Spaces:
Sleeping
Sleeping
| from pickle import dump, load | |
| from numpy import argsort, dot, array, any, isin | |
| from matplotlib import pyplot as plt | |
| from gossip_semantic_search.models import ProcessedDataset | |
| from gossip_semantic_search.utils import CustomUnpickler | |
| class Evaluator: | |
| def __init__(self, | |
| processed_dataset_path: str, | |
| k_max:int = None, | |
| path_k_plot:str = None): | |
| self.processed_dataset_path = processed_dataset_path | |
| self.k_max = k_max | |
| self.path_k_plot = path_k_plot | |
| self.processed_dataset: ProcessedDataset = None | |
| def load_dataset(self): | |
| with open(self.processed_dataset_path, 'rb') as file: | |
| unpickler = CustomUnpickler(file) | |
| return unpickler.load() | |
| def evaluate_k_context(self, | |
| k:int) -> float: | |
| similarity_matrix = dot(self.processed_dataset.embedded_queries, self.processed_dataset.embedded_context.T) | |
| k_best_context = argsort(similarity_matrix, axis=1)[:, -k:][:, ::-1] | |
| result = array([1 if any(isin(row, val)) else 0 | |
| for row, val in zip(self.processed_dataset.y_true, k_best_context)]) | |
| return result.mean() | |
| def plot_top_k_graph(self): | |
| accuracies = [] | |
| for k in range(1, self.k_max): | |
| accuracies.append(self.evaluate_k_context(k)) | |
| plt.plot(accuracies, marker='o', color='b', linestyle='-', markersize=8, linewidth=2) | |
| # Personnalisation du graphique | |
| plt.title("Taux de contextes corrects parmi les k meilleurs contextes", fontsize=14, fontweight='bold') | |
| plt.xlabel("k", fontsize=12) | |
| plt.ylabel("Accuracy", fontsize=12) | |
| plt.grid(True, which='both', linestyle='--', linewidth=0.5) | |
| plt.xticks(fontsize=10) | |
| plt.yticks(fontsize=10) | |
| plt.legend() | |
| plt.savefig(self.path_k_plot) | |
| def run(self): | |
| self.processed_dataset = self.load_dataset() | |
| if self.k_max: | |
| self.plot_top_k_graph() | |