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Machine Learning/Query Optimization example TypeError: logger() got an unexpected keyword argument 'curr_min_score' #385

Open llermaly opened 2 years ago

llermaly commented 2 years ago

Hello, I'm trying to run the optimization query of the Machine Learning/Query Optimization example and I'm getting this error:

Using configuration
 - metric: {
  "mean_reciprocal_rank": {
    "k": 100,
    "relevant_rating_threshold": 1
  }
}
 - selected method: bayesian
 - default params: {}
/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/elasticsearch/connection/base.py:209: ElasticsearchWarning: Elasticsearch built-in security features are not enabled. Without authentication, your cluster could be accessible to anyone. See https://www.elastic.co/guide/en/elasticsearch/reference/7.15/security-minimal-setup.html to enable security.
  warnings.warn(message, category=ElasticsearchWarning)
Traceback (most recent call last):
  File "bin/optimize-query", line 84, in <module>
    main()
  File "bin/optimize-query", line 71, in main
    args.template_id, queries, qrels, logger)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/qopt/optimize.py", line 223, in optimize_query
    return optimize(config, objective_fn, initial_points=None, logger_fn=logger_fn)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/qopt/optimize.py", line 265, in optimize
    x0=initial_points)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/optimizer/gp.py", line 268, in gp_minimize
    callback=callback, n_jobs=n_jobs, model_queue_size=model_queue_size)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/optimizer/base.py", line 304, in base_minimize
    if eval_callbacks(callbacks, result):
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/utils.py", line 99, in eval_callbacks
    decision = c(result)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/qopt/optimize.py", line 176, in __call__
    params=params)
TypeError: logger() got an unexpected keyword argument 'curr_min_score'

real    0m0.988s
user    0m0.927s
sys 0m0.271s

This is the method that failes

    def logger(iteration, total_iterations, score, _, duration, params):
        print(f"   - iteration {iteration}/{total_iterations} ({duration:.04f}s) scored {score:.04f} with: {json.dumps(params)}")

If I replace the "_" with curr_min_score then throws a different error:

 - iteration 39/75 (0.0159s) scored 1.0000 with: {"tie_breaker": 0.5699173276454137, "title.english|boost": 4.3273304062411615, "title|boost": 1.6393208127999483, "category|boost": 0.9826987511047194, "category.english|boost": 9.580933015419806, "descriptors|boost": 3.503736851987736, "file_name|boost": 8.566968785271335, "vendor_name|boost": 1.757893690628294, "snippet|boost": 7.494445295285921, "snippet.english|boost": 4.513989505981226, "assetId|boost": 7.035638672539559, "operator": "OR"}
Traceback (most recent call last):
  File "bin/optimize-query", line 84, in <module>
    main()
  File "bin/optimize-query", line 71, in main
    args.template_id, queries, qrels, logger)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/qopt/optimize.py", line 223, in optimize_query
    return optimize(config, objective_fn, initial_points=None, logger_fn=logger_fn)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/qopt/optimize.py", line 265, in optimize
    x0=initial_points)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/optimizer/gp.py", line 268, in gp_minimize
    callback=callback, n_jobs=n_jobs, model_queue_size=model_queue_size)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/optimizer/base.py", line 302, in base_minimize
    result = optimizer.tell(next_x, next_y)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/optimizer/optimizer.py", line 493, in tell
    return self._tell(x, y, fit=fit)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/optimizer/optimizer.py", line 536, in _tell
    est.fit(self.space.transform(self.Xi), self.yi)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/skopt/learning/gaussian_process/gpr.py", line 195, in fit
    super(GaussianProcessRegressor, self).fit(X, y)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/sklearn/gaussian_process/_gpr.py", line 234, in fit
    self.kernel_.bounds))]
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/sklearn/gaussian_process/_gpr.py", line 503, in _constrained_optimization
    bounds=bounds)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py", line 618, in minimize
    callback=callback, **options)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py", line 308, in _minimize_lbfgsb
    finite_diff_rel_step=finite_diff_rel_step)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 262, in _prepare_scalar_function
    finite_diff_rel_step, bounds, epsilon=epsilon)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py", line 76, in __init__
    self._update_fun()
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py", line 166, in _update_fun
    self._update_fun_impl()
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py", line 73, in update_fun
    self.f = fun_wrapped(self.x)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py", line 70, in fun_wrapped
    return fun(x, *args)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 74, in __call__
    self._compute_if_needed(x, *args)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 68, in _compute_if_needed
    fg = self.fun(x, *args)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/sklearn/gaussian_process/_gpr.py", line 225, in obj_func
    theta, eval_gradient=True, clone_kernel=False)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/sklearn/gaussian_process/_gpr.py", line 476, in log_marginal_likelihood
    alpha = cho_solve((L, True), y_train)  # Line 3
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/scipy/linalg/decomp_cholesky.py", line 194, in cho_solve
    b1 = asarray_chkfinite(b)
  File "/home/gllermaly/examples/Machine Learning/Query Optimization/venv/lib/python3.6/site-packages/numpy/lib/function_base.py", line 486, in asarray_chkfinite
    "array must not contain infs or NaNs")
ValueError: array must not contain infs or NaNs

It fails in the iteration number 39 , and num_initial_points is set to 40 as the default , num_iterations set to 70

Any advice would be appreciated.