boromir674 /
topic-modeling-toolkit
| Conditions | 10 |
| Total Lines | 46 |
| Code Lines | 42 |
| Lines | 0 |
| Ratio | 0 % |
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Complex classes like make_graphs.main() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
Methods with many parameters are not only hard to understand, but their parameters also often become inconsistent when you need more, or different data.
There are several approaches to avoid long parameter lists:
| 1 | #!/usr/bin/env python |
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| 34 | @click.command(context_settings=dict(help_option_names=['-h', '--help'])) |
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| 35 | @click.argument('dataset') # help="the name of the dataset/collection in which to look for trained models" |
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| 36 | @click.option('--models-labels', '-ml', cls=ModelLabelsParser, default='', help='Selects model(s) solely based on the names provided and ignores all other selection and sorting options below') |
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| 37 | @click.option('--sort', '-s', default='perplexity', show_default=True, help="How to order the results' list, from which to select; supports 'alphabetical' and rest of metrics eg: {}".format(', '.join("'{}'".format(x) for x in c))) |
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| 38 | @click.option('--model-indices', '-mi', cls=ModelLabelsParser, default='', type=int, help='Selects model(s) based on linear indices corresponding to the list of results per model (see --sort option), residing in collection directory and ignores all othe selection options below') |
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| 39 | @click.option('--top', '-t', type=int, help='Selects the first n model results from the list and ignores all othe selection options below') |
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| 40 | @click.option('--range-tuple', '-r', cls=ModelLabelsParser, default='', help='Selects model results defined by the range') |
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| 41 | @click.option('--allmetrics/--no-allmetrics', default=False, show_default=True, help='Whether to create plots for all possible metrics (maximal) discovered in the model results') |
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| 42 | @click.option('--metrics', cls=ScoreMetricsDefinitionParser, default='', help='Whether to limit plotting to the selected graph types (roughly score definition) i.e. "perplexity", "sparsity-phi-0.80", "top-tokens-coherence-10", If --allmetrics flag is enabled it has no effect.') |
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| 43 | @click.option('--tautrajectories/--no-tautrajectories', default=False, show_default=True, help="Legacy flag: turn this on explicitly in case you have used dynamic regularization coefficient trajectory feature") |
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| 44 | @click.option('--iterations', '-i', type=int, help='Whether to limit the datapoints plotted to the specified number. Defaults to plotting information for all training iterations') |
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| 45 | @click.option('--legend/--no-legend', default=True, show_default=True, help="Whether to include legend information in the resulting graph images") |
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| 46 | def main(dataset, models_labels, sort, model_indices, top, range_tuple, allmetrics, metrics, tautrajectories, iterations, legend): |
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| 47 | if models_labels: |
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| 48 | selection = models_labels |
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| 49 | print(selection) |
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| 50 | elif model_indices: |
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| 51 | selection = model_indices |
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| 52 | elif top: |
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| 53 | selection = top |
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| 54 | elif range_tuple: |
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| 55 | selection = range(*range_tuple) |
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| 56 | else: |
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| 57 | selection = 8 |
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| 58 | if allmetrics: |
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| 59 | metrics = 'all' |
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| 60 | if tautrajectories: |
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| 61 | tautrajectories = 'all' |
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| 62 | if sys.version_info[1] == 3: |
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| 63 | print("The graph building process is probably going to result in an Attribute error ('Legend' object has no attribute 'draggable') " |
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| 64 | "due to a bug of the easyplot module when requesting legend information to be inculded in the graph image built." |
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| 65 | " For now please either invoke program with python2 interpreter or use the '--no-legend' flag.") |
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| 66 | collections_dir = os.getenv('COLLECTIONS_DIR') |
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| 67 | if not collections_dir: |
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| 68 | raise RuntimeError( |
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| 69 | "Please set the COLLECTIONS_DIR environment variable with the path to a directory containing collections/datasets") |
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| 70 | graph_maker = GraphMaker(collections_dir) |
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| 71 | graph_maker.build_graphs_from_collection(dataset, selection, |
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| 72 | metric=sort, |
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| 73 | score_definitions=metrics, |
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| 74 | tau_trajectories=tautrajectories, |
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| 75 | showlegend=legend) |
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| 76 | |||
| 77 | print("\nFigures' paths:") |
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| 78 | for _ in graph_maker.saved_figures: |
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| 79 | print(_) |
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| 80 | |||
| 83 |
