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generating plots for overview paper

This commit is contained in:
Alejandro Moreo Fernandez 2024-11-18 14:45:30 +01:00
parent 5bbaf42a0e
commit 996bfd82f4
3 changed files with 244 additions and 128 deletions
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180
LeQua2024/util_scripts/prior_shift_plot.py
View File

@ -2,7 +2,8 @@ import os
from os.path import join
import pandas as pd
from scripts.data import load_vector_documents
from LeQua2024.scripts.data import load_vector_documents
from LeQua2024.scripts.constants import SAMPLE_SIZE
from quapy.data.base import LabelledCollection
import sys
# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))
@ -16,106 +17,97 @@ import matplotlib.pyplot as plt
# import seaborn as sns
from pathlib import Path
import glob
from scripts.constants import SAMPLE_SIZE
from commons import *
# os.chdir('/home/moreo/QuaPy/LeQua2024')
# print(os.getcwd())
for TASK in [1,2,4]:
qp.environ['SAMPLE_SIZE']=SAMPLE_SIZE[f'T{TASK}']
TASK=2
qp.environ['SAMPLE_SIZE']=SAMPLE_SIZE[f'T{TASK}']
true_prevs_path = f'../TruePrevalences/T{TASK}.test_prevalences/T{TASK}/public/test_prevalences.txt'
folder = F'../Results_CODALAB_2024/extracted/TASK_{TASK}'
true_prevs_path = f'../TruePrevalences/T{TASK}.test_prevalences/T{TASK}/public/test_prevalences.txt'
folder = F'../Results_CODALAB_2024/extracted/TASK_{TASK}'
method_files = glob.glob(f"{folder}/*.csv")
def load_result_file(path):
df = pd.read_csv(path, index_col=0)
id = df.index.to_numpy()
prevs = df.values
return id, prevs
desired_order = desired_order_dict[TASK]
# load the true values (sentiment prevalence, domain prevalence)
true_id, true_prevs = load_result_file(true_prevs_path)
# define the loss for evaluation
error_name = 'RAE'
error_log = False
if error_name == 'RAE':
err_function_ = qp.error.rae
elif error_name == 'AE':
err_function_ = qp.error.ae
else:
raise ValueError()
if error_log:
error_name = f'log({error_name})'
err_function = lambda x,y: np.log(err_function_(x,y))
else:
err_function = err_function_
method_files = glob.glob(f"{folder}/*.csv")
#train_prevalence = fetch_lequa2024(task=f'T{TASK}', data_home='./data')
train = LabelledCollection.load(f'../data/lequa2024/T{TASK}/public/training_data.txt', loader_func=load_vector_documents)
train_prev = train.prevalence()
#train_prev = np.tile(train_prev, (len(true_id),1))
from quapy.plot import error_by_drift, binary_diagonal
# load the participant and baseline results
method_names, estim_prevs = [], []
for method_file in method_files:
method_name = Path(method_file).name.replace('.csv', '')
# if method_name in exclude_methods:
# continue
id, method_prevs = load_result_file(join(folder, method_name+'.csv'))
assert (true_id == id).all(), f'unmatched files for {method_file}'
method_name = method_names_nice.get(method_name, method_name)
if method_name not in desired_order:
print(f'method {method_name} unknown')
raise ValueError()
method_names.append(method_name)
estim_prevs.append(method_prevs)
plt.rcParams['figure.figsize'] = [14, 6]
plt.rcParams['figure.dpi'] = 200
plt.rcParams['font.size'] = 15
true_prevs = [true_prevs]*len(method_names)
savepath = f'./t{TASK}_diagonal.png'
if TASK in [1,4]:
binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
train_prev=train.prevalence(), savepath=savepath, method_order=desired_order)
method_names_nice={
'DistMatching-y': 'DM',
'TeamGMNet': 'UniOviedo(Team1)',
'tobiaslotz': 'Lamarr'
}
box_to_ancor={
1: (0.88,0.1),
2: (0.9,0.15),
4: (0.9, 0.15),
}
exclude_methods=[
'TeamCUFE',
'hustav',
'PCC',
'CC'
]
# desired_order=[
# 'Lamarr',
# 'SLD',
# 'DM',
# 'KDEy',
# 'UniOviedo(Team1)'
# ]
# desired_order=[
# 'PCC', 'Lamarr'
# ]
# load the true values (sentiment prevalence, domain prevalence)
true_id, true_prevs = load_result_file(true_prevs_path)
# define the loss for evaluation
error_name = 'RAE'
error_log = False
if error_name == 'RAE':
err_function_ = qp.error.rae
elif error_name == 'AE':
err_function_ = qp.error.ae
else:
raise ValueError()
if error_log:
error_name = f'log({error_name})'
err_function = lambda x,y: np.log(err_function_(x,y))
else:
err_function = err_function_
#train_prevalence = fetch_lequa2024(task=f'T{TASK}', data_home='./data')
train = LabelledCollection.load(f'../data/lequa2024/T{TASK}/public/training_data.txt', loader_func=load_vector_documents)
train_prev = train.prevalence()
#train_prev = np.tile(train_prev, (len(true_id),1))
from quapy.plot import error_by_drift, binary_diagonal
# load the participant and baseline results
method_names, estim_prevs = [], []
for method_file in method_files:
method_name = Path(method_file).name.replace('.csv', '')
if method_name in exclude_methods:
continue
id, method_prevs = load_result_file(join(folder, method_name+'.csv'))
assert (true_id == id).all(), f'unmatched files for {method_file}'
method_name = method_names_nice.get(method_name, method_name)
method_names.append(method_name)
estim_prevs.append(method_prevs)
true_prevs = [true_prevs]*len(method_names)
savepath = f'./t{TASK}_diagonal.png'
if TASK==1:
binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
train_prev=train.prevalence(), savepath=savepath, method_order=None)
tr_prevs =[train.prevalence()]*len(method_names)
savepath = f'./t{TASK}_{error_name}_pps.png'
error_by_drift(method_names,
true_prevs,
estim_prevs,
tr_prevs, title=None,
error_name='rae', show_std=True, n_bins=1000,
show_density=True, vlines=[tr_prevs[0][1]], savepath=savepath)
tr_prevs =[train.prevalence()]*len(method_names)
savepath = f'./t{TASK}_{error_name}_pps.png'
binary=TASK in [1,4]
if binary:
print(f'{TASK=} has positive prevalence = {train.prevalence()[1]}')
error_by_drift(method_names,
true_prevs,
estim_prevs,
tr_prevs,
title=None,
y_error_name='rae',
x_error_name='bias_binary' if binary else 'ae',
x_axis_title=f'PPS between training set and test sample (in terms of bias)' if binary else None,
show_std=False,
n_bins=25,
logscale=True if binary else False,
show_density=True,
method_order=desired_order,
vlines=list(train.prevalence()) if binary else None,
bbox_to_anchor=box_to_ancor[TASK],
savepath=savepath)

26
quapy/error.py
View File

@ -44,6 +44,28 @@ def acce(y_true, y_pred):
"""
return 1. - (y_true == y_pred).mean()
def bias_binary(prevs, prevs_hat):
"""
Computes the (positive) bias in a binary problem. The bias is simply the difference between the
predicted positive value and the true positive value, so that a positive such value indicates the
prediction has positive bias (i.e., it tends to overestimate) the true value, and negative otherwise.
:math:`bias(p,\\hat{p})=\\hat{p}_1-p_1`,
:param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values
:param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted
prevalence values
:return: binary bias
"""
assert prevs.shape[-1] == 2 and prevs.shape[-1] == 2, f'bias_binary can only be applied to binary problems'
return prevs_hat[...,1]-prevs[...,1]
def mean_bias_binary(prevs, prevs_hat):
"""
Computes the mean of the (positive) bias in a binary problem.
:param prevs: array-like of shape `(n_classes,)` with the true prevalence values
:param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values
:return: mean binary bias
"""
return np.mean(bias_binary(prevs, prevs_hat))
def mae(prevs, prevs_hat):
"""Computes the mean absolute error (see :meth:`quapy.error.ae`) across the sample pairs.
@ -308,8 +330,8 @@ def __check_eps(eps=None):
CLASSIFICATION_ERROR = {f1e, acce}
QUANTIFICATION_ERROR = {mae, mnae, mrae, mnrae, mse, mkld, mnkld}
QUANTIFICATION_ERROR_SINGLE = {ae, nae, rae, nrae, se, kld, nkld}
QUANTIFICATION_ERROR = {mae, mnae, mrae, mnrae, mse, mkld, mnkld, mean_bias_binary}
QUANTIFICATION_ERROR_SINGLE = {ae, nae, rae, nrae, se, kld, nkld, bias_binary}
QUANTIFICATION_ERROR_SMOOTH = {kld, nkld, rae, nrae, mkld, mnkld, mrae}
CLASSIFICATION_ERROR_NAMES = {func.__name__ for func in CLASSIFICATION_ERROR}
QUANTIFICATION_ERROR_NAMES = {func.__name__ for func in QUANTIFICATION_ERROR}

156
quapy/plot.py
View File

@ -9,6 +9,7 @@ import math
import quapy as qp
plt.rcParams['figure.figsize'] = [10, 6]
plt.rcParams['figure.dpi'] = 200
plt.rcParams['font.size'] = 18
@ -212,13 +213,19 @@ def binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title=N
def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
n_bins=20, error_name='ae', show_std=False,
n_bins=20,
y_error_name='ae',
x_error_name='ae',
show_std=False,
show_density=True,
show_legend=True,
y_axis_title=None,
x_axis_title=None,
logscale=False,
title=f'Quantification error as a function of distribution shift',
title=None,
vlines=None,
method_order=None,
bbox_to_anchor=(1,1),
savepath=None):
"""
Plots the error (along the x-axis, as measured in terms of `error_name`) as a function of the train-test shift
@ -234,7 +241,10 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
for each experiment
:param tr_prevs: training prevalence of each experiment
:param n_bins: number of bins in which the y-axis is to be divided (default is 20)
:param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is "ae")
:param y_error_name: a string representing the name of an error function (as defined in `quapy.error`,
default is "ae") to be used along the y-axis
:param x_error_name: a string representing the name of an error function (as defined in `quapy.error`,
default is "ae") to be used along the x-axis
:param show_std: whether or not to show standard deviations as color bands (default is False)
:param show_density: whether or not to display the distribution of experiments for each bin (default is True)
:param show_density: whether or not to display the legend of the chart (default is True)
@ -250,31 +260,40 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
fig, ax = plt.subplots()
ax.grid()
x_error = qp.error.ae
y_error = getattr(qp.error, error_name)
if isinstance(x_error_name, str):
x_error = qp.error.from_name(x_error_name)
if isinstance(y_error_name, str):
y_error = qp.error.from_name(y_error_name)
# get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same
# order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to
# x_error function) and 'y' is the estim-test shift (computed as according to y_error)
data = _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order)
min_x = np.min([np.min(m_data['x']) for m_data in data.values()])
max_x = np.max([np.max(m_data['x']) for m_data in data.values()])
min_y = np.min([np.min(m_data['y']) for m_data in data.values()])
max_y = np.max([np.max(m_data['y']) for m_data in data.values()])
print(f'[{min_x}, {max_x}]<-')
if method_order is None:
method_order = method_names
_set_colors(ax, n_methods=len(method_order))
bins = np.linspace(0, 1, n_bins+1)
binwidth = 1 / n_bins
min_x, max_x, min_y, max_y = None, None, None, None
bins = np.linspace(min_x-1E-8, max_x+1E-8, n_bins+1)
print('bins', bins)
binwidth = (max_x-min_x) / n_bins
npoints = np.zeros(len(bins), dtype=float)
for method in method_order:
tr_test_drifts = data[method]['x']
method_drifts = data[method]['y']
if logscale:
ax.set_yscale("log")
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.yaxis.get_major_formatter().set_scientific(False)
ax.minorticks_off()
# ax.yaxis.set_major_formatter(ScalarFormatter())
# ax.yaxis.get_major_formatter().set_scientific(False)
# ax.minorticks_off()
inds = np.digitize(tr_test_drifts, bins, right=True)
@ -282,7 +301,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
for p,ind in enumerate(range(len(bins))):
selected = inds==ind
if selected.sum() > 0:
xs.append(ind*binwidth-binwidth/2)
xs.append(min_x + (ind*binwidth-binwidth/2))
ys.append(np.mean(method_drifts[selected]))
ystds.append(np.std(method_drifts[selected]))
npoints[p] += len(method_drifts[selected])
@ -291,13 +310,6 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
ys = np.asarray(ys)
ystds = np.asarray(ystds)
min_x_method, max_x_method, min_y_method, max_y_method = xs.min(), xs.max(), ys.min(), ys.max()
min_x = min_x_method if min_x is None or min_x_method < min_x else min_x
max_x = max_x_method if max_x is None or max_x_method > max_x else max_x
max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
min_y = min_y_method if min_y is None or min_y_method < min_y else min_y
max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
ax.errorbar(xs, ys, fmt='-', marker='o', color='w', markersize=8, linewidth=4, zorder=1)
ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=6, linewidth=2, zorder=2)
@ -307,31 +319,40 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
if show_density:
ax2 = ax.twinx()
densities = npoints/np.sum(npoints)
ax2.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
densities, alpha=0.15, color='g', width=binwidth, label='density')
ax2.bar([min_x + (ind * binwidth-binwidth/2) for ind in range(len(bins))],
densities, alpha=0.15, color='g', width=binwidth, label='density')
ax2.set_ylim(0,max(densities))
ax2.spines['right'].set_color('g')
ax2.tick_params(axis='y', colors='g')
ax.set(xlabel=f'Distribution shift between training set and test sample',
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
title=title)
y_axis_err_name = y_error_name.upper()
if logscale:
y_axis_err_name = f'log({y_axis_err_name})'
if y_axis_title is None:
y_axis_title=f'{y_axis_err_name} (true distribution, predicted distribution)'
if x_axis_title is None:
x_axis_title = f'PPS between training set and test sample (in terms of {x_error_name.upper()})'
ax.set(xlabel=x_axis_title, ylabel=y_axis_title, title=title)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
if vlines:
for vline in vlines:
ax.axvline(vline, 0, 1, linestyle='--', color='k')
ax.set_xlim(min_x, max_x)
margin = (max_x-min_x)*0.02
ax.set_xlim(min_x-margin, max_x+margin)
if logscale:
#nice scale for the logaritmic axis
ax.set_ylim(0,10 ** math.ceil(math.log10(max_y)))
if show_legend:
# fig.legend(loc='lower center',
# bbox_to_anchor=(1, 0.5),
# ncol=(len(method_names)+1)//2)
fig.legend(loc='lower center',
bbox_to_anchor=(1, 0.5),
ncol=(len(method_names)+1)//2)
bbox_to_anchor=bbox_to_anchor,
ncol=1)
_save_or_show(savepath)
@ -547,6 +568,7 @@ def _save_or_show(savepath):
plt.savefig(savepath, bbox_inches='tight')
else:
plt.show()
plt.close()
def _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order):
@ -568,3 +590,83 @@ def _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error
method_order.append(method)
return data
def bin_signal(x, y, n_bins, binning_type='isometric'):
"""
Bins the input data `x` and computes statistical summaries of `y` within each bin.
:param x: The independent variable to be binned. Must be a 1D array of numerical values.
:type x: array-like
:param y: The dependent variable corresponding to `x`. Must be the same length as `x`.
:type y: array-like
:param n_bins: The number of bins to create.
:type n_bins: int
:param binning_type: The method to use for binning:
- `'isometric'`: Creates bins with equal width (isometric binning).
- `'isotonic'`: Creates bins containing an equal number of instances (isotonic binning).
Defaults to `'isometric'`.
:type binning_type: str
:return: A tuple containing:
- `bin_means` (numpy.ndarray): The mean of `y` values in each bin (`np.nan` for empty bins).
- `bin_stds` (numpy.ndarray): The standard deviation (sample std) of `y` values in each bin (`np.nan` for empty bins).
- `bin_centers` (numpy.ndarray): The center points of each bin.
- `bin_lengths` (numpy.ndarray): The length (width) of each bin.
- `bin_counts` (numpy.ndarray): The number of elements in each bin.
:rtype: tuple (numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray)
:raises ValueError: If `binning_type` is not one of `'isometric'` or `'isotonic'`.
.. note::
- For isometric binning, bins are equally spaced along the range of `x`.
- For isotonic binning, bins are constructed to contain approximately equal numbers of elements, based on sorted `x`.
- If a bin is empty (no elements fall within its range), its mean and standard deviation will be `np.nan`.
:example:
>>> import numpy as np
>>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> y = np.array([10, 20, 15, 25, 30, 35, 45, 50, 40])
>>> n_bins = 3
>>> bin_signal(x, y, n_bins, binning_type='isometric')
(array([15., 30., 45.]),
array([5., 5., 5.]),
array([2.33333333, 5. , 7.66666667]),
array([2.66666667, 2.66666667, 2.66666667]),
array([3, 3, 3]))
"""
x = np.asarray(x)
y = np.asarray(y)
if binning_type == 'isometric':
# all bins are equally-sized
bin_edges = np.linspace(x.min(), x.max(), n_bins + 1)
elif binning_type == 'isotonic':
# all bins contain the same number of instances
sorted_x = np.sort(x)
bin_edges = np.interp(np.linspace(0, len(x), n_bins + 1), np.arange(len(x)), sorted_x)
else:
raise ValueError("valid binning types include 'isometric' and 'isotonic'")
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
bin_lengths = bin_edges[1:] - bin_edges[:-1]
bin_means = []
bin_stds = []
bin_counts = []
for start, end in zip(bin_edges[:-1], bin_edges[1:]):
mask = (x >= start) & (x < end) if end != bin_edges[-1] else (x >= start) & (x <= end)
count = mask.sum()
if count > 0:
y_in_bin = y[mask]
bin_means.append(np.mean(y_in_bin))
bin_stds.append(np.std(y_in_bin, ddof=1))
else:
bin_means.append(np.nan)
bin_stds.append(np.nan)
bin_counts.append(count)
return np.array(bin_means), np.array(bin_stds), np.array(bin_centers), np.array(bin_lengths), np.array(bin_counts)