def test_is_compatible(self):
regression_metrics = metrics.RegressionMetrics()
# Only compatible with RegressionScore spec.
self.assertTrue(
regression_metrics.is_compatible(types.RegressionScore()))
self.assertFalse(
regression_metrics.is_compatible(
types.MulticlassPreds(vocab=[''])))
self.assertFalse(
regression_metrics.is_compatible(types.GeneratedText()))
def test_compute(self):
regression_metrics = metrics.RegressionMetrics()
# All correct predictions.
result = regression_metrics.compute([1, 2, 3, 4], [1, 2, 3, 4],
types.RegressionScore(),
types.RegressionScore())
self.assertAlmostEqual(result, {
'mse': 0,
'pearsonr': 1.0,
'spearmanr': 1.0
})
# Some incorrect predictions.
result = regression_metrics.compute([1, 2, 3, 4], [1, 2, 5.5, 6.3],
types.RegressionScore(),
types.RegressionScore())
self.assertAlmostEqual(result, {
'mse': 2.885,
'pearsonr': 0.9656642444980974,
'spearmanr': 1.0
})
# All incorrect predictions (and not monotonic).
result = regression_metrics.compute([1, 2, 3, 4], [-5, -10, 5, 6],
types.RegressionScore(),
types.RegressionScore())
self.assertAlmostEqual(
result, {
'mse': 47.0,
'pearsonr': 0.795592252958155,
'spearmanr': 0.7999999999999999
})
# Empty labels and predictions
result = regression_metrics.compute([], [], types.RegressionScore(),
types.RegressionScore())
self.assertAlmostEqual(result, {})
def test_compute(self):
regression_metrics = metrics.RegressionMetrics()
# All correct predictions.
result = regression_metrics.compute([1, 2, 3, 4], [1, 2, 3, 4],
types.RegressionScore(),
types.RegressionScore())
testing_utils.assert_dicts_almost_equal(self, result, {
'mse': 0,
'pearsonr': 1.0,
'spearmanr': 1.0
})
# Some incorrect predictions.
result = regression_metrics.compute([1, 2, 3, 4], [1, 2, 5.5, 6.3],
types.RegressionScore(),
types.RegressionScore())
testing_utils.assert_dicts_almost_equal(self, result, {
'mse': 2.885,
'pearsonr': 0.96566,
'spearmanr': 1.0
})
# All incorrect predictions (and not monotonic).
result = regression_metrics.compute([1, 2, 3, 4], [-5, -10, 5, 6],
types.RegressionScore(),
types.RegressionScore())
testing_utils.assert_dicts_almost_equal(self, result, {
'mse': 47.0,
'pearsonr': 0.79559,
'spearmanr': 0.79999
})
# Empty labels and predictions
result = regression_metrics.compute([], [], types.RegressionScore(),
types.RegressionScore())
testing_utils.assert_dicts_almost_equal(self, result, {})
def __init__(
self,
models: Mapping[Text, lit_model.Model],
datasets: MutableMapping[Text, lit_dataset.Dataset],
generators: Optional[Mapping[Text, lit_components.Generator]] = None,
interpreters: Optional[Mapping[Text, lit_components.Interpreter]] = None,
# General server config; see server_flags.py.
data_dir: Optional[Text] = None,
warm_start: float = 0.0,
warm_projections: bool = False,
client_root: Optional[Text] = None,
demo_mode: bool = False,
default_layout: str = None,
canonical_url: str = None,
):
if client_root is None:
raise ValueError('client_root must be set on application')
self._demo_mode = demo_mode
self._default_layout = default_layout
self._canonical_url = canonical_url
if data_dir and not os.path.isdir(data_dir):
os.mkdir(data_dir)
self._models = {
name: caching.CachingModelWrapper(model, name, cache_dir=data_dir)
for name, model in models.items()
}
self._datasets = datasets
self._datasets['_union_empty'] = NoneDataset(self._models)
if generators is not None:
self._generators = generators
else:
self._generators = {
'scrambler': scrambler.Scrambler(),
'word_replacer': word_replacer.WordReplacer(),
}
if interpreters is not None:
self._interpreters = interpreters
else:
metrics_group = lit_components.ComponentGroup({
'regression': metrics.RegressionMetrics(),
'multiclass': metrics.MulticlassMetrics(),
'paired': metrics.MulticlassPairedMetrics(),
'bleu': metrics.CorpusBLEU(),
})
self._interpreters = {
'grad_norm': gradient_maps.GradientNorm(),
'lime': lime_explainer.LIME(),
'grad_dot_input': gradient_maps.GradientDotInput(),
'integrated gradients': gradient_maps.IntegratedGradients(),
'counterfactual explainer': lemon_explainer.LEMON(),
'metrics': metrics_group,
# Embedding projectors expose a standard interface, but get special
# handling so we can precompute the projections if requested.
'pca': projection.ProjectionManager(pca.PCAModel),
'umap': projection.ProjectionManager(umap.UmapModel),
}
# Information on models and datasets.
self._build_metadata()
# Optionally, run models to pre-populate cache.
if warm_projections:
logging.info(
'Projection (dimensionality reduction) warm-start requested; '
'will do full warm-start for all models since predictions are needed.'
)
warm_start = 1.0
if warm_start > 0:
self._warm_start(rate=warm_start)
self.save_cache()
# If you add a new embedding projector that should be warm-started,
# also add it to the list here.
# TODO(lit-dev): add some registry mechanism / automation if this grows to
# more than 2-3 projection types.
if warm_projections:
self._warm_projections(['pca', 'umap'])
handlers = {
# Metadata endpoints.
'/get_info': self._get_info,
# Dataset-related endpoints.
'/get_dataset': self._get_dataset,
'/get_generated': self._get_generated,
'/save_datapoints': self._save_datapoints,
'/load_datapoints': self._load_datapoints,
'/get_datapoint_ids': self._get_datapoint_ids,
# Model prediction endpoints.
'/get_preds': self._get_preds,
'/get_interpretations': self._get_interpretations,
}
self._wsgi_app = wsgi_app.App(
# Wrap endpoint fns to take (handler, request)
handlers={k: make_handler(v) for k, v in handlers.items()},
project_root=client_root,
index_file='static/index.html',
)
def __init__(
self,
models: Mapping[Text, lit_model.Model],
datasets: Mapping[Text, lit_dataset.Dataset],
generators: Optional[Mapping[Text, lit_components.Generator]] = None,
interpreters: Optional[Mapping[Text,
lit_components.Interpreter]] = None,
annotators: Optional[List[lit_components.Annotator]] = None,
layouts: Optional[dtypes.LitComponentLayouts] = None,
# General server config; see server_flags.py.
data_dir: Optional[Text] = None,
warm_start: float = 0.0,
warm_projections: bool = False,
client_root: Optional[Text] = None,
demo_mode: bool = False,
default_layout: Optional[str] = None,
canonical_url: Optional[str] = None,
page_title: Optional[str] = None,
development_demo: bool = False,
):
if client_root is None:
raise ValueError('client_root must be set on application')
self._demo_mode = demo_mode
self._development_demo = development_demo
self._default_layout = default_layout
self._canonical_url = canonical_url
self._page_title = page_title
self._data_dir = data_dir
self._layouts = layouts or {}
if data_dir and not os.path.isdir(data_dir):
os.mkdir(data_dir)
# Wrap models in caching wrapper
self._models = {
name: caching.CachingModelWrapper(model, name, cache_dir=data_dir)
for name, model in models.items()
}
self._datasets = dict(datasets)
self._datasets['_union_empty'] = lit_dataset.NoneDataset(self._models)
self._annotators = annotators or []
# Run annotation on each dataset, creating an annotated dataset and
# replace the datasets with the annotated versions.
for ds_key, ds in self._datasets.items():
self._datasets[ds_key] = self._run_annotators(ds)
# Index all datasets
self._datasets = lit_dataset.IndexedDataset.index_all(
self._datasets, caching.input_hash)
if generators is not None:
self._generators = generators
else:
self._generators = {
'Ablation Flip': ablation_flip.AblationFlip(),
'Hotflip': hotflip.HotFlip(),
'Scrambler': scrambler.Scrambler(),
'Word Replacer': word_replacer.WordReplacer(),
}
if interpreters is not None:
self._interpreters = interpreters
else:
metrics_group = lit_components.ComponentGroup({
'regression':
metrics.RegressionMetrics(),
'multiclass':
metrics.MulticlassMetrics(),
'paired':
metrics.MulticlassPairedMetrics(),
'bleu':
metrics.CorpusBLEU(),
})
self._interpreters = {
'Grad L2 Norm':
gradient_maps.GradientNorm(),
'Grad ⋅ Input':
gradient_maps.GradientDotInput(),
'Integrated Gradients':
gradient_maps.IntegratedGradients(),
'LIME':
lime_explainer.LIME(),
'Model-provided salience':
model_salience.ModelSalience(self._models),
'counterfactual explainer':
lemon_explainer.LEMON(),
'tcav':
tcav.TCAV(),
'thresholder':
thresholder.Thresholder(),
'nearest neighbors':
nearest_neighbors.NearestNeighbors(),
'metrics':
metrics_group,
'pdp':
pdp.PdpInterpreter(),
# Embedding projectors expose a standard interface, but get special
# handling so we can precompute the projections if requested.
'pca':
projection.ProjectionManager(pca.PCAModel),
'umap':
projection.ProjectionManager(umap.UmapModel),
}
# Information on models, datasets, and other components.
self._info = self._build_metadata()
# Optionally, run models to pre-populate cache.
if warm_projections:
logging.info(
'Projection (dimensionality reduction) warm-start requested; '
'will do full warm-start for all models since predictions are needed.'
)
warm_start = 1.0
if warm_start > 0:
self._warm_start(rate=warm_start)
self.save_cache()
# If you add a new embedding projector that should be warm-started,
# also add it to the list here.
# TODO(lit-dev): add some registry mechanism / automation if this grows to
# more than 2-3 projection types.
if warm_projections:
self._warm_projections(['pca', 'umap'])
handlers = {
# Metadata endpoints.
'/get_info': self._get_info,
# Dataset-related endpoints.
'/get_dataset': self._get_dataset,
'/create_dataset': self._create_dataset,
'/create_model': self._create_model,
'/get_generated': self._get_generated,
'/save_datapoints': self._save_datapoints,
'/load_datapoints': self._load_datapoints,
'/annotate_new_data': self._annotate_new_data,
# Model prediction endpoints.
'/get_preds': self._get_preds,
'/get_interpretations': self._get_interpretations,
}
self._wsgi_app = wsgi_app.App(
# Wrap endpoint fns to take (handler, request, environ)
handlers={k: self.make_handler(v)
for k, v in handlers.items()},
project_root=client_root,
index_file='static/index.html',
)
