best_tts.py

from sklearn.model_selection import train_test_split as tts 
from sklearn.metrics import r2_score,f1_score,accuracy_score, root_mean_squared_error 
import evaluationer 
import pandas as pd 
import numpy as np 
 
def best_tts(X,y,model,eva): 
 # def best_tts(X,y,test_size_range = range(10,25),random_state_range =range(1,100), stratify=None,shuffle=True,model = LinearRegression(),method = root_mean_squared_error,eva = "reg"): 
 
 if eva == "reg": 
 
 test_r2_,test_r2_ts,test_r2_rs = 0,0,0 
 for k in range(10,25,3): 
 i = k/100 
 for j in range(1,100,10): 
 X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size = i, random_state = j,) 
 
 model = model 
 model.fit(X_train,y_train) # model fitting 
 y_pred_train = model.predict(X_train) # model prediction for train 
 y_pred_test = model.predict(X_test) # model prediction for test 
 
 train_r2 = r2_score(y_train, y_pred_train) # evaluating r2 score for train 
 
 
 test_r2 = r2_score(y_test, y_pred_test) # evaluating r2 score for test 
 if test_r2_ < test_r2: 
 test_r2_ = test_r2 
 test_r2_ts = i 
 test_r2_rs = j 
 
 n_r_train, n_c_train = X_train.shape # getting no of rows and columns of train data 
 n_r_test, n_c_test = X_test.shape # getting no of rows and columns of test data 
 
 adj_r2_train = 1 - ((1 - train_r2)*(n_r_train - 1)/ (n_r_train - n_c_train - 1)) # evaluating adjusted r2 score for train 
 
 
 adj_r2_test = 1 - ((1 - test_r2)*(n_r_test - 1)/ (n_r_test - n_c_test - 1)) # evaluating adjusted r2 score for test 
 
 
 train_evaluation = root_mean_squared_error(y_train, y_pred_train) # evaluating train error 
 
 
 test_evaluation = root_mean_squared_error(y_test, y_pred_test) # evaluating test error 
 
 X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size = test_r2_ts, random_state = test_r2_rs) 
 evaluationer.evaluation("best_tts",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva) 
 return evaluationer.reg_evaluation_df,X_train,X_test,y_train,y_test 
 
 
 
 elif eva == "class": 
 global test_accuracies_,test_accuracies_ts,test_accuracies_rs 
 test_accuracies_,test_accuracies_ts,test_accuracies_rs = 0,0,0 
 
 for k in range(10,25): 
 i = k/100 
 for j in range(1,100): 
 X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size = i, random_state = j) 
 model = model 
 model.fit(X_train,y_train) # model fitting 
 y_pred_train = model.predict(X_train) # model prediction for train 
 y_pred_test = model.predict(X_test) # model prediction for test 
 # y_pred_proba_train= model.predict_proba(X_train) 
 # y_pred_proba_test= model.predict_proba(X_test) 
 
 
 unique_classes = np.unique(y_train) 
 
 # Determine the average method 
 if len(unique_classes) == 2: 
 # Binary classification 
 # print("Using 'binary' average for binary classification.") 
 average_method = 'binary' 
 elif len(unique_classes)!=2: 
 # Determine the distribution of the target column 
 class_counts = np.bincount(y_train) 
 
 # Check if the dataset is imbalanced 
 imbalance_ratio = max(class_counts) / min(class_counts) 
 
 if imbalance_ratio > 1.5: 
 # Imbalanced dataset 
 # print("Using 'weighted' average due to imbalanced dataset.") 
 average_method = 'weighted' 
 else: 
 # Balanced dataset 
 # print("Using 'macro' average due to balanced dataset.") 
 average_method = 'macro' 
 # F1 scores 
 train_f1_scores = (f1_score(y_train, y_pred_train,average=average_method)) 
 
 
 test_f1_scores = (f1_score(y_test, y_pred_test,average=average_method)) 
 
 # Accuracies 
 train_accuracies = (accuracy_score(y_train, y_pred_train)) 
 
 test_accuracies = (accuracy_score(y_test, y_pred_test)) 
 if test_accuracies_ <test_accuracies: 
 test_accuracies_,test_accuracies_ts,test_accuracies_rs =test_accuracies, i,j 
 X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size = test_accuracies_ts, random_state = test_accuracies_rs) 
 print(f"test_size = {test_accuracies_ts}, random_state = {test_accuracies_rs}") 
 
 evaluationer.evaluation("best_tts",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva) 
 
 
 return evaluationer.classification_evaluation_df,X_train,X_test,y_train,y_test