33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
import numpy as np
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from girth import ability_mle
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class Rasch:
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def __init__(self, model_params, kwargs):
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self.model_params = model_params
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self.b_param = kwargs['b_param']
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self.e = 2.71828
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self.theta = kwargs['theta']
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def result(self):
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return 0.0
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@classmethod
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def ability_estimate(self, items) -> float:
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# responses are mapped into a matrix, where each row and item
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# and each column is an exam form result
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# we'll likely have to change this to something more robust
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# when we get into more complex response types
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responses = np.array([[int(item.response)] for item in items])
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# the difficulty (b param) for each item is in an ordered list
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difficulty = np.array([item.b_param for item in items])
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# the package currently utilizes a fixed a param (discrimination)
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discrimination = np.linspace(1, 1, len(difficulty))
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# there are many methodologies to calculate ability from a data set of responses
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# this is what our client currently uses but we should expand this to allow for
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# switching between methodologies when needed
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# it also currrently only does a single ability estimation
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# at some point we can also accommodate batch ability estimates if need be
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return ability_mle(responses, difficulty, discrimination).tolist()[0]
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