[BUG] Forest Inference notebook errors on model load and provides unhelpful error message to remediate

[taureandyernv]

Describe the bug
In our latest nightlies, CUDA11.8, Python 3.10, loading the xgboost model in the Forest Inference Demo notebook says that the output_type should be set to output_type=self.output_type and is currently set to mirror. The error is not helpful and may be currently incorrect, and the docs don't provide that possible input/may require an update.

Steps/Code to reproduce bug

1. generate model file with this code:

import xgboost as xgb
import cupy
import os

from cuml.testing.utils import array_equal
from cuml.internals.import_utils import has_xgboost
from cuml.model_selection import train_test_split
from cuml.datasets import make_classification

# synthetic data size
n_rows = 10000
n_columns = 100
n_categories = 2
random_state = cupy.random.RandomState(43210)

# fraction of data used for model training
train_size = 0.8

# trained model output filename
model_path = 'xgb.model'

# num of iterations for which xgboost is trained
num_rounds = 100

# maximum tree depth in each training round
max_depth = 20

# create the dataset
X, y = make_classification(
    n_samples=n_rows,
    n_features=n_columns,
    n_informative=int(n_columns/5),
    n_classes=n_categories,
    random_state=42
)

# convert the dataset to float32
X = X.astype('float32')
y = y.astype('float32')

# split the dataset into training and validation splits
X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.8)

# set the xgboost model parameters
params = {
    'verbosity': 0, 
    'eval_metric':'error',
    'objective':'binary:logistic',
    'max_depth': max_depth,
    'tree_method': 'gpu_hist'
}

# convert training data into DMatrix
dtrain = xgb.DMatrix(X_train, label=y_train)

# train the xgboost model
trained_model = xgb.train(params, dtrain, num_rounds)

# save the trained xgboost model
trained_model.save_model(model_path)

2. read in the xgboost model file provided

from cuml import ForestInference
fil_model = ForestInference.load(
    filename="xgb.model",
    algo='BATCH_TREE_REORG',
    output_class=True,
    threshold=0.50,
    model_type='xgboost'
)

The output will be this error:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
/rapids/notebooks/cuml/Untitled.ipynb in <module>
----> 1 fil_model = ForestInference.load(
      2     filename="xgb.model",
      3     algo='BATCH_TREE_REORG',
      4     output_class=True,
      5     threshold=0.50,

/opt/conda/envs/rapids/lib/python3.10/site-packages/cuml/internals/api_decorators.py in wrapper(*args, **kwargs)
    190                         ret = func(*args, **kwargs)
    191                     else:
--> 192                         return func(*args, **kwargs)
    193 
    194                 return cm.process_return(ret)

fil.pyx in cuml.fil.fil.ForestInference.load()

/opt/conda/envs/rapids/lib/python3.10/site-packages/cuml/internals/api_decorators.py in inner_f(*args, **kwargs)
    340             kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
    341 
--> 342             return func(**kwargs)
    343 
    344         # Set this flag to prevent auto adding this decorator twice

fil.pyx in cuml.fil.fil.ForestInference.__init__()

/opt/conda/envs/rapids/lib/python3.10/site-packages/cuml/internals/api_decorators.py in inner_f(*args, **kwargs)
    340             kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
    341 
--> 342             return func(**kwargs)
    343 
    344         # Set this flag to prevent auto adding this decorator twice

base.pyx in cuml.internals.base.Base.__init__()

base.pyx in cuml.internals.base._check_output_type_str()

AssertionError: Cannot pass output_type='mirror' in Base.__init__(). Did you forget to pass `output_type=self.output_type` to a child estimator? Currently `cuml.global_settings.output_type==`mirror`

and nothing in the docs help me figure out how to fix it properly

Expected behavior
The model should load and/or the error message and corresponding docs should help me fix the issue so that I can successfully load the model

Environment details (please complete the following information):

• Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)] Docker
• Linux Distro/Architecture: [Ubuntu 22.04]
• GPU Model/Driver: [H100]
• CUDA: [11.8]
• Method of cuDF & cuML install: Docker: docker run --gpus all --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai-core-nightly-arm64:23.02-cuda11.8-runtime-ubuntu22.04-py3.10

Additional context
Add any other context about the problem here.