cmc_scores, threshold = None, rank = 1):
    probe item**. Each pair can contain up to one empty array (or ``None``),
    i.e., in case of open set recognition.
    threshold (Optional[float]): Decision threshold. If not ``None``, **all**
    threshold (:obj:`float`, optional): Decision threshold. If not ``None``, **all**
    scores will be filtered by the threshold. In an open set recognition
    problem, all open set scores (negatives with no corresponding positive)
    for which all scores are below threshold, will be counted as correctly
    rejected and **removed** from the probe list (i.e., the denominator).
    rank (Optional[int]):
    rank (:obj:`int`, optional):
    The rank for which the recognition rate should be computed, 1 by default.
    ......@@ -350,7 +350,7 @@ def detection_identification_rate(cmc_scores, threshold, rank = 1):
    threshold (float): The decision threshold :math:`\\tau``.
    rank (Optional[int]): The rank for which the curve should be plotted
    rank (:obj:`int`, optional): The rank for which the curve should be plotted
    Returns:
    ......
    bob/measure/load.py
    View file @ 5b829ad6
    ......@@ -22,10 +22,10 @@ def open_file(filename, mode='rt'):
    Parameters:
    filename (str, file): The name of the score file to open, or a file-like
    object open for reading. If a file name is given, the according file
    might be a raw text file or a (compressed) tar file containing a raw text
    file.
    filename (:obj:`str`, :obj:`file`): The name of the score file to open, or
    a file-like object open for reading. If a file name is given, the
    according file might be a raw text file or a (compressed) tar file
    containing a raw text file.
    Returns:
    ......@@ -74,8 +74,8 @@ def four_column(filename):
    Parameters:
    filename (str, file): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    filename (:obj:`str`, :obj:`file`): The file object that will be opened
    with :py:func:`open_file` containing the scores.
    Returns:
    ......@@ -119,8 +119,8 @@ def split_four_column(filename):
    Parameters:
    filename (str, file): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    filename (:obj:`str`, :obj:`file`): The file object that will be opened
    with :py:func:`open_file` containing the scores.
    Returns:
    ......@@ -155,8 +155,8 @@ def cmc_four_column(filename):
    Parameters:
    filename (str, file): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    filename (:obj:`str`, :obj:`file`): The file object that will be opened
    with :py:func:`open_file` containing the scores.
    Returns:
    ......@@ -201,7 +201,7 @@ def five_column(filename):
    Parameters:
    filename (str, file): The file object that will be opened with
    filename (:obj:`str`, :obj:`file`): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    ......@@ -248,7 +248,7 @@ def split_five_column(filename):
    Parameters:
    filename (str, file): The file object that will be opened with
    filename (:obj:`str`, :obj:`file`): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    ......@@ -284,7 +284,7 @@ def cmc_five_column(filename):
    Parameters:
    filename (str, file): The file object that will be opened with
    filename (:obj:`str`, :obj:`file`): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    ......@@ -317,12 +317,12 @@ def load_score(filename, ncolumns=None):
    Parameters:
    filename (str, file): The file object that will be opened with
    :py:func:`open_file` containing the scores.
    filename (:obj:`str`, :obj:`file`): The file object that will be opened
    with :py:func:`open_file` containing the scores.
    ncolumns (Optional[int]): 4, 5 or None (the default), specifying the number
    of columns in the score file. If None is provided, the number of columns
    will be guessed.
    ncolumns (:obj:`int`, optional): 4, 5 or None (the default), specifying the
    number of columns in the score file. If None is provided, the number of
    columns will be guessed.
    Returns:
    ......
    bob/measure/main.cpp
    View file @ 5b829ad6
    ......@@ -102,7 +102,7 @@ static auto det_doc = bob::extension::FunctionDoc(
    "[0] X axis values in the normal deviate scale for the false-accepts\n\n"
    "[1] Y axis values in the normal deviate scale for the false-rejections\n\n"
    "You can plot the results using your preferred tool to first create a plot using rows 0 and 1 from the returned value and then replace the X/Y axis annotation using a pre-determined set of tickmarks as recommended by NIST. "
    "The derivative scales are computed with the :py:func:`ppndf` function."
    "The derivative scales are computed with the :py:func:`bob.measure.ppndf` function."
    )
    .add_prototype("negatives, positives, n_points", "curve")
    .add_parameter("negatives, positives", "array_like(1D, float)", "The list of negative and positive scores to compute the DET for")
    ......@@ -264,7 +264,7 @@ static auto eer_threshold_doc = bob::extension::FunctionDoc(
    .add_prototype("negatives, positives, [is_sorted]", "threshold")
    .add_parameter("negatives, positives", "array_like(1D, float)", "The set of negative and positive scores to compute the threshold")
    .add_parameter("is_sorted", "bool", "[Default: ``False``] Are both sets of scores already in ascendantly sorted order?")
    .add_return("threshold", "float", "The threshold (i.e., as used in :py:func:`farfrr`) where FAR and FRR are as close as possible")
    .add_return("threshold", "float", "The threshold (i.e., as used in :py:func:`bob.measure.farfrr`) where FAR and FRR are as close as possible")
    ;
    static PyObject* eer_threshold(PyObject*, PyObject* args, PyObject* kwds) {
    BOB_TRY
    ......@@ -344,7 +344,7 @@ BOB_CATCH_FUNCTION("min_weighted_error_rate_threshold", 0)
    static auto min_hter_threshold_doc = bob::extension::FunctionDoc(
    "min_hter_threshold",
    "Calculates the :py:func:`min_weighted_error_rate_threshold` with ``cost=0.5``"
    "Calculates the :py:func:`bob.measure.min_weighted_error_rate_threshold` with ``cost=0.5``"
    )
    .add_prototype("negatives, positives, [is_sorted]", "threshold")
    .add_parameter("negatives, positives", "array_like(1D, float)", "The set of negative and positive scores to compute the threshold")
    ......@@ -390,7 +390,7 @@ static auto precision_recall_doc = bob::extension::FunctionDoc(
    "where :math:`tp` are the true positives, :math:`fp` are the false positives and :math:`fn` are the false negatives.\n\n"
    "``positives`` holds the score information for samples that are labeled to belong to a certain class (a.k.a., 'signal' or 'client'). "
    "``negatives`` holds the score information for samples that are labeled **not** to belong to the class (a.k.a., 'noise' or 'impostor'). "
    "For more precise details about how the method considers error rates, see :py:func:`farfrr`."
    "For more precise details about how the method considers error rates, see :py:func:`bob.measure.farfrr`."
    )
    .add_prototype("negatives, positives, threshold", "precision, recall")
    .add_parameter("negatives, positives", "array_like(1D, float)", "The set of negative and positive scores to compute the measurements")
    ......@@ -429,7 +429,7 @@ BOB_CATCH_FUNCTION("precision_recall", 0)
    static auto f_score_doc = bob::extension::FunctionDoc(
    "f_score",
    "This method computes the F-score of the accuracy of the classification",
    "The F-score is a weighted mean of precision and recall measurements, see :py:func:`precision_recall`. "
    "The F-score is a weighted mean of precision and recall measurements, see :py:func:`bob.measure.precision_recall`. "
    "It is computed as:\n\n"
    ".. math::\n\n"
    " \\mathrm{f-score} = (1 + w^2)\\frac{\\mathrm{precision}\\cdot{}\\mathrm{recall}}{w^2\\cdot{}\\mathrm{precision} + \\mathrm{recall}}\n\n"
    ......
    bob/measure/openbr.py
    View file @ 5b829ad6
    ......@@ -50,9 +50,9 @@ def write_matrix(
    The mask file defines, which values are positives, negatives or to be
    ignored. Usually, the file name extension is ``.mask``
    model_names (Optional[str]): If given, the matrix will be written in the
    same order as the given model names. The model names must be identical
    with the second column in the 5-column ``score_file``.
    model_names (:obj:`str`, optional): If given, the matrix will be written in
    the same order as the given model names. The model names must be
    identical with the second column in the 5-column ``score_file``.
    .. note::
    ......@@ -62,25 +62,27 @@ def write_matrix(
    Only the scores of the given models will be considered.
    probe_names (Optional[list]): A list of strings. If given, the matrix will
    be written in the same order as the given probe names (the ``path`` of
    the probe). The probe names are identical to the third column of the
    4-column (or the fourth column of the 5-column) ``score_file``. Only the
    scores of the given probe names will be considered in this case.
    probe_names (:obj:`list`, optional): A list of strings. If given, the
    matrix will be written in the same order as the given probe names (the
    ``path`` of the probe). The probe names are identical to the third
    column of the 4-column (or the fourth column of the 5-column)
    ``score_file``. Only the scores of the given probe names will be
    considered in this case.
    score_file_format (Optional[str]): One of ``('4column', '5column')``. The
    format, in which the ``score_file`` is; defaults to ``'4column'``
    score_file_format (:obj:`str`, optional): One of ``('4column',
    '5column')``. The format, in which the ``score_file`` is; defaults to
    ``'4column'``
    gallery_file_name (Optional[str]): The name of the gallery file that will
    be written in the header of the OpenBR files.
    gallery_file_name (:obj:`str`, optional): The name of the gallery file that
    will be written in the header of the OpenBR files.
    probe_file_name (Optional[str]): The name of the probe file that will be
    written in the header of the OpenBR files.
    probe_file_name (:obj:`str`, optional): The name of the probe file that
    will be written in the header of the OpenBR files.
    search (Optional[int]): If given, the scores will be sorted per probe,
    keeping the specified number of highest scores. If the given number is
    higher than the models, ``NaN`` values will be added, and the mask will
    contain ``0x00`` values.
    search (:obj:`int`, optional): If given, the scores will be sorted per
    probe, keeping the specified number of highest scores. If the given
    number is higher than the models, ``NaN`` values will be added, and the
    mask will contain ``0x00`` values.
    Returns:
    ......@@ -229,39 +231,41 @@ def write_score_file(
    score_file (str): Path to the 4 or 5 column style score file that should be
    written.
    models_ids (Optional[list]): A list of strings with the client ids of the
    models that will be written in the first column of the score file. If
    given, the size must be identical to the number of models (gallery
    models_ids (:obj:`list`, optional): A list of strings with the client ids
    of the models that will be written in the first column of the score file.
    If given, the size must be identical to the number of models (gallery
    templates) in the OpenBR files. If not given, client ids of the model
    will be identical to the **gallery index** in the matrix file.
    probes_ids (Optional[list]): A list of strings with the client ids of the
    probes that will be written in the second/third column of the four/five
    column score file. If given, the size must be identical to the number of
    probe templates in the OpenBR files. It will be checked that the OpenBR
    mask fits to the model/probe client ids. If not given, the probe ids
    will be estimated automatically, i.e., to fit the OpenBR matrix.
    probes_ids (:obj:`list`, optional): A list of strings with the client ids
    of the probes that will be written in the second/third column of the
    four/five column score file. If given, the size must be identical to the
    number of probe templates in the OpenBR files. It will be checked that
    the OpenBR mask fits to the model/probe client ids. If not given, the
    probe ids will be estimated automatically, i.e., to fit the OpenBR
    matrix.
    model_names (Optional[list]): A list of strings with the model path written
    in the second column of the five column score file. If not given, the
    model index in the OpenBR file will be used.
    model_names (:obj:`list`, optional): A list of strings with the model path
    written in the second column of the five column score file. If not given,
    the model index in the OpenBR file will be used.
    .. note::
    This entry is ignored in the four column score file format.
    probe_names (Optional[list]): A list of probe path to be written in the
    third/fourth column in the four/five column score file. If given, the
    probe_names (:obj:`list`, optional): A list of probe path to be written in
    the third/fourth column in the four/five column score file. If given, the
    size must be identical to the number of probe templates in the OpenBR
    files. If not given, the probe index in the OpenBR file will be used.
    score_file_format (Optional[str]): One of ``('4column', '5column')``. The
    format, in which the ``score_file`` is; defaults to ``'4column'``
    score_file_format (:obj:`str`, optional): One of ``('4column',
    '5column')``. The format, in which the ``score_file`` is; defaults to
    ``'4column'``
    replace_nan (Optional[float]): If NaN values are encountered in the OpenBR
    matrix (which are not ignored due to the mask being non-NULL), this value
    will be written instead. If ``None``, the values will not be written in
    the score file at all.
    replace_nan (:obj:`float`, optional): If NaN values are encountered in the
    OpenBR matrix (which are not ignored due to the mask being non-NULL),
    this value will be written instead. If ``None``, the values will not be
    written in the score file at all.
    Returns:
    ......
    bob/measure/plot.py
    View file @ 5b829ad6
    ......@@ -15,11 +15,11 @@ def log_values(min_step = -4, counts_per_step = 4):
    Parameters:
    min_step (Optional[int]): The power of 10 that will be the minimum value.
    min_step (:obj:`int`, optional): The power of 10 that will be the minimum value.
    E.g., the default ``-4`` will result in the first number to be
    :math:`10^{-4}` = ``0.00001`` or ``0.01%``
    counts_per_step (Optional[int]): The number of values that will be put
    counts_per_step (:obj:`int`, optional): The number of values that will be put
    between two adjacent powers of 10. With the default value ``4`` (and
    default values of ``min_step``), we will get ``log_list[0] == 1e-4``,
    ``log_list[4] == 1e-3``, ..., ``log_list[16] == 1``.
    ......@@ -65,22 +65,22 @@ def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
    "positive" (signal, class) samples of your classifier. See
    (:py:func:`bob.measure.roc`)
    npoints (Optional[int]): The number of points for the plot. See
    npoints (:obj:`int`, optional): The number of points for the plot. See
    (:py:func:`bob.measure.roc`)
    CAR (Optional[bool]): If set to ``True``, it will plot the CAR over FAR in
    CAR (:obj:`bool`, optional): If set to ``True``, it will plot the CAR over FAR in
    using :py:func:`matplotlib.pyplot.semilogx`, otherwise the FAR over FRR
    linearly using :py:func:`matplotlib.pyplot.plot`.
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    Returns:
    :py:class:`matplotlib.pyplot.line`: The line that was added as defined by
    the return value of :py:func:`matplotlib.pyplot.plot` or
    :py:func:`matplotlib.pyplot.semilogx`.
    :py:class:`list` of :py:class:`matplotlib.lines.Line2D`: The lines that
    were added as defined by the return value of
    :py:func:`matplotlib.pyplot.plot`.
    """
    ......@@ -123,17 +123,18 @@ def roc_for_far(negatives, positives, far_values = log_values(), **kwargs):
    "positive" (signal, class) samples of your classifier. See
    (:py:func:`bob.measure.roc`)
    far_values (Optional[list]): The values for the FAR, where the CAR should
    far_values (:obj:`list`, optional): The values for the FAR, where the CAR should
    be plotted; each value should be in range [0,1].
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    Returns:
    :py:class:`matplotlib.pyplot.line`: The line that was added as defined by
    the return value of :py:func:`matplotlib.pyplot.semilogx`.
    :py:class:`list` of :py:class:`matplotlib.lines.Line2D`: The lines that
    were added as defined by the return value of
    :py:func:`matplotlib.pyplot.semilogx`.
    """
    ......@@ -171,17 +172,18 @@ def precision_recall_curve(negatives, positives, npoints=100, **kwargs):
    "positive" (signal, class) samples of your classifier. See
    (:py:func:`bob.measure.precision_recall_curve`)
    npoints (Optional[int]): The number of points for the plot. See
    npoints (:obj:`int`, optional): The number of points for the plot. See
    (:py:func:`bob.measure.precision_recall_curve`)
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    Returns:
    :py:class:`matplotlib.pyplot.line`: The line that was added as defined by
    the return value of :py:func:`matplotlib.pyplot.plot`.
    :py:class:`list` of :py:class:`matplotlib.lines.Line2D`: The lines that
    were added as defined by the return value of
    :py:func:`matplotlib.pyplot.plot`.
    """
    ......@@ -236,17 +238,18 @@ def epc(dev_negatives, dev_positives, test_negatives, test_positives,
    "positive" (signal, class) samples of your classifier, from the test set.
    See (:py:func:`bob.measure.epc`)
    npoints (Optional[int]): The number of points for the plot. See
    npoints (:obj:`int`, optional): The number of points for the plot. See
    (:py:func:`bob.measure.epc`)
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    Returns:
    :py:class:`matplotlib.pyplot.line`: The line that was added as defined by
    the return value of :py:func:`matplotlib.pyplot.plot`.
    :py:class:`list` of :py:class:`matplotlib.lines.Line2D`: The lines that
    were added as defined by the return value of
    :py:func:`matplotlib.pyplot.plot`.
    """
    ......@@ -324,20 +327,21 @@ def det(negatives, positives, npoints=100, axisfontsize='x-small', **kwargs):
    "positive" (signal, class) samples of your classifier. See
    (:py:func:`bob.measure.det`)
    npoints (Optional[int]): The number of points for the plot. See
    npoints (:obj:`int`, optional): The number of points for the plot. See
    (:py:func:`bob.measure.det`)
    axisfontsize (Optional[str]): The size to be used by x/y-tick-labels to set
    axisfontsize (:obj:`str`, optional): The size to be used by x/y-tick-labels to set
    the font size on the axis
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    Returns:
    :py:class:`matplotlib.pyplot.line`: The line that was added as defined by
    the return value of :py:func:`matplotlib.pyplot.plot`.
    :py:class:`list` of :py:class:`matplotlib.lines.Line2D`: The lines that
    were added as defined by the return value of
    :py:func:`matplotlib.pyplot.plot`.
    """
    ......@@ -401,7 +405,7 @@ def det_axis(v, **kwargs):
    numbers it is passed without further inspection to
    :py:func:`matplotlib.pyplot.axis`.
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.axis`.
    ......@@ -461,11 +465,11 @@ def cmc(cmc_scores, logx = True, **kwargs):
    cmc_scores (array): 1D float array containing the CMC values (See
    :py:func:`bob.measure.cmc`)
    logx (Optional[bool]): If set (the default), plots the rank axis in
    logx (:obj:`bool`, optional): If set (the default), plots the rank axis in
    logarithmic scale using :py:func:`matplotlib.pyplot.semilogx` or in
    linear scale using :py:func:`matplotlib.pyplot.plot`
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    ......@@ -511,23 +515,24 @@ def detection_identification_curve(cmc_scores, far_values = log_values(), rank
    cmc_scores (array): 1D float array containing the CMC values (See
    :py:func:`bob.measure.cmc`)
    rank (Optional[int]): The rank for which the curve should be plotted
    rank (:obj:`int`, optional): The rank for which the curve should be plotted
    far_values (Optional[list]): The values for the FAR, where the CAR should
    far_values (:obj:`list`, optional): The values for the FAR, where the CAR should
    be plotted; each value should be in range [0,1].
    logx (Optional[bool]): If set (the default), plots the rank axis in
    logx (:obj:`bool`, optional): If set (the default), plots the rank axis in
    logarithmic scale using :py:func:`matplotlib.pyplot.semilogx` or in
    linear scale using :py:func:`matplotlib.pyplot.plot`
    kwargs (Optional[dict]): Extra plotting parameters, which are passed
    kwargs (:obj:`dict`, optional): Extra plotting parameters, which are passed
    directly to :py:func:`matplotlib.pyplot.plot`.
    Returns:
    :py:class:`matplotlib.pyplot.line`: The line that was added as defined by
    the return value of :py:func:`matplotlib.pyplot.plot`.
    :py:class:`list` of :py:class:`matplotlib.lines.Line2D`: The lines that
    were added as defined by the return value of
    :py:func:`matplotlib.pyplot.plot`.
    """
    ......
    doc/guide.rst
    View file @ 5b829ad6
    ......@@ -215,8 +215,8 @@ Plotting
    --------
    An image is worth 1000 words, they say. You can combine the capabilities of
    `Matplotlib`_ with |project| to plot a number of curves. However, you must have that
    package installed though. In this section we describe a few recipes.
    `Matplotlib`_ with |project| to plot a number of curves. However, you must have
    that package installed though. In this section we describe a few recipes.
    ROC
    ===
    ......@@ -255,18 +255,18 @@ You should see an image like the following one:
    pyplot.title('ROC')
    As can be observed, plotting methods live in the namespace
    :py:mod:`bob.measure.plot`. They work like the :py:func:`matplotlib.pyplot.plot`
    itself, except that instead of receiving the x and y point coordinates as
    parameters, they receive the two :py:class:`numpy.ndarray` arrays with
    negatives and positives, as well as an indication of the number of points the
    curve must contain.
    As in the :py:func:`matplotlib.pyplot.plot` command, you can pass optional parameters for
    the line as shown in the example to setup its color, shape and even the label.
    For an overview of the keywords accepted, please refer to the `Matplotlib`_'s
    Documentation. Other plot properties such as the plot title, axis labels,
    grids, legends should be controlled directly using the relevant `Matplotlib`_'s
    controls.
    :py:mod:`bob.measure.plot`. They work like the
    :py:func:`matplotlib.pyplot.plot` itself, except that instead of receiving the
    x and y point coordinates as parameters, they receive the two
    :py:class:`numpy.ndarray` arrays with negatives and positives, as well as an
    indication of the number of points the curve must contain.
    As in the :py:func:`matplotlib.pyplot.plot` command, you can pass optional
    parameters for the line as shown in the example to setup its color, shape and
    even the label. For an overview of the keywords accepted, please refer to the
    `Matplotlib`_'s Documentation. Other plot properties such as the plot title,
    axis labels, grids, legends should be controlled directly using the relevant
    `Matplotlib`_'s controls.
    DET
    ===
    ......@@ -358,9 +358,11 @@ This will produce an image like the following one:
    CMC
    ===
    The Cumulative Match Characteristics (CMC) curve estimates the probability that the correct model is in the *N* models with the highest similarity to a given probe.
    A CMC curve can be plotted using the :py:func:`bob.measure.plot.cmc` function.
    The CMC can be calculated from a relatively complex data structure, which defines a pair of positive and negative scores **per probe**:
    The Cumulative Match Characteristics (CMC) curve estimates the probability that
    the correct model is in the *N* models with the highest similarity to a given
    probe. A CMC curve can be plotted using the :py:func:`bob.measure.plot.cmc`
    function. The CMC can be calculated from a relatively complex data structure,
    which defines a pair of positive and negative scores **per probe**:
    .. plot::
    ......
    doc/py_api.rst
    View file @ 5b829ad6
    .. vim: set fileencoding=utf-8 :
    .. Andre Anjos <andre.dos.anjos@gmail.com>
    .. Sat 16 Nov 20:52:58 2013
    ============
    Python API
    ============
    This section includes information for using the Python API of :py:mod:`bob.measure`.
    This section includes information for using the Python API of
    :py:mod:`bob.measure`.
    Measurement
    ......
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