Using SuperAlign, you can build complicated pipelines with much ease. In our previous tutorial, all the steps in the pipeline have one input and one output. However, in quite a few cases, a step could have multiple inputs. We will use parent parameter to construct these pipelines.

Let us consider the following example.

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Example

from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
from pureml.decorators import dataset, model, transformer, load_data

@load_data()
def load_churn_data():
    '''
    Loads churn data
    '''
    df = pd.read_csv('../bigml_59c28831336c6604c800002a.csv')

    return df

@transformer()
def encode_ordinal(df):
    '''
    Encode ordinal data
    '''
    df[df.columns] = OrdinalEncoder().fit_transform(df)

    return df

@transformer()
def encode_binary(df):
    '''
    Encode binary data
    '''
    df['voice mail plan'] = df['voice mail plan'].map({'yes':1, 'no':0})
    df['international plan'] = df['international plan'].map({'yes':1, 'no':0})
    df['churn'] = df['churn'].map({True:1, False:0})

    return df

@dataset('telecom churn', parent=['encode ordinal', 'encode binary'])
def build_dataset():
    '''
    None
    '''
    df = load_churn_data()

    column_names = df.columns

    df[['state',  'phone number']] = encode_ordinal(df[['state',  'phone number']])
    df[['voice mail plan', 'international plan', 'churn']] = encode_binary(df[['voice mail plan', 'international plan', 'churn']])

    return df

df = build_dataset()

The above example generates the following pipeline structure: