ValueError: y must be an integer array. Found object. Try passing the array as y.astype(np.integer)

Question

This is my code.

import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_graphviz, export_text
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_curve, auc, f1_score, roc_auc_score
import warnings; warnings.simplefilter('ignore')

data_files = 'dataset_for_learning_decision_tree.xlsx'

data = pd.read_excel(data_files)
train_data = data[['title','category','processed_title']]

categories=train_data['category']
labels=list(set(categories))

X_train, X_test, y_train, y_test = train_test_split(train_data['processed_title'],train_data['category'],test_size=0.2,random_state=57)

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(X_train)
decisiontree=DecisionTreeClassifier()

model = Pipeline([('vect', vectorizer),
                  ('tfidf', TfidfTransformer()),
('clf', decisiontree),
])
model.fit(X_train,y_train)

predicted = model.predict(X_test)
confusion_matrix(y_test,predicted)
print('accuracy_score',accuracy_score(y_test,predicted))
print('Reporting...')
print(classification_report(y_test,predicted))

import numpy as np
from mlxtend.plotting import plot_decision_regions

X=np.array(X_train)
y=np.array(y_train)
plot_decision_regions(X=X,
                      y=y,
                      clf=model.named_steps['clf'])

I want to draw a plot_decision_region. However, when I executed this code, I got the same error as the title. When running with y=y.astype(np.integer), I get errors such as ValueError: invalid literal for int() with base 10: 'depression'. How should I fix it?

Answer 1

Convert the class labels to integers first,

import numpy as np
from mlxtend.plotting import plot_decision_regions

X = np.array(X_train)
y = np.array(y_train)
d = {'addiction':0, 'depression':1, 'normal':2}
y = list(map(lambda i : d[i], y))
plot_decision_regions(X=X,
                      y=y,
                      clf=model.named_steps['clf'])