複数の分類のスコア計算のエラー,python,SVM

accuracy_scoreや、F1_score等を求めたいのですが、

 　 print(accuracy_score(Y_test, Y_pred))
    print(precision_score(Y_test, Y_pred))
    print(recall_score(Y_test, Y_pred))
    print(F1_score(Y_test, Y_pred))

上記のように書いたところ、以下のようなエラーが発生しました。

ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

そこで、webで調べてbinary対策として次のようなコードにすると良いという事で変更したのですが、

    print(accuracy_score(Y_test, Y_pred,average=’micro’))
    print(precision_score(Y_test, Y_pred,average=’micro’))
    print(recall_score(Y_test, Y_pred,average=’micro’))
    print(F1_score(Y_test, Y_pred,average=’micro’))

以下のエラーが発生し言語認識をしてくれません。どのように変えたらいいのでしょうか。
分かる方がいらしたら教えてください。

 print(precision_score(Y_test, Y_pred,average=’micro’))
                                                       ^
SyntaxError: invalid character in identifier

コード本文です。

# -*- coding: utf-8 -*-

import scipy as sp
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier #RandomForest
from sklearn.svm import SVC # SVM用
import lightgbm as lgb
from sklearn.metrics import confusion_matrix  #混同行列
from sklearn.metrics import accuracy_score, precision_score #適合率
from sklearn.metrics import recall_score, f1_score #再現率,F1スコア
from sklearn.metrics import make_scorer



    
    
    
    
def SVM(X_train_std, X_test_std, Y_train, Y_test):
    model_SVM = SVC(random_state=0, kernel = "rbf", C = 1000 , gamma = 0.01 )
	#学習モデル構築。引数に訓練データの特徴量と、それに対応したラベル
    model_SVM.fit(X_train_std,Y_train)

	#予測したクラスラベル
    Y_pred = model_SVM.predict(X_test_std) 
 
	# .scoreで正解率を算出。
    print("\nSVM")	
    print("train score:",model_SVM.score(X_train_std,Y_train))
    print("test score:",model_SVM.score(X_test_std,Y_test))
    
    print("accuracy score:",accuracy_score(Y_test, Y_pred,average='micro'))
    print("precision score:",precision_score(Y_test, Y_pred,average='micro'))
    print("recall score:",recall_score(Y_test, Y_pred,average='micro'))
    print("f1 score:",f1_score(Y_test, Y_pred,average='micro'))
    
    

def GBM(X_train, X_test, Y_train, Y_test):
    model_GBM = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=58, 
								   max_depth=14, learning_rate=0.1, n_estimators=940, 
								   min_child_samples=40, importance_type="split", random_state=0)

	#学習モデル構築。引数に訓練データの特徴量と、それに対応したラベル
    model_GBM.fit(X_train, Y_train)

	#予測したクラスラベル
    Y_pred = model_GBM.predict(X_test) 
 
	# .scoreで正解率を算出。
    print("\nGBM")	
    print("train score:",model_GBM.score(X_train,Y_train))
    print("test score:",model_GBM.score(X_test,Y_test))
    
    print("accuracy score:",accuracy_score(Y_test, Y_pred,average='micro'))
    print("precision score:",precision_score(Y_test, Y_pred,average='micro'))
    print("recall score:",recall_score(Y_test, Y_pred,average='micro'))
    print("f1 score:",f1_score(Y_test, Y_pred,average='micro'))
    
    

    
    

	
    
def main():
    """
    TODO case_nameに任意の名前を指定
    フォルダを統一するために以降のscriptも名前を統一する
    """
    case_name = "case3"
    # ---------------------------------------

    case_dir = "./casestudy/{}/".format(case_name) 
    input_csv_name = "2_extracted_features_original.csv"
    input_csv_path = case_dir + input_csv_name
    input_df = pd.read_csv(input_csv_path, encoding="utf-8-sig")


	#すべてのデータを対象に分類を行う場合
	#-----------------------------------------------------------------------
    task = "all"
    train_df = input_df[input_df["train_test_flag"] == 0]
    test_df = input_df[input_df["train_test_flag"] == 1]
	# print(train_df)
	# print(test_df)

    X_train = train_df.loc[:, "contrast":"inverse_difference_m_norm"]
    X_test = test_df.loc[:, "contrast":"inverse_difference_m_norm"]
	# print(X_train)
	# print(X_test)
    Y_train = train_df["category"]
    Y_test = test_df["category"]
	# print(Y_train)
	# print(Y_test)

    sc = StandardScaler()
    sc.fit(X_train)
    X_train_std = sc.transform(X_train)
    X_test_std = sc.transform(X_test)
	# print(X_train_std)
	# print(X_test_std)
	# #------------------------------------------------------------------------
    SVM(X_train_std, X_test_std, Y_train, Y_test)
    GBM(X_train, X_test, Y_train, Y_test)

	
if __name__ == "__main__":
	main()

Ypred [1 1 1 ... 6 6 6]

Ytest
384     1
385     1
386     1
387     1
388     1
       ..
7675    8
7676    8
7677    8
7678    8
7679    8
Name: category, Length: 1536, dtype: in