py4ai-core 1.0.0

A Python library of data structures optimized for machine learning tasks

py4ai core

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A Python library defining data structures optimized for machine learning pipelines

What is it ?

py4ai-core is a Python package with modular design that provides powerful abstractions to build data ingestion pipelines and run end to end machine learning pipelines. The library offers lightweight object-oriented interface to MongoDB as well as Pandas based data structures. The aim of the library is to provide extensive support for developing machine learning based applications with a focus on practicing clean code and modular design.

Features

Some cool features that we are proud to mention are:

Data layers

1. Archiver: Offers an object-oriented design to perform ETL on Mongodb collections as well as Pandas DataFrames.
2. DAO: Data Access Object to allow archivers to serialize domain objects into the proper persistence layer support object (e.g. in the case of MongoDB, a DAO serializes a domain object into a MongoDB document) and to parse objects retrieved from the given persistence layer in the correct representation in our framework (e.g. a text will be parsed in a Document while tabular data will be parsed in a pandas DataFrame).
3. Database: Object representing a relational database
4. Table: Object representing a table of a relational database

Data Model

Offers the following data structures:

1. Document : Data structure specifically designed to work with NLP applications that parses a json-like document into a couple of uuid and dictionary of information.
2. Sample : Data structure representing an observation (a.k.a. sample) as used in machine learning applications
3. MultiFeatureSample : Data structure representing an observation defined by a nested list of arrays.
4. Dataset : Data structure designed to be used specifically for machine learning applications representing a collection of samples.

Installation

From pypi server

pip install py4ai-core

From source

git clone https://github.com/NicolaDonelli/py4ai-core
cd py4ai-core
make install

Tests

make tests

Checks

To run predefined checks (unit-tests, linting checks, formatting checks and static typing checks):

make checks

Examples

Data Layers

The Data Layer abstractions are designed to decouple the business layers from the detail of the persistence layer implementation. The basic abstraction that will make this possible is the Repository.

As an example, imagine to have a domain business object Entity

from pydantic import BaseModel

class Entity(BaseModel):
    my_id: int
    my_data: str

To start with, imagine we want to use csv files store on disk as a persistence layer. To do so, we will use the CsvRepository that uses pandas DataFrames stored in memory and written to the disk as csv. Thus, we need to write the business logic to serialize the Entity into a row of the pandas DataFrame, i.e. a pandas Series:

import pandas as pd
from py4ai.core.data.layer.common.serialiazer import DataSerializer

class EntitySerializer(DataSerializer[int, int, Entity, pd.Series]):
    def to_object(self, entity: Entity) -> pd.Series:
        return pd.Series(entity.dict())

    def to_entity(self, document: pd.Series) -> Entity:
        return Entity(**document)

    def to_object_key(self, key: int) -> int:
        return key

    def get_key(self, entity: Entity) -> int:
        return entity.my_id

We can now instantiate the repository class that has all the methods for reading and writing objects from the persistence layer.

from py4ai.core.data.layer.pandas.repository import CsvRepository

repo = CsvRepository(filename, EntitySerializer())

entity = Entity(my_id=1234, my_data="Important data")

# This will create the entity in the persistence layer
await repo.create(entity)

# Retrieving the entity
retrieved = repo.retrieve(key=1234)

# Retrieving all entities
all_entities = repo.list()

Imagine now that, given the data increase in size, we now would like to change the persistence layer with a proper backend into something more structured and scalable, such as a NoSQL document-based data platform, such as MongoDB. We only need to create a new business logic to serialize/deserialize our class into a json (represented in python by a dictionary):

from bson import ObjectId
from py4ai.core.data.layer.mongo.serializer import create_mongo_id
from py4ai.core.data.layer.common.serialiazer import DataSerializer

class MongoDataSerializer(DataSerializer[int, ObjectId, Entity, dict]):
    def get_key(self, entity: Entity) -> int:
        return entity.my_id

    def to_object(self, entity: Entity) -> dict:
        doc = entity.dict()
        doc["_id"] = self.to_object_key(self.get_key(entity))
        return doc

    def to_entity(self, document: dict) -> Entity:
        return Entity(**document)

    def to_object_key(self, key: int) -> ObjectId:
        # This converts a string into an hash compatible with MongoDB format
        return create_mongo_id(str(key))

A new repository based on the MongoDB persistence layer can now be created using

from py4ai.core.data.layer.mongo.repository import MongoRepository

repo = MongoRepository(collection, MongoDataSerializer())

This repository is compatible with the previous and can be used in place of the previous one, having the same signatures.

Abstracting Data Querying

The Repository abstraction also allow to retrieve data based on certain query/filters:

entities = repo.retrieve_by_criteria(criteria)

However, the format of the query also depends on the type of the persistence layer and more specifically on how the data are organized. Therefore, in order to abstract and decouple the notion of the underlying persistence layer, we need to define a general class containing the possible queries for a certain database:

from typing import Generic
from abc import ABC, abstractmethod

from py4ai.core.data.layer.common.criteria import SearchCriteria

class EntityCriteriaFactory(ABC, Generic[Q]):
    @abstractmethod
    def by_id(self, id: int) -> SearchCriteria[Q]:
        ...

When considering a particular persistence layer, the querying business logic needs to be specified

from py4ai.core.data.layer.mongo.criteria import MongoSearchCriteria

class MongoCriteriaFactory(EntityCriteriaFactory[Dict[str, Any]]):
    
    def by_id(self, my_id: int) -> MongoSearchCriteria:
        return MongoSearchCriteria({"my_id": my_id})

criteria = MongoCriteriaFactory()

entities = repo.retrieve_by_criteria(criteria.by_id(1234))

Note that SearchCriteria can be also joined using logical operators:

entities = repo.retrieve_by_criteria(
    criteria.by_id(1234) & criteria.by_id(1235) 
)

entities = repo.retrieve_by_criteria(
    criteria.by_id(1234) | criteria.by_id(1235) 
)

Data Model

Creating a PandasDataset object

import pandas as pd
import numpy as np
from py4ai.core.data.model.ml import PandasDataset

dataset = PandasDataset(features=pd.concat([pd.Series([1, np.nan, 2, 3], name="feat1"),
                                            pd.Series([1, 2, 3, 4], name="feat2")], axis=1),
                        labels=pd.Series([0, 0, 0, 1], name="Label"))

# access features as a pandas dataframe 
print(dataset.features.head())
# access labels as pandas dataframe 
print(dataset.labels.head())
# access features as a python dictionary 
dataset.getFeaturesAs('dict')
# access features as numpy array 
dataset.getFeaturesAs('array')

# indexing operations 
# access features and labels at the given index as a pandas dataframe  
print(dataset.loc([2]).features.head())
print(dataset.loc([2]).labels.head())

Creating a PandasTimeIndexedDataset object

import pandas as pd
import numpy as np
from py4ai.core.data.model.ml import PandasTimeIndexedDataset

dateStr = [str(x) for x in pd.date_range('2010-01-01', '2010-01-04')]
dataset = PandasTimeIndexedDataset(
    features=pd.concat([
        pd.Series([1, np.nan, 2, 3], index=dateStr, name="feat1"),
        pd.Series([1, 2, 3, 4], index=dateStr, name="feat2")
    ], axis=1))

How to contribute ?

We are very much willing to welcome any kind of contribution whether it is bug report, bug fixes, contributions to the existing codebase or improving the documentation.

Where to start ?

Please look at the Github issues tab to start working on open issues

Contributing to py4ai-core

Please make sure the general guidelines for contributing to the code base are respected

1. Fork the py4ai-core repository.
2. Create/choose an issue to work on in the Github issues page.
3. Create a new branch to work on the issue.
4. Commit your changes and run the tests to make sure the changes do not break any test.
5. Open a Pull Request on Github referencing the issue.
6. Once the PR is approved, the maintainers will merge it on the main branch.