def bace_rf_model(mode="classification", verbosity="high", split="20-80"):
"""Train random forests on BACE dataset."""
(bace_tasks, train_dataset, valid_dataset, test_dataset, crystal_dataset,
transformers) = load_bace(mode=mode, transform=False, split=split)
if mode == "regression":
r2_metric = Metric(metrics.r2_score, verbosity=verbosity)
rms_metric = Metric(metrics.rms_score, verbosity=verbosity)
mae_metric = Metric(metrics.mae_score, verbosity=verbosity)
all_metrics = [r2_metric, rms_metric, mae_metric]
metric = r2_metric
model_class = RandomForestRegressor
elif mode == "classification":
roc_auc_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
accuracy_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
mcc_metric = Metric(metrics.matthews_corrcoef, verbosity=verbosity)
# Note sensitivity = recall
recall_metric = Metric(metrics.recall_score, verbosity=verbosity)
model_class = RandomForestClassifier
all_metrics = [
accuracy_metric, mcc_metric, recall_metric, roc_auc_metric
]
metric = roc_auc_metric
else:
raise ValueError("Invalid mode %s" % mode)
def model_builder(tasks,
task_types,
params_dict,
model_dir,
verbosity=verbosity):
n_estimators = params_dict["n_estimators"]
max_features = params_dict["max_features"]
return SklearnModel(tasks,
task_types,
params_dict,
model_dir,
model_instance=model_class(
n_estimators=n_estimators,
max_features=max_features))
params_dict = {
"n_estimators": [10, 100],
"batch_size": [None],
"data_shape": [train_dataset.get_data_shape()],
"max_features": ["auto", "sqrt", "log2", None],
}
optimizer = HyperparamOpt(model_builder, bace_tasks,
{task: mode
for task in bace_tasks})
best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(
params_dict, train_dataset, valid_dataset, transformers, metric=metric)
if len(train_dataset) > 0:
rf_train_evaluator = Evaluator(best_rf,
train_dataset,
transformers,
verbosity=verbosity)
csv_out = "rf_%s_%s_train.csv" % (mode, split)
stats_out = "rf_%s_%s_train_stats.txt" % (mode, split)
rf_train_score = rf_train_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Train set scores: %s" % (str(rf_train_score)))
if len(valid_dataset) > 0:
rf_valid_evaluator = Evaluator(best_rf,
valid_dataset,
transformers,
verbosity=verbosity)
csv_out = "rf_%s_%s_valid.csv" % (mode, split)
stats_out = "rf_%s_%s_valid_stats.txt" % (mode, split)
rf_valid_score = rf_valid_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Valid set scores: %s" % (str(rf_valid_score)))
if len(test_dataset) > 0:
rf_test_evaluator = Evaluator(best_rf,
test_dataset,
transformers,
verbosity=verbosity)
csv_out = "rf_%s_%s_test.csv" % (mode, split)
stats_out = "rf_%s_%s_test_stats.txt" % (mode, split)
rf_test_score = rf_test_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Test set: %s" % (str(rf_test_score)))
if len(crystal_dataset) > 0:
rf_crystal_evaluator = Evaluator(best_rf, crystal_dataset,
transformers, verbosity)
csv_out = "rf_%s_%s_crystal.csv" % (mode, split)
stats_out = "rf_%s_%s_crystal_stats.txt" % (mode, split)
rf_crystal_score = rf_crystal_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Crystal set: %s" % (str(rf_crystal_score)))
def bace_dnn_model(mode="classification", verbosity="high", split="20-80"):
"""Train fully-connected DNNs on BACE dataset."""
(bace_tasks, train_dataset, valid_dataset, test_dataset, crystal_dataset,
transformers) = load_bace(mode=mode, transform=True, split=split)
if mode == "regression":
r2_metric = Metric(metrics.r2_score, verbosity=verbosity)
rms_metric = Metric(metrics.rms_score, verbosity=verbosity)
mae_metric = Metric(metrics.mae_score, verbosity=verbosity)
all_metrics = [r2_metric, rms_metric, mae_metric]
metric = r2_metric
elif mode == "classification":
roc_auc_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
accuracy_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
mcc_metric = Metric(metrics.matthews_corrcoef, verbosity=verbosity)
# Note sensitivity = recall
recall_metric = Metric(metrics.recall_score, verbosity=verbosity)
all_metrics = [
accuracy_metric, mcc_metric, recall_metric, roc_auc_metric
]
metric = roc_auc_metric
else:
raise ValueError("Invalid mode %s" % mode)
params_dict = {
"activation": ["relu"],
"momentum": [.9],
"batch_size": [50],
"init": ["glorot_uniform"],
"data_shape": [train_dataset.get_data_shape()],
"learning_rate": np.power(10., np.random.uniform(-5, -3, size=5)),
"decay": np.power(10, np.random.uniform(-6, -4, size=5)),
"nb_hidden": [1000],
"nb_epoch": [40],
"nesterov": [False],
"dropout": [.5],
"nb_layers": [1],
"batchnorm": [False],
}
optimizer = HyperparamOpt(SingleTaskDNN,
bace_tasks, {task: mode
for task in bace_tasks},
verbosity=verbosity)
best_dnn, best_hyperparams, all_results = optimizer.hyperparam_search(
params_dict, train_dataset, valid_dataset, transformers, metric=metric)
if len(train_dataset) > 0:
dnn_train_evaluator = Evaluator(best_dnn, train_dataset, transformers)
csv_out = "dnn_%s_%s_train.csv" % (mode, split)
stats_out = "dnn_%s_%s_train_stats.txt" % (mode, split)
dnn_train_score = dnn_train_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Train set %s: %s" % (metric.name, str(dnn_train_score)))
if len(valid_dataset) > 0:
dnn_valid_evaluator = Evaluator(best_dnn, valid_dataset, transformers)
csv_out = "dnn_%s_%s_valid.csv" % (mode, split)
stats_out = "dnn_%s_%s_valid_stats.txt" % (mode, split)
dnn_valid_score = dnn_valid_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Valid set %s: %s" % (metric.name, str(dnn_valid_score)))
if len(test_dataset) > 0:
dnn_test_evaluator = Evaluator(best_dnn, test_dataset, transformers)
csv_out = "dnn_%s_%s_test.csv" % (mode, split)
stats_out = "dnn_%s_%s_test_stats.txt" % (mode, split)
dnn_test_score = dnn_test_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Test set %s: %s" % (metric.name, str(dnn_test_score)))
if len(crystal_dataset) > 0:
dnn_crystal_evaluator = Evaluator(best_dnn, crystal_dataset,
transformers)
csv_out = "dnn_%s_%s_crystal.csv" % (mode, split)
stats_out = "dnn_%s_%s_crystal_stats.txt" % (mode, split)
dnn_crystal_score = dnn_crystal_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Crystal set %s: %s" % (metric.name, str(dnn_crystal_score)))
def bace_dnn_model(mode="classification", verbosity="high", split="20-80"):
"""Train fully-connected DNNs on BACE dataset."""
(bace_tasks, train_dataset, valid_dataset, test_dataset, crystal_dataset,
transformers) = load_bace(mode=mode, transform=True, split=split)
if mode == "regression":
r2_metric = Metric(metrics.r2_score, verbosity=verbosity)
rms_metric = Metric(metrics.rms_score, verbosity=verbosity)
mae_metric = Metric(metrics.mae_score, verbosity=verbosity)
all_metrics = [r2_metric, rms_metric, mae_metric]
metric = r2_metric
elif mode == "classification":
roc_auc_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
accuracy_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
mcc_metric = Metric(metrics.matthews_corrcoef, verbosity=verbosity)
# Note sensitivity = recall
recall_metric = Metric(metrics.recall_score, verbosity=verbosity)
all_metrics = [accuracy_metric, mcc_metric, recall_metric, roc_auc_metric]
metric = roc_auc_metric
else:
raise ValueError("Invalid mode %s" % mode)
params_dict = {"activation": ["relu"],
"momentum": [.9],
"batch_size": [50],
"init": ["glorot_uniform"],
"data_shape": [train_dataset.get_data_shape()],
"learning_rate": np.power(10., np.random.uniform(-5, -3, size=5)),
"decay": np.power(10, np.random.uniform(-6, -4, size=5)),
"nb_hidden": [1000],
"nb_epoch": [40],
"nesterov": [False],
"dropout": [.5],
"nb_layers": [1],
"batchnorm": [False],
}
optimizer = HyperparamOpt(SingleTaskDNN, bace_tasks,
{task: mode for task in bace_tasks},
verbosity=verbosity)
best_dnn, best_hyperparams, all_results = optimizer.hyperparam_search(
params_dict, train_dataset, valid_dataset, transformers,
metric=metric)
if len(train_dataset) > 0:
dnn_train_evaluator = Evaluator(best_dnn, train_dataset, transformers)
csv_out = "dnn_%s_%s_train.csv" % (mode, split)
stats_out = "dnn_%s_%s_train_stats.txt" % (mode, split)
dnn_train_score = dnn_train_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Train set %s: %s" % (metric.name, str(dnn_train_score)))
if len(valid_dataset) > 0:
dnn_valid_evaluator = Evaluator(best_dnn, valid_dataset, transformers)
csv_out = "dnn_%s_%s_valid.csv" % (mode, split)
stats_out = "dnn_%s_%s_valid_stats.txt" % (mode, split)
dnn_valid_score = dnn_valid_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Valid set %s: %s" % (metric.name, str(dnn_valid_score)))
if len(test_dataset) > 0:
dnn_test_evaluator = Evaluator(best_dnn, test_dataset, transformers)
csv_out = "dnn_%s_%s_test.csv" % (mode, split)
stats_out = "dnn_%s_%s_test_stats.txt" % (mode, split)
dnn_test_score = dnn_test_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Test set %s: %s" % (metric.name, str(dnn_test_score)))
if len(crystal_dataset) > 0:
dnn_crystal_evaluator = Evaluator(best_dnn, crystal_dataset, transformers)
csv_out = "dnn_%s_%s_crystal.csv" % (mode, split)
stats_out = "dnn_%s_%s_crystal_stats.txt" % (mode, split)
dnn_crystal_score = dnn_crystal_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("DNN Crystal set %s: %s" % (metric.name, str(dnn_crystal_score)))
def bace_rf_model(mode="classification", verbosity="high", split="20-80"):
"""Train random forests on BACE dataset."""
(bace_tasks, train_dataset, valid_dataset, test_dataset, crystal_dataset,
transformers) = load_bace(mode=mode, transform=False, split=split)
if mode == "regression":
r2_metric = Metric(metrics.r2_score, verbosity=verbosity)
rms_metric = Metric(metrics.rms_score, verbosity=verbosity)
mae_metric = Metric(metrics.mae_score, verbosity=verbosity)
all_metrics = [r2_metric, rms_metric, mae_metric]
metric = r2_metric
model_class = RandomForestRegressor
elif mode == "classification":
roc_auc_metric = Metric(metrics.roc_auc_score, verbosity=verbosity)
accuracy_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
mcc_metric = Metric(metrics.matthews_corrcoef, verbosity=verbosity)
# Note sensitivity = recall
recall_metric = Metric(metrics.recall_score, verbosity=verbosity)
model_class = RandomForestClassifier
all_metrics = [accuracy_metric, mcc_metric, recall_metric, roc_auc_metric]
metric = roc_auc_metric
else:
raise ValueError("Invalid mode %s" % mode)
def model_builder(tasks, task_types, params_dict, model_dir, verbosity=verbosity):
n_estimators = params_dict["n_estimators"]
max_features = params_dict["max_features"]
return SklearnModel(
tasks, task_types, params_dict, model_dir,
model_instance=model_class(n_estimators=n_estimators,
max_features=max_features))
params_dict = {
"n_estimators": [10, 100],
"batch_size": [None],
"data_shape": [train_dataset.get_data_shape()],
"max_features": ["auto", "sqrt", "log2", None],
}
optimizer = HyperparamOpt(model_builder, bace_tasks,
{task: mode for task in bace_tasks})
best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(
params_dict, train_dataset, valid_dataset, transformers,
metric=metric)
if len(train_dataset) > 0:
rf_train_evaluator = Evaluator(best_rf, train_dataset, transformers,
verbosity=verbosity)
csv_out = "rf_%s_%s_train.csv" % (mode, split)
stats_out = "rf_%s_%s_train_stats.txt" % (mode, split)
rf_train_score = rf_train_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Train set scores: %s" % (str(rf_train_score)))
if len(valid_dataset) > 0:
rf_valid_evaluator = Evaluator(best_rf, valid_dataset, transformers,
verbosity=verbosity)
csv_out = "rf_%s_%s_valid.csv" % (mode, split)
stats_out = "rf_%s_%s_valid_stats.txt" % (mode, split)
rf_valid_score = rf_valid_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Valid set scores: %s" % (str(rf_valid_score)))
if len(test_dataset) > 0:
rf_test_evaluator = Evaluator(best_rf, test_dataset, transformers,
verbosity=verbosity)
csv_out = "rf_%s_%s_test.csv" % (mode, split)
stats_out = "rf_%s_%s_test_stats.txt" % (mode, split)
rf_test_score = rf_test_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Test set: %s" % (str(rf_test_score)))
if len(crystal_dataset) > 0:
rf_crystal_evaluator = Evaluator(best_rf, crystal_dataset, transformers,
verbosity)
csv_out = "rf_%s_%s_crystal.csv" % (mode, split)
stats_out = "rf_%s_%s_crystal_stats.txt" % (mode, split)
rf_crystal_score = rf_crystal_evaluator.compute_model_performance(
all_metrics, csv_out=csv_out, stats_out=stats_out)
print("RF Crystal set: %s" % (str(rf_crystal_score)))
