Spaces:
Sleeping
Sleeping
| from .config import * | |
| def create_bias_weights(): | |
| """ | |
| Create a weights vector for bias based on the given tags and weights. | |
| The function initializes a weights vector to zero, then maps the weights from the weights_tags_vector to the appropriate positions in the weights_vector based on the tags present in the destinations. | |
| """ | |
| weights_tags_vector = [ | |
| [15, 15, 0.9, 15, 15, 10, 1, 5, 0.6, 0.9, 0.9, 0.8, 10, 10, 1, 15], | |
| [15, 15, 0.9, 15, 15, 10, 15, 1, 10, 0.6, 0.9, 0.9, 0.8, 10, 10, 15, 0.8, 15], | |
| [15, 0.9, 0.8, 15, 15, 1, 10, 10, 0.6, 0.9, 0.9, 0.8, 5, 5, 1, 15], | |
| [15, 15, 0.9, 15, 0.7, 15, 15, 15, 1, 10, 10, 1, 0.9, 0.9, 0.9, 5, 5, 15, 0.8, 15], | |
| [10, 10, 15, 15, 0.8, 0.9, 15, 15, 15, 1, 10, 10, 0.6, 0.5, 0.9, 0.9, 0.8, 0.7, 15, 15, 15, 15, 15], | |
| [0.8, 0.9, 15, 0.8, 15, 0.9, 10, 15, 0.9, 0.9, 0.9, 0.8, 15, 10, 1, 15], | |
| [0.9, 0.8, 5, 1, 0.9, 10, 15, 0.9, 0.9, 0.9, 0.9, 0.8, 15, 1, 1, 15], | |
| [0.8, 0.9, 5, 1, 15, 15, 0.9, 0.9, 0.9, 0.8, 15, 1, 15], | |
| [0.8, 0.7, 15, 15, 1, 10, 0.7, 0.7, 0.6, 5, 5, 15], | |
| [0.8, 5, 1, 15, 15, 15, 0.7, 0.7, 15], | |
| [0.8, 0.7, 1, 15, 15, 0.7, 0.7, 15], | |
| [0.8, 0.7, 1, 15, 15, 15, 0.7, 0.9, 15], | |
| [0.8, 0.7, 1, 15, 15, 0.7, 0.7, 15], | |
| [0.8, 0.7, 1, 15, 15, 15, 0.7, 0.7, 15], | |
| [0.8, 0.7, 1, 15, 15, 15, 1, 10, 15], | |
| [10, 0.9, 0.8, 1, 15, 15, 15, 0.8, 10, 15], | |
| [0.8, 15, 1, 15, 15, 0.8, 10, 15], | |
| [10, 0.8, 1, 15, 1, 0.9, 0.8, 5, 0.8], | |
| [0.8, 15, 1, 5, 0.9, 0.8, 0.7, 0.7], | |
| [0.9, 0.8, 15, 1, 15, 0.7, 0.8, 0.7, 0.7, 5, 5, 15], | |
| [0.8, 0.7, 1, 5, 0.9, 10, 10, 15], | |
| [0.8, 1, 15, 15, 1, 0.9, 0.8, 0.8, 15], | |
| [0.8, 1, 10, 5, 5, 15], | |
| [0.8, 0.7, 1, 15, 15, 0.8, 0.9, 15], | |
| [10, 10, 10, 1, 10, 0.8, 1, 5, 10, 10, 10, 10, 1, 0.9, 1, 1, 15], | |
| [0.8, 0.7, 1, 15, 15, 0.8, 0.9, 15], | |
| [0.8, 0.7, 1, 10, 10, 0.8, 0.9, 15], | |
| [10, 0.8, 0.7, 15, 15, 1, 15, 15, 0.7, 0.7, 0.6, 5, 5, 1, 15], | |
| [5, 0.8, 0.7, 5, 5, 1, 10, 10, 0.7, 0.7, 0.6, 5, 5, 1, 15], | |
| [0.8, 0.7, 15, 5, 1, 10, 10, 10, 0.8, 0.7, 0.7, 5, 5, 5, 10, 15], | |
| [5, 5, 10, 15, 15, 15, 15, 0.9, 0.8, 0.7, 0.7, 1, 15], | |
| [10, 10, 15, 15, 10, 5, 1, 15, 15, 15, 15, 0.7, 5, 5, 0.8, 1, 15], | |
| [10, 15, 15, 15, 10, 10, 1, 1, 1, 15, 15, 5, 5], | |
| [0.8, 0.7, 0.6, 0.8, 1, 1, 1, 0.9, 0.8, 0.7, 0.7, 0.6, 5, 5, 1, 15], | |
| [1, 0.8, 0.9, 0.7, 0.6, 1, 0.9, 0.8, 1, 1, 0.9, 0.8, 0.8, 0.7, 0.9, 5, 5, 15], | |
| [1, 0.8, 0.9, 0.7, 0.6, 1, 0.9, 0.8, 1, 1, 0.9, 0.7, 0.6, 0.8, 0.8, 0.8, 0.7, 5, 5, 1, 0.7, 0.6, 15], | |
| [0.9, 0.7, 1, 1, 0.8, 0.7, 0.8, 0.8, 0.7, 1, 1, 1, 1, 15] | |
| ] | |
| #Create a weights vector initialized to zero | |
| weights_vector = np.zeros(tags_vector.shape) | |
| # Map weights to the appropriate positions in the weights_vector | |
| for i, row in enumerate(destinations["tags"][1:].values): | |
| tags = row.split() | |
| for tag, weight in zip(tags, weights_tags_vector[i]): | |
| index = np.where(feature_names == tag.lower())[0][0] | |
| weights_vector[i][index] = weight | |
| np.save("Datasets/Weights/weights_bias.npy", weights_vector) | |
| def create_freq_weights(): | |
| """ | |
| This function creates a weights vector for frequency-based weights based on the given tags and their frequencies. | |
| The function initializes a weights vector to zero, then maps the weights from the sorted_tags_dict to the appropriate positions in the weights_vector based on the tags present in the destinations. | |
| The weights are calculated as the ratio of the tag's frequency to the maximum frequency among all tags. | |
| Parameters: | |
| tags_vector (numpy.ndarray): A 2D numpy array representing the tags vector. Each row corresponds to a destination, and each column corresponds to a tag. The value at each position is 1 if the tag is present in the destination, and 0 otherwise. | |
| sorted_tags_dict (dict): A dictionary where the keys are the tags and the values are their frequencies. | |
| feature_names (numpy.ndarray): A 1D numpy array representing the names of the features (tags). | |
| destinations (pandas.DataFrame): A pandas DataFrame containing the destinations data, including the tags column. | |
| Returns: | |
| numpy.ndarray: A 2D numpy array representing the weights vector for frequency-based weights. 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. | |
| """ | |
| #Create a weights vector initialized to zero | |
| weights_vector = np.zeros(tags_vector.shape) | |
| max_freq = max(sorted_tags_dict.values()) | |
| # Map weights to the appropriate positions in the weights_vector | |
| for i, row in enumerate(destinations['tags'][1:].values): | |
| tags = row.split() | |
| for tag in tags: | |
| index = np.where(feature_names == tag.lower())[0][0] | |
| weights_vector[i][index] = f"{(sorted_tags_dict[tag.replace('_', ' ')]/max_freq):.2f}" | |
| np.save("Datasets/Weights/weights_freq.npy", weights_vector) | |
| create_bias_weights() | |
| create_freq_weights() | |
| weights_bias_vector = np.load("Datasets/Weights/weights_bias.npy") | |
| weights_freq = np.load("Datasets/Weights/weights_freq.npy") | |
| weighted_tags_vector = weights_bias_vector | |