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Update xgboost examples #2619

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69 changes: 35 additions & 34 deletions doc/source/tutorial-quickstart-xgboost.rst
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Expand Up @@ -88,7 +88,8 @@ Prior to local training, we require loading the HIGGS dataset from Flower Datase
.. code-block:: python

# Load (HIGGS) dataset and conduct partitioning
partitioner = IidPartitioner(num_partitions=2)
# We use a small subset (num_partitions=30) of the dataset for demonstration to speed up the data loading process.
partitioner = IidPartitioner(num_partitions=30)
fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})

# Load the partition for this `node_id`
Expand Down Expand Up @@ -544,39 +545,39 @@ You should now see how the training does in the very first terminal (the one tha

.. code-block:: shell

INFO flwr 2023-11-19 18:36:33,599 | app.py:163 | Starting Flower server, config: ServerConfig(num_rounds=5, round_timeout=None)
INFO flwr 2023-11-19 18:36:33,629 | app.py:176 | Flower ECE: gRPC server running (5 rounds), SSL is disabled
INFO flwr 2023-11-19 18:36:33,629 | server.py:89 | Initializing global parameters
INFO flwr 2023-11-19 18:36:33,629 | server.py:276 | Requesting initial parameters from one random client
INFO flwr 2023-11-19 18:40:03,997 | server.py:280 | Received initial parameters from one random client
INFO flwr 2023-11-19 18:40:03,999 | server.py:91 | Evaluating initial parameters
INFO flwr 2023-11-19 18:40:04,000 | server.py:104 | FL starting
DEBUG flwr 2023-11-19 18:40:04,098 | server.py:222 | fit_round 1: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:09,097 | server.py:236 | fit_round 1 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:09,108 | server.py:173 | evaluate_round 1: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:09,236 | server.py:187 | evaluate_round 1 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:09,237 | server.py:222 | fit_round 2: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:09,819 | server.py:236 | fit_round 2 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:09,855 | server.py:173 | evaluate_round 2: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:10,007 | server.py:187 | evaluate_round 2 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:10,007 | server.py:222 | fit_round 3: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:10,623 | server.py:236 | fit_round 3 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:10,674 | server.py:173 | evaluate_round 3: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:10,847 | server.py:187 | evaluate_round 3 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:10,847 | server.py:222 | fit_round 4: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:11,515 | server.py:236 | fit_round 4 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:11,581 | server.py:173 | evaluate_round 4: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:11,775 | server.py:187 | evaluate_round 4 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:11,775 | server.py:222 | fit_round 5: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:12,568 | server.py:236 | fit_round 5 received 2 results and 0 failures
DEBUG flwr 2023-11-19 18:40:12,648 | server.py:173 | evaluate_round 5: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-19 18:40:12,862 | server.py:187 | evaluate_round 5 received 2 results and 0 failures
INFO flwr 2023-11-19 18:40:12,862 | server.py:153 | FL finished in 8.86196927200001
INFO flwr 2023-11-19 18:40:12,864 | app.py:226 | app_fit: losses_distributed [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)]
INFO flwr 2023-11-19 18:40:12,864 | app.py:227 | app_fit: metrics_distributed_fit {}
INFO flwr 2023-11-19 18:40:12,864 | app.py:228 | app_fit: metrics_distributed {'AUC': [(1, 0.76315), (2, 0.7734), (3, 0.7783), (4, 0.7824), (5, 0.78595)]}
INFO flwr 2023-11-19 18:40:12,864 | app.py:229 | app_fit: losses_centralized []
INFO flwr 2023-11-19 18:40:12,864 | app.py:230 | app_fit: metrics_centralized {}
INFO flwr 2023-11-20 11:21:56,454 | app.py:163 | Starting Flower server, config: ServerConfig(num_rounds=5, round_timeout=None)
INFO flwr 2023-11-20 11:21:56,473 | app.py:176 | Flower ECE: gRPC server running (5 rounds), SSL is disabled
INFO flwr 2023-11-20 11:21:56,473 | server.py:89 | Initializing global parameters
INFO flwr 2023-11-20 11:21:56,473 | server.py:276 | Requesting initial parameters from one random client
INFO flwr 2023-11-20 11:22:38,302 | server.py:280 | Received initial parameters from one random client
INFO flwr 2023-11-20 11:22:38,302 | server.py:91 | Evaluating initial parameters
INFO flwr 2023-11-20 11:22:38,302 | server.py:104 | FL starting
DEBUG flwr 2023-11-20 11:22:38,302 | server.py:222 | fit_round 1: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,636 | server.py:236 | fit_round 1 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:38,643 | server.py:173 | evaluate_round 1: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,653 | server.py:187 | evaluate_round 1 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:38,653 | server.py:222 | fit_round 2: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,721 | server.py:236 | fit_round 2 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:38,745 | server.py:173 | evaluate_round 2: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,756 | server.py:187 | evaluate_round 2 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:38,756 | server.py:222 | fit_round 3: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,831 | server.py:236 | fit_round 3 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:38,868 | server.py:173 | evaluate_round 3: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,881 | server.py:187 | evaluate_round 3 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:38,881 | server.py:222 | fit_round 4: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:38,960 | server.py:236 | fit_round 4 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:39,012 | server.py:173 | evaluate_round 4: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:39,026 | server.py:187 | evaluate_round 4 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:39,026 | server.py:222 | fit_round 5: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:39,111 | server.py:236 | fit_round 5 received 2 results and 0 failures
DEBUG flwr 2023-11-20 11:22:39,177 | server.py:173 | evaluate_round 5: strategy sampled 2 clients (out of 2)
DEBUG flwr 2023-11-20 11:22:39,193 | server.py:187 | evaluate_round 5 received 2 results and 0 failures
INFO flwr 2023-11-20 11:22:39,193 | server.py:153 | FL finished in 0.8905023969999988
INFO flwr 2023-11-20 11:22:39,193 | app.py:226 | app_fit: losses_distributed [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)]
INFO flwr 2023-11-20 11:22:39,193 | app.py:227 | app_fit: metrics_distributed_fit {}
INFO flwr 2023-11-20 11:22:39,193 | app.py:228 | app_fit: metrics_distributed {'AUC': [(1, 0.7572), (2, 0.7705), (3, 0.77595), (4, 0.78), (5, 0.78385)]}
INFO flwr 2023-11-20 11:22:39,193 | app.py:229 | app_fit: losses_centralized []
INFO flwr 2023-11-20 11:22:39,193 | app.py:230 | app_fit: metrics_centralized {}

Congratulations!
You've successfully built and run your first federated XGBoost system.
Expand Down
3 changes: 2 additions & 1 deletion examples/xgboost-comprehensive/README.md
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@@ -1,7 +1,8 @@
# Flower Example using XGBoost (Comprehensive)

This example demonstrates a comprehensive federated learning setup using Flower with XGBoost.
It differs from the quickstart example in the following ways:
We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset to perform a binary classification task.
It differs from the [xgboost-quickstart](https:/adap/flower/tree/main/examples/xgboost-quickstart) example in the following ways:

- Arguments parsers of server and clients for hyperparameters selection.
- Customised FL settings.
Expand Down
2 changes: 2 additions & 0 deletions examples/xgboost-comprehensive/client.py
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Expand Up @@ -47,6 +47,7 @@
)

# Load the partition for this `node_id`
log(INFO, "Loading partition...")
node_id = args.node_id
partition = fds.load_partition(idx=node_id, split="train")
partition.set_format("numpy")
Expand All @@ -67,6 +68,7 @@
)

# Reformat data to DMatrix for xgboost
log(INFO, "Reformatting data...")
train_dmatrix = transform_dataset_to_dmatrix(train_data)
valid_dmatrix = transform_dataset_to_dmatrix(valid_data)

Expand Down
1 change: 1 addition & 0 deletions examples/xgboost-quickstart/README.md
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@@ -1,6 +1,7 @@
# Flower Example using XGBoost

This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using `xgboost` package.
We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset for this example to perform a binary classification task.
Tree-based with bagging method is used for aggregation on the server.

This project provides a minimal code example to enable you to get stated quickly. For a more comprehensive code example, take a look at [xgboost-comprehensive](https:/adap/flower/tree/main/examples/xgboost-comprehensive).
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5 changes: 4 additions & 1 deletion examples/xgboost-quickstart/client.py
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Expand Up @@ -57,10 +57,12 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.


# Load (HIGGS) dataset and conduct partitioning
partitioner = IidPartitioner(num_partitions=2)
# We use a small subset (num_partitions=30) of the dataset for demonstration to speed up the data loading process.
partitioner = IidPartitioner(num_partitions=30)
fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})

# Load the partition for this `node_id`
log(INFO, "Loading partition...")
partition = fds.load_partition(idx=args.node_id, split="train")
partition.set_format("numpy")

Expand All @@ -70,6 +72,7 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
)

# Reformat data to DMatrix for xgboost
log(INFO, "Reformatting data...")
train_dmatrix = transform_dataset_to_dmatrix(train_data)
valid_dmatrix = transform_dataset_to_dmatrix(valid_data)

Expand Down