[ENH] Scatter Plot Graph and Scaling can handle metas #2699
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,8 @@ | ||
| from itertools import chain | ||
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| import numpy as np | ||
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| from Orange.data import Domain | ||
| from Orange.statistics.basic_stats import DomainBasicStats | ||
| from Orange.widgets.settings import Setting | ||
| from Orange.widgets.utils import checksum | ||
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@@ -12,12 +15,11 @@ class ScaleData: | |
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| def _reset_data(self): | ||
| self.domain = None | ||
| self.data = None # as Orange Table | ||
| self.scaled_data = None # in [0, 1] | ||
| self.jittered_data = None | ||
| self.attr_values = {} | ||
| self.domain_data_stat = {} | ||
| self.valid_data_array = None | ||
| self.attribute_flip_info = {} # dictionary with attr: 0/1 if flipped | ||
| self.jitter_seed = 0 | ||
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@@ -30,53 +32,58 @@ def rescale_data(self): | |
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| def _compute_domain_data_stat(self): | ||
| stt = self.domain_data_stat = \ | ||
| getCached(self.data, DomainBasicStats, (self.data, True)) | ||
| domain = self.domain | ||
| for attr in chain(domain.variables, domain.metas): | ||
| if attr.is_discrete: | ||
| self.attr_values[attr] = [0, len(attr.values)] | ||
| elif attr.is_continuous: | ||
| self.attr_values[attr] = [stt[attr].min, stt[attr].max] | ||
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| def _compute_scaled_data(self): | ||
| data = self.data | ||
| # We cache scaled_data and validArray to share them between widgets | ||
| cached = getCached(data, "visualizationData") | ||
| if cached: | ||
| self.data, self.scaled_data, self.valid_data_array = cached | ||
| return | ||
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| Y = data.Y if data.Y.ndim == 2 else np.atleast_2d(data.Y).T | ||
| if np.any(data.metas): | ||
| all_data = (data.X, Y, data.metas) | ||
| else: | ||
| all_data = (data.X, Y) | ||
| all_data = np.hstack(all_data).T | ||
|
There was a problem hiding this comment. This can have dtype==object when stacking with metas which will cause np.isfinite to fail 2 lines below There was a problem hiding this comment. Fixed here, but I get similar errors elsewhere (see sentry report). There was a problem hiding this comment. I found another error caused by Corpus (issue: biolab/orange3-text#324 ) |
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| self.scaled_data = self.data.copy() | ||
| self.valid_data_array = np.isfinite(all_data) | ||
| domain = self.domain | ||
| for attr in chain(domain.attributes, domain.class_vars, domain.metas): | ||
| c = self.scaled_data.get_column_view(attr)[0] | ||
| if attr.is_discrete: | ||
| c += 0.5 | ||
| c /= len(attr.values) | ||
| else: | ||
| dstat = self.domain_data_stat[attr] | ||
| c -= dstat.min | ||
| if dstat.max != dstat.min: | ||
| c /= dstat.max - dstat.min | ||
| setCached(data, "visualizationData", | ||
| (self.data, self.scaled_data, self.valid_data_array)) | ||
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| def _compute_jittered_data(self): | ||
| data = self.data | ||
| self.jittered_data = self.scaled_data.copy() | ||
| random = np.random.RandomState(seed=self.jitter_seed) | ||
| domain = self.domain | ||
| for attr in chain(domain.variables, domain.metas): | ||
| # Need to use a different seed for each feature | ||
| if attr.is_discrete: | ||
| off = self.jitter_size / (25 * max(1, len(attr.values))) | ||
| elif attr.is_continuous and self.jitter_continuous: | ||
| off = self.jitter_size / 25 | ||
| else: | ||
| continue | ||
| col = self.jittered_data.get_column_view(attr)[0] | ||
| col += random.uniform(-off, off, len(data)) | ||
| # fix values outside [0, 1] | ||
| col = np.absolute(col) | ||
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@@ -92,8 +99,13 @@ def set_data(self, data, skip_if_same=False, no_data=False): | |
| if data is None: | ||
| return | ||
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| domain = data.domain | ||
| new_domain = Domain(attributes=domain.attributes, | ||
| class_vars=domain.class_vars, | ||
| metas=tuple(v for v in domain.metas if v.is_primitive())) | ||
| self.data = data.transform(new_domain) | ||
| self.data.metas = self.data.metas.astype(float) | ||
| self.domain = self.data.domain | ||
| self.attribute_flip_info = {} | ||
| if not no_data: | ||
| self._compute_domain_data_stat() | ||
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@@ -103,67 +115,73 @@ def set_data(self, data, skip_if_same=False, no_data=False): | |
| def flip_attribute(self, attr): | ||
| if attr.is_discrete: | ||
| return 0 | ||
| self.attribute_flip_info[attr] = 1 - self.attribute_flip_info.get(attr, 0) | ||
| if attr.is_continuous: | ||
| self.attr_values[attr] = [-self.attr_values[attr][1], | ||
| -self.attr_values[attr][0]] | ||
| col = self.jittered_data.get_column_view(attr)[0] | ||
| col *= -1 | ||
| col += 1 | ||
| col = self.scaled_data.get_column_view(attr)[0] | ||
| col *= -1 | ||
| col += 1 | ||
| return 1 | ||
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| def get_valid_list(self, attrs): | ||
| """ | ||
| Get array of 0 and 1 of len = len(self.data). If there is a missing | ||
| value at any attribute in indices return 0 for that instance. | ||
| """ | ||
| if self.valid_data_array is None or len(self.valid_data_array) == 0: | ||
| return np.array([], np.bool) | ||
| domain = self.domain | ||
| indices = [] | ||
| for index, attr in enumerate(chain(domain.variables, domain.metas)): | ||
| if attr in attrs: | ||
| indices.append(index) | ||
| return np.all(self.valid_data_array[indices], axis=0) | ||
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| def get_valid_indices(self, attrs): | ||
| """ | ||
| Get array with numbers that represent the instance indices that have a | ||
| valid data value. | ||
| """ | ||
| valid_list = self.get_valid_list(attrs) | ||
| return np.nonzero(valid_list)[0] | ||
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| class ScaleScatterPlotData(ScaleData): | ||
| def get_xy_data_positions(self, attr_x, attr_y, filter_valid=False, | ||
| copy=True): | ||
| """ | ||
| Create x-y projection of attributes in attrlist. | ||
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| """ | ||
| jit = self.jittered_data | ||
| if filter_valid is True: | ||
| filter_valid = self.get_valid_list([attr_x, attr_y]) | ||
| if isinstance(filter_valid, np.ndarray): | ||
| data_x = jit.get_column_view(attr_x)[0][filter_valid] | ||
| data_y = jit.get_column_view(attr_y)[0][filter_valid] | ||
| elif copy: | ||
| data_x = jit.get_column_view(attr_x)[0].copy() | ||
| data_y = jit.get_column_view(attr_y)[0].copy() | ||
| else: | ||
| data_x = jit.get_column_view(attr_x)[0] | ||
| data_y = jit.get_column_view(attr_y)[0] | ||
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| if attr_x.is_discrete: | ||
| data_x *= len(attr_x.values) | ||
| data_x -= 0.5 | ||
| else: | ||
| data_x *= self.attr_values[attr_x][1] - self.attr_values[attr_x][0] | ||
| data_x += float(self.attr_values[attr_x][0]) | ||
| if attr_y.is_discrete: | ||
| data_y *= len(attr_y.values) | ||
| data_y -= 0.5 | ||
| else: | ||
| data_y *= self.attr_values[attr_y][1] - self.attr_values[attr_y][0] | ||
| data_y += float(self.attr_values[attr_y][0]) | ||
| return data_x, data_y | ||
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| getXYDataPositions = get_xy_data_positions | ||
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@BlazZupan, do we want the columns that contain x and y coordinates from the MDS to appear as ordinary attributes or as metas?
They used to be in metas, but now that visualization widgets can show metas as well, I would tend to store x and y as metas to not pollute that feature set.
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Actually this line here is not related to that (it's for constructing data for plotting).
Output data has new coordinates in metas as we want, see commit function below (L671)