CODEWITHSUNDEEP
pythonpandasxgboost
bjarkemoensted
bjarkemoensted

Reputation: 2766

How to predict with an xgboost model, on a single row in a dataframe

I'm fitting an xgboost model to some data which is stored in a dataframe. After fitting, I would like to run the .predict method of the classifier/regressor on a single row from the dataframe.

Following, is a minimal example, which predicts fine on the full dataframe, yet crashes when running on only the second row of the dataframe.

from sklearn.datasets import load_iris
import xgboost

# Load iris data such that X is a dataframe
X, y = load_iris(return_X_y=True, as_frame=True)

clf = xgboost.XGBClassifier()
clf.fit(X, y)

# Predict for all rows - works fine
y_pred = clf.predict(X)

# Predict for single row. Crashes.
# Error: '('Expecting 2 dimensional numpy.ndarray, got: ', (4,))'
secondrow = X.iloc[1]
secondpred = clf.predict(secondrow)

Error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-45-a06c6820c458> in <module>
     11 # Error: '('Expecting 2 dimensional numpy.ndarray, got: ', (4,))'
     12 secondrow = X.iloc[1]
---> 13 secondpred = clf.predict(secondrow)

e:\Anaconda3\envs\py37\lib\site-packages\xgboost\sklearn.py in predict(self, data, output_margin, ntree_limit, validate_features)
    789                                                  output_margin=output_margin,
    790                                                  ntree_limit=ntree_limit,
--> 791                                                  validate_features=validate_features)
    792         if output_margin:
    793             # If output_margin is active, simply return the scores

e:\Anaconda3\envs\py37\lib\site-packages\xgboost\core.py in predict(self, data, output_margin, ntree_limit, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features)
   1282 
   1283         if validate_features:
-> 1284             self._validate_features(data)
   1285 
   1286         length = c_bst_ulong()

e:\Anaconda3\envs\py37\lib\site-packages\xgboost\core.py in _validate_features(self, data)
   1688 
   1689                 raise ValueError(msg.format(self.feature_names,
-> 1690                                             data.feature_names))
   1691 
   1692     def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True):

ValueError: feature_names mismatch: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] ['f0', 'f1', 'f2', 'f3']
expected petal length (cm), petal width (cm), sepal length (cm), sepal width (cm) in input data
training data did not have the following fields: f1, f3, f0, f2

Upvotes: 3

Views: 8750

Answers (1)

Trenton McKinney
Trenton McKinney

Reputation: 62413

  • predict expects an array of a specific shape, based upon the model fit.
  • The issue is, secondrow is a one dimensional pandas.Series, which does not match the shape of the model.
X.iloc[1]

sepal length (cm)    4.9
sepal width (cm)     3.0
petal length (cm)    1.4
petal width (cm)     0.2
Name: 1, dtype: float64

# look at the array
X.iloc[1].values

array([4.9, 3. , 1.4, 0.2])  # note this is a 1-d array

# look at the shape
secondrow.values.shape

(4,)
  • You can look at a single row, by passing data in the correct shape, which is a 2 dimensional array
  • Convert the Series selection to a DataFrame, and transpose it to the correct shape for .predict.
secondrow = pd.DataFrame(X.iloc[1]).T

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
1                4.9               3.0                1.4               0.2

# look at secondrow as an array
secondrow.values

array([[4.9, 3. , 1.4, 0.2]])  # note this is a 2-d array

# look at the shape
secondrow.values.shape

(1, 4)

# predict
secondpred = clf.predict(secondrow)

# result
array([0])

Upvotes: 6

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