def optimize_model(dataset,
features,
model_cfg=None,
scorer=None,
n_samples=None,
max_evals=10):
if model_cfg is None:
model_cfg = {}
if scorer is None:
def scorer(**kwargs):
return 0.0
if n_samples is None:
n_samples = len(dataset[0][0])
n_samples = 6000 if n_samples >= 6000 else n_samples
ds_text, ds_labels = resample(dataset[0][0],
dataset[0][1],
n_samples=n_samples,
random_state=0)
text_train, text_test, y_train, y_test = train_test_split(ds_text,
ds_labels,
test_size=0.4)
feature_extractor = make_fvector_pipeline(dataset, features)
X_train = feature_extractor.fit_transform(text_train, y_train)
X_test = feature_extractor.transform(text_test)
clf = SVC
opt = HyperoptOptimizer(clf, model_cfg, scorer,
max_evals).optimize(X_train, X_test, y_train,
y_test)
return opt
def build_model(dataset, features, model, **kwargs):
feature_extractor = make_fvector_pipeline(dataset, features)
model_params = {}
if 'C' in model:
model_params['C'] = model['C']
clf = SVC(**model_params)
return make_pipeline(feature_extractor, clf).fit(dataset[0][0],
dataset[0][1])
def build_model(dataset, features=None, model=None, **kwargs):
if features is None:
features = []
if model is None:
model = {}
embedding_extractor = make_fvector_pipeline(dataset, features)
clf = xgb_clf(**model)
return make_pipeline(embedding_extractor, clf).fit(dataset[0][0],
dataset[0][1])
def build_model(dataset, features=None, model=None, **kwargs):
if features is None:
features = []
if model is None:
model = {}
feature_extractor = make_fvector_pipeline(dataset, features)
clf = FNNClassifier(**model)
if 'early_stopping' in model:
clf.early_stopping(**model)
if 'validation_split' in model:
clf.validation_split(**model)
pipe = make_pipeline(feature_extractor, clf).fit(dataset[0][0], dataset[0][1])
dash_line = '\n' + '-' * 50 + '\n'
print('{}Training Completed.\nFeature Size: {}{}'.format(dash_line, clf.num_features_, dash_line))
return pipe
def optimize_model(dataset, features=None, model_cfg=None, scorer=None, n_samples=None, max_evals=15):
if features is None:
features = []
if model_cfg is None:
model_cfg = {}
if scorer is None:
def scorer(**kwargs):
return 0.0
if n_samples is None:
text_train, text_test, y_train, y_test = dataset[0][0], dataset[1][0], dataset[0][1], dataset[1][1]
else:
ds_text, ds_labels = resample(dataset[0][0], dataset[0][1], n_samples=n_samples, random_state=0)
text_train, text_test, y_train, y_test = train_test_split(ds_text, ds_labels, test_size=0.4)
embedding_extractor = make_fvector_pipeline(dataset, features)
X_train = embedding_extractor.fit_transform(text_train, y_train)
X_test = embedding_extractor.transform(text_test)
opt = HyperoptOptimizer(FNNClassifier, model_cfg, scorer, max_evals, callbacks=[print_info]).optimize(X_train,
X_test,
y_train,
y_test)
return opt