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paper_pictures_synthetics_NIPS.py
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paper_pictures_synthetics_NIPS.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 6 17:50:54 2018
@author: jeremiasknoblauch
Description: Plot pictures for demo/artificial data
"""
import pickle
import numpy as np
import scipy
from Evaluation_tool import EvaluationTool
from matplotlib import pyplot as plt
from matplotlib import rcParams
import matplotlib as mpl
import csv
import datetime
import string
#only needed if you want to generate demo data yourself
from cp_probability_model import CpModel
from detector import Detector
#import synthetic_simulations_prepare_data
#import synthetic_simulations_prepare_models
result_path = ("//Users//jeremiasknoblauch//Documents//OxWaSP//BOCPDMS//Code//" +
"SpatialBOCD//PaperNIPS//syntheticNIPS")
#date_file = "portfolio_dates.csv"
results_file_KL= ("//KL_K=5_k=1_T=600_2CPs_results_arld=015_ap=02_nolearning"+
"_rld_dpd_int=100_shrink=100.txt")
results_file_DPD= ("//DPD_K=5_k=1_T=600_2CPs_results_arld=015_ap=02_" +
"nolearning_rld_dpd_int=100_shrink=100.txt" )
singlePlot = True
doublePlot = False
"""#CREATE THE DATA THAT WAS USED IN SIMULATION#"""
"""STEP 1: Set up the simulation"""
normalize = True
offset = True
max_num_plotted_series = 5
K = 5 #number of series
k = 1 #number of contaminated series
T = 600
burn_in = 100
data = np.zeros((T,K,1))
AR_coefs = [np.ones(K) * (-0.5),
np.ones(K) * 0.75, #,
np.ones(K) * -0.7]
levels = [np.ones(K) * 0.3,
np.ones(K) * (-0.25), #,
np.ones(K) * 0.3]
CP_loc = [200,400]
contamination_df = 4
contamination_scale = np.sqrt(5)
"""STEP 2: Run the simulation with contamination for i<k"""
for cp in range(0, len(CP_loc) + 1):
#Retrieve the correct number of obs in segment
if cp == 0:
T_ = CP_loc[0] + burn_in
start = 0
fin = CP_loc[0]
elif cp==len(CP_loc):
T_ = T - CP_loc[cp-1]
start = CP_loc[cp-1]
fin = T #DEBUG: probably wrong.
else:
T_ = CP_loc[cp] - CP_loc[cp-1]
start = CP_loc[cp-1]
fin = CP_loc[cp]
#Generate AR(1)
for i in range(0,K):
np.random.seed(i)
next_AR1 = np.random.normal(0,1,size=T_)
for j in range(1, T_):
next_AR1[j] = next_AR1[j-1]*AR_coefs[cp][i] + next_AR1[j] + levels[cp][i]
#if i < k, do contamination
if i<k:
np.random.seed(i*20)
contam = contamination_scale*scipy.stats.t.rvs(contamination_df, size=T_)
contam[np.where(contam <3)] = 0
next_AR1 = (next_AR1 + contam)
#if first segment, cut off the burn-in
if cp == 0:
next_AR1 = next_AR1[burn_in:]
#add the next AR 1 stream into 'data'
data[start:fin,i,0] = next_AR1
"""STEP 3: Set up analysis parameters"""
S1, S2 = K,1 #S1, S2 give you spatial dimensions
if normalize:
data = (data - np.mean(data))/np.sqrt(np.var(data))
"""STEP 4: Offset"""
if offset:
for i in range(0, min(K, max_num_plotted_series)):
data[:,i,:] = data[:,i,:] + i*7
"""#CREATE THE PICTURES USING THE STORED RESULTS#"""
EvTKL = EvaluationTool()
EvTKL.build_EvaluationTool_via_results(result_path + "//" + results_file_KL)
EvTDPD = EvaluationTool()
EvTDPD.build_EvaluationTool_via_results(result_path + "//" + results_file_DPD)
"""STEP 1: Set up the plot configs"""
#mpl.rcParams.update(mpl.rcParamsDefault)
#height_ratio =[5,5,5,5,5]
#custom_colors = ["blue", "purple"]
#fig, ax_array = plt.subplots(5, figsize=(5,5), sharex = True,
# gridspec_kw = {'height_ratios':height_ratio})
#plt.subplots_adjust(hspace = .35, left = None, bottom = None,
# right = None, top = None)
if singlePlot:
fig, ax = plt.subplots(1, figsize=(8,5))
ylabel_coords = [-0.065, 0.5]
elif doublePlot:
#height_ratio =[3,5]
width_ratio = [3,5]
fig, ax_array = plt.subplots(1,2, figsize=(10,3.5), #paper: (10,3.5)
gridspec_kw = {'width_ratios':width_ratio})
plt.subplots_adjust(hspace = .05, wspace = .175, left = None, bottom = None,
right = None, top = None)
ylabel_coords = [-0.065, 0.5]
#INSERT THE ADDITIONAL CPs KL DECLARES AS VERTICAL LINES
#Plot of raw Time Series
if singlePlot:
EvTKL.plot_raw_TS(data.reshape(T,S1,S2), indices = [0,1,2,3,4],
show_MAP_CPs = True,
time_range = np.linspace(1,T, T, dtype=int),
print_plt = False,
ylab = "",
xlab = "Time",
ax = ax, #ax_array[0],
#all_dates = np.linspace(622 + 1, 1284, 1284 - (622 + 1), dtype = int),
custom_colors_series = ["black"]*5,
custom_colors_CPs = ["red"]* 100,
custom_linestyles = [":"]*100,
custom_linewidth = 2,
ylab_fontsize = 14,
xlab_fontsize = 14,
ylabel_coords = ylabel_coords,
additional_CPs = EvTDPD.results[EvTDPD.names.index("MAP CPs")][-2],
custom_colors_additional_CPs = ["blue"] * 100,
custom_linewidth_additional_CPs = 5.0,
custom_linestyles_additional_CPs = ["-"] * 10)
fig.savefig(result_path + "5SeriesRes_solidline.pdf",
format = "pdf", dpi = 800)
elif doublePlot:
"""Set up the influence plot first"""
dir_ = ("//Users//jeremiasknoblauch//Documents//OxWaSP//BOCPDMS//Code//" +
"SpatialBOCD//PaperNIPS//influencePlot")
well_file = dir_ + "//InfluencePlotData.csv"
"""STEP 1: Read in the data"""
raw_data = []
count = 0
with open(well_file) as csvfile:
reader = csv.reader(csvfile)
for row in reader:
if count > 0: #skip header
raw_data += row
count = count +1
raw_data_float = []
for entry in raw_data:
raw_data_float.append(float(entry))
raw_data = raw_data_float
num_cols, num_rows = 5, int(len(raw_data)/5)
data_ = np.zeros((num_rows, num_cols))
count = 0
for entry, count in zip(raw_data, range(0, int(len(raw_data)))):
ind_col = count % 5
ind_row = int(count/5)
data_[ind_row, ind_col] = entry
"""STEP 2: Plot"""
xlabsize, ylabsize, legendsize = 12, 12, 11
linewidths = [2]*5
linestyles = [0,"-",":", "--", "-."]
linecolors = [0, "navy", "purple", "red", "orange"]
#ax, fig = plt.subplots(1, figsize = (3.5,4.5))
ax = ax_array[0]
handles, labels = ax.get_legend_handles_labels()
for i in range(1, 5):
handle, = ax.plot(data_[:,0], data_[:,i], linewidth = linewidths[i],
linestyle = linestyles[i],
color = linecolors[i])
handles.append(handle)
ax.set_xlabel("Standard Deviations", size = xlabsize)
ax.set_ylabel("Influence", size = ylabsize)
labels = ["KLD", r'$\beta=0.05$', r'$\beta=0.2$',r'$\beta=0.25$']
ax.legend(handles, labels, prop = {'size':legendsize})
ax.text(-0.11, 0.98, string.ascii_uppercase[0], transform=ax.transAxes,
size=20, weight='bold')
"""STEP 3: Second plot"""
EvTKL.plot_raw_TS(data.reshape(T,S1*S2), indices = [0,1,2,3,4],
show_MAP_CPs = True,
time_range = np.linspace(1,T, T, dtype=int),
print_plt = False,
ylab = "Value",
xlab = "Time",
ax = ax_array[1], #ax_array[0],
#all_dates = np.linspace(622 + 1, 1284, 1284 - (622 + 1), dtype = int),
custom_colors_series = ["black"]*5,
custom_colors_CPs = ["red"]* 100,
custom_linestyles = [":"]*100,
custom_linewidth = 2,
ylab_fontsize = ylabsize,
xlab_fontsize = xlabsize,
ylabel_coords = ylabel_coords,
additional_CPs = EvTDPD.results[EvTDPD.names.index("MAP CPs")][-2],
custom_colors_additional_CPs = ["blue"] * 100,
custom_linewidth_additional_CPs = 2.5,
custom_linestyles_additional_CPs = ["-"] * 10)
ax_array[1].text(-0.07, 0.98, string.ascii_uppercase[1],
transform=ax_array[1].transAxes,
size=20, weight='bold')
fig.savefig(result_path + "InfluenceAndAR5.pdf",
format = "pdf", dpi = 800)
fig.savefig(result_path + "InfluenceAndAR5.jpg",
format = "jpg", dpi = 800)