Source code for pyqstrat.optimize

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import matplotlib.cm as cm
from scipy.interpolate import griddata
import os
import sys
import concurrent

from pyqstrat.pq_utils import set_defaults, has_display
from pyqstrat.plot import *

set_defaults()

[docs]class Experiment: '''An Experiment stores a suggestion and its result Attributes: suggestion: A dictionary of variable name -> value cost: A float representing output of the function we are testing with this suggestion as input. other_costs: A dictionary of other results we want to store and look at later. ''' def __init__(self, suggestion, cost, other_costs): self.suggestion = suggestion self.cost = cost self.other_costs = other_costs
[docs] def valid(self): ''' Returns True if all suggestions and costs are finite, i.e not NaN or +/- Infinity ''' if not all(np.isfinite(list(self.suggestion.values()))): return False if not np.isfinite(self.cost): return False if not all(np.isfinite(list(self.other_costs.values()))): return False return True
def __repr__(self): return f'suggestion: {self.suggestion} cost: {self.cost} other costs: {self.other_costs}'
[docs]class Optimizer: '''Optimizer is used to optimize parameters for a strategy.'''
[docs] def __init__(self, name, generator, cost_func, max_processes = None): ''' Args: name: string used to display title in plotting, etc. generator: A generator (see Python Generators) that takes no inputs and yields a list of dictionaries with parameter name -> parameter value. cost_func: A function that takes a dictionary of parameter name -> parameter value as input and outputs cost for that set of parameters. max_processes: If not set, the Optimizer will look at the number of CPU cores on your machine to figure out how many processes to run. ''' self.name = name self.generator = generator self.cost_func = cost_func import sys if sys.platform in ['win32', 'cygwin']: if max_processes is not None and max_processes != 1: raise Exception("max_processes must be 1 on Microsoft Windows") max_processes = 1 self.max_processes = max_processes self.experiments = []
def _run_single_process(self): try: for suggestion in self.generator: if suggestion is None: continue cost, other_costs = self.cost_func(suggestion) self.generator.send(cost) self.experiments.append(Experiment(suggestion, cost, other_costs)) except StopIteration: # Exhausted generator return #TODO: Needs to be rewritten to send costs back to generator when we do parallel gradient descent, etc. def _run_multi_process(self, raise_on_error): fut_map = {} with concurrent.futures.ProcessPoolExecutor(self.max_processes) as executor: for suggestion in self.generator: if suggestion is None: continue future = executor.submit(self.cost_func, suggestion) fut_map[future] = suggestion for future in concurrent.futures.as_completed(fut_map): try: cost, other_costs = future.result() except Exception as e: new_exc = type(e)(f'Exception: {str(e)} with suggestion: {suggestion}').with_traceback(sys.exc_info()[2]) if raise_on_error: raise new_exc else: print(str(new_exc)) continue suggestion = fut_map[future] self.experiments.append(Experiment(suggestion, cost, other_costs))
[docs] def run(self, raise_on_error = False): '''Run the optimizer. Args: raise_on_error: If set to True, even if we are running a multiprocess optimization, any Exceptions will bubble up and stop the Optimizer. This can be useful for debugging to see stack traces for Exceptions. ''' if self.max_processes == 1: self._run_single_process() else: self._run_multi_process(raise_on_error)
[docs] def experiment_list(self, sort_order = 'lowest_cost'): '''Returns the list of experiments we have run Args: sort_order: Can be set to lowest_cost, highest_cost or sequence. If set to sequence, experiments are returned in the sequence in which they were run ''' if sort_order == 'lowest_cost': experiments = sorted(self.experiments, key = lambda x : x.cost, reverse = True) elif sort_order == 'highest_cost': experiments = sorted(self.experiments, key = lambda x : x.cost, reverse = False) elif sort_order == 'sequence': # in order in which experiment was run experiments = self.experiments else: raise Exception(f'invalid sort order: {sort}') return experiments
[docs] def df_experiments(self, sort_column = 'cost', ascending = True): ''' Returns a dataframe containing experiment data, sorted by sort_column (default "cost") ''' if len(self.experiments) == 0: return None pc_keys = list(self.experiments[0].other_costs.keys()) sugg_keys = list(self.experiments[0].suggestion.keys()) records = [[exp.suggestion[k] for k in sugg_keys] + [exp.cost] + [exp.other_costs[k] for k in pc_keys] for exp in self.experiments if exp.valid()] df = pd.DataFrame.from_records(records, columns = sugg_keys + ['cost'] + pc_keys) df.sort_values(by = [sort_column], ascending = ascending, inplace = True) return df
[docs] def plot_3d(self, x, y, z = 'all', plot_type = 'surface', figsize = (15,15), interpolation = 'linear', cmap = 'viridis', marker = 'X', marker_size = 50, marker_color = 'r', xlim = None, ylim = None, hspace = None): """Creates a 3D plot of the optimization output for plotting 2 parameters and costs. Args: x: Name of the parameter to plot on the x axis, corresponding to the same name in the generator. y: Name of the parameter to plot on the y axis, corresponding to the same name in the generator. z: Can be one of: "cost" The name of another cost variable corresponding to the output from the cost function "all", which creates a subplot for cost plus all other costs plot_type: surface or contour (default surface) figsize: Figure size interpolation: Can be ‘linear’, ‘nearest’ or ‘cubic’ for plotting z points between the ones passed in. See scipy.interpolate.griddata for details cmap: Colormap to use (default viridis). See matplotlib colormap for details marker: Adds a marker to each point in x, y, z to show the actual data used for interpolation. You can set this to None to turn markers off. hspace: Vertical space between subplots """ if len(self.experiments) == 0: print('No experiments found') return if not has_display(): return # Get rid of nans since matplotlib does not like them experiments = [experiment for experiment in self.experiments if experiment.valid()] if not len(experiments): print('No valid experiments found') return if xlim: experiments = [experiment for experiment in experiments if experiment.suggestion[x] >= xlim[0] and experiment.suggestion[x] <= xlim[1]] if ylim: experiments = [experiment for experiment in experiments if experiment.suggestion[y] >= ylim[0] and experiment.suggestion[y] <= ylim[1]] xvalues = [experiment.suggestion[x] for experiment in experiments] yvalues = [experiment.suggestion[y] for experiment in experiments] zvalues = [] if z == 'all': zvalues.append(('cost', np.array([experiment.cost for experiment in experiments]))) if len(experiments[0].other_costs): other_cost_keys = experiments[0].other_costs.keys() for key in other_cost_keys: zvalues.append((key, np.array([experiment.other_costs[key] for experiment in experiments]))) elif z == 'cost': zvalues.append(('cost', np.array([experiment.cost for experiment in experiments]))) else: zvalues.append((z, np.array([experiment.other_costs[zname] for experiment in experiments]))) subplots = [] for tup in zvalues: name = tup[0] zarray = tup[1] if plot_type == 'contour': zlabel = None title = name else: zlabel = name title = None subplots.append(Subplot(data_list = [ XYZData(name, xvalues, yvalues, zarray, plot_type = plot_type, marker = marker, marker_size = marker_size, marker_color = marker_color, interpolation = interpolation, cmap = cmap )], title = title, xlabel = x, ylabel = y, zlabel = zlabel, xlim = xlim, ylim = ylim)) plot = Plot(subplots, figsize = figsize, title = 'Optimizer 2D Test', hspace = hspace) plot.draw()
[docs] def plot_2d(self, x, y = 'all', plot_type = 'line', figsize = (15,8), marker = 'X', marker_size = 50, marker_color = 'r', xlim = None, hspace = None): """Creates a 2D plot of the optimization output for plotting 1 parameter and costs. Args: x: Name of the parameter to plot on the x axis, corresponding to the same name in the generator. y: Can be one of: "cost" The name of another cost variable corresponding to the output from the cost function "all", which creates a subplot for cost plus all other costs plot_type: line or scatter (default line) figsize: Figure size marker: Adds a marker to each point in x, y to show the actual data used for interpolation. You can set this to None to turn markers off. hspace: Vertical space between subplots """ if len(self.experiments) == 0: return if not has_display(): return # Get rid of nans since matplotlib does not like them experiments = [experiment for experiment in self.experiments if experiment.valid()] if xlim: experiments = [experiment for experiment in experiments if experiment.suggestion[x] >= xlim[0] and experiment.suggestion[x] <= xlim[1]] xvalues = [experiment.suggestion[x] for experiment in experiments] yvalues = [] if y == 'all': yvalues.append(('cost', np.array([experiment.cost for experiment in experiments]))) other_cost_keys = experiments[0].other_costs.keys() for key in other_cost_keys: yvalues.append((key, np.array([experiment.other_costs[key] for experiment in experiments]))) elif y == 'cost': yvalues.append(('cost', np.array([experiment.cost for experiment in experiments]))) else: yvalues.append((y, np.array([experiment.other_costs[zname] for experiment in experiments]))) xvalues = np.array(xvalues) x_sort_indices = np.argsort(xvalues) xvalues = xvalues[x_sort_indices] subplots = [] for tup in yvalues: name = tup[0] yarray = tup[1] idx = np.argsort(xvalues) yarray = yarray[x_sort_indices] subplots.append(Subplot(data_list = [ XYData(name, xvalues, yarray, plot_type = plot_type, marker = marker, marker_size = marker_size, marker_color = marker_color )], xlabel = x, ylabel = name, xlim = xlim)) plot = Plot(subplots, figsize = figsize, title = 'Optimizer 1D Test') plot.draw()
# Functions used in unit testing def _generator_1d(): for x in np.arange(0, np.pi * 2, 0.1): costs = (yield {'x' : x}) def _cost_func_1d(suggestion): x = suggestion['x'] cost = np.sin(x) ret = cost, {'std' : -0.1 * cost} return ret def _generator_2d(): for x in np.arange(0, np.pi * 2, 0.5): for y in np.arange(0, np.pi * 2, 0.5): costs = (yield {'x' : x, 'y' : y}) def _cost_func_2d(suggestion): x = suggestion['x'] y = suggestion['y'] cost = np.sin(np.sqrt(x**2 + y ** 2)) return cost, {'sharpe' : cost, 'std' : -0.1 * cost}
[docs]def test_optimize(): max_processes = 1 if os.name == 'nt' else 4 optimizer_1d = Optimizer('test', _generator_1d(), _cost_func_1d, max_processes = max_processes) optimizer_1d.run(raise_on_error = True) optimizer_1d.plot_2d(x = 'x', plot_type = 'line', marker = 'o', marker_color = 'blue') optimizer_2d = Optimizer('test', _generator_2d(), _cost_func_2d, max_processes = max_processes) optimizer_2d.run() optimizer_2d.plot_3d(x = 'x', y = 'y')
if __name__ == "__main__": test_optimize() import doctest doctest.testmod(optionflags = doctest.NORMALIZE_WHITESPACE)