Htydjtk

I am working on a dataset where I predict the risks of developing pancreatic cancer with respect to a number of variables. I have created a random forest, and want to find the feature contributions. I have already used the "Treeinterpreter" library, resulting in a contributions array that is three-dimensional. I want to display the contributions in the array beside the name of the factor/variable. I have used the code below to do so, however, the code responsible for displaying the contributions does not work. I have tried multiple methods, including converting the dataframe to a numpy array, and other methods such as .all() and .any(). However, none are producing the desired result.

What can be the right way to display the feature contributions with respect to each of the feature it represents?

     # -*- coding: utf-8 -*-

"""

Created on Mon Apr 15 13:39:19 2019



@author: GoodManMcGee

"""



import pandas as pd

from sklearn.metrics import accuracy_score

from sklearn import tree

from sklearn.model_selection import train_test_split

from sklearn import preprocessing

from sklearn.metrics import confusion_matrix

from sklearn.ensemble import RandomForestClassifier 

from IPython.display import Image

from sklearn.tree import export_graphviz

from treeinterpreter import treeinterpreter as ti

import matplotlib.pyplot as plt

import numpy as np

import itertools



data = pd.read_csv("pancreatic_cancer_smokers.csv")

target = data['case (1: case, 0: control)']

data.drop('case (1: case, 0: control)', axis=1, inplace=True)

x_train, x_test, y_train, y_test = train_test_split(data, target, test_size = 0.2)

clf = RandomForestClassifier(n_estimators=100)

clf.fit(x_train, y_train)

y_pred = clf.predict(x_test)

clf_accuracy = accuracy_score(y_test, y_pred)

clf_pred, clf_bias, contributions = ti.predict(clf, x_test)





#The code below was taken from DataDive's treeinterpreter tutorial. 

#The aforementioned messages applies to all code between the underscores

#///////////////////////////////////////////



for i in range(len(x_test)):

    print ("Instance", i)

    print ("Bias (trainset mean)", clf_bias[i])

    print ("Feature contributions:")

    for c, feature in sorted(zip(contributions[i], data.feature_names), 

                             key=lambda x: -abs(x[0])):

#An error occurs in the "data.feature_names" method in the code above:AttributeError: 'DataFrame' object has no attribute 'feature_names'. I have tried referenceing columns from datasets also, but that also leads to errors: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

        print (feature, round(c, 2))

    print ("-"*20)   

#///////////////////////////////////////////