def train(fpath, max_depth, max_features, n_estimators):
"""
:param params: hyperparameters. Its structure is consistent with how search space is defined. See below.
:param fpath: Path or URL for the training data used with the model.
:param max_depth: RF max_depth parameter
:param max_features: RF max_features parameter
:param n_estimators: RF n_estimators parameter
:return: trained model
"""
X_train, X_test, y_train, y_test = load_data(fpath)
mod = RandomForestClassifier(max_depth=max_depth,
max_features=max_features,
n_estimators=n_estimators)
mod.fit(X_train, y_train)
preds = mod.predict(X_test)
acc = accuracy_score(y_test, preds)
mlparams = {
"max_depth": str(max_depth),
"max_features": str(max_features),
"n_estimators": str(n_estimators),
}
mlflow.log_params(mlparams)
mlflow.log_metric("accuracy", acc)
mlflow.sklearn.log_model(mod, "saved_models")
return mod
def fit(self, X_train, y_train):
""" Fit decision tree model """
if 'XGBoost' in self.hpo_config.model_type:
hpo_log.info('> fit xgboost model')
dtrain = xgboost.DMatrix(data=X_train, label=y_train)
num_boost_round = self.hpo_config.model_params['num_boost_round']
trained_model = xgboost.train(dtrain=dtrain,
params=self.hpo_config.model_params,
num_boost_round=num_boost_round)
elif 'RandomForest' in self.hpo_config.model_type:
hpo_log.info('> fit randomforest model')
trained_model = RandomForestClassifier(
n_estimators=self.hpo_config.model_params['n_estimators'],
max_depth=self.hpo_config.model_params['max_depth'],
max_features=self.hpo_config.model_params['max_features'],
n_bins=self.hpo_config.model_params['n_bins']).fit(
X_train, y_train.astype('int32'))
elif 'KMeans' in self.hpo_config.model_type:
hpo_log.info('> fit kmeans model')
trained_model = KMeans(
n_clusters=self.hpo_config.model_params['n_clusters'],
max_iter=self.hpo_config.model_params['max_iter'],
random_state=self.hpo_config.model_params['random_state'],
init=self.hpo_config.model_params['init']).fit(X_train)
return trained_model
def GridSearch_random_forest(X_train, y_train):
# Encode as float32
X_train = X_train.to_numpy().astype('float32')
y_train = y_train.to_numpy().astype('float32')
# Init Kfolds
folds = KFold(n_splits=5)
# Init hyperparam vals
n_estimators_lst = [128, 256, 512, 1024]
max_features_lst = ['sqrt', 'log2']
fin_arr = []
# Run GridSearch for all hyperparam combos
for n_estimators in n_estimators_lst:
for max_features in max_features_lst:
# Init clf
clf = RandomForestClassifier(n_estimators=n_estimators,
max_features=max_features)
predicted_y = []
true_y = []
# Run CV and calc metrics
for train, holdout in folds.split(X_train):
clf.fit(X_train[train], y_train[train])
predicted_y.append(clf.predict(X_train[holdout]))
true_y.append(y_train[holdout])
predicted_y = np.concatenate(predicted_y)
true_y = np.concatenate(true_y)
accuracy_train = accuracy_score(true_y, predicted_y)
f1_train = f1_score(true_y, predicted_y)
roc_auc_train = roc_auc_score(true_y, predicted_y)
fin_arr.append([
n_estimators, max_features, accuracy_train, f1_train,
roc_auc_train
])
# Create final dataframe from GridSearch results
fin_arr = np.array(fin_arr).reshape(
(len(n_estimators_lst) * len(max_features_lst)), 5)
columns = [
'n_estimators', 'max_features', 'mean_accuracy', 'mean_f1', 'mean_auc'
]
results = pd.DataFrame(data=fin_arr, columns=columns)
results.n_estimators = results.n_estimators.astype(int)
return results
def train_and_eval(X_param, y_param, max_depth=16, n_estimators=100):
X_train, X_valid, y_train, y_valid = train_test_split(X_param,
y_param,
random_state=77)
classifier = RandomForestClassifier(max_depth=max_depth,
n_estimators=n_estimators)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_valid)
score = accuracy_score(y_valid, y_pred)
return score
def fit(X, y):
global clf
clf = RandomForestClassifier(split_criterion=params.criterion,
split_algo=params.split_algorithm,
n_estimators=params.num_trees,
max_depth=params.max_depth,
max_features=params.max_features,
min_samples_split=params.min_samples_split,
max_leaves=params.max_leaf_nodes,
min_impurity_decrease=params.min_impurity_decrease,
bootstrap=params.bootstrap)
return clf.fit(X, y)
def fit(self, X_train, y_train):
"""Fit decision tree model"""
if "XGBoost" in self.hpo_config.model_type:
hpo_log.info("> fit xgboost model")
dtrain = xgboost.DMatrix(data=X_train, label=y_train)
num_boost_round = self.hpo_config.model_params["num_boost_round"]
trained_model = xgboost.train(dtrain=dtrain,
params=self.hpo_config.model_params,
num_boost_round=num_boost_round)
elif "RandomForest" in self.hpo_config.model_type:
hpo_log.info("> fit randomforest model")
trained_model = RandomForestClassifier(
n_estimators=self.hpo_config.model_params["n_estimators"],
max_depth=self.hpo_config.model_params["max_depth"],
max_features=self.hpo_config.model_params["max_features"],
n_bins=self.hpo_config.model_params["n_bins"],
).fit(X_train, y_train.astype("int32"))
return trained_model
def _train(params, fpath, hyperopt=False):
"""
:param params: hyperparameters. Its structure is consistent with how search space is defined. See below.
:param fpath: Path or URL for the training data used with the model.
:param hyperopt: Use hyperopt for hyperparameter search during training.
:return: dict with fields 'loss' (scalar loss) and 'status' (success/failure status of run)
"""
max_depth, max_features, n_estimators = params
max_depth, max_features, n_estimators = (int(max_depth),
float(max_features),
int(n_estimators))
# Log all of our training parameters for this run.
pyver = sys.version_info
mlparams = {
'cudf_version': str(cudf.__version__),
'cuml_version': str(cuml.__version__),
'max_depth': str(max_depth),
'max_features': str(max_features),
'n_estimators': str(n_estimators),
'python_version': f"{pyver[0]}.{pyver[1]}.{pyver[2]}.{pyver[3]}",
}
mlflow.log_params(mlparams)
X_train, X_test, y_train, y_test = load_data(fpath)
mod = RandomForestClassifier(max_depth=max_depth,
max_features=max_features,
n_estimators=n_estimators)
mod.fit(X_train, y_train)
preds = mod.predict(X_test)
acc = accuracy_score(y_test, preds)
mlflow.log_metric("accuracy", acc)
mlflow.sklearn.log_model(mod, "saved_models")
if not hyperopt:
return mod
return {"loss": acc, "status": STATUS_OK}
def _train(params, fpath, hyperopt=False):
"""
:param params: hyperparameters. Its structure is consistent with how search space is defined. See below.
:param fpath: Path or URL for the training data used with the model.
:param hyperopt: Use hyperopt for hyperparameter search during training.
:return: dict with fields 'loss' (scalar loss) and 'status' (success/failure status of run)
"""
max_depth, max_features, n_estimators = params
max_depth, max_features, n_estimators = (int(max_depth),
float(max_features),
int(n_estimators))
X_train, X_test, y_train, y_test = load_data(fpath)
mod = RandomForestClassifier(max_depth=max_depth,
max_features=max_features,
n_estimators=n_estimators)
mod.fit(X_train, y_train)
preds = mod.predict(X_test)
acc = accuracy_score(y_test, preds)
mlparams = {
"max_depth": str(max_depth),
"max_features": str(max_features),
"n_estimators": str(n_estimators)
}
mlflow.log_params(mlparams)
mlflow.log_metric("accuracy", acc)
mlflow.sklearn.log_model(mod, "saved_models")
if (not hyperopt):
return mod
return {'loss': acc, 'status': STATUS_OK}
print('cu_y_train:', type(cu_y_train), 'shape:', cu_y_train.shape)
print('Copying data to GPU done in {:.2f} seconds'.format(time() - t0))
# ### Learning
#
# Random forest classifiers are quick to train, quite robust to
# hyperparameter values, and often work relatively well.
print()
print('Learning begins')
t0 = time()
n_estimators = 100
max_depth = 16
clf_rf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth)
print(clf_rf)
clf_rf.fit(cu_X_train, cu_y_train)
print('Learning done in {:.2f} seconds'.format(time() - t0))
# ### Inference
#
# We will use GPU-based inference to predict the classes for the test
# data.
print()
print('Inference begins')
t0 = time()
pred_rf = clf_rf.predict(X_test, predict_model='GPU')
def run_random_forest(scaled_df):
raw_train_arr = []
raw_test_arr = []
# Over five trials
for i in range(5):
# Split data into train and test
X_train, X_test, y_train, y_test = train_test_split(
scaled_df.iloc[:, :-1], scaled_df.y, train_size=5000)
# Run GridSearch
search_results = GridSearch_random_forest(X_train, y_train)
results = search_results
# Get optimal clfs using gridsearch results
opt_acc_inf = results.sort_values(by='mean_accuracy',
ascending=False).iloc[0]
opt_f1_inf = results.sort_values(by='mean_f1', ascending=False).iloc[0]
opt_auc_inf = results.sort_values(by='mean_auc',
ascending=False).iloc[0]
# Init optimal clfs
opt_acc_clf = RandomForestClassifier(
n_estimators=opt_acc_inf.n_estimators,
max_features=opt_acc_inf.max_features)
opt_f1_clf = RandomForestClassifier(
n_estimators=opt_f1_inf.n_estimators,
max_features=opt_f1_inf.max_features)
opt_auc_clf = RandomForestClassifier(
n_estimators=opt_auc_inf.n_estimators,
max_features=opt_auc_inf.max_features)
# Encode as float32 for cuML
X_train_np = X_train.to_numpy().astype('float32')
y_train_np = y_train.to_numpy().astype('float32')
X_test_np = X_test.to_numpy().astype('float32')
y_test_np = y_test.to_numpy().astype('float32')
# Fit clfs
opt_acc_clf.fit(X_train_np, y_train_np)
opt_f1_clf.fit(X_train_np, y_train_np)
opt_auc_clf.fit(X_train_np, y_train_np)
# Get train and test metrics
train_score_acc = opt_acc_clf.score(X_train_np, y_train_np)
train_score_f1 = f1_score(y_train_np, opt_f1_clf.predict(X_train_np))
train_score_auc = roc_auc_score(y_train_np,
opt_auc_clf.predict(X_train_np))
test_score_acc = opt_acc_clf.score(X_test_np, y_test_np)
test_score_f1 = f1_score(y_test_np, opt_f1_clf.predict(X_test_np))
test_score_auc = roc_auc_score(y_test_np,
opt_auc_clf.predict(X_test_np))
raw_train_arr.append(
[train_score_acc, train_score_f1, train_score_auc])
raw_test_arr.append([test_score_acc, test_score_f1, test_score_auc])
raw_train_arr = np.array(raw_train_arr).reshape(5, 3)
raw_test_arr = np.array(raw_test_arr).reshape(5, 3)
raw_train_df = pd.DataFrame(data=raw_train_arr,
columns=['accuracy', 'f1', 'auc'])
raw_test_df = pd.DataFrame(data=raw_test_arr,
columns=['accuracy', 'f1', 'auc'])
return raw_train_df, raw_test_df
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(**alg.input_variables.__dict__)
elif alg.name == 'AdaBoost' and alg.type == 'classification':
from sklearn.ensemble import AdaBoostClassifier
model = AdaBoostClassifier(**alg.input_variables.__dict__)
warn_not_gpu_support(alg)
elif alg.name == 'GradientBoosting' and alg.type == 'classification':
from sklearn.ensemble import GradientBoostingClassifier
model = GradientBoostingClassifier(**alg.input_variables.__dict__)
warn_not_gpu_support(alg)
elif alg.name == 'RandomForest' and alg.type == 'classification':
if NVIDIA_RAPIDS_ENABLED:
from cuml.ensemble import RandomForestClassifier
else:
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(**alg.input_variables.__dict__)
elif alg.name == 'XGBoost' and alg.type == 'classification':
from xgboost.sklearn import XGBClassifier
"""
Note from NVIDIA RAPIDS >= 0.17 (no error for == 0.13)
ValueError: The option use_label_encoder=True is incompatible with inputs of type cuDF or cuPy.
Please set use_label_encoder=False when constructing XGBClassifier object.
NOTE: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release.
To remove this warning, do the following:
1) Pass option use_label_encoder=False when constructing XGBClassifier object; and
2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1]
"""
if NVIDIA_RAPIDS_ENABLED:
model = XGBClassifier(**alg.input_variables.__dict__, use_label_encoder=False, tree_method="gpu_hist")
else:
model = XGBClassifier(**alg.input_variables.__dict__)