diff options
Diffstat (limited to 'python-verticapy.spec')
| -rw-r--r-- | python-verticapy.spec | 812 |
1 files changed, 812 insertions, 0 deletions
diff --git a/python-verticapy.spec b/python-verticapy.spec new file mode 100644 index 0000000..8548803 --- /dev/null +++ b/python-verticapy.spec @@ -0,0 +1,812 @@ +%global _empty_manifest_terminate_build 0 +Name: python-verticapy +Version: 0.12.0 +Release: 1 +Summary: VerticaPy simplifies data exploration, data cleaning, and machine learning in Vertica. +License: Apache Software License +URL: https://github.com/vertica/VerticaPy +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/85/8c/bc3f23f356017e0d3a8e4a142c8559741ea9fc25d80bf3fafd245fc56afa/verticapy-0.12.0.tar.gz +BuildArch: noarch + +Requires: python3-matplotlib +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-highcharts +Requires: python3-scipy +Requires: python3-tqdm +Requires: python3-vertica-python +Requires: python3-descartes +Requires: python3-geopandas +Requires: python3-graphviz +Requires: python3-shapely + +%description +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/logo.png' width="180px"> +</p> + +:star: 2022-12-01: VerticaPy secures 100 stars. + +: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 <a href='https://www.vertica.com/python/documentation_last/whats-new.php'>changelog</a>. + +# VerticaPy + +[](https://badge.fury.io/py/verticapy) +[](https://opensource.org/licenses/Apache-2.0) +[](https://www.python.org/downloads/) +[](https://codecov.io/gh/vertica/VerticaPy) + +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/benefits.png' width="92%"> +</p> + +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. +<br><br> +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. +<br><br> +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. +<br><br> +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. +<br><br> +**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. +<br><br> +3 years in the making, we're proud to bring you VerticaPy. +<br><br> +Main Advantages: +<ul> + <li> Easy Data Exploration.</li> + <li> Fast Data Preparation.</li> + <li> In-Database Machine Learning.</li> + <li> Easy Model Evaluation.</li> + <li> Easy Model Deployment.</li> +</ul> + +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/architecture.png' width="92%"> +</p> + +## Installation + +To install <b>VerticaPy</b> with pip: +```shell +# 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 <b>VerticaPy</b> from source, run the following command from the root directory: +```shell +root@ubuntu:~$ python3 setup.py install +``` + +A detailed installation guide is available at: <br> + +https://www.vertica.com/python/installation.php + +## Documentation + +Documentation is available at: <br> + +https://www.vertica.com/python/documentation_last/ + +## Use-cases + +Examples and case-studies: <br> + +https://www.vertica.com/python/examples/ + +<p align="center"> +<img src="https://raw.githubusercontent.com/vertica/VerticaPy/master/img/examples.gif" width="92%"> +</p> + +## SQL Magic + +You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see <a href='https://www.vertica.com/python/documentation_last/extensions/sql/'>SQL Magic</a>: + +### Example + +Load the SQL extension. +```python +%load_ext verticapy.sql +``` +Execute your SQL queries. +```python +%%sql +SELECT version(); + +# Output +# Vertica Analytic Database v11.0.1-0 +``` + +## Charts + +A gallery of VerticaPy-generated charts is available at:<br> + +https://www.vertica.com/python/gallery/ + +<p align="center"> +<img src="https://raw.githubusercontent.com/vertica/VerticaPy/master/img/charts.gif" width="92%"> +</p> + +## Contributing + +For a short guide on contribution standards, see <a href='https://github.com/vertica/VerticaPy/blob/master/CONTRIBUTING.md'>CONTRIBUTING.md</a> + +## Connecting to the Database + +VerticaPy is compatible with several clients. For details, see the <a href='https://www.vertica.com/python/connection.php'>connection page</a>.<br> + +## Quickstart + +The following example follows the <a href='https://www.vertica.com/python/quick-start.php'>VerticaPy quickstart guide</a>. + +Install the library using with <b>pip</b>. +```shell +root@ubuntu:~$ pip3 install verticapy[all] +``` +Create a new Vertica connection: +```python +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: +```python +vp.connect("Vertica_New_Connection") +``` +Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself): +```python +vp.create_verticapy_schema() +``` +Create a vDataFrame of your relation: +```python +from verticapy import vDataFrame +vdf = vDataFrame("my_relation") +``` +Load a sample dataset: +```python +from verticapy.datasets import load_titanic +vdf = load_titanic() +``` +Examine your data: +```python +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 <b>set_option</b>: +```python +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. +```python +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! + + +%package -n python3-verticapy +Summary: VerticaPy simplifies data exploration, data cleaning, and machine learning in Vertica. +Provides: python-verticapy +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-verticapy +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/logo.png' width="180px"> +</p> + +:star: 2022-12-01: VerticaPy secures 100 stars. + +: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 <a href='https://www.vertica.com/python/documentation_last/whats-new.php'>changelog</a>. + +# VerticaPy + +[](https://badge.fury.io/py/verticapy) +[](https://opensource.org/licenses/Apache-2.0) +[](https://www.python.org/downloads/) +[](https://codecov.io/gh/vertica/VerticaPy) + +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/benefits.png' width="92%"> +</p> + +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. +<br><br> +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. +<br><br> +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. +<br><br> +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. +<br><br> +**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. +<br><br> +3 years in the making, we're proud to bring you VerticaPy. +<br><br> +Main Advantages: +<ul> + <li> Easy Data Exploration.</li> + <li> Fast Data Preparation.</li> + <li> In-Database Machine Learning.</li> + <li> Easy Model Evaluation.</li> + <li> Easy Model Deployment.</li> +</ul> + +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/architecture.png' width="92%"> +</p> + +## Installation + +To install <b>VerticaPy</b> with pip: +```shell +# 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 <b>VerticaPy</b> from source, run the following command from the root directory: +```shell +root@ubuntu:~$ python3 setup.py install +``` + +A detailed installation guide is available at: <br> + +https://www.vertica.com/python/installation.php + +## Documentation + +Documentation is available at: <br> + +https://www.vertica.com/python/documentation_last/ + +## Use-cases + +Examples and case-studies: <br> + +https://www.vertica.com/python/examples/ + +<p align="center"> +<img src="https://raw.githubusercontent.com/vertica/VerticaPy/master/img/examples.gif" width="92%"> +</p> + +## SQL Magic + +You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see <a href='https://www.vertica.com/python/documentation_last/extensions/sql/'>SQL Magic</a>: + +### Example + +Load the SQL extension. +```python +%load_ext verticapy.sql +``` +Execute your SQL queries. +```python +%%sql +SELECT version(); + +# Output +# Vertica Analytic Database v11.0.1-0 +``` + +## Charts + +A gallery of VerticaPy-generated charts is available at:<br> + +https://www.vertica.com/python/gallery/ + +<p align="center"> +<img src="https://raw.githubusercontent.com/vertica/VerticaPy/master/img/charts.gif" width="92%"> +</p> + +## Contributing + +For a short guide on contribution standards, see <a href='https://github.com/vertica/VerticaPy/blob/master/CONTRIBUTING.md'>CONTRIBUTING.md</a> + +## Connecting to the Database + +VerticaPy is compatible with several clients. For details, see the <a href='https://www.vertica.com/python/connection.php'>connection page</a>.<br> + +## Quickstart + +The following example follows the <a href='https://www.vertica.com/python/quick-start.php'>VerticaPy quickstart guide</a>. + +Install the library using with <b>pip</b>. +```shell +root@ubuntu:~$ pip3 install verticapy[all] +``` +Create a new Vertica connection: +```python +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: +```python +vp.connect("Vertica_New_Connection") +``` +Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself): +```python +vp.create_verticapy_schema() +``` +Create a vDataFrame of your relation: +```python +from verticapy import vDataFrame +vdf = vDataFrame("my_relation") +``` +Load a sample dataset: +```python +from verticapy.datasets import load_titanic +vdf = load_titanic() +``` +Examine your data: +```python +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 <b>set_option</b>: +```python +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. +```python +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! + + +%package help +Summary: Development documents and examples for verticapy +Provides: python3-verticapy-doc +%description help +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/logo.png' width="180px"> +</p> + +:star: 2022-12-01: VerticaPy secures 100 stars. + +: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 <a href='https://www.vertica.com/python/documentation_last/whats-new.php'>changelog</a>. + +# VerticaPy + +[](https://badge.fury.io/py/verticapy) +[](https://opensource.org/licenses/Apache-2.0) +[](https://www.python.org/downloads/) +[](https://codecov.io/gh/vertica/VerticaPy) + +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/benefits.png' width="92%"> +</p> + +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. +<br><br> +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. +<br><br> +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. +<br><br> +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. +<br><br> +**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. +<br><br> +3 years in the making, we're proud to bring you VerticaPy. +<br><br> +Main Advantages: +<ul> + <li> Easy Data Exploration.</li> + <li> Fast Data Preparation.</li> + <li> In-Database Machine Learning.</li> + <li> Easy Model Evaluation.</li> + <li> Easy Model Deployment.</li> +</ul> + +<p align="center"> +<img src='https://raw.githubusercontent.com/vertica/VerticaPy/master/img/architecture.png' width="92%"> +</p> + +## Installation + +To install <b>VerticaPy</b> with pip: +```shell +# 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 <b>VerticaPy</b> from source, run the following command from the root directory: +```shell +root@ubuntu:~$ python3 setup.py install +``` + +A detailed installation guide is available at: <br> + +https://www.vertica.com/python/installation.php + +## Documentation + +Documentation is available at: <br> + +https://www.vertica.com/python/documentation_last/ + +## Use-cases + +Examples and case-studies: <br> + +https://www.vertica.com/python/examples/ + +<p align="center"> +<img src="https://raw.githubusercontent.com/vertica/VerticaPy/master/img/examples.gif" width="92%"> +</p> + +## SQL Magic + +You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see <a href='https://www.vertica.com/python/documentation_last/extensions/sql/'>SQL Magic</a>: + +### Example + +Load the SQL extension. +```python +%load_ext verticapy.sql +``` +Execute your SQL queries. +```python +%%sql +SELECT version(); + +# Output +# Vertica Analytic Database v11.0.1-0 +``` + +## Charts + +A gallery of VerticaPy-generated charts is available at:<br> + +https://www.vertica.com/python/gallery/ + +<p align="center"> +<img src="https://raw.githubusercontent.com/vertica/VerticaPy/master/img/charts.gif" width="92%"> +</p> + +## Contributing + +For a short guide on contribution standards, see <a href='https://github.com/vertica/VerticaPy/blob/master/CONTRIBUTING.md'>CONTRIBUTING.md</a> + +## Connecting to the Database + +VerticaPy is compatible with several clients. For details, see the <a href='https://www.vertica.com/python/connection.php'>connection page</a>.<br> + +## Quickstart + +The following example follows the <a href='https://www.vertica.com/python/quick-start.php'>VerticaPy quickstart guide</a>. + +Install the library using with <b>pip</b>. +```shell +root@ubuntu:~$ pip3 install verticapy[all] +``` +Create a new Vertica connection: +```python +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: +```python +vp.connect("Vertica_New_Connection") +``` +Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself): +```python +vp.create_verticapy_schema() +``` +Create a vDataFrame of your relation: +```python +from verticapy import vDataFrame +vdf = vDataFrame("my_relation") +``` +Load a sample dataset: +```python +from verticapy.datasets import load_titanic +vdf = load_titanic() +``` +Examine your data: +```python +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 <b>set_option</b>: +```python +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. +```python +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! + + +%prep +%autosetup -n verticapy-0.12.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-verticapy -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.12.0-1 +- Package Spec generated |