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QuaPy
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svm-perf leaks model, fixing...

This commit is contained in:
Alejandro Moreo Fernandez 2021-06-15 07:49:16 +02:00
parent 9fd9d096f6
commit e8c3e29911
3 changed files with 13 additions and 2 deletions
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2
quapy/classification/methods.py
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@ -32,7 +32,7 @@ class PCALR(BaseEstimator):
self.pca = TruncatedSVD(self.n_components).fit(X, y) self.pca = TruncatedSVD(self.n_components).fit(X, y)
self.classes_ = self.learner.classes_ self.classes_ = self.learner.classes_
return self return self
def predict(self, X): def predict(self, X):
# X = self.transform(X) # X = self.transform(X)

4
quapy/classification/svmperf.py
View File

@ -58,6 +58,8 @@ class SVMperf(BaseEstimator, ClassifierMixin):
if self.verbose: if self.verbose:
print('[Running]', cmd) print('[Running]', cmd)
p = subprocess.run(cmd.split(), stdout=PIPE, stderr=STDOUT) p = subprocess.run(cmd.split(), stdout=PIPE, stderr=STDOUT)
if not exists(self.model):
print(p.stderr.decode('utf-8'))
remove(traindat) remove(traindat)
if self.verbose: if self.verbose:
@ -102,5 +104,5 @@ class SVMperf(BaseEstimator, ClassifierMixin):
def __del__(self): def __del__(self):
if hasattr(self, 'tmpdir'): if hasattr(self, 'tmpdir'):
shutil.rmtree(self.tmpdir) pass # shutil.rmtree(self.tmpdir, ignore_errors=True)

9
quapy/plot.py
View File

@ -172,6 +172,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
# join all data, and keep the order in which the methods appeared for the first time # join all data, and keep the order in which the methods appeared for the first time
data = defaultdict(lambda:{'x':np.empty(shape=(0)), 'y':np.empty(shape=(0))}) data = defaultdict(lambda:{'x':np.empty(shape=(0)), 'y':np.empty(shape=(0))})
method_order = [] method_order = []
for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs): for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs):
tr_prev_i = np.repeat(tr_prev_i.reshape(1,-1), repeats=test_prevs_i.shape[0], axis=0) tr_prev_i = np.repeat(tr_prev_i.reshape(1,-1), repeats=test_prevs_i.shape[0], axis=0)
@ -185,6 +186,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
method_order.append(method) method_order.append(method)
bins = np.linspace(0, 1, n_bins+1) bins = np.linspace(0, 1, n_bins+1)
inds_histogram_global = np.zeros(n_bins, dtype=np.float) # we use this to keep track of how many datapoits contribute to each bin
binwidth = 1 / n_bins binwidth = 1 / n_bins
min_x, max_x = None, None min_x, max_x = None, None
for method in method_order: for method in method_order:
@ -194,6 +196,8 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
method_drifts=np.log(1+method_drifts) method_drifts=np.log(1+method_drifts)
inds = np.digitize(tr_test_drifts, bins, right=True) inds = np.digitize(tr_test_drifts, bins, right=True)
inds_histogram_global += np.histogram(tr_test_drifts, density=True, bins=bins)[0]
xs, ys, ystds = [], [], [] xs, ys, ystds = [], [], []
for ind in range(len(bins)): for ind in range(len(bins)):
selected = inds==ind selected = inds==ind
@ -214,6 +218,11 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
if show_std: if show_std:
ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25) ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
# xs = bins[:-1]
# ys = inds_histogram_global
# print(xs.shape, ys.shape)
# ax.errorbar(xs, ys, label='density')
ax.set(xlabel=f'Distribution shift between training set and test sample', ax.set(xlabel=f'Distribution shift between training set and test sample',
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)', ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
title=title) title=title)