Python library for simplifying data science
Project description
Atlantis
Atlantis is a Python library for simplifying programming with Python for data science.
Installation
You can just use pip to install Atlantis:
pip install atlantis
Modules
- collections helps with working with collections.
- colour simplifies using colours.
- ds (datascience) provides tools for:
- data wrangling,
- validation,
- tuning,
- sampling,
- evaluation,
- clustering, and
- parallel processing of machine learning models.
- functions manages higher order functions.
- hash simplifies and standardizes hashing.
- text makes working with texts and strings easy.
- time
- provides methods for interacting with time and date as well as
- progress bars
collections
This module of the package atlantis helps with working with collections.
flatten
from atlantis.collections import flatten
flatten([1, 2, [3, 4, [5, 6], 7], 8])
returns: [1, 2, 3, 4, 5, 6, 7, 8]
List
This class inherits from Python's list class but implements a few additional functionalities.
from atlantis.collections import List
l = List(1, 2, 3, 4, 2, [1, 2], [1, 2])
Flattening:
l.flatten()
>>> List: [1, 2, 3, 4, 2, 1, 2, 1, 2]
Finding duplicates:
l.get_duplicates()
>>> List: [2, List: [1, 2]]
Note: the list elements of a List automatically get converted to Lists, recursively.
ds (Data Science)
This module provides data science tools for:
- data wrangling,
- validation,
- tuning,
- sampling,
- evaluation,
- clustering, and
- parallel processing of machine learning models.
KMeans Clustering
I have used the KMeans
class from both sklearn and that of pyspark and was frustrated
by two problems: (a) even though the two classes do exactly the same thing their interfaces
are vastly different and (b) some of the simplest operations are very hard to do with
both classes. I solved this problem by creating my own KMeans
class that is a wrapper
aroung both of those classes and uses the appropriate one automatically without
complicating it for the data scientist programmer.
Usage
from atlantis.ds.clustering import KMeans
kmeans = KMeans(n_clusters=3, n_jobs=10)
kmeans.fit(X=X)
predictions = kmeans.predict(X=X)
transformed_x = kmeans.transform(X=X)
Clustering Optimization
Usage
from atlantis.ds.clustering import ClusteringOptimizer
clustering_optimizer = ClusteringOptimizer(min_k=2, max_k=16, n_jobs=10)
clustering_optimizer.fit(X=X)
print(f'best number of clusters: {clustering_optimizer.optimal_number_of_clusters}')
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