def bayesian_optimize(clf,
param_grid,
X_train,
X_test,
y_train,
y_test,
beta=0.2,
threshold=0.1,
n_iter=20,
verbose=0,
n_jobs=1):
"""
Wrapper for Bayesian Optimization for hyperparameter tuning.
Arguments:
clf -- input classifier. May be of sklearn class.
param_grid -- input parameter grid, in a dictionary format.
X_train, X_test, y_train, y_test -- X and y data.
beta -- float, beta < 1 favors more on NPV, while beta > 1 more on specificity
threshold -- float, Threshold for classifying positive vs. negative classes.
n_iter -- int, number of iterations to carry out Bayesian Optimization.
verbose -- int, verbosity controller.
n_jobs -- int, number of processes/threads being used.
Returns:
opt_rf -- The optimized classifier.
"""
# RandomForest + Bayesian optimization
opt_rf = BayesSearchCV(clf,
param_grid,
n_iter=n_iter,
verbose=verbose,
scoring=make_scorer(adjusted_neg_Fbeta_score,
needs_proba=True,
beta=beta,
threshold=threshold),
n_jobs=n_jobs)
# callback handler
def on_step(optim_result):
score = opt_rf.best_score_
print("\t- best score: %s" % score)
if score >= 0.99:
print('* Interrupting...')
return True
opt_rf.fit(X_train, y_train.ravel(), callback=[on_step])
y_prob = np.asarray(opt_rf.predict_proba(X_test))
y_test = np.asarray(y_test)
summarize_res(opt_rf, y_prob, y_test)
return opt_rf
def run_shallow(data_dir: str, results_dir: str, splits: List[str],
metric: str, n_iter: int, n_points: int, n_folds: int,
n_jobs: int) -> None:
"""Evaluate shallow baselines on the scruples resource.
Train shallow baseline models on the scruples resource, reading
the dataset from DATA_DIR, and writing trained models, logs, and
other results to RESULTS_DIR. Performance is reported for each split
provided as an argument.
"""
# Step 1: Manage and construct paths.
logger.info('Creating the results directory.')
os.makedirs(results_dir)
model_paths = {}
metrics_paths = collections.defaultdict(dict)
predictions_paths = collections.defaultdict(dict)
for baseline in baselines.resource.SHALLOW_BASELINES.keys():
os.makedirs(os.path.join(results_dir, baseline))
model_paths[baseline] = os.path.join(results_dir, baseline,
'model.pkl')
for split in splits:
os.makedirs(os.path.join(results_dir, baseline, split))
metrics_paths[baseline][split] = os.path.join(
results_dir, baseline, split, 'metrics.json')
predictions_paths[baseline][split] = os.path.join(
results_dir, baseline, split, 'predictions.jsonl')
# Step 2: Load the data.
logger.info(f'Loading the data from {data_dir}.')
dataset = ScruplesResource(data_dir=data_dir)
# Step 3: Run the baselines.
logger.info('Running the baselines.')
for baseline, (Model, hyper_parameter_space) in tqdm.tqdm(
baselines.resource.SHALLOW_BASELINES.items(),
**settings.TQDM_KWARGS):
# tune the hyper-parameters and train the model
ids, features, labels, label_scores = dataset.train
if hyper_parameter_space:
model = BayesSearchCV(
Model,
hyper_parameter_space,
scoring=make_scorer(score_func=METRICS[metric][1],
**METRICS[metric][2]),
n_iter=n_iter,
n_points=n_points,
cv=n_folds,
n_jobs=os.cpu_count() if n_jobs == 0 else n_jobs,
refit=True)
else:
model = Model
model.fit(features, labels)
# Step 4: Save the model.
with open(model_paths[baseline], 'wb') as model_file:
dill.dump(model, model_file)
# Step 5: Run evaluation on the splits.
for split in splits:
ids, features, labels, label_scores = getattr(dataset, split)
predictions = model.predict(features)
probabilities = model.predict_proba(features)
with open(metrics_paths[baseline][split], 'w') as metrics_file:
json.dump(
{
key: metric(
y_true=labels,
y_pred=probabilities
if scorer_kwargs['needs_proba'] else predictions)
for key, (_, metric, scorer_kwargs) in METRICS.items()
}, metrics_file)
with open(predictions_paths[baseline][split], 'w')\
as predictions_file:
for id_, probs, prediction in zip(ids, probabilities,
predictions):
predictions_file.write(
json.dumps({
'id': id_,
'label': prediction.tolist(),
'label_scores': probs.tolist()
}) + '\n')
test_df = as_category(test_df)
test_X = test_df.drop(['CONTACT_DATE', 'SNAP_DATE'], axis=1)
if clf_name != 'FeaturePredictor':
cols = list(set(test_X.columns).difference(test_X.select_dtypes(include='category').columns))
test_X.loc[:, cols] = test_X.loc[:, cols].fillna(0).replace([np.inf, -np.inf], 0)
for c in test_X.select_dtypes(include='category').columns:
test_X.loc[:, c] = test_X.loc[:, c].cat.codes
adv_auc = 0
adv_train_x, adv_train_y, adv_test_x, adv_test_y = adversial_train_test_split(train_X.loc[:, features], train_y,
test_X.loc[:, features],
topK=1000)
bayes_cv_tuner._fit_best_model(adv_train_x, adv_train_y)
adv_pred_y = bayes_cv_tuner.predict_proba(adv_test_x)[:, 1]
adv_auc = roc_auc_score(adv_test_y, adv_pred_y)
print(f'Adversial AUC = {adv_auc} by {len(adv_test_y)} samples')
bayes_cv_tuner._fit_best_model(train_X, train_y)
test_y = bayes_cv_tuner.predict_proba(test_X)
df = pd.DataFrame(test_y[:, 1])
df.to_csv(f"submits/"
f"{best_estimator.__class__.__name__}"
f"_{datetime.now().strftime('%d_%H_%M')}"
f"_{bayes_cv_tuner.best_score_:0.4f}"
f"_{adv_auc:0.4f}.csv",
header=None,
index=None)
}
# scorer
metric = make_scorer(score_func=log_loss,
greater_is_better=False,
needs_proba=True,
labels=train['Category'].unique())
# cv
kfold_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# bayessearch cv
bayes_tuned_pipeline = BayesSearchCV(estimator=estimator_pipeline,
search_spaces=search_space,
n_iter=10,
scoring=metric,
cv=kfold_cv,
verbose=12,
n_jobs=-1,
refit=True)
bayes_tuned_pipeline.fit(X_train, y_train)
# Saving model using pickle
pickle.dump(bayes_tuned_pipeline, open('logistic_tuned_pipeline.pkl', 'wb'))
phat_val = bayes_tuned_pipeline.predict_proba(X_val)
log_loss(y_val, phat_val)
make_submission_file(bayes_tuned_pipeline, test, 'onsite_logistic.csv')
###### CatBoost with Tuning
cb_param_grid = {'iterations': Integer(10, 1000),
'depth': Integer(1, 8),
'learning_rate': Real(0.01, 1.0, 'log-uniform'),
'random_strength': Real(1e-9, 10, 'log-uniform'),
'bagging_temperature': Real(0.0, 1.0),
'border_count': Integer(1, 255),
'l2_leaf_reg': Integer(2, 30),
'scale_pos_weight':Real(0.01, 1.0, 'uniform')}
cb_bs = BayesSearchCV(cb, cb_param_grid, scoring = 'roc_auc', n_iter = 100, n_jobs = 1,
return_train_score = False, refit = True, optimizer_kwargs = {'base_estimator': 'GP'},
random_state = 123)
cb_bs.fit(x_train, y_train)
y_probs = cb_bs.predict_proba(x_test)
y_probs = y_probs[:, 1]
y_pred = cb_bs.predict(x_test)
print(classification_report(y_test, y_pred))
print(roc_auc_score(y_test, y_probs)) ### 0.903
fpr, tpr, thresholds = roc_curve(y_test, y_probs)
plot_roc_curve(fpr, tpr)
# Find the best parameters
cb_bs.best_params_
# Use the parameters to re-run the model
cb_tuned = CatBoostClassifier(iterations = 1000, depth = 8,
learning_rate = 0.11574, random_strength = 1e-9,
bagging_temperature = 1.0,
'min_samples_leaf': Integer(2, 6),
'bootstrap': Categorical([False]),
},
n_iter=300,
random_state=42,
cv=5,
n_jobs=6,
verbose=1)
# y_metales.values.reshape(-1,1)
# Fit the random search model
opt.fit(X_train, y_train)
opt.best_params_
opt.best_score_
opt_pred = opt.predict_proba(X_new)[:, 1]
Confusion_Matrix(y_new, opt_pred, pred_prob=True)
Confusion_Matrix(np.array(pd.concat([y_test, y_new], axis=0)),
opt.predict_proba(pd.concat([X_test, X_new], axis=0))[:, 1],
pred_prob=bool)
'''
Best parameters
'''
best_parameters = {
'GridSearchCV': {
'bootstrap': False,
'criterion': 'gini',
'max_depth': 10,
'max_features': 'auto',
