Typeerror: Only Integer Scalar Arrays Can Be Converted to a Scalar Index Site:stackoverflow.com

I just started to contact machine learning, and I encountered some problems when I used model fusion, and the following code was some of the issues I encountered in studying other people's code.I want to stack with Xgboost，ExtraTreesRegressor，RandomForestRegressor，Ridge，Lasso; here are codes:

          # coding=utf-8   import pandas as pd import numpy every bit np from scipy.stats import skew import xgboost as xgb from sklearn.cross_validation import KFold from sklearn.ensemble import ExtraTreesRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, Lasso from math import sqrt  NTRAIN = 100 NTEST = 0 X_train = [] Y_train = [] X_test = [] NFOLDS = 3 SEED = 0 kf = KFold(NTRAIN, n_folds=NFOLDS, shuffle=True, random_state=SEED)   grade SklearnWrapper(object):     def __init__(self, clf, seed=0, params=None):         params['random_state'] = seed         self.clf = clf(**params)      def train(self, x_train, y_train):         self.clf.fit(x_train, y_train)      def predict(self, 10):         return self.clf.predict(x)   class XgbWrapper(object):     def __init__(self, seed=0, params=None):         self.param = params         self.param['seed'] = seed         self.nrounds = params.pop('nrounds', 250)      def train(self, x_train, y_train):         dtrain = xgb.DMatrix(x_train, label=y_train)         cocky.gbdt = xgb.train(self.param, dtrain, self.nrounds)      def predict(cocky, x):         return cocky.gbdt.predict(xgb.DMatrix(ten))   def get_oof(clf):     oof_train = np.zeros((NTRAIN,))     oof_test = np.zeros((NTEST,))     oof_test_skf = np.empty((NFOLDS, NTEST))      for i, (train_index, test_index) in enumerate(kf):         impress("TRAIN:", train_index, "TEST:", test_index)          x_tr = X_train[train_index]         y_tr = Y_train[train_index]         x_te = X_train[test_index]          clf.train(x_tr, y_tr)          oof_train[test_index] = clf.predict(x_te)         oof_test_skf[i, :] = clf.predict(X_test)      oof_test[:] = oof_test_skf.mean(axis=0)     return oof_train.reshape(-one, 1), oof_test.reshape(-1, i)   def stacking(nfolds, seed, et_params, rf_params, xgb_params, rd_params, ls_params, x_train, y_train, x_test):     NTRAIN = x_train.shape[0]     NTEST = len(x_test)     NFOLDS = nfolds     SEED = seed     X_train = x_train     Y_train = y_train     X_test = x_test      kf = KFold(NTRAIN, n_folds=NFOLDS, shuffle=True, random_state=SEED)      xg = XgbWrapper(seed=SEED, params=xgb_params)     et = SklearnWrapper(clf=ExtraTreesRegressor, seed=SEED, params=et_params)     rf = SklearnWrapper(clf=RandomForestRegressor, seed=SEED, params=rf_params)     rd = SklearnWrapper(clf=Ridge, seed=SEED, params=rd_params)     ls = SklearnWrapper(clf=Lasso, seed=SEED, params=ls_params)      xg_oof_train, xg_oof_test = get_oof(xg)     et_oof_train, et_oof_test = get_oof(et)     rf_oof_train, rf_oof_test = get_oof(rf)     rd_oof_train, rd_oof_test = get_oof(rd)     ls_oof_train, ls_oof_test = get_oof(ls)      impress("XG-CV: {}".format(sqrt(mean_squared_error(Y_train, xg_oof_train))))     print("ET-CV: {}".format(sqrt(mean_squared_error(Y_train, et_oof_train))))     print("RF-CV: {}".format(sqrt(mean_squared_error(Y_train, rf_oof_train))))     print("RD-CV: {}".format(sqrt(mean_squared_error(Y_train, rd_oof_train))))     impress("LS-CV: {}".format(sqrt(mean_squared_error(Y_train, ls_oof_train))))      dtrain = xgb.DMatrix(X_train, label=Y_train)     dtest = xgb.DMatrix(X_test)      res = xgb.cv(xgb_params, dtrain, num_boost_round=1000, nfold=4, seed=SEED, stratified=Imitation,                  early_stopping_rounds=25, verbose_eval=10, show_stdv=True)      best_nrounds = res.shape[0] - i     cv_mean = res.iloc[-1, 0]     cv_std = res.iloc[-1, 1]      print('Ensemble-CV: {0}+{one}'.format(cv_mean, cv_std))      gbdt = xgb.train(xgb_params, dtrain, best_nrounds)     submission = pd.read_csv("../house_price/information/submission_xgboosting2.csv")     submission.iloc[:, 1] = gbdt.predict(dtest)     print(submission['SalePrice'])     saleprice = np.exp(submission['SalePrice']) - 1     print(saleprice)     submission['SalePrice'] = saleprice     submission.to_csv('xgstacker_starter.sub.csv', index=None)                  

I have this mistake when trying to run the stacking method on my data :

TypeError: only integer scalar arrays tin exist converted to a scalar index

The fault is coming from line x_tr = X_train[train_index] in the get_oof method.

many people encountered it when using TensorFlow, simply I think it looks like a bug in Numpy?

wadsworthladest78.blogspot.com

Source: https://stackoverflow.com/questions/49775365/typeerror-only-integer-scalar-arrays-can-be-converted-to-a-scalar-index-while-i

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