def test_incremental_basic(scheduler, xy_classification):
X, y = xy_classification
with scheduler() as (s, [a, b]):
est1 = SGDClassifier(random_state=0, tol=1e-3)
est2 = clone(est1)
clf = Incremental(est1)
result = clf.fit(X, y, classes=[0, 1])
for slice_ in da.core.slices_from_chunks(X.chunks):
est2.partial_fit(X[slice_], y[slice_[0]], classes=[0, 1])
assert result is clf
assert isinstance(result.estimator.coef_, np.ndarray)
np.testing.assert_array_almost_equal(result.estimator.coef_,
est2.coef_)
assert_estimator_equal(clf.estimator, est2, exclude=['loss_function_'])
# Predict
result = clf.predict(X)
expected = est2.predict(X)
assert isinstance(result, da.Array)
assert_eq(result, expected)
# score
result = clf.score(X, y)
expected = est2.score(X, y)
# assert isinstance(result, da.Array)
assert_eq(result, expected)
clf = Incremental(SGDClassifier(random_state=0, tol=1e-3))
clf.partial_fit(X, y, classes=[0, 1])
assert_estimator_equal(clf.estimator, est2, exclude=['loss_function_'])
def test_incremental_basic(scheduler, dataframes):
# Create observations that we know linear models can recover
n, d = 100, 3
rng = da.random.RandomState(42)
X = rng.normal(size=(n, d), chunks=30)
coef_star = rng.uniform(size=d, chunks=d)
y = da.sign(X.dot(coef_star))
y = (y + 1) / 2
if dataframes:
X = dd.from_array(X)
y = dd.from_array(y)
with scheduler() as (s, [_, _]):
est1 = SGDClassifier(random_state=0, tol=1e-3, average=True)
est2 = clone(est1)
clf = Incremental(est1, random_state=0)
result = clf.fit(X, y, classes=[0, 1])
assert result is clf
# est2 is a sklearn optimizer; this is just a benchmark
if dataframes:
X = X.to_dask_array(lengths=True)
y = y.to_dask_array(lengths=True)
for slice_ in da.core.slices_from_chunks(X.chunks):
est2.partial_fit(X[slice_].compute(),
y[slice_[0]].compute(),
classes=[0, 1])
assert isinstance(result.estimator_.coef_, np.ndarray)
rel_error = np.linalg.norm(clf.coef_ - est2.coef_)
rel_error /= np.linalg.norm(clf.coef_)
assert rel_error < 0.9
assert set(dir(clf.estimator_)) == set(dir(est2))
# Predict
result = clf.predict(X)
expected = est2.predict(X)
assert isinstance(result, da.Array)
if dataframes:
# Compute is needed because chunk sizes of this array are unknown
result = result.compute()
rel_error = np.linalg.norm(result - expected)
rel_error /= np.linalg.norm(expected)
assert rel_error < 0.3
# score
result = clf.score(X, y)
expected = est2.score(*dask.compute(X, y))
assert abs(result - expected) < 0.1
clf = Incremental(SGDClassifier(random_state=0, tol=1e-3,
average=True))
clf.partial_fit(X, y, classes=[0, 1])
assert set(dir(clf.estimator_)) == set(dir(est2))
def test_incremental_basic(scheduler):
# Create observations that we know linear models can recover
n, d = 100, 3
rng = da.random.RandomState(42)
X = rng.normal(size=(n, d), chunks=30)
coef_star = rng.uniform(size=d, chunks=d)
y = da.sign(X.dot(coef_star))
y = (y + 1) / 2
with scheduler() as (s, [_, _]):
est1 = SGDClassifier(random_state=0, tol=1e-3, average=True)
est2 = clone(est1)
clf = Incremental(est1, random_state=0)
result = clf.fit(X, y, classes=[0, 1])
for slice_ in da.core.slices_from_chunks(X.chunks):
est2.partial_fit(X[slice_], y[slice_[0]], classes=[0, 1])
assert result is clf
assert isinstance(result.estimator_.coef_, np.ndarray)
rel_error = np.linalg.norm(clf.coef_ - est2.coef_)
rel_error /= np.linalg.norm(clf.coef_)
assert rel_error < 0.9
assert set(dir(clf.estimator_)) == set(dir(est2))
# Predict
result = clf.predict(X)
expected = est2.predict(X)
assert isinstance(result, da.Array)
rel_error = np.linalg.norm(result - expected)
rel_error /= np.linalg.norm(expected)
assert rel_error < 0.2
# score
result = clf.score(X, y)
expected = est2.score(X, y)
assert abs(result - expected) < 0.1
clf = Incremental(SGDClassifier(random_state=0, tol=1e-3,
average=True))
clf.partial_fit(X, y, classes=[0, 1])
assert set(dir(clf.estimator_)) == set(dir(est2))
x_testPDscaled = scalerPD.transform(x_testPD)
##### MLP Model
learnermlpPD.fit(x_trainPDscaled, y_trainPD, classes=numpy.unique(y_trainPD))
print('PD done training model')
#result = mlpPD.predict([[1,1,1,1]])
#prob_results = mlpPD.predict_proba([[1,1,1,1]])
##### Random Forest Model
##learnerrfPD.fit(x_trainPDscaled,y_trainPD, classes=numpy.unique(y_trainPD))
##
##forest_result = model.predict([[1,1,1,1]])
##forest_prob_result = model.predict_proba([[1,1,1,1]])
#####Testing
predictions_mlpPD = learnermlpPD.predict(x_testPDscaled)
##predictions_forestPD = learnerrfPD.predict(x_testPDscaled)
print('PD done predicting')
print('NN PD Confusion Matrix\n' +
str(confusion_matrix(y_testPD, predictions_mlpPD)) + '\n')
print('NN PD Classification Report \n' +
classification_report(y_testPD, predictions_mlpPD))
##print('RF Confusion Matrix\n' + str(confusion_matrix(y_testPD,predictions_forestPD)) + '\n')
##print('RF Classification Report \n'+ classification_report(y_testPD,predictions_forestPD))
##
############### Delay Amount
#Create one single learner instance to be used throughout this code block
mlpDA = MLPClassifier(hidden_layer_sizes=(4, 4),
max_iter=300,
def main():
t0 = time.time()
basepath = "/home/eline/OneDrive/__NiiFormat1" # Path to the patient folders.
patientPaths, patientIDs = getData.GetPatients(basepath)
patientIDs = np.array(patientIDs)
# Choose which scans to include.
t2 = ["T2"]
dwi = [
"DWI_b00", "DWI_b01", "DWI_b02", "DWI_b03", "DWI_b04", "DWI_b05",
"DWI_b06"
]
ffe = []
t1t2sense = []
scantypes = [t2, dwi, ffe, t1t2sense]
scans = []
for type in scantypes:
if type:
scans.append(type)
# Choose the mask/ground truth.
maskchoice = "union" # an, shh, intersection or union
# Creating dictionaries to store patient image data and the masks.
dataDict, groundTruthDict, imsizes = buildData.buildDataset(
patientPaths, patientIDs, scans, maskchoice)
# Choose cross-validator.
crossvalidator = options.select_cross_validator(
"leave-One-Out") # K-fold or leave-One-Out
loadtime = time.time()
zeroIndex = {}
dice = []
# Train model.
for train_index, test_index in crossvalidator.split(patientIDs):
# First splitting the data and building dask arrays.
trainingX, trainingY = buildData.get_data_for_training(
dataDict, groundTruthDict, patientIDs[train_index])
testX, testY = buildData.get_data_for_test(dataDict, groundTruthDict,
patientIDs[test_index],
zeroIndex)
# Using incremental learning (out of core learning) because of the large amount of data.
estimator = sklearn.linear_model.SGDClassifier(
) # Estimator have to have partial_fit API implemented.
clf = Incremental(estimator, scoring='accuracy')
clf.fit(trainingX, trainingY, classes=[True, False])
data = clf.predict(testX)
# Per patient predictions.
index = 0
for patientID in patientIDs[test_index]:
# Get the voxels belonging to the patient.
size = len(groundTruthDict[patientID])
pred = data[index:index + size].compute(
) # compute() is needed to access the values in a Dask array.
truth = testY[index:index + size].compute()
# Set rows which contained at least one zero as background (0).
for element in zeroIndex[patientID]:
pred[element] = 0
# Remove small areas/volumes from the predicted mask.
pred = processResults.remove_small_areas2D(pred,
imsizes[patientID])
#pred = processResults.remove_small_areas3D(pred, imsizes[patientID])
# Calculate the confusion matrix.
confusionMatrix = confusion_matrix(truth, pred)
# Calculate the DICE score.
diceScore = processResults.calculate_dice(confusionMatrix)
dice.append([patientID, diceScore])
# Save prediction as nifti file.
filename = 'predict' + patientID + '.nii'
predimage = processResults.array_to_image(pred, imsizes[patientID])
sitk.WriteImage(predimage, filename)
# Increase index to the starting index of the next patient.
index += size
t1 = time.time()
print('loadtime: ' + str(loadtime - t0))
print('traintime: ' + str(t1 - loadtime))
print('runtime: ' + str(t1 - t0))
# Save the DICE scores in a text file.
processResults.save_dice_scores(dice, "diceScores")
# Calculate the mean DSC value.
sum = 0
n = 0
for i in dice:
sum += i[1]
n += 1
print(sum / n)
