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visualization_mlp_performance.py
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import argparse
import tensorflow as tf
from tensorflow import keras
import numpy as np
import yaml
import pandas as pd
from itertools import product
import pickle
import uuid
import seaborn as sns
import os
import cddm_data_simulation as cds
import cdwiener as cdw
import boundary_functions as bf
import kde_training_utilities as kde_utils
import kde_class as kdec
import ckeras_to_numpy as ktnp
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
from matplotlib import cm
# INITIALIZATIONS -------------------------------------------------------------
def kde_vs_mlp_likelihoods(ax_titles = [],
title = 'Likelihoods KDE - MLP',
network_dir = '',
x_labels = [],
parameter_matrix = [],
cols = 3,
model = 'angle',
data_signature = '',
n_samples = 10,
nreps = 10,
save = True,
show = False,
machine = 'home',
method = 'mlp',
traindatanalytic = 0,
plot_format = 'svg'):
mpl.rcParams['text.usetex'] = True
#matplotlib.rcParams['pdf.fonttype'] = 42
mpl.rcParams['svg.fonttype'] = 'none'
# Initialize rows and graph parameters
rows = int(np.ceil(len(ax_titles) / cols))
sns.set(style = "white",
palette = "muted",
color_codes = True,
font_scale = 2)
fig, ax = plt.subplots(rows, cols,
figsize = (10, 10),
sharex = True,
sharey = False)
fig.suptitle(title + ': ' + model.upper().replace('_', '-'), fontsize = 40)
sns.despine(right = True)
# Data template
plot_data = np.zeros((4000, 2))
plot_data[:, 0] = np.concatenate(([i * 0.0025 for i in range(2000, 0, -1)], [i * 0.0025 for i in range(1, 2001, 1)]))
plot_data[:, 1] = np.concatenate((np.repeat(-1, 2000), np.repeat(1, 2000)))
# Load Keras model and initialize batch container
keras_model = keras.models.load_model(network_dir + 'model_final.h5')
keras_input_batch = np.zeros((4000, parameter_matrix.shape[1] + 2))
keras_input_batch[:, parameter_matrix.shape[1]:] = plot_data
for i in range(len(ax_titles)):
print('Making Plot: ', i)
row_tmp = int(np.floor(i / cols))
col_tmp = i - (cols * row_tmp)
# Get predictions from keras model
keras_input_batch[:, :parameter_matrix.shape[1]] = parameter_matrix[i, :]
ll_out_keras = keras_model.predict(keras_input_batch,
batch_size = 100)
# Get prediction from navarro if traindatanalytic = 1
if traindatanalytic:
ll_out_gt = cdw.batch_fptd(plot_data[:, 0] * plot_data[:, 1],
v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
ndt = parameter_matrix[i, 3])
sns.lineplot(plot_data[:, 0] * plot_data[:, 1],
ll_out_gt,
color = 'black',
alpha = 0.5,
label = 'TRUE',
ax = ax[row_tmp, col_tmp])
# Get predictions from simulations /kde
if not traindatanalytic:
for j in range(nreps):
if model == 'ddm' or model == 'ddm_analytic':
out = cds.ddm_flexbound(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
ndt = parameter_matrix[i, 3],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.constant,
boundary_multiplicative = True,
boundary_params = {})
if model == 'ddm_sdv':
out = cds.ddm_sdv(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
ndt = parameter_matrix[i, 3],
sdv = parameter_matrix[i, 4],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.constant,
boundary_multiplicative = True,
boundary_params = {})
if model == 'full_ddm' or model == 'full_ddm2':
out = cds.full_ddm(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
ndt = parameter_matrix[i, 3],
dw = parameter_matrix[i, 4],
sdv = parameter_matrix[i, 5],
dndt = parameter_matrix[i, 6],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.constant,
boundary_multiplicative = True,
boundary_params = {})
if model == 'angle' or model == 'angle2':
out = cds.ddm_flexbound(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
ndt = parameter_matrix[i, 3],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.angle,
boundary_multiplicative = False,
boundary_params = {'theta': parameter_matrix[i, 4]})
if model == 'weibull_cdf' or model == 'weibull_cdf2':
out = cds.ddm_flexbound(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
ndt = parameter_matrix[i, 3],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.weibull_cdf,
boundary_multiplicative = True,
boundary_params = {'alpha': parameter_matrix[i, 4],
'beta': parameter_matrix[i, 5]})
if model == 'levy':
out = cds.levy_flexbound(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
alpha_diff = parameter_matrix[i, 3],
ndt = parameter_matrix[i, 4],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.constant,
boundary_multiplicative = True,
boundary_params = {})
if model == 'ornstein':
out = cds.ornstein_uhlenbeck(v = parameter_matrix[i, 0],
a = parameter_matrix[i, 1],
w = parameter_matrix[i, 2],
g = parameter_matrix[i, 3],
ndt = parameter_matrix[i, 4],
s = 1,
delta_t = 0.001,
max_t = 20,
n_samples = n_samples,
print_info = False,
boundary_fun = bf.constant,
boundary_multiplicative = True,
boundary_params = {})
mykde = kdec.logkde((out[0], out[1], out[2]))
ll_out_gt = mykde.kde_eval((plot_data[:, 0], plot_data[:, 1]))
# Plot kde predictions
if j == 0:
sns.lineplot(plot_data[:, 0] * plot_data[:, 1],
np.exp(ll_out_gt),
color = 'black',
alpha = 0.5,
label = 'KDE',
ax = ax[row_tmp, col_tmp])
elif j > 0:
sns.lineplot(plot_data[:, 0] * plot_data[:, 1],
np.exp(ll_out_gt),
color = 'black',
alpha = 0.5,
ax = ax[row_tmp, col_tmp])
# Plot keras predictions
sns.lineplot(plot_data[:, 0] * plot_data[:, 1],
np.exp(ll_out_keras[:, 0]),
color = 'green',
label = 'MLP',
alpha = 1,
ax = ax[row_tmp, col_tmp])
# Legend adjustments
if row_tmp == 0 and col_tmp == 0:
ax[row_tmp, col_tmp].legend(loc = 'upper left',
fancybox = True,
shadow = True,
fontsize = 12)
else:
ax[row_tmp, col_tmp].legend().set_visible(False)
if row_tmp == rows - 1:
ax[row_tmp, col_tmp].set_xlabel('rt',
fontsize = 24);
else:
ax[row_tmp, col_tmp].tick_params(color = 'white')
if col_tmp == 0:
ax[row_tmp, col_tmp].set_ylabel('likelihood',
fontsize = 24);
ax[row_tmp, col_tmp].set_title(ax_titles[i],
fontsize = 20)
ax[row_tmp, col_tmp].tick_params(axis = 'y', size = 16)
ax[row_tmp, col_tmp].tick_params(axis = 'x', size = 16)
for i in range(len(ax_titles), rows * cols, 1):
row_tmp = int(np.floor(i / cols))
col_tmp = i - (cols * row_tmp)
ax[row_tmp, col_tmp].axis('off')
if save == True:
if machine == 'home':
fig_dir = "/users/afengler/OneDrive/git_repos/nn_likelihoods/figures/" + method + "/likelihoods/"
if not os.path.isdir(fig_dir):
os.mkdir(fig_dir)
figure_name = 'mlp_vs_kde_likelihood_'
plt.subplots_adjust(top = 0.9)
plt.subplots_adjust(hspace = 0.3, wspace = 0.3)
if traindatanalytic == 1:
if plot_format == 'svg':
plt.savefig(fig_dir + '/' + figure_name + model + data_signature + '_' + train_data_type + '.svg',
format = 'svg',
transparent = True,
frameon = False)
if plot_format == 'png':
plt.savefig(fig_dir + '/' + figure_name + model + '_analytic' + '.png',
dpi = 300) #, bbox_inches = 'tight')
else:
if plot_format == 'svg':
plt.savefig(fig_dir + '/' + figure_name + model + '_kde' + '.svg',
format = 'svg',
transparent = True,
frameon = False)
if plot_format == 'png':
plt.savefig(fig_dir + '/' + figure_name + model + '_kde' + '.png',
dpi = 300) #, bbox_inches = 'tight')
if show:
return plt.show()
else:
plt.close()
return 'finished'
# Predict
def mlp_manifold(params = [],
vary_idx = [],
vary_range = [],
vary_name = [],
n_levels = 25,
network_dir = [],
save = True,
show = True,
title = 'MLP Manifold',
model = 'ddm',
traindatanalytic = 0,
plot_format = 'svg',
):
mpl.rcParams.update(mpl.rcParamsDefault)
mpl.rcParams['text.usetex'] = True
#matplotlib.rcParams['pdf.fonttype'] = 42
mpl.rcParams['svg.fonttype'] = 'none'
# Load Keras model and initialize batch container
keras_model = keras.models.load_model(network_dir + 'model_final.h5')
# Prepare data structures
# Data template
plot_data = np.zeros((4000, 2))
plot_data[:, 0] = np.concatenate(([i * 0.005 for i in range(2000, 0, -1)], [i * 0.005 for i in range(1, 2001, 1)]))
plot_data[:, 1] = np.concatenate((np.repeat(-1, 2000), np.repeat(1, 2000)))
data_var = np.zeros((4000 * n_levels, len(params) + 3))
cnt = 0
for par_tmp in np.linspace(vary_range[0], vary_range[1], n_levels):
tmp_begin = 4000 * cnt
tmp_end = 4000 * (cnt + 1)
params[vary_idx] = par_tmp
data_var[tmp_begin:tmp_end, :len(params)] = params
data_var[tmp_begin:tmp_end, len(params):(len(params) + 2)] = plot_data
# print(data_var.shape)
data_var[tmp_begin:tmp_end, (len(params) + 2)] = np.squeeze(np.exp(keras_model.predict(data_var[tmp_begin:tmp_end, :-1],
batch_size = 100)))
cnt += 1
fig = plt.figure(figsize=(8, 5.5))
ax = fig.add_subplot(111, projection = '3d')
ax.plot_trisurf(data_var[:, -2] * data_var[:, -3],
data_var[:, vary_idx],
data_var[:, -1],
linewidth = 0.5,
alpha = 1.0,
cmap = cm.coolwarm)
ax.set_ylabel(vary_name.upper().replace('_', '-'),
fontsize = 16,
labelpad = 20)
ax.set_xlabel('RT',
fontsize = 16,
labelpad = 20)
ax.set_zlabel('Likelihood',
fontsize = 16,
labelpad = 20)
ax.set_zticks(np.round(np.linspace(min(data_var[:, -1]),
max(data_var[:, -1]),
5),
1))
ax.set_yticks(np.round(np.linspace(min(data_var[:, vary_idx]),
max(data_var[:, vary_idx]),
5),
1))
ax.set_xticks(np.round(np.linspace(min(data_var[:, -2] * data_var[:, -3]),
max(data_var[:, -2] * data_var[:, -3]),
5),
1))
ax.tick_params(labelsize = 16)
ax.set_title(model.upper().replace('_', '-') + ' - MLP: Manifold',
fontsize = 20,
pad = 20)
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
if traindatanalytic:
if plot_format == 'svg':
plt.savefig('./figures/mlp/manifolds/mlp_manifold_' + model + '_vary_' + vary_name + '_analytic' + '.svg',
format = 'svg',
transparent = True,
frameon = False)
if plot_format == 'png':
plt.savefig('./figures/mlp/manifolds/mlp_manifold_' + model + '_vary_' + vary_name + '_analytic' + '.png',
bbox_inches = 'tight',
dpi = 300)
else:
if plot_format == 'svg':
plt.savefig('./figures/mlp/manifolds/mlp_manifold_' + model + '_vary_' + vary_name + '_kde' + '.svg',
format = 'svg',
transparent = True,
frameon = False)
if plot_format == 'png':
plt.savefig('./figures/mlp/manifolds/mlp_manifold_' + model + '_vary_' + vary_name + '_kde' + '.png',
bbox_inches = 'tight',
dpi = 300)
if show:
return plt.show()
else:
plt.close()
return 'finished ...'
if __name__ == "__main__":
CLI = argparse.ArgumentParser()
CLI.add_argument("--model",
type = str,
default = 'ddm')
CLI.add_argument("--machine",
type = str,
default = 'machine')
CLI.add_argument("--traindatanalytic",
type = int,
default = 0)
CLI.add_argument("--ngraphs",
type = int,
default = 9)
CLI.add_argument("--trainfileidx",
type = int,
default = 0)
CLI.add_argument("--networkidx",
type = int,
default = -1)
CLI.add_argument("--mlekdereps",
type = int,
default = 10)
CLI.add_argument("--manifoldlayers",
type = int,
default = 50)
CLI.add_argument("--modelidentifier",
type = str,
default = '')
args = CLI.parse_args()
print(args)
model = args.model
machine = args.machine
ngraphs = args.ngraphs
trainfileidx = args.trainfileidx
networkidx = args.networkidx
traindatanalytic = args.traindatanalytic
mlekdereps = args.mlekdereps
manifoldlayers = args.manifoldlayers
if args.modelidentifier == 'None':
modelidentifier = ''
else:
modelidentifier = args.modelidentifier
# INSERT HERE
# machine = 'home' # put custom if you want to keep your network_path
# model = 'weibull_cdf'
if machine == 'x7':
stats = pickle.load(open("/media/data_cifs/afengler/git_repos/nn_likelihoods/kde_stats.pickle", "rb"))
model_params = stats[model]
output_folder = model_params['output_folder_x7']
with open("model_paths_x7.yaml") as tmp_file:
network_path = yaml.load(tmp_file)[model]
if machine == 'ccv':
stats = pickle.load(open("/users/afengler/git_repos/nn_likelihoods/kde_stats.pickle", "rb"))
model_params = stats[model]
ndt_idx = model_params['param_names'].index('ndt')
output_folder = model_params['output_folder']
with open("model_paths.yaml") as tmp_file:
if networkidx == -1:
network_path = yaml.load(tmp_file)[model]
#network_id = network_path[list(re.finditer('/', network_path))[-2].end():]
else:
network_path = yaml.load(tmp_file)[model + '_batch'][networkidx]
#network_id = network_path[list(re.finditer('/', network_path))[-2].end():]
if machine == 'home':
stats = pickle.load(open("/users/afengler/OneDrive/git_repos/nn_likelihoods/kde_stats.pickle", "rb"))
model_params = stats[model]
ndt_idx = model_params['param_names'].index('ndt')
output_folder = model_params['output_folder']
with open("model_paths_home.yaml") as tmp_file:
print('NETWOK ID:', networkidx)
if networkidx == -1:
network_path = yaml.load(tmp_file)[model + modelidentifier]
#network_id = network_path[list(re.finditer('/', network_path))[-2].end():]
else:
network_path = yaml.load(tmp_file)[model + '_batch'][networkidx]
#network_id = network_path[list(re.finditer('/', network_path))[-2].end():]
print('Why am I passing here ?????')
# Load training data
if machine == 'home':
if traindatanalytic:
training_file_folder = '/users/afengler/OneDrive/project_nn_likelihoods/data/analytic/' + model + '/training_data_binned_0_nbins_0_n_20000/'
else:
training_file_folder = '/users/afengler/OneDrive/project_nn_likelihoods/data/kde/' + model + '/training_data_binned_0_nbins_0_n_20000/'
if machine == 'ccv':
if traindatanalytic:
training_file_folder = '/users/afengler/data/analytic/' + model + '/training_data_binned_0_nbins_0_n_20000/'
else:
training_file_folder = '/users/afengler/data/kde/' + model + '/training_data_binned_0_nbins_0_n_20000/'
training_files = os.listdir(training_file_folder)
train_dat = pickle.load(open(training_file_folder + training_files[trainfileidx], 'rb'))
# TD HERE WE SHOULD ACTUALLY HAVE > NDT NOT 0
train_dat = train_dat[train_dat[:, -3] > train_dat[:, ndt_idx], :]
# Preprocess
train_dat[:, -1] = np.maximum(train_dat[:, -1], np.log(1e-7))
# Predict Likelihoods
keras_model = keras.models.load_model(network_path + 'model_final.h5')
prediction = keras_model.predict(train_dat[:, :-1],
batch_size = 100)
prediction = prediction[:, 0]
prediction = np.maximum(prediction, np.log(1e-7))
prediction_error = (prediction - train_dat[:, -1])
# Distribution of Prediction Errors
plt.hist(prediction_error,
bins = np.arange(-0.2, 0.2, 0.01),
density = True,
histtype = 'step',
color = 'black',
fill = 'black')
plt.xlabel('MLP Prediction Error',
size = 20)
plt.title('Prediction Error: ' + model.upper().replace('_', '-'),
size = 24)
if traindatanalytic:
plt.savefig('/Users/afengler/OneDrive/git_repos/nn_likelihoods/figures/mlp/prediction_errors/prediction_error_distribution_' + model + '_analytic' + '.png',
dpi = 300,
bbox_inches = 'tight')
else:
plt.savefig('/Users/afengler/OneDrive/git_repos/nn_likelihoods/figures/mlp/prediction_errors/prediction_error_distribution_' + model + '_kde' + '.png',
dpi = 300,
bbox_inches = 'tight')
plt.close()
# Graph showing number of cases where prediction error is larger than 1, with loglikelihood of prediction on the x axis
cnts = {}
for err in np.arange(0.1, 1, 0.1):
cnts[int(err * 10)] = []
for i in range(17):
cnts[int(err * 10)].append(np.sum(np.asarray(prediction_error > err) * np.asarray(prediction > - i)) / np.sum(prediction > - i))
plt.scatter(-np.arange(0, 17, 1), cnts[int(err * 10)], color = 'black', alpha = err, label = str(round(err, 1)))
plt.xlabel('log likelihood', size = 20)
plt.ylabel('Proportion Error >= ', size = 20)
plt.title('Prediction Error / ll: ' + model.upper().replace('_', '-'), size = 24)
plt.ylim((0, 0.05))
plt.legend(title = 'Error size', title_fontsize = 14, labelspacing = 0.1, fontsize = 10)
if traindatanalytic:
plt.savefig('/Users/afengler/OneDrive/git_repos/nn_likelihoods/figures/mlp/prediction_errors/prediction_error_vs_likelihood_' + model + '_analytic' + '.png',
dpi = 300,
bbox_inches = 'tight')
else:
plt.savefig('/Users/afengler/OneDrive/git_repos/nn_likelihoods/figures/mlp/prediction_errors/prediction_error_vs_likelihood_' + model + '_kde' + '.png',
dpi = 300,
bbox_inches = 'tight')
plt.close()
param_bounds = np.array(model_params['param_bounds_sampler'] + model_params['boundary_param_bounds_sampler'])
n_params = param_bounds.shape[0]
parameter_matrix = np.random.uniform(low = param_bounds[:, 0],
high = param_bounds[:, 1],
size = (ngraphs, n_params))
if model == 'angle2':
model2 = 'angle'
if model == 'ddm_analytic':
model2 = 'ddm'
if model == 'weibull_cdf2':
model2 = 'weibull_cdf'
if model == 'full_ddm2':
model2 = 'full_ddm'
else:
model2 = model
print('Now Plotting KDE vs. MLP Likelihoods')
kde_vs_mlp_likelihoods(ax_titles = [str(i) for i in range(1, ngraphs + 1, 1)],
parameter_matrix = parameter_matrix,
network_dir = network_path,
cols = 3,
model = model2,
n_samples = 20000,
nreps = mlekdereps,
save = True,
show = False,
machine = 'home',
method = 'mlp',
traindatanalytic = traindatanalytic)
# MANIFOLD PLOTS
# if model == 'ddm' or model == 'ddm_analytic':
# vary_idx_vec = [0, 1, 2, 3]
# start_params = [0, 1.5, 0.5, 1]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [0, 2]]
# vary_name_vec = ['v', 'a', 'w', 'ndt']
# if model == 'angle2' or model == 'angle':
# vary_idx_vec = [0, 1, 2, 3, 4]
# start_params = [0, 1.5, 0.5, 1, 0.2]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [0, 2], [0, 1]]
# vary_name_vec = ['v', 'a', 'w', 'ndt', 'angle']
# if model == 'ornstein':
# vary_idx_vec = [0, 1, 2, 3, 4]
# start_params = [0, 1.5, 0.5, 0.0, 1]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [-1, 1], [0, 2]]
# vary_name_vec = ['v', 'a', 'w', 'ndt', 'Inhibition']
# if model == 'full_ddm':
# vary_idx_vec = [0, 1, 2, 3, 4, 5, 6]
# start_params = [0.0, 1.5, 0.5, 1.0, 0.0, 0.0, 0.0]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [0.25, 2], [0.0, 0.2], [0.0, 1.0], [0.0, 0.25]]
# vary_name_vec = ['v', 'a', 'w', 'ndt', 'w noise', 'v noise', 'ndt noise']
# # add full_ddm2
# if model == 'levy':
# vary_idx_vec = [0, 1, 2, 3, 4]
# start_params = [0.0, 1.5, 0.5, 1.5, 1.0]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [1.0 , 2.0], [0.0, 2.0]]
# vary_name_vec = ['v', 'a', 'w', 'noise alpha', 'ndt']
# if model == 'weibull_cdf':
# vary_idx_vec = [0, 1, 2, 3, 4, 5]
# start_params = [0, 1.5, 0.5, 1.0, 2.0, 2.0]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [0.0 , 2.0], [0.3, 5.0], [0.3, 7.0]]
# vary_name_vec = ['v', 'a', 'w', 'ndt', 'alpha bound', 'beta boundary']
# if model == 'ddm_sdv' or model == 'ddm_sdv_analytic':
# vary_idx_vec = [0, 1, 2, 3, 4]
# start_params = [0.0, 1.5, 0.5, 1.0, 0.5]
# vary_range_vec = [[-2, 2], [0.3, 2], [0.2, 0.8], [0.25, 2], [0.0, 2.0]]
# vary_name_vec = ['v', 'a', 'w', 'ndt', 'v_noise']
# print('Now plotting MLP Likelihood Manifolds')
# for i in range(len(start_params)):
# print(i)
# mlp_manifold(params = start_params.copy(),
# vary_idx = vary_idx_vec[i],
# vary_range = vary_range_vec[i],
# vary_name = vary_name_vec[i],
# n_levels = manifoldlayers,
# network_dir = network_path,
# save = True,
# show = False,
# title = 'MLP Manifold',
# model = model,
# traindatanalytic = traindatanalytic)