add files

.gitignore

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+.venv
+.vscode

LICENSE

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+MIT License
+
+Copyright (c) 2020 Cristian Garcia
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# point-cloud-mnist-2D

main.py

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+import json
+import os
+from pathlib import Path
+import random
+
+import numpy as np
+import pandas as pd
+import typer
+from tqdm import tqdm
+from datasets.load import load_dataset
+
+np.random.seed(42)
+random.seed(420)
+
+
+def main(viz: bool = False):
+
+    dataset = load_dataset("mnist")
+    dataset.set_format("np")
+    X_train = dataset["train"]["image"]
+    y_train = dataset["train"]["label"]
+    X_test = dataset["test"]["image"]
+    y_test = dataset["test"]["label"]
+
+    X_train = to_sparse(X_train)
+    X_test = to_sparse(X_test)
+
+    max_length = max(x.shape[0] for x in X_train + X_test)
+
+    X_train = shuffle_and_pad(X_train, max_length, "train").astype(np.uint8)
+    X_train = X_train.reshape(X_train.shape[0], -1)
+    X_test = shuffle_and_pad(X_test, max_length, "test").astype(np.uint8)
+    X_test = X_test.reshape(X_test.shape[0], -1)
+
+    data_path = Path("data")
+    np.save(data_path / "X_train.npy", X_train)
+    np.save(data_path / "y_train.npy", y_train)
+    np.save(data_path / "X_test.npy", X_test)
+    np.save(data_path / "y_test.npy", y_test)
+
+    return
+
+    data_train = np.concatenate([y_train[:, None], X_train], axis=-1)
+    data_test = np.concatenate([y_test[:, None], X_test], axis=-1)
+
+
+    columns = [[f"x{i}", f"y{i}", f"v{i}"] for i in range(max_length)]
+    columns = ["label"] + sum(columns, [])
+
+    df_train = pd.DataFrame(data_train, columns=columns)
+    df_test = pd.DataFrame(data_test, columns=columns)
+    test_labels = pd.DataFrame(data_test[:, :1], columns=columns[:1])
+
+    os.makedirs("data/point-cloud-mnist-2D", exist_ok=True)
+
+    print("saving train")
+    df_train.to_csv("data/point-cloud-mnist-2D/train.csv", index=False)
+    print("saving test")
+    df_test.to_csv("data/point-cloud-mnist-2D/test.csv", index=False)
+    print("saving test labels")
+    test_labels.to_csv("data/point-cloud-mnist-2D/test_labels.csv", index=False)
+
+
+def shuffle_and_pad(X, k, name):
+
+    samples = []
+
+    padding = np.array([[-1, -1, -1]], dtype=np.int32)
+
+    for x in tqdm(X, desc=f"Padding {name}_{k}"):
+        N = len(x)
+
+        np.random.shuffle(x)
+
+        if N < k:
+            x = np.concatenate([x, np.tile(padding, (k - N, 1))])
+
+        samples.append(x)
+
+    samples = np.stack(samples, axis=0).astype(np.int32)
+
+    return samples
+
+
+def to_sparse(X: np.ndarray) -> np.ndarray:
+
+    N = len(X)
+    w, h = X.shape[1:]
+
+    xx, yy = np.meshgrid(np.arange(w), np.arange(h)[::-1])
+
+    xx = xx[None]
+
+    xx = np.tile(xx, [N, 1, 1])
+
+    yy = yy[None]
+    yy = np.tile(yy, [N, 1, 1])
+
+    X = np.stack([xx, yy, X], axis=-1)
+    X = X.reshape(N, -1, 3)
+
+    return [Xi[Xi[:, 2] > 0] for Xi in X]
+
+
+if __name__ == "__main__":
+    typer.run(main)

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pyproject.toml

@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "point-cloud-mnist-2d"
+version = "0.1.0"
+description = ""
+authors = ["Cristian Garcia <cgarcia.e88@gmail.com>"]
+
+[tool.poetry.dependencies]
+python = "^3.7"
+numpy = "^1.18.1"
+pandas = "^1.0.1"
+typer = "^0.0.8"
+idx2numpy = "^1.2.2"
+tqdm = "^4.42.1"
+datasets = "^1.14.0"
+
+[tool.poetry.dev-dependencies]
+debugpy = "^1.5.1"
+
+[build-system]
+requires = ["poetry>=0.12"]
+build-backend = "poetry.masonry.api"