aiuna 0.2101.6

Science as data transformation

aiuna scientific data for the classroom

WARNING: This project is still subject to major changes, e.g., in the next rewrite.

Installation

Examples

Creating data from ARFF file

from aiuna import *

d = file("iris.arff").data

print(d.Xd)
"""
['sepallength', 'sepalwidth', 'petallength', 'petalwidth']
"""

print(d.X[:5])
"""
[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]]
"""

print(d.y[:5])
"""
['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa']
"""

from pandas import DataFrame
print(DataFrame(d.y).value_counts())
"""
Iris-setosa        50
Iris-versicolor    50
Iris-virginica     50
dtype: int64
"""

cessing a data field as a pandas DataFrame

#from aiuna import *

#d = dataset.data  # 'iris' is the default dataset
#df = d.X_pd
#print(df.head())
#...

#mycol = d.X_pd["petal length (cm)"]
#print(mycol[:5])
#...

Creating data from numpy arrays

from aiuna import *
import numpy as np

X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([0, 1, 1])
d = new(X=X, y=y)
print(d)
"""
{
    "uuid": "06NLDM4mLEMrHPOaJvEBqdo",
    "uuids": {
        "changed": "3Sc2JjUPMlnNtlq3qdx9Afy",
        "X": "13zbQMwRwU3WB8IjMGaXbtf",
        "Y": "1IkmDz3ATFmgzeYnzygvwDu"
    },
    "step": {
        "id": "06NLDM4mLEMrHPOT2pd5lzo",
        "desc": {
            "name": "New",
            "path": "aiuna.step.new",
            "config": {
                "hashes": {
                    "X": "586962852295d584ec08e7214393f8b2",
                    "Y": "f043eb8b1ab0a9618ad1dc53a00d759e"
                }
            }
        }
    },
    "changed": [
        "X",
        "Y"
    ],
    "X": [
        "[[1 2 3]",
        " [4 5 6]",
        " [7 8 9]]"
    ],
    "Y": [
        "[[0]",
        " [1]",
        " [1]]"
    ]
}
"""

Checking history

from aiuna import *

d = dataset.data  # 'iris' is the default dataset
print(d.history)
"""
{
    "02o8BsNH0fhOYFF6JqxwaLF": {
        "name": "New",
        "path": "aiuna.step.new",
        "config": {
            "hashes": {
                "X": "19b2d27779bc2d2444c11f5cc24c98ee",
                "Y": "8baa54c6c205d73f99bc1215b7d46c9c",
                "Xd": "0af9062dccbecaa0524ac71978aa79d3",
                "Yd": "04ceed329f7c3eb43f93efd981fde313",
                "Xt": "60d4f429fcd642bbaf1d976002479ea2",
                "Yt": "4660adc31e2c25d02cb751dcb96ecfd3"
            }
        }
    }
}
"""

del d["X"]
print(d.history)
"""
{
    "02o8BsNH0fhOYFF6JqxwaLF": {
        "name": "New",
        "path": "aiuna.step.new",
        "config": {
            "hashes": {
                "X": "19b2d27779bc2d2444c11f5cc24c98ee",
                "Y": "8baa54c6c205d73f99bc1215b7d46c9c",
                "Xd": "0af9062dccbecaa0524ac71978aa79d3",
                "Yd": "04ceed329f7c3eb43f93efd981fde313",
                "Xt": "60d4f429fcd642bbaf1d976002479ea2",
                "Yt": "4660adc31e2c25d02cb751dcb96ecfd3"
            }
        }
    },
    "06fV1rbQVC1WfPelDNTxEPI": {
        "name": "Del",
        "path": "aiuna.step.delete",
        "config": {
            "field": "X"
        }
    }
}
"""

d["Z"] = 42
print(d.Z, type(d.Z))
"""
[[42]] <class 'numpy.ndarray'>
"""

print(d.history)
"""
{
    "02o8BsNH0fhOYFF6JqxwaLF": {
        "name": "New",
        "path": "aiuna.step.new",
        "config": {
            "hashes": {
                "X": "19b2d27779bc2d2444c11f5cc24c98ee",
                "Y": "8baa54c6c205d73f99bc1215b7d46c9c",
                "Xd": "0af9062dccbecaa0524ac71978aa79d3",
                "Yd": "04ceed329f7c3eb43f93efd981fde313",
                "Xt": "60d4f429fcd642bbaf1d976002479ea2",
                "Yt": "4660adc31e2c25d02cb751dcb96ecfd3"
            }
        }
    },
    "06fV1rbQVC1WfPelDNTxEPI": {
        "name": "Del",
        "path": "aiuna.step.delete",
        "config": {
            "field": "X"
        }
    },
    "05eIWbfCJS7vWJsXBXjoUAh": {
        "name": "Let",
        "path": "aiuna.step.let",
        "config": {
            "field": "Z",
            "value": 42
        }
    }
}
"""

Grants

Part of the effort spent in the present code was kindly supported by Fapesp under supervision of Prof. André C. P. L. F. de Carvalho at CEPID-CeMEAI (Grants 2013/07375-0 – 2019/01735-0).

History

The novel ideias presented here are a result of a years-long process of drafts, thinking, trial/error and rewrittings from scratch in several languages from Delphi, passing through Haskell, Java and Scala to Python - including frustration with well stablished libraries at the time. The fundamental concepts were lightly borrowed from basic category theory concepts like algebraic data structures that permeate many recent tendencies, e.g., in programming language design.

For more details, refer to https://github.com/davips/kururu