LogisticRegression for Breastcancer data.py

#Import Libraries
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import roc_auc_score
from sklearn.metrics import zero_one_loss
#----------------------------------------------------

#load breast cancer data

BreastData = load_breast_cancer()

#X Data
X = BreastData.data

#y Data
y = BreastData.target

#----------------------------------------------------
#Splitting data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=44, shuffle =True)


#----------------------------------------------------
#Applying LogisticRegression Model 

'''
#linear_model.LogisticRegression(penalty='l2’,dual=False,tol=0.0001,C=1.0,fit_intercept=True,intercept_scaling=1,
#                                class_weight=None,random_state=None,solver='warn’,max_iter=100,
#                                multi_class='warn’, verbose=0,warm_start=False, n_jobs=None)
'''

LogisticRegressionModel = LogisticRegression(penalty='l2',max_iter=1000,solver='sag',C=1.0,random_state=33)
LogisticRegressionModel.fit(X_train, y_train)

#Calculating Details
print('LogisticRegressionModel Train Score is : ' , LogisticRegressionModel.score(X_train, y_train))
print('LogisticRegressionModel Test Score is : ' , LogisticRegressionModel.score(X_test, y_test))
print('LogisticRegressionModel Classes are : ' , LogisticRegressionModel.classes_)
print('LogisticRegressionModel No. of iteratios is : ' , LogisticRegressionModel.n_iter_)
print('----------------------------------------------------')

#Calculating Prediction
y_pred = LogisticRegressionModel.predict(X_test)
y_pred_prob = LogisticRegressionModel.predict_proba(X_test)
print('Predicted Value for LogisticRegressionModel is : ' , y_pred[:10])
print('Prediction Probabilities Value for LogisticRegressionModel is : ' , y_pred_prob[:10])

#----------------------------------------------------
#Calculating Confusion Matrix
CM = confusion_matrix(y_test, y_pred)
print('Confusion Matrix is : \n', CM)

# drawing confusion matrix
sns.heatmap(CM, center = True)
plt.show()

#----------------------------------------------------
#Calculating Accuracy Score  : ((TP + TN) / float(TP + TN + FP + FN))
AccScore = accuracy_score(y_test, y_pred, normalize=False)
print('Accuracy Score is : ', AccScore)

#----------------------------------------------------
#Calculating F1 Score  : 2 * (precision * recall) / (precision + recall)
# f1_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)

F1Score = f1_score(y_test, y_pred, average='micro') #it can be : binary,macro,weighted,samples
print('F1 Score is : ', F1Score)

#----------------------------------------------------
#Calculating Recall Score : (Sensitivity) (TP / float(TP + FN))   1 / 1+2  
# recall_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)

RecallScore = recall_score(y_test, y_pred, average='micro') #it can be : binary,macro,weighted,samples
print('Recall Score is : ', RecallScore)

#----------------------------------------------------
#Calculating Precision Score : (Specificity) #(TP / float(TP + FP))  
# precision_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’,sample_weight=None)

PrecisionScore = precision_score(y_test, y_pred, average='micro') #it can be : binary,macro,weighted,samples
print('Precision Score is : ', PrecisionScore)

#----------------------------------------------------
#Calculating Precision recall Score :  
#metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=
#                                        None, warn_for = ('precision’,’recall’, ’f-score’), sample_weight=None)

PrecisionRecallScore = precision_recall_fscore_support(y_test, y_pred, average='micro') #it can be : binary,macro,weighted,samples
print('Precision Recall Score is : ', PrecisionRecallScore)

#----------------------------------------------------
#Calculating Precision recall Curve :  
# precision_recall_curve(y_true, probas_pred, pos_label=None, sample_weight=None)

PrecisionValue, RecallValue, ThresholdsValue = precision_recall_curve(y_test,y_pred)
print('Precision Value is : ', PrecisionValue)
print('Recall Value is : ', RecallValue)
print('Thresholds Value is : ', ThresholdsValue)

#----------------------------------------------------
#Calculating classification Report :  
#classification_report(y_true, y_pred, labels=None, target_names=None,sample_weight=None, digits=2, output_dict=False)

ClassificationReport = classification_report(y_test,y_pred)
print('Classification Report is : ', ClassificationReport )

#----------------------------------------------------
#Calculating Area Under the Curve :  

fprValue2, tprValue2, thresholdsValue2 = roc_curve(y_test,y_pred)
AUCValue = auc(fprValue2, tprValue2)
print('AUC Value  : ', AUCValue)

#----------------------------------------------------
#Calculating Receiver Operating Characteristic :  
#roc_curve(y_true, y_score, pos_label=None, sample_weight=None,drop_intermediate=True)

fprValue, tprValue, thresholdsValue = roc_curve(y_test,y_pred)
print('fpr Value  : ', fprValue)
print('tpr Value  : ', tprValue)
print('thresholds Value  : ', thresholdsValue)

#----------------------------------------------------
#Calculating ROC AUC Score:  
#roc_auc_score(y_true, y_score, average=’macro’, sample_weight=None,max_fpr=None)

ROCAUCScore = roc_auc_score(y_test,y_pred, average='micro') #it can be : macro,weighted,samples
print('ROCAUC Score : ', ROCAUCScore)

#----------------------------------------------------
#Calculating Zero One Loss:  
#zero_one_loss(y_true, y_pred, normalize = True, sample_weight = None)

ZeroOneLossValue = zero_one_loss(y_test,y_pred,normalize=False) 
print('Zero One Loss Value : ', ZeroOneLossValue )