def testRaiseErrorWhenMoreThanOneMultiClassKeyAreProvided(self):
with self.assertRaises(ValueError):
util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_2, self.slice_c)]),
top_k=3,
class_id=0)
def testRaiseErrorWhenBothLabelAndPlotKeyAreProvided(self):
with self.assertRaises(ValueError):
util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_2, self.slice_c)]),
label='head_a',
output_name='')
def testGetPlotUsingLabel(self):
data, _ = util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_2, self.slice_c)]),
label='head_a')
self.assertEqual(data, self.plots_data_c2['label/head_a'])
def render_plot(
result: view_types.EvalResult,
slicing_spec: Optional[Union[slicer.SingleSliceSpec,
config_pb2.SlicingSpec]] = None,
output_name: Optional[Text] = None,
class_id: Optional[int] = None,
top_k: Optional[int] = None,
k: Optional[int] = None,
label: Optional[Text] = None,
) -> Optional[visualization.PlotViewer]: # pytype: disable=invalid-annotation
"""Renders the plot view as widget.
Args:
result: An tfma.EvalResult.
slicing_spec: The tfma.SlicingSpec to identify the slice. Show overall if
unset.
output_name: A string representing the output name.
class_id: A number representing the class id if multi class.
top_k: The k used to compute prediction in the top k position.
k: The k used to compute prediciton at the kth position.
label: A partial label used to match a set of plots in the results.
Returns:
A PlotViewer object if in Jupyter notebook; None if in Colab.
"""
if slicing_spec and isinstance(slicing_spec, config_pb2.SlicingSpec):
slicing_spec = slicer.SingleSliceSpec(spec=slicing_spec)
slice_spec_to_use = slicing_spec if slicing_spec else slicer.SingleSliceSpec(
)
data, cfg = util.get_plot_data_and_config(result.plots, slice_spec_to_use,
output_name, class_id, top_k, k,
label)
return visualization.render_plot(data, cfg)
def testGetPlotDataAndConfig(self):
data, eval_config = util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_1, self.slice_a)]))
self.assertEqual(data, self.plots_data_a)
self.assertEqual(eval_config['sliceName'], self.column_a)
def testReplaceNaNInPlotWithNone(self):
data, _ = util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_1, self.slice_c)]))
self.assertEquals(
data, {
metric_keys.CALIBRATION_PLOT_BOUNDARIES: [0.5, None],
metric_keys.CALIBRATION_PLOT_MATRICES: [[1, 2], [2, None, 3],
[3, 4]]
})
def testReplaceNaNInPlotWithNone(self):
data, _ = util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_1, self.slice_c)]))
self.assertEqual(data, {
'calibrationHistogramBuckets': {
'buckets': [{
'v': 0.5
}, {
'v': None
}],
}
})
def render_plot(result, slicing_spec=None):
"""Renders the plot view as widget.
Args:
result: An tfma.EvalResult.
slicing_spec: The slicing spec to identify the slice. Show overall if unset.
Returns:
A PlotViewer object if in Jupyter notebook; None if in Colab.
"""
slice_spec_to_use = slicing_spec if slicing_spec else SingleSliceSpec()
data, config = util.get_plot_data_and_config(result.plots,
slice_spec_to_use)
return visualization.render_plot(data, config)
def render_plot(result,
slicing_spec = None):
"""Renders the plot view as widget.
Args:
result: An tfma.EvalResult.
slicing_spec: The slicing spec to identify the slice. Show overall if unset.
Returns:
A PlotViewer object.
"""
slice_spec_to_use = slicing_spec if slicing_spec else SingleSliceSpec()
view = tfma_widget.PlotViewer()
view.data, view.config = util.get_plot_data_and_config(
result.plots, slice_spec_to_use)
return view
def render_plot(
result: model_eval_lib.EvalResult,
slicing_spec: Optional[SingleSliceSpec] = None,
label: Optional[Text] = None,
) -> Optional[visualization.PlotViewer]:
"""Renders the plot view as widget.
Args:
result: An tfma.EvalResult.
slicing_spec: The slicing spec to identify the slice. Show overall if unset.
label: A partial label used to match a set of plots in the results.
Returns:
A PlotViewer object if in Jupyter notebook; None if in Colab.
"""
slice_spec_to_use = slicing_spec if slicing_spec else SingleSliceSpec()
data, config = util.get_plot_data_and_config(result.plots,
slice_spec_to_use, label)
return visualization.render_plot(data, config)
def testRaisesErrorWhenMultipleMatchForLabel(self):
with self.assertRaises(ValueError):
util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_2, self.slice_c)]),
label='head_')
def testRaisesErrorWhenLabelNotProvided(self):
with self.assertRaises(ValueError):
util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_2, self.slice_c)]))
def testRaisesErrorWhenMultipleSlicesMatchInPlotData(self):
with self.assertRaises(ValueError):
util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_1, self.slice_b)]))
def testRaisesErrorWhenNoMatchAvailableInPlotData(self):
with self.assertRaises(ValueError):
util.get_plot_data_and_config(
self._makeTestPlotsData(),
SingleSliceSpec(features=[(self.column_2, self.slice_a)]))
