from sklearn.model_selection import learning_curve

#The function below builds the model and returns cross validation scores, train score and learning curve data
 def learn_curve(X,y,c):
 ''' param X: Matrix of input features
        param y: Vector of Target/Label
        c: Inverse Regularization variable to control overfitting (high value causes overfitting, low value causes underfitting)
    '''
 '''We aren't splitting the data into train and test because we will use StratifiedKFoldCV.
        KFold CV is a preferred method compared to hold out CV, since the model is tested on all the examples.
        Hold out CV is preferred when the model takes too long to train and we have a huge test set that truly represents the universe
    '''
     
     le = LabelEncoder() # Label encoding the target
     sc = StandardScaler() # Scaling the input features
     y = le.fit_transform(y)#Label Encoding the target
 log_reg = LogisticRegression(max_iter=200,random_state=11,C=c) # LogisticRegression model
 # Pipeline with scaling and classification as steps, must use a pipelne since we are using KFoldCV
     lr = Pipeline(steps=(['scaler',sc],
                        ['classifier',log_reg]))
     
     
     cv = StratifiedKFold(n_splits=5,random_state=11,shuffle=True) # Creating a StratifiedKFold object with 5 folds
 cv_scores = cross_val_score(lr,X,y,scoring="accuracy",cv=cv) # Storing the CV scores (accuracy) of each fold
     
     
     lr.fit(X,y) # Fitting the model
 
     train_score = lr.score(X,y) # Scoring the model on train set
     
     #Building the learning curve
 train_size,train_scores,test_scores =learning_curve(estimator=lr,X=X,y=y,cv=cv,scoring="accuracy",random_state=11)
 train_scores = 1-np.mean(train_scores,axis=1)#converting the accuracy score to misclassification rate
     test_scores = 1-np.mean(test_scores,axis=1)#converting the accuracy score to misclassification rate
 lc =pd.DataFrame({"Training_size":train_size,"Training_loss":train_scores,"Validation_loss":test_scores}).melt(id_vars="Training_size")
     return {"cv_scores":cv_scores,
            "train_score":train_score,
            "learning_curve":lc}

lc = learn_curve(X,y,1)
 print(f'Cross Validation Accuracies:\n{"-"*25}\n{list(lc["cv_scores"])}\n\n\
 Mean Cross Validation Accuracy:\n{"-"*25}\n{np.mean(lc["cv_scores"])}\n\n\
 Standard Deviation of Deep HUB Cross Validation Accuracy:\n{"-"*25}\n{np.std(lc["cv_scores"])}\n\n\
 Training Accuracy:\n{"-"*15}\n{lc["train_score"]}\n\n')
 sns.lineplot(data=lc["learning_curve"],x="Training_size",y="value",hue="variable")
 plt.title("Learning Curve of Good Fit Model")
 plt.ylabel("Misclassification Rate/Loss");

lc = learn_curve(X,y,10000)
 print(f'Cross Validation Accuracies:\n{"-"*25}\n{list(lc["cv_scores"])}\n\n\
 Mean Cross Validation Deep HUB Accuracy:\n{"-"*25}\n{np.mean(lc["cv_scores"])}\n\n\
 Standard Deviation of Cross Validation Accuracy:\n{"-"*25}\n{np.std(lc["cv_scores"])} (High Variance)\n\n\
 Training Accuracy:\n{"-"*15}\n{lc["train_score"]}\n\n')
 sns.lineplot(data=lc["learning_curve"],x="Training_size",y="value",hue="variable")
 plt.title("Learning Curve of an Overfit Model")
 plt.ylabel("Misclassification Rate/Loss");

lc = learn_curve(X,y,1/10000)
 print(f'Cross Validation Accuracies:\n{"-"*25}\n{list(lc["cv_scores"])}\n\n\
 Mean Cross Validation Accuracy:\n{"-"*25}\n{np.mean(lc["cv_scores"])}\n\n\
 Standard Deviation of Cross Validation Accuracy:\n{"-"*25}\n{np.std(lc["cv_scores"])} (Low variance)\n\n\
 Training Deep HUB Accuracy:\n{"-"*15}\n{lc["train_score"]}\n\n')
 sns.lineplot(data=lc["learning_curve"],x="Training_size",y="value",hue="variable")
 plt.title("Learning Curve of an Underfit Model")
 plt.ylabel("Misclassification Rate/Loss");