metrics

'access_key_id': 'YOUR_ACCESS_KEY_ID', 'secret_access_key': 'YOUR_SECRET_ACCESS_KEY' } } results = {} for library in libraries: evaluator = StreamingEvaluator(library, configurations[library]) latency = evaluat…

'storage_efficiency': storage_efficiency, 'scalability': scalability, 'ease_of_use': ease_of_use, 'cost': cost } for library, metrics in results.items(): print(f"Library: {library}") print(f"Sear…

for metric, value in metrics.items(): print(f"{metric.capitalize()}: {value / len(documents)}") ``` ->-> 7,20 [Turn 1177] Assistant: Certainly! Designing a proof of concept (PoC) to evaluate the performance of different retriev…

import random # Define the retrieval tools tools = ['tool1', 'tool2'] # Define the documents documents = [f'document{i}' for i in range(400)] # Define the evaluation metrics metrics = ['recall', 'precision', 'f1_score'] # Initialize the…

for tool, metrics in average_results.items(): print(f"Tool: {tool}") for metric, value in metrics.items(): print(f"{metric.capitalize()}: {value:.4f}") ``` ### Explanation 1. **Define the Retrieval Tools**: - List the r…

# Example usage engine = { 'search': lambda x: np.random.choice([0, 1], size=x.shape[0]) } metrics = test_sparse_retrieval_engine(engine) print(f"Average Duration: {metrics['average_duration']:.4f} seconds") print(f"Average Throughput: …

print(f"Average Duration: {metrics['average_duration']:.4f} seconds") print(f"Average Throughput: {metrics['average_throughput']:.2f} queries/second") print(f"Average Latency: {metrics['average_latency']:.4f} seconds") print(f"Average Preci…

total_duration += timer.duration total_throughput += num_queries / timer.duration latencies.append(timer.duration) # Assuming results is a binary array indicating relevance precision = precision_scor…

Here's an example of how you can use Okta's built-in analytics to monitor and optimize your authentication flow: ```python import okta import logging from okta.analytics import AnalyticsClient from okta.errors import OktaError # Set up lo…

hit_rate = (self.metrics['hits'] / self.metrics['total_requests']) * 100 if self.metrics['total_requests'] > 0 else 0 miss_rate = (self.metrics['misses'] / self.metrics['total_requests']) * 100 if self.metrics['total_request…

improved_percentage = (improved_steps / steps) * 100 # Initialize a dictionary to store the metrics metrics = { 'Improved Steps': improved_steps, 'Improved Percentage': improved_percentage } # A…