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How to use test_picker method in Molotov
Best Python code snippet using molotov_python
test_brain.py
Source:test_brain.py 
1# -*- coding: utf-8 -*-2#3# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>4# Eric Larson <larson.eric.d@gmail.com>5# Joan Massich <mailsik@gmail.com>6# Guillaume Favelier <guillaume.favelier@gmail.com>7# Oleh Kozynets <ok7mailbox@gmail.com>8#9# License: Simplified BSD10import os11import os.path as op12import sys13import pytest14import numpy as np15from numpy.testing import assert_allclose, assert_array_equal16from mne import (read_source_estimate, read_evokeds, read_cov,17 read_forward_solution, pick_types_forward,18 SourceEstimate, MixedSourceEstimate, write_surface,19 VolSourceEstimate, vertex_to_mni)20from mne.minimum_norm import apply_inverse, make_inverse_operator21from mne.source_space import (read_source_spaces,22 setup_volume_source_space)23from mne.datasets import testing24from mne.fixes import _cell_data25from mne.io import read_info26from mne.utils import check_version27from mne.label import read_label28from mne.viz._brain import Brain, _LinkViewer, _BrainScraper, _LayeredMesh29from mne.viz._brain.colormap import calculate_lut30from matplotlib import cm, image31from matplotlib.lines import Line2D32data_path = testing.data_path(download=False)33subject_id = 'sample'34subjects_dir = op.join(data_path, 'subjects')35sample_dir = op.join(data_path, 'MEG', 'sample')36fname_raw_testing = op.join(sample_dir, 'sample_audvis_trunc_raw.fif')37fname_trans = op.join(sample_dir, 'sample_audvis_trunc-trans.fif')38fname_stc = op.join(sample_dir, 'sample_audvis_trunc-meg')39fname_label = op.join(sample_dir, 'labels', 'Vis-lh.label')40fname_cov = op.join(sample_dir, 'sample_audvis_trunc-cov.fif')41fname_evoked = op.join(sample_dir, 'sample_audvis_trunc-ave.fif')42fname_fwd = op.join(sample_dir, 'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')43src_fname = op.join(subjects_dir, subject_id, 'bem', 'sample-oct-6-src.fif')44class _Collection(object):45 def __init__(self, actors):46 self._actors = actors47 def GetNumberOfItems(self):48 return len(self._actors)49 def GetItemAsObject(self, ii):50 return self._actors[ii]51class TstVTKPicker(object):52 """Class to test cell picking."""53 def __init__(self, mesh, cell_id, hemi, brain):54 self.mesh = mesh55 self.cell_id = cell_id56 self.point_id = None57 self.hemi = hemi58 self.brain = brain59 self._actors = ()60 def GetCellId(self):61 """Return the picked cell."""62 return self.cell_id63 def GetDataSet(self):64 """Return the picked mesh."""65 return self.mesh66 def GetPickPosition(self):67 """Return the picked position."""68 if self.hemi == 'vol':69 self.point_id = self.cell_id70 return self.brain._data['vol']['grid_coords'][self.cell_id]71 else:72 vtk_cell = self.mesh.GetCell(self.cell_id)73 cell = [vtk_cell.GetPointId(point_id) for point_id74 in range(vtk_cell.GetNumberOfPoints())]75 self.point_id = cell[0]76 return self.mesh.points[self.point_id]77 def GetProp3Ds(self):78 """Return all picked Prop3Ds."""79 return _Collection(self._actors)80 def GetRenderer(self):81 """Return the "renderer"."""82 return self # set this to also be the renderer and active camera83 GetActiveCamera = GetRenderer84 def GetPosition(self):85 """Return the position."""86 return np.array(self.GetPickPosition()) - (0, 0, 100)87def test_layered_mesh(renderer_interactive_pyvistaqt):88 """Test management of scalars/colormap overlay."""89 mesh = _LayeredMesh(90 renderer=renderer_interactive_pyvistaqt._get_renderer(size=(300, 300)),91 vertices=np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]]),92 triangles=np.array([[0, 1, 2], [1, 2, 3]]),93 normals=np.array([[0, 0, 1]] * 4),94 )95 assert not mesh._is_mapped96 mesh.map()97 assert mesh._is_mapped98 assert mesh._current_colors is None99 assert mesh._cached_colors is None100 mesh.update()101 assert len(mesh._overlays) == 0102 mesh.add_overlay(103 scalars=np.array([0, 1, 1, 0]),104 colormap=np.array([(1, 1, 1, 1), (0, 0, 0, 0)]),105 rng=[0, 1],106 opacity=None,107 name='test1',108 )109 assert mesh._current_colors is not None110 assert mesh._cached_colors is None111 assert len(mesh._overlays) == 1112 assert 'test1' in mesh._overlays113 mesh.add_overlay(114 scalars=np.array([1, 0, 0, 1]),115 colormap=np.array([(1, 1, 1, 1), (0, 0, 0, 0)]),116 rng=[0, 1],117 opacity=None,118 name='test2',119 )120 assert mesh._current_colors is not None121 assert mesh._cached_colors is not None122 assert len(mesh._overlays) == 2123 assert 'test2' in mesh._overlays124 mesh.remove_overlay('test2')125 assert 'test2' not in mesh._overlays126 mesh.update()127 assert len(mesh._overlays) == 1128 mesh._clean()129@testing.requires_testing_data130def test_brain_gc(renderer_pyvistaqt, brain_gc):131 """Test that a minimal version of Brain gets GC'ed."""132 brain = Brain('fsaverage', 'both', 'inflated', subjects_dir=subjects_dir)133 brain.close()134@testing.requires_testing_data135def test_brain_routines(renderer, brain_gc):136 """Test backend agnostic Brain routines."""137 brain_klass = renderer.get_brain_class()138 from mne.viz._brain import Brain139 assert brain_klass == Brain140@testing.requires_testing_data141def test_brain_init(renderer_pyvistaqt, tmp_path, pixel_ratio, brain_gc):142 """Test initialization of the Brain instance."""143 from mne.source_estimate import _BaseSourceEstimate144 class FakeSTC(_BaseSourceEstimate):145 def __init__(self):146 pass147 hemi = 'lh'148 surf = 'inflated'149 cortex = 'low_contrast'150 title = 'test'151 size = (300, 300)152 kwargs = dict(subject_id=subject_id, subjects_dir=subjects_dir)153 with pytest.raises(ValueError, match='"size" parameter must be'):154 Brain(hemi=hemi, surf=surf, size=[1, 2, 3], **kwargs)155 with pytest.raises(ValueError, match='.*hemi.*Allowed values.*'):156 Brain(hemi='foo', surf=surf, **kwargs)157 with pytest.raises(ValueError, match='.*view.*Allowed values.*'):158 Brain(hemi='lh', surf=surf, views='foo', **kwargs)159 with pytest.raises(TypeError, match='figure'):160 Brain(hemi=hemi, surf=surf, figure='foo', **kwargs)161 with pytest.raises(TypeError, match='interaction'):162 Brain(hemi=hemi, surf=surf, interaction=0, **kwargs)163 with pytest.raises(ValueError, match='interaction'):164 Brain(hemi=hemi, surf=surf, interaction='foo', **kwargs)165 with pytest.raises(FileNotFoundError, match=r'lh\.whatever'):166 Brain(subject_id, 'lh', 'whatever')167 with pytest.raises(ValueError, match='`surf` cannot be seghead'):168 Brain(hemi='lh', surf='seghead', **kwargs)169 with pytest.raises(ValueError, match='RGB argument'):170 Brain('sample', cortex='badcolor')171 Brain(subject_id, hemi=None, surf=None) # test no surfaces172 renderer_pyvistaqt.backend._close_all()173 brain = Brain(hemi=hemi, surf=surf, size=size, title=title,174 cortex=cortex, units='m',175 silhouette=dict(decimate=0.95), **kwargs)176 assert 'data' not in brain._actors177 with pytest.raises(TypeError, match='not supported'):178 brain._check_stc(hemi='lh', array=FakeSTC(), vertices=None)179 with pytest.raises(ValueError, match='add_data'):180 brain.setup_time_viewer(time_viewer=True)181 brain._hemi = 'foo' # for testing: hemis182 with pytest.raises(ValueError, match='not be None'):183 brain._check_hemi(hemi=None)184 with pytest.raises(ValueError, match='Invalid.*hemi.*Allowed'):185 brain._check_hemi(hemi='foo')186 brain._hemi = hemi # end testing: hemis187 with pytest.raises(ValueError, match='bool or positive'):188 brain._to_borders(None, None, 'foo')189 assert brain.interaction == 'trackball'190 # add_data191 stc = read_source_estimate(fname_stc)192 fmin = stc.data.min()193 fmax = stc.data.max()194 for h in brain._hemis:195 if h == 'lh':196 hi = 0197 else:198 hi = 1199 hemi_data = stc.data[:len(stc.vertices[hi]), 10]200 hemi_vertices = stc.vertices[hi]201 with pytest.raises(TypeError, match='scale_factor'):202 brain.add_data(hemi_data, hemi=h, scale_factor='foo')203 with pytest.raises(TypeError, match='vector_alpha'):204 brain.add_data(hemi_data, hemi=h, vector_alpha='foo')205 with pytest.raises(ValueError, match='thresh'):206 brain.add_data(hemi_data, hemi=h, thresh=-1)207 with pytest.raises(ValueError, match='remove_existing'):208 brain.add_data(hemi_data, hemi=h, remove_existing=-1)209 with pytest.raises(ValueError, match='time_label_size'):210 brain.add_data(hemi_data, hemi=h, time_label_size=-1,211 vertices=hemi_vertices)212 with pytest.raises(ValueError, match='is positive'):213 brain.add_data(hemi_data, hemi=h, smoothing_steps=-1,214 vertices=hemi_vertices)215 with pytest.raises(TypeError, match='int or NoneType'):216 brain.add_data(hemi_data, hemi=h, smoothing_steps='foo')217 with pytest.raises(ValueError, match='dimension mismatch'):218 brain.add_data(array=np.array([0, 1, 2]), hemi=h,219 vertices=hemi_vertices)220 with pytest.raises(ValueError, match='vertices parameter must not be'):221 brain.add_data(hemi_data, fmin=fmin, hemi=hemi,222 fmax=fmax, vertices=None)223 with pytest.raises(ValueError, match='has shape'):224 brain.add_data(hemi_data[:, np.newaxis], fmin=fmin, hemi=hemi,225 fmax=fmax, vertices=None, time=[0, 1])226 brain.add_data(hemi_data, fmin=fmin, hemi=h, fmax=fmax,227 colormap='hot', vertices=hemi_vertices,228 smoothing_steps='nearest', colorbar=(0, 0), time=None)229 with pytest.raises(ValueError, match='brain has no defined times'):230 brain.set_time(0.)231 assert brain.data['lh']['array'] is hemi_data232 assert brain.views == ['lateral']233 assert brain.hemis == ('lh',)234 brain.add_data(hemi_data[:, np.newaxis], fmin=fmin, hemi=h, fmax=fmax,235 colormap='hot', vertices=hemi_vertices,236 smoothing_steps=1, initial_time=0., colorbar=False,237 time=[0])238 with pytest.raises(ValueError, match='the range of available times'):239 brain.set_time(7.)240 brain.set_time(0.)241 brain.set_time_point(0) # should hit _safe_interp1d242 with pytest.raises(ValueError, match='consistent with'):243 brain.add_data(hemi_data[:, np.newaxis], fmin=fmin, hemi=h,244 fmax=fmax, colormap='hot', vertices=hemi_vertices,245 smoothing_steps='nearest', colorbar=False,246 time=[1])247 with pytest.raises(ValueError, match='different from'):248 brain.add_data(hemi_data[:, np.newaxis][:, [0, 0]],249 fmin=fmin, hemi=h, fmax=fmax, colormap='hot',250 vertices=hemi_vertices)251 with pytest.raises(ValueError, match='need shape'):252 brain.add_data(hemi_data[:, np.newaxis], time=[0, 1],253 fmin=fmin, hemi=h, fmax=fmax, colormap='hot',254 vertices=hemi_vertices)255 with pytest.raises(ValueError, match='If array has 3'):256 brain.add_data(hemi_data[:, np.newaxis, np.newaxis],257 fmin=fmin, hemi=h, fmax=fmax, colormap='hot',258 vertices=hemi_vertices)259 assert len(brain._actors['data']) == 4260 brain.remove_data()261 assert 'data' not in brain._actors262 # add label263 label = read_label(fname_label)264 with pytest.raises(ValueError, match="not a filename"):265 brain.add_label(0)266 with pytest.raises(ValueError, match="does not exist"):267 brain.add_label('foo', subdir='bar')268 label.name = None # test unnamed label269 brain.add_label(label, scalar_thresh=0., color="green")270 assert isinstance(brain.labels[label.hemi], list)271 overlays = brain._layered_meshes[label.hemi]._overlays272 assert 'unnamed0' in overlays273 assert np.allclose(overlays['unnamed0']._colormap[0],274 [0, 0, 0, 0]) # first component is transparent275 assert np.allclose(overlays['unnamed0']._colormap[1],276 [0, 128, 0, 255]) # second is green277 brain.remove_labels()278 assert 'unnamed0' not in overlays279 brain.add_label(fname_label)280 brain.add_label('V1', borders=True)281 brain.remove_labels()282 brain.remove_labels()283 # add foci284 brain.add_foci([0], coords_as_verts=True,285 hemi=hemi, color='blue')286 # add head and skull287 brain.add_head(color='red', alpha=0.1)288 brain.remove_head()289 brain.add_skull(outer=True, color='green', alpha=0.1)290 brain.remove_skull()291 # add volume labels292 brain.add_volume_labels(293 aseg='aseg', labels=('Brain-Stem', 'Left-Hippocampus',294 'Left-Amygdala'))295 brain.remove_volume_labels()296 # add sensors297 info = read_info(fname_raw_testing)298 brain.add_sensors(info, trans=fname_trans)299 for kind in ('meg', 'eeg', 'fnirs', 'ecog', 'seeg', 'dbs', 'helmet'):300 brain.remove_sensors(kind)301 brain.add_sensors(info, trans=fname_trans)302 brain.remove_sensors()303 info['chs'][0]['coord_frame'] = 99304 with pytest.raises(RuntimeError, match='must be "meg", "head" or "mri"'):305 brain.add_sensors(info, trans=fname_trans)306 # add text307 brain.add_text(x=0, y=0, text='foo')308 with pytest.raises(ValueError, match='already exists'):309 brain.add_text(x=0, y=0, text='foo')310 brain.remove_text('foo')311 brain.add_text(x=0, y=0, text='foo')312 brain.remove_text()313 brain.close()314 # add annotation315 annots = ['aparc', op.join(subjects_dir, 'fsaverage', 'label',316 'lh.PALS_B12_Lobes.annot')]317 borders = [True, 2]318 alphas = [1, 0.5]319 colors = [None, 'r']320 brain = Brain(subject_id='fsaverage', hemi='both', size=size,321 surf='inflated', subjects_dir=subjects_dir)322 with pytest.raises(RuntimeError, match="both hemispheres"):323 brain.add_annotation(annots[-1])324 with pytest.raises(ValueError, match="does not exist"):325 brain.add_annotation('foo')326 brain.close()327 brain = Brain(subject_id='fsaverage', hemi=hemi, size=size,328 surf='inflated', subjects_dir=subjects_dir)329 for a, b, p, color in zip(annots, borders, alphas, colors):330 brain.add_annotation(a, b, p, color=color)331 view_args = dict(roll=1, distance=500, focalpoint=(1e-5, 1e-5, 1e-5))332 cam = brain._renderer.figure.plotter.camera333 previous_roll = cam.GetRoll()334 brain.show_view(**view_args)335 assert_allclose(cam.GetFocalPoint(), view_args["focalpoint"])336 assert_allclose(cam.GetDistance(), view_args["distance"])337 assert_allclose(cam.GetRoll(), previous_roll + view_args["roll"])338 del view_args339 # image and screenshot340 fname = op.join(str(tmp_path), 'test.png')341 assert not op.isfile(fname)342 brain.save_image(fname)343 assert op.isfile(fname)344 fp = np.array(345 brain._renderer.figure.plotter.renderer.ComputeVisiblePropBounds())346 fp = (fp[1::2] + fp[::2]) * 0.5347 azimuth, elevation = 180., 90.348 for view_args in (dict(azimuth=azimuth, elevation=elevation,349 focalpoint='auto'),350 dict(view='lateral', hemi='lh')):351 brain.show_view(**view_args)352 assert_allclose(brain._renderer.figure._azimuth, azimuth)353 assert_allclose(brain._renderer.figure._elevation, elevation)354 assert_allclose(cam.GetFocalPoint(), fp)355 del view_args356 img = brain.screenshot(mode='rgba')357 want_size = np.array([size[0] * pixel_ratio, size[1] * pixel_ratio, 4])358 assert_allclose(img.shape, want_size)359 brain.close()360@testing.requires_testing_data361@pytest.mark.skipif(os.getenv('CI_OS_NAME', '') == 'osx',362 reason='Unreliable/segfault on macOS CI')363@pytest.mark.parametrize('hemi', ('lh', 'rh'))364def test_single_hemi(hemi, renderer_interactive_pyvistaqt, brain_gc):365 """Test single hemi support."""366 stc = read_source_estimate(fname_stc)367 idx, order = (0, 1) if hemi == 'lh' else (1, -1)368 stc = SourceEstimate(369 getattr(stc, f'{hemi}_data'), [stc.vertices[idx], []][::order],370 0, 1, 'sample')371 brain = stc.plot(372 subjects_dir=subjects_dir, hemi='both', size=300,373 cortex='0.5') # single cortex string arg374 brain.close()375 # test skipping when len(vertices) == 0376 stc.vertices[1 - idx] = np.array([])377 brain = stc.plot(378 subjects_dir=subjects_dir, hemi=hemi, size=300)379 brain.close()380@testing.requires_testing_data381@pytest.mark.slowtest382def test_brain_save_movie(tmp_path, renderer, brain_gc):383 """Test saving a movie of a Brain instance."""384 from imageio_ffmpeg import count_frames_and_secs385 brain = _create_testing_brain(hemi='lh', time_viewer=False,386 cortex=['r', 'b']) # custom binarized387 filename = str(op.join(tmp_path, "brain_test.mov"))388 for interactive_state in (False, True):389 # for coverage, we set interactivity390 if interactive_state:391 brain._renderer.plotter.enable()392 else:393 brain._renderer.plotter.disable()394 with pytest.raises(TypeError, match='unexpected keyword argument'):395 brain.save_movie(filename, time_dilation=1, tmin=1, tmax=1.1,396 bad_name='blah')397 assert not op.isfile(filename)398 tmin = 1399 tmax = 5400 duration = np.floor(tmax - tmin)401 brain.save_movie(filename, time_dilation=1., tmin=tmin,402 tmax=tmax, interpolation='nearest')403 assert op.isfile(filename)404 _, nsecs = count_frames_and_secs(filename)405 assert_allclose(duration, nsecs, atol=0.2)406 os.remove(filename)407 brain.close()408_TINY_SIZE = (350, 300)409def tiny(tmp_path):410 """Create a tiny fake brain."""411 # This is a minimal version of what we need for our viz-with-timeviewer412 # support currently413 subject = 'test'414 (tmp_path / subject).mkdir()415 subject_dir = tmp_path / subject416 (subject_dir / 'surf').mkdir()417 surf_dir = subject_dir / 'surf'418 rng = np.random.RandomState(0)419 rr = rng.randn(4, 3)420 tris = np.array([[0, 1, 2], [2, 1, 3]])421 curv = rng.randn(len(rr))422 with open(surf_dir / 'lh.curv', 'wb') as fid:423 fid.write(np.array([255, 255, 255], dtype=np.uint8))424 fid.write(np.array([len(rr), 0, 1], dtype='>i4'))425 fid.write(curv.astype('>f4'))426 write_surface(surf_dir / 'lh.white', rr, tris)427 write_surface(surf_dir / 'rh.white', rr, tris) # needed for vertex tc428 vertices = [np.arange(len(rr)), []]429 data = rng.randn(len(rr), 10)430 stc = SourceEstimate(data, vertices, 0, 1, subject)431 brain = stc.plot(subjects_dir=tmp_path, hemi='lh', surface='white',432 size=_TINY_SIZE)433 # in principle this should be sufficient:434 #435 # ratio = brain.mpl_canvas.canvas.window().devicePixelRatio()436 #437 # but in practice VTK can mess up sizes, so let's just calculate it.438 sz = brain.plotter.size()439 sz = (sz.width(), sz.height())440 sz_ren = brain.plotter.renderer.GetSize()441 ratio = np.median(np.array(sz_ren) / np.array(sz))442 return brain, ratio443@pytest.mark.filterwarnings('ignore:.*constrained_layout not applied.*:')444def test_brain_screenshot(renderer_interactive_pyvistaqt, tmp_path, brain_gc):445 """Test time viewer screenshot."""446 # XXX disable for sprint because it's too unreliable447 if sys.platform == 'darwin' and os.getenv('GITHUB_ACTIONS', '') == 'true':448 pytest.skip('Test is unreliable on GitHub Actions macOS')449 tiny_brain, ratio = tiny(tmp_path)450 img_nv = tiny_brain.screenshot(time_viewer=False)451 want = (_TINY_SIZE[1] * ratio, _TINY_SIZE[0] * ratio, 3)452 assert img_nv.shape == want453 img_v = tiny_brain.screenshot(time_viewer=True)454 assert img_v.shape[1:] == want[1:]455 assert_allclose(img_v.shape[0], want[0] * 4 / 3, atol=3) # some slop456 tiny_brain.close()457def _assert_brain_range(brain, rng):458 __tracebackhide__ = True459 assert brain._cmap_range == rng, 'brain._cmap_range == rng'460 for hemi, layerer in brain._layered_meshes.items():461 for key, mesh in layerer._overlays.items():462 if key == 'curv':463 continue464 assert mesh._rng == rng, \465 f'_layered_meshes[{repr(hemi)}][{repr(key)}]._rng != {rng}'466@testing.requires_testing_data467@pytest.mark.slowtest468def test_brain_time_viewer(renderer_interactive_pyvistaqt, pixel_ratio,469 brain_gc):470 """Test time viewer primitives."""471 with pytest.raises(ValueError, match="between 0 and 1"):472 _create_testing_brain(hemi='lh', show_traces=-1.0)473 with pytest.raises(ValueError, match="got unknown keys"):474 _create_testing_brain(hemi='lh', surf='white', src='volume',475 volume_options={'foo': 'bar'})476 brain = _create_testing_brain(477 hemi='both', show_traces=False,478 brain_kwargs=dict(silhouette=dict(decimate=0.95))479 )480 # test sub routines when show_traces=False481 brain._on_pick(None, None)482 brain._configure_vertex_time_course()483 brain._configure_label_time_course()484 brain.setup_time_viewer() # for coverage485 brain.callbacks["time"](value=0)486 assert "renderer" not in brain.callbacks487 brain.callbacks["orientation"](488 value='lat',489 update_widget=True490 )491 brain.callbacks["orientation"](492 value='medial',493 update_widget=True494 )495 brain.callbacks["time"](496 value=0.0,497 time_as_index=False,498 )499 # Need to process events for old Qt500 brain.callbacks["smoothing"](value=1)501 _assert_brain_range(brain, [0.1, 0.3])502 from mne.utils import use_log_level503 print('\nCallback fmin\n')504 with use_log_level('debug'):505 brain.callbacks["fmin"](value=12.0)506 assert brain._data["fmin"] == 12.0507 brain.callbacks["fmax"](value=4.0)508 _assert_brain_range(brain, [4.0, 4.0])509 brain.callbacks["fmid"](value=6.0)510 _assert_brain_range(brain, [4.0, 6.0])511 brain.callbacks["fmid"](value=4.0)512 brain.callbacks["fplus"]()513 brain.callbacks["fminus"]()514 brain.callbacks["fmin"](value=12.0)515 brain.callbacks["fmid"](value=4.0)516 _assert_brain_range(brain, [4.0, 12.0])517 brain._shift_time(op=lambda x, y: x + y)518 brain._shift_time(op=lambda x, y: x - y)519 brain._rotate_azimuth(15)520 brain._rotate_elevation(15)521 brain.toggle_interface()522 brain.toggle_interface(value=False)523 brain.callbacks["playback_speed"](value=0.1)524 brain.toggle_playback()525 brain.toggle_playback(value=False)526 brain.apply_auto_scaling()527 brain.restore_user_scaling()528 brain.reset()529 assert brain.help_canvas is not None530 assert not brain.help_canvas.canvas.isVisible()531 brain.help()532 assert brain.help_canvas.canvas.isVisible()533 # screenshot534 # Need to turn the interface back on otherwise the window is too wide535 # (it keeps the window size and expands the 3D area when the interface536 # is toggled off)537 brain.toggle_interface(value=True)538 brain.show_view(azimuth=180., elevation=90.)539 img = brain.screenshot(mode='rgb')540 want_shape = np.array([300 * pixel_ratio, 300 * pixel_ratio, 3])541 assert_allclose(img.shape, want_shape)542 brain.close()543@testing.requires_testing_data544@pytest.mark.parametrize('hemi', [545 'lh',546 pytest.param('rh', marks=pytest.mark.slowtest),547 pytest.param('split', marks=pytest.mark.slowtest),548 pytest.param('both', marks=pytest.mark.slowtest),549])550@pytest.mark.parametrize('src', [551 'surface',552 pytest.param('vector', marks=pytest.mark.slowtest),553 pytest.param('volume', marks=pytest.mark.slowtest),554 pytest.param('mixed', marks=pytest.mark.slowtest),555])556@pytest.mark.slowtest557def test_brain_traces(renderer_interactive_pyvistaqt, hemi, src, tmp_path,558 brain_gc):559 """Test brain traces."""560 hemi_str = list()561 if src in ('surface', 'vector', 'mixed'):562 hemi_str.extend([hemi] if hemi in ('lh', 'rh') else ['lh', 'rh'])563 if src in ('mixed', 'volume'):564 hemi_str.extend(['vol'])565 # label traces566 brain = _create_testing_brain(567 hemi=hemi, surf='white', src=src, show_traces='label',568 volume_options=None, # for speed, don't upsample569 n_time=5, initial_time=0,570 )571 if src == 'surface':572 brain._data['src'] = None # test src=None573 if src in ('surface', 'vector', 'mixed'):574 assert brain.show_traces575 assert brain.traces_mode == 'label'576 brain.widgets["extract_mode"].set_value('max')577 # test picking a cell at random578 rng = np.random.RandomState(0)579 for idx, current_hemi in enumerate(hemi_str):580 if current_hemi == 'vol':581 continue582 current_mesh = brain._layered_meshes[current_hemi]._polydata583 cell_id = rng.randint(0, current_mesh.n_cells)584 test_picker = TstVTKPicker(585 current_mesh, cell_id, current_hemi, brain)586 assert len(brain.picked_patches[current_hemi]) == 0587 brain._on_pick(test_picker, None)588 assert len(brain.picked_patches[current_hemi]) == 1589 for label_id in list(brain.picked_patches[current_hemi]):590 label = brain._annotation_labels[current_hemi][label_id]591 assert isinstance(label._line, Line2D)592 brain.widgets["extract_mode"].set_value('mean')593 brain.clear_glyphs()594 assert len(brain.picked_patches[current_hemi]) == 0595 brain._on_pick(test_picker, None) # picked and added596 assert len(brain.picked_patches[current_hemi]) == 1597 brain._on_pick(test_picker, None) # picked again so removed598 assert len(brain.picked_patches[current_hemi]) == 0599 # test switching from 'label' to 'vertex'600 brain.widgets["annotation"].set_value('None')601 brain.widgets["extract_mode"].set_value('max')602 else: # volume603 assert "annotation" not in brain.widgets604 assert "extract_mode" not in brain.widgets605 brain.close()606 # test colormap607 if src != 'vector':608 brain = _create_testing_brain(609 hemi=hemi, surf='white', src=src, show_traces=0.5,610 initial_time=0,611 volume_options=None, # for speed, don't upsample612 n_time=1 if src == 'mixed' else 5, diverging=True,613 add_data_kwargs=dict(colorbar_kwargs=dict(n_labels=3)),614 )615 # mne_analyze should be chosen616 ctab = brain._data['ctable']617 assert_array_equal(ctab[0], [0, 255, 255, 255]) # opaque cyan618 assert_array_equal(ctab[-1], [255, 255, 0, 255]) # opaque yellow619 assert_allclose(ctab[len(ctab) // 2], [128, 128, 128, 0], atol=3)620 brain.close()621 # vertex traces622 brain = _create_testing_brain(623 hemi=hemi, surf='white', src=src, show_traces=0.5,624 initial_time=0,625 volume_options=None, # for speed, don't upsample626 n_time=1 if src == 'mixed' else 5,627 add_data_kwargs=dict(colorbar_kwargs=dict(n_labels=3)),628 )629 assert brain.show_traces630 assert brain.traces_mode == 'vertex'631 assert hasattr(brain, "picked_points")632 assert hasattr(brain, "_spheres")633 assert brain._scalar_bar.GetNumberOfLabels() == 3634 # add foci should work for volumes635 brain.add_foci([[0, 0, 0]], hemi='lh' if src == 'surface' else 'vol')636 # test points picked by default637 picked_points = brain.get_picked_points()638 spheres = brain._spheres639 for current_hemi in hemi_str:640 assert len(picked_points[current_hemi]) == 1641 n_spheres = len(hemi_str)642 n_actors = n_spheres643 if hemi == 'split' and src in ('mixed', 'volume'):644 n_spheres += 1645 assert len(spheres) == n_spheres646 # test that there are actually enough actors647 assert len(brain._actors['data']) == n_actors648 # test switching from 'vertex' to 'label'649 if src == 'surface':650 brain.widgets["annotation"].set_value('aparc')651 brain.widgets["annotation"].set_value('None')652 # test removing points653 brain.clear_glyphs()654 assert len(spheres) == 0655 for key in ('lh', 'rh', 'vol'):656 assert len(picked_points[key]) == 0657 # test picking a cell at random658 rng = np.random.RandomState(0)659 for idx, current_hemi in enumerate(hemi_str):660 assert len(spheres) == 0661 if current_hemi == 'vol':662 current_mesh = brain._data['vol']['grid']663 vertices = brain._data['vol']['vertices']664 values = _cell_data(current_mesh)['values'][vertices]665 cell_id = vertices[np.argmax(np.abs(values))]666 else:667 current_mesh = brain._layered_meshes[current_hemi]._polydata668 cell_id = rng.randint(0, current_mesh.n_cells)669 test_picker = TstVTKPicker(None, None, current_hemi, brain)670 assert brain._on_pick(test_picker, None) is None671 test_picker = TstVTKPicker(672 current_mesh, cell_id, current_hemi, brain)673 assert cell_id == test_picker.cell_id674 assert test_picker.point_id is None675 brain._on_pick(test_picker, None)676 brain._on_pick(test_picker, None)677 assert test_picker.point_id is not None678 assert len(picked_points[current_hemi]) == 1679 assert picked_points[current_hemi][0] == test_picker.point_id680 assert len(spheres) > 0681 sphere = spheres[-1]682 vertex_id = sphere._vertex_id683 assert vertex_id == test_picker.point_id684 line = sphere._line685 hemi_prefix = current_hemi[0].upper()686 if current_hemi == 'vol':687 assert hemi_prefix + ':' in line.get_label()688 assert 'MNI' in line.get_label()689 continue # the MNI conversion is more complex690 hemi_int = 0 if current_hemi == 'lh' else 1691 mni = vertex_to_mni(692 vertices=vertex_id,693 hemis=hemi_int,694 subject=brain._subject_id,695 subjects_dir=brain._subjects_dir696 )697 label = "{}:{} MNI: {}".format(698 hemi_prefix, str(vertex_id).ljust(6),699 ', '.join('%5.1f' % m for m in mni))700 assert line.get_label() == label701 # remove the sphere by clicking in its vicinity702 old_len = len(spheres)703 test_picker._actors = sum((s._actors for s in spheres), [])704 brain._on_pick(test_picker, None)705 assert len(spheres) < old_len706 screenshot = brain.screenshot()707 screenshot_all = brain.screenshot(time_viewer=True)708 assert screenshot.shape[0] < screenshot_all.shape[0]709 # and the scraper for it (will close the instance)710 # only test one condition to save time711 if not (hemi == 'rh' and src == 'surface' and712 check_version('sphinx_gallery')):713 brain.close()714 return715 fnames = [str(tmp_path / f'temp_{ii}.png') for ii in range(2)]716 block_vars = dict(image_path_iterator=iter(fnames),717 example_globals=dict(brain=brain))718 block = ('code', """719something720# brain.save_movie(time_dilation=1, framerate=1,721# interpolation='linear', time_viewer=True)722#723""", 1)724 gallery_conf = dict(src_dir=str(tmp_path), compress_images=[])725 scraper = _BrainScraper()726 rst = scraper(block, block_vars, gallery_conf)727 assert brain.plotter is None # closed728 gif_0 = fnames[0][:-3] + 'gif'729 for fname in (gif_0, fnames[1]):730 assert op.basename(fname) in rst731 assert op.isfile(fname)732 img = image.imread(fname)733 assert img.shape[1] == screenshot.shape[1] # same width734 assert img.shape[0] > screenshot.shape[0] # larger height735 assert img.shape[:2] == screenshot_all.shape[:2]736@testing.requires_testing_data737@pytest.mark.slowtest738def test_brain_linkviewer(renderer_interactive_pyvistaqt, brain_gc):739 """Test _LinkViewer primitives."""740 brain1 = _create_testing_brain(hemi='lh', show_traces=False)741 brain2 = _create_testing_brain(hemi='lh', show_traces='separate')742 brain1._times = brain1._times * 2743 with pytest.warns(RuntimeWarning, match='linking time'):744 link_viewer = _LinkViewer(745 [brain1, brain2],746 time=True,747 camera=False,748 colorbar=False,749 picking=False,750 )751 brain1.close()752 brain_data = _create_testing_brain(hemi='split', show_traces='vertex')753 link_viewer = _LinkViewer(754 [brain2, brain_data],755 time=True,756 camera=True,757 colorbar=True,758 picking=True,759 )760 link_viewer.leader.set_time_point(0)761 link_viewer.leader.mpl_canvas.time_func(0)762 link_viewer.leader.callbacks["fmin"](0)763 link_viewer.leader.callbacks["fmid"](0.5)764 link_viewer.leader.callbacks["fmax"](1)765 link_viewer.leader.set_playback_speed(0.1)766 link_viewer.leader.toggle_playback()767 brain2.close()768 brain_data.close()769def test_calculate_lut():770 """Test brain's colormap functions."""771 colormap = "coolwarm"772 alpha = 1.0773 fmin = 0.0774 fmid = 0.5775 fmax = 1.0776 center = None777 calculate_lut(colormap, alpha=alpha, fmin=fmin,778 fmid=fmid, fmax=fmax, center=center)779 center = 0.0780 colormap = cm.get_cmap(colormap)781 calculate_lut(colormap, alpha=alpha, fmin=fmin,782 fmid=fmid, fmax=fmax, center=center)783 cmap = cm.get_cmap(colormap)784 zero_alpha = np.array([1., 1., 1., 0])785 half_alpha = np.array([1., 1., 1., 0.5])786 atol = 1.5 / 256.787 # fmin < fmid < fmax788 lut = calculate_lut(colormap, alpha, 1, 2, 3)789 assert lut.shape == (256, 4)790 assert_allclose(lut[0], cmap(0) * zero_alpha, atol=atol)791 assert_allclose(lut[127], cmap(0.5), atol=atol)792 assert_allclose(lut[-1], cmap(1.), atol=atol)793 # divergent794 lut = calculate_lut(colormap, alpha, 0, 1, 2, 0)795 assert lut.shape == (256, 4)796 assert_allclose(lut[0], cmap(0), atol=atol)797 assert_allclose(lut[63], cmap(0.25), atol=atol)798 assert_allclose(lut[127], cmap(0.5) * zero_alpha, atol=atol)799 assert_allclose(lut[192], cmap(0.75), atol=atol)800 assert_allclose(lut[-1], cmap(1.), atol=atol)801 # fmin == fmid == fmax802 lut = calculate_lut(colormap, alpha, 1, 1, 1)803 zero_alpha = np.array([1., 1., 1., 0])804 assert lut.shape == (256, 4)805 assert_allclose(lut[0], cmap(0) * zero_alpha, atol=atol)806 assert_allclose(lut[1], cmap(0.5), atol=atol)807 assert_allclose(lut[-1], cmap(1.), atol=atol)808 # divergent809 lut = calculate_lut(colormap, alpha, 0, 0, 0, 0)810 assert lut.shape == (256, 4)811 assert_allclose(lut[0], cmap(0), atol=atol)812 assert_allclose(lut[127], cmap(0.5) * zero_alpha, atol=atol)813 assert_allclose(lut[-1], cmap(1.), atol=atol)814 # fmin == fmid < fmax815 lut = calculate_lut(colormap, alpha, 1, 1, 2)816 assert lut.shape == (256, 4)817 assert_allclose(lut[0], cmap(0.) * zero_alpha, atol=atol)818 assert_allclose(lut[1], cmap(0.5), atol=atol)819 assert_allclose(lut[-1], cmap(1.), atol=atol)820 # divergent821 lut = calculate_lut(colormap, alpha, 1, 1, 2, 0)822 assert lut.shape == (256, 4)823 assert_allclose(lut[0], cmap(0), atol=atol)824 assert_allclose(lut[62], cmap(0.245), atol=atol)825 assert_allclose(lut[64], cmap(0.5) * zero_alpha, atol=atol)826 assert_allclose(lut[127], cmap(0.5) * zero_alpha, atol=atol)827 assert_allclose(lut[191], cmap(0.5) * zero_alpha, atol=atol)828 assert_allclose(lut[193], cmap(0.755), atol=atol)829 assert_allclose(lut[-1], cmap(1.), atol=atol)830 lut = calculate_lut(colormap, alpha, 0, 0, 1, 0)831 assert lut.shape == (256, 4)832 assert_allclose(lut[0], cmap(0), atol=atol)833 assert_allclose(lut[126], cmap(0.25), atol=atol)834 assert_allclose(lut[127], cmap(0.5) * zero_alpha, atol=atol)835 assert_allclose(lut[129], cmap(0.75), atol=atol)836 assert_allclose(lut[-1], cmap(1.), atol=atol)837 # fmin < fmid == fmax838 lut = calculate_lut(colormap, alpha, 1, 2, 2)839 assert lut.shape == (256, 4)840 assert_allclose(lut[0], cmap(0) * zero_alpha, atol=atol)841 assert_allclose(lut[-2], cmap(0.5), atol=atol)842 assert_allclose(lut[-1], cmap(1.), atol=atol)843 # divergent844 lut = calculate_lut(colormap, alpha, 1, 2, 2, 0)845 assert lut.shape == (256, 4)846 assert_allclose(lut[0], cmap(0), atol=atol)847 assert_allclose(lut[1], cmap(0.25), atol=2 * atol)848 assert_allclose(lut[32], cmap(0.375) * half_alpha, atol=atol)849 assert_allclose(lut[64], cmap(0.5) * zero_alpha, atol=atol)850 assert_allclose(lut[127], cmap(0.5) * zero_alpha, atol=atol)851 assert_allclose(lut[191], cmap(0.5) * zero_alpha, atol=atol)852 assert_allclose(lut[223], cmap(0.625) * half_alpha, atol=atol)853 assert_allclose(lut[-2], cmap(0.7475), atol=2 * atol)854 assert_allclose(lut[-1], cmap(1.), atol=2 * atol)855 lut = calculate_lut(colormap, alpha, 0, 1, 1, 0)856 assert lut.shape == (256, 4)857 assert_allclose(lut[0], cmap(0), atol=atol)858 assert_allclose(lut[1], cmap(0.25), atol=2 * atol)859 assert_allclose(lut[64], cmap(0.375) * half_alpha, atol=atol)860 assert_allclose(lut[127], cmap(0.5) * zero_alpha, atol=atol)861 assert_allclose(lut[191], cmap(0.625) * half_alpha, atol=atol)862 assert_allclose(lut[-2], cmap(0.75), atol=2 * atol)863 assert_allclose(lut[-1], cmap(1.), atol=atol)864 with pytest.raises(ValueError, match=r'.*fmin \(1\) <= fmid \(0\) <= fma'):865 calculate_lut(colormap, alpha, 1, 0, 2)866def _create_testing_brain(hemi, surf='inflated', src='surface',867 size=300, n_time=5, diverging=False, **kwargs):868 assert src in ('surface', 'vector', 'mixed', 'volume')869 meth = 'plot'870 if src in ('surface', 'mixed'):871 sample_src = read_source_spaces(src_fname)872 klass = MixedSourceEstimate if src == 'mixed' else SourceEstimate873 if src == 'vector':874 fwd = read_forward_solution(fname_fwd)875 fwd = pick_types_forward(fwd, meg=True, eeg=False)876 evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0]877 noise_cov = read_cov(fname_cov)878 free = make_inverse_operator(879 evoked.info, fwd, noise_cov, loose=1.)880 stc = apply_inverse(evoked, free, pick_ori='vector')881 return stc.plot(882 subject=subject_id, hemi=hemi, size=size,883 subjects_dir=subjects_dir, colormap='auto',884 **kwargs)885 if src in ('volume', 'mixed'):886 vol_src = setup_volume_source_space(887 subject_id, 7., mri='aseg.mgz',888 volume_label='Left-Cerebellum-Cortex',889 subjects_dir=subjects_dir, add_interpolator=False)890 assert len(vol_src) == 1891 assert vol_src[0]['nuse'] == 150892 if src == 'mixed':893 sample_src = sample_src + vol_src894 else:895 sample_src = vol_src896 klass = VolSourceEstimate897 meth = 'plot_3d'898 assert sample_src.kind == src899 # dense version900 rng = np.random.RandomState(0)901 vertices = [s['vertno'] for s in sample_src]902 n_verts = sum(len(v) for v in vertices)903 stc_data = np.zeros((n_verts * n_time))904 stc_size = stc_data.size905 stc_data[(rng.rand(stc_size // 20) * stc_size).astype(int)] = \906 rng.rand(stc_data.size // 20)907 stc_data.shape = (n_verts, n_time)908 if diverging:909 stc_data -= 0.5910 stc = klass(stc_data, vertices, 1, 1)911 clim = dict(kind='value', lims=[0.1, 0.2, 0.3])912 if diverging:913 clim['pos_lims'] = clim.pop('lims')914 brain_data = getattr(stc, meth)(915 subject=subject_id, hemi=hemi, surface=surf, size=size,916 subjects_dir=subjects_dir, colormap='auto',917 clim=clim, src=sample_src,918 **kwargs)...
validate_best_model.py
Source:validate_best_model.py
1import pandas as pd2import numpy as np3from sklearn.neighbors import KNeighborsClassifier4import matplotlib.pyplot as plt5import seaborn as sns6from sklearn.dummy import DummyClassifier7from util import train_test_model, print_results_cv, read_dataset, input_data, rescale_data8from sklearn.model_selection import GroupKFold, cross_val_score, train_test_split9from sklearn.naive_bayes import GaussianNB10from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, BaggingClassifier11from sklearn.tree import DecisionTreeClassifier12import time13from sklearn.manifold import TSNE14from sklearn.feature_selection import RFE, RFECV15from sklearn.feature_selection import SelectKBest16from sklearn.feature_selection import mutual_info_classif17from imblearn.under_sampling import RandomUnderSampler18# Calculando correlação e criando figura19def calc_corr_fig(standardized_values):20 plt.figure(figsize=(17, 15))21 matrix_correlation = standardized_values.corr()22 sns.heatmap(matrix_correlation, annot=True, fmt=".1f")23 plt.show()24# Visualizar em 2 dimensões os dados para facilitar o direcionamento da análise25def tsne_scatterplot(x_without_corr_feat, y):26 tsne = TSNE(n_components=2)27 tsne_x = tsne.fit_transform(x_without_corr_feat)28 sns.scatterplot(x=tsne_x[:, 0], y=tsne_x[:, 1], hue=y)29 plt.show()30def select_features(model, train_x, train_y, picker, test_x=None):31 picker.fit(train_x, train_y)32 train_picker = picker.transform(train_x)33 test_picker = None34 if test_x is not None:35 test_picker = picker.transform(test_x)36 return {'train_picker': train_picker, 'test_picker': test_picker, 'picker': picker}37def validate_models_holdout(train_x, train_y, test_x, test_y, models, k_size):38 for model in models:39 print("Imprimindo resultados da abordagem holdout para o %s" %40 model.__class__)41 t0 = time.time()42 model = train_test_model(model, train_x, train_y, test_x, test_y)43 # Se o classificador não possuir lista de importância de caracterÃsticas, seleciona-se por métrica de filtro44 try:45 picker = select_features(model, train_x, train_y, RFE(46 estimator=model, n_features_to_select=k_size, step=1), test_x)47 except:48 picker = select_features(model, train_x, train_y, SelectKBest(49 mutual_info_classif, k=k_size), test_x)50 print("Selecionando-se as caracterÃsticas com %s" % picker['picker'])51 train_test_model(model, picker['train_picker'],52 train_y, picker['test_picker'], test_y)53 print("Tempo do modelo %s: %d" %54 (model.__class__, round(time.time()-t0, 3)))55def validate_models_cv(x, y, random_groups, models, k_size):56 for model in models:57 print("Imprimindo resultados da abordagem por validação cruzada para o %s" %58 model.__class__)59 cv = GroupKFold(n_splits=5)60 t0 = time.time()61 results = cross_val_score(62 model, x, y, cv=cv, groups=random_groups, scoring='f1_micro')63 print_results_cv(results)64 try:65 picker = select_features(model, x, y, RFECV(estimator=model, cv=5, step=1, scoring="f1_micro"), None)66 except:67 picker = select_features(model, x, y, SelectKBest(68 mutual_info_classif, k=k_size), None)69 print("Selecionando-se as caracterÃsticas com %s" % picker['picker'])70 results = cross_val_score(71 model, picker['train_picker'], y, cv=cv, groups=random_groups, scoring='f1_micro')72 print("Tempo do modelo %s: %d" %73 (model.__class__, round(time.time()-t0, 3)))74 print_results_cv(results)75def main():76 dataset = read_dataset("https://raw.githubusercontent.com/dataminerdbm/test_data_scientist/main/treino.csv")77 dataset.replace(to_replace=[None], value=np.nan, inplace=True)78 raw_dataset_values = dataset.drop(columns=['inadimplente'])79 transformed_values = input_data(raw_dataset_values)80 standardized_values = rescale_data(transformed_values, raw_dataset_values)81 # calc_corr_fig(standardized_values)82 x = standardized_values83 # Remove-se as demais caracterÃsticas correlacionadas, mantendo-se apenas uma84 x_without_corr_feat = standardized_values.drop(85 columns=['vezes_passou_de_30_59_dias', 'numero_de_vezes_que_passou_60_89_dias'])86 y = dataset.inadimplente87 SEED = 770788 np.random.seed(SEED)89 # Realiza-se a estratificação dos dados tendo em vista o desbalanceamento da base90 train_x, test_x, train_y, test_y = train_test_split(91 x, y, test_size=0.3, stratify=y)92 train_x_without_corr_feat, test_x_without_corr_feat, train_y_without_corr_feat, test_y_without_corr_feat = train_test_split(93 x_without_corr_feat, y, test_size=0.3, stratify=y)94 undersample = RandomUnderSampler(sampling_strategy='majority')95 X_without_corr_feat_under, y_without_corr_feat_under = undersample.fit_resample(x_without_corr_feat, y)96 x_under, y_under = undersample.fit_resample(x, y)97 train_x_under, train_y_under = undersample.fit_resample(train_x, train_y)98 train_x_without_corr_feat_under, train_y_without_corr_feat_under = undersample.fit_resample(train_x_without_corr_feat, train_y_without_corr_feat)99 #tsne_scatterplot(x_without_corr_feat, y)100 # Os classificadores validados foram escolhidos de acordo com o aspecto da base de dados:101 # caracterÃsticas numéricas, multidimensional com alto número de instâncias e problema não linearmente separável102 models = [DummyClassifier(), KNeighborsClassifier(), DecisionTreeClassifier(),103 GaussianNB(), AdaBoostClassifier(n_estimators=100), RandomForestClassifier(),104 BaggingClassifier(base_estimator=GaussianNB(), n_estimators=100)]105 k_size = 5106 # Criando aleatoridade nos grupos de folds (para evitar repetição). Abordagem mais adequada para bases desbalanceadas107 # https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold108 x_under['idade_r'] = x_under.idade + np.random.randint(-2, 3, size=14662)109 x_under.idade_r = x_under.idade + abs(x_under.idade.min()) + 1110 print("Validando modelos com todas as caracterÃsticas")111 validate_models_cv(x_under, y_under, x_under.idade_r, models, k_size)112 validate_models_holdout(train_x_under, train_y_under, test_x, test_y, models, k_size)113 print("Validando modelos sem as caracterÃsticas correlacionadas")114 validate_models_cv(X_without_corr_feat_under, y_without_corr_feat_under, x_under.idade_r, models, k_size)115 validate_models_holdout(train_x_without_corr_feat_under, train_y_without_corr_feat_under,116 test_x_without_corr_feat, test_y_without_corr_feat, models, k_size)117if __name__ == "__main__":...
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