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optimisation_from_db_example.py
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#! /usr/bin/env python
"""Sequentially optimisation entries from a database."""
# Python imports
import sys
import logging
# Module imports
import numpy as np
import pandas as pd
from tqdm import tqdm
import sqlite3
# Local imports
from src import solve_system, Optimiser
logger = logging.getLogger(__file__)
def main():
"""Main script for optimisation against csv records. """
db_path = "../heat_response/data/exercise/processed_data.sqlite3"
con = sqlite3.connect(db_path)
cursor = con.cursor()
col_names = (
'id', 'temp', 't',
'sv', 'sys', 'dia',
'hr', 'pr', 'qrs', 'qt',
'core', 'core_ref', 'skin', 'skin_ref',
)
table = 'Model_Inputs_BA'
out_table_name = 'Model_Outputs_BA'
cursor.execute(f"SELECT {', '.join(col_names)} FROM {table}")
df = pd.DataFrame(cursor.fetchall(), columns=col_names)
prev = {
'id': None,
'temp': None,
't': 60,
'e_scale': 1,
'v_scale': 1,
'r_scale': 1,
'c_scale': 1,
}
for i in tqdm(range(df.shape[0]), position=0, file=sys.stdout, leave=True):
########################
# Sets up model inputs #
########################
row = df.iloc[i]
if row['id'] != prev['id'] and row['temp'] != prev['temp']:
prev = {
'id': row['id'],
'temp': row['temp'],
't': row['t'],
'e_scale': 1,
'v_scale': 1,
'r_scale': 1,
'c_scale': 1,
'k_dil': 1,
'k_con': 1,
}
new_pid = True
else:
# Diminishes the effect of the previous scale with time.
# When only 60s has passed, current starting scale is
# the same as the previous scale.
# when 300s has passed the current starting scale is
# 0.5 * previous scale + 0.5
# when around 30 minutes has passed the current starting scale is
# 0.005 * previous scale + 0.995
# Checks if the new value is within bounds before updating
dt = row['t'] - prev['t']
alpha = np.exp(- np.log(2)/4 * (dt/60 - 1))
for d in list(params.keys()):
for key in list(params[d].keys()):
new_val = alpha * prev[key] + (1 - alpha)
if d == 'thermal_system':
new_val *= 75 if key == 'k_dil' else 0.5
if params[d][key][0] <= new_val <= params[d][key][1]:
prev[key] = alpha * prev[key] + (1 - alpha)
new_pid = False
default_inputs = {
'generic_params': {
'period': 60/row['hr'],
},
'ecg': {
"t1": row['pr'] / 3,
"t2": row['pr'] + row['qrs']/2,
"t3": row['pr'] + row['qrs'] + 0.75 * (row['qt'] - row['qrs']),
"t4": row['pr'] + row['qt'],
},
'thermal_system': {
't_cr': row['core'],
't_cr_ref': row['core_ref'],
't_sk': row['skin'],
't_sk_ref': row['skin_ref'],
},
}
###################################################
# Optimises for blood pressure and stroke volume #
###################################################
inputs = default_inputs
params = {
"generic_params": {
"r_scale": [0.1, 10, prev['r_scale']],
"c_scale": [0.1, 10, prev['c_scale']],
'e_scale': [0.25, 4, prev['e_scale']],
},
"thermal_system": {
"k_dil": [37, 113, 75 * prev['k_dil']],
"k_con": [0.25, 0.75, 0.5 * prev['k_con']],
},
}
if new_pid:
params['generic_params']['v_scale'] = [0.9, 1.1, prev['v_scale']]
else:
inputs['generic_params']['v_scale'] = prev['v_scale']
opt = Optimiser(
optimiser="TwoPointsDE",
inputs=inputs,
params=params,
budget=1000,
num_workers=16,
multi_objective=True,
tol=1e-3,
pbar=True,
pbar_pos=1,
)
best_params = opt.run(sbp=row['sys'], dbp=row['dia'], sv=row['sv'])
#####################
# Saves the results #
#####################
for j, best_p in enumerate(best_params):
sol = solve_system(**best_p)
systolic, diastolic = opt.get_systemic_sysdia_pres(sol)
sv = opt.get_stroke_volume(sol)
loss = (
np.abs(systolic - row['sys']) / row['sys']
+ np.abs(diastolic - row['dia']) / row['dia']
+ np.abs(sv - row['sv']) / row['sv']
)
outputs = {
'id': row['id'],
'temp': row['temp'],
't': row['t'],
'sys': systolic,
'sys_target': row['sys'],
'dia': diastolic,
'dia_target': row['dia'],
'sv': sv,
'sv_target': row['sv'],
'loss': loss,
**opt.flat_inputs_raw,
**opt.recommendation[j],
}
if i == 0 and j == 0:
pd.DataFrame([outputs]).to_sql(
out_table_name, con, if_exists='replace', index=False,
)
prev_loss = loss
else:
pd.DataFrame([outputs]).to_sql(
out_table_name, con, if_exists='append', index=False,
)
if loss <= prev_loss:
for d in list(params.keys()):
for param in list(params[d].keys()):
if d == "thermal_system":
def_val = 75 if param == 'k_dil' else 0.5
prev[param] = best_p[d][param] / def_val
else:
prev[param] = best_p[d][param]
tqdm._instances.clear()
if __name__ == '__main__':
main()