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nile_ICML18.py
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nile_ICML18.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 27 15:45:27 2018
@author: Jeremias Knoblauch, j.knoblauch@warwick.ac.uk
Description: Reads in the nile data and processes it as a demo
"""
# System packages/modules
import argparse
import numpy as np
import matplotlib.pyplot as plt
import scipy
import csv
import os
# Modules of the BOCPDMS algorithm
from cp_probability_model import CpModel
from BVAR_NIG import BVARNIG
from detector import Detector
from Evaluation_tool import EvaluationTool
def load_nile_data(path_to_data):
"""Read in the Nile dataset and convert it to a format suitable for use with the Detector class.
The original dataset is available from http://mldata.org/repository/data/viewslug/nile-water-level/"""
"""STEP 1: Read in the nile data from nile.txt"""
raw_data = []
with open(path_to_data) as csvfile:
reader = csv.reader(csvfile)
for row in reader:
raw_data += row
raw_data_float = []
for entry in raw_data:
raw_data_float.append(float(entry))
raw_data = raw_data_float
"""STEP 2: Format the nile data so that it can be processed with a Detector
object and instantiations of ProbabilityModel subclasses"""
T = int(len(raw_data) / 2)
S1, S2 = 1, 1 # S1, S2 give you spatial dimensions, but Nile is univariate.
data = np.array(raw_data).reshape(T, 2)
dates = data[:, 0]
river_height = data[:, 1]
mean, variance = np.mean(river_height), np.var(river_height)
"""STEP 3: Standardize in order to be able to compare with GP-approaches"""
standardised_river_height = (river_height - mean) / np.sqrt(variance)
return T, S1, S2, standardised_river_height, river_height, dates
if __name__ == "__main__":
"""STEP 1: Set the current working directory and read in the data."""
baseline_working_directory = os.getcwd()
nile_file = os.path.join(baseline_working_directory, "Data", "nile.txt")
T, S1, S2, river_height, __, __ = load_nile_data(nile_file) # Use standardised river height
"""STEP 2: Set up initial hyperparameters (will be optimized throughout
the algorithm) and lag lengths"""
# Set up the parser
parser = argparse.ArgumentParser(
description="Options for applying the BOCPDMS algorithm to the Nile river height dataset.")
parser.add_argument("-a", "--prior_a", type=float, default=1.0, help="Initial value of a")
parser.add_argument("-b", "--prior_b", type=float, default=1.0, help="Initial value of b")
parser.add_argument("-ms", "--prior_mean_scale", type=float, default=0.0,
help="Mean scale used to calculate beta_0")
parser.add_argument("-vs", "--prior_var_scale", type=float, default=0.0075,
help="Variance scale used to calculate V_0")
parser.add_argument("-i", "--intensity", type=float, default=100, help="Intensity")
parser.add_argument("-lAR", "--lower_AR", type=int, default=1, help="Lower lag length for AR models")
parser.add_argument("-uAR", "--upper_AR", type=int, default=3, help="Upper lag length for AR models")
args = parser.parse_args()
prior_a = args.prior_a # a, b: inverse Gamma hyperparameters; will be optimized inside detector
prior_b = args.prior_b
prior_mean_scale = args.prior_mean_scale
prior_var_scale = args.prior_var_scale
intensity = args.intensity
lower_AR = args.lower_AR # In the paper, we suggest int(mult*pow(float(T)/np.log(T), 0.25) + 1) for mult = 1
upper_AR = args.upper_AR # as the maximum lag length, and 1 for the minimum lag length, but the algorithm is
# relatively insensitive to choosing different pairs
# Changepoint prior is a constant hazard function
cp_model = CpModel(intensity)
# And optimise the hyperparameters online
hyperpar_opt = "online"
"""STEP 3: Set up the AR-models, run algorithm"""
AR_models = []
for lag in range(lower_AR, upper_AR + 1):
"""Generate next model object"""
AR_models += [BVARNIG(
prior_a=prior_a, prior_b=prior_b,
S1=S1, S2=S2,
prior_mean_scale=prior_mean_scale,
prior_var_scale=prior_var_scale,
intercept_grouping=None,
nbh_sequence=[0] * lag,
restriction_sequence=[0] * lag,
hyperparameter_optimization=hyperpar_opt)]
"""STEP 4: Put all model objects together, create model universe, model priors"""
model_universe = np.array(AR_models)
model_prior = np.array([1 / len(model_universe)] * len(model_universe))
"""STEP 5: Build and run detector, i.e. the object responsible for executing
BOCPDMS with multiple (previously specified) models for the segments and a
CP model specified by cp_model"""
detector = Detector(
data=river_height,
model_universe=model_universe,
model_prior=model_prior,
cp_model=cp_model,
S1=S1, S2=S2, T=T,
store_rl=True, store_mrl=True,
trim_type="keep_K", threshold=50,
notifications=50,
save_performance_indicators=True,
generalized_bayes_rld="kullback_leibler",
alpha_param_learning="individual",
alpha_param=0.01,
alpha_param_opt_t=30,
alpha_rld_learning=True,
loss_der_rld_learning="squared_loss",
loss_param_learning="squared_loss")
detector.run()
"""STEP 6: Store results into EvaluationTool object with plotting capability"""
EvT = EvaluationTool()
EvT.build_EvaluationTool_via_run_detector(detector)
EvT.store_results_to_HD(os.path.join(baseline_working_directory, "Output", "results_nile.txt"))
"""STEP 7: Inspect convergence of the hyperparameters"""
for lag in range(0, upper_AR + 1 - lower_AR):
plt.plot(np.linspace(1, len(detector.model_universe[lag].a_list),
len(detector.model_universe[lag].a_list)),
np.array(detector.model_universe[lag].a_list))
plt.plot(np.linspace(1, len(detector.model_universe[lag].b_list),
len(detector.model_universe[lag].b_list)),
np.array(detector.model_universe[lag].b_list))
plt.savefig(os.path.join(baseline_working_directory, "Output",
"ICML18_ExtraFigure_Nile_ab_lag" +
str(detector.model_universe[lag].lag_length) + ".pdf"),
format="pdf", dpi=800)
plt.cla()
"""STEP 8: Also plot some performance indicators (will usually be printed
to the console before the plots)"""
print("\nCPs are ", detector.CPs[-2])
print("\n***** Predictive MSE + NLL from Table 1 in ICML 2018 paper *****")
print("MSE is %.5g with 95%% error of %.5g" %
(np.mean(detector.MSE), 1.96 * scipy.stats.sem(detector.MSE)))
print("NLL is %.5g with 95%% error of %.5g" %
(np.mean(detector.negative_log_likelihood), 1.96 * scipy.stats.sem(detector.negative_log_likelihood)))
"""STEP 9: Print out the settings used to get these results"""
print("\n")
print("***** Parameter values and other options *****")
print("prior_a:", prior_a)
print("prior_b:", prior_b)
print("prior_mean_scale:", prior_mean_scale)
print("prior_var_scale:", prior_var_scale)
print("intensity:", intensity)
print("lower_AR:", lower_AR)
print("upper_AR:", upper_AR)
print("hyperpar_opt:", hyperpar_opt)