def train_tagger(essays_TD, essays_VD, wd_test_tags, wd_train_tags):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ,
sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(
td_feats), feature_transformer.transform(vd_feats)
wd_td_ys_bytag = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
wd_vd_ys_bytag = get_wordlevel_ys_by_code(vd_tags, wd_train_tags)
""" TRAIN Tagger """
tag2word_classifier = train_classifier_per_code(
td_X,
wd_td_ys_bytag,
lambda: LogisticRegression(),
wd_train_tags,
verbose=False)
""" TEST Tagger """
td_wd_predictions_by_code = test_classifier_per_code(
td_X, tag2word_classifier, wd_test_tags)
vd_wd_predictions_by_code = test_classifier_per_code(
vd_X, tag2word_classifier, wd_test_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_bytag, wd_vd_ys_bytag
def train_tagger(fold, essays_TD, essays_VD, wd_test_tags, wd_train_tags):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
print "\nFold %s" % fold
print "Training Tagging Model"
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(td_feats), feature_transformer.transform(vd_feats)
wd_td_ys = get_wordlevel_powerset_ys(td_tags, wd_train_tags)
wd_vd_ys = get_wordlevel_powerset_ys(vd_tags, wd_train_tags)
wd_td_ys_by_code = get_by_code_from_powerset_predictions(wd_td_ys, wd_test_tags)
wd_vd_ys_by_code = get_by_code_from_powerset_predictions(wd_vd_ys, wd_test_tags)
""" TRAIN Tagger """
model = fn_create_wd_cls()
model.fit(td_X, wd_td_ys)
wd_td_pred = model.predict(td_X)
wd_vd_pred = model.predict(vd_X)
""" TEST Tagger """
td_wd_predictions_by_code = get_by_code_from_powerset_predictions(wd_td_pred, wd_test_tags)
vd_wd_predictions_by_code = get_by_code_from_powerset_predictions(wd_vd_pred, wd_test_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_by_code, wd_vd_ys_by_code
def train_tagger(fold, essays_TD, essays_VD, wd_test_tags, wd_train_tags,
dual, C, penalty, fit_intercept, multi_class):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(td_feats), feature_transformer.transform(vd_feats)
""" compute most common tags per word for training only (but not for evaluation) """
wd_td_ys = get_wordlevel_mostfrequent_ys(td_tags, wd_train_tags, tag_freq)
""" TRAIN Tagger """
solver = 'liblinear'
if multi_class == 'multinomial':
solver = "lbfgs"
model = LogisticRegression(dual=dual, C=C, penalty=penalty, fit_intercept=fit_intercept, multi_class=multi_class, solver=solver)
if fold == 0:
print(model)
model.fit(td_X, wd_td_ys)
wd_td_pred = model.predict(td_X)
wd_vd_pred = model.predict(vd_X)
""" TEST Tagger """
td_wd_predictions_by_code = get_by_code_from_powerset_predictions(wd_td_pred, wd_test_tags)
vd_wd_predictions_by_code = get_by_code_from_powerset_predictions(wd_vd_pred, wd_test_tags)
""" Get Actual Ys by code (dict of label to predictions """
wd_td_ys_by_code = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
wd_vd_ys_by_code = get_wordlevel_ys_by_code(vd_tags, wd_train_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_by_code, wd_vd_ys_by_code
def train_tagger(fold, essays_TD, essays_VD, wd_test_tags, wd_train_tags):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
print "\nFold %s" % fold
print "Training Tagging Model"
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ,
sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(
td_feats), feature_transformer.transform(vd_feats)
wd_td_ys = get_wordlevel_powerset_ys(td_tags, wd_train_tags)
wd_vd_ys = get_wordlevel_powerset_ys(vd_tags, wd_train_tags)
wd_td_ys_by_code = get_by_code_from_powerset_predictions(
wd_td_ys, wd_test_tags)
wd_vd_ys_by_code = get_by_code_from_powerset_predictions(
wd_vd_ys, wd_test_tags)
""" TRAIN Tagger """
model = fn_create_wd_cls()
model.fit(td_X, wd_td_ys)
wd_td_pred = model.predict(td_X)
wd_vd_pred = model.predict(vd_X)
""" TEST Tagger """
td_wd_predictions_by_code = get_by_code_from_powerset_predictions(
wd_td_pred, wd_test_tags)
vd_wd_predictions_by_code = get_by_code_from_powerset_predictions(
wd_vd_pred, wd_test_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_by_code, wd_vd_ys_by_code
def train_tagger(essays_TD, essays_VD, wd_test_tags, wd_train_tags):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(td_feats), feature_transformer.transform(vd_feats)
return td_X.shape, vd_X.shape
def train_tagger(essays_TD, essays_VD, wd_test_tags, wd_train_tags):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ,
sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(
td_feats), feature_transformer.transform(vd_feats)
return td_X.shape, vd_X.shape
def train_crel_models(self, examples):
feat_vectorizer = FeatureVectorizer(
min_feature_freq=self.min_feature_freq, sparse=self.sparse)
model = self.crel_learner_fact()
xs = feat_vectorizer.fit_transform(examples.xs)
ys = examples.get_labels()
# There are no weights here as this is a simple binary classification problem
model.fit(xs, ys)
self.crel_models.append(model)
self.crel_feat_vectorizers.append(feat_vectorizer)
def train_crel_models(self, examples):
feat_vectorizer = FeatureVectorizer(min_feature_freq=self.min_feature_freq,
sparse=self.sparse)
model = self.crel_learner_fact()
xs = feat_vectorizer.fit_transform(examples.xs)
ys = examples.get_labels()
# There are no weights here as this is a simple binary classification problem
model.fit(xs, ys)
self.crel_models.append(model)
self.crel_feat_vectorizers.append(feat_vectorizer)
def train_tagger(fold, essays_TD, essays_VD, wd_test_tags, wd_train_tags, dual,
C, penalty, fit_intercept, multi_class):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ,
sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(
td_feats), feature_transformer.transform(vd_feats)
wd_td_ys = get_wordlevel_powerset_ys(td_tags, wd_train_tags)
wd_vd_ys = get_wordlevel_powerset_ys(vd_tags, wd_train_tags)
wd_td_ys_by_code = get_by_code_from_powerset_predictions(
wd_td_ys, wd_test_tags)
wd_vd_ys_by_code = get_by_code_from_powerset_predictions(
wd_vd_ys, wd_test_tags)
""" TRAIN Tagger """
solver = 'liblinear'
if multi_class == 'multinomial':
solver = "lbfgs"
model = LogisticRegression(dual=dual,
C=C,
penalty=penalty,
fit_intercept=fit_intercept,
multi_class=multi_class,
solver=solver)
if fold == 0:
print(model)
model.fit(td_X, wd_td_ys)
wd_td_pred = model.predict(td_X)
wd_vd_pred = model.predict(vd_X)
""" TEST Tagger """
td_wd_predictions_by_code = get_by_code_from_powerset_predictions(
wd_td_pred, wd_test_tags)
vd_wd_predictions_by_code = get_by_code_from_powerset_predictions(
wd_vd_pred, wd_test_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_by_code, wd_vd_ys_by_code
def train_tagger(essays_TD, essays_VD, wd_test_tags, wd_train_tags):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(td_feats), feature_transformer.transform(vd_feats)
wd_td_ys_bytag = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
wd_vd_ys_bytag = get_wordlevel_ys_by_code(vd_tags, wd_train_tags)
""" TRAIN Tagger """
tag2word_classifier = train_classifier_per_code(td_X, wd_td_ys_bytag, lambda: LogisticRegression(),
wd_train_tags, verbose=False)
""" TEST Tagger """
td_wd_predictions_by_code = test_classifier_per_code(td_X, tag2word_classifier, wd_test_tags)
vd_wd_predictions_by_code = test_classifier_per_code(vd_X, tag2word_classifier, wd_test_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_bytag, wd_vd_ys_bytag
def train_tagger(fold, essays_TD, essays_VD, wd_test_tags, wd_train_tags):
wd_train_tags = set(wd_train_tags)
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
print "\nFold %s" % fold
print "Training Tagging Model"
_, lst_every_tag = flatten_to_wordlevel_feat_tags(essay_feats)
tag_freq = Counter(flatten(lst_every_tag))
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(td_feats), feature_transformer.transform(vd_feats)
#TODO: compute most common tags per word for training only (but not for evaluation)
wd_td_ys = get_wordlevel_mostfrequent_ys(td_tags, wd_train_tags, tag_freq)
# Get Actual Ys by code (dict of label to predictions
wd_td_ys_by_code = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
wd_vd_ys_by_code = get_wordlevel_ys_by_code(vd_tags, wd_train_tags)
#TODO: get most common tags for each word, predict from that using multi class method
""" TRAIN Tagger """
model = fn_create_wd_cls()
model.fit(td_X, wd_td_ys)
wd_td_pred = model.predict(td_X)
wd_vd_pred = model.predict(vd_X)
""" TEST Tagger """
td_wd_predictions_by_code = get_by_code_from_powerset_predictions(wd_td_pred, wd_test_tags)
vd_wd_predictions_by_code = get_by_code_from_powerset_predictions(wd_vd_pred, wd_test_tags)
return td_wd_predictions_by_code, vd_wd_predictions_by_code, wd_td_ys_by_code, wd_vd_ys_by_code
def train_parse_models(self, examples):
models = {}
feat_vectorizer = FeatureVectorizer(min_feature_freq=self.min_feature_freq, sparse=self.sparse)
xs = feat_vectorizer.fit_transform(examples.xs)
for action in PARSE_ACTIONS:
ys = [1 if i > 0 else 0 for i in examples.get_labels_for(action)]
weights = examples.get_weights_for(action)
# filter out zero cost actions
# triples = zip(xs, ys, weights)
# triple_no_zeros = [(x,y,c) for (x,y,c) in triples if c > 0.0]
# tmp_xs, ys, weights = zip(*triple_no_zeros)
# # need to re-constitute the matrix
# xs = scipy.sparse.vstack(tmp_xs)
mdl = self.base_learner_fact()
mdl.fit(xs, ys, sample_weight=weights)
models[action] = mdl
self.parser_models.append(models)
self.parser_feature_vectorizers.append(feat_vectorizer)
class SearnModelTemplateFeaturesRegression(SearnModelTemplateFeatures):
def __init__(self,
ngram_extractor,
feature_extractor,
cost_function,
min_feature_freq,
cr_tags,
base_learner_fact,
crel_learner_fact,
beta=0.2,
positive_val=1,
sparse=True,
log_fn=lambda s: print(s)):
super(SearnModelTemplateFeaturesRegression,
self).__init__(ngram_extractor=ngram_extractor,
feature_extractor=feature_extractor,
cost_function=cost_function,
min_feature_freq=min_feature_freq,
cr_tags=cr_tags,
base_learner_fact=base_learner_fact,
beta=beta,
positive_val=positive_val,
sparse=sparse,
log_fn=log_fn)
self.crel_learner_fact = crel_learner_fact
def train_parse_models(self, examples):
models = {}
self.current_parser_feat_vectorizer = FeatureVectorizer(
min_feature_freq=self.min_feature_freq, sparse=self.sparse)
xs = self.current_parser_feat_vectorizer.fit_transform(examples.xs)
for action in PARSE_ACTIONS:
# positive examples have negative cost, negative examples have positive cost
lbls = [
-1 if i > 0 else 1 for i in examples.get_labels_for(action)
] # type: List[int]
costs = examples.get_weights_for(action) # type: List[float]
# Ensure the costs is > 0 so that the lost cost examples provide some more info
#ys = [lbl * max(0.1, cost) for (lbl,cost) in zip(lbls, costs)]
ys = [lbl * cost for (lbl, cost) in zip(lbls, costs)]
mdl = self.base_learner_fact()
mdl.fit(xs, ys)
models[action] = mdl
self.current_parser_models = models
self.parser_models.append(models)
def predict_parse_action(self, feats, tos):
xs = self.current_parser_feat_vectorizer.transform(feats)
pred_by_label = {}
for action in self.randomize_actions():
if not allowed_action(action, tos):
continue
pred_by_label[action] = self.current_parser_models[action].predict(
xs)[0]
# Get label with the lowest cost
min_act, min_val = min(pred_by_label.items(), key=lambda tpl: tpl[1])
return min_act
cv_sent_vd_ys_by_tag, cv_sent_vd_predictions_by_tag = defaultdict(list), defaultdict(list)
folds = cross_validation(essay_feats, CV_FOLDS)
#TODO Parallelize
for i,(essays_TD, essays_VD) in enumerate(folds):
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
print "\nFold %s" % i
print "Training Tagging Model"
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
vd_feats, vd_tags = flatten_to_wordlevel_feat_tags(essays_VD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X, vd_X = feature_transformer.fit_transform(td_feats), feature_transformer.transform(vd_feats)
wd_td_ys_bytag = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
wd_vd_ys_bytag = get_wordlevel_ys_by_code(vd_tags, wd_train_tags)
""" TRAIN Tagger """
tag2word_classifier = train_classifier_per_code(td_X, wd_td_ys_bytag, fn_create_wd_cls, wd_train_tags)
""" TEST Tagger """
td_wd_predictions_by_code = test_classifier_per_code(td_X, tag2word_classifier, wd_test_tags)
vd_wd_predictions_by_code = test_classifier_per_code(vd_X, tag2word_classifier, wd_test_tags)
print "\nTraining Sentence Model"
""" SENTENCE LEVEL PREDICTIONS FROM STACKING """
sent_td_xs, sent_td_ys_bycode = get_sent_feature_for_stacking_from_tagging_model(sent_input_feat_tags, sent_input_interaction_tags, essays_TD, td_X, wd_td_ys_bytag, tag2word_classifier, SPARSE_SENT_FEATS, LOOK_BACK)
sent_vd_xs, sent_vd_ys_bycode = get_sent_feature_for_stacking_from_tagging_model(sent_input_feat_tags, sent_input_interaction_tags, essays_VD, vd_X, wd_vd_ys_bytag, tag2word_classifier, SPARSE_SENT_FEATS, LOOK_BACK)
class SearnModelTemplateFeaturesMultinomialLogisticRegression(SearnModelTemplateFeatures):
def __init__(self, ngram_extractor, feature_extractor, cost_function, min_feature_freq, cr_tags,
base_learner_fact, crel_learner_fact,
beta=0.2, positive_val=1, sparse=True, log_fn=lambda s: print(s)):
super(SearnModelTemplateFeaturesMultinomialLogisticRegression, self).__init__(ngram_extractor=ngram_extractor,
feature_extractor=feature_extractor,
cost_function=cost_function,
min_feature_freq=min_feature_freq,
cr_tags=cr_tags,
base_learner_fact=base_learner_fact,
beta=beta,
positive_val=positive_val,
sparse=sparse,
log_fn=log_fn)
self.crel_learner_fact = crel_learner_fact
def train_parse_models(self, examples):
self.current_parser_feat_vectorizer = FeatureVectorizer(min_feature_freq=self.min_feature_freq,
sparse=self.sparse)
xs = self.current_parser_feat_vectorizer.fit_transform(examples.xs)
ys = examples.get_labels()
weights = []
for ix in range(xs.shape[0]):
costs_by_action = {}
gold_action = ys[ix]
gold_action_wt = 0
for action in PARSE_ACTIONS:
cost = examples.get_weights_for(action)[ix]
if action == gold_action:
gold_action_wt = cost
else:
costs_by_action[action] = cost
worse_action, worse_cost = max(costs_by_action.items(), key= lambda tpl: tpl[1])
assert gold_action_wt >=0 and worse_cost >= 0, "Costs should be non negative"
# Weight of example is the difference between the best action and the worse action
# as both are positive, we simply add them up
weight = gold_action_wt + worse_cost
weights.append(weight)
mdl = self.base_learner_fact()
mdl.fit(xs, ys, sample_weight=weights)
#cost = examples.get_weights_for(action)[ix]
self.current_parser_models = mdl
self.parser_models.append(mdl)
def predict_parse_action(self, feats, tos):
model = self.current_parser_models
xs = self.current_parser_feat_vectorizer.transform(feats)
# get first row, as just looking at one data point
ys_probs = model.predict_proba(xs)[0]
prob_by_label = {}
for action, prob in zip(model.classes_, ys_probs):
if not allowed_action(action, tos):
continue
prob_by_label[action] = prob
items = list(prob_by_label.items())
# randomize order so that max returns different items in the case of a tie
np.random.shuffle(items)
max_act, max_prob = max(items, key=lambda tpl: tpl[1])
return max_act
# NOTE - GBT is stochastic in the SPLITS, and so you will get non-deterministic results
#fn_create_sent_cls = lambda : GradientBoostingClassifier() #F1 = 0.5312 on numeric + 5b + casual codes for sentences
if type(fn_create_sent_cls()) == GradientBoostingClassifier:
SPARSE_SENT_FEATS = False
essays_TD = essay_feats
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
print "Training Tagging Model"
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X = feature_transformer.fit_transform(td_feats)
wd_td_ys_bytag = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
""" TRAIN Tagger """
tag2word_classifier = train_classifier_per_code(td_X, wd_td_ys_bytag, fn_create_wd_cls, wd_train_tags)
print "\nTraining Sentence Model"
""" SENTENCE LEVEL PREDICTIONS FROM STACKING """
sent_td_xs, sent_td_ys_bycode = get_sent_feature_for_stacking_from_tagging_model(sent_input_feat_tags, sent_input_interaction_tags, essays_TD, td_X, wd_td_ys_bytag, tag2word_classifier, SPARSE_SENT_FEATS, LOOK_BACK)
""" Train Stacked Classifier """
tag2sent_classifier = train_classifier_per_code(sent_td_xs, sent_td_ys_bycode , fn_create_sent_cls, sent_output_train_test_tags)
""" Persist Models """
model_store.store(feature_transformer, tag2word_classifier, tag2sent_classifier)
# NOTE - GBT is stochastic in the SPLITS, and so you will get non-deterministic results
if type(fn_create_sent_cls()) == GradientBoostingClassifier:
SPARSE_SENT_FEATS = False
#TODO Parallelize
essays_TD = essay_feats
# TD and VD are lists of Essay objects. The sentences are lists
# of featureextractortransformer.Word objects
print("Training Tagging Model")
""" Data Partitioning and Training """
td_feats, td_tags = flatten_to_wordlevel_feat_tags(essays_TD)
feature_transformer = FeatureVectorizer(min_feature_freq=MIN_FEAT_FREQ, sparse=SPARSE_WD_FEATS)
td_X = feature_transformer.fit_transform(td_feats)
wd_td_ys_bytag = get_wordlevel_ys_by_code(td_tags, wd_train_tags)
""" TRAIN Tagger """
tag2word_classifier = train_classifier_per_code(td_X, wd_td_ys_bytag, fn_create_wd_cls, wd_train_tags)
print("\nTraining Sentence Model")
""" SENTENCE LEVEL PREDICTIONS FROM STACKING """
sent_td_xs, sent_td_ys_bycode = get_sent_feature_for_stacking_from_tagging_model(sent_input_feat_tags, sent_input_interaction_tags, essays_TD, td_X, wd_td_ys_bytag, tag2word_classifier, SPARSE_SENT_FEATS, LOOK_BACK)
""" Train Stacked Classifier """
tag2sent_classifier = train_classifier_per_code(sent_td_xs, sent_td_ys_bycode , fn_create_sent_cls, sent_output_train_test_tags)
""" Persist Models """
model_store.store(feature_transformer, tag2word_classifier, tag2sent_classifier)
class SearnModelTemplateFeaturesMultinomialLogisticRegression(
SearnModelTemplateFeatures):
def __init__(self,
ngram_extractor,
feature_extractor,
cost_function,
min_feature_freq,
cr_tags,
base_learner_fact,
crel_learner_fact,
beta=0.2,
positive_val=1,
sparse=True,
log_fn=lambda s: print(s)):
super(SearnModelTemplateFeaturesMultinomialLogisticRegression,
self).__init__(ngram_extractor=ngram_extractor,
feature_extractor=feature_extractor,
cost_function=cost_function,
min_feature_freq=min_feature_freq,
cr_tags=cr_tags,
base_learner_fact=base_learner_fact,
beta=beta,
positive_val=positive_val,
sparse=sparse,
log_fn=log_fn)
self.crel_learner_fact = crel_learner_fact
def train_parse_models(self, examples):
self.current_parser_feat_vectorizer = FeatureVectorizer(
min_feature_freq=self.min_feature_freq, sparse=self.sparse)
xs = self.current_parser_feat_vectorizer.fit_transform(examples.xs)
ys = examples.get_labels()
weights = []
for ix in range(xs.shape[0]):
costs_by_action = {}
gold_action = ys[ix]
gold_action_wt = 0
for action in PARSE_ACTIONS:
cost = examples.get_weights_for(action)[ix]
if action == gold_action:
gold_action_wt = cost
else:
costs_by_action[action] = cost
worse_action, worse_cost = max(costs_by_action.items(),
key=lambda tpl: tpl[1])
assert gold_action_wt >= 0 and worse_cost >= 0, "Costs should be non negative"
# Weight of example is the difference between the best action and the worse action
# as both are positive, we simply add them up
weight = gold_action_wt + worse_cost
weights.append(weight)
mdl = self.base_learner_fact()
mdl.fit(xs, ys, sample_weight=weights)
#cost = examples.get_weights_for(action)[ix]
self.current_parser_models = mdl
self.parser_models.append(mdl)
def predict_parse_action(self, feats, tos):
model = self.current_parser_models
xs = self.current_parser_feat_vectorizer.transform(feats)
# get first row, as just looking at one data point
ys_probs = model.predict_proba(xs)[0]
prob_by_label = {}
for action, prob in zip(model.classes_, ys_probs):
if not allowed_action(action, tos):
continue
prob_by_label[action] = prob
items = list(prob_by_label.items())
# randomize order so that max returns different items in the case of a tie
np.random.shuffle(items)
max_act, max_prob = max(items, key=lambda tpl: tpl[1])
return max_act