utils.py

import matplotlib.pyplot as plt
import numpy as np
import itertools
from sklearn.metrics import confusion_matrix, classification_report, recall_score, roc_auc_score
import pandas as pd
import seaborn as sns

def score_fn(gt, pred):
    return 0.1 * recall_score(gt, pred, average='macro', zero_division=0) + 0.9 * roc_auc_score(gt, pred, multi_class='ovo')

def plot_confusion_matrix(y_true,
                          y_pred,
                          target_names=['0', '1'],
                          title='Confusion matrix',
                          cmap='Purples',
                          normalize=True):

    cm = confusion_matrix(y_true, y_pred)
    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")


    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
    plt.show()
    print(classification_report(y_true, y_pred))
    print(f'target: {score_fn(y_true, y_pred)}')
    

def plot_feature_importance(importance, names, model_type='unk', figsize=(10,10)):
    
    #Create arrays from feature importance and feature names
    feature_importance = np.array(importance)
    feature_names = np.array(names)
    
    #Create a DataFrame using a Dictionary
    data={'feature_names':feature_names,'feature_importance':feature_importance}
    fi_df = pd.DataFrame(data)
    
    #Sort the DataFrame in order decreasing feature importance
    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
    
    #Define size of bar plot
    plt.figure(figsize=figsize)
    #Plot Searborn bar chart
    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
    #Add chart labels
    plt.title(model_type + 'FEATURE IMPORTANCE')
    plt.xlabel('FEATURE IMPORTANCE')
    plt.ylabel('FEATURE NAMES')