Calculating score of regression model gives me dimensionality error

Question

I want to make regression model with Scikit learn. I have features that are categorical and numerical. This is how I handled that.

features = df[["text", "title_len", "lead_len", "exclamation_question", "number_of_ent", "punct_count"]]
results = df["shares"]

features = features.to_numpy()
results = results.to_numpy()
print("Shape of Features:", features.shape) # Shape of Features: (14706, 6)
print("Shape of Result:", results.shape) # Shape of Result: (14706,)


# Creating vectorizer
transformerVectoriser = ColumnTransformer(transformers=[('text_vocab', TfidfVectorizer(analyzer='word', ngram_range=(1, 4), vocabulary=vocabulary, lowercase = True), 0)
                                                       ],
                                          remainder='passthrough'
                                          )

# Making final prediction with classifiation report and confusion matrix with model with highest accuracy
x_train, x_test, y_train, y_test = train_test_split(features, results, test_size=0.25, random_state=0)

print("X Train Shape", x_train.shape) # X Train Shape (11029, 6)
print("Y Train Shape", y_train.shape) # Y Train Shape (11029,)

print("X Test Shape", x_test.shape) # X Test Shape (3677, 6)
print("Y Test Shape", y_test.shape) # Y Test Shape (3677,)

x_train = transformerVectoriser.fit_transform(x_train)
x_test = transformerVectoriser.transform(x_test)

print("X Train Vectorized Shape", x_train.shape) # X Train Vectorized Shape (11029, 1091)
print("X Test Vectorized Shape", x_test.shape) # X Test Vectorized Shape (3677, 1091)

This is how I created a model:

regression_models = [["SVR C1", SVR(kernel='rbf', gamma='scale', C=1.0)],
                     ["SVR C2", SVR(kernel='rbf', gamma='scale', C=2.0)],
                     ["SVR C5", SVR(kernel='rbf', gamma='scale', C=5.0)]]

for regressor in regression_models:

   name = regressor[0]
   regressor = regressor[1]

   print("Model Name:", name)

   model = regressor.fit(x_train, y_train)

   y_pred = model.predict(x_test)
   score = model.score(y_test, y_pred) # ERROR
   print(score)

The error that I get:

ValueError                                Traceback (most recent call last)
<ipython-input-1351-c5fbe26b2474> in <module>
     22     print(y_test)
     23     print(y_test.shape)
---> 24     score = model.score(y_test, y_pred)
     25     print(score)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/base.py in score(self, X, y, sample_weight)
    551 
    552         from .metrics import r2_score
--> 553         y_pred = self.predict(X)
    554         return r2_score(y, y_pred, sample_weight=sample_weight)
    555 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_base.py in predict(self, X)
    236             Returns predicted values.
    237         """
--> 238         return self._decision_function(X)
    239 
    240     _preprocess_data = staticmethod(_preprocess_data)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_base.py in _decision_function(self, X)
    218         check_is_fitted(self)
    219 
--> 220         X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
    221         return safe_sparse_dot(X, self.coef_.T,
    222                                dense_output=True) + self.intercept_

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    692             # If input is 1D raise error
    693             if array.ndim == 1:
--> 694                 raise ValueError(
    695                     "Expected 2D array, got 1D array instead:\narray={}.\n"
    696                     "Reshape your data either using array.reshape(-1, 1) if "

ValueError: Expected 2D array, got 1D array instead:
array=[13.  8. 71. ... 43. 61. 55.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

What am I doing wrong?

If i print:

print(y_pred)
print(y_pred.shape)

print(y_test)
print(y_test.shape) 

Im getting this:

[ 81.54398324  43.34783895 111.73805915 ...  75.27910881  89.46342907
  78.93812588]

(4235,)
[13.  8. 71. ... 43. 61. 55.]
(4235,)

Answer 1

Based on documentation , the input of score is X and y. Therefore, it should be changed to

score = model.score(x_test, y_test) 

Else, you can do

from sklearn.metrics import r2_score
score = r2_score(y_test, y_pred)