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import numpy as np
from .config import vectorizer
from .get_default_weight import destinations, weights_bias_vector
def get_des_accumulation(question_vector, weights_bias_vector):
accumulation = 0
for index in range(len(weights_bias_vector)):
if question_vector[index]==1:
accumulation += weights_bias_vector[index]
return accumulation
def get_destinations_list(question_vector, top_k):
des = destinations
des = des[1:].reset_index(drop=True)
"""
This function calculates the accumulated scores for each destination based on the given question vector and weights vector.
It then selects the top 5 destinations with the highest scores and returns their names.
Parameters:
question_vector (numpy.ndarray): A 1D numpy array representing the question vector. Each element corresponds to a tag, and its value is 1 if the tag is present in the question, and 0 otherwise.
weights_bias_vector (numpy.ndarray): A 2D numpy array representing the weights vector. Each row corresponds to a destination, and each column corresponds to a tag. The value at each position represents the weight of the tag for that destination.
Returns:
destinations_list: A list of strings representing the names of the top k destinations with the highest scores.
"""
accumulation_dict = {}
for index in range(len(weights_bias_vector)):
accumulation = get_des_accumulation(question_vector[0], weights_bias_vector[index])
accumulation_dict[str(index)] = accumulation
top_keys = sorted(accumulation_dict, key=accumulation_dict.get, reverse=True)[:top_k]
scores = [accumulation_dict[key] for key in top_keys]
q1_score = np.percentile(scores, 25)
destinations_list = []
for key in top_keys:
if accumulation_dict[key] > q1_score:
destinations_list.append(des["name"][int(key)])
print(f"{des['name'][int(key)]}: {accumulation_dict[key]}")
return destinations_list
def get_question_vector(question_tags):
"""
Generate a question vector based on the given list of question tags.
Parameters:
question_tags (list): A list of strings representing the tags associated with the question.
Each tag is a word or phrase that describes a characteristic of a destination.
Returns:
numpy.ndarray: A 2D numpy array representing the question vector.
The array is transformed from the input list of question tags using a vectorizer.
Each row in the array corresponds to a tag, and its value is either 0 or 1.
The length of each row is equal to the number of unique tags in the dataset.
"""
question_tags = [question_tags]
question_vector = vectorizer.transform(question_tags).toarray()
return question_vector
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