def grid_search(
embeddings,
train_X,
train_y,
cv_X,
cv_y,
grid_params,
):
results = list()
err = False
total_p = len(grid_params)
for i, params in enumerate(grid_params):
print("Running [%d/%d]:\n%s" % (i + 1, total_p, str(params)))
m = build_keras_embedding_classifier(embeddings, **params)
cb = [
EarlyStopping(patience=17),
TerminateOnNaN(),
ReduceLROnPlateau(verbose=1)
]
m.fit(train_X,
train_y,
epochs=100,
batch_size=128,
validation_data=(cv_X, cv_y),
callbacks=cb)
test_preds = m.predict(cv_X).round().astype(int)
test_actual = Y_test
pred = m.predict(np.concatenate([sequences,
test_sequences])).round().astype(int)
actual = np.concatenate([Y, Y_test])
try:
perf_metrics = binary_classification_metrics(actual, pred)
test_metrics = binary_classification_metrics(
test_actual, test_preds)
test_metrics = {"test_%s" % k: v for k, v in test_metrics.items()}
print("HOLDOUT PERFORMANCE: %s" % str(test_metrics))
except ValueError as ve:
print("VALUE ERROR")
perf_metrics = dict()
err = True
m_id = str(uuid.uuid4())[:7]
m.save('./saved_models/model_%s.keras' % m_id)
results.append(
dict(error=err,
model_id=m_id,
**params,
**perf_metrics,
**test_metrics))
# embedding distance - embed from different sources with same seed of important words, say from Glove. then tune all others around thos
# can then compare them.
return results
def tune_parameters(target_label, window_sizes, embed_dims, model=None):
if model is None:
model = GradientBoostingClassifier(n_estimators=400, verbose=1)
results = list()
for ws in window_sizes:
for ed in embed_dims:
print("Window size: %d" % ws)
model_df, count_vec_model, model_features = create_model_ready(
ixes=range(41),
window_size=ws,
target_type_label=target_label,
neg_include_proba=.25,
embed_dim=ed)
cv_df, cv_vec_model, cv_features = create_model_ready(
ixes=range(41, 53),
window_size=ws,
target_type_label=target_label,
count_vec_model=count_vec_model,
neg_include_proba=1.,
embed_dim=ed)
fm = model.fit(model_df[model_features], model_df.is_target)
fm_preds = fm.predict_proba(cv_df[cv_features])
cv_metrics = binary_classification_metrics(cv_df.is_target,
fm_preds[:, 1] > 0.5)
cv_metrics['window_size'] = ws
print(classification_report(cv_df.is_target, fm_preds[:, 1] > 0.5))
print(cv_metrics)
results.append(cv_metrics)
return results
def compare_auto_label():
file_ixs = list(range(39))
cvec_X, Y, cvec_X_test, Y_test = load_train_and_test_bow(
train_ixes=file_ixs[:23], test_ixes=file_ixs[23:31])
preds = GradientBoostingClassifier().fit(cvec_X, Y).predict(cvec_X_test)
#preds = DecisionTreeClassifier().fit(cvec_X, Y).predict(cvec_X_test)
print("Labeled Only")
metrics = utils.binary_classification_metrics(Y_test, preds)
print(metrics)
auto_label_p = 'top_10_auto_labeled_from_brown_external_att.txt'
cvec_X, Y, cvec_X_test, Y_test = load_train_and_test_bow(
train_ixes=file_ixs[:23],
test_ixes=file_ixs[23:31],
include_auto_labeled=auto_label_p)
preds = GradientBoostingClassifier().fit(cvec_X, Y).predict(cvec_X_test)
#preds = DecisionTreeClassifier().fit(cvec_X, Y).predict(cvec_X_test)
print("AUTO LABELED")
metrics = utils.binary_classification_metrics(Y_test, preds)
print(metrics)
def eval_on_holdout(target_label,
window_size,
embed_dim,
embed_type,
model=None,
eval_holdout=False):
if model is None:
model = GradientBoostingClassifier(n_estimators=400, verbose=1)
print("Window size: %d" % window_size)
model_df, count_vec_model, model_features = create_model_ready(
ixes=range(41),
window_size=window_size,
target_type_label=target_label,
neg_include_proba=.35,
embed_dim=embed_dim,
embed_type=embed_type)
cv_df, cv_vec_model, cv_features = create_model_ready(
ixes=range(41, 53),
window_size=window_size,
target_type_label=target_label,
count_vec_model=count_vec_model,
neg_include_proba=1.,
embed_dim=embed_dim,
embed_type=embed_type)
holdout_df, holdout_vec_model, holdout_features = create_model_ready(
ixes=range(53, 65),
window_size=window_size,
target_type_label=target_label,
count_vec_model=count_vec_model,
neg_include_proba=1.,
embed_dim=embed_dim,
embed_type=embed_type)
if eval_holdout:
train_df = pd.concat([model_df, cv_df]).sample(frac=1.)
test_df = holdout_df
else:
train_df = model_df
test_df = cv_df
fm = model.fit(train_df[model_features], train_df.is_target)
fm_preds = fm.predict_proba(test_df[model_features])
metrics = binary_classification_metrics(test_df.is_target,
fm_preds[:, 1] > 0.5)
metrics['window_size'] = window_size
metrics['embed_dim'] = embed_dim
metrics['embed_type'] = embed_type
metrics['model'] = str(model.__class__.__name__)
pprint(metrics)
return metrics
def evaluate_models_on_holdout(models_to_test, top_n_to_inc=None):
ixes = list(range(39))
train_X, train_Y, test_X, test_Y = load_train_and_test_bow(
train_ixes=ixes[:31], test_ixes=ixes[31:], top_n_to_inc=top_n_to_inc)
metrics = dict()
for m_name, m in models_to_test:
print("Running %s" % str(m_name))
fit_m = m.fit(train_X, train_Y)
preds = fit_m.predict(test_X)
metrics[m_name] = utils.binary_classification_metrics(
test_Y, preds.round())
return metrics
ret = load_data(embedding_dim=args.embed_dim, return_holdout=True)
embeddings, sequences, Y, test_sequences, Y_test, holdout_sequences, Y_holdout = ret
if args.model_load is not None:
print("Keras model loading broken!")
sys.exit(1)
print("Loading model at %s" % str(args.model_load))
m = keras.models.load_model(args.model_load)
print("Running predictions on datasets")
train_pred = m.predict(sequences)
dev_pred = m.predict(test_sequences)
holdout_pred = m.predict(holdout_sequences)
train_metrics = binary_classification_metrics(Y, train_pred.round())
dev_metrics = binary_classification_metrics(Y_test, dev_pred.round())
holdout_metrics = binary_classification_metrics(
Y_holdout, holdout_pred.round())
print("Train: %s" % str(train_metrics))
print("Dev: %s" % str(dev_metrics))
print("Holdout: %s" % str(holdout_metrics))
elif args.grid_search:
grid_params = build_params_from_grid(
activations=['tanh'],
hidden_size=[15, 20, 25],
depth=[4, 5, 6], #range(1, 3),
lr=[0.0000002],
dropout=[.5],
def run_gridsearch(model_type='dt', n_jobs=2, resample_n=1500, top_n_to_inc=0):
# Recurse into this if given a list of model types
if isinstance(model_type, list):
return {
mt: run_gridsearch(mt,
n_jobs=n_jobs,
resample_n=resample_n,
top_n_to_inc=top_n_to_inc)
for mt in model_type
}
file_ixs = list(range(65))
top_n = top_n_to_inc if top_n_to_inc != 0 else None
#auto_label_p = 'top_10_auto_labeled_from_brown_external_att.txt'
cvec_X, Y, cvec_X_test, Y_test = load_train_and_test_bow(
train_ixes=file_ixs[:41],
test_ixes=file_ixs[41:53],
top_n_to_inc=top_n,
resample=resample_n)
print("train X shape: %s" % str(cvec_X.shape))
cv_kwargs = dict(n_jobs=n_jobs, X=cvec_X, Y=Y)
#Best Params: {'criterion': 'entropy', 'max_depth': None,
# 'max_leaf_nodes': 12, 'min_samples_leaf': 2, 'min_samples_split': 2}
#---
#Best Params: {'criterion': 'entropy', 'max_depth': None,
# 'max_leaf_nodes': 12, 'min_samples_leaf': 2, 'min_samples_split': 2}
#---
#Best Params: {'criterion': 'gini', 'max_depth': 25,
# 'max_leaf_nodes': None, 'min_samples_leaf': 3, 'min_samples_split': 4}
if model_type == 'dt':
cv_m = grid_search(DecisionTreeClassifier(),
param_grid=dict(
criterion=['gini', 'entropy'],
max_depth=[None] + list(range(24, 30, 3)),
max_leaf_nodes=[None] + list(range(13, 20, 2)),
min_samples_leaf=list(range(2, 5, 2)),
min_samples_split=list(range(2, 5, 2))),
**cv_kwargs)
elif model_type == 'gb':
cv_m = grid_search(
GradientBoostingClassifier(),
param_grid=dict(
max_depth=list(range(3, 7, 1)),
max_leaf_nodes=[None], #+ list(range(9, 22, 2)),
min_samples_leaf=list(range(2, 4, 1)),
min_samples_split=list(range(2, 5, 2)),
learning_rate=np.arange(0.7, 1.5, 0.33),
n_estimators=range(100, 251, 50)),
**cv_kwargs)
elif model_type == 'rf':
cv_m = grid_search(RandomForestClassifier(),
param_grid=dict(max_depth=range(2, 35, 4),
min_samples_split=range(2, 45, 4),
n_estimators=range(25, 226, 25)),
**cv_kwargs)
elif model_type == 'nb':
cv_m = grid_search(
MultinomialNB(),
param_grid=dict(alpha=[10**a for a in range(-3, 4, 1)]),
#[.5, 1.5, 3.5, 9, 17, 25]),#np.arange(.1, 3.5, .35)),
**cv_kwargs)
else:
raise ValueError("No model type %s" % model_type)
pred_Y = cv_m.predict(cvec_X)
metrics = utils.binary_classification_metrics(Y, pred_Y)
print("Best Params: %s" % str(cv_m.best_params_))
print("Train + CV Overall Performance:")
print("%s: %s" % (model_type, str(metrics)))
pred_Y_test = cv_m.predict(cvec_X_test)
test_metrics = utils.binary_classification_metrics(Y_test, pred_Y_test)
print("Hold out performance:")
print("%s: %s" % (model_type, str(test_metrics)))
test_metrics = {"test_%s" % k: v for k, v in test_metrics.items()}
metrics.update(test_metrics)
metrics['best_params'] = dict(cv_m.best_params_)
return metrics