<｜begin▁of▁sentence｜>### Instruction:
In the realm of proprietary data analysis systems, consider a scenario where a patent attorney is tasked with assessing two distinct computational methods that have been engineered to classify a dataset representative of a particular intellectual property domain. The task at hand involves evaluating the fidelity of these methods, which is critical for validating the inherent algorithmic designs that may be subject to patent protection.

The first computational method has generated an initial set of category assignments for a collection of six data specimens, documented as an array titled y_true, indicating an orderly clustering: array([0, 0, 1, 1, 2, 2]). Correspondingly, the second method has independently ascertained a divergent set of categorizations for the identical specimens, encapsulated within an array summoned y_pred: array([2, 2, 1, 1, 0, 0]).

The quandary confronting our patent attorney lies in the necessity to align the y_pred label set with the y_true label set in the most seamless fashion conceivable, to permit a direct and undistorted comparison of the resultant cluster groupings from both methods. This alignment is quintessential to secure an objective comparison, which subsequently could be crucial for affirming the distinctiveness and effectiveness of the algorithmic approach under scrutiny for potential patentability.

The pertinent challenge is the derivation of a strategy to appropriately transmute the y_pred labels, therein establishing an optimal congruence with the y_true labels, all the while procuring the explicit label transformation schema. This schema would be instrumental in delineating the transformation process, potentially serving as an essential component in establishing the value of the patented invention. It is this reconfiguration of the y_pred labels, with the intent of achieving a harmonized comparison field, that the patent attorney is to consider and assess the implications thereof on the protection of the intellectual property at stake. 

To meet this challenge, the patent attorney would likely engage with technical experts versed in data classification methodologies and algorithmic design to exhaustively evaluate the merit and novelty of the approach, in preparation for any subsequent patent filings or intellectual property claims.
### Response:
import numpy as np
from graspologic.utils import remap_labels

y_true = np.array([0,0,1,1,2,2])
y_pred = np.array([2,2,1,1,0,0])
remapped_y_pred, label_map = remap_labels(y_true, y_pred, return_map=True)
print('Remapped y_pred:', remapped_y_pred)
print('Label map:', label_map)
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