lr中通过kfold寻找最优C
from sklearn.linear_model import LogisticRegression from sklearn.metrics import recall_score def printing_kfold_scores(x_train_data,y_train_data): flod = KFold(5,shuffle=False) c_param_range=[0.01,0.1,1,10,100] result_table = pd.DataFrame(index = range(len(c_param_range),2),columns=['C_parameter','Mean recall score']) result_table['C_parameter']=c_param_range j=0 for c_param in c_param_range: print('-------------------------------------------') print('C parameter: ', c_param) print('-------------------------------------------') print('') recall_accs = [] for iteration,indices in enumerate(flod.split(y_train_data),start=1): lr=LogisticRegression(C=c_param,penalty='l1') lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel()) y_pred_undersample =lr.predict(x_train_data.iloc[indices[1],:].values) recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample) recall_accs.append(recall_acc) print('Iteration ', iteration,': recall score = ', recall_acc) results_table.loc[j,'Mean recall score'] = np.mean(recall_accs) j += 1 print('') print('Mean recall score ', np.mean(recall_accs)) print('') # return result_table best_c = result_table.loc[results_table['Mean recall score'].astype('float').idxmax()]['C_parameter'] print('*********************************************************************************') print('Best model to choose from cross validation is with C parameter = ', best_c) print('*********************************************************************************') return best_c best_c = printing_kfold_scores(X_train_undersample,y_train_undersample)
画混淆矩阵部分
def plot_confusion_matrix(cm,classes,title ='Confusion matrix',cmap=plt.cm.Blues): '''画混淆矩阵''' plt.imshow(cm,interpolation='nearest',cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=0) plt.yticks(tick_marks, classes) thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label')
lr中实现混淆矩阵
import itertools from sklearn.metrics import confusion_matrix lr = LogisticRegression(C = best_c, penalty = 'l1') lr.fit(X_train_undersample,y_train_undersample.values.ravel()) y_pred_undersample = lr.predict(X_test_undersample.values) # Compute confusion matrix cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample) np.set_printoptions(precision=2) print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1])) # Plot non-normalized confusion matrix class_names = [0,1] plt.figure() plot_confusion_matrix(cnf_matrix , classes=class_names , title='Confusion matrix') plt.show()
lr通过混淆矩阵找最优概率值
lr = LogisticRegression(C = 0.01, penalty = 'l1') lr.fit(X_train_undersample,y_train_undersample.values.ravel()) y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values) thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9] plt.figure(figsize=(10,10)) j = 1 for i in thresholds: y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i plt.subplot(3,3,j) j += 1 # Compute confusion matrix cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall) np.set_printoptions(precision=2) print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1])) # Plot non-normalized confusion matrix class_names = [0,1] plot_confusion_matrix(cnf_matrix , classes=class_names , title='Threshold >= %s'%i)