error in example doc with scikit-optimize #838
Description
The example doc "Parameter Heatmap", section "Model-based optimization". https://kernc.github.io/backtesting.py/doc/examples/Parameter%20Heatmap%20&%20Optimization.html
When I run this snippet I get an error.
stats_skopt, heatmap, optimize_result = backtest.optimize(
n1=[10, 100], # Note: For method="skopt", we
n2=[20, 200], # only need interval end-points
n_enter=[10, 40],
n_exit=[10, 30],
constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,
maximize='Equity Final [$]',
method='skopt',
max_tries=200,
random_state=0,
return_heatmap=True,
return_optimization=True)---------------------------------------------------------------------------
InvalidParameterError Traceback (most recent call last)
File <timed exec>:1, in <module>
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/backtesting/backtesting.py:1490, in Backtest.optimize(self, maximize, method, max_tries, constraint, return_heatmap, return_optimization, random_state, **kwargs)
1488 output = _optimize_grid()
1489 elif method == 'skopt':
-> 1490 output = _optimize_skopt()
1491 else:
1492 raise ValueError(f"Method should be 'grid' or 'skopt', not {method!r}")
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/backtesting/backtesting.py:1456, in Backtest.optimize.<locals>._optimize_skopt()
1452 with warnings.catch_warnings():
1453 warnings.filterwarnings(
1454 'ignore', 'The objective has been evaluated at this point before.')
-> 1456 res = forest_minimize(
1457 func=objective_function,
1458 dimensions=dimensions,
1459 n_calls=max_tries,
1460 base_estimator=ExtraTreesRegressor(n_estimators=20, min_samples_leaf=2),
1461 acq_func='LCB',
1462 kappa=3,
1463 n_initial_points=min(max_tries, 20 + 3 * len(kwargs)),
1464 initial_point_generator='lhs', # 'sobel' requires n_initial_points ~ 2**N
1465 callback=DeltaXStopper(9e-7),
1466 random_state=random_state)
1468 stats = self.run(**dict(zip(kwargs.keys(), res.x)))
1469 output = [stats]
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/forest.py:186, in forest_minimize(func, dimensions, base_estimator, n_calls, n_random_starts, n_initial_points, acq_func, initial_point_generator, x0, y0, random_state, verbose, callback, n_points, xi, kappa, n_jobs, model_queue_size)
10 def forest_minimize(func, dimensions, base_estimator="ET", n_calls=100,
11 n_random_starts=None, n_initial_points=10, acq_func="EI",
12 initial_point_generator="random",
13 x0=None, y0=None, random_state=None, verbose=False,
14 callback=None, n_points=10000, xi=0.01, kappa=1.96,
15 n_jobs=1, model_queue_size=None):
16 """Sequential optimisation using decision trees.
17
18 A tree based regression model is used to model the expensive to evaluate
(...)
184 :class:`skopt.dummy_minimize`, :class:`skopt.gbrt_minimize`
185 """
--> 186 return base_minimize(func, dimensions, base_estimator,
187 n_calls=n_calls, n_points=n_points,
188 n_random_starts=n_random_starts,
189 n_initial_points=n_initial_points,
190 initial_point_generator=initial_point_generator,
191 x0=x0, y0=y0, random_state=random_state,
192 n_jobs=n_jobs,
193 acq_func=acq_func,
194 xi=xi, kappa=kappa, verbose=verbose,
195 callback=callback, acq_optimizer="sampling",
196 model_queue_size=model_queue_size)
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/base.py:300, in base_minimize(func, dimensions, base_estimator, n_calls, n_random_starts, n_initial_points, initial_point_generator, acq_func, acq_optimizer, x0, y0, random_state, verbose, callback, n_points, n_restarts_optimizer, xi, kappa, n_jobs, model_queue_size)
298 next_x = optimizer.ask()
299 next_y = func(next_x)
--> 300 result = optimizer.tell(next_x, next_y)
301 result.specs = specs
302 if eval_callbacks(callbacks, result):
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:493, in Optimizer.tell(self, x, y, fit)
490 y = list(y)
491 y[1] = log(y[1])
--> 493 return self._tell(x, y, fit=fit)
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:536, in Optimizer._tell(self, x, y, fit)
534 with warnings.catch_warnings():
535 warnings.simplefilter("ignore")
--> 536 est.fit(self.space.transform(self.Xi), self.yi)
538 if hasattr(self, "next_xs_") and self.acq_func == "gp_hedge":
539 self.gains_ -= est.predict(np.vstack(self.next_xs_))
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:341, in BaseForest.fit(self, X, y, sample_weight)
314 def fit(self, X, y, sample_weight=None):
315 """
316 Build a forest of trees from the training set (X, y).
317
(...)
339 Fitted estimator.
340 """
--> 341 self._validate_params()
343 # Validate or convert input data
344 if issparse(y):
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/sklearn/base.py:570, in BaseEstimator._validate_params(self)
562 def _validate_params(self):
563 """Validate types and values of constructor parameters
564
565 The expected type and values must be defined in the `_parameter_constraints`
(...)
568 accepted constraints.
569 """
--> 570 validate_parameter_constraints(
571 self._parameter_constraints,
572 self.get_params(deep=False),
573 caller_name=self.__class__.__name__,
574 )
File ~/anaconda3/envs/trade/lib/python3.8/site-packages/sklearn/utils/_param_validation.py:97, in validate_parameter_constraints(parameter_constraints, params, caller_name)
91 else:
92 constraints_str = (
93 f"{', '.join([str(c) for c in constraints[:-1]])} or"
94 f" {constraints[-1]}"
95 )
---> 97 raise InvalidParameterError(
98 f"The {param_name!r} parameter of {caller_name} must be"
99 f" {constraints_str}. Got {param_val!r} instead."
100 )
InvalidParameterError: The 'criterion' parameter of ExtraTreesRegressor must be a str among {'squared_error', 'absolute_error', 'poisson', 'friedman_mse'}. Got 'mse' instead.- Backtesting version: 0.3.3
bokeh.__version__: Python 3.11, bokeh 2.4.3; Python 3.8.11, bokeh 3.0.3;- scikit-optimize 0.9.0
- OS: Ubuntu 20.04