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

Issue

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?

Solution

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.

Answered By - Sal_H