適合率、再現率の計算でわからないこと

前回の質問のかいもあってコードを実行することができ、実際に実行したのですがaccuracyやf1等のscoreがすべて同じ値になりました。これはどういう状況でしょうか。それとも現在組んでいるコードでは足りない箇所があるのでしょうか。
分かる方がいらしたら教えてください。
実際のコードが以下です

# -*- 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(kernel = "rbf", C = 763.8 , gamma = 0.0197 )
    #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) 
    #print(Y_pred.shape)
    #print(Y_pred)
    #print(Y_test.shape)
    #print(Y_test)

    # .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))

    Y_test=Y_test.values


    print("accuracy score:",accuracy_score(Y_test, Y_pred))
    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))

    Y_test=Y_test.values

    print("accuracy score:",accuracy_score(Y_test, Y_pred))
    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)

    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()

実際に出た結果がこちらです。

SVM
train score: 0.8728841145833334
test score: 0.771484375
accuracy score: 0.771484375
precision score: 0.771484375
recall score: 0.771484375
f1 score: 0.771484375

GBM
train score: 1.0
test score: 0.7096354166666666
accuracy score: 0.7096354166666666
precision score: 0.7096354166666666
recall score: 0.7096354166666666
f1 score: 0.7096354166666666