# Load your dataset using polars.
>>> import polars as pl
>>> df = pl.read_csv("titanic.csv")

# Define a custom policy for your data.
# In this example, requests that aggregate at least 10 rows are safe.
# Other requests will be reviewed by the data owner.
>>> from bastionlab.polars.policy import Policy, Aggregation, Review
>>> policy = Policy(safe_zone=Aggregation(min_agg_size=10), unsafe_handling=Review())

# Upload your dataset to the server.
# Optionally anonymize sensitive columns.
# The server returns a remote object that can be used to query the dataset.
>>> from bastionlab import Connection
>>> with Connection("bastionlab.example.com") as client:
...     rdf = client.polars.send_df(df, policy=policy, sanitized_columns=["Name"])
...     rdf
...
FetchableLazyFrame(identifier=3a2d15c5-9f9d-4ced-9234-d9465050edb1)

# List the datasets made available by the data owner, select one and get a remote object.
>>> from bastionlab import Connection
>>> connection = Connection("localhost")
>>> all_remote_dfs = connection.client.polars.list_dfs()
>>> remote_df = all_remote_dfs[0]

# Run unsafe queries such as displaying the five first rows.
# According to the policy, unsafe queries require the data owner's approval.
>>> remote_df.head(5).collect().fetch()
Warning: non privacy-preserving queries necessitate data owner's approval.
Reason: Only 1 subrules matched but at least 2 are required.
Failed sub rules are:
Rule #1: Cannot fetch a DataFrame that does not aggregate at least 10 rows of the initial dataframe uploaded by the data owner.

A notification has been sent to the data owner. The request will be pending until the data owner accepts or denies it or until timeout seconds elapse.
The query has been accepted by the data owner.
shape: (5, 12)
┌─────────────┬──────────┬────────┬──────┬─────┬──────────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ ... ┆ Ticket           ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆     ┆ ---              ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆     ┆ str              ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═════╪══════════════════╪═════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ null ┆ ... ┆ A/5 21171        ┆ 7.25    ┆ null  ┆ S        │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2           ┆ 1        ┆ 1      ┆ null ┆ ... ┆ PC 17599         ┆ 71.2833 ┆ C85   ┆ C        │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 3           ┆ 1        ┆ 3      ┆ null ┆ ... ┆ STON/O2. 3101282 ┆ 7.925   ┆ null  ┆ S        │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 4           ┆ 1        ┆ 1      ┆ null ┆ ... ┆ 113803           ┆ 53.1    ┆ C123  ┆ S        │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 5           ┆ 0        ┆ 3      ┆ null ┆ ... ┆ 373450           ┆ 8.05    ┆ null  ┆ S        │
└─────────────┴──────────┴────────┴──────┴─────┴──────────────────┴─────────┴───────┴──────────┘

# Run safe queries and get the result right away.
>>> (
... remote_df
... .select([pl.col("Pclass"), pl.col("Survived")])
... .groupby(pl.col("Pclass"))
... .agg(pl.col("Survived").mean())
... .sort("Survived", reverse=True)
... .collect()
... .fetch()
... )
shape: (3, 2)
┌────────┬──────────┐
│ Pclass ┆ Survived │
│ ---    ┆ ---      │
│ i64    ┆ f64      │
╞════════╪══════════╡
│ 1      ┆ 0.62963  │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2      ┆ 0.472826 │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 3      ┆ 0.242363 │
└────────┴──────────┘

>>> from torchvision.datasets import CIFAR100
>>> from torchvision.transforms import ToTensor, Normalize, Compose
>>> from bastionlab.client import Connection

# Define a transformation pipeline for the CIFAR dataset.
# The last step is there for shape compatibility reasons.
>>> transform = Compose([
...     ToTensor(),
...     Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
...     lambda x: [x.squeeze(0)],
... ])

# Define train and test datasets
>>> train_dataset = CIFAR100("data", train=True, transform=transform, download=True)
Files already downloaded and verified
>>> test_dataset = CIFAR100("data", train=False, transform=transform, download=True)
Files already downloaded and verified

# Send them to the server by instantiating a RemoteDataset.
>>> with Connection("localhost") as client:
...     client.torch.RemoteDataset(train_dataset, test_dataset, name="CIFAR100")
...
Sending CIFAR100: 100%|████████████████████| 615M/615M [00:04<00:00, 150MB/s]  
Sending CIFAR100 (test): 100%|████████████████████| 123M/123M [00:00<00:00, 150MB/s]
<bastionlab.torch.learner.RemoteDataset object at 0x7f1220063ac0>

>>> from torchvision.models import efficientnet_b0
>>> from bastionlab.client import Connection

# Define the model
>>> model = efficientnet_b0()

# List the datasets made available by the data owner, select one and get a remote object.
>>> connection = Connection("localhost")
>>> remote_datasets = connection.client.torch.list_remote_datasets()
>>> remote_dataset = remote_datasets[0]

# Send the model to the server by instantiating a RemoteLearner
# The RemoteLearner objects references the RemoteDataset.
>>> remote_learner = connection.client.torch.RemoteLearner(
...     model,
...     remote_dataset,
...     max_batch_size=64,
...     loss="cross_entropy",
...     model_name="EfficientNet-B0",
...     device="cpu",
... )
Sending EfficientNet-B0: 100%|████████████████████| 21.7M/21.7M [00:00<00:00, 531MB/s]

# Train the remote model for given amount of epochs
>>> remote_learner.fit(nb_epochs=1)
Epoch 1/1 - train: 100%|████████████████████| 781/781 [04:06<00:00,  3.17batch/s, cross_entropy=4.1798 (+/- 0.0000)]

# Test the remote model
>>> remote_learner.test(metric="accuracy")
Epoch 1/1 - test: 100%|████████████████████| 156/156 [00:14<00:00, 10.62batch/s, accuracy=0.1123 (+/- 0.0000)]