from sklearn.datasets import load_iris
import pandas as pd
import featuretools as ft

# Load data and put into dataframe
iris = load_iris()
df = pd.DataFrame(iris.data, columns = iris.feature_names)
df['species'] = iris.target
df['species'] = df['species'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})

# Make an entityset and add the entity
es = ft.EntitySet(id = 'id')
es.entity_from_dataframe(
                        entity_id = 'data', 
                        dataframe = df, 
                        make_index = True, 
                        index = 'index')
# Run deep feature synthesis with transformation primitives
feature_matrix, feature_defs = ft.dfs(entityset = es, target_entity = 'data',
                                      trans_primitives = ['add_numeric', 'multiply_numeric'])

feature_matrix.head()

import featuretools as ft
ft.list_primitives()