pip install sklearn2vantage

conda install sklearn2vantage -c temporary-recipes

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:[email protected]: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)

import pandas as pd
import sklearn2vantage as s2v
from sqlalchemy import create_engine
engine = create_engine("teradata://dbc:[email protected]: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")