wehub-resource-sync
129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from datasets import load_dataset
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from datasets.arrow_dataset import Dataset
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from PIL import Image
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from sklearn.neighbors import NearestNeighbors
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from cleanlab.datalab.datalab import Datalab
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SEED = 42
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LABEL_NAME = "star"
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@pytest.fixture
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def dataset():
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data_dict = {
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"id": [
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"7bd227d9-afc9-11e6-aba1-c4b301cdf627",
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"7bd22905-afc9-11e6-a5dc-c4b301cdf627",
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"7bd2299c-afc9-11e6-85d6-c4b301cdf627",
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"7bd22a26-afc9-11e6-9309-c4b301cdf627",
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"7bd22aba-afc9-11e6-8293-c4b301cdf627",
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],
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"package_name": [
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"com.mantz_it.rfanalyzer",
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"com.mantz_it.rfanalyzer",
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"com.mantz_it.rfanalyzer",
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"com.mantz_it.rfanalyzer",
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"com.mantz_it.rfanalyzer",
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],
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"review": [
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"Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.",
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"Great It's not fully optimised and has some issues with crashing but still a nice app especially considering the price and it's open source.",
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"Works on a Nexus 6p I'm still messing around with my hackrf but it works with my Nexus 6p Trond usb-c to usb host adapter. Thanks!",
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"The bandwidth seemed to be limited to maximum 2 MHz or so. I tried to increase the bandwidth but not possible. I purchased this is because one of the pictures in the advertisement showed the 2.4GHz band with around 10MHz or more bandwidth. Is it not possible to increase the bandwidth? If not it is just the same performance as other free APPs.",
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"Works well with my Hackrf Hopefully new updates will arrive for extra functions",
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],
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"date": [
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"October 12 2016",
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"August 23 2016",
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"August 04 2016",
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"July 25 2016",
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"July 22 2016",
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],
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"star": [4, 4, 5, 3, 5],
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"version_id": [1487, 1487, 1487, 1487, 1487],
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}
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return Dataset.from_dict(data_dict)
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@pytest.fixture
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def label_name():
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return LABEL_NAME
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@pytest.fixture
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def lab(dataset, label_name):
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return Datalab(data=dataset, label_name=label_name)
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@pytest.fixture
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def large_lab():
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np.random.seed(SEED)
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N = 100
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K = 2
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data = np.random.rand(N, 2)
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labels = np.random.randint(0, K, size=N)
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pred_probs = np.random.rand(N, K)
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pred_probs /= pred_probs.sum(axis=1, keepdims=True)
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lab = Datalab(
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data={"features": data, "label": labels, "pred_probs": pred_probs}, label_name="label"
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)
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knn = NearestNeighbors(n_neighbors=25, metric="euclidean").fit(data)
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knn_graph = knn.kneighbors_graph(mode="distance")
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lab.info["statistics"]["unit_test_knn_graph"] = knn_graph
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return lab
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@pytest.fixture
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def pred_probs(dataset):
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np.random.seed(SEED)
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return np.random.rand(len(dataset), 3)
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@pytest.fixture
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def custom_issue_manager():
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from cleanlab.datalab.internal.issue_manager.issue_manager import IssueManager
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class CustomIssueManager(IssueManager):
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issue_name = "custom_issue"
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def find_issues(self, custom_argument: int = 1, **_) -> None:
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# Flag example as an issue if the custom argument equals its index
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scores = [
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abs(i - custom_argument) / (i + custom_argument)
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for i in range(len(self.datalab.data))
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]
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self.issues = pd.DataFrame(
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{
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f"is_{self.issue_name}_issue": [
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i == custom_argument for i in range(len(self.datalab.data))
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],
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self.issue_score_key: scores,
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},
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)
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summary_score = np.mean(scores)
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self.summary = self.make_summary(score=summary_score)
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return CustomIssueManager
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def generate_image():
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arr = np.random.randint(low=0, high=256, size=(300, 300, 3), dtype=np.uint8)
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img = Image.fromarray(arr, mode="RGB")
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return img
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@pytest.fixture
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def image_dataset():
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data_path = "./tests/datalab/data"
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dataset = load_dataset(
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"imagefolder",
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data_dir=data_path,
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split="train",
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)
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return dataset
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