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Update hyperopt.py

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Italo 2022-02-06 10:33:49 +00:00
parent 6c1729e20b
commit adf8f6b2d5
1 changed files with 17 additions and 39 deletions
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freqtrade/optimize/hyperopt.py
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@ -32,18 +32,17 @@ from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
from skopt.plots import plot_convergence, plot_regret, plot_evaluations, plot_objective
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import random import random
from sklearn.base import clone
# Suppress scikit-learn FutureWarnings from skopt # Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=FutureWarning)
from skopt import Optimizer from skopt import Optimizer
from skopt.space import Dimension from skopt.space import Dimension
from sklearn.model_selection import cross_val_score
from skopt.plots import plot_convergence, plot_regret, plot_evaluations, plot_objective
progressbar.streams.wrap_stderr() progressbar.streams.wrap_stderr()
progressbar.streams.wrap_stdout() progressbar.streams.wrap_stdout()
@ -483,7 +482,7 @@ class Hyperopt:
f_val = self.run_optimizer_parallel(parallel, asked, i) f_val = self.run_optimizer_parallel(parallel, asked, i)
res = self.opt.tell(asked, [v['loss'] for v in f_val]) res = self.opt.tell(asked, [v['loss'] for v in f_val])
self.plot_optimizer(res, path='user_data/scripts', convergence=False, regret=False, mse=True, objective=True, jobs=jobs) self.plot_optimizer(res, path='user_data/scripts', convergence=False, regret=False, r2=True, objective=True, jobs=jobs)
if res.models and hasattr(res.models[-1], "kernel_"): if res.models and hasattr(res.models[-1], "kernel_"):
print(f'kernel: {res.models[-1].kernel_}') print(f'kernel: {res.models[-1].kernel_}')
@ -532,41 +531,21 @@ class Hyperopt:
# a chance to be evaluated. # a chance to be evaluated.
print("No epochs evaluated yet, no best result.") print("No epochs evaluated yet, no best result.")
def plot_mse(self, res, ax, jobs): def plot_r2(self, res, ax, jobs):
from sklearn.model_selection import cross_val_score
if len(res.x_iters) < 10: if len(res.x_iters) < 10:
return return
if not hasattr(self, 'mse_list'): if not hasattr(self, 'r2_list'):
self.mse_list = [] self.r2_list = []
# model = clone(res.models[-1]) r2 = cross_val_score(res.models[-1], X=res.x_iters, y=res.func_vals, scoring='r2', cv=5, n_jobs=jobs).mean()
# i_subset = random.sample(range(len(res.x_iters)), 100) if len(res.x_iters) > 100 else range(len(res.x_iters)) print(f'R2: {r2}')
r2 = r2 if r2 > -5 else -5
# i_train = random.sample(i_subset, round(.8*len(i_subset))) # get 80% random indices self.r2_list.append(r2)
# x_train = [x for i, x in enumerate(res.x_iters) if i in i_train]
# y_train = [y for i, y in enumerate(res.func_vals) if i in i_train]
# i_test = [i for i in i_subset if i not in i_train] # get 20% random indices
# x_test = [x for i, x in enumerate(res.x_iters) if i in i_test]
# y_test = [y for i, y in enumerate(res.func_vals) if i in i_test]
# model.fit(res.x_iters, res.func_vals)
# Perform a cross-validation estimate of the coefficient of determination using
# the cross_validation module using all CPUs available on the machine
# K = 5 # folds
with warnings.catch_warnings():
warnings.simplefilter("ignore")
R2 = cross_val_score(res.models[-1], X=res.x_iters, y=res.func_vals, cv=5, n_jobs=jobs).mean()
print(f'R2: {R2}')
R2 = R2 if R2 > -5 else -5
self.mse_list.append(R2)
# y_pred, sigma = model.predict(np.array(x_test), return_std=True)
# mse = np.mean((y_test - y_pred) ** 2)
# self.mse_list.append(mse)
ax.plot(range(INITIAL_POINTS, INITIAL_POINTS + jobs * len(self.mse_list), jobs), self.mse_list, label='MSE', marker=".", markersize=12, lw=2) ax.plot(range(INITIAL_POINTS, INITIAL_POINTS + jobs * len(self.r2_list), jobs), self.r2_list, label='R2', marker=".", markersize=12, lw=2)
def plot_optimizer(self, res, path, jobs, convergence=True, regret=True, evaluations=True, objective=True, mse=True): def plot_optimizer(self, res, path, jobs, convergence=True, regret=True, evaluations=True, objective=True, r2=True):
path = Path(path) path = Path(path)
if convergence: if convergence:
ax = plot_convergence(res) ax = plot_convergence(res)
@ -586,11 +565,10 @@ class Hyperopt:
ax = plot_objective(res, sample_source='result', n_samples=50, n_points=10) ax = plot_objective(res, sample_source='result', n_samples=50, n_points=10)
ax.flatten()[0].figure.savefig(path / 'objective.png') ax.flatten()[0].figure.savefig(path / 'objective.png')
if mse and res.models: if r2 and res.models:
# print('mse')
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.set_ylabel('MSE') ax.set_ylabel('R2')
ax.set_xlabel('Epoch') ax.set_xlabel('Epoch')
ax.set_title('MSE') ax.set_title('R2')
ax = self.plot_mse(res, ax, jobs) ax = self.plot_r2(res, ax, jobs)
fig.savefig(path / 'mse.png') fig.savefig(path / 'r2.png')