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pythonxgboost

feature_names mismach in xgboost despite having same columns

I have training (X) and test data (test_data_process) set with the same columns and order, as indicated below:

enter image description here

But when I do 

predictions = my_model.predict(test_data_process)

It gives the following error:

ValueError: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20', 'f21', 'f22', 'f23', 'f24', 'f25', 'f26', 'f27', 'f28', 'f29', 'f30', 'f31', 'f32', 'f33', 'f34'] ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'YrMoSold'] expected f22, f25, f0, f34, f32, f5, f20, f3, f33, f15, f24, f31, f28, f9, f8, f19, f14, f18, f17, f2, f13, f4, f27, f16, f1, f29, f11, f26, f10, f7, f21, f30, f23, f6, f12 in input data training data did not have the following fields: OpenPorchSF, BsmtFinSF1, LotFrontage, GrLivArea, YrMoSold, FullBath, TotRmsAbvGrd, GarageCars, YearRemodAdd, BedroomAbvGr, PoolArea, KitchenAbvGr, LotArea, HalfBath, MiscVal, EnclosedPorch, BsmtUnfSF, MSSubClass, BsmtFullBath, YearBuilt, 1stFlrSF, ScreenPorch, 3SsnPorch, TotalBsmtSF, GarageYrBlt, MasVnrArea, OverallQual, Fireplaces, WoodDeckSF, 2ndFlrSF, BsmtFinSF2, BsmtHalfBath, LowQualFinSF, OverallCond, GarageArea

So it complains that the training data (X) does not have those fields, whereas it has.

How to solve this issue?

[UPDATE]:

My code:

X = data.select_dtypes(exclude=['object']).drop(columns=['Id'])
X['YrMoSold'] = X['YrSold'] * 12 + X['MoSold']
X = X.drop(columns=['YrSold', 'MoSold', 'SalePrice'])
X = X.fillna(0.0000001)

train_X, val_X, train_y, val_y = train_test_split(X.values, y.values, test_size=0.2)

my_model = XGBRegressor(n_estimators=100, learning_rate=0.05, booster='gbtree')
my_model.fit(train_X, train_y, early_stopping_rounds=5, 
    eval_set=[(val_X, val_y)], verbose=False)

test_data_process = test_data.select_dtypes(exclude=['object']).drop(columns=['Id'])
test_data_process['YrMoSold'] = test_data_process['YrSold'] * 12 + test_data['MoSold']
test_data_process = test_data_process.drop(columns=['YrSold', 'MoSold'])
test_data_process = test_data_process.fillna(0.0000001)
test_data_process = test_data_process[X.columns]

predictions = my_model.predict(test_data_process)
Solution

  • Thats an honest mistake.

    When feeding your data you are using np arrays:

    train_X, val_X, train_y, val_y = train_test_split(X.values, y.values, test_size=0.2)

    (X.values is a np.array)

    which do not have column names defined

    when entering the data set for prediction you are using a dataframe

    you should use a numpy array, you can convert it by using:

    predictions = my_model.predict(test_data_process.values)

    (add .values)