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analysis.py
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analysis.py
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import numpy as np
import pylab as pl
from pystruct.learners import OneSlackSSVM
from latent_structured_svm import LatentSSVM
from heterogenous_crf import HCRF
from test_weak_labeled import msrc_load
from test_weak_labeled import split_test_train
from data_loader import load_syntetic
def msrc_train_score_per_iter(result, only_weak=False, plot=True):
w_history = result.data['w_history']
meta_data = result.meta
n_full = meta_data['n_full']
n_train = meta_data['n_train']
n_inference_iter = meta_data['n_inference_iter']
n_full = meta_data['n_full']
n_train = meta_data['n_train']
C = meta_data['C']
latent_iter = meta_data['latent_iter']
max_iter = meta_data['max_iter']
inner_tol = meta_data['inner_tol']
outer_tol = meta_data['outer_tol']
alpha = meta_data['alpha']
min_changes = meta_data['min_changes']
initialize = meta_data['initialize']
crf = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=alpha,
inference_method='gco', n_iter=n_inference_iter)
base_clf = OneSlackSSVM(crf, max_iter=max_iter, C=C, verbose=2,
tol=inner_tol, n_jobs=4, inference_cache=10)
clf = LatentSSVM(base_clf, latent_iter=latent_iter, verbose=2,
tol=outer_tol, min_changes=min_changes, n_jobs=4)
Xtrain, Ytrain, Ytrain_full, Xtest, Ytest = msrc_load(n_full, n_train)
if only_weak:
Xtrain = [x for (i, x) in enumerate(Xtrain) if not Ytrain[i].full_labeled]
Ytrain_full = [y for (i, y) in enumerate(Ytrain_full) if not Ytrain[i].full_labeled]
base_clf.w = None
clf.w_history_ = w_history
clf.iter_done = w_history.shape[0]
train_scores = []
for score in clf.staged_score(Xtrain, Ytrain_full):
train_scores.append(score)
train_scores = np.array(train_scores)
if plot:
x = np.arange(0, train_scores.size)
pl.rc('text', usetex=True)
pl.rc('font', family='serif')
pl.figure(figsize=(10,10), dpi=96)
pl.title('score on train set')
pl.plot(x, train_scores)
pl.scatter(x, train_scores)
pl.xlabel('iteration')
pl.xlim([-0.5, train_scores.size + 1])
return train_scores
def syntetic_train_score_per_iter(result, only_weak=False, plot=True):
w_history = result.data['w_history']
meta_data = result.meta
n_full = meta_data['n_full']
n_train = meta_data['n_train']
n_inference_iter = meta_data['n_inference_iter']
n_full = meta_data['n_full']
n_train = meta_data['n_train']
dataset = meta_data['dataset']
C = meta_data['C']
latent_iter = meta_data['latent_iter']
max_iter = meta_data['max_iter']
inner_tol = meta_data['inner_tol']
outer_tol = meta_data['outer_tol']
alpha = meta_data['alpha']
min_changes = meta_data['min_changes']
initialize = meta_data['initialize']
crf = HCRF(n_states=10, n_features=10, n_edge_features=2, alpha=alpha,
inference_method='gco', n_iter=n_inference_iter)
base_clf = OneSlackSSVM(crf, max_iter=max_iter, C=C, verbose=0,
tol=inner_tol, n_jobs=4, inference_cache=100)
clf = LatentSSVM(base_clf, latent_iter=latent_iter, verbose=2,
tol=outer_tol, min_changes=min_changes, n_jobs=4)
X, Y = load_syntetic(dataset)
Xtrain, Ytrain, Ytrain_full, Xtest, Ytest = \
split_test_train(X, Y, n_full, n_train)
if only_weak:
Xtrain = [x for (i, x) in enumerate(Xtrain) if not Ytrain[i].full_labeled]
Ytrain_full = [y for (i, y) in enumerate(Ytrain_full) if not Ytrain[i].full_labeled]
base_clf.w = None
clf.w_history_ = w_history
clf.iter_done = w_history.shape[0]
train_scores = []
for score in clf.staged_score(Xtrain, Ytrain_full):
train_scores.append(score)
train_scores = np.array(train_scores)
if plot:
x = np.arange(0, train_scores.size)
pl.rc('text', usetex=True)
pl.rc('font', family='serif')
pl.figure(figsize=(10,10), dpi=96)
pl.title('score on train set')
pl.plot(x, train_scores)
pl.scatter(x, train_scores)
pl.xlabel('iteration')
pl.xlim([-0.5, train_scores.size + 1])
return train_scores