Tube segmentation

from brainlit.utils import session
from brainlit.feature_extraction import *
import napari

/opt/buildhome/python3.7/lib/python3.7/site-packages/nilearn/datasets/__init__.py:96: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
  "Numpy arrays.", FutureWarning)

url = "s3://open-neurodata/brainlit/brain1"
sess = session.NeuroglancerSession(url=url, url_segments=url+"_segments", mip=0)
SEGLIST=[101,103,106,107,109,11,111,112,115,11,12,120,124,126,127,129,13,132,133,136,137,14,140,141,142,143,144,145,146,147,149,150]
SEGLIST = SEGLIST[:1]

# %%capture
nbr = NeighborhoodFeatures(url=url, radius=1, offset=[50,50,50], segment_url=url+"_segments")
nbr.fit(seg_ids=SEGLIST, num_verts=10, file_path='demo', batch_size=10)

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import glob, feather
feathers = glob.glob('*.feather')


for count, feather_file in enumerate(feathers):
    if count == 0:
        data = feather.read_dataframe(feather_file)
    else:
        df = feather.read_dataframe(feather_file)
        data = pd.concat([data, df])
data.shape

(20, 30)

data.head()

	Segment	Vertex	Label	0	1	2	3	4	5	6	...	17	18	19	20	21	22	23	24	25	26
0	101	0	1	28778	30111	30120	28438	29909	30092	28315	...	28751	30383	30683	29420	29565	30769	29762	28865	29364	29687
1	101	0	0	12290	12090	12222	12340	12215	12376	12185	...	12208	12144	11845	12030	12682	12340	12336	12194	12383	12067
2	101	1	1	15429	16558	17587	15353	16662	17459	15462	...	18105	14926	16441	17088	15187	16712	17474	15401	16939	18366
3	101	1	0	12166	12290	12180	12162	12129	12124	12058	...	12297	12090	12038	12144	11959	11929	12116	12093	12298	12407
4	101	2	1	13005	12535	12333	12903	12660	12402	12846	...	12502	12388	12404	12471	12860	12526	12441	12951	12771	12571

5 rows × 30 columns

from sklearn.preprocessing import StandardScaler
X = data.iloc[:, 3:]
X = StandardScaler().fit_transform(X)

y = data["Label"]

from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=1)
clf = MLPClassifier(hidden_layer_sizes=4, activation="logistic", alpha=1, max_iter=1000).fit(X_train, y_train)
y_score = clf.predict_proba(X_test)

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
fpr, tpr, _ = roc_curve(y_test, y_score[:,1])
roc_auc = auc(fpr, tpr)

plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('MLP ROC')
plt.legend(loc="lower right")
plt.show()

from brainlit.feature_extraction.neighborhood import subsample

X.shape

(20, 27)

from sklearn.linear_model import LogisticRegression
Xc_train, Xc_test, yc_train, yc_test = train_test_split(X, y, stratify=y, random_state=1)
clf = LogisticRegression(random_state=1, max_iter=2000).fit(Xc_train, yc_train)
yc_score = clf.predict_proba(Xc_test)

fpr_c, tpr_c, _ = roc_curve(yc_test, yc_score[:,1])
roc_auc_c = auc(fpr_c, tpr_c)

plt.figure()
lw = 2
plt.plot(fpr_c, tpr_c, color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc_c)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('LogRegression Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()