Logistic regression applied to iris dataset

iris_logistic_regression.py

import matplotlib.pyplot as plt
from sklearn import datasets
import numpy as np
import seaborn as sns
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import roc_curve, auc

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import seaborn as sns
import pandas as pd
#Split the data into 80% training and 20% testing




iris = datasets.load_iris()
X = iris.data
y = iris.target


x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)



#Feature Scaling :
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)

# Fitting Logistic Regression to the Training set

classifier = LogisticRegression(random_state = 0, solver='lbfgs', multi_class='auto')
classifier.fit(x_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(x_test)

# Predict probabilities
probs_y=classifier.predict_proba(x_test)

### Print results 
probs_y = np.round(probs_y, 2)

res = "{:<10} | {:<10} | {:<10} | {:<13} | {:<5}".format("y_test", "y_pred", "Setosa(%)", "versicolor(%)", "virginica(%)\n")
res += "-"*65+"\n"
res += "\n".join("{:<10} | {:<10} | {:<10} | {:<13} | {:<10}".format(x, y, a, b, c) for x, y, a, b, c in zip(y_test, y_pred, probs_y[:,0], probs_y[:,1], probs_y[:,2]))
res += "\n"+"-"*65+"\n"
print(res)

#Check precision, recall, f1-score
print( classification_report(y_test, y_pred) )

print( accuracy_score(y_test, y_pred))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)

# Plot confusion matrix

# confusion matrix sns heatmap 
ax = plt.axes()
df_cm = cm
sns.heatmap(df_cm, annot=True, annot_kws={"size": 30}, fmt='d',cmap="Blues", ax = ax )
ax.set_title('Confusion Matrix')
plt.show()