Adansons Base Document
- Product Concept
- 0. Get Access Key
- 1. Installation
- 2. Configuration
- 3. Tutorial 1: Organize meta data and Create dataset
- 4. API Reference
Product Concept
- Adansons Base is a data management tool that organizes metadata of unstructured data and creates and organizes datasets.
- It makes dataset creation more effective and helps find essential insights from training results and improves AI performance.
More detail ↓↓↓
See our product page: https://adansons.wraptas.site
0. Get Access Key
Type your email into the form below to join our slack and get the access key.
Invitation Form: https://share.hsforms.com/1KG8Hp2kwSjC6fjVwwlklZA8moen
1. Installation
Adansons Base contains Command Line Interface (CLI) and Python SDK, and you can install both with pip command.
pip install git+https://github.com/adansons/base
Note: if you want to use CLI in any directory, you have to install with the python globally installed on your computer.
2. Configuration
2.1 with CLI
when you run any Base CLI command for the first time, Base will ask your access key provided on our slack.
then, Base will verify the specified access key was correct.
if you don't have any access key, please see 0. Get Access Key.
this command will show you what projects you have
base list
Output
Welcome to Adansons Base!!
Let's start with your access key provided on our slack.
Please register your access_key: xxxxxxxxxx
Successfully configured as [email protected]
projects
========
2.2 Environment Variables
if you don’t want to configure interactively, you can use environment variables for configuration.
BASE_USER_ID is used for identification of users, this is the email address you submitted via our form.
export BASE_ACCESS_KEY=xxxxxxxxxx
export [email protected]
3. Tutorial 1: Organize meta data and Create dataset
let’s start Base tutorial with mnist dataset.
Step 0. prepare sample dataset
install dependencied for download dataset at first.
pip install pypng
then, download a script for mnist from our Base repository
curl -sSL https://raw.githubusercontent.com/adansons/base/main/download_mnist.py > download_mnist.py
run download-mnist script. you can specify any folder for downloading as last argument(default “~/dataset/mnist”). if you run this command on Windows, please replace it to windows path like “C:\dataset\mnist”
python3 ./download_mnist.py ~/dataset/mnist
Note: Base can link the data files if you put anywhere in local computer. So if you already downloaded mnist dataset, you can use it
after downloading, you can see data files in ~/dataset/mnist.
~
└── dataset
└── mnist
├── train
│ ├── 0
│ │ ├── 1.png
│ │ ├── ...
│ │ └── 59987.png
│ ├── ...
│ └── 9
└── test
├── 0
└── ...
Step 1. create new project
create mnist project with base new command.
base new mnist
Output
Your Project UID
----------------
abcdefghij0123456789
save Project UID in local file (~/.base/projects)
Base will issue a Project Unique ID and automatically save it in local file.
Step 2. import data files
after the step 0, you have many png image files on ”~/dataset/mnist” directory.
let’s upload meta data related their paths into mnist project with base import command.
base import mnist --directory ~/dataset/mnist --extension png --parse "{dataType}/{label}/{id}.png"
Note: if you changed download folder, please replace “~/dataset/mnist” in above command.
Output
Check datafiles...
found 70000 files with png extension.
Success!
Step 3. import external metadata files
if you have external meta data files, you can integrate them into existing project database with —-external-file option.
in this time, we use wrongImagesInMNISTTestset.csv published at Github by youkaichao.
https://github.com/youkaichao/mnist-wrong-test
this is the extra meta data which correct wrong label on mnist test dataset.
you can evaluate your model more strictly and correctly by using these extra meta data with Base.
download external csv
curl -SL https://raw.githubusercontent.com/youkaichao/mnist-wrong-test/master/wrongImagesInMNISTTestset.csv > ~/Downloads/wrongImagesInMNISTTestset.csv
base import mnist --external-file --path ~/Downloads/wrongImagesInMNISTTestset.csv -a dataType:test
Output
1 tables found!
now estimating the rule for table joining...
1 table joining rule was estimated!
Below table joining rule will be applied...
Rule no.1
key 'index' -> connected to 'id' key on exist table
key 'originalLabel' -> connected to 'label' key on exist table
key 'correction' -> newly added
1 tables will be applied
Table 1 sample record:
{'index': 8, 'originalLabel': 5, 'correction': '-1'}
Do you want to perform table join?
Base will join tables with that rule described above.
'y' will be accepted to approve.
Enter a value: y
Success!
Step 4. filter and export dataset with CLI
now, we are ready to create dataset.
let’s pick up a part of data files, label is 0, 1, or 2 for training, from project mnist with base search command.
you can use --conditions option for magical search filter and --query option for advanced filter.
be careful that you may get so large output on your console without -s, --summary option.
(check search docs for more information).
base search mnist --conditions "train" --query "label in ['1','2','3']"
Note: in query option, you have to specified each component as string in list without space like
“[’1’,’2’,’3’]”, when you want to operateinornot inquery.
Output
18831 files
========
'/home/xxxx/dataset/mnist/train/1/42485.png'
...
Note: If you specify no conditions or query, Base will return whole data files.
Step 5. filter and export dataset with Python SDK
in python script, you can filter and export dataset easily and simply with Project class and Files class. (see SDK docs)
from base import Project, Dataset
# export dataset as you want to use
project = Project("mnist")
files = project.files(conditions="train", query=["label in ['1','2','3']"])
print(files[0])
# this returns path-like `File` object
# -> '/home/xxxx/dataset/mnist/0/12909.png'
print(files[0].label)
# this returns the value of attribute 'lable' of first `File` object
# -> '0'
dataset = Dataset(files, target_key="label", transform=preprocess_func)
x_train, x_test, y_train, y_test = dataset.train_test_split(split_rate=0.2)
# or use with torch
import torch
dataset = Dataset(files, target_key="label", transform=preprocess_func)
loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
finally, let’s try one of most characteristic use cases on Adansons Base.
in the external file you imported in step.3, some mnist test data files are annotated as “-1” in correction column. this means that it is difficult to classify that files even for human.
so, you should exclude that files from your dataset to evaluate your AI models more properly.
# you can exclude files which have "-1" on "correction" with below code
eval_files = project.files(conditions="test", query=["correction != -1"])
print(len(eval_files))
# this returns the number of files matched with requested conditions or query
# -> 9963
eval_dataset = Dataset(eval_files, target_key="label", transform=preprocess_func)
2 Jan 11, 2022
9 Dec 28, 2022
21 Jan 6, 2023
261 Jan 4, 2023
4 Oct 26, 2021
7 Oct 30, 2022
1 Oct 17, 2022
4 Jan 26, 2022
42 Dec 22, 2022
3 Apr 5, 2022
3 Nov 18, 2021
8 Sep 13, 2022
18.6k Jan 2, 2023
16 Oct 6, 2022
9 Nov 27, 2022
1 Jan 2, 2022
1 Jan 10, 2022
2 Nov 12, 2021
2 Jan 2, 2022