# ***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***](#collections) helps with working with collections.
- [***colour***](about_colour.md) simplifies using colours.
- [***ds (datascience)***](#ds-data-science) provides tools for:
- data wrangling,
- validation,
- tuning,
- sampling,
- evaluation,
- clustering, and
- parallel processing of machine learning models.
- [***functions***](about_functions.md) manages higher order functions.
- [***hash***](about_hash.md) simplifies and standardizes hashing.
- [***text***](about_text.md) makes working with texts and strings easy.
- [***time***](about_time.md)
- provides methods for interacting with time and date as well as
- progress bars
## *collections*
This module of the package [***atlantis***](README.md) helps with working with collections.
### *`flatten`*
```python
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.
```python
from atlantis.collections import List
l = List(1, 2, 3, 4, 2, [1, 2], [1, 2])
```
Flattening:
```python
l.flatten()
>>> List: [1, 2, 3, 4, 2, 1, 2, 1, 2]
```
Finding duplicates:
```python
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
```python
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
```python
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}')
```
