Kaggle titanic challenge

Im trying to solve kaggle titanic challenge, but I get an error:

tuple object is not callable.

First i had an error with .loc then a fixed that with X_df=X_df.copy() but the tuple error had been there for a long time here is my code for feature_extractor and for the Classifier:

import pandas as pd
import random
class FeatureExtractor():
  def __init__(self):
    pass


  def fit(self, X_df, y):
    pass

  def transform(self, X_df):
    X_df=X_df.copy()
    #Adding family size
    family_size=X_df['SibSp']+X_df['Parch']
    X_df['family_size']=family_size
    #Cleaning Age
    mean=int(X_df['Age'].mean())
    std=int(X_df['Age'].std())
    X_df['Age']=X_df['Age'].fillna(random.randint(mean-std,mean+std))
    #age category
    mask=X_df.Age <= 16
    X_df.loc[mask,'Age']=0
    mask1=(X_df.Age <=32) & (X_df.Age >16)
    X_df.loc[mask1,'Age']=1
    mask2=(X_df.Age <=48) & (X_df.Age >32)
    X_df.loc[mask2,'Age']=2
    mask3=(X_df.Age <=64) & (X_df.Age >48)
    X_df.loc[mask3,'Age']=3
    mask4=X_df.Age>64
    X_df.loc[mask4,'Age']=4
    X_df_new = pd.concat(
        [X_df.get(['Fare', 'Age', 'family_size']),
         pd.get_dummies(X_df.Sex, prefix='Sex', drop_first=True),
         pd.get_dummies(X_df.Pclass, prefix='Pclass', drop_first=True),
         pd.get_dummies(X_df.Embarked, prefix='Embarked', drop_first=True)],axis=1)

    XX = X_df_new.values
    return XX

from sklearn.ensemble import GradientBoostingClassifier,RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors.nearest_centroid import NearestCentroid
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import Imputer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator

class Classifier(BaseEstimator):
  def __init__(self):
    self.clf = Pipeline([
        ('imputer', SimpleImputer(strategy='median'))
        ('classifier', GradientBoostingClassifier(n_estimators=100,learning_rate=0.05,max_depth=2,subsample=0.6))
    ])

  def fit(self, X, y):
    self.clf.fit(X, y)

  def predict_proba(self, X):
    return self.clf.predict_proba(X)

Can you find any solutions because I tried a lot to solve but in vain?