rsi-dstools 0.0.2
RSI DataScience Tools Python package
Requires Python: >=3.7

# Data Science Tools
Helper functions to be used with database, Azure ML, plotting, and generic ML needs.

### Introduction
There are several categories of tools for use on data science/engineering tasks. If these become more copious and distinct codebases in the future, then we may parcel them up into different repos.



## AZURE UTILS 
Helper functions to be used to interact with Azure ML model training, etc.

#### Introduction
Interacting with Azure ML services requires navigating a fairly complex set up process. The `AzureRun` class is intended to simplify the set up while at the same time familiarizing the user with the use of Azure ML functions/classes.

#### Usage
See the [`azureutils README.md`](dstools/azureutils/README.md) for more details on the set up, the `AzureMLRun` class is located in the [`azureutils/azurerun.py`](./dstools/azureutils/azurerun.py) module.
```
from dstools.azureutils.azurerun import AzureMLRun

arun = AzureMLRun('./',debug=0)
arun.setup_run()
print(arun.__repr__())

arun.run(debug=True)
arun.metrics()
```

## SQL UTILS 
Helper functions to be used with SQL Alchemy / ODBC and Pandas

#### Introduction
SQL Alchemy provides a fairly usable interface. The primary tools added by this package are predefined calls using `pandas.DataFrame` SQL API and `with` context manager for opening, querying, and closing database connections as needed.

#### Usage
The additional tools are located in [`sqlutils/utils.py`](./dstools/sqlutils/util.py). 
    
- the `DbConnectGenerator` class produces an object which can be used to generate temporary database  connection using the `with` context via (the behavior within the context manager opens the DB connection, executes the query/command, and then closes the DB connection):
```
import sqlalchemy as sql
from sql_utils.util_sqlalchemy import DbConnectGenerator

dbMine = DbConnectGenerator(**connectionKWargs)
with dbMine.gen_connection() as conn:
    tbl = conn.get_table('table_name')
    data = conn.execute_query(sql.select([tbl]))
```

- Alternatively, calls to the SQL connection object can be made directly from a `pandas` `DataFrame` object

```
import sqlalchemy as sql
import pandas as pd
from sql_utils.util_sqlalchemy import DbConnectGenerator, gettypes

# write
dbMine = DbConnectGenerator(**connectionKWargs)
with dbMine.gen_connection() as conn:
    df.to_sql(name='new_table_name', con=conn.engine, 
            if_exists='append', # could also use: 'replace'
            index=False, dtype=gettypes(df))
# read
dbMine = DbConnectGenerator(**connectionKWargs)
with dbMine.gen_connection() as conn:
    df = pd.read_sql('SELECT * FROM Table1 WHERE alpha=1', 
            con=conn.engine)
```


## ML UTILS
Helper functions to be used to generate features and data labels

#### Introduction
Tools in this `mlutils` module are for use with corporate tax audit feature generation. The primary tools added by this package use the data resulting from the `pandas.DataFrame` SQL API queries (defined in the `sqlutils` package)to generate features for machine learning models.

#### Usage
The primary call in [`mlutils/corp_tax_audit_features.py`](./dstools/mlutils/corp_tax_audit_features.py) is `get_labeled_data_features`, which utilizes the `Duration` and `Encoder` classes to fit the queried data into usable features. These were mainly developed for use with a particular client, as so may need heavy modification to work with future clients (but the skeleton is at least there).


## PLOT UTILS
Helper functions to be used with matplotlib

#### Introduction
Tools in this `plotutils` module are for streamlining some common visualization workflows.

#### Usage
Forthcoming...as we develop regularly used plotting activities/functions.