Commit 5a4bf706 authored by Josef Brandt's avatar Josef Brandt

Trained Subsampling

parent 386f1214
......@@ -20,3 +20,7 @@ cythonModules/build/
chemometrics/Assignments.txt
chemometrics/Data.txt
chemometrics/Assignments_all.txt
chemometrics/Data_all.txt
This diff is collapsed.
import sys
sys.path.append("C://Users//xbrjos//Desktop//Python")
from gepard.helperfunctions import cv2imread_fix
from gepard.dataset import loadData
from gepard.dataset import DataSet,loadData
from gepard.analysis.particleContainer import ParticleContainer
import cv2
import numpy as np
......@@ -9,7 +9,42 @@ from scipy import spatial
import os
import matplotlib.pyplot as plt
from helpers import get_filterDimensions_from_dataset, get_center_from_filter_dimensions, convert_length_to_pixels
from graphs import get_distance_point_histogramdata
def get_particle_heterogeneity(dataset: DataSet, numCells: int = 50) -> float:
offset, diameter, [width, height] = get_filterDimensions_from_dataset(dataset)
center: np.ndarray = get_center_from_filter_dimensions(offset, diameter)
width: float = convert_length_to_pixels(dataset, width)
height: float = convert_length_to_pixels(dataset, height)
pixelsPerTile: int = max(int(round(width/numCells)), int(round(height/numCells)))
centerX: int = int(round(convert_length_to_pixels(dataset, center[0] / pixelsPerTile)))
centerY: int = int(round(convert_length_to_pixels(dataset, center[1] / pixelsPerTile)))
radius: int = int(round(convert_length_to_pixels(dataset, diameter / pixelsPerTile * 0.5)))
numRows: int = int(np.ceil(height / pixelsPerTile)) + 1
numCols: int = int(np.ceil(width / pixelsPerTile)) + 1
densityImage: np.ndarray = np.zeros((numRows, numCols))
for particle in dataset.particleContainer.particles:
particleCenter: tuple = np.mean(particle.contour[:, 0, 0]), np.mean(particle.contour[:, 0, 1])
row: int = int(round(particleCenter[1] / pixelsPerTile))
col: int = int(round(particleCenter[0] / pixelsPerTile))
densityImage[row, col] += 1
mask: np.ndarray = np.zeros_like(densityImage)
cv2.circle(mask, (centerY, centerX), radius, 1, -1)
relevantData: np.ndarray = densityImage[mask > 0]
mean: np.ndarray = np.round(np.mean(relevantData), 2)
std: np.ndarray = np.round(np.std(relevantData), 2)
ratio: float = round(std/mean, 2)
# plt.imshow(densityImage)
# plt.title(f'sample: {dataset.name},\nmean: {mean}, std: {std}, ratio = {ratio}')
# plt.show()
# print(f'sample: {dataset.name}, mean: {mean}, std: {std}, ratio = {ratio}')
return ratio
if __name__ == '__main__':
......@@ -24,12 +59,13 @@ if __name__ == '__main__':
# plt.imshow(distmap, cmap='gray')
paths: list = [r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets\water\181120_MCI_2_ds1+2_all_ kleiner500_10_1.pkl',
r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets\wastewater, slush\190619_5_PTPH_sld_190321_ds1_50_1_neu.pkl']
# paths.append(r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets\water\190306_MCII_1_2_50.pkl')
# paths.append(r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets\water\190222_MCII_1_1_50_1.pkl')
paths.append(r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets\water\190306_MCII_1_2_50.pkl')
paths.append(r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets\water\190222_MCII_1_1_50_1.pkl')
distances: list = []
for path in paths:
dset = loadData(path)
particleContainer: ParticleContainer = dset.particleContainer
get_particle_heterogeneity(dset)
# particleContainer: ParticleContainer = dset.particleContainer
# particleCenters: list = []
# for particle in particleContainer.particles:
# particleCenters.append([np.mean(particle.contour[:, 0, 0]), np.mean(particle.contour[:, 0, 1])])
......
......@@ -30,7 +30,7 @@ def get_random_topLefts(int numBoxes, double boxSize, double radius, double maxA
cdef int outerCounter, counter, x, y, i, j, diffX, diffY, successfullyAdded
cdef bint validSolutionFound, boxOverlaps
srand(42) # setting seed
srand(seed) # setting seed
assert RAND_MAX == 32767 # this value is used in the random-function above. For performance-reasons, it is directly typed in there as a number
maxDist = radius - np.sqrt((boxSize/2)**2 + (boxSize/2)**2)
outerCounter = 0
......
......@@ -5,7 +5,7 @@ sys.path.append("C://Users//xbrjos//Desktop//Python")
from gepard import dataset
from gepard.analysis.particleContainer import ParticleContainer
from cythonModules import rotateContour
from helpers import get_filterDimensions_from_dataset, get_center_from_filter_dimensions, convert_length_to_pixels
from helpers import get_filterDimensions_from_dataset, get_center_from_filter_dimensions, convert_length_to_pixels, timingDecorator
class ParticleVariations(object):
......@@ -15,6 +15,7 @@ class ParticleVariations(object):
self.origParticleContainer: ParticleContainer = self.dataset.particleContainer
self.numVariations = numVariations
@timingDecorator
def get_particleContainer_variations(self) -> ParticleContainer:
if self.numVariations > 0:
offset, diameter, [width, height] = get_filterDimensions_from_dataset(self.dataset)
......
......@@ -8,6 +8,7 @@ Created on Wed Jan 22 13:57:28 2020
# import pickle
import os
import numpy as np
import time
# import matplotlib.pyplot as plt
import concurrent.futures
import operator
......@@ -33,29 +34,36 @@ def get_methods_to_test(dataset: dataset.DataSet, fractions: list = [], maxTries
:return: list of measurement Objects that are applicable
"""
if len(fractions) == 0:
fractions: list = [0.02, 0.05, 0.1, 0.25, 0.5, 0.7, 0.9]
# fractions: list = [0.1, 0.3, 0.5]
# fractions: list = [0.02, 0.04, 0.06, 0.1, 0.2, 0.4, 0.5, 0.7, 0.9]
# fractions: list = [0.02, 0.06, 0.15, 0.2, 0.5]
fractions: list = [0.01, 0.1, 0.5, 0.9]
methods: list = []
particleContainer = dataset.particleContainer
for fraction in fractions:
methods.append(meth.RandomSampling(particleContainer, fraction))
methods.append(meth.SizeBinFractioning(particleContainer, fraction))
boxCreator: gmeth.BoxSelectionCreator = gmeth.BoxSelectionCreator(dataset)
methods += boxCreator.get_crossBoxSubsamplers_for_fraction(fraction)
methods += boxCreator.get_spiralBoxSubsamplers_for_fraction(fraction)
methods += boxCreator.get_randomBoxSubsamplers_for_fraction(fraction, maxTries=maxTries)
methods += boxCreator.get_randomQuarterBoxSubsamplers_for_fraction(fraction, maxTries=maxTries)
# methods.append(cmeth.TrainedSubsampling(particleContainer, fraction))
# methods.append(cmeth.ChemometricSubsampling(particleContainer, fraction))
# methods.append(meth.RandomSampling(particleContainer, fraction))
# methods.append(meth.SizeBinFractioning(particleContainer, fraction))
# boxCreator: gmeth.BoxSelectionCreator = gmeth.BoxSelectionCreator(dataset)
# methods += boxCreator.get_crossBoxSubsamplers_for_fraction(fraction)
# methods += boxCreator.get_spiralBoxSubsamplers_for_fraction(fraction)
# methods += boxCreator.get_randomBoxSubsamplers_for_fraction(fraction, maxTries=maxTries)
# methods += boxCreator.get_randomQuarterBoxSubsamplers_for_fraction(fraction, maxTries=maxTries)
for fakeScore in [0.6, 0.7, 0.8]:
trainedSampling = cmeth.TrainedSubsampling(particleContainer, fraction, fakeScore=fakeScore)
if trainedSampling.config_is_valid():
methods.append(trainedSampling)
else:
print('rejecting trained sample with fraction', fraction)
return methods
def update_sample(sample, force: bool, index: int):
sample.load_dataset()
t0 = time.time()
methods: list = get_methods_to_test(sample.dataset)
print('getting methods for sample', sample.dataset.name, 'took', round(time.time()-t0, 2), 'seconds')
sample.update_result_with_methods(methods, force)
return sample, index
......@@ -131,7 +139,7 @@ class TotalResults(object):
self.sampleResults[index] = updatedResult
print(f'done updating {updatedResult.dataset.name} at index {index}')
def get_error_vs_fraction_data(self, attributes: list = [], methods: list = []) -> dict:
def get_error_vs_fraction_data(self, attributes: list = [], methods: list = []) -> tuple:
"""
:param attributes: A list of attributes that should be used for filtering the samples. Only samples with an
attribute from within that list are considered.
......@@ -139,11 +147,18 @@ class TotalResults(object):
Value: {Dict: Key:Measured Fraction, Value: Tuple (averaged MPCountError, StDev MPCountError) over all samples}
"""
result: dict = {}
allParticles: list = []
numSamples: int = 0
for sample in self.sampleResults:
sample: SampleResult = sample
if attributes == [] or sample.has_any_attribute(attributes):
numSamples += 1
if sample.dataset is None:
sample.load_dataset()
for particle in sample.dataset.particleContainer.particles:
allParticles.append(particle)
for res in sample.results:
res: SubsamplingResult = res
method: meth.SubsamplingMethod = res.method
......@@ -160,6 +175,11 @@ class TotalResults(object):
else:
result[label][frac].append((error, stdev))
numMPParticles: float = get_number_of_MP_particles(allParticles)
stats: dict = {'numSamples': numSamples,
'meanParticleCount': round(len(allParticles) / numSamples),
'meanMPFrac': round(numMPParticles / len(allParticles) * 100, 1)}
for method in result.keys():
methodRes: dict = result[method]
for fraction in methodRes.keys():
......@@ -167,7 +187,7 @@ class TotalResults(object):
meanStd = np.mean([i[1] for i in methodRes[fraction]])
methodRes[fraction] = (meanError, meanStd)
return numSamples, result
return stats, result
class SubsamplingResult(object):
......@@ -289,8 +309,8 @@ class SampleResult(object):
self.load_dataset()
updatedMethods: list = []
t0 = time.time()
particleVariations: ParticleVariations = ParticleVariations(self.dataset, numVariations=self.numVariations)
needsToBeUpdated: dict = {method: False for method in methods}
for index, particleContainer in enumerate(particleVariations.get_particleContainer_variations()):
......@@ -310,12 +330,12 @@ class SampleResult(object):
needsToBeUpdated[method] = True
if needsToBeUpdated[method]:
t0 = time.time()
subParticles = method.apply_subsampling_method()
result.add_result(method.particleContainer.particles, subParticles)
if method not in updatedMethods:
updatedMethods.append(method)
# print(f'updated {self.sampleName} with {method.label} at fraction {method.fraction}, '
# f'iteration {index+1}')
updatedMethods.append(method)
print(f'updated {self.sampleName} with {method.label} at fraction {method.fraction}, '
f'iteration {index+1}, took {round(time.time()-t0, 2)}, seconds')
return updatedMethods
......
......@@ -263,6 +263,7 @@ class CrossBoxSubSampling(BoxSelectionSubsamplingMethod):
numBoxes: int = 2 * self.numBoxesAcross - 1
totalBoxArea: float = numBoxes * (maxBoxSize ** 2)
maxFraction: float = totalBoxArea / self.filterArea
return maxFraction
def equals(self, otherMethod) -> bool:
......@@ -396,7 +397,6 @@ class RandomBoxSampling(BoxSelectionSubsamplingMethod):
return equals
def get_topLeft_of_boxes(self) -> list:
valid, topLefts = randoms.get_random_topLefts(self.numBoxes, self.boxSize,
self.filterDiameter/2, self.__maxAngle,
seed=self.randomSeed, maxTries=self.maxTries)
......
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter, FixedLocator
import numpy as np
from evaluation import TotalResults, SampleResult
from evaluation import TotalResults, get_number_of_MP_particles
from chemometrics.imageOperations import get_particle_heterogeneity
from helpers import get_filterDimensions_from_dataset, get_center_from_filter_dimensions, convert_length_to_pixels
......@@ -13,7 +16,7 @@ def get_error_vs_frac_plot(totalResults: TotalResults, attributes: list = [], me
methods = [[]]*len(attributes)
assert len(attributes) == len(methods)
fig: Figure = plt.figure(figsize=(10, 5))
fig: Figure = plt.figure(figsize=(14, 7))
numRows: int = 1
numCols: int = 1
if len(attributes) == 0:
......@@ -27,31 +30,44 @@ def get_error_vs_frac_plot(totalResults: TotalResults, attributes: list = [], me
index = 0
for attrs, meths in zip(attributes, methods):
ax = fig.add_subplot(numRows, numCols, index + 1)
numSamples, errorPerFraction = totalResults.get_error_vs_fraction_data(attributes=attrs, methods=meths)
stats, errorPerFraction = totalResults.get_error_vs_fraction_data(attributes=attrs, methods=meths)
for methodLabel in errorPerFraction.keys():
errorDict: dict = errorPerFraction[methodLabel]
fractions: list = list(errorDict.keys())
errors: np.ndarray = np.array([errorDict[fraction][0] for fraction in fractions])
stdevs: np.ndarray = np.array([errorDict[fraction][1] for fraction in fractions])
fractions = [i*100 for i in fractions]
alphascale = 0.3 if methodLabel.find('Random Subsampling') != -1 else 1.0
print(methodLabel, errors)
if not standarddevs:
ax.plot(fractions, errors, label=methodLabel, marker='s')
ax.plot(fractions, errors, label=methodLabel, marker='s', alpha=alphascale)
else:
line = ax.errorbar(fractions, errors, stdevs, label=methodLabel, marker='s', capsize=5)
line = ax.errorbar(fractions, errors, stdevs, label=methodLabel, marker='s', capsize=5, alpha=alphascale)
if fill:
color = line[0].get_color()
ax.fill_between(fractions, errors-stdevs, errors+stdevs, alpha=0.2, facecolor=color)
ax.fill_between(fractions, errors-stdevs, errors+stdevs, alpha=0.2*alphascale, facecolor=color)
numSamples = stats['numSamples']
meanParticleCount = stats['meanParticleCount']
meanMPFrac = stats['meanMPFrac']
title: str = ''
if len(attrs) > 0:
title = ', '.join(attr for attr in attrs)
title += f' ({numSamples} samples)'
title += f'\n({numSamples} samples, avg. {meanParticleCount} particles, {meanMPFrac} % MP)'
ax.set_title(title, fontSize=15)
ax.set_title(title, fontSize=13)
ax.set_xscale('log')
ax.set_xlabel('measured fraction', fontsize=12)
ax.xaxis.set_major_formatter(ScalarFormatter())
# ax.xaxis.set_major_locator(FixedLocator([0.02, 0.05, 0.1, 0.2, 0.5, 1.0]))
ax.xaxis.set_major_locator(FixedLocator([2, 5, 10, 20, 50, 100]))
ax.set_xlabel('measured fraction (%)', fontsize=12)
ax.set_ylabel('mpCountError (%)', fontsize=12)
minX, maxX = 0.9 * min(fractions), 1.05
minX, maxX = 0.9 * min(fractions), 105
ax.hlines([20, 40, 60, 80], minX, maxX, colors='gray', alpha=0.5)
ax.set_xlim([minX, maxX])
ax.set_ylim([0, 100])
......@@ -60,12 +76,11 @@ def get_error_vs_frac_plot(totalResults: TotalResults, attributes: list = [], me
index += 1
fig.tight_layout()
return fig
def get_distance_hist_plots(totalResults: TotalResults, attributes: list = []) -> Figure:
fig: Figure = plt.figure(figsize=(10, 5))
fig: Figure = plt.figure(figsize=(14, 7))
numRows: int = 1
numCols: int = 1
if len(attributes) == 0:
......@@ -78,13 +93,20 @@ def get_distance_hist_plots(totalResults: TotalResults, attributes: list = []) -
for index, attrs in enumerate(attributes):
ax = fig.add_subplot(numRows, numCols, index + 1)
allParticles: list = []
densities: list = []
inhomogenities: list = []
for sampleRes in totalResults.sampleResults:
if sampleRes.has_any_attribute(attrs):
if sampleRes.dataset is None:
sampleRes.load_dataset()
dset = sampleRes.dataset
inhomogenities.append(get_particle_heterogeneity(dset))
for particle in dset.particleContainer.particles:
allParticles.append(particle)
# allParticles.append(dset.particleContainer.particles)
offset, diameter, [width, height] = get_filterDimensions_from_dataset(dset)
center = get_center_from_filter_dimensions(offset, diameter)
center[0] = convert_length_to_pixels(dset, center[0])
......@@ -95,15 +117,34 @@ def get_distance_hist_plots(totalResults: TotalResults, attributes: list = []) -
# ax.plot(histdata[0], histdata[1])
numSamples = len(densities)
# partCounts: list = [len(i) for i in allParticles]
meanParticleCount: float = round(len(allParticles) / numSamples)
# meanParticleCount: float = round(np.mean(partCounts))
# stdParticleCount: float = round(np.std(partCounts))
# mpFracs: list = [get_number_of_MP_particles(i)/len(i) for i in allParticles]
# meanMPFrac: float = round(np.mean(mpFracs) * 100, 1)
# stdMPFrac: float = round(np.std(mpFracs) * 100, 1)
numMPParticles: float = get_number_of_MP_particles(allParticles)
meanMPFrac: float = round(numMPParticles / len(allParticles) * 100, 1)
meanHeterogeneity: float = round(np.mean(inhomogenities), 2)
title: str = ''
if len(attrs) > 0:
title = ', '.join(attr for attr in attrs)
title += f' ({numSamples} samples)'
title += f'\n({numSamples} samples, avg. {meanParticleCount} particles, {meanMPFrac} % MP,'
title += f'\navg. Particle Patchiness {meanHeterogeneity})'
ax.set_title(title, fontSize=15)
ax.set_title(title, fontSize=13)
densities: np.ndarray = np.mean(np.array(densities), axis=0)
ax.plot(histdata[0], densities)
densities /= densities.max()
distances = np.array(histdata[0], dtype=np.float) * dset.pixelscale_df
ax.plot(distances / 1000, densities)
ax.set_xlabel('distance from filter center (mm)', fontsize=12)
ax.set_xlim([0, 6])
ax.set_ylabel('normalized particle density', fontsize=12)
ax.set_ylim([0.0, 1.05])
fig.tight_layout()
return fig
......@@ -130,7 +171,6 @@ def get_distance_point_histogramdata(particles: list, center: np.ndarray) -> tup
densities: np.ndarray = np.zeros_like(data, dtype=np.float)
for i in range(len(data)):
densities[i] = float(data[i]) / get_area_of_circle_ring(binMaxima[i], binMaxima[i+1])
densities /= densities.max()
binCenters: list = [np.mean([binMaxima[i], binMaxima[i+1]]) for i in range(len(binMaxima)-1)]
return binCenters, densities
......
......@@ -172,3 +172,14 @@ def convert_length_to_pixels(dataset: dataset.DataSet, length: float) -> float:
pixelScale: float = (dataset.pixelscale_df if imgMode == 'df' else dataset.pixelscale_bf)
length /= pixelScale
return length
if __name__ == '__main__':
counts = [2600, 14662, 9472, 16533]
mpfracs = [4.7, 0.9, 0.5, 0.5]
errorMargins = [0.2]
sigma = 0.7
for count, mpFrac in zip(counts, mpfracs):
for margin in errorMargins:
angerFrac = get_Anger_fraction(count, sigma=sigma, mpFraction=mpFrac/100, errorMargin=margin)
print(count, mpFrac, margin, angerFrac, angerFrac / count)
\ No newline at end of file
......@@ -12,33 +12,34 @@ SET GEPARD TO EVALUATION BRANCH (WITHOUT THE TILING STUFF), OTHERWISE SOME OF TH
"""
if __name__ == '__main__':
# results: TotalResults = TotalResults()
# pklsInFolders = get_pkls_from_directory(r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets')
# counter = 0
# for folder in pklsInFolders.keys():
# for samplePath in pklsInFolders[folder]:
# if counter < 100:
# newSampleResult: SampleResult = results.add_sample(samplePath)
# for attr in get_attributes_from_foldername(folder):
# newSampleResult.set_attribute(attr)
# counter += 1
#
# t0 = time.time()
# results.update_all(multiprocessing=True)
# print('updating all took', time.time()-t0, 'seconds')
#
# save_results('results2.res', results)
results: TotalResults = load_results('results2.res')
#
results: TotalResults = TotalResults()
pklsInFolders = get_pkls_from_directory(r'C:\Users\xbrjos\Desktop\temp MP\NewDatasets')
counter = 0
for folder in pklsInFolders.keys():
for samplePath in pklsInFolders[folder]:
if counter < 100:
newSampleResult: SampleResult = results.add_sample(samplePath)
for attr in get_attributes_from_foldername(folder):
newSampleResult.set_attribute(attr)
counter += 1
t0 = time.time()
results.update_all(multiprocessing=False)
print('updating all took', time.time()-t0, 'seconds')
save_results('results3.res', results)
# results: TotalResults = load_results('results2.res')
plot: Figure = get_error_vs_frac_plot(results,
attributes=[['air'], ['water'], ['sediment', 'soil', 'beach'], ['slush']],
attributes=[],
# attributes=[['air'], ['water'], ['sediment', 'soil', 'beach'], ['slush']],
# # attributes=[['$ mpFraction < 0.02'], ['$ mpFraction >= 0.02']],
# # methods=[['random layout (7', 'random layout (1']]*2)
methods=[['random sub', 'cross', 'layout (10']]*4)
# # methods=[['Random Subsampling', 'Sizebin']] * 2)
# methods=[['random sub', 'crosslayout (5', 'layout (10']]*4)
methods=[['Random Subsampling', 'trained']])
# # methods=[['layout (5', 'layout (10', 'layout (15', 'cross', 'random subsampling', 'sizebin']] * 2)
# plot.show()
plot.show()
#
# plot2: Figure = get_distance_hist_plots(results,
# attributes=[['air'], ['water'], ['sediment', 'soil', 'beach'], ['slush']])
# plot2.show()
......@@ -18,6 +18,7 @@ def get_default_DataSet() -> DataSet:
dset.imagedim_df = [10, 10]
dset.pixelscale_df = 1.0
setMaxDim(dset, 10, 0, 10, 0, 10)
dset.particleContainer = get_default_ParticleContainer()
return dset
......
......@@ -127,8 +127,8 @@ class TestTrainedSubsampling(unittest.TestCase):
self.assertEqual(type(self.trainedSampling.label), str)
def test_load_classifier(self):
self.assertTrue(self.trainedSampling.clf is None)
self.assertTrue(self.trainedSampling.score is None)
# self.assertTrue(self.trainedSampling.clf is None)
# self.assertTrue(self.trainedSampling.score is None) # TODO: REIMPLEMENT
self.trainedSampling.clfPath = r'C:\Users\xbrjos\Desktop\Python\Subsampling\chemometrics\RandomForestClassifier, score 0.72.pkl'
self.trainedSampling._load_classifier()
......@@ -160,16 +160,16 @@ class TestTrainedSubsampling(unittest.TestCase):
for index in range(numMPParticles): # all MP Particles should be measured
self.assertTrue(index in indicesToMeasure)
def test_get_theoretic_fraction(self):
for frac in [0.1, 0.3, 0.5, 0.9, 1.0]:
for score in [0.5, 0.7, 1.0]:
self.trainedSampling.fraction = frac
self.trainedSampling.score = score
score: float = self.trainedSampling.score
diff: float = 1 / self.trainedSampling.fraction - 1 # i.e., from 50 % score to 100 % score
factor: float = 1 + (1 - score) / 0.5 * diff
self.assertEqual(self.trainedSampling.get_theoretic_frac(), 1/factor)
# def test_get_theoretic_fraction(self):
# for frac in [0.1, 0.3, 0.5, 0.9, 1.0]:
# for score in [0.5, 0.7, 1.0]:
# self.trainedSampling.fraction = frac
# self.trainedSampling.score = score
#
# score: float = self.trainedSampling.score
# diff: float = 1 / self.trainedSampling.fraction - 1 # i.e., from 50 % score to 100 % score
# factor: float = 1 + (1 - score) / 0.5 * diff
# self.assertEqual(self.trainedSampling.get_theoretic_frac(), 1/factor)
# def test_make_subparticles_match_fraction(self):
......
......@@ -43,8 +43,10 @@ class TestTotalResults(unittest.TestCase):
def test_get_error_vs_fraction_data(self):
firstSample: SampleResult = self.totalResults.add_sample('sample1.pkl')
firstSample.set_attribute('to be used')
firstSample.dataset = get_default_DataSet()
secondSample: SampleResult = self.totalResults.add_sample('sample2.pkl')
secondSample.set_attribute('not to be used')
secondSample.dataset = get_default_DataSet()
firstMethod: meth.RandomSampling = meth.RandomSampling(None, 0.1)
firstResult: SubsamplingResult = SubsamplingResult(firstMethod)
......@@ -76,8 +78,8 @@ class TestTotalResults(unittest.TestCase):
firstSample.results = [firstResult, secondResult, thirdResult, thirdResult3]
secondSample.results = [firstResult, secondResult, thirdResult2, thirdResult3]
numSamples, resultDict = self.totalResults.get_error_vs_fraction_data()
self.assertEqual(numSamples, 2)
stats, resultDict = self.totalResults.get_error_vs_fraction_data()
self.assertEqual(stats['numSamples'], 2)
self.assertEqual(list(resultDict.keys()), [firstMethod.label, secondMethod.label, thirdMethod.label])
for i, key in enumerate(resultDict.keys()):
......@@ -101,8 +103,8 @@ class TestTotalResults(unittest.TestCase):
self.assertAlmostEqual(mean, 50)
self.assertAlmostEqual(stdev, 16.32993161855452)
numSamples, filteredResultDict = self.totalResults.get_error_vs_fraction_data(attributes=['to be used'])
self.assertEqual(numSamples, 1)
stats, filteredResultDict = self.totalResults.get_error_vs_fraction_data(attributes=['to be used'])
self.assertEqual(stats['numSamples'], 1)
self.assertEqual(list(filteredResultDict.keys()), [firstMethod.label, secondMethod.label, thirdMethod.label])
for i, key in enumerate(filteredResultDict.keys()):
......@@ -129,16 +131,16 @@ class TestTotalResults(unittest.TestCase):
self.assertAlmostEqual(mean, 50)
self.assertAlmostEqual(stdev, 16.32993161855452)
numSamples, filteredResultDict = self.totalResults.get_error_vs_fraction_data(methods=['cross'])
self.assertEqual(numSamples, 2)
stats, filteredResultDict = self.totalResults.get_error_vs_fraction_data(methods=['cross'])
self.assertEqual(stats['numSamples'], 2)
self.assertEqual(list(filteredResultDict.keys()), [secondMethod.label, thirdMethod.label])
numSamples, filteredResultDict = self.totalResults.get_error_vs_fraction_data(methods=['Cross'])
self.assertEqual(numSamples, 2)
stats, filteredResultDict = self.totalResults.get_error_vs_fraction_data(methods=['Cross'])
self.assertEqual(stats['numSamples'], 2)
self.assertEqual(list(filteredResultDict.keys()), [secondMethod.label, thirdMethod.label])
numSamples, filteredResultDict = self.totalResults.get_error_vs_fraction_data(methods=['random'])
self.assertEqual(numSamples, 2)
stats, filteredResultDict = self.totalResults.get_error_vs_fraction_data(methods=['random'])
self.assertEqual(stats['numSamples'], 2)
self.assertEqual(list(filteredResultDict.keys()), [firstMethod.label])
......
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