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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
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