def getNextTransition(transition, sent, classifier, dictVectorizer, feats):
legalTansDic = transition.getLegalTransDic()
if len(legalTansDic) == 1:
return Transition.initialize(legalTansDic.keys()[0], sent)
featDic = Extractor.getFeatures(transition, sent)
if not isinstance(featDic, list):
featDic = [featDic]
transTypeValue = classifier.predict(
dictVectorizer.transform(featDic))[0]
transType = TransitionType.getType(transTypeValue)
if transType in legalTansDic:
return legalTansDic[transType]
if len(legalTansDic):
return Transition.initialize(legalTansDic.keys()[0], sent)
raise
def parse(corpus, clf):
"""
Iterate on sentences of the test set and predict transitions for it.
The predicted transition will be applied and and the integer of it will be appended to labels (list) andd to saved in the featuresInfo of the sentence object
:param corpus: corpus object with sentence objects of testing set
:param clf: classifier model
"""
count_transitions = Counter()
all_labels = list()
if XPParams.useCrossValidation:
corpus.initializeSents(training=False)
label_file_content = list()
folder = os.path.join(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
"Results/")
if Paths.descriptionPath:
folder += Paths.descriptionPath + "/"
if not os.path.exists(folder):
os.makedirs(folder)
with open(
EmbeddingOracle.get_path(
folder + 'features/' +
str(XPParams.numberModuloReduction) + '/', corpus.langName,
0, "test_modulo_features.csv"), "w+") as f:
f.write("")
if XPParams.use_extern_labels:
i = 0
with open(
os.path.join(Paths.projectPath,
Paths.extern_labels)) as f: # FA_test_labels
label_file_content = f.readlines()
label_file_content = [
int(l.split(" ")[0]) for l in label_file_content
]
for sent in corpus.testingSents:
sent.initialTransition = EmbeddingTransition(None,
isInitial=True,
sent=sent)
# new emdiddingtransition which inherit everything from Transition -> full buffer, empty stack
transition = sent.initialTransition
labels = []
while not transition.configuration.isTerminalConf(
) and i < len(label_file_content):
transTypeValue = label_file_content[i]
transType = TransitionType.getType(transTypeValue)
# transType: enumeration object with transitiontype
legalTansDic = transition.getLegalTransDic()
if transType in legalTansDic:
newTransition = legalTansDic[transType]
elif len(legalTansDic):
newTransition = Transition.initialize(
legalTansDic.keys()[0], sent)
else:
print("no trans")
newTransition = None
if newTransition:
# newTransition: Transition object
newTransition.apply(transition, sent, parse=True)
labels.append(newTransition.type.value)
count_transitions[newTransition.type.value] += 1
# labels array/list contains the predicted labels (integer) of the sentence
transition = newTransition
i += 1
sent.featuresInfo = [
labels, []
] # second argument is needed to have the same structure as in training (features.py -> Extractor.extract)
else:
for sent in corpus.testingSents:
sent.initialTransition = EmbeddingTransition(None,
isInitial=True,
sent=sent)
# new emdiddingtransition which inherit everything from Transition -> full buffer, empty stack
transition = sent.initialTransition
labels = []
while not transition.configuration.isTerminalConf():
# while configuration is not None -> buffer and stack unequal to 0 (config.py)
# feats used here BQ
# clf[0] SVM classifier(clf) and clf[1] DictVectorizer(vec) without values, clf[2] cnn (if used)
newTransition = Parser.getNextTransition(
transition, sent, clf, corpus, folder)
if newTransition:
# newTransition: Transition object
newTransition.apply(transition, sent, parse=True)
labels.append(newTransition.type.value)
count_transitions[newTransition.type.value] += 1
# labels array/list contains the predicted labels (integer) of the sentence
transition = newTransition
sent.featuresInfo = [
labels, []
] # second argument is needed to have the same structure as in training (features.py -> Extractor.extract)
all_labels.extend(labels)
with open(
EmbeddingOracle.get_path(
folder + 'labels/' +
str(XPParams.numberModuloReduction) + '/',
corpus.langName, 0, "test_labels.txt"),
"w+") as file_labels:
file_labels.write('\n'.join(str(x) for x in all_labels))
def getNextTransition(transition, sent, clf, corpus, folder):
"""
extract features of the sentence of the trainingset and predict a transition for it
:param transition:
:param sent: sentence object of test corpus (corpus.py)
:param clf[0]: trained model in oracles.py (sklearn.multiclass OutputCodeClassifier),
clf[1]: DictVectorizer (vec) without values,
clf[2] cnn model
:return: transition object
"""
if not XPParams.use_extern_labels:
classifier = clf[0]
dictVectorizer = clf[1]
if XPParams.use_feature_selection_chi:
feat_selection = clf[2]
elif XPParams.use_feature_selection_mi:
feat_selection = clf[2]
else:
feat_selection = ''
legalTansDic = transition.getLegalTransDic()
# legalTansDic: dictionary which contains transition names as keys (<TransitionType.SHIFT: 0>) and the transition object as value (transitions.Shift)
if len(legalTansDic) == 1:
return Transition.initialize(legalTansDic.keys()[0], sent)
# ----- Extract Features ---- #
featDic = Extractor.getFeatures(transition, sent)
if not isinstance(featDic, list):
featDic = [featDic]
X = dictVectorizer.transform(
featDic) # transforms valueDict to sparse matrix
# ---- PREDICTION ------- #
if XPParams.use_feature_selection_chi:
# mi = SelectKBest(mutual_info_classif)
# mi = SelectPercentile()
# print("pred", X.shape)
X = feat_selection.transform(X)
# chi = SelectKBest()
if XPParams.use_feature_selection_mi:
X = feat_selection.transform(X)
# modulo reduction and prediction
if XPParams.useModuloReduction:
n_new_features = XPParams.numberModuloReduction
if XPParams.twoD_feats:
X = ModuloReduction.reduce_matrix_with_modulo(
X,
n_new_features,
twoD_feats=True,
new_y_number=XPParams.new_y_number)
else:
X = ModuloReduction.reduce_matrix_with_modulo(
X, n_new_features, calc_ppmi=XPParams.usePMICalc)
# print(type(NewX), NewX)
# transTypeValue = classifier.predict(X)[0]
if XPParams.useCNNandSVM:
# layer_model = Model(inputs=cnn.input, outputs=cnn.get_layer(XPParams.CNNlayerName).output)
if not XPParams.useMultiChannel:
X = X.toarray()
X = X.reshape((1, 1, X.shape[1], 1))
layer_model = clf[3]
cnn_prediction = layer_model.predict(X)
clf_prediction = classifier.predict(cnn_prediction)[0]
# print(clf_prediction)
# transTypeValue = argmax(clf_prediction) # numpy function which convert onehotvector back to integer
transTypeValue = clf_prediction
elif XPParams.useCNNOnly:
# layer_model = Model(inputs=cnn.input, outputs=cnn.get_layer(XPParams.CNNlayerName).output)
if not XPParams.twoD_feats:
X = X.toarray()
with open(
EmbeddingOracle.get_path(
folder + 'features/' +
str(XPParams.numberModuloReduction) + '/',
corpus.langName, 0, "test_modulo_features.csv"),
"a+") as f:
numpy.savetxt(f, X, delimiter="\t", fmt='%i')
if not XPParams.useMultiChannel:
X = X.reshape((1, 1, X.shape[1], 1))
else:
X = X.reshape((1, 1, X.shape[1], X.shape[2]))
cnn_prediction = classifier.predict(
X) # vector with one element per class
# print("cnn",cnn_prediction)
# clf_prediction = classifier.predict(cnn_prediction)[0]
# print(clf_prediction)
# transTypeValue = argmax(clf_prediction) # numpy function which convert onehotvector back to integer
transTypeValue = numpy.argmax(cnn_prediction)
# print(transTypeValue, "transtypevalue")
else:
transTypeValue = classifier.predict(X.toarray())[0]
# transTypeValue: type integer, value of predicted transition
# ----------------------- #
transType = TransitionType.getType(transTypeValue)
# transType: enumeration object with transitiontype
if transType in legalTansDic:
# legalTansDic[transType]: transition
return legalTansDic[transType]
if len(legalTansDic):
# if only one possible transition in dict then initialize and return it
return Transition.initialize(legalTansDic.keys()[0], sent)
# raise an error if transtype is not in legaltransdict and legaltransdict contains one element
raise ValueError("No transition found.")
