sklearn2vantage

sklearn2vantage is a Python module for converting sklearn model to Teradata Vantage model table.

This module has 2 feature. One is converting scikit-learn model to Teradata Vantage model and another is uploading pandas dataframe to Teradata.

Installation

Dependencies

sklearn2vantage requires:

• Python
• NumPy
• pandas
• SQLAlchemy
• scikit-learn
• paramiko
• scp
• teradata
• sqlalchemy-teradata
• teradatasql
• teradatasqlalchemy

Supported model

Following models are supported.

scikit-learn	Teradata Vantage
RandomForestClassifier	DecisionForestPredict
RandomForestRegressor	DecisionForestPredict
GradientBoostRegressor	DecisionForestPredict
LinearRegression	GLMPredict
Lasso	GLMPredict
Ridge	GLMPredict
Linear	GLMPredict
LogisticRegression	GLMPredict
GaussianNB	NaiveBayesPredict
CategoricalNB	NaiveBayesPredict
DecisionTreeClassifier	DecisionTreePredict
DecusionTreeRegressor	DecisionTreePredict

Some models in statsmodels are also supported.

statsmodels	Teradata Vantage
Logit	GLMPredict
OLS	GLMPredict

User installation

pip install sklearn2vantage

or

conda install sklearn2vantage -c temporary-recipes

Example: conveting model

import sklearn2vantage as s2v
import pandas as pd
from sqlalchemy import create_engine
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

engine = create_engine("teradata://dbc:dbc@173.168.56.128:1025/tdwork")

df = pd.read_sql_query("select * from some_data sample 50000", engine)
X = df.drop("target", axis=1)
y = df.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

rf_clf = RandomForestClassifier()
rf_clf.fit(X_train, y_train)

rf_clf_table = \
  s2v.make_model_table_forest(rf_clf, X_train.columns,
                              ['setosa', 'versicolor', 'virginica'])

s2v.load_model_forest(rf_clf_table, engine, "rf_clf_table")
pd.read_sql_query("""
  select * from DecisionForestPredict (
    on iris partition by any
    on rf_clf_table as ModelTable DIMENSION
    USING
    NumerixInputs ('sepal_length', 'sepal_width',
                  'petal_length', 'petal_width')
    IdColumn ('id')
    Accumulate ('species')
    Detailed ('false')
) as dt""", engine)

For further usage, please see HowToUse.ipynb.

Example: data loading

import pandas as pd
import sklearn2vantage as s2v
from sqlalchemy import create_engine
engine = create_engine("teradata://dbc:dbc@173.168.56.128:1025/tdwork")
df_titanic = pd.read_csv("titanic/train.csv").set_index("PassengerId")
s2v.tdload_df(df_titanic, engine, tablename="titanic_train",
              ifExists="replace", ssh_ip="173.168.56.128",
              ssh_username="root", ssh_password="root")

For further usage, please see HowToUseDataloader.ipynb.