py4ai-data 0.0.1
A Python library of data structures optimized for machine learning tasks
License: MIT
Requires Python: >=3.8

py4ai data
====

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A Python library defining data structures optimized for machine learning pipelines 


## What is it ?
**py4ai-data** 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. 
The detailed documentation for all its features is available [here](https://nicoladonelli.github.io/py4ai-data/intro/readme.html).

## Features
Some cool features that we are proud to mention are: 

### [Data layers](https://nicoladonelli.github.io/py4ai-data/api/py4ai.data.layer.html) 
The Data Layer abstractions are designed to decouple the business layers from 
the detail of the persistence layer implementation.
We currently implemented a few data layers based on some common abstractions:
1. Repository: class representing the access point to data. Specifies the methods to read/write data from/to persistence layer. It must be instantiated by passing it a Serializer, to specify how to read/write data from/to persistence layer to/from memory. 
2. Serializer: class that specifies how to read/write data from/to persistence layer to/from memory.
3. Criteria: class implementing query method to filter data retrieved from repository.
The data layer currently implemented are:
* file-system: access generic data stored in the file system as generic files, handling them in memory as generic indexed objects.
* pandas: access tabular data stored in the file system as csv files, handling them in memory as pandas objects.
* mongo: access data stored in MongoDB. Serializers for this layer should be implemented by the user according to its specific needs.
* sqlalchemy: access data stored in a database handled with sqlalchemy ORM. Serializers for this layer should be implemented by the user according to its specific needs.

### [Data Model](https://nicoladonelli.github.io/py4ai-data/api/py4ai.data.model.html)
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-data
```

From source
```
git clone https://github.com/NicolaDonelli/py4ai-data
cd py4ai-data
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` that associates some string data to an integer id.

```python
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:

```python
import pandas as pd
from py4ai.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. 

```python
from py4ai.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):

```python
from bson import ObjectId
from py4ai.data.layer.mongo.serializer import create_mongo_id
from py4ai.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

```python
from py4ai.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 queries/filters:

```python
entities = repo.retrieve_by_query(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:

```python
from typing import Generic
from abc import ABC, abstractmethod

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


class EntityQueryFactory(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

```python
from py4ai.data.layer.mongo.criteria import MongoSearchCriteria


class MongoQueryFactory(EntityQueryFactory[Dict[str, Any]]):

    def by_id(self, my_id: int) -> MongoSearchCriteria:
        return MongoSearchCriteria({"my_id": my_id})


criteria = MongoQueryFactory()

entities = repo.retrieve_by_query(criteria.by_id(1234))
```

Note that `SearchCriteria` can be also joined using logical operators:

```python

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

entities = repo.retrieve_by_query(
    criteria.by_id(1234) | criteria.by_id(1235) 
)
```


### Data Model

The _data.model_ module contains data structures commonly used.
The most relevant structures defined in the _core_ submodule are `CachedIterable` and `LazyIterable` 
that represent iterable objects that are either completely stored in memory or accessed lazily. In 
the same submodule we also define `Range` and `CompositeRange` that represent time and numeric ranges, 
either continuous or not, with methods to compare two instances checking for overlaps or performing 
range union by sum. 

The _ml_ submodule on the other hand defines some data structures that are of common use in 
Machine Learning applications.
The basic structure we define is the `Sample`, which is a simple serializable object characterized
by a set of features, an optional set of labels and an optional name identifier:
```python
class Sample(DillSerialization, Generic[FeatType, LabType]):
    """Base class for representing a sample/observation."""

    def __init__(
        self,
        features: FeatType,
        label: Optional[LabType] = None,
        name: Optional[Union[int, str, Any]] = None,
    ) -> None:
        """
        Return an object representing a single sample of a training or test set.

        :param features: features of the sample
        :param label: labels of the sample (optional)
        :param name: id of the sample (optional)
        """
        self.features: FeatType = features
        self.label: Optional[LabType] = label
        self.name: Optional[Union[str, int, Any]] = name
```
We also define a specialization of this class, called `MultiFeatureSample`,
thought for Deep Learning applications, for which the features attribute is a list of numpy arrays.
The most used objects defined in this module are the datasets, which represents collections of Samples
in various fashions. All the datasets share some common method allowing to retrieve the type of the Samples,
to check the names of the samples, to retrieve features and labels in a form to be chosen among 
`pd.DataFrame`, `np.ndarray`, `dict`, `list`, or a generator of samples. In particular, we defined:
* `LazyDataset`: a collection with lazy access to samples
* `CachedDataset`: a collection of samples fully stored in memory
* `PandasDataset`: a collection of samples, fully stored in memory, where features and labels are pd.DataFrames
* `PandasTimeIndexedDataset`: a collection of samples, fully stored in memory, where features and labels are pd.DataFrames with time index

Creating a PandasDataset object

```python
import pandas as pd
import numpy as np
from py4ai.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

```python
import pandas as pd
import numpy as np
from py4ai.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](https://github.com/NicolaDonelli/py4ai-data/issues) to start working on open 
issues 

### Contributing to py4ai-data 
Please make sure the general guidelines for contributing to the code base are respected
1. [Fork](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) the py4ai-data repository. 
2. Create/choose an issue to work on in the [Github issues page](https://github.com/NicolaDonelli/py4ai-data/issues). 
3. [Create a new branch](https://docs.github.com/en/get-started/quickstart/github-flow) 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.