pydatasci 0.0.51

End-to-end machine learning on your desktop or server.

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pre-alpha; in active development

Value Proposition

PyDataSci is an open source, autoML tool that keeps track of the moving parts of machine learning so that data scientists can perform best practice machine learning without the coding overhead. So hypertune to your heart's content, visually compare models, and know that you've found the best one with the proof to back it up.

TLDR

pip install pydatasci

import pydatasci as pds
pds.create_folder()
pds.create_config()

from pydatasci import aidb
aidb.create_db()

Mission

• Empowering Universities & Institutes Everywhere
  We empower non-cloud users: the academic/ institute HPCers, the private clouders, the remote server SSH'ers, and everyday desktop hackers - with the same quality ML tooling as present in public clouds (e.g. AWS SageMaker).
  
  

• Reproducibly Persistent & Embedded
  No more black boxes. No more screenshotting parameters and loss-accuracy graphs. A record of every: dataset, feature, label, sample, split, fold, parameter, model, training job, and result - is automatically persisted in a lightweight, file-based database that is automatically configured when you import the package. Submit your aidb database file alongside your publications/ papers and model zoo entries as a proof.
  
  

• Queue Hypertuning Jobs
  Queue many hypertuning jobs locally, or delegate big jobs to the cloud to run in parallel by setting cloud_queue = True.
  
  

• Visually Compare Performance Metrics
  Compare models using pre-defined plots for assessing performance, including: quantitative metrics (e.g. accuracy, loss, variance, precision, recall, etc.), training histories, and confusion/ contingency matrices.
  
  

• Code-Integrated & Agnostic
  We don’t disrupt the natural workflow of data scientists by forcing them into the confines of a GUI app or specific IDE. Instead, we weave automated tracking into their existing scripts so that PyDataSci is compatible with any data science toolset.
  
  

Functionality

Initially focusing on tabular data before expanding to multi-file use cases.

• [Done] Compress a dataset (csv, tsv, parquet, pandas dataframe, numpy ndarray) to be analyzed.
• [Done] Split samples by index while treating validation sets (3rd split) and cross-folds (k-fold) as first-level citizens.
• [Done] Generate hyperparameter combinations for preprocessing, model building, model training, and model evaluation.
• [In progress] Queue hypertuning jobs and batches based on hyperparameter combinations.
• [In progress] Evaluate and save the performance metrics of each model.
• [ToDo] Visually compare model metrics in Jupyter Notebooks with Plotly Dash to find the best one.
• [Future] Derive informative featuresets from that dataset using supervised and unsupervised methods.
• [Future] Behind the scenes, stream rows from your datasets and use generators to keep a low memory footprint.
• Scale out to run cloud jobs in parallel by toggling cloud_queue = True.

Painpoint Solved

At the time, I was deep in an unstable, remote Linux workspace trying to finish a meta-analysis of methods for interpreting neural network activation values as an alternative approach to predictions based on the traditional feedforward weighted sum. I was running so many variations of models from different versions of graph neural network algorithms, CNNs, LSTMs... the analysis was really starting to pile up. First I started taking screenshots of my loss-accuracy graphs and that worked fine for a while. Then I started taking screenshots of my hyper-params; I couldn't be bothered to write down every combination of parameters I was running and the performance metrics they spit out every time. But, then again, I hadn't generated confusion matrices to compare and I should really record my feature importance ranking... and then the wheels really fell off when I started questioning if my df in-memory was really the df I thought it was last week.

Slamming my head on the keyboard a few times, I thought to myself "I don't want to do all of that..." So I did what any good hacker would do and started a full blown project around it. I had done the hardest part in figuring out the science, but this permuted world was just a mess when it came time to systematically prove it. It wasn't conducive to the scientific method. I had also been keeping an eye on other tools in the space. They seemed lacking in that they were either: cloud-only, dependent on an external database, the integration processes were too complex for data scientists/ statisticians/ researchers, the tech wasn't distributed properly, or they were just too proprietary/ walled garden/ biased toward corporate ecosystems.

Community

Much to automate there is. Simple it must be. ML is a broad space with a lot of challenges to solve. Let us know if you want to get involved. We plan to host monthly dev jam sessions and data science lightning talks.

• Data types to support: tabular, time series, image, graph, audio, video, gaming.
• Analysis types to support for each data type: classification, regression, dimensionality reduction, feature engineering, recurrent, generative, NLP, reinforcement.

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Installation

Requires Python 3+ (check your deep learning library's Python requirements). You will only need to perform these steps the first time you use the package.

Enter the following commands one-by-one and follow any instructions returned by the command prompt to resolve errors should they arise.

Starting from the command line:

$ pip install --upgrade pydatasci
$ python

Once inside a Python shell:

>>> import pydatasci as pds
>>> pds.create_folder()
>>> pds.create_config()

>>> from pydatasci import aidb
>>> aidb.create_db()

PyDataSci makes use of the Python package, appdirs, for an operating system (OS) agnostic location to store configuration and database files. This not only keeps your $HOME directory clean, but also helps prevent careless users from deleting your database.

The installation process checks not only that the corresponding appdirs folder exists on your system but also that you have the permissions neceessary to read from and write to that location. If these conditions are not met, then you will be provided instructions during the installation about how to create the folder and/ or grant yourself the appropriate permissions.

We have attempted to support both Windows (icacls permissions and backslashes C:\\) as well as POSIX including Mac and Linux including containers & Google Colab (chmod letters permissions and slashes /). Note: due to variations in the ordering of appdirs author and app directories in different OS', we do not make use of the appdirs appauthor directory, only the appname directory.

If you run into trouble with the installation process on your OS, please submit a GitHub issue so that we can attempt to resolve, document, and release a fix as quickly as possible.

Installation Location Based on OS
import appdirs; appdirs.user_data_dir('pydatasci');:

• Mac:
  /Users/Username/Library/Application Support/pydatasci
  
  
• Linux - Alpine and Ubuntu:
  /root/.local/share/pydatasci
  
  
• Windows:
  C:\Users\Username\AppData\Local\pydatasci

create_db() is equivalent to a migration in Django or Rails in that it creates the tables found in the Object Relational Model (ORM). We use the peewee ORM as it is simpler than SQLAlchemy, has good documentation, and found the project to be actively maintained (saw same-day GitHub response to issues on a Saturday). With the addition of Dash-Plotly, this will make for a full-stack experience that also works directly in an IDE like Jupyter or VS Code.

Deleting & Recreating the Database

When deleting the database, you need to either reload the aidb module or restart the Python shell before you can attempt to recreate the database.

>>> from pydatasci import aidb
>>> aidb.delete_db(True)

>>> from importlib import reload
>>> reload(aidb)
>>> create_db()

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Usage

If you've already completed the Installation section above, let's get started.

1. Import the Library

import pydatasci as pds
from pydatasci import aidb

2. Ingest a Dataset as a compressed file.

Supported tabular file formats include CSV, TSV, Apache Parquet.

# From a file.
dataset = aidb.Dataset.from_file(
	path = 'iris.tsv' # files must have column names as their first row
	, file_format = 'tsv'
	, name = 'tab-separated plants'
	, perform_gzip = True
	, dtype = 'float64' # or a dict or dtype by column name.
)

# From an in-memory data structure.
dataset = aidb.Dataset.from_pandas(
	dataframe = df
	, file_format = 'csv'
	, name = 'comma-separated plants'
	, perform_gzip = False
	, dtype = None # None infers from dataframe provided
	, rename_columns = None
)

dataset = aidb.Dataset.from_numpy(
	ndarray = arr
	, file_format = 'parquet'
	, name = 'chunking plants'
	, perform_gzip = True
	, dtype = None # feeds pd.Dataframe(dtype)
	, column_names = None # feeds pd.Dataframe(columns)
)

Apart from read_numpy(), it's best if you provide your own column names ahead of time as the first row of your files and DataFrames that you want to ingest.

The bytes of the data will be stored as a BlobField in the SQLite database file. Storing the data in the database not only (a) provides an entity that we can use to keep track of experiments and link relational data to but also (b) makes the data less mutable than keeping it in the open filesystem.

You can choose whether or not you want to gzip compress the file when importing it with the perform_gzip=bool parameter. This compression not only enables you to store up to 90% more data on your local machine, but also helps overcome the maximum BlobField size of 2.147 GB. We handle the zipping and unzipping on the fly for you, so you don't even notice it.

Optionally, dtype, as seen in pandas.DataFrame.astype(dtype), can be specified as either a single type for all columns, or as a dict that maps a specific type to each column name. This encodes features for analysis. We read NumPy into Pandas before persisting it, so columns and dtype are read directly by pd.DataFrame().

At this point, the project's support for Parquet is extremely minimal.

If you leave name blank, it will default to a human-readble timestamp with the appropriate file extension (e.g. '2020_10_13-01_28_13_PM.tsv').

Fetch a Dataset with either Pandas or NumPy.

Supported in-memory formats include NumPy Structured Array and Pandas DataFrame.

# Implicit IDs
df = dataset.to_pandas()
df.head()

arr = dataset.to_numpy()
arr[:4]

# Explicit IDs
df = aidb.Dataset.to_pandas(id=dataset.id)
df.head()

arr = aidb.Dataset.to_numpy(id=dataset.id)
arr[:4]

For the sake of simplicity, we are reading into NumPy via Pandas. That way, if we want to revert to a simpler ndarray in the future, then we won't have to rewrite the function to read NumPy.

3. Select the Label column(s).

From a Dataset, pick the column(s) that you want to train against/ predict. If you are planning on training an unsupervised model, then you don't need to do this.

# Implicit IDs
label_column = 'species'
label = dataset.make_label(columns=[label_column])

# Explicit IDs
label = aidb.Label.from_dataset(
	dataset_id=dataset.id
	, columns=[label_column]
)

Again, read a Label into memory with to_pandas() and to_numpy() methods.

Labels accept multiple columns for situations like one-hot encoding (OHE).

4. Select the Featureset column(s).

Creating a Featureset won't duplicate your data! It simply records the columns to be used in training.

Here, we'll just exclude a Label column in preparation for supervised learning, but you can either exlcude or include any columns you see fit.

# Implicit IDs
featureset = dataset.make_featureset(exclude_columns=[label_column])

# Explicit IDs
featureset = aidb.Featureset.from_dataset(
	dataset_id = dataset.id
	, include_columns = None
	, exclude_columns = [label_column]
)

>>> featureset.columns
['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

>>> featureset.columns_excluded
['species']

Again, read a Featureset into memory with to_pandas() and to_numpy() methods.

The include_columns and exclude_columns parameters are provided to expedite column extraction:

• If both include_columns=None and exclude_columns=None then all columns in the Dataset will be used.
• If exclude_columns=[...] is specified, then all other columns will be included.
• If include_columns=[...] is specified, then all other columns will be excluded.
• Remember, these parameters accept [lists], not raw strings.

5. Generate splits of samples.

Divide the Dataset rows into Splitsets based on how you want to train, validate (optional), and test your models.

Again, creating a Splitset won't duplicate your data. It simply records the samples (aka rows) to be used in your train, validation, and test splits.

splitset_train75_test25 = featureset.make_splitset(label_id=label.id)

splitset_train70_test30 = featureset.make_splitset(
	label_id = label.id
	, size_test = 0.30
)

splitset_train68_val12_test20 = featureset.make_splitset(
	label_id = label.id
	, size_test = 0.20
	, size_validation = 0.12
)

splitset_unsupervised = featureset.make_splitset()

Label-based stratification is used to ensure equally distributed label classes for both categorical and continuous data.

The size_test and size_validation parameters are provided to expedite splitting samples:

• If you leave size_test=None, it will default to 0.30 when a Label is provided.
• You cannot specify size_validation without also specifying size_test.

Again, read a Splitset into memory with to_pandas() and to_numpy() methods. Note: this will return a dict of either data frames or arrays.

>>> splitset_train68_val12_test20.sizes
{
	'train': {
		'percent': 0.68, 	
		'count': 102},

	'validation': {
		'percent': 0.12,
		'count': 18}, 

	'test':	{
		'percent': 0.2, 	
		'count': 30}
}

>>> splitset_train68_val12_test20.to_numpy()
{
	'train': {
		'features': <ndarray>,
		'labels': <ndarray>},

	'validation': {
		'features': <ndarray>,
		'labels': <ndarray>},

	'test': {
		'features': <ndarray>,
		'labels': <ndarray>}
}

6. Optionally, create Folds of samples for cross-fold validation.

7. Optionally, create a Preprocess.

If you want to either encode, standardize, normalize, or scale you Features and/ or Labels - then you can make use of sklearn.preprocessing methods. If you already

8. Create an Algorithm aka model to fit to your splits.

9. Create combinations of Hyperparamsets for your algorithms.

10. Create a Batch of Job's to keep track of training.

11. Visually compare the performance of your hypertuned Algorithms.

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PyPI Package

Steps to Build & Upload

$ pyenv activate pydatasci
$ pip3 install --upgrade wheel twine
$ python3 setup.py sdist bdist_wheel
$ python3 -m twine upload --repository pypi dist/*
$ rm -r build dist pydatasci.egg-info
# proactively update the version number in setup.py next time
$ pip install --upgrade pydatasci; pip install --upgrade pydatasci