Build decision tree using Python and embed in Rapid Miner

10383721

Hi guys, 

 

I am doing a project where I need to create decision tree using Python and then embed it in Rapid Miner using Execute Python operator. 

These are screenshots of my process:

 

Subprocess in Cross Validation

 

 

This is my code for the decision tree:

import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree

# rm_main is a mandatory function, 
# the number of arguments has to be the number of input ports (can be none)
def rm_main(data):
	#import data
	file = '04_Class_4.1_german-credit-decoded.xlsx'
	xl = pd.ExcelFile(file)
	print(xl.sheet_names)

	#load a sheet into a DataFrame 
	gr_raw = xl.parse('RapidMiner Data')

	#create arrays for the features, X, and response, y, variable
	y = gr_raw['Credit Rating=Good'].values
	X = gr_raw.drop('Credit Rating=Good', axis=1).values

	#split data into training and testing set
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=50)

	#build decision tree classifier using gini index
	clf_gini = DecisionTreeClassifier(criterion='gini', random_state=50, max_depth=10, min_samples_leaf=5)
	clf_gini.fit(X_train, y_train)

	return clf_gini

When executed it gives me an error, I am not sure which part of this code that I should ignore for a successfule execution. 

Would appreciate any advice or help on this! 

Thank you. 

 

Regards, 

Azmir F

10383721

Thanks guys for the solutions you have provided. I have managed to come up with my own solution. 

I did not know that python needs numerical data to apply the model. So I have modified my process and used Execute Python operators twice, once in Training and once in Testing. I used the Numerical to Binominal operator after the second Excecute Python operator. 

Note that I have renamed it to Build Model and Apply Model.

 

This is my updated process:

 

Cross Validation Subprocess

 

 

My Python script for Build Model is as below:

from sklearn.tree import DecisionTreeClassifier
def rm_main(data):

# build decision tree
    X = data[['Age', 'Duration in month', 'Installment rate in % of disposable income','Credit Amount', 'Present residence since', 'Number of existing credits', 'Number of dependents']]
    y = data[['Credit Rating']]
    clf = DecisionTreeClassifier(min_samples_split = 20, max_depth = 10, random_state = 99)
    clf.fit(X, y)  

    return clf

My Python script for Apply model is as below:

from sklearn.tree import DecisionTreeClassifier
def rm_main(model, data):
    X = data[['Age', 'Duration in month', 'Installment rate in % of disposable income','Credit Amount', 'Present residence since', 'Number of existing credits', 'Number of dependents']]
    data['prediction'] = model.predict(X)

    #set role of prediction attribute to prediction
    data.rm_metadata['prediction']=(None,'prediction')
    return data

Let me know if you have other relevant solution or better script to produce a more stable model. 

Thank you. 

 

Regards,

Azmir F

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