fix: <LinAlgError: SVD did not converge>

[nikospps]

When i use some transformed values 'macrodata.csv' the corresponding error returned:
/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/utils/correlations.py:73: RuntimeWarning: invalid value encountered in double_scalars
return np.sqrt(phi_sq_hat / np.min([k_hat - 1, r_hat - 1])) # Note: this is strictly positive
/home/nikospps/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/utils/correlations.py:73: RuntimeWarning: invalid value encountered in double_scalars
return np.sqrt(phi_sq_hat / np.min([k_hat - 1, r_hat - 1])) # Note: this is strictly positive
/home/nikospps/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/utils/correlations.py:73: RuntimeWarning: invalid value encountered in double_scalars
return np.sqrt(phi_sq_hat / np.min([k_hat - 1, r_hat - 1])) # Note: this is strictly positive
/home/nikospps/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/utils/correlations.py:73: RuntimeWarning: invalid value encountered in double_scalars
return np.sqrt(phi_sq_hat / np.min([k_hat - 1, r_hat - 1])) # Note: this is strictly positive

LinAlgError Traceback (most recent call last)
/tmp/ipykernel_9595/4020703881.py in
----> 1 dq = DataQuality(df=df).evaluate() # create the main class that holds all quality modules
2 # results = dq.evaluate() # run the tests

~/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/core/data_quality.py in evaluate(self, summary)
165 """
166 results = {
--> 167 name: engine.evaluate(*self._eval_args.get(name,[]), summary=False) for name, engine in self.engines.items()
168 }
169 self._store_warnings() # fetch all warnings from the engines

~/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/core/data_quality.py in (.0)
165 """
166 results = {
--> 167 name: engine.evaluate(*self._eval_args.get(name,[]), summary=False) for name, engine in self.engines.items()
168 }
169 self._store_warnings() # fetch all warnings from the engines

~/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/data_relations/engine.py in evaluate(self, df, dtypes, label, corr_th, vif_th, p_th, plot, summary)
83 results = {}
84 corr_mat, _ = correlation_matrix(df, self.dtypes, True)
---> 85 p_corr_mat = partial_correlation_matrix(corr_mat)
86 results['Correlations'] = {'Correlation matrix': corr_mat, 'Partial correlation matrix': p_corr_mat}
87 if plot:

~/.conda/envs/testingdata/lib/python3.7/site-packages/ydata_quality/utils/correlations.py in partial_correlation_matrix(corr_matrix)
134 """Returns the matrix of full order partial correlations.
135 Uses the covariance matrix inversion method."""
--> 136 inv_corr_matrix = np.linalg.pinv(corr_matrix)
137 diag = np.diag(inv_corr_matrix)
138 if np.isnan(diag).any() or (diag <= 0).any():

<array_function internals> in pinv(*args, **kwargs)

~/.conda/envs/testingdata/lib/python3.7/site-packages/numpy/linalg/linalg.py in pinv(a, rcond, hermitian)
2000 return wrap(res)
2001 a = a.conjugate()
-> 2002 u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
2003
2004 # discard small singular values

<array_function internals> in svd(*args, **kwargs)

~/.conda/envs/testingdata/lib/python3.7/site-packages/numpy/linalg/linalg.py in svd(a, full_matrices, compute_uv, hermitian)
1658
1659 signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-> 1660 u, s, vh = gufunc(a, signature=signature, extobj=extobj)
1661 u = u.astype(result_t, copy=False)
1662 s = s.astype(_realType(result_t), copy=False)

~/.conda/envs/testingdata/lib/python3.7/site-packages/numpy/linalg/linalg.py in _raise_linalgerror_svd_nonconvergence(err, flag)
95
96 def _raise_linalgerror_svd_nonconvergence(err, flag):
---> 97 raise LinAlgError("SVD did not converge")
98
99 def _raise_linalgerror_lstsq(err, flag):

LinAlgError: SVD did not converge