verticapy 0.10.2

VerticaPy simplifies data exploration, data cleaning, and machine learning in Vertica.

:loudspeaker: 2020-06-27: Vertica-ML-Python has been renamed to VerticaPy.

:warning: VerticaPy 0.9.0 includes several significant changes and is therefore not backward compatible with older versions. For details, see the changelog.

VerticaPy

VerticaPy is a Python library with scikit-like functionality used to conduct data science projects on data stored in Vertica, taking advantage Vertica’s speed and built-in analytics and machine learning features. VerticaPy offers robust support for the entire data science life cycle, uses a 'pipeline' mechanism to sequentialize data transformation operations, and offers beautiful graphical options.

Nowadays, 'Big Data' is one of the main topics in the data science world, and data scientists are often at the center of any organization. The benefits of becoming more data-driven are undeniable and are often needed to survive in the industry.

Vertica was the first real analytic columnar database and is still the fastest in the market. However, SQL alone isn't flexible enough to meet the needs of data scientists.

Python has quickly become the most popular tool in this domain, owing much of its flexibility to its high-level of abstraction and impressively large and ever-growing set of libraries. Its accessibility has led to the development of popular and perfomant APIs, like pandas and scikit-learn, and a dedicated community of data scientists. Unfortunately, Python only works in-memory as a single-node process. This problem has led to the rise of distributed programming languages, but they too, are limited as in-memory processes and, as such, will never be able to process all of your data in this era, and moving data for processing is prohobitively expensive. On top of all of this, data scientists must also find convenient ways to deploy their data and models. The whole process is time consuming.

VerticaPy aims to solve all of these problems. The idea is simple: instead of moving data around for processing, VerticaPy brings the logic to the data.

3 years in the making, we're proud to bring you VerticaPy.

Main Advantages:

• Easy Data Exploration.
• Fast Data Preparation.
• In-Database Machine Learning.
• Easy Model Evaluation.
• Easy Model Deployment.

Installation

To install VerticaPy with pip:

# Latest release version
root@ubuntu:~$ pip3 install verticapy[all]

# Latest commit on master branch
root@ubuntu:~$ pip3 install git+https://github.com/vertica/verticapy.git@master

To install VerticaPy from source, run the following command from the root directory:

root@ubuntu:~$ python3 setup.py install

A detailed installation guide is available at:

https://www.vertica.com/python/installation.php

Documentation

Documentation is available at:

https://www.vertica.com/python/documentation_last/

Use-cases

Examples and case-studies:

https://www.vertica.com/python/examples/

SQL Magic

You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see SQL Magic:

Example

Load the SQL extension.

%load_ext verticapy.sql

Execute your SQL queries.

%%sql
SELECT version();

# Output
# Vertica Analytic Database v11.0.1-0

Charts

A gallery of VerticaPy-generated charts is available at:

https://www.vertica.com/python/gallery/

Contributing

For a short guide on contribution standards, see CONTRIBUTING.md

Connecting to the Database

VerticaPy is compatible with several clients. For details, see the connection page.

Quickstart

The following example follows the VerticaPy quickstart guide.

Install the library using with pip.

root@ubuntu:~$ pip3 install verticapy[all]

Create a new Vertica connection:

import verticapy as vp
vp.new_connection({"host": "10.211.55.14", 
                   "port": "5433", 
                   "database": "testdb", 
                   "password": "XxX", 
                   "user": "dbadmin"},
                   name = "Vertica_New_Connection")

Use the newly created connection:

vp.connect("Vertica_New_Connection")

Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself):

vp.create_verticapy_schema()

Create a vDataFrame of your relation:

from verticapy import vDataFrame
vdf = vDataFrame("my_relation")

Load a sample dataset:

from verticapy.datasets import load_titanic
vdf = load_titanic()

Examine your data:

vdf.describe()

# Output
                count                 mean                  std     min
"pclass"         1234     2.28444084278768    0.842485636190292     1.0 
"survived"       1234    0.364667747163696    0.481532018641288     0.0
"age"             997     30.1524573721163     14.4353046299159    0.33
"sibsp"          1234    0.504051863857374     1.04111727241629     0.0 
"parch"          1234    0.378444084278768    0.868604707790393     0.0 
"fare"           1233      33.963793673966     52.6460729831293     0.0 
"body"            118      164.14406779661     96.5760207557808     1.0 
                approx_25%    approx_50%    approx_75%         max  
"pclass"               1.0           3.0           3.0         3.0  
"survived"             0.0           0.0           1.0         1.0  
"age"                 21.0          28.0          39.0        80.0  
"sibsp"                0.0           0.0           1.0         8.0  
"parch"                0.0           0.0           0.0         9.0  
"fare"              7.8958       14.4542       31.3875    512.3292  
"body"               79.25         160.5         257.5       328.0  
Rows: 1-7 | Columns: 9

Print the SQL query with set_option:

set_option("sql_on", True)
vdf.describe()

# Output
## Compute the descriptive statistics of all the numerical columns ##

SELECT 
  SUMMARIZE_NUMCOL("pclass", "survived", "age", "sibsp", "parch", "fare", "body") OVER ()
FROM public.titanic

With VerticaPy, it is now possible to solve a ML problem with few lines of code.

from verticapy.learn.model_selection import cross_validate
from verticapy.learn.ensemble import RandomForestClassifier

# Data Preparation
vdf["sex"].label_encode()["boat"].fillna(method = "0ifnull")["name"].str_extract(' ([A-Za-z]+)\.').eval("family_size", expr = "parch + sibsp + 1").drop(columns = ["cabin", "body", "ticket", "home.dest"])["fare"].fill_outliers().fillna()

# Model Evaluation
cross_validate(RandomForestClassifier("rf_titanic", cur, max_leaf_nodes = 100, n_estimators = 30), 
               vdf, 
               ["age", "family_size", "sex", "pclass", "fare", "boat"], 
               "survived", 
               cutoff = 0.35)

# Output
                           auc               prc_auc   
1-fold      0.9877114427860691    0.9530465915039339   
2-fold      0.9965555014605642    0.7676485351425721   
3-fold      0.9927239216549301    0.6419135521132449   
avg             0.992330288634        0.787536226253   
std           0.00362128464093         0.12779562393   
                     accuracy              log_loss   
1-fold      0.971291866028708    0.0502052541223871   
2-fold      0.983253588516746    0.0298167751798457   
3-fold      0.964824120603015    0.0392745694400433   
avg            0.973123191716       0.0397655329141   
std           0.0076344236729      0.00833079837099   
                     precision                recall   
1-fold                    0.96                  0.96   
2-fold      0.9556962025316456                   1.0   
3-fold      0.9647887323943662    0.9383561643835616   
avg             0.960161644975        0.966118721461   
std           0.00371376912311        0.025535200301   
                      f1-score                   mcc   
1-fold      0.9687259282082884    0.9376119402985075   
2-fold      0.9867172675521821    0.9646971010878469   
3-fold      0.9588020287309097    0.9240569687684576   
avg              0.97141507483        0.942122003385   
std            0.0115538960753       0.0168949813163   
                  informedness            markedness   
1-fold      0.9376119402985075    0.9376119402985075   
2-fold      0.9737827715355807    0.9556962025316456   
3-fold      0.9185148945422918    0.9296324823943662   
avg             0.943303202125        0.940980208408   
std            0.0229190954261       0.0109037699717   
                           csi  
1-fold      0.9230769230769231  
2-fold      0.9556962025316456  
3-fold      0.9072847682119205  
avg             0.928685964607  
std            0.0201579224026

Enjoy!