52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import numpy as np
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import pandas as pd
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from .explainer import Explainer
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class ALSExplainer(Explainer):
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def __init__(self, model, recommendations, data, number_of_contributions=10):
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super(ALSExplainer, self).__init__(model, recommendations, data)
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self.number_of_contributions = number_of_contributions
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def explain_recommendation_to_user(self, user_id: int, item_id: int):
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"""
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Measuring the contribution of each item to the recommendation.
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:param model:
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:param item_id:
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:param user_id:
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:return: returns a dataframe with the contribution to the recommendation of each previously interacted with item.
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"""
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current_interactions = np.zeros(self.num_items)
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current_interactions[self.get_user_items(user_id)] = 1
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c_u = np.diag(current_interactions)
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y_t = self.model.item_embedding().transpose()
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temp = np.matmul(y_t, c_u)
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temp = np.matmul(temp, self.model.item_embedding())
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temp = temp + np.diag([self.model.reg_term] * self.model.latent_dim)
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if len(self.get_user_items(user_id)) > 1:
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weight_mtr = np.linalg.inv(temp)
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else:
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weight_mtr = np.linalg.pinv(temp)
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temp = np.matmul(self.model.item_embedding(), weight_mtr)
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sim_to_rec_id = temp.dot(self.model.item_embedding()[item_id, :])
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sim_to_rec_id = sim_to_rec_id[self.get_user_items(user_id)]
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contribution = {
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"item": self.get_user_items(user_id),
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"contribution": sim_to_rec_id,
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}
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contribution = pd.DataFrame(contribution)
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contribution = contribution.sort_values(by=["contribution"], ascending=False)
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return {
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"item": contribution.item[: self.number_of_contributions],
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"contribution": contribution.contribution[: self.number_of_contributions],
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}
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