Python client for oka repository
Project description
oka - Client for OKA repository
Overview
oka is a client for Oka repository.
It also provides utilities to process data.
Installation
...as a standalone lib
# Set up a virtualenv.
python3 -m venv venv
source venv/bin/activate
# Install from PyPI...
pip install --upgrade pip
pip install -U oka
pip install -U oka[full] # use the flag 'full' for extra functionality (recommended)
# ...or, install from updated source code.
pip install git+https://github.com/rabizao/oka
...from source
sudo apt install python3.8-venv python3.8-dev python3.8-distutils # For Debian-like systems.
git clone https://github.com/rabizao/oka
cd oka
python3.8 -m venv venv
source venv/bin/activate
pip install -e .
Usage
Hello world
from oka import Oka, generate_token, toy_df
# Create a pandas dataframe.
df = toy_df()
print(df.head())
"""
attr1 attr2 class
0 5.1 6.4 0
1 1.1 2.5 1
2 6.1 3.6 0
3 1.1 3.5 1
4 3.1 2.5 0
"""
# Login.
token = generate_token("http://localhost:5000")
client = Oka(token, "http://localhost:5000")
# Store.
id = client.send(df)
# Store again.
id = client.send(df)
"""
Content already stored for id iJ_e4463c51904e9efb800533d25082af2a7bf77
"""
# Fetch.
df = client.get(id)
print(df.head())
"""
attr1 attr2 class
0 5.1 6.4 0
1 1.1 2.5 1
2 6.1 3.6 0
3 1.1 3.5 1
4 3.1 2.5 0
"""
DataFrame by hand
import pandas as pd
from oka import Oka, generate_token
# Create a pandas dataframe.
df = pd.DataFrame(
[[1, 2, "+"],
[3, 4, "-"]],
index=["row 1", "row 2"],
columns=["col 1", "col 2", "class"],
)
print(df.head())
"""
col 1 col 2 class
row 1 1 2 +
row 2 3 4 -
"""
# Login.
token = generate_token("http://localhost:5000")
client = Oka(token, "http://localhost:5000")
# Store.
id = client.send(df)
# Store again.
id = client.send(df)
"""
Content already stored for id f7_6b9deafec2562edde56bfdc573b336b55cb16
"""
# Fetch.
df = client.get(id)
print(df.head())
"""
col 1 col 2 class
row 1 1 2 +
row 2 3 4 -
"""
Machine Learning workflow
from pprint import pprint
from idict import let, idict
from idict.function.classification import fit, predict
from idict.function.evaluation import split
from sklearn.ensemble import RandomForestClassifier as RF
d = (
idict.fromtoy()
>> split
>> let(fit, algorithm=RF, config={"n_estimators": 55}, Xin="Xtr", yin="ytr")
>> let(predict, Xin="Xts")
)
print(d.z)
"""
[1 0 1 0 1 1 1]
"""
pprint(d.history)
"""
{'fit--------------------------------idict': {'code': 'def f(algorithm=None, '
"config={}, Xin='X', "
"yin='y', "
"output='model', "
'version=0, **kwargs):\n'
'obj = '
'algorithm(**config)\n'
'obj.fit(kwargs[Xin], '
'kwargs[yin])\n'
'return {output: obj, '
"'_history': ...}",
'description': 'Induce a model.',
'name': 'fit',
'parameters': {'Xin': 'Xtr',
'algorithm': <class 'sklearn.ensemble._forest.RandomForestClassifier'>,
'config': {'n_estimators': 55},
'output': 'model',
'version': 0,
'yin': 'ytr'}},
'predict----------------------------idict': {'code': "def f(input='model', "
"Xin='X', yout='z', "
'version=0, **kwargs):\n'
'return {yout: '
'kwargs[input].predict(kwargs[Xin]), '
"'_history': ...}",
'description': 'Predict values '
'according to a '
'model.',
'name': 'predict',
'parameters': {'Xin': 'Xts',
'input': 'model',
'version': 0,
'yout': 'z'}},
'split----------------------sklearn-1.0.1': {'code': "def f(input=['X', 'y'], "
"config={'test_size': "
"0.33, 'shuffle': True, "
"'stratify': 'y', "
"'random_state': 0}, "
'**kwargs):\n'
"if input != ['X', "
"'y']:\n"
' raise '
'Exception(f"Not '
'implemented for '
'input/output different '
'from default values: '
'{input}")\n'
'from '
'sklearn.model_selection '
'import '
'train_test_split\n'
'args = {}\n'
'for i, _ in '
'enumerate(input):\n'
' args[input[i]] = '
'kwargs[input[i]]\n'
'else:\n'
" if 'stratify' in "
'config:\n'
' if '
"isinstance(config['stratify'], "
'str):\n'
' if '
"config['stratify'] not "
'in input:\n'
' raise '
'Exception(f"Missing '
'field '
"{config['stratify']} "
'for stratification.")\n'
' '
"config['stratify'] = "
"args[config['stratify']]\n"
' Xtr, Xts, ytr, yts '
'= '
'train_test_split(*(args.values)(), '
'**config)\n'
" return {'Xtr':Xtr, "
"'ytr':ytr, 'Xts':Xts, "
"'yts':yts, "
"'_history':...}",
'description': 'Split data in '
'two sets.',
'name': 'split',
'parameters': {'config': {'random_state': 0,
'shuffle': True,
'stratify': array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]),
'test_size': 0.33},
'input': ['X',
'y']}}}
"""
More info
Aside from the papers on identification and on similarity (not ready yet), the PyPI package and GitHub repository,
A lower level perspective is provided in the API documentation.
Grants
This work was supported by Fapesp under supervision of Prof. André C. P. L. F. de Carvalho at CEPID-CeMEAI (Grants 2013/07375-0 – 2019/01735-0).
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