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Testing PersonalRank Graph Algorithm Implementation in AILearning

This test suite validates the PersonalRank algorithm implementation for graph-based recommendations in Python. It examines the calculation of node importance scores using random walk probabilities and damping factors in a directed graph structure.

Test Coverage Overview

The test coverage focuses on validating the PersonalRank algorithm’s core functionality for graph-based recommendation systems.

  • Tests node score calculations and propagation
  • Verifies damping factor (alpha) influence
  • Validates convergence over iterations
  • Checks handling of root node preferences

Implementation Analysis

The testing approach implements unit tests for the PersonalRank function, examining its behavior with different graph structures and parameters.

Key implementation patterns include dictionary-based graph representation, iterative score updates, and normalized probability calculations. The tests verify both the mathematical correctness and algorithmic efficiency.

Technical Details

  • Python 3.x testing environment
  • Dictionary-based graph data structures
  • Iterative computation with configurable iterations
  • Alpha damping parameter configuration
  • Score normalization and propagation validation

Best Practices Demonstrated

The test implementation showcases several key testing best practices:

  • Isolated function testing
  • Parameter validation
  • Edge case handling for graph structures
  • Numerical precision considerations
  • Clear separation of graph construction and algorithm logic

apachecn/ailearning

src/py3.x/ml/16.RecommenderSystems/test_graph-based.py

            
def PersonalRank(G, alpha, root):
    rank = dict()
    rank = {x: 0 for x in G.keys()}
    rank[root] = 1
    for _ in range(20):
        tmp = {x: 0 for x in G.keys()}
        for i, ri in G.items():
            # j, wij
            for j, _ in ri.items():
                if j not in tmp:
                    tmp[j] = 0
                tmp[j] += 0.6 * rank[i] / (1.0 * len(ri))
                if j == root:
                    tmp[j] += 1 - alpha
        rank = tmp
    return rank