CODEWITHSUNDEEP
pythonpandasscikit-learn
Sal_H
Sal_H

Reputation: 75

TypeError: invalid type promotion while fitting a logistic regression model in Scikit-learn

I am building a logistic Regression model using Sci-kit Learn. My data is made up mainly of float and int types except for the date column which is of datetime64[ns](Its type was first object, then I converted it using

df['date'] = pd.to_datetime(df['date'],infer_datetime_format=True)

I did split my data to train and test and when trying to fit the model using logr.fit(X,Y) I get the following error:

---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
<ipython-input-15-18dd45102c66> in <module>
----> 1 logr.fit(X,Y)

/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py in fit(self, X, y, sample_weight)
   1342         X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,
   1343                                    order="C",
-> 1344                                    accept_large_sparse=solver != 'liblinear')
   1345         check_classification_targets(y)
   1346         self.classes_ = np.unique(y)

/opt/anaconda3/lib/python3.7/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    430                 y = check_array(y, **check_y_params)
    431             else:
--> 432                 X, y = check_X_y(X, y, **check_params)
    433             out = X, y
    434 

/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
    801                     ensure_min_samples=ensure_min_samples,
    802                     ensure_min_features=ensure_min_features,
--> 803                     estimator=estimator)
    804     if multi_output:
    805         y = check_array(y, accept_sparse='csr', force_all_finite=True,

/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

/opt/anaconda3/lib/python3.7/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)
    532 
    533         if all(isinstance(dtype, np.dtype) for dtype in dtypes_orig):
--> 534             dtype_orig = np.result_type(*dtypes_orig)
    535 
    536     if dtype_numeric:

<__array_function__ internals> in result_type(*args, **kwargs)

TypeError: invalid type promotion.

I am not able to understand what this error is pointing to. However from research, I found that it could be related to the date type but found nothing in the error that points out to date in particular. Any idea?

Upvotes: 2

Views: 2717

Answers (1)

Sal_H
Sal_H

Reputation: 75

After converting the date using:

df['date'] = pd.to_datetime(df['date'],infer_datetime_format=True)

I applied the following statement:

df['date']=df['date'].apply(lambda x: x.toordinal())

And it worked perfectly.

Upvotes: 3

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