Confusion Matrix Plot (Python)

eval_conf_mat.py

import seaborn as sn
from numpy import newaxis
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score, average_precision_score, recall_score
from sklearn.metrics import precision_recall_fscore_support as score
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

def plot_heat_map(ax, X, Y, Z, xlabel, ylabel, format='d', title='Heat Map'):
 
  sn.set(font_scale=1.4)  # for label size  
  sn.heatmap(Z, annot=True,fmt=format, annot_kws={"size": 12}, cmap="YlGnBu", 
             xticklabels=X, yticklabels=Y, ax=ax)  # font size
  ax.set_title(title)
  ax.set_xlabel(xlabel)
  ax.set_ylabel(ylabel)

def plot_confusion_matrix(ax, cm, class_names, normalize= False, title='Confusion Matrix'):
  format = 'd'
  if normalize:
      cm = cm.astype('float') / cm.sum(axis=1)[:, newaxis]
      format = '.2f'

  plot_heat_map(ax, class_names, class_names, cm,  'Predicted', 'True Classes', format, title)

# evalute classifer
def eval_conf(classifier, x_train, y_train, x_test, y_test, plot_conf=False, class_names=[],  title = "",  ax = None, normalized_conf=True, figsize=(10,8)):
  classifier.fit(x_train, y_train)
  y_pred = classifier.predict(x_test)
  acc = accuracy_score(y_test, y_pred)  
  cr = classification_report(y_test, y_pred)
  # plot confusion matrix
  if (plot_conf):
		  cm = confusion_matrix(y_test, y_pred)
		  precision,recall,fscore,support=score(y_test,y_pred,average='macro')
		  if ax == None:
			  fig, ax = plt.subplots(figsize=figsize)
		  if title == "":
			  title=f"Confusion matrix"
		  title += f", acc-{acc.round(2)} pr-{precision.round(2)} re-{recall.round(2)} f1-{fscore.round(2)}"
		  plot_confusion_matrix(ax, cm, class_names, normalized_conf, title)
  
  return acc, cr
 
def evalsplit_conf(classifier, x_train, y_train, test_size, plot_conf=False, class_names=[],  title = "",  ax = None, normalized_conf=True, figsize=(10,8)):
		x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=test_size, random_state=42)
		acc, cr = eval_conf(classifier, x_train, y_train, x_test, y_test, plot_conf, class_names,  title,  ax, normalized_conf,figsize)
		return acc, cr

 
def plot_dict(dict, ax=None):
    if ax == None:
        fig, ax = plt.subplots(figsize=(10,8))
    lists = sorted(dict.items()) # sorted by key, return a list of tuples
    x, y = zip(*lists) # unpack a list of pairs into two tuples
    ax.plot(x, y)