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@@ -420,9 +420,10 @@
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| 420 | 420 | """Return predicted labels, with ties broken according to policy. |
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| 421 | 421 | ||
| 422 | 422 | Policies to break ties include: |
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| 423 | - | "abstain": return an abstain vote (-1) |
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| 424 | - | "true-random": randomly choose among the tied options |
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| 425 | - | "random": randomly choose among tied option using deterministic hash |
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| 423 | + | ||
| 424 | + | - "abstain": return an abstain vote (-1) |
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| 425 | + | - "true-random": randomly choose among the tied options |
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| 426 | + | - "random": randomly choose among tied option using deterministic hash |
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| 426 | 427 | ||
| 427 | 428 | NOTE: if tie_break_policy="true-random", repeated runs may have slightly different |
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| 428 | 429 | results due to difference in broken ties |
@@ -472,9 +473,14 @@
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| 472 | 473 | Y |
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| 473 | 474 | Gold labels associated with data points in L |
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| 474 | 475 | metrics |
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| 475 | - | A list of metric names |
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| 476 | + | A list of metric names. Possbile metrics are - `accuracy`, `coverage`, |
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| 477 | + | `precision`, `recall`, `f1`, `f1_micro`, `f1_macro`, `fbeta`, |
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| 478 | + | `matthews_corrcoef`, `roc_auc`. See `sklearn.metrics |
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| 479 | + | <https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics>`_ |
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| 480 | + | for details on the metrics. |
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| 476 | 481 | tie_break_policy |
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| 477 | - | Policy to break ties when converting probabilistic labels to predictions |
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| 482 | + | Policy to break ties when converting probabilistic labels to predictions. |
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| 483 | + | Same as :func:`.predict` method above. |
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| 478 | 484 | ||
| 479 | 485 | ||
| 480 | 486 | Returns |
@@ -810,7 +816,35 @@
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| 810 | 816 | class_balance |
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| 811 | 817 | Each class's percentage of the population, by default None |
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| 812 | 818 | **kwargs |
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| 813 | - | Arguments for changing train config defaults |
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| 819 | + | Arguments for changing train config defaults. |
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| 820 | + | ||
| 821 | + | n_epochs |
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| 822 | + | The number of epochs to train (where each epoch is a single |
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| 823 | + | optimization step), default is 100 |
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| 824 | + | lr |
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| 825 | + | Base learning rate (will also be affected by lr_scheduler choice |
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| 826 | + | and settings), default is 0.01 |
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| 827 | + | l2 |
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| 828 | + | Centered L2 regularization strength, default is 0.0 |
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| 829 | + | optimizer |
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| 830 | + | Which optimizer to use (one of ["sgd", "adam", "adamax"]), |
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| 831 | + | default is "sgd" |
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| 832 | + | optimizer_config |
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| 833 | + | Settings for the optimizer |
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| 834 | + | lr_scheduler |
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| 835 | + | Which lr_scheduler to use (one of ["constant", "linear", |
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| 836 | + | "exponential", "step"]), default is "constant" |
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| 837 | + | lr_scheduler_config |
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| 838 | + | Settings for the LRScheduler |
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| 839 | + | prec_init |
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| 840 | + | LF precision initializations / priors, default is 0.7 |
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| 841 | + | seed |
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| 842 | + | A random seed to initialize the random number generator with |
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| 843 | + | log_freq |
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| 844 | + | Report loss every this many epochs (steps), default is 10 |
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| 845 | + | mu_eps |
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| 846 | + | Restrict the learned conditional probabilities to |
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| 847 | + | [mu_eps, 1-mu_eps], default is None |
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| 814 | 848 | ||
| 815 | 849 | Raises |
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| 816 | 850 | ------ |
@@ -823,8 +857,8 @@
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| 823 | 857 | >>> Y_dev = [0, 1, 0] |
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| 824 | 858 | >>> label_model = LabelModel(verbose=False) |
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| 825 | 859 | >>> label_model.fit(L) |
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| 826 | - | >>> label_model.fit(L, Y_dev=Y_dev) |
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| 827 | - | >>> label_model.fit(L, class_balance=[0.7, 0.3]) |
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| 860 | + | >>> label_model.fit(L, Y_dev=Y_dev, seed=2020, lr=0.05) |
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| 861 | + | >>> label_model.fit(L, class_balance=[0.7, 0.3], n_epochs=200, l2=0.4) |
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| 828 | 862 | """ |
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| 829 | 863 | # Set random seed |
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| 830 | 864 | self.train_config: TrainConfig = merge_config( # type:ignore |
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snorkel/slicing/sf/nlp.py
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| Files | Coverage |
|---|---|
| snorkel | -0.03% 97.18% |
| Project Totals (67 files) | 97.18% |
#15923ea77b31544f9d8ef3ba5ed77718