Created
February 2, 2022 09:49
-
-
Save VincentRouvreau/db57e57ab12ba66dbc7a68b2242b6494 to your computer and use it in GitHub Desktop.
Not square cubical complexes
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# Standard scientific Python imports | |
import numpy as np | |
# Standard scikit-learn imports | |
from sklearn.datasets import fetch_openml | |
from sklearn.pipeline import Pipeline | |
from sklearn.model_selection import train_test_split | |
from sklearn.svm import SVC | |
from sklearn import metrics | |
# Import TDA pipeline requirements | |
from gudhi.sklearn.cubical_persistence import CubicalPersistence | |
from gudhi.representations import PersistenceImage, DiagramSelector | |
X_, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) | |
X = np.zeros(shape=(70000, 840)) | |
for idx in range(len(X)): | |
X[idx] = np.append(X_[idx],np.zeros(56)) | |
# Target is: "is an eight ?" | |
y = (y == "8") * 1 | |
print("There are", np.sum(y), "eights out of", len(y), "numbers.") | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0) | |
pipe = Pipeline( | |
[ | |
("cub_pers", CubicalPersistence(persistence_dimension=0, dimensions=[28, 30], n_jobs=-2)), | |
("finite_diags", DiagramSelector(use=True, point_type="finite")), | |
( | |
"pers_img", | |
PersistenceImage(bandwidth=50, weight=lambda x: x[1] ** 2, im_range=[0, 256, 0, 256], resolution=[20, 20]), | |
), | |
("svc", SVC()), | |
] | |
) | |
# Learn from the train subset | |
pipe.fit(X_train, y_train) | |
# Predict from the test subset | |
predicted = pipe.predict(X_test) | |
print(f"Classification report for TDA pipeline {pipe}:\n" f"{metrics.classification_report(y_test, predicted)}\n") |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment