diff --git a/.gitignore b/.gitignore index e5251d79..b878b2c7 100644 --- a/.gitignore +++ b/.gitignore @@ -37,3 +37,9 @@ Thumbs.db # Vim *.swp + +# pycharm +.idea/* + +# docs +docs/_build/* \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index d1238612..8a0a5202 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -193,6 +193,14 @@ The following subsections document each submodule. :show-inheritance: :member-order: bysource +:mod:`mir_eval.transcription` +----------------------------- +.. automodule:: mir_eval.transcription + :members: + :undoc-members: + :show-inheritance: + :member-order: bysource + Indices and tables ================== diff --git a/evaluators/transcription_eval.py b/evaluators/transcription_eval.py new file mode 100644 index 00000000..8c9ec525 --- /dev/null +++ b/evaluators/transcription_eval.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python +''' +CREATED: 2/9/16 2:59 PM by Justin Salamon + +Compute note transcription evaluation metrics + +Usage: + +./transcription_eval.py REFERENCE.TXT ESTIMATED.TXT +''' + +from __future__ import print_function +import argparse +import sys +import os +import eval_utilities + +import mir_eval + + +def process_arguments(): + '''Argparse function to get the program parameters''' + + parser = argparse.ArgumentParser(description='mir_eval transcription ' + 'evaluation') + + parser.add_argument('-o', + dest='output_file', + default=None, + type=str, + action='store', + help='Store results in json format') + + parser.add_argument('reference_file', + action='store', + help='path to the reference annotation file') + + parser.add_argument('estimated_file', + action='store', + help='path to the estimated annotation file') + + return vars(parser.parse_args(sys.argv[1:])) + +if __name__ == '__main__': + # Get the parameters + parameters = process_arguments() + + # Load in data + ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals( + parameters['reference_file']) + est_intervals, est_pitches = mir_eval.io.load_valued_intervals( + parameters['estimated_file']) + # Compute all the scores + scores = mir_eval.transcription.evaluate(ref_intervals, ref_pitches, + est_intervals, est_pitches) + print("{} vs. {}".format(os.path.basename(parameters['reference_file']), + os.path.basename(parameters['estimated_file']))) + eval_utilities.print_evaluation(scores) + + if parameters['output_file']: + print('Saving results to: ', parameters['output_file']) + eval_utilities.save_results(scores, parameters['output_file']) diff --git a/mir_eval/__init__.py b/mir_eval/__init__.py index 386d59c4..85873898 100644 --- a/mir_eval/__init__.py +++ b/mir_eval/__init__.py @@ -14,5 +14,6 @@ from . import pattern from . import tempo from . import hierarchy +from . import transcription __version__ = '0.2' diff --git a/mir_eval/io.py b/mir_eval/io.py index 60fa2ccb..05608033 100644 --- a/mir_eval/io.py +++ b/mir_eval/io.py @@ -399,3 +399,44 @@ def load_wav(path, mono=True): if mono and audio_data.ndim != 1: audio_data = audio_data.mean(axis=1) return audio_data, fs + + +def load_valued_intervals(filename, delimiter=r'\s+'): + r"""Import valued intervals from an annotation file. The file should + consist of three columns: Two consisting of numeric values corresponding to + start and end time of each interval and a third, also of numeric values, + corresponding to the value of each interval. This is primarily useful for + processing events which span a duration and have a numeric value, such as + piano-roll notes which have an onset, offset, and a pitch value. + + Parameters + ---------- + filename : str + Path to the annotation file + delimiter : str + Separator regular expression. + By default, lines will be split by any amount of whitespace. + + Returns + ------- + intervals : np.ndarray, shape=(n_events, 2) + Array of event start and end times + values : np.ndarray, shape=(n_events,) + Array of values + + """ + # Use our universal function to load in the events + starts, ends, values = load_delimited(filename, [float, float, float], + delimiter) + # Stack into an interval matrix + intervals = np.array([starts, ends]).T + # Validate them, but throw a warning in place of an error + try: + util.validate_intervals(intervals) + except ValueError as error: + warnings.warn(error.args[0]) + + # return values as np.ndarray + values = np.array(values) + + return intervals, values diff --git a/mir_eval/transcription.py b/mir_eval/transcription.py new file mode 100644 index 00000000..3bb2db77 --- /dev/null +++ b/mir_eval/transcription.py @@ -0,0 +1,411 @@ +''' +The aim of a transcription algorithm is to produce a symbolic representation of +a recorded piece of music in the form of a set of discrete notes. There are +different ways to represent notes symbolically. Here we use the piano-roll +convention, meaning each note has a start time, a duration (or end time), and +a single, constant, pitch value. Pitch values can be quantized (e.g. to a +semitone grid tuned to 440 Hz), but do not have to be. Also, the transcription +can contain the notes of a single instrument or voice (for example the melody), +or the notes of all instruments/voices in the recording. This module is +instrument agnostic: all notes in the estimate are compared against all notes +in the reference. + +There are many metrics for evaluating transcription algorithms. Here we limit +ourselves to the most simple and commonly used: given two sets of notes, we +count how many estimate notes match the reference, and how many do not. Based +on these counts we compute the precision, recall, and f-measure of the estimate +given the reference. The default criteria for considering two notes to be a +match are adopted from the `MIREX Multiple fundamental frequency estimation and +tracking, Note Tracking subtask (task 2) `_: + +"This subtask is evaluated in two different ways. In the first setup , a +returned note is assumed correct if its onset is within +-50ms of a ref note +and its F0 is within +- quarter tone of the corresponding reference note, +ignoring the returned offset values. In the second setup, on top of the above +requirements, a correct returned note is required to have an offset value +within 20% of the ref notes duration around the ref note's offset, or within +50ms whichever is larger." + +In short, we compute precision, recall and f-measure, once without taking +offsets into account, and the second time with. + +For further details see Salamon, 2013 (page 186), and references therein: + + Salamon, J. (2013). Melody Extraction from Polyphonic Music Signals. + Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Spain, 2013. + + +IMPORTANT NOTE: the evaluation code in ``mir_eval`` contains several important +differences with respect to the code used in MIREX 2015 for the Note Tracking +subtask on the Su dataset (henceforth "MIREX"): + +1. ``mir_eval`` uses bipartite graph matching to find the optimal pairing of + reference notes to estimated notes. MIREX uses a greedy matching algorithm, + which can produce sub-optimal note matching. This will result in + ``mir_eval``'s metrics being slightly higher compared to MIREX. +2. MIREX rounds down the onset and offset times of each note to 2 decimal + points using ``new_time = 0.01 * floor(time*100)``. ``mir_eval`` doesn't + modify the note onset and offset times. This will bring our metrics down a + notch compared to the MIREX results. +3. In the MIREX wiki, the criterion for matching offsets is that they must be + within 0.2 * ref_duration **or 0.05 from each other, whichever is greater** + (i.e. ``offset_dif <= max(0.2 * ref_duration, 0.05)``. The MIREX code + however only uses a threshold of 0.2 * ref_duration, without the 0.05 + minimum. Since ``mir_eval`` does include this minimum, it might produce + slightly higher results compared to MIREX. + +This means that differences 1 and 3 bring ``mir_eval``'s metrics up compared to +MIREX, whilst 2 brings them down. Based on internal testing, overall the effect +of these three differences is that the Precision, Recall and F-measure returned +by ``mir_eval`` will be higher compared to MIREX by about 1%-2%. + +Finally, note that different evaluation scripts have been used for the Multi-F0 +Note Tracking task in MIREX over the years. In particular, some scripts used +``<`` for matching onsets, offsets, and pitch values, whilst the others used +``<=`` for these checks. ``mir_eval`` provides both options: by default the +latter (``<=``) is used, but you can set ``strict=True`` when calling +:func:`mir_eval.transcription.precision_recall_f1()` in which case ``<`` will +be used. The default value (``strict=False``) is the same as that used in +MIREX 2015 for the Note Tracking subtask on the Su dataset. + + +Conventions +----------- + +Notes should be provided in the form of an interval array and a pitch array. +The interval array contains two columns, one for note onsets and the second +for note offsets (each row represents a single note). The pitch array contains +one column with the corresponding note pitch values (one value per note), +represented by their fundamental frequency (f0) in Hertz. + +Metrics +------- + +* :func:`mir_eval.transcription.precision_recall_f1`: The precision, + recall, and F-measure of the note transcription, where an estimated note is + considered correct if its pitch, onset and (optionally) offset are + sufficiently close to a reference note + +''' + +import numpy as np +import collections +from . import util +import warnings + + +# The number of decimals to keep for onset/offset threshold checks +N_DECIMALS = 4 + + +def validate(ref_intervals, ref_pitches, est_intervals, est_pitches): + """Checks that the input annotations to a metric look like time intervals + and a pitch list, and throws helpful errors if not. + + Parameters + ---------- + ref_intervals : np.ndarray, shape=(n,2) + Array of reference notes time intervals (onset and offset times) + ref_pitches: np.ndarray, shape=(n,) + Array of reference pitch values in Hertz + est_intervals : np.ndarray, shape=(m,2) + Array of estimated notes time intervals (onset and offset times) + est_pitches : np.ndarray, shape=(m,) + Array of estimated pitch values in Hertz + """ + # If reference or estimated notes are empty, warn + if ref_intervals.size == 0: + warnings.warn("Reference notes are empty.") + if est_intervals.size == 0: + warnings.warn("Estimate notes are empty.") + + # Make sure intervals and pitches match in length + if not ref_intervals.shape[0] == ref_pitches.shape[0]: + raise ValueError('Reference intervals and pitches have different ' + 'lengths.') + if not est_intervals.shape[0] == est_pitches.shape[0]: + raise ValueError('Estimate intervals and pitches have different ' + 'lengths.') + + # Make sure all pitch values are positive + if ref_pitches.size > 0 and np.min(ref_pitches) <= 0: + raise ValueError("Reference contains at least one non-positive pitch " + "value") + if est_pitches.size > 0 and np.min(est_pitches) <= 0: + raise ValueError("Estimate contains at least one non-positive pitch " + "value") + + +def match_notes(ref_intervals, ref_pitches, est_intervals, est_pitches, + onset_tolerance=0.05, pitch_tolerance=50.0, offset_ratio=0.2, + offset_min_tolerance=0.05, strict=False): + """Compute a maximum matching between reference and estimated notes, + subject to onset, pitch and (optionally) offset constraints. + + Given two note sequences represented by ``ref_intervals``, ``ref_pitches``, + ``est_intervals`` and ``est_pitches`` + (see :func:`mir_eval.io.load_valued_intervals`), we seek the largest set + of correspondences ``(i, j)`` such that: + + 1. The onset of ref note i is within ``onset_tolerance`` of the onset of + est note j. + 2. The pitch of ref note i is within ``pitch_tolerance`` of the pitch of + est note j. + 3. If ``offset_ratio`` is not ``None``, the offset of ref note i has to be + within ``offset_tolerance`` of the offset of est note j, where + ``offset_tolerance`` is equal to ``offset_ratio`` times the ref note's + duration, i.e. ``offset_ratio * ref_duration[i]`` where + ``ref_duration[i] = ref_intervals[i, 1] - ref_intervals[i, 0]``. If the + resulting ``offset_tolerance`` is less than 0.05 (50 ms), 0.05 is used + instead. + 4. If ``offset_ratio`` is ``None``, note offsets are ignored, and only + criteria 1 and 2 are taken into consideration. + + Every ref note is matched against at most one est note. + + This is useful for computing precision/recall metrics for note + transcription. + + Parameters + ---------- + ref_intervals : np.ndarray, shape=(n,2) + Array of reference notes time intervals (onset and offset times) + ref_pitches: np.ndarray, shape=(n,) + Array of reference pitch values in Hertz + est_intervals : np.ndarray, shape=(m,2) + Array of estimated notes time intervals (onset and offset times) + est_pitches : np.ndarray, shape=(m,) + Array of estimated pitch values in Hertz + onset_tolerance : float > 0 + The tolerance for an estimated note's onset deviating from the + reference note's onset, in seconds. Default is 0.05 (50 ms). + pitch_tolerance: float > 0 + The tolerance for an estimated note's pitch deviating from the + reference note's pitch, in cents. Default is 50.0 (50 cents). + offset_ratio: float > 0 or None + The ratio of the reference note's duration used to define the + offset_tolerance. Default is 0.2 (20%), meaning the offset_tolerance + will equal the ref_duration * 0.2, or 0.05 (50 ms), whichever is + greater. If ``offset_ratio`` is set to ``None``, offsets are ignored in + the matching. + offset_min_tolerance: float > 0 + The minimum tolerance for offset matching. See offset_ratio description + for an explanation of how the offset tolerance is determined. Note: + this parameter only influences the results if ``offset_ratio`` is not + ``None``. + strict: bool + If ``strict=False`` (the default), threshold checks for onset, offset, + and pitch matching are performed using ``<=`` (less than or equal). If + ``strict=True``, the threshold checks are performed using ``<`` (less + than). + + Returns + ------- + matching : list of tuples + A list of matched reference and estimated notes. + ``matching[i] == (i, j)`` where reference note i matches estimate note + j. + """ + # set the comparison function + if strict: + cmp_func = np.less + else: + cmp_func = np.less_equal + + # check for onset matches + onset_distances = np.abs(np.subtract.outer(ref_intervals[:, 0], + est_intervals[:, 0])) + # Round distances to a target precision to avoid the situation where + # if the distance is exactly 50ms (and strict=False) it erroneously + # doesn't match the notes because of precision issues. + onset_distances = np.around(onset_distances, decimals=N_DECIMALS) + onset_hit_matrix = cmp_func(onset_distances, onset_tolerance) + + # check for pitch matches + pitch_distances = np.abs(1200*np.subtract.outer(np.log2(ref_pitches), + np.log2(est_pitches))) + pitch_hit_matrix = cmp_func(pitch_distances, pitch_tolerance) + + # check for offset matches if offset_ratio is not None + if offset_ratio is not None: + offset_distances = np.abs(np.subtract.outer(ref_intervals[:, 1], + est_intervals[:, 1])) + # Round distances to a target precision to avoid the situation where + # if the distance is exactly 50ms (and strict=False) it erroneously + # doesn't match the notes because of precision issues. + offset_distances = np.around(offset_distances, decimals=N_DECIMALS) + ref_durations = util.intervals_to_durations(ref_intervals) + offset_tolerances = np.maximum(offset_ratio * ref_durations, + offset_min_tolerance) + offset_hit_matrix = ( + cmp_func(offset_distances, offset_tolerances.reshape(-1, 1))) + else: + offset_hit_matrix = True + + # check for overall matches + note_hit_matrix = onset_hit_matrix * pitch_hit_matrix * offset_hit_matrix + hits = np.where(note_hit_matrix) + + # Construct the graph input + # Flip graph so that 'matching' is a list of tuples where the first item + # in each tuple is the reference note index, and the second item is the + # estimate note index. + G = {} + for ref_i, est_i in zip(*hits): + if est_i not in G: + G[est_i] = [] + G[est_i].append(ref_i) + + # Compute the maximum matching + matching = sorted(util._bipartite_match(G).items()) + + return matching + + +def precision_recall_f1(ref_intervals, ref_pitches, est_intervals, est_pitches, + onset_tolerance=0.05, pitch_tolerance=50.0, + offset_ratio=0.2, offset_min_tolerance=0.05, + strict=False): + """Compute the Precision, Recall and F-measure of correct vs incorrectly + transcribed notes. "Correctness" is determined based on note onset, pitch + and (optionally) offset: an estimated note is assumed correct if its onset + is within +-50ms of a ref note and its pitch (F0) is within +- quarter tone + (50 cents) of the corresponding reference note. If with_offset is False, + note offsets are ignored in the comparison. It with_offset is True, + on top of the above requirements, a correct returned note is required to + have an offset value within 20% (by default, adjustable via the + offset_ratio parameter) of the ref note's duration around the ref note's + offset, or within offset_min_tolerance (50 ms by default), whichever is + larger. + + Examples + -------- + >>> ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals( + ... 'reference.txt') + >>> est_intervals, est_pitches = mir_eval.io.load_valued_intervals( + ... 'estimated.txt') + >>> (precision, + ... recall, + ... f_measure) = mir_eval.transcription.precision_recall_f1( + ... ref_intervals, ref_pitches, est_intervals, est_pitches) + >>> (precision_no_offset, + ... recall_no_offset, + ... f_measure_no_offset) = mir_eval.transcription.precision_recall_f1( + ... ref_intervals, ref_pitches, est_intervals, est_pitches, + ... offset_ratio=None) + + Parameters + ---------- + ref_intervals : np.ndarray, shape=(n,2) + Array of reference notes time intervals (onset and offset times) + ref_pitches: np.ndarray, shape=(n,) + Array of reference pitch values in Hertz + est_intervals : np.ndarray, shape=(m,2) + Array of estimated notes time intervals (onset and offset times) + est_pitches : np.ndarray, shape=(m,) + Array of estimated pitch values in Hertz + onset_tolerance : float > 0 + The tolerance for an estimated note's onset deviating from the + reference note's onset, in seconds. Default is 0.05 (50 ms). + pitch_tolerance: float > 0 + The tolerance for an estimated note's pitch deviating from the + reference note's pitch, in cents. Default is 50.0 (50 cents). + offset_ratio: float > 0 or None + The ratio of the reference note's duration used to define the + offset_tolerance. Default is 0.2 (20%), meaning the offset_tolerance + will equal the ref_duration * 0.2, or min_offset_tolerance (0.05 by + default, i.e. 50 ms), whichever is greater. If ``offset_ratio`` is set + to ``None``, offsets are ignored in the evaluation. + offset_min_tolerance: float > 0 + The minimum tolerance for offset matching. See offset_ratio description + for an explanation of how the offset tolerance is determined. Note: + this parameter only influences the results if offset_ratio is not + ``None``. + strict: bool + If ``strict=False`` (the default), threshold checks for onset, offset, + and pitch matching are performed using ``<=`` (less than or equal). If + ``strict=True``, the threshold checks are performed using ``<`` (less + than). + + Returns + ------- + precision : float + The computed precision score + recall : float + The computed recall score + f_measure : float + The computed F-measure score + """ + validate(ref_intervals, ref_pitches, est_intervals, est_pitches) + # When reference notes are empty, metrics are undefined, return 0's + if len(ref_pitches) == 0 or len(est_pitches) == 0: + return 0., 0., 0. + + matching = match_notes(ref_intervals, ref_pitches, est_intervals, + est_pitches, onset_tolerance=onset_tolerance, + pitch_tolerance=pitch_tolerance, + offset_ratio=offset_ratio, + offset_min_tolerance=offset_min_tolerance, + strict=strict) + + precision = float(len(matching))/len(est_pitches) + recall = float(len(matching))/len(ref_pitches) + f_measure = util.f_measure(precision, recall) + return precision, recall, f_measure + + +def evaluate(ref_intervals, ref_pitches, est_intervals, est_pitches, **kwargs): + """Compute all metrics for the given reference and estimated annotations. + + Examples + -------- + >>> ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals( + ... 'reference.txt') + >>> est_intervals, est_pitches = mir_eval.io.load_valued_intervals( + ... 'estimate.txt') + >>> scores = mir_eval.transcription.evaluate(ref_intervals, ref_pitches, + ... est_intervals, est_pitches) + + Parameters + ---------- + ref_intervals : np.ndarray, shape=(n,2) + Array of reference notes time intervals (onset and offset times) + ref_pitches: np.ndarray, shape=(n,) + Array of reference pitch values in Hertz + est_intervals : np.ndarray, shape=(m,2) + Array of estimated notes time intervals (onset and offset times) + est_pitches : np.ndarray, shape=(m,) + Array of estimated pitch values in Hertz + kwargs + Additional keyword arguments which will be passed to the + appropriate metric or preprocessing functions. + + Returns + ------- + scores : dict + Dictionary of scores, where the key is the metric name (str) and + the value is the (float) score achieved. + """ + # Compute all the metrics + scores = collections.OrderedDict() + + # Precision, recall and f-measure taking note offsets into account + kwargs.setdefault('offset_ratio', 0.2) + if kwargs['offset_ratio'] is not None: + (scores['Precision'], + scores['Recall'], + scores['F-measure']) = util.filter_kwargs( + precision_recall_f1, ref_intervals, ref_pitches, est_intervals, + est_pitches, **kwargs) + + # Precision, recall and f-measure NOT taking note offsets into account + kwargs['offset_ratio'] = None + (scores['Precision_no_offset'], + scores['Recall_no_offset'], + scores['F-measure_no_offset']) = ( + util.filter_kwargs(precision_recall_f1, ref_intervals, ref_pitches, + est_intervals, est_pitches, **kwargs)) + + return scores diff --git a/mir_eval/util.py b/mir_eval/util.py index e4082055..b39bd2e9 100644 --- a/mir_eval/util.py +++ b/mir_eval/util.py @@ -623,9 +623,9 @@ def match_events(ref, est, window): # Construct the graph input G = {} for ref_i, est_i in zip(*hits): - if ref_i not in G: - G[ref_i] = [] - G[ref_i].append(est_i) + if est_i not in G: + G[est_i] = [] + G[est_i].append(ref_i) # Compute the maximum matching matching = sorted(_bipartite_match(G).items()) diff --git a/tests/data/transcription/est00.txt b/tests/data/transcription/est00.txt new file mode 100755 index 00000000..a59f5157 --- /dev/null +++ b/tests/data/transcription/est00.txt @@ -0,0 +1,208 @@ +0.57 1.12 415.30 +1.08 1.51 329.63 +1.52 2.54 415.30 +1.82 2.00 329.63 +2.49 2.86 311.13 +2.59 3.16 415.30 +3.00 3.51 246.94 +3.61 3.77 493.88 +3.82 3.93 493.88 +4.26 4.46 207.65 +4.37 5.00 311.13 +4.47 4.56 932.33 +4.63 4.97 932.33 +6.79 6.99 659.26 +7.02 8.02 155.56 +7.04 7.23 220.00 +7.28 7.34 739.99 +7.44 7.60 440.00 +7.49 7.76 369.99 +7.65 7.95 440.00 +7.76 7.88 739.99 +8.00 8.90 554.37 +8.04 8.39 164.81 +8.32 8.99 415.30 +8.65 8.81 164.81 +8.90 9.13 164.81 +8.92 9.25 277.18 +9.11 10.01 415.30 +9.64 9.78 164.81 +9.81 10.01 493.88 +9.90 10.87 155.56 +9.99 10.85 246.94 +10.16 10.27 415.30 +10.50 11.45 659.26 +10.83 10.99 138.59 +10.85 11.94 233.08 +10.90 11.10 246.94 +11.08 11.22 138.59 +11.92 12.01 329.63 +11.97 12.85 415.30 +12.11 13.61 329.63 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a/tests/data/transcription/output00.json b/tests/data/transcription/output00.json new file mode 100644 index 00000000..33b06962 --- /dev/null +++ b/tests/data/transcription/output00.json @@ -0,0 +1 @@ +{"Precision": 0.08173076923076923, "Recall": 0.07234042553191489, "F-measure": 0.07674943566591423, "Precision_no_offset": 0.16826923076923078, "Recall_no_offset": 0.14893617021276595, "F-measure_no_offset": 0.1580135440180587} \ No newline at end of file diff --git a/tests/data/transcription/output01.json b/tests/data/transcription/output01.json new file mode 100644 index 00000000..d0b993eb --- /dev/null +++ b/tests/data/transcription/output01.json @@ -0,0 +1 @@ +{"Precision": 0.2446043165467626, "Recall": 0.11486486486486487, "F-measure": 0.15632183908045977, "Precision_no_offset": 0.5467625899280576, "Recall_no_offset": 0.25675675675675674, "F-measure_no_offset": 0.34942528735632183} \ No newline at end of file diff --git a/tests/data/transcription/output02.json 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index 00000000..7976b783 --- /dev/null +++ b/tests/data/transcription/output04.json @@ -0,0 +1 @@ +{"Precision": 0.1270718232044199, "Recall": 0.11917098445595854, "F-measure": 0.12299465240641712, "Precision_no_offset": 0.7734806629834254, "Recall_no_offset": 0.7253886010362695, "F-measure_no_offset": 0.748663101604278} \ No newline at end of file diff --git a/tests/data/transcription/output05.json b/tests/data/transcription/output05.json new file mode 100644 index 00000000..0ce64191 --- /dev/null +++ b/tests/data/transcription/output05.json @@ -0,0 +1 @@ +{"Precision": 0.0, "Recall": 0.0, "F-measure": 0.0, "Precision_no_offset": 0.03333333333333333, "Recall_no_offset": 0.07291666666666667, "F-measure_no_offset": 0.0457516339869281} \ No newline at end of file diff --git a/tests/data/transcription/output06.json b/tests/data/transcription/output06.json new file mode 100644 index 00000000..8e263cd6 --- /dev/null +++ b/tests/data/transcription/output06.json @@ -0,0 +1 @@ +{"Precision": 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13.56 987.77 +12.43 12.86 830.61 +12.80 13.24 369.99 +12.81 13.20 739.99 +13.19 14.01 659.26 +13.20 14.36 329.63 +13.54 13.99 554.37 +13.54 14.03 554.37 +13.56 13.95 1760.00 +13.59 13.99 277.18 +13.95 14.40 1661.22 +13.96 14.40 493.88 +13.98 14.33 293.66 +13.99 14.75 493.88 +14.32 14.76 293.66 +14.33 14.80 587.33 +14.34 14.76 69.30 +14.35 14.77 164.81 +14.35 14.74 1479.98 +14.72 15.15 73.42 +14.73 15.84 1318.51 +14.73 15.14 185.00 +14.74 15.11 277.18 +14.74 15.15 554.37 +15.07 15.50 82.41 +15.08 15.31 246.94 +15.10 15.45 207.65 +15.11 15.28 493.88 +15.27 15.48 554.37 +15.28 15.51 277.18 +15.44 15.68 220.00 +15.48 15.68 440.00 +15.49 17.09 92.50 +15.49 15.82 220.00 +15.65 15.83 277.18 +15.66 15.85 554.37 +15.81 15.96 293.66 +15.81 16.18 1174.66 +15.81 15.99 185.00 +15.82 15.98 587.33 +15.96 16.18 207.65 +15.96 16.19 329.63 +15.98 16.16 659.26 +16.15 16.50 220.00 +16.16 16.50 739.99 +16.16 16.51 1108.73 +16.16 16.51 369.99 +16.49 16.69 185.00 +16.50 16.71 987.77 +16.51 16.73 329.63 +16.52 16.71 659.26 +16.65 16.86 207.65 +16.68 16.89 1108.73 +16.69 16.91 587.33 +16.69 16.91 293.66 +16.86 17.19 220.00 +16.88 18.07 277.18 +16.88 17.11 880.00 +16.89 17.69 554.37 +17.07 17.29 1108.73 +17.25 17.45 1174.66 +17.31 17.56 55.00 +17.32 17.56 110.00 +17.43 17.64 1318.51 +17.62 18.00 1479.98 +17.64 18.06 92.50 +17.64 18.00 46.25 +17.68 18.04 440.00 +17.98 18.19 1318.51 +18.02 18.37 110.00 +18.03 18.31 55.00 +18.05 18.49 739.99 +18.06 18.50 369.99 +18.16 18.39 1174.66 +18.36 19.27 1108.73 +18.43 19.30 138.59 +18.44 20.14 69.30 +18.45 19.24 349.23 +18.47 18.88 698.46 +18.84 19.20 277.18 +18.85 19.25 554.37 +19.19 19.62 293.66 +19.22 19.94 369.99 +19.23 19.59 587.33 +19.58 20.04 493.88 +19.60 20.11 246.94 +19.60 20.04 1479.98 +19.97 20.88 349.23 +19.99 20.84 415.30 +20.00 20.31 1396.91 +20.04 20.85 138.59 +20.05 20.80 207.65 +20.41 20.83 1108.73 +20.81 21.17 1174.66 +20.82 21.35 123.47 +20.84 21.61 369.99 +21.16 21.62 987.77 +21.20 21.60 739.99 +21.21 21.64 369.99 +21.57 22.69 830.61 +21.58 21.98 349.23 +21.58 22.23 207.65 +21.60 22.83 138.59 +21.64 22.31 349.23 +21.65 21.94 698.46 +21.91 22.33 554.37 +21.97 22.30 277.18 +22.30 22.51 246.94 +22.30 23.20 293.66 +22.31 22.65 369.99 +22.31 22.55 493.88 +22.48 22.72 220.00 +22.51 22.73 440.00 +22.69 23.11 1479.98 +22.69 22.92 207.65 +22.70 22.91 415.30 +22.87 23.14 369.99 +22.89 23.17 185.00 +23.10 23.44 1396.91 +23.11 24.23 138.59 +23.12 23.88 415.30 +23.12 23.90 349.23 +23.13 23.85 207.65 +23.41 23.81 1108.73 +23.80 24.01 987.77 +23.83 24.22 220.00 +23.85 24.68 369.99 +23.99 24.19 880.00 +24.17 24.37 830.61 +24.20 24.53 277.18 +24.22 24.54 554.37 +24.36 24.64 739.99 +24.59 25.90 830.61 +24.61 25.87 138.59 +24.62 25.48 329.63 +24.62 25.07 277.18 +24.63 25.03 554.37 +25.03 25.34 207.65 +25.03 25.31 415.30 +25.41 25.84 415.30 +25.45 25.85 207.65 +25.82 26.05 440.00 +25.83 26.11 329.63 +25.83 26.03 220.00 +25.84 26.28 92.50 +25.87 26.19 1108.73 +26.01 26.22 554.37 +26.01 26.27 277.18 +26.20 27.43 659.26 +26.25 26.50 329.63 +26.27 26.62 1108.73 +26.27 27.35 138.59 +26.29 26.65 207.65 +26.61 26.88 830.61 +26.63 26.94 329.63 +26.63 27.01 277.18 +26.95 27.32 830.61 +26.97 27.37 246.94 +27.04 27.39 415.30 +27.28 27.52 880.00 +27.33 27.75 220.00 +27.33 27.72 92.50 +27.37 27.69 369.99 +27.38 28.28 277.18 +27.40 28.19 554.37 +27.48 27.71 1108.73 +27.68 28.31 138.59 +27.69 28.47 1318.51 +27.69 28.28 207.65 +27.72 28.29 329.63 diff --git a/tests/generate_data.py b/tests/generate_data.py index 68ca2e6f..e2bed131 100755 --- a/tests/generate_data.py +++ b/tests/generate_data.py @@ -65,6 +65,9 @@ def load_separation_data(folder): 'tests/data/segment/{}*.lab') tasks['separation'] = (mir_eval.separation, load_separation_data, 'tests/data/separation/{}*') + tasks['transcription'] = (mir_eval.transcription, + mir_eval.io.load_valued_intervals, + 'tests/data/transcription/{}*.txt') # Get task keys from argv for task in sys.argv[1:]: print('Generating data for {}'.format(task)) diff --git a/tests/test_input_output.py b/tests/test_input_output.py index 65de09ae..3d8ac752 100644 --- a/tests/test_input_output.py +++ b/tests/test_input_output.py @@ -96,3 +96,21 @@ def test_load_labeled_intervals(): # Make sure intervals were read in correctly assert np.all(intervals == [[10, 9], [9, 10]]) assert labels == ['a', 'b'] + + +def test_load_valued_intervals(): + # Test for a value error when invalid valued events are supplied + with tempfile.TemporaryFile('r+') as f: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + # Non-increasing is invalid + f.write('10 9 5\n9 10 6') + f.seek(0) + intervals, values = mir_eval.io.load_valued_intervals(f) + assert len(w) == 1 + assert issubclass(w[-1].category, UserWarning) + assert (str(w[-1].message) == + 'All interval durations must be strictly positive') + # Make sure intervals were read in correctly + assert np.all(intervals == [[10, 9], [9, 10]]) + assert np.all(values == [5, 6]) diff --git a/tests/test_transcription.py b/tests/test_transcription.py new file mode 100644 index 00000000..14465f82 --- /dev/null +++ b/tests/test_transcription.py @@ -0,0 +1,201 @@ +# CREATED: 2/9/16 2:27 PM by Justin Salamon + +import mir_eval +import numpy as np +import glob +import json +from nose.tools import raises +import warnings + +A_TOL = 1e-12 + +# Path to the fixture files +REF_GLOB = 'tests/data/transcription/ref*.txt' +EST_GLOB = 'tests/data/transcription/est*.txt' +SCORES_GLOB = 'tests/data/transcription/output*.json' + +REF = np.array([ + [0.100, 0.300, 220.000], + [0.300, 0.400, 246.942], + [0.500, 0.600, 277.183], + [0.550, 0.650, 293.665]]) + +EST = np.array([ + [0.120, 0.290, 225.000], + [0.300, 0.340, 246.942], + [0.500, 0.600, 500.000], + [0.550, 0.600, 293.665], + [0.560, 0.650, 293.665]]) + +SCORES = { + "Precision": 0.4, + "Recall": 0.5, + "F-measure": 0.4444444444444445, + "Precision_no_offset": 0.6, + "Recall_no_offset": 0.75, + "F-measure_no_offset": 0.6666666666666665 +} + + +def test_match_notes(): + + ref_int, ref_pitch = REF[:, :2], REF[:, 2] + est_int, est_pitch = EST[:, :2], EST[:, 2] + + matching = ( + mir_eval.transcription.match_notes(ref_int, ref_pitch, est_int, + est_pitch)) + + assert matching == [(0, 0), (3, 3)] + + matching = ( + mir_eval.transcription.match_notes(ref_int, ref_pitch, est_int, + est_pitch, offset_ratio=None)) + + assert matching == [(0, 0), (1, 1), (3, 3)] + + +def test_match_notes_strict(): + + ref_int, ref_pitch = np.array([[0, 1]]), np.array([100]) + est_int, est_pitch = np.array([[0.05, 1]]), np.array([100]) + + matching = ( + mir_eval.transcription.match_notes(ref_int, ref_pitch, est_int, + est_pitch, strict=True)) + + assert matching == [] + + +def test_precision_recall_f1(): + + # load test data + ref_int, ref_pitch = REF[:, :2], REF[:, 2] + est_int, est_pitch = EST[:, :2], EST[:, 2] + + precision, recall, f_measure = ( + mir_eval.transcription.precision_recall_f1(ref_int, ref_pitch, est_int, + est_pitch)) + + scores_gen = np.array([precision, recall, f_measure]) + scores_exp = np.array([SCORES['Precision'], SCORES['Recall'], + SCORES['F-measure']]) + assert np.allclose(scores_exp, scores_gen, atol=A_TOL) + + precision, recall, f_measure = ( + mir_eval.transcription.precision_recall_f1(ref_int, ref_pitch, est_int, + est_pitch, + offset_ratio=None)) + + scores_gen = np.array([precision, recall, f_measure]) + scores_exp = np.array([SCORES['Precision_no_offset'], + SCORES['Recall_no_offset'], + SCORES['F-measure_no_offset']]) + assert np.allclose(scores_exp, scores_gen, atol=A_TOL) + + +def __check_score(score, expected_score): + assert np.allclose(score, expected_score, atol=A_TOL) + + +def test_regression(): + + # Regression tests + ref_files = sorted(glob.glob(REF_GLOB)) + est_files = sorted(glob.glob(EST_GLOB)) + sco_files = sorted(glob.glob(SCORES_GLOB)) + + for ref_f, est_f, sco_f in zip(ref_files, est_files, sco_files): + with open(sco_f, 'r') as f: + expected_scores = json.load(f) + # Load in reference transcription + ref_int, ref_pitch = mir_eval.io.load_valued_intervals(ref_f) + # Load in estimated transcription + est_int, est_pitch = mir_eval.io.load_valued_intervals(est_f) + scores = mir_eval.transcription.evaluate(ref_int, ref_pitch, est_int, + est_pitch) + for metric in scores: + # This is a simple hack to make nosetest's messages more useful + yield (__check_score, scores[metric], expected_scores[metric]) + + +def test_invalid_pitch(): + + ref_int, ref_pitch = np.array([[0, 1]]), np.array([-100]) + est_int, est_pitch = np.array([[0, 1]]), np.array([100]) + + yield (raises(ValueError)(mir_eval.transcription.validate), + ref_int, ref_pitch, est_int, est_pitch) + yield (raises(ValueError)(mir_eval.transcription.validate), + est_int, est_pitch, ref_int, ref_pitch) + + +def test_inconsistent_int_pitch(): + + ref_int, ref_pitch = np.array([[0, 1], [2, 3]]), np.array([100]) + est_int, est_pitch = np.array([[0, 1]]), np.array([100]) + + yield (raises(ValueError)(mir_eval.transcription.validate), + ref_int, ref_pitch, est_int, est_pitch) + yield (raises(ValueError)(mir_eval.transcription.validate), + est_int, est_pitch, ref_int, ref_pitch) + + +def test_empty_ref(): + + warnings.resetwarnings() + warnings.simplefilter('always') + with warnings.catch_warnings(record=True) as out: + + ref_int, ref_pitch = np.array([]), np.array([]) + est_int, est_pitch = np.array([[0, 1]]), np.array([100]) + + mir_eval.transcription.validate(ref_int, ref_pitch, est_int, est_pitch) + + # Make sure that the warning triggered + assert len(out) > 0 + + # And that the category is correct + assert out[0].category is UserWarning + + # And that it says the right thing (roughly) + assert 'empty' in str(out[0].message).lower() + + +def test_empty_est(): + + warnings.resetwarnings() + warnings.simplefilter('always') + with warnings.catch_warnings(record=True) as out: + + ref_int, ref_pitch = np.array([[0, 1]]), np.array([100]) + est_int, est_pitch = np.array([]), np.array([]) + + mir_eval.transcription.validate(ref_int, ref_pitch, est_int, est_pitch) + + # Make sure that the warning triggered + assert len(out) > 0 + + # And that the category is correct + assert out[0].category is UserWarning + + # And that it says the right thing (roughly) + assert 'empty' in str(out[0].message).lower() + + +def test_precision_recall_f1_empty(): + + ref_int, ref_pitch = np.array([]), np.array([]) + est_int, est_pitch = np.array([[0, 1]]), np.array([100]) + + precision, recall, f1 = ( + mir_eval.transcription.precision_recall_f1(ref_int, ref_pitch, est_int, + est_pitch)) + + assert (precision, recall, f1) == (0, 0, 0) + + precision, recall, f1 = ( + mir_eval.transcription.precision_recall_f1(est_int, est_pitch, ref_int, + ref_pitch)) + + assert (precision, recall, f1) == (0, 0, 0)