#!/usr/bin/env python3
# coding: utf-8
""" Live multi-panel backtest figure for neural-network training.
Provides :class:`BacktestNeuralNet`, which composes the dynamic
loss / accuracy / performance plots from
:mod:`fynance.backtest.dynamic_plot_backtest` into a single updating figure.
"""
__all__ = ['BacktestNeuralNet']
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class BacktestNeuralNet:
def __init__(self, figsize=(9, 6), loss_xlim=None, perf_xlim=None,
accu_xlim=None, plot_accuracy=False, plot_loss=True,
plot_perf=False, **subplot_kw):
from matplotlib import pyplot as plt # lazy: keeps matplotlib off the
# `import fynance` path (live training viz only).
# Set dynamic plot object
n_rows = plot_accuracy + plot_loss + plot_perf
self.f, self.axes = plt.subplots(n_rows, 1, figsize=figsize,
**subplot_kw)
if n_rows == 1:
self.axes = [self.axes]
plt.ion()
self.accu_is_plot = False
self.loss_is_plot = False
self.perf_is_plot = False
if plot_accuracy:
self.set_plot_accuracy(self.axes[0], accu_xlim=accu_xlim)
if plot_loss:
self.set_plot_loss(self.axes[int(plot_accuracy)],
loss_xlim=loss_xlim)
if plot_perf:
self.set_plot_perf(self.axes[int(plot_accuracy + plot_loss)],
perf_xlim=perf_xlim)
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def set_plot_accuracy(self, ax, accu_xlim=None):
""" Set plot accuracy object. """
from fynance.backtest.dynamic_plot_backtest import DynaPlotAccuracy
self.dp_accu = DynaPlotAccuracy(self.f, ax)
self.dp_accu.ax.grid()
self.dp_accu.ax.set_autoscaley_on(True)
if accu_xlim is not None:
self.dp_accu.ax.set_xlim(*accu_xlim, auto=False)
self.dp_accu.ax.set_autoscalex_on(False)
else:
self.dp_accu.ax.set_autoscalex_on(True)
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def plot_accuracy(self, test, eval, train=None, clear=True):
""" Plot accuracy scores for test and evaluate set. """
self.dp_accu.plot(test=test, eval=eval, train=train, clear=clear)
self.accu_is_plot = True
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def update_accuracy(self, test, eval, train=None):
""" Plot accuracy scores for test and evaluate set. """
self.dp_accu.update(test=test, eval=eval, train=train)
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def set_plot_loss(self, ax, loss_xlim=None):
""" Set plot loss object. """
from fynance.backtest.dynamic_plot_backtest import DynaPlotLoss
self.dp_loss = DynaPlotLoss(self.f, ax)
self.dp_loss.ax.grid()
self.dp_loss.ax.set_autoscaley_on(True)
if loss_xlim is not None:
self.dp_loss.ax.set_xlim(*loss_xlim, auto=False)
self.dp_loss.ax.set_autoscalex_on(False)
else:
self.dp_loss.ax.set_autoscalex_on(True)
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def plot_loss(self, test, eval, train=None, clear=True):
""" Plot loss function values for test and evaluate set. """
self.dp_loss.plot(test=test, eval=eval, train=train, clear=clear)
self.loss_is_plot = True
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def update_loss(self, test, eval, train=None):
""" Plot loss function values for test and evaluate set. """
self.dp_loss.update(test=test, eval=eval, train=train)
def set_plot_perf(self, ax, perf_xlim=None):
# set perf plot
from fynance.backtest.dynamic_plot_backtest import DynaPlotPerf
self.dp_perf = DynaPlotPerf(self.f, ax)
self.dp_perf.ax.grid()
self.dp_perf.ax.set_autoscaley_on(True)
if perf_xlim is not None:
self.dp_perf.ax.set_xlim(*perf_xlim, auto=False)
self.dp_perf.ax.set_autoscalex_on(False)
else:
self.dp_perf.ax.set_autoscalex_on(True)
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def plot_perf(self, test, eval, underlying=None, index=None, clear=True):
""" Plot performance values for test and eval set. """
self.dp_perf.plot(test=test, eval=eval, underlying=underlying,
index=index, clear=clear)
self.perf_is_plot = True
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def update_perf(self, test, eval, underlying=None, index=None, clear=True):
""" Update performance values for test and eval set. """
self.dp_perf.update(test=test, eval=eval, underlying=underlying,
index=index, clear=clear)
