profile_query_score.py

import line_profiler

import numpy as np
from sample_pool import TreeSamplePool
from random_steiner_tree.util import from_gt
from graph_helpers import remove_filters
from core1 import query_score, prediction_error, matching_trees_cython
from core1_python import query_score as query_score_py, prediction_error as prediction_error_py, matching_trees
from graph_tool.generation import lattice



g = lattice((10, 10))
gv = remove_filters(g)
gi = from_gt(g)
pool = TreeSamplePool(gv,
                      n_samples=20,
                      method='cut',
                      gi=gi,
                      return_tree_nodes=True  # using tree nodes
)

n_obs = 10
obs = np.random.permutation(g.num_vertices())[:n_obs]

print(obs)
pool.fill(obs)

q = 0
hidden_nodes = set(map(int, g.vertices())) - {q}- set(obs)

func_name = "prediction_error"


if func_name == 'query_score':
    profile = line_profiler.LineProfiler(query_score)
    profile.runcall(query_score, q, pool.samples, hidden_nodes)
    profile.print_stats()

    profile = line_profiler.LineProfiler(query_score_py)
    profile.runcall(query_score_py, q, pool.samples, hidden_nodes)
    profile.print_stats()

elif func_name == 'prediction_error':
    profile = line_profiler.LineProfiler(prediction_error)
    profile.runcall(prediction_error, q, 1, pool.samples, hidden_nodes)
    profile.print_stats()

    profile = line_profiler.LineProfiler(prediction_error_py)
    profile.runcall(prediction_error_py, q, 1, pool.samples, hidden_nodes)
    profile.print_stats()

elif func_name == 'matching_trees':
    profile = line_profiler.LineProfiler(matching_trees_cython)
    profile.runcall(matching_trees_cython, pool.samples, q, 1)
    profile.print_stats()

    profile = line_profiler.LineProfiler(matching_trees)
    profile.runcall(matching_trees, pool.samples, q, 1)
    profile.print_stats()