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510 progress bar #559

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510 progress bar #559

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1591b28
Add progress bar in Gridsearch using tqdm
Aug 14, 2020
0097ed3
Add description to progress bar
Aug 14, 2020
d914f47
Make tqdm a soft-dependency
Aug 14, 2020
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1 change: 1 addition & 0 deletions CHANGES.md
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Expand Up @@ -41,6 +41,7 @@
* Add new constraints and objectives in `ThresholdOptimizer`
* Add class `InterpolatedThresholder` to represent the fitted `ThresholdOptimizer`
* Add `fairlearn.datasets` module.
* Add optional `verbose` argument to `GridSearch.fit` to diplay progress using `tqdm`.

### v0.4.6

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16 changes: 15 additions & 1 deletion fairlearn/reductions/_grid_search/grid_search.py
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Expand Up @@ -18,6 +18,7 @@
logger = logging.getLogger(__name__)

TRADEOFF_OPTIMIZATION = "tradeoff_optimization"
_TQDM_IMPORT_ERROR_MESSAGE = "To use `verbose=True` in GridSearch.fit, please install tqdm."


class GridSearch(BaseEstimator, MetaEstimatorMixin):
Expand Down Expand Up @@ -105,12 +106,24 @@ def fit(self, X, y, **kwargs):
:param sensitive_features: A (currently) required keyword argument listing the
feature used by the constraints object
:type sensitive_features: numpy.ndarray, pandas.DataFrame, pandas.Series, or list (for now)

:param verbose: An optional argument to enable/disable the progress bar.
Default: False
:type verbose: bool
"""
self.predictors_ = []
self.lambda_vecs_ = pd.DataFrame(dtype=np.float64)
self.objectives_ = []
self.gammas_ = pd.DataFrame(dtype=np.float64)
self.oracle_execution_times_ = []
verbose = kwargs.pop('verbose', False)

if verbose:
# We need tqdm to show the progress bar.
try:
from tqdm import tqdm
except ImportError:
raise RuntimeError(_TQDM_IMPORT_ERROR_MESSAGE)
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Rather than raise an exception, since verbose=True is the default, I think it would be kinder to log a warning and behave as if verbose was false.

Tagging @adrinjalali , who first raised a caution around tqdm

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I set verbose default to False so that the user ia not forced to install tqdm.


if isinstance(self.constraints, ClassificationMoment):
logger.debug("Classification problem detected")
Expand Down Expand Up @@ -151,7 +164,8 @@ def fit(self, X, y, **kwargs):

# Fit the estimates
logger.debug("Setup complete. Starting grid search")
for i in grid.columns:
grid_iter = tqdm(grid.columns, desc="Grid search progress") if verbose else grid.columns
for i in grid_iter:
lambda_vec = grid[i]
logger.debug("Obtaining weights")
weights = self.constraints.signed_weights(lambda_vec)
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