input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The function should load a CSV file containing text and corresponding categories, vectorize the text, convert target labels to numerical values, split the data into training and testing sets, train a Naive Bayes classifier, test the model, and print out the classification report.
  assert classify_text_data()==None
output: def classify_text_data():
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      data = pd.read_csv('data.csv')
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data['text'])
      y = data['category']
      y = y.map({'category1': 0, 'category2': 1, 'category3': 2})
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      classifier = MultinomialNB()
      classifier.fit(X_train, y_train)
  
      y_pred = classifier.predict(X_test)
      print(classification_report(y_test, y_pred))