def test_mbsgd_regressor_vs_skl(lrate, penalty, make_dataset):
nrows, datatype, X_train, X_test, y_train, y_test = make_dataset
if nrows < 500000:
cu_mbsgd_regressor = cumlMBSGRegressor(learning_rate=lrate,
eta0=0.005,
epochs=100,
fit_intercept=True,
batch_size=2,
tol=0.0,
penalty=penalty)
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test)
cu_r2 = r2_score(cp.asnumpy(cu_pred),
cp.asnumpy(y_test),
convert_dtype=datatype)
skl_sgd_regressor = SGDRegressor(learning_rate=lrate,
eta0=0.005,
max_iter=100,
fit_intercept=True,
tol=0.0,
penalty=penalty,
random_state=0)
skl_sgd_regressor.fit(cp.asnumpy(X_train), cp.asnumpy(y_train).ravel())
skl_pred = skl_sgd_regressor.predict(cp.asnumpy(X_test))
skl_r2 = r2_score(skl_pred, cp.asnumpy(y_test), convert_dtype=datatype)
assert abs(cu_r2 - skl_r2) <= 0.02
def test_rf_regression_float64(large_reg, datatype):
X, y = large_reg
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.8,
random_state=0)
X_train = X_train.astype(datatype[0])
y_train = y_train.astype(datatype[0])
X_test = X_test.astype(datatype[1])
y_test = y_test.astype(datatype[1])
# Initialize, fit and predict using cuML's
# random forest classification model
cuml_model = curfr()
cuml_model.fit(X_train, y_train)
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype[0])
# sklearn random forest classification model
# initialization, fit and predict
if X.shape[0] < 500000:
sk_model = skrfr(max_depth=16, random_state=10)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype[0])
assert cu_r2 >= (sk_r2 - 0.09)
# predict using cuML's GPU based prediction
fil_preds = cuml_model.predict(X_test,
predict_model="GPU",
convert_dtype=True)
fil_preds = np.reshape(fil_preds, np.shape(cu_preds))
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype[0])
assert fil_r2 >= (cu_r2 - 0.02)
def test_elastic_net(dtype, alpha, algorithm, nrows, column_info, n_parts,
client, delayed):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
n_parts=n_parts,
client=client,
dtype=dtype)
elasticnet = ElasticNet(alpha=np.array([alpha]),
fit_intercept=True,
normalize=False,
max_iter=1000,
selection=algorithm,
tol=1e-10,
client=client)
elasticnet.fit(X, y)
y_hat = elasticnet.predict(X, delayed=delayed)
# based on differences with scikit-learn 0.22
if alpha == 0.2:
assert r2_score(y.compute(), y_hat.compute()) >= 0.96
else:
assert r2_score(y.compute(), y_hat.compute()) >= 0.80
def test_mbsgd_regressor(datatype, lrate, input_type, penalty,
nrows, column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows, n_features=ncols,
n_informative=n_info, random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
cu_mbsgd_regressor = cumlMBSGRegressor(learning_rate=lrate, eta0=0.005,
epochs=100, fit_intercept=True,
batch_size=2, tol=0.0,
penalty=penalty)
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test).to_array()
skl_sgd_regressor = SGDRegressor(learning_rate=lrate, eta0=0.005,
max_iter=100, fit_intercept=True,
tol=0.0, penalty=penalty,
random_state=0)
skl_sgd_regressor.fit(X_train, y_train)
skl_pred = skl_sgd_regressor.predict(X_test)
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
skl_r2 = r2_score(skl_pred, y_test, convert_dtype=datatype)
assert abs(cu_r2 - skl_r2) <= 0.02
def test_lasso_default(datatype, nrows, column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.8,
random_state=0)
cu_lasso = cuLasso()
cu_lasso.fit(X_train, y_train)
assert cu_lasso.coef_ is not None
cu_predict = cu_lasso.predict(X_test)
cu_r2 = r2_score(y_test, cu_predict)
sk_lasso = Lasso()
sk_lasso.fit(X_train, y_train)
sk_predict = sk_lasso.predict(X_test)
sk_r2 = r2_score(y_test, sk_predict)
assert cu_r2 >= sk_r2 - 0.07
def test_lasso(datatype, X_type, alpha, algorithm,
nrows, column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows, n_features=ncols,
n_informative=n_info, random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
cu_lasso = cuLasso(alpha=np.array([alpha]), fit_intercept=True,
normalize=False, max_iter=1000,
selection=algorithm, tol=1e-10)
cu_lasso.fit(X_train, y_train)
assert cu_lasso.coef_ is not None
cu_predict = cu_lasso.predict(X_test)
cu_r2 = r2_score(y_test, cu_predict)
if nrows < 500000:
sk_lasso = Lasso(alpha=np.array([alpha]), fit_intercept=True,
normalize=False, max_iter=1000,
selection=algorithm, tol=1e-10)
sk_lasso.fit(X_train, y_train)
sk_predict = sk_lasso.predict(X_test)
sk_r2 = r2_score(y_test, sk_predict)
assert cu_r2 >= sk_r2 - 0.07
def test_mbsgd_regressor_default(datatype, nrows,
column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows, n_features=ncols,
n_informative=n_info, random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
cu_mbsgd_regressor = cumlMBSGRegressor()
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test).to_array()
skl_sgd_regressor = SGDRegressor()
skl_sgd_regressor.fit(X_train, y_train)
skl_pred = skl_sgd_regressor.predict(X_test)
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
skl_r2 = r2_score(skl_pred, y_test, convert_dtype=datatype)
try:
assert abs(cu_r2 - skl_r2) <= 0.02
except AssertionError:
pytest.xfail("failed due to AssertionError error, "
"fix will be merged soon")
def test_rf_regression_sparse(special_reg, datatype, fil_sparse_format, algo):
use_handle = True
num_treees = 50
X, y = special_reg
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
# Create a handle for the cuml model
handle, stream = get_handle(use_handle, n_streams=1)
# Initialize and fit using cuML's random forest regression model
cuml_model = curfr(n_bins=16, split_criterion=2,
min_rows_per_node=2, random_state=123, n_streams=1,
n_estimators=num_treees, handle=handle, max_leaves=-1,
max_depth=40, accuracy_metric='mse')
cuml_model.fit(X_train, y_train)
# predict using FIL
if ((not fil_sparse_format or algo == 'tree_reorg' or
algo == 'batch_tree_reorg') or
fil_sparse_format == 'not_supported'):
with pytest.raises(ValueError):
fil_preds = cuml_model.predict(X_test, predict_model="GPU",
fil_sparse_format=fil_sparse_format,
algo=algo)
else:
fil_preds = cuml_model.predict(X_test, predict_model="GPU",
fil_sparse_format=fil_sparse_format,
algo=algo)
fil_preds = np.reshape(fil_preds, np.shape(y_test))
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype)
fil_model = cuml_model.convert_to_fil_model()
input_type = 'numpy'
fil_model_preds = fil_model.predict(X_test,
output_type=input_type)
fil_model_preds = np.reshape(fil_model_preds, np.shape(y_test))
fil_model_r2 = r2_score(y_test, fil_model_preds,
convert_dtype=datatype)
assert fil_r2 == fil_model_r2
tl_model = cuml_model.convert_to_treelite_model()
assert num_treees == tl_model.num_trees
assert X.shape[1] == tl_model.num_features
# Initialize, fit and predict using
# sklearn's random forest regression model
if X.shape[0] < 1000: # mode != "stress":
sk_model = skrfr(n_estimators=50, max_depth=40,
min_samples_split=2,
random_state=10)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype)
assert fil_r2 >= (sk_r2 - 0.07)
def test_rf_regression(
special_reg, datatype, max_features, max_samples, n_bins
):
use_handle = True
X, y = special_reg
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=0.8, random_state=0
)
# Create a handle for the cuml model
handle, stream = get_handle(use_handle, n_streams=1)
# Initialize and fit using cuML's random forest regression model
cuml_model = curfr(
max_features=max_features,
max_samples=max_samples,
n_bins=n_bins,
split_criterion=2,
min_samples_leaf=2,
random_state=123,
n_streams=1,
n_estimators=50,
handle=handle,
max_leaves=-1,
max_depth=16,
accuracy_metric="mse",
)
cuml_model.fit(X_train, y_train)
# predict using FIL
fil_preds = cuml_model.predict(X_test, predict_model="GPU")
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
fil_preds = np.reshape(fil_preds, np.shape(cu_preds))
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype)
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype)
# Initialize, fit and predict using
# sklearn's random forest regression model
if X.shape[0] < 1000: # mode != "stress"
sk_model = skrfr(
n_estimators=50,
max_depth=16,
min_samples_split=2,
max_features=max_features,
random_state=10,
)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype)
assert fil_r2 >= (sk_r2 - 0.07)
assert fil_r2 >= (cu_r2 - 0.02)
def test_rf_regression_float64(large_reg, datatype):
X, y = large_reg
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=0.8, random_state=0
)
X_train = X_train.astype(datatype[0])
y_train = y_train.astype(datatype[0])
X_test = X_test.astype(datatype[1])
y_test = y_test.astype(datatype[1])
# Initialize, fit and predict using cuML's
# random forest classification model
cuml_model = curfr()
cuml_model.fit(X_train, y_train)
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype[0])
# sklearn random forest classification model
# initialization, fit and predict
if X.shape[0] < 500000:
sk_model = skrfr(max_depth=16, random_state=10)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype[0])
assert cu_r2 >= (sk_r2 - 0.09)
# predict using cuML's GPU based prediction
if datatype[0] == np.float32:
fil_preds = cuml_model.predict(
X_test, predict_model="GPU", convert_dtype=True
)
fil_preds = np.reshape(fil_preds, np.shape(cu_preds))
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype[0])
assert fil_r2 >= (cu_r2 - 0.02)
# because datatype[0] != np.float32 or datatype[0] != datatype[1]
# display warning when GPU-predict cannot be used and revert to CPU-predict
elif datatype[1] == np.float64:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
fil_preds = cuml_model.predict(
X_test, predict_model="GPU"
)
assert("GPU based predict only accepts "
"np.float32 data. The model was "
"trained on np.float64 data hence "
"cannot use GPU-based prediction! "
"\nDefaulting to CPU-based Prediction. "
"\nTo predict on float-64 data, set "
"parameter predict_model = 'CPU'"
in str(w[-1].message))
def test_mbsgd_regressor_default(make_dataset):
nrows, datatype, X_train, X_test, y_train, y_test = make_dataset
cu_mbsgd_regressor = cumlMBSGRegressor()
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test)
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
if nrows < 500000:
skl_sgd_regressor = SGDRegressor()
skl_sgd_regressor.fit(X_train, y_train)
skl_pred = skl_sgd_regressor.predict(X_test)
skl_r2 = r2_score(skl_pred, y_test, convert_dtype=datatype)
assert abs(cu_r2 - skl_r2) <= 0.02
def test_lasso_default(dtype, nrows, column_info, n_parts, cluster):
client = Client(cluster)
ncols, n_info = column_info
try:
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
client=client,
dtype=dtype)
wait(X)
lasso = Lasso(client=client)
lasso.fit(X, y)
y_hat = lasso.predict(X)
assert r2_score(y.compute(), y_hat.compute()) >= 0.99
finally:
client.close()
def test_lasso(dtype, alpha, algorithm,
nrows, column_info, n_parts, delayed, cluster):
client = Client(cluster)
ncols, n_info = column_info
try:
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
n_parts=n_parts,
client=client,
dtype=dtype)
wait(X)
lasso = Lasso(alpha=np.array([alpha]), fit_intercept=True,
normalize=False, max_iter=1000,
selection=algorithm, tol=1e-10,
client=client)
lasso.fit(X, y)
y_hat = lasso.predict(X, delayed=delayed)
assert r2_score(y.compute(), y_hat.compute()) >= 0.99
finally:
client.close()
def test_elastic_net_default(dtype, nrows, column_info, n_parts, cluster):
client = Client(cluster)
ncols, n_info = column_info
try:
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
n_parts=n_parts,
client=client,
dtype=dtype)
wait(X)
elasticnet = ElasticNet(client=client)
elasticnet.fit(X, y)
y_hat = elasticnet.predict(X)
assert r2_score(y.compute(), y_hat.compute()) >= 0.96
finally:
client.close()
def test_rf_regression_float64(datatype, column_info, nrows, convert_dtype):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
random_state=123)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.8,
random_state=0)
X_train = X_train.astype(datatype[0])
y_train = y_train.astype(datatype[0])
X_test = X_test.astype(datatype[1])
y_test = y_test.astype(datatype[1])
# Initialize, fit and predict using cuML's
# random forest classification model
cuml_model = curfr()
cuml_model.fit(X_train, y_train)
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype[0])
# sklearn random forest classification model
# initialization, fit and predict
if nrows < 500000:
sk_model = skrfr(max_depth=16, random_state=10)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype[0])
assert cu_r2 >= (sk_r2 - 0.09)
# predict using cuML's GPU based prediction
if datatype[0] == np.float32 and convert_dtype:
fil_preds = cuml_model.predict(X_test,
predict_model="GPU",
convert_dtype=convert_dtype)
fil_preds = np.reshape(fil_preds, np.shape(cu_preds))
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype[0])
assert fil_r2 >= (cu_r2 - 0.02)
else:
with pytest.raises(TypeError):
fil_preds = cuml_model.predict(X_test,
predict_model="GPU",
convert_dtype=convert_dtype)
def test_rf_regression_default(datatype, column_info, nrows):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows,
n_features=ncols,
n_informative=n_info,
random_state=123)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.8,
random_state=0)
# Initialize, fit and predict using cuML's
# random forest classification model
cuml_model = curfr()
cuml_model.fit(X_train, y_train)
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype)
# predict using FIL
fil_preds = cuml_model.predict(X_test, predict_model="GPU")
fil_preds = np.reshape(fil_preds, np.shape(cu_preds))
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype)
# score function should be equivalent
score_mse = cuml_model.score(X_test, y_test, predict_model="GPU")
sk_mse = mean_squared_error(y_test, fil_preds)
assert sk_mse == pytest.approx(score_mse)
# Initialize, fit and predict using
# sklearn's random forest regression model
if nrows < 500000:
sk_model = skrfr(max_depth=16, random_state=10)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype)
# XXX Accuracy gap exists with default parameters, requires
# further investigation for next release
assert fil_r2 >= (sk_r2 - 0.08)
assert fil_r2 >= (cu_r2 - 0.02)
def test_mbsgd_regressor_default(make_dataset):
nrows, datatype, X_train, X_test, y_train, y_test = make_dataset
cu_mbsgd_regressor = cumlMBSGRegressor(batch_size=nrows / 100)
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test)
cu_r2 = r2_score(cp.asnumpy(cu_pred),
cp.asnumpy(y_test),
convert_dtype=datatype)
assert cu_r2 > 0.9
def test_elastic_net_default(datatype, nrows, column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows, n_features=ncols,
n_informative=n_info, random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
elastic_cu = cuElasticNet()
elastic_cu.fit(X_train, y_train)
cu_predict = elastic_cu.predict(X_test)
cu_r2 = r2_score(y_test, cu_predict)
elastic_sk = ElasticNet()
elastic_sk.fit(X_train, y_train)
sk_predict = elastic_sk.predict(X_test)
sk_r2 = r2_score(y_test, sk_predict)
assert cu_r2 >= sk_r2 - 0.07
def test_mbsgd_regressor_default(datatype, nrows,
column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows, n_features=ncols,
n_informative=n_info, random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
cu_mbsgd_regressor = cumlMBSGRegressor()
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test).to_array()
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
if nrows < 500000:
skl_sgd_regressor = SGDRegressor()
skl_sgd_regressor.fit(X_train, y_train)
skl_pred = skl_sgd_regressor.predict(X_test)
skl_r2 = r2_score(skl_pred, y_test, convert_dtype=datatype)
assert abs(cu_r2 - skl_r2) <= 0.02
def test_mbsgd_regressor(lrate, penalty, make_dataset):
nrows, datatype, X_train, X_test, y_train, y_test = make_dataset
cu_mbsgd_regressor = cumlMBSGRegressor(learning_rate=lrate,
eta0=0.005,
epochs=100,
fit_intercept=True,
batch_size=nrows / 100,
tol=0.0,
penalty=penalty)
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test)
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
assert cu_r2 >= 0.9
def _r2(y_true, y_pred):
return r2_score(y_true=y_true, y_pred=y_pred)
def test_rf_regression_sparse(datatype, split_algo, mode, column_info,
max_features, rows_sample, fil_sparse_format,
algo):
ncols, n_info = column_info
use_handle = True
num_treees = 50
if mode == 'unit':
X, y = make_regression(n_samples=500,
n_features=ncols,
n_informative=n_info,
random_state=123)
elif mode == 'quality':
X, y = fetch_california_housing(return_X_y=True)
else:
X, y = make_regression(n_samples=100000,
n_features=ncols,
n_informative=n_info,
random_state=123)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.8,
random_state=0)
# Create a handle for the cuml model
handle, stream = get_handle(use_handle, n_streams=1)
# Initialize and fit using cuML's random forest regression model
cuml_model = curfr(max_features=max_features,
rows_sample=rows_sample,
n_bins=16,
split_algo=split_algo,
split_criterion=2,
min_rows_per_node=2,
seed=123,
n_streams=1,
n_estimators=num_treees,
handle=handle,
max_leaves=-1,
max_depth=40,
accuracy_metric='mse')
cuml_model.fit(X_train, y_train)
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype)
# predict using FIL
if ((not fil_sparse_format or algo == 'tree_reorg'
or algo == 'batch_tree_reorg')
or fil_sparse_format == 'not_supported'):
with pytest.raises(ValueError):
fil_preds = cuml_model.predict(X_test,
predict_model="GPU",
fil_sparse_format=fil_sparse_format,
algo=algo)
else:
fil_preds = cuml_model.predict(X_test,
predict_model="GPU",
fil_sparse_format=fil_sparse_format,
algo=algo)
fil_preds = np.reshape(fil_preds, np.shape(cu_preds))
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype)
fil_model = cuml_model.convert_to_fil_model()
input_type = 'numpy'
fil_model_preds = fil_model.predict(X_test, output_type=input_type)
fil_model_preds = np.reshape(fil_model_preds, np.shape(cu_preds))
fil_model_r2 = r2_score(y_test,
fil_model_preds,
convert_dtype=datatype)
assert fil_r2 == fil_model_r2
tl_model = cuml_model.convert_to_treelite_model()
assert num_treees == tl_model.num_trees
assert ncols == tl_model.num_features
del tl_model
# Initialize, fit and predict using
# sklearn's random forest regression model
if mode != "stress":
sk_model = skrfr(n_estimators=50,
max_depth=40,
min_samples_split=2,
max_features=max_features,
random_state=10)
sk_model.fit(X_train, y_train)
sk_preds = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_preds, convert_dtype=datatype)
assert fil_r2 >= (sk_r2 - 0.07)
assert fil_r2 >= (cu_r2 - 0.02)
def _calc_score_cuml(y_true, y_preds, y_proba=None, metrics=('accuracy',), task=const.TASK_BINARY, pos_label=1,
classes=None, average=None):
if y_proba is None:
y_proba = y_preds
if len(y_proba.shape) == 2 and y_proba.shape[-1] == 1:
y_proba = y_proba.reshape(-1)
if len(y_preds.shape) == 2 and y_preds.shape[-1] == 1:
y_preds = y_preds.reshape(-1)
y_true = _to_dtype(y_true, 'float64')
y_preds = _to_dtype(y_preds, 'float64')
y_proba = _to_dtype(y_proba, 'float64')
if task == const.TASK_REGRESSION:
if isinstance(y_true, cudf.Series):
y_true = y_true.values
if isinstance(y_preds, cudf.Series):
y_preds = y_preds.values
if isinstance(y_proba, cudf.Series):
y_proba = y_proba.values
scores = {}
for metric in metrics:
if callable(metric):
scores[metric.__name__] = metric(y_true, y_preds)
else:
metric_lower = metric.lower()
if metric_lower == 'auc':
if len(y_proba.shape) == 2:
# if task == const.TASK_MULTICLASS:
# s = cu_metrics.roc_auc_score(y_true, y_proba, multi_class='ovo', labels=classes)
# else:
# s = cu_metrics.roc_auc_score(y_true, y_proba[:, 1])
s = cu_metrics.roc_auc_score(y_true, y_proba[:, 1])
else:
s = cu_metrics.roc_auc_score(y_true, y_proba)
elif metric_lower == 'accuracy':
if y_preds is None:
s = 0
else:
s = cu_metrics.accuracy_score(y_true, y_preds)
# elif metric_lower == 'recall':
# s = cu_metrics.recall_score(y_true, y_preds, **recall_options)
# elif metric_lower == 'precision':
# s = cu_metrics.precision_score(y_true, y_preds, **recall_options)
# elif metric_lower == 'f1':
# s = cu_metrics.f1_score(y_true, y_preds, **recall_options)
elif metric_lower == 'mse':
s = cu_metrics.mean_squared_error(y_true, y_preds)
elif metric_lower == 'mae':
s = cu_metrics.mean_absolute_error(y_true, y_preds)
elif metric_lower == 'msle':
s = cu_metrics.mean_squared_log_error(y_true, y_preds)
elif metric_lower in {'rmse', 'rootmeansquarederror', 'root_mean_squared_error'}:
s = cu_metrics.mean_squared_error(y_true, y_preds, squared=False)
elif metric_lower == 'r2':
s = cu_metrics.r2_score(y_true, y_preds)
elif metric_lower in {'logloss', 'log_loss'}:
# s = cu_metrics.log_loss(y_true, y_proba, labels=classes)
s = cu_metrics.log_loss(y_true, y_proba)
else:
logger.warning(f'unknown metric: {metric}')
continue
if isinstance(s, cp.ndarray):
s = float(cp.asnumpy(s))
scores[metric] = s
return scores
def test_rf_regression(datatype, split_algo, mode, column_info, max_features,
rows_sample):
ncols, n_info = column_info
use_handle = True
if mode == 'unit':
X, y = make_regression(n_samples=500,
n_features=ncols,
n_informative=n_info,
random_state=123)
elif mode == 'quality':
X, y = fetch_california_housing(return_X_y=True)
else:
X, y = make_regression(n_samples=100000,
n_features=ncols,
n_informative=n_info,
random_state=123)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.8,
random_state=0)
# Create a handle for the cuml model
handle, stream = get_handle(use_handle, n_streams=1)
# Initialize and fit using cuML's random forest regression model
cuml_model = curfr(max_features=max_features,
rows_sample=rows_sample,
n_bins=16,
split_algo=split_algo,
split_criterion=2,
min_rows_per_node=2,
seed=123,
n_streams=1,
n_estimators=50,
handle=handle,
max_leaves=-1,
max_depth=16,
accuracy_metric='mse')
cuml_model.fit(X_train, y_train)
# predict using FIL
fil_preds = cuml_model.predict(X_test, predict_model="GPU")
cu_preds = cuml_model.predict(X_test, predict_model="CPU")
cu_r2 = r2_score(y_test, cu_preds, convert_dtype=datatype)
fil_r2 = r2_score(y_test, fil_preds, convert_dtype=datatype)
# Initialize, fit and predict using
# sklearn's random forest regression model
if mode != "stress":
sk_model = skrfr(n_estimators=50,
max_depth=16,
min_samples_split=2,
max_features=max_features,
random_state=10)
sk_model.fit(X_train, y_train)
sk_predict = sk_model.predict(X_test)
sk_r2 = r2_score(y_test, sk_predict, convert_dtype=datatype)
assert fil_r2 >= (sk_r2 - 0.07)
assert fil_r2 >= (cu_r2 - 0.02)
