Move TODOs to Issues

Issues now written to address later: #98, #99, #100, #101, #102
issues #103 #104 #105 #106 #107 and #108

make_dataset.py

@@ -99,7 +99,6 @@ def parse_args(args_input=None):
     parser.add_argument('--local-midi-dirs', metavar='midi_dir', type=str,
                         nargs='*', help='directories containing midi files to '
                         'include in the dataset', default=[])
-    # TODO: check this works!
     parser.add_argument('--local-csv-dirs', metavar='csv_dir', type=str,
                         nargs='*', help='directories containing csv files to '
                         'include in the dataset', default=[])
@@ -114,12 +113,10 @@ def parse_args(args_input=None):
                         '--download-cache-dir and --clear-download-cache. To '
                         'download no data, provide an input of "None"',
                         )
-    # TODO: check this works!
     parser.add_argument('--download-cache-dir', type=str,
                         default=downloaders.DEFAULT_CACHE_PATH, help='The '
                         'directory to use for storing intermediate downloaded '
                         'data e.g. zip files, and prior to preprocessing.')
-    # TODO: check this works!
     parser.add_argument('--clear-download-cache', action='store_true',
                         help='clear downloaded data cache')
     parser.add_argument('--degradations', metavar='deg_name', nargs='*',
@@ -136,7 +133,6 @@ def parse_args(args_input=None):
     parser.add_argument('--min-notes', metavar='N', type=int, default=10,
                         help='The minimum number of notes required for an '
                         'excerpt to be valid.')
-    # TODO: check this works!
     parser.add_argument('--degradation-kwargs', metavar='json_string',
                         help='json with keyword arguments for the '
                         'degradation functions. First provide the degradation '
@@ -145,7 +141,6 @@ def parse_args(args_input=None):
                         'kwarg. e.g. {"pitch_shift__distribution": "poisson", '
                         '"pitch_shift__min_pitch: 5"}',
                         type=json.loads, default=None)
-    # TODO: check this works!
     parser.add_argument('--degradation-kwarg-json', metavar='json_file',
                         help='A file containing parameters as described in '
                         '--degradation-kwargs. If this file is given, '
@@ -242,7 +237,6 @@ def parse_args(args_input=None):
 
 
     # Instantiate downloaders =================================================
-    # TODO: make OVERWRITE this an arg for the script
     OVERWRITE = None
     ds_names = ARGS.datasets
     if len(ds_names) == 1 and ds_names[0].lower() == 'none':
@@ -568,5 +562,4 @@ def parse_args(args_input=None):
 
     print('\nTo reproduce this dataset again, run the script with argument '
           f'--seed {seed}')
-    #TODO: print('see the examples directory for baseline models using this data')
     print(LOGO)

mdtk/data_structures.py

@@ -230,7 +230,6 @@ def fix_overlapping_notes(df):
     bad_note = np.append(bad_note, False)  # last note always fine
     df.loc[bad_note, 'dur'] = (next_note_on[bad_note[:-1]]
                                - df.loc[bad_note, 'onset'].values)
-    # TODO: add an assertion to catch dur==0 and add a test
     return df
 
 
@@ -246,7 +245,6 @@ def fix_overlapping_pitches(df):
     bad_note = np.append(bad_note, False)  # last note always fine
     df.loc[bad_note, 'dur'] = (next_note_on[bad_note[:-1]]
                                - df.loc[bad_note, 'onset'].values)
-    # TODO: add an assertion to catch dur==0 and add a test
     return df
 
 
@@ -930,10 +928,6 @@ def __init__(self, note_df=None, csv_path=None,
             self.note_df = note_df
             # We do not assume that the supplied note_df is correctly formed,
             # and simply bomb out if it is not
-            # TODO: implement df methods to fix issues instead e.g. overlaps.
-            #       Copy code from read_note_csv. e.g.:
-            #       * reorder columns
-            #       * if all columns but track and no extra cols, assume 1 trk
             if self.monophonic_tracks is not None:
                 make_monophonic(self.note_df, tracks=monophonic_tracks)
             if self.max_note_len is not None:

mdtk/downloaders.py

@@ -19,9 +19,6 @@
 
 
 # Classes =====================================================================
-# TODO: make attributes useful to users standard e.g. beat-aligned=True/False
-# TODO: some things are likely to be important re preprocessing e.g. the unit
-#       for the onset and duration of notes. Add these as attributes too.
 class DataDownloader:
     """Base class for data downloaders"""
     def __init__(self, cache_path=DEFAULT_CACHE_PATH):
@@ -60,20 +57,14 @@ def download_csv(self, output_path, cache_path=None, overwrite=None,
                                   'implement the download_csv method.')
 
 
-# TODO: since these datasets already have CSV in the right format, we should
-#       implement download_csv() methods to use in favour of the download_midi
-#       this method would reformat csv to be correct (cols need renaming etc.)
-# TODO: handle conversion from quarters to ms - use tempo data, but use min/max
-#       tempo values as some were a bit spurious
+
 class PPDDSep2018Monophonic(DataDownloader):
     """Patterns for Preditction Development Dataset. Monophonic data only.
 
     References
     ----------
     https://www.music-ir.org/mirex/wiki/2019:Patterns_for_Prediction
     """
-    # TODO: add 'sample_size', to allow only a small random sample of the
-    #       total midi files to be copied to the output
     def __init__(self, cache_path=DEFAULT_CACHE_PATH,
                  sizes=['small', 'medium', 'large'], clean=False):
         super().__init__(cache_path = cache_path)
@@ -136,8 +127,6 @@ class PPDDSep2018Polyphonic(PPDDSep2018Monophonic):
     ----------
     https://www.music-ir.org/mirex/wiki/2019:Patterns_for_Prediction
     """
-    # TODO: add 'sample_size', to allow only a small random sample of the
-    #       total midi files to be copied to the output
     def __init__(self, cache_path=DEFAULT_CACHE_PATH,
                  sizes=['small', 'medium', 'large'], clean=False):
         super().__init__(cache_path=cache_path, sizes=sizes, clean=clean)

mdtk/filesystem_utils.py

@@ -67,8 +67,7 @@ def make_directory(path, overwrite=None, verbose=False):
 
 
 def extract_zip(zip_path, out_path, overwrite=None, verbose=False):
-    """Convenience function to extract zip file to out_path.
-    TODO: make work for all types of zip files."""
+    """Convenience function to extract zip file to out_path."""
     if verbose:
         print(f'Extracting {zip_path} to {out_path}')
     dirname = os.path.splitext(os.path.basename(zip_path))[0]

mdtk/formatters.py

@@ -31,9 +31,7 @@ def diff_pd(df1, df2):
         return pd.DataFrame({'from': changed_from, 'to': changed_to},
                             index=changed.index)
 
-# TODO: later can auto detect vocab from corpus if necessary
-#       I'm doing things this way just for ability to change things
-#       later with ease
+
 class CommandVocab(object):
     def __init__(self, min_pitch=MIN_PITCH_DEFAULT,
                  max_pitch=MAX_PITCH_DEFAULT,
@@ -351,7 +349,6 @@ def df_to_command_str(df, min_pitch=MIN_PITCH_DEFAULT, max_pitch=MAX_PITCH_DEFAU
     assert time_increment > 0, "time_increment must be positive."
     assert max_time_shift > 0, "max_time_shift must be positive."
 
-    # TODO: This rounding may result in notes of length 0.
     note_off = df.loc[:, ['onset', 'pitch']]
     note_off['onset'] = note_off['onset'] + df['dur']
     note_off['cmd'] = note_off['pitch'].apply(lambda x: f'f{x}')

mdtk/pytorch_models.py

@@ -29,7 +29,6 @@ def __init__(self, vocab_size, embedding_dim, hidden_dim, output_size=2,
         self.vocab_size = vocab_size
 
         self.embedding = nn.Embedding(vocab_size, embedding_dim)
-        # TODO: try getting batch_first to work on this model
         self.lstm = nn.LSTM(embedding_dim, hidden_dim,
                             num_layers=num_lstm_layers)
 
@@ -49,7 +48,6 @@ def forward(self, batch, input_lengths=None):
         device = batch.device
         self.hidden = self.init_hidden(batch_size, device=device)
         embeds = self.embedding(batch).permute(1, 0, 2)
-        # TODO: try getting batch_first to work on this model
 #        embeds = self.embedding(batch)
         outputs, (ht, ct) = self.lstm(embeds, self.hidden)
         # ht is the last hidden state of the sequences

mdtk/pytorch_trainers.py

@@ -10,9 +10,6 @@
 
 
 
-# TODO: I don't like the fomatter being passed in here - would prefer these
-# Trainers to be more general except for the iteration method for which you
-# hardcode how to do the train/test iteration.
 class BaseTrainer:
     """Provides methods to train pytorch models. Adapted from:
     https://github.com/codertimo/BERT-pytorch/blob/master/bert_pytorch/trainer/pretrain.py"""
@@ -137,7 +134,6 @@ def save(self, file_path=None, epoch=None):
         print(f"Model saved {output_path}")
         return output_path
 
-#   TODO: implement load method (for use with load from checkpoint)
 
 
 class ErrorDetectionTrainer(BaseTrainer):
@@ -706,13 +702,6 @@ def iteration(self, epoch, data_loader, train=True, evaluate=False):
                 total_data_points += len(input_data)
                 for in_data, out_data, clean_data in \
                         zip(input_data, model_output, labels):
-                    # TODO: Only 1 of these calls is necessary. deg and clean
-                    # could conceivably be returned by the data loader.
-                    # N.B. Currently, the precise min and max pitch don't
-                    # matter here. The converter just treats them all the same,
-                    # corrects and warns if the range doesn't make sense.
-                    # However, if loading deg and clean from the original df,
-                    # using the correct min and max pitch will be important.
                     with warnings.catch_warnings():
                         warnings.simplefilter("ignore")
                         deg_df = self.formatter['model_to_df'](

mdtk/tests/test_data_structures.py

@@ -445,18 +445,7 @@ def test_pianoroll_all_pitches():
     assert (pianoroll == np.ones((1, 2, 128, 1), dtype='uint8')).all()
 
 
-# TODO: test all note_on occur with sounding
-
-# TODO: test all note_off occur with sounding
-
-# TODO: test all sounding begin note_on and end_note_off
-
-# TODO: test all methods in pianoroll and all attributes
-
-
 # Composition class tests =====================================================
-# TODO: write import from csv tests
-
 def test_composition_df_assertions():
     """Essentially the same tests as test_check_note_df"""
     assertion = False
@@ -504,16 +493,6 @@ def test_composition_all_pitches():
 
 
 
-# TODO: reimplement this if and when we implement auto fix of note_df
-#def test_auto_sort_onset_and_pitch():
-#    comp = Composition(note_df=note_df_2pitch_aligned, fix_note_df=True)
-#    assert comp.note_df.equals(
-#        note_df_2pitch_aligned
-#            .sort_values(['onset', 'pitch'])
-#            .reset_index(drop=True)
-#    )
-
-
 def test_not_ending_in_silence():
     for df in ALL_VALID_DF.values():
         comp = Composition(note_df=df)
@@ -555,19 +534,11 @@ def test_composition_read_csv():
         comp.plot()
         comp.synthesize()
 
-def test_csv_and_df_imports_same():
-    # TODO: write test that imports from all csvs and checks same as
-    # importing from df
-    pass
 
 
-# TODO: Check if anything alters input data - loop over all functions and
-#       methods
 
 
 # Cleanup =====================================================================
-# TODO: This isn't technichally a test...should probably be some other function
-#       look up the proper way to do this.
 def test_remove_csvs():
     for csv in ALL_CSV:
         os.remove(csv)