From e20a54df356d23262ac7f09278b88677a9adb5e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Mon, 20 Feb 2023 11:00:01 +0100 Subject: [PATCH 01/20] Update t-SNE defaults to match openTSNE --- Orange/projection/manifold.py | 8 ++++---- Orange/widgets/unsupervised/owtsne.py | 19 +------------------ 2 files changed, 5 insertions(+), 22 deletions(-) diff --git a/Orange/projection/manifold.py b/Orange/projection/manifold.py index 24248872565..476f2035f1f 100644 --- a/Orange/projection/manifold.py +++ b/Orange/projection/manifold.py @@ -382,12 +382,12 @@ class TSNE(Projector): Orange.preprocess.SklImpute(), ] - def __init__(self, n_components=2, perplexity=30, learning_rate=200, + def __init__(self, n_components=2, perplexity=30, learning_rate="auto", early_exaggeration_iter=250, early_exaggeration=12, - n_iter=750, exaggeration=None, theta=0.5, + n_iter=500, exaggeration=None, theta=0.5, min_num_intervals=10, ints_in_interval=1, initialization="pca", metric="euclidean", n_jobs=1, - neighbors="exact", negative_gradient_method="bh", + neighbors="auto", negative_gradient_method="auto", multiscale=False, callbacks=None, callbacks_every_iters=50, random_state=None, preprocessors=None): super().__init__(preprocessors=preprocessors) @@ -498,7 +498,7 @@ def fit(self, X: np.ndarray, Y: np.ndarray = None) -> openTSNE.TSNEEmbedding: # Run standard t-SNE optimization embedding.optimize( n_iter=self.early_exaggeration_iter, exaggeration=self.early_exaggeration, - inplace=True, momentum=0.5, propagate_exception=True, + inplace=True, momentum=0.8, propagate_exception=True, ) embedding.optimize( n_iter=self.n_iter, exaggeration=self.exaggeration, diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 3dd7f64ab56..3188995e98b 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -62,28 +62,11 @@ def prepare_tsne_obj(data, perplexity, multiscale, exaggeration): else: perplexity = perplexity - # Determine whether to use settings for large data sets - if n_samples > 10_000: - neighbor_method, gradient_method = "approx", "fft" - else: - neighbor_method, gradient_method = "exact", "bh" - - # Larger data sets need a larger number of iterations - if n_samples > 100_000: - early_exagg_iter, n_iter = 500, 1000 - else: - early_exagg_iter, n_iter = 250, 750 - return manifold.TSNE( n_components=2, perplexity=perplexity, multiscale=multiscale, - early_exaggeration_iter=early_exagg_iter, - n_iter=n_iter, exaggeration=exaggeration, - neighbors=neighbor_method, - negative_gradient_method=gradient_method, - theta=0.8, random_state=0, ) @@ -159,7 +142,7 @@ def run_optimization(tsne_params: dict, iterations_needed: int) -> bool: # Run early exaggeration phase was_interrupted = run_optimization( - dict(exaggeration=tsne.early_exaggeration, momentum=0.5, inplace=False), + dict(exaggeration=tsne.early_exaggeration, momentum=0.8, inplace=False), iterations_needed=tsne.early_exaggeration_iter, ) if was_interrupted: From e9c129345a7f8247b5cd3fb4967b8d6bbd90189b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 16:40:48 +0100 Subject: [PATCH 02/20] owtsne: Allow user to disable PCA preprocessing --- Orange/widgets/unsupervised/owtsne.py | 213 ++++++++++++------ .../widgets/unsupervised/tests/test_owtsne.py | 63 +++++- 2 files changed, 209 insertions(+), 67 deletions(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 3188995e98b..ef9f3c31eb5 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -27,27 +27,47 @@ class Task(namespace): """Completely determines the t-SNE task spec and intermediate results.""" - data = None # type: Optional[Table] - normalize = None # type: Optional[bool] - pca_components = None # type: Optional[int] - pca_projection = None # type: Optional[Table] - perplexity = None # type: Optional[float] - multiscale = None # type: Optional[bool] - exaggeration = None # type: Optional[float] - initialization = None # type: Optional[np.ndarray] - affinities = None # type: Optional[openTSNE.affinity.Affinities] - tsne_embedding = None # type: Optional[manifold.TSNEModel] - iterations_done = 0 # type: int + data = None # type: Optional[Table] + + normalize = None # type: Optional[bool] + normalized_data = None # type: Optional[Table] + + use_pca_preprocessing = None # type: Optional[bool] + pca_components = None # type: Optional[int] + pca_projection = None # type: Optional[Table] + + perplexity = None # type: Optional[float] + multiscale = None # type: Optional[bool] + exaggeration = None # type: Optional[float] + initialization = None # type: Optional[np.ndarray] + affinities = None # type: Optional[openTSNE.affinity.Affinities] + tsne_embedding = None # type: Optional[manifold.TSNEModel] + iterations_done = 0 # type: int # These attributes need not be set by the widget - tsne = None # type: Optional[manifold.TSNE] + tsne = None # type: Optional[manifold.TSNE] + # `effective_data` stores the current working matrix which should be used + # for any steps depending on the data matrix. For instance, normalization + # should use the effective data (presumably the original data), and set it + # to the normalized version upon completion. This can then later be used for + # PCA preprocessing + effective_data = None # type: Optional[Table] + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # Set `effective_data` to data if data provided, and effective data + # not provided + if self.effective_data is None and self.data is not None: + self.effective_data = self.data -def pca_preprocessing(data, n_components, normalize): - projector = PCA(n_components=n_components, random_state=0) - if normalize: - projector.preprocessors += (preprocess.Normalize(),) +def data_normalization(data): + normalization = preprocess.Normalize() + return normalization(data) + + +def pca_preprocessing(data, n_components): + projector = PCA(n_components=n_components, random_state=0) model = projector(data) return model(data) @@ -73,32 +93,38 @@ def prepare_tsne_obj(data, perplexity, multiscale, exaggeration): class TSNERunner: @staticmethod - def compute_pca(task, state, **_): + def compute_normalization(task: Task, state: TaskState, **_) -> None: + state.set_status("Normalizing data...") + task.normalized_data = data_normalization(task.effective_data) + task.effective_data = task.normalized_data + state.set_partial_result(("normalized_data", task)) + + @staticmethod + def compute_pca(task: Task, state: TaskState, **_) -> None: # Perform PCA preprocessing state.set_status("Computing PCA...") - pca_projection = pca_preprocessing( - task.data, task.pca_components, task.normalize - ) + pca_projection = pca_preprocessing(task.effective_data, task.pca_components) # Apply t-SNE's preprocessors to the data task.pca_projection = task.tsne.preprocess(pca_projection) + task.effective_data = task.pca_projection state.set_partial_result(("pca_projection", task)) @staticmethod - def compute_initialization(task, state, **_): + def compute_initialization(task: Task, state: TaskState, **_) -> None: # Prepare initial positions for t-SNE state.set_status("Preparing initialization...") - task.initialization = task.tsne.compute_initialization(task.pca_projection.X) + task.initialization = task.tsne.compute_initialization(task.effective_data.X) state.set_partial_result(("initialization", task)) @staticmethod - def compute_affinities(task, state, **_): + def compute_affinities(task: Task, state: TaskState, **_) -> None: # Compute affinities state.set_status("Finding nearest neighbors...") - task.affinities = task.tsne.compute_affinities(task.pca_projection.X) + task.affinities = task.tsne.compute_affinities(task.effective_data.X) state.set_partial_result(("affinities", task)) @staticmethod - def compute_tsne(task, state, progress_callback=None): + def compute_tsne(task: Task, state: TaskState, progress_callback=None) -> None: tsne = task.tsne state.set_status("Running optimization...") @@ -111,7 +137,7 @@ def compute_tsne(task, state, progress_callback=None): task.affinities, task.initialization ) task.tsne_embedding = tsne.convert_embedding_to_model( - task.pca_projection, task.tsne_embedding + task.effective_data, task.tsne_embedding ) state.set_partial_result(("tsne_embedding", task)) @@ -121,8 +147,8 @@ def compute_tsne(task, state, progress_callback=None): total_iterations_needed = tsne.early_exaggeration_iter + tsne.n_iter def run_optimization(tsne_params: dict, iterations_needed: int) -> bool: - """Run t-SNE optimization phase. Return value indicates whether or - not the optimization was interrupted.""" + """Run t-SNE optimization phase. Return value indicates whether the + optimization was interrupted.""" while task.iterations_done < iterations_needed: # Step size can't be larger than the remaining number of iterations step_size = min(_STEP_SIZE, iterations_needed - task.iterations_done) @@ -154,11 +180,9 @@ def run_optimization(tsne_params: dict, iterations_needed: int) -> bool: ) @classmethod - def run(cls, task, state): - # type: (Task, TaskState) -> Task - + def run(cls, task: Task, state: TaskState) -> Task: # Assign weights to each job indicating how much time will be spent on each - weights = {"pca": 1, "init": 1, "aff": 23, "tsne": 75} + weights = {"normalization": 1, "pca": 1, "init": 1, "aff": 25, "tsne": 50} total_weight = sum(weights.values()) # Prepare the tsne object and add it to the spec @@ -168,7 +192,12 @@ def run(cls, task, state): job_queue = [] # Add the tasks that still need to be run to the job queue - if task.pca_projection is None: + task.effective_data = task.data + + if task.normalize and task.normalized_data is None: + job_queue.append((cls.compute_normalization, weights["normalization"])) + + if task.use_pca_preprocessing and task.pca_projection is None: job_queue.append((cls.compute_pca, weights["pca"])) if task.initialization is None: @@ -183,6 +212,14 @@ def run(cls, task, state): job_queue = [(partial(f, task, state), w) for f, w in job_queue] + # Ensure the effective data is set to the appropriate, potentially + # precomputed matrix + task.effective_data = task.data + if task.normalize and task.normalized_data is not None: + task.effective_data = task.normalized_data + if task.use_pca_preprocessing and task.pca_projection is not None: + task.effective_data = task.pca_projection + # Figure out the total weight of the jobs job_weight = sum(j[1] for j in job_queue) progress_done = total_weight - job_weight @@ -215,20 +252,27 @@ def update_coordinates(self): class invalidated: # pylint: disable=invalid-name - pca_projection = affinities = tsne_embedding = False + normalized_data = pca_projection = affinities = tsne_embedding = False def __set__(self, instance, value): # `self._invalidate = True` should invalidate everything - self.pca_projection = self.affinities = self.tsne_embedding = value + self.normalized_data = value + self.pca_projection = value + self.affinities = value + self.tsne_embedding = value def __bool__(self): # If any of the values are invalidated, this should return true - return self.pca_projection or self.affinities or self.tsne_embedding + return ( + self.normalized_data or self.pca_projection or self.affinities or + self.tsne_embedding + ) def __str__(self): return "%s(%s)" % (self.__class__.__name__, ", ".join( "=".join([k, str(getattr(self, k))]) - for k in ["pca_projection", "affinities", "tsne_embedding"] + for k in ["normalized_data", "pca_projection", "affinities", + "tsne_embedding"] )) @@ -243,14 +287,16 @@ class OWtSNE(OWDataProjectionWidget, ConcurrentWidgetMixin): perplexity = ContextSetting(30) multiscale = ContextSetting(False) exaggeration = ContextSetting(1) - pca_components = ContextSetting(_DEFAULT_PCA_COMPONENTS) + normalize = ContextSetting(True) + use_pca_preprocessing = ContextSetting(True) + pca_components = ContextSetting(_DEFAULT_PCA_COMPONENTS) GRAPH_CLASS = OWtSNEGraph graph = SettingProvider(OWtSNEGraph) embedding_variables_names = ("t-SNE-x", "t-SNE-y") - # Use `invalidated` descriptor so we don't break the usage of + # Use `invalidated` descriptor, so we don't break the usage of # `_invalidated` in `OWDataProjectionWidget`, but still allow finer control # over which parts of the embedding to invalidate _invalidated = invalidated() @@ -268,11 +314,13 @@ class Error(OWDataProjectionWidget.Error): def __init__(self): OWDataProjectionWidget.__init__(self) ConcurrentWidgetMixin.__init__(self) - self.pca_projection = None # type: Optional[Table] - self.initialization = None # type: Optional[np.ndarray] - self.affinities = None # type: Optional[openTSNE.affinity.Affinities] - self.tsne_embedding = None # type: Optional[manifold.TSNEModel] - self.iterations_done = 0 # type: int + # Intermediate results + self.normalized_data = None # type: Optional[Table] + self.pca_projection = None # type: Optional[Table] + self.initialization = None # type: Optional[np.ndarray] + self.affinities = None # type: Optional[openTSNE.affinity.Affinities] + self.tsne_embedding = None # type: Optional[manifold.TSNEModel] + self.iterations_done = 0 # type: int @property def effective_data(self): @@ -283,7 +331,22 @@ def _add_controls(self): super()._add_controls() def _add_controls_start_box(self): - box = gui.vBox(self.controlArea, box="Optimize") + preprocessing_box = gui.vBox(self.controlArea, box="Preprocessing") + self.normalize_cbx = gui.checkBox( + preprocessing_box, self, "normalize", "Normalize data", + callback=self._invalidate_normalized_data, + ) + self.pca_preprocessing_cbx = gui.checkBox( + preprocessing_box, self, "use_pca_preprocessing", "Preprocess using PCA", + callback=self._pca_preprocessing_changed, + ) + self.pca_component_slider = gui.hSlider( + preprocessing_box, self, "pca_components", label="PCA Components:", + minValue=2, maxValue=_MAX_PCA_COMPONENTS, step=1, + callback=self._invalidate_pca_projection, + ) + + box = gui.vBox(self.controlArea, box="Parameters") form = QFormLayout( labelAlignment=Qt.AlignLeft, formAlignment=Qt.AlignLeft, @@ -308,27 +371,27 @@ def _add_controls_start_box(self): ) form.addRow("Exaggeration:", sbe) - sbp = gui.hBox(self.controlArea, False, addToLayout=False) - gui.hSlider( - sbp, self, "pca_components", minValue=2, maxValue=_MAX_PCA_COMPONENTS, - step=1, callback=self._invalidate_pca_projection, - ) - form.addRow("PCA components:", sbp) - - self.normalize_cbx = gui.checkBox( - box, self, "normalize", "Normalize data", - callback=self._invalidate_pca_projection, addToLayout=False - ) - form.addRow(self.normalize_cbx) - box.layout().addLayout(form) self.run_button = gui.button(box, self, "Start", callback=self._toggle_run) + # Control callbacks + def _normalize_data_changed(self): + self._invalidate_normalized_data() + + def _pca_preprocessing_changed(self): + self.controls.pca_components.setEnabled(self.use_pca_preprocessing) + self._invalidate_pca_projection() + def _multiscale_changed(self): self.controls.perplexity.setDisabled(self.multiscale) self._invalidate_affinities() + # Invalidateion cascade + def _invalidate_normalized_data(self): + self._invalidated.normalized_data = True + self._invalidate_pca_projection() + def _invalidate_pca_projection(self): self._invalidated.pca_projection = True self._invalidate_affinities() @@ -358,7 +421,7 @@ def error(err): err() self.data = None - # `super().check_data()` clears all messages so we have to remember if + # `super().check_data()` clears all messages, so we have to remember if # it was shown # pylint: disable=assignment-from-no-return should_show_modified_message = self.Information.modified.is_shown() @@ -483,25 +546,39 @@ def run(self): task = Task( data=self.data, + # Normalization normalize=self.normalize, + normalized_data=self.normalized_data, + # PCA preprocessing + use_pca_preprocessing=self.use_pca_preprocessing, pca_components=self.pca_components, pca_projection=self.pca_projection, + # t-SNE parameters perplexity=self.perplexity, multiscale=self.multiscale, exaggeration=self.exaggeration, initialization=self.initialization, affinities=self.affinities, + # Misc tsne_embedding=self.tsne_embedding, iterations_done=self.iterations_done, ) return self.start(TSNERunner.run, task) - def __ensure_task_same_for_pca(self, task: Task): + def __ensure_task_same_for_normalization(self, task: Task): assert self.data is not None assert task.normalize == self.normalize - assert task.pca_components == self.pca_components - assert isinstance(task.pca_projection, Table) and \ - len(task.pca_projection) == len(self.data) + if task.normalize: + assert isinstance(task.normalized_data, Table) and \ + len(task.normalized_data) == len(self.data) + + def __ensure_task_same_for_pca(self, task: Task): + assert self.data is not None + assert task.use_pca_preprocessing == self.use_pca_preprocessing + if task.use_pca_preprocessing: + assert task.pca_components == self.pca_components + assert isinstance(task.pca_projection, Table) and \ + len(task.pca_projection) == len(self.data) def __ensure_task_same_for_initialization(self, task: Task): assert isinstance(task.initialization, np.ndarray) and \ @@ -520,18 +597,25 @@ def on_partial_result(self, value): # type: (Tuple[str, Task]) -> None which, task = value - if which == "pca_projection": + if which == "normalized_data": + self.__ensure_task_same_for_normalization(task) + self.normalized_data = task.normalized_data + elif which == "pca_projection": + self.__ensure_task_same_for_normalization(task) self.__ensure_task_same_for_pca(task) self.pca_projection = task.pca_projection elif which == "initialization": + self.__ensure_task_same_for_normalization(task) self.__ensure_task_same_for_pca(task) self.__ensure_task_same_for_initialization(task) self.initialization = task.initialization elif which == "affinities": + self.__ensure_task_same_for_normalization(task) self.__ensure_task_same_for_pca(task) self.__ensure_task_same_for_affinities(task) self.affinities = task.affinities elif which == "tsne_embedding": + self.__ensure_task_same_for_normalization(task) self.__ensure_task_same_for_pca(task) self.__ensure_task_same_for_initialization(task) self.__ensure_task_same_for_affinities(task) @@ -597,6 +681,7 @@ def clear(self): super().clear() self.run_button.setText("Start") self.cancel() + self.normalized_data = None self.pca_projection = None self.initialization = None self.affinities = None diff --git a/Orange/widgets/unsupervised/tests/test_owtsne.py b/Orange/widgets/unsupervised/tests/test_owtsne.py index f388ce8437f..f99f29583d6 100644 --- a/Orange/widgets/unsupervised/tests/test_owtsne.py +++ b/Orange/widgets/unsupervised/tests/test_owtsne.py @@ -306,11 +306,13 @@ def test_invalidation_flow(self): self.send_signal(w.Inputs.data, self.data) self.wait_until_finished() self.assertFalse(self.widget.Information.modified.is_shown()) - # All the embedding components should computed + # All the embedding components should be computed + self.assertIsNotNone(w.normalized_data) self.assertIsNotNone(w.pca_projection) self.assertIsNotNone(w.affinities) self.assertIsNotNone(w.tsne_embedding) # All the invalidation flags should be set to false + self.assertFalse(w._invalidated.normalized_data) self.assertFalse(w._invalidated.pca_projection) self.assertFalse(w._invalidated.affinities) self.assertFalse(w._invalidated.tsne_embedding) @@ -320,12 +322,14 @@ def test_invalidation_flow(self): self.assertTrue(self.widget.Information.modified.is_shown()) # Setting `multiscale` to true should set the invalidate flags for # the affinities and embedding, but not the pca_projection + self.assertFalse(w._invalidated.normalized_data) self.assertFalse(w._invalidated.pca_projection) self.assertTrue(w._invalidated.affinities) self.assertTrue(w._invalidated.tsne_embedding) # The flags should now be set, but the embedding should still be # available when selecting a subset of data and such + self.assertIsNotNone(w.normalized_data) self.assertIsNotNone(w.pca_projection) self.assertIsNotNone(w.affinities) self.assertIsNotNone(w.tsne_embedding) @@ -356,11 +360,60 @@ class TestTSNERunner(unittest.TestCase): def setUpClass(cls): cls.data = Table("iris") - def test_run(self): + def test_run_with_normalization_and_pca_preprocessing(self): state = Mock() state.is_interruption_requested = Mock(return_value=False) - task = TSNERunner.run(Task(data=self.data, perplexity=30), state) + task = Task( + normalize=True, use_pca_preprocessing=True, data=self.data, perplexity=30 + ) + task = TSNERunner.run(task, state) + + self.assertEqual(len(state.set_status.mock_calls), 5) + state.set_status.assert_has_calls([ + call("Normalizing data..."), + call("Computing PCA..."), + call("Preparing initialization..."), + call("Finding nearest neighbors..."), + call("Running optimization..."), + ]) + + self.assertIsInstance(task.normalized_data, Table) + self.assertIsInstance(task.pca_projection, Table) + self.assertIsInstance(task.tsne, TSNE) + self.assertIsInstance(task.tsne_embedding, TSNEModel) + + def test_run_with_normalization(self): + state = Mock() + state.is_interruption_requested = Mock(return_value=False) + + task = Task( + normalize=True, use_pca_preprocessing=False, data=self.data, perplexity=30 + ) + task = TSNERunner.run(task, state) + + self.assertEqual(len(state.set_status.mock_calls), 4) + state.set_status.assert_has_calls([ + call("Normalizing data..."), + call("Preparing initialization..."), + call("Finding nearest neighbors..."), + call("Running optimization..."), + ]) + + self.assertIsNone(task.pca_projection, Table) + + self.assertIsInstance(task.normalized_data, Table) + self.assertIsInstance(task.tsne, TSNE) + self.assertIsInstance(task.tsne_embedding, TSNEModel) + + def test_run_with_pca_preprocessing(self): + state = Mock() + state.is_interruption_requested = Mock(return_value=False) + + task = Task( + normalize=False, use_pca_preprocessing=True, data=self.data, perplexity=30 + ) + task = TSNERunner.run(task, state) self.assertEqual(len(state.set_status.mock_calls), 4) state.set_status.assert_has_calls([ @@ -369,6 +422,9 @@ def test_run(self): call("Finding nearest neighbors..."), call("Running optimization..."), ]) + state.set_status.assert_has_calls + + self.assertIsNone(task.normalized_data, Table) self.assertIsInstance(task.pca_projection, Table) self.assertIsInstance(task.tsne, TSNE) @@ -383,6 +439,7 @@ def test_run_do_not_modify_model_inplace(self): task.tsne = prepare_tsne_obj( task.data, task.perplexity, task.multiscale, task.exaggeration ) + TSNERunner.compute_normalization(task, state) TSNERunner.compute_pca(task, state) TSNERunner.compute_initialization(task, state) TSNERunner.compute_affinities(task, state) From 659130d3cb87fa9aec1eddbb5636b2c646fc1542 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 17:31:42 +0100 Subject: [PATCH 03/20] owtsne: Warn if PCA disabled and large number of features --- Orange/widgets/unsupervised/owtsne.py | 12 +++ .../widgets/unsupervised/tests/test_owtsne.py | 73 +++++++++++++++++++ 2 files changed, 85 insertions(+) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index ef9f3c31eb5..5bb8777eafb 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -305,6 +305,12 @@ class Information(OWDataProjectionWidget.Information): modified = Msg("The parameter settings have been changed. Press " "\"Start\" to rerun with the new settings.") + class Warning(OWDataProjectionWidget.Warning): + consider_using_pca_preprocessing = Msg( + "The input data contains a large number of features, which may slow" + " down t-SNE computation. Consider enabling PCA preprocessing." + ) + class Error(OWDataProjectionWidget.Error): not_enough_rows = Msg("Input data needs at least 2 rows") not_enough_cols = Msg("Input data needs at least 2 attributes") @@ -383,6 +389,12 @@ def _pca_preprocessing_changed(self): self.controls.pca_components.setEnabled(self.use_pca_preprocessing) self._invalidate_pca_projection() + should_warn_pca = False + if self.data is not None and not self.use_pca_preprocessing: + if len(self.data.domain.attributes) >= _MAX_PCA_COMPONENTS: + should_warn_pca = True + self.Warning.consider_using_pca_preprocessing(shown=should_warn_pca) + def _multiscale_changed(self): self.controls.perplexity.setDisabled(self.multiscale) self._invalidate_affinities() diff --git a/Orange/widgets/unsupervised/tests/test_owtsne.py b/Orange/widgets/unsupervised/tests/test_owtsne.py index f99f29583d6..362c86dc4e4 100644 --- a/Orange/widgets/unsupervised/tests/test_owtsne.py +++ b/Orange/widgets/unsupervised/tests/test_owtsne.py @@ -297,6 +297,14 @@ def test_modified_info_message_behaviour(self): "The information message was not cleared on no data" ) + self.send_signal(self.widget.Inputs.data, self.data) + self.wait_until_stop_blocking() + self.assertFalse( + self.widget.Information.modified.is_shown(), + "The modified info message should be hidden after the widget " + "computes the embedding" + ) + def test_invalidation_flow(self): # pylint: disable=protected-access w = self.widget @@ -354,6 +362,71 @@ def test_invalidation_flow(self): # All of the inavalidation flags should have been cleared self.assertFalse(w._invalidated) + def test_pca_preprocessing_warning_with_large_number_of_features(self): + self.assertFalse( + self.widget.Warning.consider_using_pca_preprocessing.is_shown(), + "The PCA warning should be hidden by default" + ) + + self.widget.controls.use_pca_preprocessing.setChecked(False) + self.assertFalse( + self.widget.Warning.consider_using_pca_preprocessing.is_shown(), + "The PCA warning should be hidden even after toggling options if " + "no data is on input" + ) + + # Setup data classes + x_small = np.random.normal(0, 1, size=(50, 4)) + data_small = Table.from_numpy(Domain.from_numpy(x_small), x_small) + x_large = np.random.normal(0, 1, size=(50, 250)) + data_large = Table.from_numpy(Domain.from_numpy(x_large), x_large) + + # SMALL data with PCA preprocessing ENABLED + self.send_signal(self.widget.Inputs.data, data_small) + self.widget.controls.use_pca_preprocessing.setChecked(True) + self.widget.run_button.click(), self.wait_until_stop_blocking() + self.assertFalse( + self.widget.Warning.consider_using_pca_preprocessing.is_shown(), + "The PCA warning should be hidden when PCA preprocessing enabled " + "when the data has <50 features" + ) + + # SMALL data with PCA preprocessing DISABLED + self.send_signal(self.widget.Inputs.data, data_small) + self.widget.controls.use_pca_preprocessing.setChecked(False) + self.widget.run_button.click(), self.wait_until_stop_blocking() + self.assertFalse( + self.widget.Warning.consider_using_pca_preprocessing.is_shown(), + "The PCA warning should be hidden with disabled PCA preprocessing " + "when the data has <50 features" + ) + + # LARGE data with PCA preprocessing ENABLED + self.send_signal(self.widget.Inputs.data, data_large) + self.widget.controls.use_pca_preprocessing.setChecked(True) + self.widget.run_button.click(), self.wait_until_stop_blocking() + self.assertFalse( + self.widget.Warning.consider_using_pca_preprocessing.is_shown(), + "The PCA warning should be hidden when PCA preprocessing enabled " + "when has >50 features" + ) + + # LARGE data with PCA preprocessing DISABLED + self.send_signal(self.widget.Inputs.data, data_large) + self.widget.controls.use_pca_preprocessing.setChecked(False) + self.widget.run_button.click(), self.wait_until_stop_blocking() + self.assertTrue( + self.widget.Warning.consider_using_pca_preprocessing.is_shown(), + "The PCA warning should be shown when PCA preprocessing disabled " + "when the data has >50 features" + ) + + self.send_signal(self.widget.Inputs.data, None) + self.assertFalse( + self.widget.Information.modified.is_shown(), + "The PCA warning should be cleared when problematic data is removed" + ) + class TestTSNERunner(unittest.TestCase): @classmethod From 9b0abb0bb4da971ee68a76dbb74a5dbf8475d410 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 19:03:06 +0100 Subject: [PATCH 04/20] owtsne: Add support for spectral initialization --- Orange/projection/manifold.py | 4 ++ Orange/widgets/unsupervised/owtsne.py | 68 ++++++++++++++++++++------- 2 files changed, 56 insertions(+), 16 deletions(-) diff --git a/Orange/projection/manifold.py b/Orange/projection/manifold.py index 476f2035f1f..f78f597b8d6 100644 --- a/Orange/projection/manifold.py +++ b/Orange/projection/manifold.py @@ -461,6 +461,10 @@ def compute_initialization(self, X): initialization = openTSNE.initialization.pca( X, self.n_components, random_state=self.random_state ) + elif self.initialization == "spectral": + initialization = openTSNE.initialization.spectral( + X, self.n_components, random_state=self.random_state, + ) elif self.initialization == "random": initialization = openTSNE.initialization.random( X, self.n_components, random_state=self.random_state diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 5bb8777eafb..582e352b2fb 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -24,6 +24,8 @@ _MAX_PCA_COMPONENTS = 50 _DEFAULT_PCA_COMPONENTS = 20 +INITIALIZATIONS = [("PCA", "pca"), ("Spectral", "spectral")] + class Task(namespace): """Completely determines the t-SNE task spec and intermediate results.""" @@ -39,6 +41,7 @@ class Task(namespace): perplexity = None # type: Optional[float] multiscale = None # type: Optional[bool] exaggeration = None # type: Optional[float] + initialization_method = None # type: Optional[str] initialization = None # type: Optional[np.ndarray] affinities = None # type: Optional[openTSNE.affinity.Affinities] tsne_embedding = None # type: Optional[manifold.TSNEModel] @@ -55,8 +58,8 @@ class Task(namespace): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - # Set `effective_data` to data if data provided, and effective data - # not provided + # Set `effective_data` to `data` if provided and effective data is not + # provided if self.effective_data is None and self.data is not None: self.effective_data = self.data @@ -72,7 +75,7 @@ def pca_preprocessing(data, n_components): return model(data) -def prepare_tsne_obj(data, perplexity, multiscale, exaggeration): +def prepare_tsne_obj(data, initialization_method, perplexity, multiscale, exaggeration): # type: (Table, float, bool, float) -> manifold.TSNE """Automatically determine the best parameters for the given data set.""" # Compute perplexity settings for multiscale @@ -84,6 +87,7 @@ def prepare_tsne_obj(data, perplexity, multiscale, exaggeration): return manifold.TSNE( n_components=2, + initialization=initialization_method, perplexity=perplexity, multiscale=multiscale, exaggeration=exaggeration, @@ -113,7 +117,16 @@ def compute_pca(task: Task, state: TaskState, **_) -> None: def compute_initialization(task: Task, state: TaskState, **_) -> None: # Prepare initial positions for t-SNE state.set_status("Preparing initialization...") - task.initialization = task.tsne.compute_initialization(task.effective_data.X) + if task.initialization_method == "pca": + x = task.effective_data.X + elif task.initialization_method == "spectral": + assert task.affinities is not None + x = task.affinities.P + else: + raise RuntimeError( + f"Unrecognized initialization scheme `{task.initialization_method}`!" + ) + task.initialization = task.tsne.compute_initialization(x) state.set_partial_result(("initialization", task)) @staticmethod @@ -187,7 +200,11 @@ def run(cls, task: Task, state: TaskState) -> Task: # Prepare the tsne object and add it to the spec task.tsne = prepare_tsne_obj( - task.data, task.perplexity, task.multiscale, task.exaggeration + task.data, + task.initialization_method, + task.perplexity, + task.multiscale, + task.exaggeration, ) job_queue = [] @@ -200,12 +217,12 @@ def run(cls, task: Task, state: TaskState) -> Task: if task.use_pca_preprocessing and task.pca_projection is None: job_queue.append((cls.compute_pca, weights["pca"])) - if task.initialization is None: - job_queue.append((cls.compute_initialization, weights["init"])) - if task.affinities is None: job_queue.append((cls.compute_affinities, weights["aff"])) + if task.initialization is None: + job_queue.append((cls.compute_initialization, weights["init"])) + total_iterations = task.tsne.early_exaggeration_iter + task.tsne.n_iter if task.tsne_embedding is None or task.iterations_done < total_iterations: job_queue.append((cls.compute_tsne, weights["tsne"])) @@ -252,27 +269,28 @@ def update_coordinates(self): class invalidated: # pylint: disable=invalid-name - normalized_data = pca_projection = affinities = tsne_embedding = False + normalized_data = pca_projection = initialization = affinities = tsne_embedding = False def __set__(self, instance, value): # `self._invalidate = True` should invalidate everything self.normalized_data = value self.pca_projection = value + self.initialization = value self.affinities = value self.tsne_embedding = value def __bool__(self): # If any of the values are invalidated, this should return true return ( - self.normalized_data or self.pca_projection or self.affinities or - self.tsne_embedding + self.normalized_data or self.pca_projection or self.initialization or + self.affinities or self.tsne_embedding ) def __str__(self): return "%s(%s)" % (self.__class__.__name__, ", ".join( "=".join([k, str(getattr(self, k))]) - for k in ["normalized_data", "pca_projection", "affinities", - "tsne_embedding"] + for k in ["normalized_data", "pca_projection", "initialization", + "affinities", "tsne_embedding"] )) @@ -287,6 +305,7 @@ class OWtSNE(OWDataProjectionWidget, ConcurrentWidgetMixin): perplexity = ContextSetting(30) multiscale = ContextSetting(False) exaggeration = ContextSetting(1) + initialization_method_idx = ContextSetting(0) normalize = ContextSetting(True) use_pca_preprocessing = ContextSetting(True) @@ -359,6 +378,12 @@ def _add_controls_start_box(self): fieldGrowthPolicy=QFormLayout.AllNonFixedFieldsGrow, ) + self.initialization_combo = gui.comboBox( + self.controlArea, self, "initialization_method_idx", + items=[m[0] for m in INITIALIZATIONS], + callback=self._invalidate_initialization, + ) + form.addRow("Initialization:", self.initialization_combo) self.perplexity_spin = gui.spin( box, self, "perplexity", 1, 500, step=1, alignment=Qt.AlignRight, callback=self._invalidate_affinities, addToLayout=False @@ -381,7 +406,7 @@ def _add_controls_start_box(self): self.run_button = gui.button(box, self, "Start", callback=self._toggle_run) - # Control callbacks + # GUI control callbacks def _normalize_data_changed(self): self._invalidate_normalized_data() @@ -399,7 +424,7 @@ def _multiscale_changed(self): self.controls.perplexity.setDisabled(self.multiscale) self._invalidate_affinities() - # Invalidateion cascade + # Invalidation cascade def _invalidate_normalized_data(self): self._invalidated.normalized_data = True self._invalidate_pca_projection() @@ -412,6 +437,10 @@ def _invalidate_affinities(self): self._invalidated.affinities = True self._invalidate_tsne_embedding() + def _invalidate_initialization(self): + self._invalidated.initialization = True + self._invalidate_tsne_embedding() + def _invalidate_tsne_embedding(self): self._invalidated.tsne_embedding = True self._stop_running_task() @@ -538,6 +567,8 @@ def run(self): self.pca_projection = None if self._invalidated.affinities: self.affinities = None + if self._invalidated.initialization: + self.initialization = None if self._invalidated.tsne_embedding: self.iterations_done = 0 self.tsne_embedding = None @@ -556,6 +587,8 @@ def run(self): if self.data is None: return + initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] + task = Task( data=self.data, # Normalization @@ -566,10 +599,11 @@ def run(self): pca_components=self.pca_components, pca_projection=self.pca_projection, # t-SNE parameters + initialization_method=initialization_method, + initialization=self.initialization, perplexity=self.perplexity, multiscale=self.multiscale, exaggeration=self.exaggeration, - initialization=self.initialization, affinities=self.affinities, # Misc tsne_embedding=self.tsne_embedding, @@ -593,6 +627,8 @@ def __ensure_task_same_for_pca(self, task: Task): len(task.pca_projection) == len(self.data) def __ensure_task_same_for_initialization(self, task: Task): + initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] + assert task.initialization_method == initialization_method assert isinstance(task.initialization, np.ndarray) and \ len(task.initialization) == len(self.data) From 62dc75ad064688ce1f867fc58f5feeaa5b9f07c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 19:47:43 +0100 Subject: [PATCH 05/20] owtsne: Add support for l1, cosine distances --- Orange/widgets/unsupervised/owtsne.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 582e352b2fb..eb4cd714a56 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -25,6 +25,7 @@ _DEFAULT_PCA_COMPONENTS = 20 INITIALIZATIONS = [("PCA", "pca"), ("Spectral", "spectral")] +DISTANCE_METRICS = [("Euclidean", "l2"), ("Manhattan", "l1"), ("Cosine", "cosine")] class Task(namespace): @@ -38,6 +39,7 @@ class Task(namespace): pca_components = None # type: Optional[int] pca_projection = None # type: Optional[Table] + distance_metric = None # type: Optional[str] perplexity = None # type: Optional[float] multiscale = None # type: Optional[bool] exaggeration = None # type: Optional[float] @@ -75,7 +77,8 @@ def pca_preprocessing(data, n_components): return model(data) -def prepare_tsne_obj(data, initialization_method, perplexity, multiscale, exaggeration): +def prepare_tsne_obj(data, initialization_method, distance_metric, perplexity, + multiscale, exaggeration): # type: (Table, float, bool, float) -> manifold.TSNE """Automatically determine the best parameters for the given data set.""" # Compute perplexity settings for multiscale @@ -88,6 +91,7 @@ def prepare_tsne_obj(data, initialization_method, perplexity, multiscale, exagge return manifold.TSNE( n_components=2, initialization=initialization_method, + metric=distance_metric, perplexity=perplexity, multiscale=multiscale, exaggeration=exaggeration, @@ -202,6 +206,7 @@ def run(cls, task: Task, state: TaskState) -> Task: task.tsne = prepare_tsne_obj( task.data, task.initialization_method, + task.distance_metric, task.perplexity, task.multiscale, task.exaggeration, @@ -306,6 +311,7 @@ class OWtSNE(OWDataProjectionWidget, ConcurrentWidgetMixin): multiscale = ContextSetting(False) exaggeration = ContextSetting(1) initialization_method_idx = ContextSetting(0) + distance_metric_idx = ContextSetting(0) normalize = ContextSetting(True) use_pca_preprocessing = ContextSetting(True) @@ -384,12 +390,21 @@ def _add_controls_start_box(self): callback=self._invalidate_initialization, ) form.addRow("Initialization:", self.initialization_combo) + + self.distance_metric_combo = gui.comboBox( + self.controlArea, self, "distance_metric_idx", + items=[m[0] for m in DISTANCE_METRICS], + callback=self._invalidate_affinities, + ) + form.addRow("Distance metric:", self.distance_metric_combo) + self.perplexity_spin = gui.spin( box, self, "perplexity", 1, 500, step=1, alignment=Qt.AlignRight, callback=self._invalidate_affinities, addToLayout=False ) self.controls.perplexity.setDisabled(self.multiscale) form.addRow("Perplexity:", self.perplexity_spin) + form.addRow(gui.checkBox( box, self, "multiscale", label="Preserve global structure", callback=self._multiscale_changed, addToLayout=False @@ -588,6 +603,7 @@ def run(self): return initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] + distance_metric = DISTANCE_METRICS[self.distance_metric_idx][1] task = Task( data=self.data, @@ -601,6 +617,7 @@ def run(self): # t-SNE parameters initialization_method=initialization_method, initialization=self.initialization, + distance_metric=distance_metric, perplexity=self.perplexity, multiscale=self.multiscale, exaggeration=self.exaggeration, @@ -635,6 +652,8 @@ def __ensure_task_same_for_initialization(self, task: Task): def __ensure_task_same_for_affinities(self, task: Task): assert task.perplexity == self.perplexity assert task.multiscale == self.multiscale + distance_metric = DISTANCE_METRICS[self.distance_metric_idx][1] + assert task.distance_metric == distance_metric def __ensure_task_same_for_embedding(self, task: Task): assert task.exaggeration == self.exaggeration @@ -688,7 +707,7 @@ def on_done(self, task): # type: (Task) -> None self.run_button.setText("Start") # NOTE: All of these have already been set by on_partial_result, - # we double check that they are aliases + # we double-check that they are aliases if task.pca_projection is not None: self.__ensure_task_same_for_pca(task) assert task.pca_projection is self.pca_projection From 083ef115df6d38500720fa479b0180070e1ae1f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 19:48:15 +0100 Subject: [PATCH 06/20] owtsne: Fix label to be more consistent with rest of Orange --- Orange/widgets/unsupervised/owtsne.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index eb4cd714a56..da42fb82313 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -368,7 +368,7 @@ def _add_controls_start_box(self): callback=self._invalidate_normalized_data, ) self.pca_preprocessing_cbx = gui.checkBox( - preprocessing_box, self, "use_pca_preprocessing", "Preprocess using PCA", + preprocessing_box, self, "use_pca_preprocessing", "Apply PCA preprocessing", callback=self._pca_preprocessing_changed, ) self.pca_component_slider = gui.hSlider( From febfcab52a35fdc1b7b5f84d3cfba3a12a3d1b2d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 19:48:47 +0100 Subject: [PATCH 07/20] owtsne: Change exaggeration slider interval to 0.25 --- Orange/widgets/unsupervised/owtsne.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index da42fb82313..90491b72da4 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -412,7 +412,8 @@ def _add_controls_start_box(self): sbe = gui.hBox(self.controlArea, False, addToLayout=False) gui.hSlider( - sbe, self, "exaggeration", minValue=1, maxValue=4, step=1, + sbe, self, "exaggeration", minValue=1, maxValue=4, step=0.25, + intOnly=False, labelFormat="%.2f", callback=self._invalidate_tsne_embedding, ) form.addRow("Exaggeration:", sbe) From be467ca354323e87acd5f432ff0578d4d308de53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Tue, 21 Feb 2023 19:56:38 +0100 Subject: [PATCH 08/20] owtsne: Fix normalized_data not being invalidated properly --- Orange/widgets/unsupervised/owtsne.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 90491b72da4..3265bcdefe3 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -579,6 +579,8 @@ def enable_controls(self): def run(self): # Reset invalidated values as indicated by the flags + if self._invalidated.normalized_data: + self.normalized_data = None if self._invalidated.pca_projection: self.pca_projection = None if self._invalidated.affinities: @@ -709,6 +711,9 @@ def on_done(self, task): self.run_button.setText("Start") # NOTE: All of these have already been set by on_partial_result, # we double-check that they are aliases + if task.normalized_data is not None: + self.__ensure_task_same_for_normalization(task) + assert task.normalized_data is self.normalized_data if task.pca_projection is not None: self.__ensure_task_same_for_pca(task) assert task.pca_projection is self.pca_projection From 99e39a3183db1b0560410e6a9b1ffb85459429c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Wed, 22 Feb 2023 22:14:07 +0100 Subject: [PATCH 09/20] owtsne: Add Distances signal --- Orange/projection/manifold.py | 38 +- Orange/widgets/unsupervised/owtsne.py | 350 +++++++++++--- .../widgets/unsupervised/tests/test_owtsne.py | 429 +++++++++++++++++- 3 files changed, 746 insertions(+), 71 deletions(-) diff --git a/Orange/projection/manifold.py b/Orange/projection/manifold.py index f78f597b8d6..fde353d0703 100644 --- a/Orange/projection/manifold.py +++ b/Orange/projection/manifold.py @@ -1,3 +1,5 @@ +from typing import Union + import logging import warnings from collections.abc import Iterable @@ -13,6 +15,7 @@ from Orange.data import Table, Domain, ContinuousVariable from Orange.data.util import get_unique_names from Orange.distance import Distance, DistanceModel, Euclidean +from Orange.misc import DistMatrix from Orange.projection import SklProjector, Projector, Projection from Orange.projection.base import TransformDomain, ComputeValueProjector @@ -511,17 +514,40 @@ def fit(self, X: np.ndarray, Y: np.ndarray = None) -> openTSNE.TSNEEmbedding: return embedding - def convert_embedding_to_model(self, data, embedding): + def convert_embedding_to_model(self, data: Union[Table, DistMatrix], embedding: np.ndarray): # The results should be accessible in an Orange table, which doesn't # need the full embedding attributes and is cast into a regular array n = self.n_components + + if self.metric == "precomputed": + if not isinstance(data, DistMatrix): + raise ValueError( + f"Expected `data` to be instance of " + f"{DistMatrix.__class__.__name__} when using " + f"`metric='precomputed'. Got {data.__class__.__name__} " + f"instead!" + ) + # The distance matrix need not come attached with the original data + if data.row_items is not None: + data = data.row_items + else: + data = Table.from_domain(Domain([])) + + # Determine variable names postfixes = ["x", "y"] if n == 2 else list(range(1, n + 1)) + tsne_colnames = [f"t-SNE-{p}" for p in postfixes] names = [var.name for var in chain(data.domain.class_vars, data.domain.metas) if var] - proposed = [(f"t-SNE-{p}") for p in postfixes] - uniq_names = get_unique_names(names, proposed) - tsne_cols = [ContinuousVariable(name) for name in uniq_names] - embedding_domain = Domain(tsne_cols, data.domain.class_vars, data.domain.metas) - embedding_table = Table(embedding_domain, embedding.view(np.ndarray), data.Y, data.metas) + tsne_colnames = get_unique_names(names, tsne_colnames) + tsne_cols = [ContinuousVariable(name) for name in tsne_colnames] + + # Distance matrices need not come attached with the original data + if len(data.domain) == 0: + embedding_domain = Domain(tsne_cols) + embedding_table = Table(embedding_domain, embedding.view(np.ndarray)) + + else: # data table was available + embedding_domain = Domain(tsne_cols, data.domain.class_vars, data.domain.metas) + embedding_table = Table(embedding_domain, embedding.view(np.ndarray), data.Y, data.metas) # Create a model object which will be capable of transforming new data # into the existing embedding diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 3265bcdefe3..fdb3fe567bc 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -1,14 +1,14 @@ +import numpy as np import warnings +from AnyQt.QtCore import Qt +from AnyQt.QtWidgets import QFormLayout from functools import partial from types import SimpleNamespace as namespace from typing import Optional # pylint: disable=unused-import -import numpy as np - -from AnyQt.QtCore import Qt -from AnyQt.QtWidgets import QFormLayout - from Orange.data import Table, Domain +from Orange.data.util import array_equal +from Orange.misc import DistMatrix from Orange.preprocess import preprocess from Orange.projection import PCA from Orange.projection import manifold @@ -19,6 +19,7 @@ from Orange.widgets.visualize.owscatterplotgraph import OWScatterPlotBase from Orange.widgets.visualize.utils.widget import OWDataProjectionWidget from Orange.widgets.widget import Msg +from orangewidget.utils.signals import Input _STEP_SIZE = 25 _MAX_PCA_COMPONENTS = 50 @@ -31,6 +32,7 @@ class Task(namespace): """Completely determines the t-SNE task spec and intermediate results.""" data = None # type: Optional[Table] + distance_matrix = None # type: Optional[DistMatrix] normalize = None # type: Optional[bool] normalized_data = None # type: Optional[Table] @@ -65,6 +67,37 @@ def __init__(self, *args, **kwargs): if self.effective_data is None and self.data is not None: self.effective_data = self.data + class ValidationError(ValueError): + pass + + def validate(self) -> "Task": + def error(msg): + raise Task.ValidationError(msg) + + if self.data is None and self.distance_matrix is None: + error("Both `distance_matrix` and `data` cannot be `None`") + + if self.distance_matrix is not None: + if self.distance_metric != "precomputed": + error( + "`distance_metric` must be set to `precomputed` when using " + "a distance matrix" + ) + if self.initialization_method != "spectral": + error( + "`initialization_method` must be set to `spectral` when " + "using a distance matrix" + ) + + if self.distance_matrix is None: + if self.distance_metric == "precomputed": + error( + "`distance_metric` cannot be set to `precomputed` when no " + "distance matrix is provided" + ) + + return self + def data_normalization(data): normalization = preprocess.Normalize() @@ -77,12 +110,11 @@ def pca_preprocessing(data, n_components): return model(data) -def prepare_tsne_obj(data, initialization_method, distance_metric, perplexity, - multiscale, exaggeration): - # type: (Table, float, bool, float) -> manifold.TSNE +def prepare_tsne_obj(n_samples: int, initialization_method: str, + distance_metric: str, perplexity: float, + multiscale: bool, exaggeration: float): """Automatically determine the best parameters for the given data set.""" # Compute perplexity settings for multiscale - n_samples = data.X.shape[0] if multiscale: perplexity = min((n_samples - 1) / 3, 50), min((n_samples - 1) / 3, 500) else: @@ -135,9 +167,17 @@ def compute_initialization(task: Task, state: TaskState, **_) -> None: @staticmethod def compute_affinities(task: Task, state: TaskState, **_) -> None: - # Compute affinities state.set_status("Finding nearest neighbors...") - task.affinities = task.tsne.compute_affinities(task.effective_data.X) + + if task.distance_metric == "precomputed": + assert task.distance_matrix is not None + x = task.distance_matrix + else: + assert task.data is not None + assert task.effective_data is not None + x = task.effective_data.X + + task.affinities = task.tsne.compute_affinities(x) state.set_partial_result(("affinities", task)) @staticmethod @@ -153,8 +193,15 @@ def compute_tsne(task: Task, state: TaskState, progress_callback=None) -> None: task.tsne_embedding = tsne.prepare_embedding( task.affinities, task.initialization ) + + if task.distance_metric == "precomputed": + x = task.distance_matrix + else: + assert task.effective_data is not None + x = task.effective_data + task.tsne_embedding = tsne.convert_embedding_to_model( - task.effective_data, task.tsne_embedding + x, task.tsne_embedding ) state.set_partial_result(("tsne_embedding", task)) @@ -198,13 +245,21 @@ def run_optimization(tsne_params: dict, iterations_needed: int) -> bool: @classmethod def run(cls, task: Task, state: TaskState) -> Task: + task.validate() + # Assign weights to each job indicating how much time will be spent on each weights = {"normalization": 1, "pca": 1, "init": 1, "aff": 25, "tsne": 50} total_weight = sum(weights.values()) # Prepare the tsne object and add it to the spec + if task.distance_matrix is not None: + n_samples = task.distance_matrix.shape[0] + else: + assert task.data is not None + n_samples = task.data.X.shape[0] + task.tsne = prepare_tsne_obj( - task.data, + n_samples, task.initialization_method, task.distance_metric, task.perplexity, @@ -214,13 +269,14 @@ def run(cls, task: Task, state: TaskState) -> Task: job_queue = [] # Add the tasks that still need to be run to the job queue - task.effective_data = task.data + if task.distance_metric != "precomputed": + task.effective_data = task.data - if task.normalize and task.normalized_data is None: - job_queue.append((cls.compute_normalization, weights["normalization"])) + if task.normalize and task.normalized_data is None: + job_queue.append((cls.compute_normalization, weights["normalization"])) - if task.use_pca_preprocessing and task.pca_projection is None: - job_queue.append((cls.compute_pca, weights["pca"])) + if task.use_pca_preprocessing and task.pca_projection is None: + job_queue.append((cls.compute_pca, weights["pca"])) if task.affinities is None: job_queue.append((cls.compute_affinities, weights["aff"])) @@ -326,6 +382,9 @@ class OWtSNE(OWDataProjectionWidget, ConcurrentWidgetMixin): # over which parts of the embedding to invalidate _invalidated = invalidated() + class Inputs(OWDataProjectionWidget.Inputs): + distances = Input("Distances", DistMatrix) + class Information(OWDataProjectionWidget.Information): modified = Msg("The parameter settings have been changed. Press " "\"Start\" to rerun with the new settings.") @@ -342,9 +401,22 @@ class Error(OWDataProjectionWidget.Error): constant_data = Msg("Input data is constant") no_valid_data = Msg("No projection due to no valid data") + distance_matrix_not_symmetric = Msg("Distance matrix is not symmetric") + distance_matrix_too_small = Msg("Input matrix must be at least 2x2") + + dimension_mismatch = Msg("Data and distance dimensions do not match") + def __init__(self): OWDataProjectionWidget.__init__(self) ConcurrentWidgetMixin.__init__(self) + + # Distance matrix from `Distances` signal + self.distance_matrix = None # type: Optional[DistMatrix] + # Data table from the `self.matrix.row_items` (if present) + self.distance_matrix_data = None # type: Optional[Table] + # Data table from `Data` signal + self.signal_data = None # type: Optional[Table] + # Intermediate results self.normalized_data = None # type: Optional[Table] self.pca_projection = None # type: Optional[Table] @@ -374,7 +446,7 @@ def _add_controls_start_box(self): self.pca_component_slider = gui.hSlider( preprocessing_box, self, "pca_components", label="PCA Components:", minValue=2, maxValue=_MAX_PCA_COMPONENTS, step=1, - callback=self._invalidate_pca_projection, + callback=self._pca_slider_changed, ) box = gui.vBox(self.controlArea, box="Parameters") @@ -424,17 +496,36 @@ def _add_controls_start_box(self): # GUI control callbacks def _normalize_data_changed(self): - self._invalidate_normalized_data() + # We only care about the normalization checkbox if there is no distance + # matrix provided. This is not user-settable anyway, but is triggered + # when we programmatically enable/disable the checkbox in + # `enable_controls` + if self.distance_matrix is None: + self._invalidate_normalized_data() def _pca_preprocessing_changed(self): - self.controls.pca_components.setEnabled(self.use_pca_preprocessing) - self._invalidate_pca_projection() - - should_warn_pca = False - if self.data is not None and not self.use_pca_preprocessing: - if len(self.data.domain.attributes) >= _MAX_PCA_COMPONENTS: - should_warn_pca = True - self.Warning.consider_using_pca_preprocessing(shown=should_warn_pca) + # We only care about the PCA checkbox if there is no distance + # matrix provided. This is not user-settable anyway, but is triggered + # when we programmatically enable/disable the checkbox in + # `enable_controls` + if self.distance_matrix is None: + self.controls.pca_components.setEnabled(self.use_pca_preprocessing) + + self._invalidate_pca_projection() + + should_warn_pca = False + if self.data is not None and not self.use_pca_preprocessing: + if len(self.data.domain.attributes) >= _MAX_PCA_COMPONENTS: + should_warn_pca = True + self.Warning.consider_using_pca_preprocessing(shown=should_warn_pca) + + def _pca_slider_changed(self): + # We only care about the PCA slider if there is no distance + # matrix provided. This is not user-settable anyway, but is triggered + # when we programmatically enable/disable the checkbox in + # `enable_controls` + if self.distance_matrix is None: + self._invalidate_pca_projection() def _multiscale_changed(self): self.controls.perplexity.setDisabled(self.multiscale) @@ -474,21 +565,30 @@ def _set_modified(self, state): self.Information.modified(shown=state) def check_data(self): + self.Error.dimension_mismatch.clear() + self.Error.not_enough_rows.clear() + self.Error.not_enough_cols.clear() + self.Error.no_valid_data.clear() + self.Error.constant_data.clear() + + if self.data is None: + return + def error(err): err() self.data = None - # `super().check_data()` clears all messages, so we have to remember if - # it was shown - # pylint: disable=assignment-from-no-return - should_show_modified_message = self.Information.modified.is_shown() - super().check_data() + if ( + self.data is not None and self.distance_matrix is not None and + len(self.data) != len(self.distance_matrix) + ): + error(self.Error.dimension_mismatch) - if self.data is None: + # The errors below are relevant only if the distance matrix is not + # provided + if self.distance_matrix is not None: return - self.Information.modified(shown=should_show_modified_message) - if len(self.data) < 2: error(self.Error.not_enough_rows) @@ -500,12 +600,28 @@ def error(err): error(self.Error.no_valid_data) else: with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", "Degrees of freedom .*", RuntimeWarning) - if np.nan_to_num(np.nanstd(self.data.X, axis=0)).sum() \ - == 0: + warnings.filterwarnings("ignore", "Degrees of freedom .*", RuntimeWarning) + if np.nan_to_num(np.nanstd(self.data.X, axis=0)).sum() == 0: error(self.Error.constant_data) + def check_distance_matrix(self): + self.Error.distance_matrix_not_symmetric.clear() + self.Error.distance_matrix_too_small.clear() + + if self.distance_matrix is None: + return + + def error(err): + err() + self.distance_matrix = self.distance_matrix_data = None + + # Check for matrix validity + if self.distance_matrix is not None: + if not self.distance_matrix.is_symmetric(): + error(self.Error.distance_matrix_not_symmetric) + elif len(self.distance_matrix) < 2: + error(self.Error.distance_matrix_too_small) + def get_embedding(self): if self.tsne_embedding is None: self.valid_data = None @@ -529,7 +645,89 @@ def _toggle_run(self): else: self.run() + @Inputs.data + def set_data(self, data): + self.signal_data = data + # Data checking will be performed in `handleNewSignals` since the data + # can also be set from the `distance_matrix.row_items` if no additional + # data is provided + + @Inputs.distances + def set_distances(self, matrix: DistMatrix): + had_distance_matrix = self.distance_matrix is not None + prev_distance_matrix = self.distance_matrix + + self.distance_matrix = matrix + self.distance_matrix_data = matrix.row_items if matrix is not None else None + self.check_distance_matrix() + + # If there was no distance matrix before, but there is data now, invalidate + if self.distance_matrix is not None and not had_distance_matrix: + self._invalidated = True + + # If the new distance matrix is invalid or None, invalidate + elif self.distance_matrix is None and had_distance_matrix: + self._invalidated = True + + # If the distance matrix has changed, invalidate + elif ( + had_distance_matrix and self.distance_matrix is not None and + not array_equal(prev_distance_matrix, self.distance_matrix) + ): + self._invalidated = True + def handleNewSignals(self): + had_data = self.data is not None + prev_data = self.effective_data if had_data else None + + self.cancel() # clear any running jobs + self.data = None + self.closeContext() + + if self.signal_data is not None: + self.data = self.signal_data + elif self.distance_matrix_data is not None: + self.data = self.distance_matrix_data + + self.check_data() + + # If we have any errors, there's something wrong with the inputs or + # their combination, so we clear the graph and the outputs + if len(self.Error.active) > 0: + self.clear() + self._invalidated = True + # Set data to None so that the output signal will be cleared + self.data = None + self.init_attr_values() + self.commit.now() + return + + # We only invalidate based on data if there is no distance matrix, as + # otherwise, the embedding will remain in-tact + if self.distance_matrix is None: + # If there was no data before, but there is data now, invalidate + if self.data is not None and not had_data: + self._invalidated = True + + # If the new data is invalid or None, invalidate + elif self.data is None and had_data: + self._invalidated = True + + # If the data table has changed, invalidate + elif ( + had_data and self.data is not None and + not array_equal(prev_data.X, self.effective_data.X) + ): + self._invalidated = True + + self.init_attr_values() + self.openContext(self.data) + self.enable_controls() + + if self._invalidated: + self.clear() + self.input_changed.emit(self.data) + # We don't bother with the granular invalidation flags because # `super().handleNewSignals` will just set all of them to False or will # do nothing. However, it's important we remember its state because we @@ -551,7 +749,7 @@ def init_attr_values(self): else: max_components = _MAX_PCA_COMPONENTS - # We set this to the default number of components here so it resets + # We set this to the default number of components here, so it resets # properly, any previous settings will be restored from context # settings a little later self.controls.pca_components.setMaximum(max_components) @@ -562,17 +760,39 @@ def init_attr_values(self): def enable_controls(self): super().enable_controls() - if self.data is not None: + if self.signal_data is None and self.distance_matrix is not None: + self.normalize = False + self.normalize_cbx.setDisabled(True) + + self.use_pca_preprocessing = False + self.pca_preprocessing_cbx.setDisabled(True) + self.pca_component_slider.setDisabled(True) + + # Only spectral init is valid with a precomputed distance matrix + spectral_init_idx = self.initialization_combo.findText("Spectral") + self.initialization_method_idx = spectral_init_idx + self.initialization_combo.setCurrentIndex(spectral_init_idx) + self.initialization_combo.setDisabled(True) + + self.distance_metric_combo.setDisabled(True) + self.distance_metric_combo.setCurrentIndex(-1) + + elif self.data is not None: # PCA doesn't support normalization on sparse data, as this would # require centering and normalizing the matrix - self.normalize_cbx.setDisabled(self.data.is_sparse()) if self.data.is_sparse(): self.normalize = False + self.normalize_cbx.setDisabled(True) self.normalize_cbx.setToolTip( "Data normalization is not supported on sparse matrices." ) - else: - self.normalize_cbx.setToolTip("") + else: + self.normalize_cbx.setDisabled(False) + self.normalize_cbx.setToolTip("") + self.pca_preprocessing_cbx.setDisabled(False) + self.pca_component_slider.setDisabled(False) + self.initialization_combo.setDisabled(False) + self.distance_metric_combo.setDisabled(False) # Disable the perplexity spin box if multiscale is turned on self.controls.perplexity.setDisabled(self.multiscale) @@ -602,14 +822,18 @@ def run(self): # Cancel current running task self.cancel() - if self.data is None: + if self.data is None and self.distance_matrix is None: return initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] distance_metric = DISTANCE_METRICS[self.distance_metric_idx][1] + if self.distance_matrix is not None: + distance_metric = "precomputed" + task = Task( data=self.data, + distance_matrix=self.distance_matrix, # Normalization normalize=self.normalize, normalized_data=self.normalized_data, @@ -632,14 +856,14 @@ def run(self): return self.start(TSNERunner.run, task) def __ensure_task_same_for_normalization(self, task: Task): - assert self.data is not None + assert task.data is self.data assert task.normalize == self.normalize if task.normalize: assert isinstance(task.normalized_data, Table) and \ len(task.normalized_data) == len(self.data) def __ensure_task_same_for_pca(self, task: Task): - assert self.data is not None + assert task.data is self.data assert task.use_pca_preprocessing == self.use_pca_preprocessing if task.use_pca_preprocessing: assert task.pca_components == self.pca_components @@ -649,19 +873,31 @@ def __ensure_task_same_for_pca(self, task: Task): def __ensure_task_same_for_initialization(self, task: Task): initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] assert task.initialization_method == initialization_method + if self.distance_matrix is not None: + n_samples = self.distance_matrix.shape[0] + else: + assert self.data is not None + n_samples = self.data.X.shape[0] assert isinstance(task.initialization, np.ndarray) and \ - len(task.initialization) == len(self.data) + len(task.initialization) == n_samples def __ensure_task_same_for_affinities(self, task: Task): assert task.perplexity == self.perplexity assert task.multiscale == self.multiscale distance_metric = DISTANCE_METRICS[self.distance_metric_idx][1] - assert task.distance_metric == distance_metric + # Precomputed distances will never match the combo box value + if task.distance_metric != "precomputed": + assert task.distance_metric == distance_metric def __ensure_task_same_for_embedding(self, task: Task): assert task.exaggeration == self.exaggeration + if self.distance_matrix is not None: + n_samples = self.distance_matrix.shape[0] + else: + assert self.data is not None + n_samples = self.data.X.shape[0] assert isinstance(task.tsne_embedding, manifold.TSNEModel) and \ - len(task.tsne_embedding.embedding) == len(self.data) + len(task.tsne_embedding.embedding) == n_samples def on_partial_result(self, value): # type: (Tuple[str, Task]) -> None @@ -694,7 +930,7 @@ def on_partial_result(self, value): prev_embedding, self.tsne_embedding = self.tsne_embedding, task.tsne_embedding self.iterations_done = task.iterations_done # If this is the first partial result we've gotten, we've got to - # setup the plot + # set up the plot if prev_embedding is None: self.setup_plot() # Otherwise, just update the point positions @@ -749,10 +985,13 @@ def _get_projection_data(self): ) return data + def cancel(self): + self.run_button.setText("Start") + super().cancel() + def clear(self): """Clear widget state. Note that this doesn't clear the data.""" super().clear() - self.run_button.setText("Start") self.cancel() self.normalized_data = None self.pca_projection = None @@ -788,7 +1027,10 @@ def migrate_context(cls, context, version): if __name__ == "__main__": import sys data = Table(sys.argv[1] if len(sys.argv) > 1 else "iris") + from Orange.distance import Euclidean + dist_matrix = Euclidean(data, normalize=True) WidgetPreview(OWtSNE).run( - set_data=data, + # set_data=data, + set_distances=dist_matrix, set_subset_data=data[np.random.choice(len(data), 10)], ) diff --git a/Orange/widgets/unsupervised/tests/test_owtsne.py b/Orange/widgets/unsupervised/tests/test_owtsne.py index 362c86dc4e4..a4a666b097a 100644 --- a/Orange/widgets/unsupervised/tests/test_owtsne.py +++ b/Orange/widgets/unsupervised/tests/test_owtsne.py @@ -1,9 +1,13 @@ +import os + import unittest from unittest.mock import patch, Mock, call import numpy as np from Orange.data import DiscreteVariable, ContinuousVariable, Domain, Table +from Orange.distance import Euclidean +from Orange.misc import DistMatrix from Orange.preprocess import Normalize from Orange.projection import manifold, TSNE from Orange.projection.manifold import TSNEModel @@ -36,6 +40,19 @@ def setUpClass(cls): cls.signal_name = OWtSNE.Inputs.data cls.signal_data = cls.data + cls.iris = Table("iris") + cls.iris_distances = Euclidean(cls.iris) + cls.housing = Table("housing")[:200] + cls.housing_distances = Euclidean(cls.housing) + + # Load distance-only DistMatrix, without accompanying `.row_items` + my_dir = os.path.dirname(__file__) + datasets_dir = os.path.join(my_dir, '..', '..', '..', 'datasets') + cls.datasets_dir = os.path.realpath(datasets_dir) + cls.towns = DistMatrix.from_file( + os.path.join(cls.datasets_dir, "slovenian-towns.dst") + ) + def setUp(self): # For almost all the tests, we won't need to verify t-SNE validity and # the tests will run much faster if we dummy them out @@ -345,7 +362,7 @@ def test_invalidation_flow(self): # We should still be able to send a data subset to the input and have # the points be highlighted self.send_signal(w.Inputs.data_subset, self.data[:10]) - self.wait_until_stop_blocking() + self.wait_until_finished() subset = [brush.color().name() == "#46befa" for brush in w.graph.scatterplot_item.data["brush"][:10]] other = [brush.color().name() == "#000000" for brush in @@ -357,8 +374,8 @@ def test_invalidation_flow(self): self.send_signal(w.Inputs.data_subset, None) # Run the optimization - self.widget.run_button.clicked.emit() - self.wait_until_stop_blocking() + self.widget.run_button.click() + self.wait_until_finished() # All of the inavalidation flags should have been cleared self.assertFalse(w._invalidated) @@ -427,18 +444,264 @@ def test_pca_preprocessing_warning_with_large_number_of_features(self): "The PCA warning should be cleared when problematic data is removed" ) + def test_distance_matrix_not_symmetric(self): + w = self.widget + self.assertFalse(w.Error.distance_matrix_not_symmetric.is_shown()) + + self.send_signal(w.Inputs.distances, DistMatrix([[1, 2, 3], [4, 5, 6]])) + self.assertTrue(w.Error.distance_matrix_not_symmetric.is_shown()) + + self.send_signal(w.Inputs.distances, None) + self.assertFalse(w.Error.distance_matrix_not_symmetric.is_shown()) + + def test_matrix_too_small(self): + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, DistMatrix([[1]])) + self.assertTrue(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, None) + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + def test_mismatching_distances_and_data_size(self): + w = self.widget + self.assertFalse(w.Error.dimension_mismatch.is_shown()) + + # Send incompatible combination + self.send_signal(w.Inputs.distances, self.iris_distances) + self.send_signal(w.Inputs.data, self.housing) + self.wait_until_finished() + self.assertTrue(w.Error.dimension_mismatch.is_shown()) + + # Remove offending data + self.send_signal(w.Inputs.data, None) + self.wait_until_finished() + self.assertFalse(w.Error.dimension_mismatch.is_shown()) + + # Send incompatible combination + self.send_signal(w.Inputs.distances, None) + self.send_signal(w.Inputs.distances, self.iris_distances) + self.send_signal(w.Inputs.data, self.housing) + self.wait_until_finished() + + # Remove offending distance matrix + self.send_signal(w.Inputs.distances, None) + self.wait_until_finished() + self.assertFalse(w.Error.dimension_mismatch.is_shown()) + + # Clear any data + self.send_signal(w.Inputs.distances, None) + self.assertFalse(w.Error.dimension_mismatch.is_shown()) + + def test_invalid_distance_matrix_with_valid_data_signal_1(self): + """Provide valid data table and an invalid distance matrix at the + same time.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, DistMatrix([[1]])) + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + def test_invalid_distance_matrix_with_valid_data_signal_2(self): + """Provide an invalid distance matrix, wait to finish, then provide a + valid data table.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, DistMatrix([[1]])) + self.wait_until_finished() + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + def test_invalid_distance_matrix_with_valid_data_signal_3(self): + """Provide a valid data table, wait to finish, then provide an + invalid distance matrix.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + self.send_signal(w.Inputs.distances, DistMatrix([[1]])) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + def test_valid_distance_matrix_with_mismatching_data_signal_1(self): + """Provide valid distance matrix and a mismatching data table at the + same time.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.send_signal(w.Inputs.data, self.iris[:5]) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + def test_valid_distance_matrix_with_mismatching_data_signal_2(self): + """Provide valid distance matrix, wait to finish, then provide a + mismatching data table.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.wait_until_finished() + self.send_signal(w.Inputs.data, self.iris[:5]) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + def test_invalid_combination_followed_by_valid_combination(self): + """Provide valid distance matrix and a mismatching data table at the + same time.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.send_signal(w.Inputs.data, self.iris[:5]) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + self.assertIsNotNone(w.graph.scatterplot_item) + self.assertIsNotNone(self.get_output(w.Outputs.annotated_data)) + + def test_invalid_combination_followed_by_valid_distance_only(self): + """Provide valid distance matrix and a mismatching data table at the + same time.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.send_signal(w.Inputs.data, self.iris[:5]) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + self.send_signal(w.Inputs.data, None) + self.wait_until_finished() + self.assertIsNotNone(w.graph.scatterplot_item) + self.assertIsNotNone(self.get_output(w.Outputs.annotated_data)) + + def test_invalid_combination_followed_by_valid_data_only(self): + """Provide valid distance matrix and a mismatching data table at the + same time.""" + w = self.widget + self.assertFalse(w.Error.distance_matrix_too_small.is_shown()) + + self.send_signal(w.Inputs.distances, DistMatrix([[1]])) + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + + self.assertIsNone(w.graph.scatterplot_item) + self.assertIsNone(self.get_output(w.Outputs.annotated_data)) + + self.send_signal(w.Inputs.distances, None) + self.wait_until_finished() + self.assertIsNotNone(w.graph.scatterplot_item) + self.assertIsNotNone(self.get_output(w.Outputs.annotated_data)) + + def test_adding_data_table_to_distance_matrix_doesnt_trigger_rerun(self): + """If the embedding is already constructed, and we just update the data + signal, then we don't need to recompute the embedding.""" + w = self.widget + + with patch("Orange.widgets.unsupervised.owtsne.TSNERunner.run", wraps=TSNERunner.run) as runner: + self.send_signal(w.Inputs.distances, Euclidean(self.housing[:150])) + self.wait_until_finished() + + housing_colors = [ + brush.color().name() for brush in + w.graph.scatterplot_item.data["brush"] + ] + + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + iris_colors = [ + brush.color().name() for brush in + w.graph.scatterplot_item.data["brush"] + ] + + # Ensure the colors have changed + self.assertTrue(all(c1 != c2 for c1, c2 in zip(housing_colors, iris_colors))) + # And that the embedding has not been recomputed + runner.assert_called_once() + + def test_data_change_doesnt_crash(self): + w = self.widget + + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + + self.send_signal(w.Inputs.data, self.housing) + self.wait_until_finished() + + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + + def test_distance_change_doesnt_crash(self): + w = self.widget + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.wait_until_finished() + + self.send_signal(w.Inputs.distances, self.housing_distances) + self.wait_until_finished() + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.wait_until_finished() + + def test_distances_without_data_axis_0(self): + w = self.widget + signal_data = Euclidean(self.data, axis=0) + signal_data.row_items = None + self.send_signal(w.Inputs.distances, signal_data) + + def test_distances_without_data_axis_1(self): + signal_data = Euclidean(self.data, axis=1) + signal_data.row_items = None + self.send_signal("Distances", signal_data) + + def test_data_table_with_no_attributes_with_distance_matrix_works(self): + w = self.widget + self.send_signal(w.Inputs.distances, self.towns) + self.wait_until_finished() + # No errors should be shown + self.assertEqual(len(w.Error.active), 0) + class TestTSNERunner(unittest.TestCase): @classmethod def setUpClass(cls): cls.data = Table("iris") + cls.distances = Euclidean(cls.data) def test_run_with_normalization_and_pca_preprocessing(self): state = Mock() state.is_interruption_requested = Mock(return_value=False) task = Task( - normalize=True, use_pca_preprocessing=True, data=self.data, perplexity=30 + normalize=True, + use_pca_preprocessing=True, + data=self.data, + perplexity=30, + initialization_method="pca", + distance_metric="l2", ) task = TSNERunner.run(task, state) @@ -446,8 +709,8 @@ def test_run_with_normalization_and_pca_preprocessing(self): state.set_status.assert_has_calls([ call("Normalizing data..."), call("Computing PCA..."), - call("Preparing initialization..."), call("Finding nearest neighbors..."), + call("Preparing initialization..."), call("Running optimization..."), ]) @@ -461,15 +724,20 @@ def test_run_with_normalization(self): state.is_interruption_requested = Mock(return_value=False) task = Task( - normalize=True, use_pca_preprocessing=False, data=self.data, perplexity=30 + normalize=True, + use_pca_preprocessing=False, + data=self.data, + initialization_method="pca", + distance_metric="l2", + perplexity=30, ) task = TSNERunner.run(task, state) self.assertEqual(len(state.set_status.mock_calls), 4) state.set_status.assert_has_calls([ call("Normalizing data..."), - call("Preparing initialization..."), call("Finding nearest neighbors..."), + call("Preparing initialization..."), call("Running optimization..."), ]) @@ -484,15 +752,20 @@ def test_run_with_pca_preprocessing(self): state.is_interruption_requested = Mock(return_value=False) task = Task( - normalize=False, use_pca_preprocessing=True, data=self.data, perplexity=30 + normalize=False, + use_pca_preprocessing=True, + data=self.data, + initialization_method="pca", + distance_metric="l2", + perplexity=30, ) task = TSNERunner.run(task, state) self.assertEqual(len(state.set_status.mock_calls), 4) state.set_status.assert_has_calls([ call("Computing PCA..."), - call("Preparing initialization..."), call("Finding nearest neighbors..."), + call("Preparing initialization..."), call("Running optimization..."), ]) state.set_status.assert_has_calls @@ -507,10 +780,22 @@ def test_run_do_not_modify_model_inplace(self): state = Mock() state.is_interruption_requested.return_value = True - task = Task(data=self.data, perplexity=30, multiscale=False, exaggeration=1) + task = Task( + data=self.data, + initialization_method="pca", + distance_metric="l2", + perplexity=30, + multiscale=False, + exaggeration=1, + ) # Run through all the steps to prepare the t-SNE object task.tsne = prepare_tsne_obj( - task.data, task.perplexity, task.multiscale, task.exaggeration + task.data.X.shape[0], + task.initialization_method, + task.distance_metric, + task.perplexity, + task.multiscale, + task.exaggeration, ) TSNERunner.compute_normalization(task, state) TSNERunner.compute_pca(task, state) @@ -528,6 +813,128 @@ def test_run_do_not_modify_model_inplace(self): state.set_partial_result.assert_called_once() self.assertIsNot(tsne_obj_before, tsne_obj_after) + def test_run_with_distance_matrix(self): + state = Mock() + state.is_interruption_requested = Mock(return_value=False) + + task = Task( + normalize=False, + use_pca_preprocessing=False, + # data=self.data, + distance_matrix=self.distances, + perplexity=30, + initialization_method="spectral", + distance_metric="precomputed", + ) + task = TSNERunner.run(task, state) + + self.assertEqual(len(state.set_status.mock_calls), 3) + state.set_status.assert_has_calls([ + call("Finding nearest neighbors..."), + call("Preparing initialization..."), + call("Running optimization..."), + ]) + + self.assertIsNone(task.normalized_data) + self.assertIsNone(task.pca_projection) + self.assertIsInstance(task.initialization, np.ndarray) + self.assertIsInstance(task.tsne, TSNE) + self.assertIsInstance(task.tsne_embedding, TSNEModel) + + def test_task_validation(self): + # distance matrix with no data table + Task( + normalize=True, + use_pca_preprocessing=True, + distance_matrix=self.distances, + perplexity=30, + initialization_method="spectral", + distance_metric="precomputed", + ).validate() + + # both distance matrix and data table are provided + Task( + normalize=True, + use_pca_preprocessing=True, + data=self.data, + distance_matrix=self.distances, + perplexity=30, + initialization_method="spectral", + distance_metric="precomputed", + ).validate() + + # data table with no distance matrix + Task( + normalize=True, + use_pca_preprocessing=True, + data=self.data, + perplexity=30, + initialization_method="pca", + distance_metric="cosine", + ).validate() + + # distance_metric="precomputed" with no distance matrix + with self.assertRaises(Task.ValidationError): + Task( + normalize=True, + use_pca_preprocessing=True, + data=self.data, + perplexity=30, + initialization_method="spectral", + distance_metric="precomputed", + ).validate() + + # initialization_method="pca" with distance matrix + with self.assertRaises(Task.ValidationError): + Task( + normalize=True, + use_pca_preprocessing=True, + data=self.data, + distance_matrix=self.distances, + perplexity=30, + initialization_method="pca", + distance_metric="precomputed", + ).validate() + + # distance_metric="l2" with distance matrix + with self.assertRaises(Task.ValidationError): + Task( + normalize=True, + use_pca_preprocessing=True, + data=self.data, + distance_matrix=self.distances, + perplexity=30, + initialization_method="spectral", + distance_metric="l2", + ).validate() + + def test_run_with_distance_matrix_ignores_preprocessing(self): + state = Mock() + state.is_interruption_requested = Mock(return_value=False) + + task = Task( + normalize=True, + use_pca_preprocessing=True, + distance_matrix=self.distances, + perplexity=30, + initialization_method="spectral", + distance_metric="precomputed", + ) + task = TSNERunner.run(task, state) + + self.assertEqual(len(state.set_status.mock_calls), 3) + state.set_status.assert_has_calls([ + call("Finding nearest neighbors..."), + call("Preparing initialization..."), + call("Running optimization..."), + ]) + + self.assertIsNone(task.normalized_data) + self.assertIsNone(task.pca_projection) + self.assertIsInstance(task.initialization, np.ndarray) + self.assertIsInstance(task.tsne, TSNE) + self.assertIsInstance(task.tsne_embedding, TSNEModel) + if __name__ == "__main__": unittest.main() From 64294a9061c6d5eba576c0d42ecde182b508d58a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Thu, 23 Feb 2023 13:33:37 +0100 Subject: [PATCH 10/20] owtsne: normalize and pca preprocessing enabled by default --- Orange/widgets/unsupervised/owtsne.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index fdb3fe567bc..e51d25dc736 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -756,6 +756,8 @@ def init_attr_values(self): self.controls.pca_components.setValue(_DEFAULT_PCA_COMPONENTS) self.exaggeration = 1 + self.normalize = True + self.use_pca_preprocessing = True def enable_controls(self): super().enable_controls() From c82f9bd1221a003a1a77b22575edcb92150d079d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Thu, 23 Feb 2023 13:34:02 +0100 Subject: [PATCH 11/20] owtsne: remove _get_projection_data; updated in parent --- Orange/widgets/unsupervised/owtsne.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index e51d25dc736..e2bfe313604 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -966,27 +966,6 @@ def on_done(self, task): self.commit.deferred() - def _get_projection_data(self): - if self.data is None: - return None - - data = self.data.transform( - Domain( - self.data.domain.attributes, - self.data.domain.class_vars, - self.data.domain.metas + self._get_projection_variables() - ) - ) - with data.unlocked(data.metas): - data.metas[:, -2:] = self.get_embedding() - if self.tsne_embedding is not None: - data.domain = Domain( - self.data.domain.attributes, - self.data.domain.class_vars, - self.data.domain.metas + self.tsne_embedding.domain.attributes, - ) - return data - def cancel(self): self.run_button.setText("Start") super().cancel() From a9fca3fd48bf4a0096ac0e2a254f518b0b0f0867 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Thu, 23 Feb 2023 13:55:16 +0100 Subject: [PATCH 12/20] owtsne: Disable preprocessing controls whenever distance matrix provided --- Orange/widgets/unsupervised/owtsne.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index e2bfe313604..1d0ab5fee38 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -762,7 +762,7 @@ def init_attr_values(self): def enable_controls(self): super().enable_controls() - if self.signal_data is None and self.distance_matrix is not None: + if self.distance_matrix is not None: self.normalize = False self.normalize_cbx.setDisabled(True) From 1e7bf330aba2402a00773c7f5254ddef7df4eb24 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Thu, 23 Feb 2023 16:00:12 +0100 Subject: [PATCH 13/20] owtsne: Fix DummyTSNE in tests --- .../widgets/unsupervised/tests/test_owtsne.py | 25 +++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/Orange/widgets/unsupervised/tests/test_owtsne.py b/Orange/widgets/unsupervised/tests/test_owtsne.py index a4a666b097a..867e6f435d0 100644 --- a/Orange/widgets/unsupervised/tests/test_owtsne.py +++ b/Orange/widgets/unsupervised/tests/test_owtsne.py @@ -4,7 +4,9 @@ from unittest.mock import patch, Mock, call import numpy as np +import scipy.sparse as sp +import openTSNE.affinity from Orange.data import DiscreteVariable, ContinuousVariable, Domain, Table from Orange.distance import Euclidean from Orange.misc import DistMatrix @@ -18,13 +20,32 @@ class DummyTSNE(manifold.TSNE): + def compute_affinities(self, X): + + class DummyAffinities(openTSNE.affinity.Affinities): + def __init__(self, data=None, *args, **kwargs): + n_samples = data.shape[0] + self.P = sp.random(n_samples, n_samples, density=0.1) + self.P /= self.P.sum() + + def to_new(self, data, return_distances=False): + ones = np.ones((len(data), 2), float) + if return_distances: + return ones, ones + return ones + + return DummyAffinities(X) + + def compute_initialization(self, X): + return np.ones((X.shape[0], 2), float) + def fit(self, X, Y=None): - return np.ones((len(X), 2), float) + return np.ones((X.shape[0], 2), float) class DummyTSNEModel(manifold.TSNEModel): def transform(self, X, **kwargs): - return np.ones((len(X), 2), float) + return np.ones((X.shape[0], 2), float) def optimize(self, n_iter, **kwargs): return self From c38af384422298a07d772eb7bac4d150b592f9ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Thu, 23 Feb 2023 18:18:33 +0100 Subject: [PATCH 14/20] owtsne: Fixup enabled/disabled controls with distance matrix --- Orange/widgets/unsupervised/owtsne.py | 80 ++++++++++++------- .../widgets/unsupervised/tests/test_owtsne.py | 53 ++++++++++++ 2 files changed, 105 insertions(+), 28 deletions(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 1d0ab5fee38..40053673803 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -434,22 +434,22 @@ def _add_controls(self): super()._add_controls() def _add_controls_start_box(self): - preprocessing_box = gui.vBox(self.controlArea, box="Preprocessing") + self.preprocessing_box = gui.vBox(self.controlArea, box="Preprocessing") self.normalize_cbx = gui.checkBox( - preprocessing_box, self, "normalize", "Normalize data", + self.preprocessing_box, self, "normalize", "Normalize data", callback=self._invalidate_normalized_data, ) self.pca_preprocessing_cbx = gui.checkBox( - preprocessing_box, self, "use_pca_preprocessing", "Apply PCA preprocessing", + self.preprocessing_box, self, "use_pca_preprocessing", "Apply PCA preprocessing", callback=self._pca_preprocessing_changed, ) self.pca_component_slider = gui.hSlider( - preprocessing_box, self, "pca_components", label="PCA Components:", + self.preprocessing_box, self, "pca_components", label="PCA Components:", minValue=2, maxValue=_MAX_PCA_COMPONENTS, step=1, callback=self._pca_slider_changed, ) - box = gui.vBox(self.controlArea, box="Parameters") + self.parameter_box = gui.vBox(self.controlArea, box="Parameters") form = QFormLayout( labelAlignment=Qt.AlignLeft, formAlignment=Qt.AlignLeft, @@ -471,14 +471,14 @@ def _add_controls_start_box(self): form.addRow("Distance metric:", self.distance_metric_combo) self.perplexity_spin = gui.spin( - box, self, "perplexity", 1, 500, step=1, alignment=Qt.AlignRight, - callback=self._invalidate_affinities, addToLayout=False + self.controlArea, self, "perplexity", 1, 500, step=1, + alignment=Qt.AlignRight, addToLayout=False, + callback=self._invalidate_affinities, ) - self.controls.perplexity.setDisabled(self.multiscale) form.addRow("Perplexity:", self.perplexity_spin) form.addRow(gui.checkBox( - box, self, "multiscale", label="Preserve global structure", + self.controlArea, self, "multiscale", label="Preserve global structure", callback=self._multiscale_changed, addToLayout=False )) @@ -490,9 +490,11 @@ def _add_controls_start_box(self): ) form.addRow("Exaggeration:", sbe) - box.layout().addLayout(form) + self.parameter_box.layout().addLayout(form) - self.run_button = gui.button(box, self, "Start", callback=self._toggle_run) + self.run_button = gui.button( + self.parameter_box, self, "Start", callback=self._toggle_run + ) # GUI control callbacks def _normalize_data_changed(self): @@ -509,7 +511,7 @@ def _pca_preprocessing_changed(self): # when we programmatically enable/disable the checkbox in # `enable_controls` if self.distance_matrix is None: - self.controls.pca_components.setEnabled(self.use_pca_preprocessing) + self.controls.pca_components.box.setEnabled(self.use_pca_preprocessing) self._invalidate_pca_projection() @@ -528,7 +530,11 @@ def _pca_slider_changed(self): self._invalidate_pca_projection() def _multiscale_changed(self): + form = self.parameter_box.layout().itemAt(0) + assert isinstance(form, QFormLayout) + form.labelForField(self.perplexity_spin).setDisabled(self.multiscale) self.controls.perplexity.setDisabled(self.multiscale) + self._invalidate_affinities() # Invalidation cascade @@ -746,8 +752,10 @@ def init_attr_values(self): if self.data is not None: n_attrs = len(self.data.domain.attributes) max_components = min(_MAX_PCA_COMPONENTS, n_attrs) + should_use_pca = len(self.data.domain.attributes) > 10 else: max_components = _MAX_PCA_COMPONENTS + should_use_pca = False # We set this to the default number of components here, so it resets # properly, any previous settings will be restored from context @@ -757,47 +765,63 @@ def init_attr_values(self): self.exaggeration = 1 self.normalize = True - self.use_pca_preprocessing = True + self.use_pca_preprocessing = should_use_pca + self.distance_metric_idx = 0 + self.initialization_method_idx = 0 def enable_controls(self): super().enable_controls() - if self.distance_matrix is not None: + has_distance_matrix = self.distance_matrix is not None + has_data = self.data is not None + + # When we disable controls in the form layout, we also want to ensure + # the labels are disabled, to be consistent with the preprocessing box + form = self.parameter_box.layout().itemAt(0) + assert isinstance(form, QFormLayout) + + if has_distance_matrix: self.normalize = False self.normalize_cbx.setDisabled(True) self.use_pca_preprocessing = False self.pca_preprocessing_cbx.setDisabled(True) - self.pca_component_slider.setDisabled(True) # Only spectral init is valid with a precomputed distance matrix spectral_init_idx = self.initialization_combo.findText("Spectral") self.initialization_method_idx = spectral_init_idx self.initialization_combo.setCurrentIndex(spectral_init_idx) self.initialization_combo.setDisabled(True) + form.labelForField(self.initialization_combo).setDisabled(True) self.distance_metric_combo.setDisabled(True) self.distance_metric_combo.setCurrentIndex(-1) - - elif self.data is not None: - # PCA doesn't support normalization on sparse data, as this would - # require centering and normalizing the matrix - if self.data.is_sparse(): - self.normalize = False - self.normalize_cbx.setDisabled(True) - self.normalize_cbx.setToolTip( - "Data normalization is not supported on sparse matrices." - ) + form.labelForField(self.distance_metric_combo).setDisabled(True) else: self.normalize_cbx.setDisabled(False) - self.normalize_cbx.setToolTip("") self.pca_preprocessing_cbx.setDisabled(False) - self.pca_component_slider.setDisabled(False) self.initialization_combo.setDisabled(False) self.distance_metric_combo.setDisabled(False) + form.labelForField(self.initialization_combo).setDisabled(False) + form.labelForField(self.distance_metric_combo).setDisabled(False) + + # PCA doesn't support normalization on sparse data, as this would + # require centering and normalizing the matrix + if not has_distance_matrix and has_data and self.data.is_sparse(): + self.normalize = False + self.normalize_cbx.setDisabled(True) + self.normalize_cbx.setToolTip( + "Data normalization is not supported on sparse matrices." + ) + else: + self.normalize_cbx.setToolTip("") + + # Disable slider parent, because we want to disable the labels too + self.pca_component_slider.parent().setEnabled(self.use_pca_preprocessing) # Disable the perplexity spin box if multiscale is turned on - self.controls.perplexity.setDisabled(self.multiscale) + self.perplexity_spin.setDisabled(self.multiscale) + form.labelForField(self.perplexity_spin).setDisabled(self.multiscale) def run(self): # Reset invalidated values as indicated by the flags diff --git a/Orange/widgets/unsupervised/tests/test_owtsne.py b/Orange/widgets/unsupervised/tests/test_owtsne.py index 867e6f435d0..dd0e9c74426 100644 --- a/Orange/widgets/unsupervised/tests/test_owtsne.py +++ b/Orange/widgets/unsupervised/tests/test_owtsne.py @@ -705,6 +705,59 @@ def test_data_table_with_no_attributes_with_distance_matrix_works(self): # No errors should be shown self.assertEqual(len(w.Error.active), 0) + def test_controls_are_properly_disabled_with_distance_matrix_1(self): + """Send both signals first, then disconnect distances.""" + w = self.widget + + disabled_fields = [ + "normalize", "use_pca_preprocessing", "pca_components", + "initialization_method_idx", "distance_metric_idx", + ] + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.send_signal(w.Inputs.data, self.iris) + self.wait_until_finished() + + for field in disabled_fields: + self.assertFalse(getattr(w.controls, field).isEnabled()) + + # Remove distance matrix, can use data, so fields should be enabled + self.send_signal(w.Inputs.distances, None) + # Ensure PCA checkbox is ticked, to enable slider + w.controls.use_pca_preprocessing.setChecked(True) + self.wait_until_finished() + + for field in disabled_fields: + self.assertTrue(getattr(w.controls, field).isEnabled()) + + def test_controls_are_properly_disabled_with_distance_matrix_2(self): + """Send distances first, disconnect distances, then send data.""" + w = self.widget + + disabled_fields = [ + "normalize", "use_pca_preprocessing", "pca_components", + "initialization_method_idx", "distance_metric_idx", + ] + + self.send_signal(w.Inputs.distances, self.iris_distances) + self.wait_until_finished() + + for field in disabled_fields: + self.assertFalse(getattr(w.controls, field).isEnabled()) + + # Remove distance matrix + self.send_signal(w.Inputs.distances, None) + self.wait_until_finished() + # Send data + self.send_signal(w.Inputs.data, self.iris) + # Ensure PCA checkbox is ticked, to enable slider + w.controls.use_pca_preprocessing.setChecked(True) + self.wait_until_finished() + + # Should now be enabled + for field in disabled_fields: + self.assertTrue(getattr(w.controls, field).isEnabled()) + class TestTSNERunner(unittest.TestCase): @classmethod From ef865df20ba1c4919b9bafced52c759bc83a849b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Thu, 23 Feb 2023 18:46:34 +0100 Subject: [PATCH 15/20] owtsne: Add tooltips to disabled controls when distance matrix --- Orange/widgets/unsupervised/owtsne.py | 37 ++++++++++++++++++++------- 1 file changed, 28 insertions(+), 9 deletions(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 40053673803..1bc23fff8f6 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -780,30 +780,51 @@ def enable_controls(self): form = self.parameter_box.layout().itemAt(0) assert isinstance(form, QFormLayout) + # Reset all tooltips and controls + self.normalize_cbx.setDisabled(False) + self.normalize_cbx.setToolTip("") + + self.pca_preprocessing_cbx.setDisabled(False) + self.pca_preprocessing_cbx.setToolTip("") + + self.initialization_combo.setDisabled(False) + self.initialization_combo.setToolTip("") + form.labelForField(self.initialization_combo).setDisabled(False) + + self.distance_metric_combo.setDisabled(False) + self.distance_metric_combo.setToolTip("") + form.labelForField(self.distance_metric_combo).setDisabled(False) + if has_distance_matrix: self.normalize = False self.normalize_cbx.setDisabled(True) + self.normalize_cbx.setToolTip( + "Precomputed distances provided. Preprocessing is unnecessary!" + ) self.use_pca_preprocessing = False self.pca_preprocessing_cbx.setDisabled(True) + self.pca_preprocessing_cbx.setToolTip( + "Precomputed distances provided. Preprocessing is unnecessary!" + ) # Only spectral init is valid with a precomputed distance matrix spectral_init_idx = self.initialization_combo.findText("Spectral") self.initialization_method_idx = spectral_init_idx self.initialization_combo.setCurrentIndex(spectral_init_idx) self.initialization_combo.setDisabled(True) + self.initialization_combo.setToolTip( + "Only spectral intialization is supported with precomputed " + "distance matrices." + ) form.labelForField(self.initialization_combo).setDisabled(True) self.distance_metric_combo.setDisabled(True) self.distance_metric_combo.setCurrentIndex(-1) + self.distance_metric_combo.setToolTip( + "Precomputed distances provided." + ) form.labelForField(self.distance_metric_combo).setDisabled(True) - else: - self.normalize_cbx.setDisabled(False) - self.pca_preprocessing_cbx.setDisabled(False) - self.initialization_combo.setDisabled(False) - self.distance_metric_combo.setDisabled(False) - form.labelForField(self.initialization_combo).setDisabled(False) - form.labelForField(self.distance_metric_combo).setDisabled(False) # PCA doesn't support normalization on sparse data, as this would # require centering and normalizing the matrix @@ -813,8 +834,6 @@ def enable_controls(self): self.normalize_cbx.setToolTip( "Data normalization is not supported on sparse matrices." ) - else: - self.normalize_cbx.setToolTip("") # Disable slider parent, because we want to disable the labels too self.pca_component_slider.parent().setEnabled(self.use_pca_preprocessing) From 416707ab4317e4cb94e6b98ba2834e3c465dc5f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pavlin=20Poli=C4=8Dar?= Date: Mon, 27 Feb 2023 19:06:48 +0100 Subject: [PATCH 16/20] owtsne: Ensure data table-only settings properly restored --- Orange/widgets/unsupervised/owtsne.py | 25 ++++----- .../widgets/unsupervised/tests/test_owtsne.py | 55 +++++++++++++++++++ 2 files changed, 66 insertions(+), 14 deletions(-) diff --git a/Orange/widgets/unsupervised/owtsne.py b/Orange/widgets/unsupervised/owtsne.py index 1bc23fff8f6..ef47eae52d6 100644 --- a/Orange/widgets/unsupervised/owtsne.py +++ b/Orange/widgets/unsupervised/owtsne.py @@ -437,11 +437,11 @@ def _add_controls_start_box(self): self.preprocessing_box = gui.vBox(self.controlArea, box="Preprocessing") self.normalize_cbx = gui.checkBox( self.preprocessing_box, self, "normalize", "Normalize data", - callback=self._invalidate_normalized_data, + callback=self._invalidate_normalized_data, stateWhenDisabled=False, ) self.pca_preprocessing_cbx = gui.checkBox( self.preprocessing_box, self, "use_pca_preprocessing", "Apply PCA preprocessing", - callback=self._pca_preprocessing_changed, + callback=self._pca_preprocessing_changed, stateWhenDisabled=False, ) self.pca_component_slider = gui.hSlider( self.preprocessing_box, self, "pca_components", label="PCA Components:", @@ -796,13 +796,11 @@ def enable_controls(self): form.labelForField(self.distance_metric_combo).setDisabled(False) if has_distance_matrix: - self.normalize = False self.normalize_cbx.setDisabled(True) self.normalize_cbx.setToolTip( "Precomputed distances provided. Preprocessing is unnecessary!" ) - self.use_pca_preprocessing = False self.pca_preprocessing_cbx.setDisabled(True) self.pca_preprocessing_cbx.setToolTip( "Precomputed distances provided. Preprocessing is unnecessary!" @@ -810,7 +808,6 @@ def enable_controls(self): # Only spectral init is valid with a precomputed distance matrix spectral_init_idx = self.initialization_combo.findText("Spectral") - self.initialization_method_idx = spectral_init_idx self.initialization_combo.setCurrentIndex(spectral_init_idx) self.initialization_combo.setDisabled(True) self.initialization_combo.setToolTip( @@ -829,7 +826,6 @@ def enable_controls(self): # PCA doesn't support normalization on sparse data, as this would # require centering and normalizing the matrix if not has_distance_matrix and has_data and self.data.is_sparse(): - self.normalize = False self.normalize_cbx.setDisabled(True) self.normalize_cbx.setToolTip( "Data normalization is not supported on sparse matrices." @@ -872,9 +868,9 @@ def run(self): initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] distance_metric = DISTANCE_METRICS[self.distance_metric_idx][1] - if self.distance_matrix is not None: distance_metric = "precomputed" + initialization_method = "spectral" task = Task( data=self.data, @@ -901,26 +897,28 @@ def run(self): return self.start(TSNERunner.run, task) def __ensure_task_same_for_normalization(self, task: Task): - assert task.data is self.data assert task.normalize == self.normalize - if task.normalize: + if task.normalize and task.distance_metric != "precomputed": + assert task.data is self.data assert isinstance(task.normalized_data, Table) and \ len(task.normalized_data) == len(self.data) def __ensure_task_same_for_pca(self, task: Task): - assert task.data is self.data assert task.use_pca_preprocessing == self.use_pca_preprocessing - if task.use_pca_preprocessing: + if task.use_pca_preprocessing and task.distance_metric != "precomputed": + assert task.data is self.data assert task.pca_components == self.pca_components assert isinstance(task.pca_projection, Table) and \ len(task.pca_projection) == len(self.data) def __ensure_task_same_for_initialization(self, task: Task): - initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] - assert task.initialization_method == initialization_method if self.distance_matrix is not None: n_samples = self.distance_matrix.shape[0] else: + initialization_method = INITIALIZATIONS[self.initialization_method_idx][1] + # If distance matrix is provided, the control value will be set to + # whatever it was from the context, but we will use `spectral` + assert task.initialization_method == initialization_method assert self.data is not None n_samples = self.data.X.shape[0] assert isinstance(task.initialization, np.ndarray) and \ @@ -1054,7 +1052,6 @@ def migrate_context(cls, context, version): from Orange.distance import Euclidean dist_matrix = Euclidean(data, normalize=True) WidgetPreview(OWtSNE).run( - # set_data=data, set_distances=dist_matrix, set_subset_data=data[np.random.choice(len(data), 10)], ) diff --git a/Orange/widgets/unsupervised/tests/test_owtsne.py b/Orange/widgets/unsupervised/tests/test_owtsne.py index dd0e9c74426..e0525b76d1a 100644 --- a/Orange/widgets/unsupervised/tests/test_owtsne.py +++ b/Orange/widgets/unsupervised/tests/test_owtsne.py @@ -758,6 +758,61 @@ def test_controls_are_properly_disabled_with_distance_matrix_2(self): for field in disabled_fields: self.assertTrue(getattr(w.controls, field).isEnabled()) + def test_controls_ignored_by_distance_matrix_retain_values_on_table_signal(self): + """The controls for `normalize`, `pca_preprocessing`, `metric`, and + `initialization` are overridden/ignored when using a distance matrix + signal. However, we want to remember their values when using Data + table signals.""" + w = self.widget + + self.send_signal(w.Inputs.data, self.iris) + w.normalize_cbx.setChecked(True) + w.pca_preprocessing_cbx.setChecked(True) + w.pca_component_slider.setValue(3) + w.initialization_combo.setCurrentIndex(0) + w.distance_metric_combo.setCurrentIndex(2) + w.perplexity_spin.setValue(42) + # Disconnect data, save context settings + self.send_signal(w.Inputs.data, None) + + # Send distances signal + self.send_signal(w.Inputs.distances, self.iris_distances) + + self.assertFalse(w.normalize_cbx.isEnabled()) + + self.assertFalse(w.pca_preprocessing_cbx.isEnabled()) + self.assertFalse(w.pca_component_slider.isEnabled()) + + self.assertFalse(w.initialization_combo.isEnabled()) + self.assertEqual(w.initialization_combo.currentText(), "Spectral") + + self.assertFalse(w.distance_metric_combo.isEnabled()) + self.assertEqual(w.distance_metric_combo.currentText(), "") + + self.assertTrue(w.perplexity_spin.isEnabled()) + self.assertEqual(w.perplexity_spin.value(), 42) + # Disconnect signal, the context settings should not be overridden + self.send_signal(w.Inputs.distances, None) + + # Send data signal, the data-only settings should be restored + self.send_signal(w.Inputs.data, self.iris) + + self.assertTrue(w.normalize_cbx.isEnabled()) + self.assertTrue(w.normalize_cbx.isChecked()) + + self.assertTrue(w.pca_preprocessing_cbx.isEnabled()) + self.assertTrue(w.pca_preprocessing_cbx.isChecked()) + self.assertTrue(w.pca_component_slider.isEnabled()) + + self.assertTrue(w.initialization_combo.isEnabled()) + self.assertTrue(w.initialization_combo.currentText(), "PCA") + + self.assertTrue(w.distance_metric_combo.isEnabled()) + self.assertEqual(w.distance_metric_combo.currentIndex(), 2) + + self.assertTrue(w.perplexity_spin.isEnabled()) + self.assertEqual(w.perplexity_spin.value(), 42) + class TestTSNERunner(unittest.TestCase): @classmethod From 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