def optimize_syntactic_feature_sets2():
# We'll write results for this hyperparameter optimization here:
out_path = os.path.join(HYPERPARAMETER_TUNING_DIR,
'optimize_syntactic_feature_sets2.tsv')
# Read in the training set splits and the features
train, test = utils.get_train_test_split()
features_path = os.path.join(DATA_DIR,
'relational-noun-features-lexical-wordnet',
'0ba')
features = extract_features.FeatureAccumulator(
vocabulary=utils.read_wordnet_index(), load=features_path)
# Define the ranges over which parameters should be varied
parameter_ranges = {
'syntax_feature_types': [
#[],
#['baseline'],
#['dependency'],
#['hand_picked'],
['pos_unigram'],
['pos_unigram', 'pos_bigram'],
['lemma_unigram'],
['lemma_unigram', 'lemma_bigram'],
['surface_unigram', 'surface_bigram'],
#['dependency', 'hand_picked'],
#['baseline', 'hand_picked'],
#['baseline', 'dependency'],
#['baseline', 'dependency', 'hand_picked'],
]
}
# Define the values of parameters to be held constant
constants = {
'kind': 'svm',
'on_unk': False,
'C': 0.01,
'semantic_similarity': 'res',
'include_suffix': True,
'syntactic_multiplier': 10.0,
'semantic_multiplier': 2.0,
'suffix_multiplier': 0.2
}
# Generate all combinations of variable parameters, while including
# constant paramteres.
classifier_definitions = test_classifier.generate_classifier_definitions(
parameter_ranges, constants)
# Evaluate the classifier when running for all classifier definitions
test_classifier.optimize_classifier(classifier_definitions,
features,
train['pos'],
train['neg'],
test['pos'],
test['neg'],
out_path,
num_procs=1)
def optimize_pruning2():
# We'll write results for this hyperparameter optimization here:
out_path = os.path.join(HYPERPARAMETER_TUNING_DIR, 'optimize_pruning2.tsv')
# Read in the training set splits and the features
train, test = utils.get_train_test_split()
features = extract_features.FeatureAccumulator(load=os.path.join(
DATA_DIR, 'relational-noun-features-wordnet-only', 'accumulated'))
# Define the ranges over which parameters should be varied
parameter_ranges = {
'min_feature_frequency': [
200,
500,
1000,
2000,
5000,
10000,
#20000, 50000, 100000, 200000, 500000, 1000000,
]
}
# Define the values of parameters to be held constant
constants = {
'kind': 'svm',
'on_unk': False,
'C': 0.01,
'syntax_feature_types': ['baseline', 'dependency', 'hand_picked'],
'semantic_similarity': 'res',
'include_suffix': True,
'syntactic_multiplier': 0.33,
'semantic_multiplier': 0.33,
'suffix_multiplier': 0.33,
}
# Generate all combinations of variable parameters, while including
# constant paramteres.
classifier_definitions = test_classifier.generate_classifier_definitions(
parameter_ranges, constants)
# Evaluate the classifier when running for all classifier definitions
test_classifier.optimize_classifier(classifier_definitions,
features,
train['pos'],
train['neg'],
test['pos'],
test['neg'],
out_path,
num_procs=12)