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| # Fit and transform the Vectorizer based on the feature selection results X_names | |
| vectorizer = feature_extraction.text.CountVectorizer(vocabulary=X_names) | |
| vectorizer.fit(corpus) | |
| X_train = vectorizer.transform(corpus) | |
| # Testing ML models are Naive Bayes, Random Forest and Decision Trees | |
| NB_Classifier = naive_bayes.MultinomialNB() | |
| RForest_Classifier = RandomForestClassifier() | |
| DTree_Classifier = DecisionTreeClassifier() | |
| # NB_Classifier Pipeline with CountVectorizer | |
| NB_Pipeline = pipeline.Pipeline([("vectorizer", vectorizer), | |
| ("classifier", NB_Classifier)]) | |
| # RForest_Classifier Pipeline with CountVectorizer | |
| RForest_Pipeline = pipeline.Pipeline([("vectorizer", vectorizer), | |
| ("classifier", RForest_Classifier)]) | |
| # DTree_Classifier Pipeline with CountVectorizer | |
| DTree_Pipeline = pipeline.Pipeline([("vectorizer", vectorizer), | |
| ("classifier", DTree_Classifier)]) | |
| # Define a plot function returns the heatmap of selected model pipeline's confusion matrix | |
| def model_plot(pipeline): | |
| # train classifier | |
| pipeline["classifier"].fit(X_train, y_train) | |
| # test | |
| X_test = df_test.Questions.values | |
| predicted = pipeline.predict(X_test) | |
| predicted_prob = pipeline.predict_proba(X_test) | |
| # Plot confusion matrix | |
| cm = confusion_matrix(y_test, predicted) | |
| ax= plt.subplot() | |
| sns.heatmap(cm, annot=True, fmt='g', ax=ax, cmap="BuPu"); #annot=True to annotate cells, ftm='g' to disable scientific notation | |
| # labels, title and ticks | |
| ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); | |
| ax.set_title('Confusion Matrix'); | |
| ax.xaxis.set_ticklabels(['EDA', 'Method', 'Model', 'Statistics']); ax.yaxis.set_ticklabels(['EDA', 'Method', 'Model', 'Statistics']) |
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