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"""Helper file for Thompson sampling"""
import pickle
import random
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
from tenacity import retry, stop_after_attempt, wait_fixed
import config as cfg
random.seed(42)
class ThompsonSampler:
def __init__(self):
self.placeholder = st.empty()
self.latent_elasticity = cfg.LATENT_ELASTICITY
self.price_observations = np.concatenate(
[np.repeat(10,10), np.repeat(7.5,25), np.repeat(11,15)]
)
self.update_demand_observations()
self.possible_prices = np.linspace(0, 20, 100)
self.price_samples = []
self.latent_demand = self.calc_latent_demand()
self.latent_price = self.calc_optimal_price(self.latent_demand, sample=False)
self.update_posteriors()
def update_demand_observations(self):
self.demand_observations = np.exp(
np.random.normal(
loc=-self.latent_elasticity*self.price_observations+cfg.LATENT_SHAPE,
scale=cfg.LATENT_STDEV,
)
)
def update_elasticity(self):
self.latent_elasticity = st.session_state.latent_elasticity
self.price_samples = []
self.latent_demand = self.calc_latent_demand()
self.update_demand_observations()
self.latent_price = self.calc_optimal_price(self.latent_demand, sample=False)
self.update_posteriors(samples=75)
self.create_plots()
def create_plots(self, highlighted_sample=None):
with self.placeholder.container():
posterior_plot, price_plot = st.columns(2)
with posterior_plot:
st.markdown("## Demands")
fig = self.create_posteriors_plot(highlighted_sample)
st.write(fig)
plt.close(fig)
with price_plot:
st.markdown("## Prices")
fig = self.create_price_plot()
st.write(fig)
plt.close(fig)
def create_price_plot(self):
fig = plt.figure()
plt.xlabel("Price")
plt.xlim(0,20)
plt.yticks(color='w')
price_distr = [self.calc_optimal_price(post_demand, sample=False)
for post_demand in self.posterior]
plt.violinplot(price_distr, vert=False, showextrema=False)
for price in self.price_samples:
plt.plot(price, 1, marker='o', markersize = 5, color='grey', label="Price sample")
plt.axhline(1, color='black')
plt.axvline(self.latent_price, 0, color='red', label="Latent optimal price")
_plot_legend()
return fig
def create_posteriors_plot(self, highlighted_sample=None):
fig = plt.figure()
plt.xlabel("Price")
plt.ylabel("Demand")
plt.xlim(0,20)
plt.ylim(0,10)
plt.scatter(self.price_observations, self.demand_observations, label="Demand observations")
plt.plot(self.possible_prices, self.latent_demand, color="red", label="Latent demand")
for posterior_sample in self.posterior_samples:
plt.plot(
self.possible_prices, posterior_sample,
color="grey", alpha=0.15, label="Posterior demand"
)
if highlighted_sample is not None:
plt.plot(
self.possible_prices, highlighted_sample,
color="black", label="Thompson sampled demand"
)
_plot_legend()
return fig
def calc_latent_demand(self):
return np.exp(
-self.latent_elasticity*self.possible_prices + cfg.LATENT_SHAPE
)
@staticmethod
@np.vectorize
def _cost(demand):
return cfg.VARIABLE_COST*demand + cfg.FIXED_COST
def calc_optimal_price(self, sampled_demand, sample=False):
revenue = self.possible_prices * sampled_demand
profit = revenue - self._cost(sampled_demand)
optimal_price = self.possible_prices[np.argmax(profit)]
if sample:
self.price_samples.append(optimal_price)
if len(self.price_samples) > cfg.MAX_PRICE_SAMPLES:
self.price_samples.pop(0)
return optimal_price
def update_posteriors(self, samples=75):
with open(f"assets/precalc_results/posterior_{self.latent_elasticity}.pkl", "rb") as post:
self.posterior = pickle.load(post)
self.posterior_samples = random.sample(self.posterior, samples)
def pick_posterior(self):
posterior_sample = random.choice(self.posterior_samples)
self.calc_optimal_price(posterior_sample, sample=True)
self.create_plots(highlighted_sample=posterior_sample)
@retry(stop=stop_after_attempt(5), wait=wait_fixed(0.25))
def run(self):
if st.session_state.latent_elasticity != self.latent_elasticity:
self.update_elasticity()
self.pick_posterior()
def _plot_legend():
handles, labels = plt.gca().get_legend_handles_labels()
labels, ids = np.unique(labels, return_index=True)
handles = [handles[i] for i in ids]
plt.legend(handles, labels, loc='upper right')
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