docs: add dataframe documentation (#576)

docs/README.md

@@ -27,7 +27,7 @@ Learn the basics of Concrete ML, set it up, and make it run with ease.
 
 Start building with Concrete ML by exploring its core features, discovering essential guides, and learning more with user-friendly tutorials.
 
-<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th><th data-hidden data-card-cover data-type="files"></th></tr></thead><tbody><tr><td><strong>Fundamentals</strong></td><td>Explore core features and basics of Concrete ML.<br><br></td><td><ul><li><a href="tutorials/ml_examples.md">Build-in models</a></li><li><a href="tutorials/dl_examples.md">Deep learning</a></li></ul></td><td></td><td><a href=".gitbook/assets/orange1.png">orange1.png</a></td></tr><tr><td><strong>Guides</strong></td><td>Discover essential guides to work with Concrete ML.<br><br></td><td><ul><li><a href="guides/prediction_with_fhe.md">Prediction with FHE</a></li><li><a href="guides/client_server.md">Production deployment</a></li><li><a href="guides/hybrid-models.md">Hybrid models</a></li></ul></td><td></td><td><a href=".gitbook/assets/orange2.png">orange2.png</a></td></tr><tr><td><strong>Tutorials</strong></td><td>Learn more about Concrete ML with our tutorials.<br><br></td><td><ul><li><a href="tutorials/showcase.md#start-here">Start here</a></li><li><a href="tutorials/showcase.md#go-further">Go further</a></li><li><a href="tutorials/showcase.md">See all tutorials</a></li></ul></td><td></td><td><a href=".gitbook/assets/orange3.png">orange3.png</a></td></tr></tbody></table>
+<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th><th data-hidden data-card-cover data-type="files"></th></tr></thead><tbody><tr><td><strong>Fundamentals</strong></td><td>Explore core features and basics of Concrete ML.<br><br></td><td><ul><li><a href="tutorials/ml_examples.md">Built-in models</a></li><li><a href="built-in-models/encrypted_dataframe.md">Encrypted data-frames</a></li><li><a href="tutorials/dl_examples.md">Deep learning</a></li></ul></td><td></td><td><a href=".gitbook/assets/orange1.png">orange1.png</a></td></tr><tr><td><strong>Guides</strong></td><td>Discover essential guides to work with Concrete ML.<br><br></td><td><ul><li><a href="guides/prediction_with_fhe.md">Prediction with FHE</a></li><li><a href="guides/client_server.md">Production deployment</a></li><li><a href="guides/hybrid-models.md">Hybrid models</a></li></ul></td><td></td><td><a href=".gitbook/assets/orange2.png">orange2.png</a></td></tr><tr><td><strong>Tutorials</strong></td><td>Learn more about Concrete ML with our tutorials.<br><br></td><td><ul><li><a href="tutorials/showcase.md#start-here">Start here</a></li><li><a href="tutorials/showcase.md#go-further">Go further</a></li><li><a href="tutorials/showcase.md">See all tutorials</a></li></ul></td><td></td><td><a href=".gitbook/assets/orange3.png">orange3.png</a></td></tr></tbody></table>
 
 ## Explore more
 

docs/SUMMARY.md

@@ -15,7 +15,7 @@
 - [Tree-based models](built-in-models/tree.md)
 - [Neural networks](built-in-models/neural-networks.md)
 - [Nearest neighbors](built-in-models/nearest-neighbors.md)
-- [Pandas](built-in-models/pandas.md)
+- [Encrypted dataframe](built-in-models/encrypted_dataframe.md)
 - [Encrypted training](built-in-models/training.md)
 
 ## Deep Learning
@@ -41,62 +41,8 @@
 
 ## References
 
-<!-- auto-created, do not edit, begin -->
-
 - [API](references/api/README.md)
-  - [concrete.ml.common.check_inputs.md](references/api/concrete.ml.common.check_inputs.md)
-  - [concrete.ml.common.debugging.custom_assert.md](references/api/concrete.ml.common.debugging.custom_assert.md)
-  - [concrete.ml.common.debugging.md](references/api/concrete.ml.common.debugging.md)
-  - [concrete.ml.common.md](references/api/concrete.ml.common.md)
-  - [concrete.ml.common.serialization.decoder.md](references/api/concrete.ml.common.serialization.decoder.md)
-  - [concrete.ml.common.serialization.dumpers.md](references/api/concrete.ml.common.serialization.dumpers.md)
-  - [concrete.ml.common.serialization.encoder.md](references/api/concrete.ml.common.serialization.encoder.md)
-  - [concrete.ml.common.serialization.loaders.md](references/api/concrete.ml.common.serialization.loaders.md)
-  - [concrete.ml.common.serialization.md](references/api/concrete.ml.common.serialization.md)
-  - [concrete.ml.common.utils.md](references/api/concrete.ml.common.utils.md)
-  - [concrete.ml.deployment.deploy_to_aws.md](references/api/concrete.ml.deployment.deploy_to_aws.md)
-  - [concrete.ml.deployment.deploy_to_docker.md](references/api/concrete.ml.deployment.deploy_to_docker.md)
-  - [concrete.ml.deployment.fhe_client_server.md](references/api/concrete.ml.deployment.fhe_client_server.md)
-  - [concrete.ml.deployment.md](references/api/concrete.ml.deployment.md)
-  - [concrete.ml.deployment.server.md](references/api/concrete.ml.deployment.server.md)
-  - [concrete.ml.deployment.utils.md](references/api/concrete.ml.deployment.utils.md)
-  - [concrete.ml.onnx.convert.md](references/api/concrete.ml.onnx.convert.md)
-  - [concrete.ml.onnx.md](references/api/concrete.ml.onnx.md)
-  - [concrete.ml.onnx.onnx_impl_utils.md](references/api/concrete.ml.onnx.onnx_impl_utils.md)
-  - [concrete.ml.onnx.onnx_model_manipulations.md](references/api/concrete.ml.onnx.onnx_model_manipulations.md)
-  - [concrete.ml.onnx.onnx_utils.md](references/api/concrete.ml.onnx.onnx_utils.md)
-  - [concrete.ml.onnx.ops_impl.md](references/api/concrete.ml.onnx.ops_impl.md)
-  - [concrete.ml.pytest.md](references/api/concrete.ml.pytest.md)
-  - [concrete.ml.pytest.torch_models.md](references/api/concrete.ml.pytest.torch_models.md)
-  - [concrete.ml.pytest.utils.md](references/api/concrete.ml.pytest.utils.md)
-  - [concrete.ml.quantization.base_quantized_op.md](references/api/concrete.ml.quantization.base_quantized_op.md)
-  - [concrete.ml.quantization.md](references/api/concrete.ml.quantization.md)
-  - [concrete.ml.quantization.post_training.md](references/api/concrete.ml.quantization.post_training.md)
-  - [concrete.ml.quantization.quantized_module.md](references/api/concrete.ml.quantization.quantized_module.md)
-  - [concrete.ml.quantization.quantized_module_passes.md](references/api/concrete.ml.quantization.quantized_module_passes.md)
-  - [concrete.ml.quantization.quantized_ops.md](references/api/concrete.ml.quantization.quantized_ops.md)
-  - [concrete.ml.quantization.quantizers.md](references/api/concrete.ml.quantization.quantizers.md)
-  - [concrete.ml.search_parameters.md](references/api/concrete.ml.search_parameters.md)
-  - [concrete.ml.search_parameters.p_error_search.md](references/api/concrete.ml.search_parameters.p_error_search.md)
-  - [concrete.ml.sklearn.base.md](references/api/concrete.ml.sklearn.base.md)
-  - [concrete.ml.sklearn.glm.md](references/api/concrete.ml.sklearn.glm.md)
-  - [concrete.ml.sklearn.linear_model.md](references/api/concrete.ml.sklearn.linear_model.md)
-  - [concrete.ml.sklearn.md](references/api/concrete.ml.sklearn.md)
-  - [concrete.ml.sklearn.neighbors.md](references/api/concrete.ml.sklearn.neighbors.md)
-  - [concrete.ml.sklearn.qnn.md](references/api/concrete.ml.sklearn.qnn.md)
-  - [concrete.ml.sklearn.qnn_module.md](references/api/concrete.ml.sklearn.qnn_module.md)
-  - [concrete.ml.sklearn.rf.md](references/api/concrete.ml.sklearn.rf.md)
-  - [concrete.ml.sklearn.svm.md](references/api/concrete.ml.sklearn.svm.md)
-  - [concrete.ml.sklearn.tree.md](references/api/concrete.ml.sklearn.tree.md)
-  - [concrete.ml.sklearn.tree_to_numpy.md](references/api/concrete.ml.sklearn.tree_to_numpy.md)
-  - [concrete.ml.sklearn.xgb.md](references/api/concrete.ml.sklearn.xgb.md)
-  - [concrete.ml.torch.compile.md](references/api/concrete.ml.torch.compile.md)
-  - [concrete.ml.torch.hybrid_model.md](references/api/concrete.ml.torch.hybrid_model.md)
-  - [concrete.ml.torch.md](references/api/concrete.ml.torch.md)
-  - [concrete.ml.torch.numpy_module.md](references/api/concrete.ml.torch.numpy_module.md)
-  - [concrete.ml.version.md](references/api/concrete.ml.version.md)
-
-<!-- auto-created, do not edit, end -->
+- [Pandas support](references/pandas.md)
 
 ## Explanations
 

docs/built-in-models/encrypted_dataframe.md

@@ -0,0 +1,116 @@
+# Working with Encrypted DataFrames
+
+Concrete ML builds upon the pandas data-frame functionality by introducing the capability to construct and perform operations on encrypted data-frames using FHE. This API ensures data scientists can leverage well-known pandas-like operations while maintaining privacy throughout the whole process.
+
+Encrypted data-frames are a storage format for encrypted tabular data and they can be exchanged with third-parties without security risks.
+
+Potential applications include:
+
+- Encrypted storage of tabular datasets
+- Joint data analysis efforts between multiple parties
+- Data preparation steps before machine learning tasks, such as inference or training
+- Secure outsourcing of data analysis to untrusted third parties
+
+## Encrypt and Decrypt a DataFrame
+
+To encrypt a pandas `DataFrame`, you must construct a `ClientEngine` which manages keys. Then call the `encrypt_from_pandas` function:
+
+```python
+from concrete.ml.pandas import ClientEngine
+from io import StringIO
+import pandas
+
+data_left = """index,total_bill,tip,sex,smoker
+1,12.54,2.5,Male,No
+2,11.17,1.5,Female,No
+3,20.29,2.75,Female,No
+"""
+
+# Load your pandas DataFrame
+df = pandas.read_csv(StringIO(data_left))
+
+# Obtain client object
+client = ClientEngine(keys_path="my_keys")
+
+# Encrypt the DataFrame
+df_encrypted = client.encrypt_from_pandas(df)
+
+# Decrypt the DataFrame to produce a pandas DataFrame
+df_decrypted = client.decrypt_to_pandas(df_encrypted)
+```
+
+## Supported Data Types and Schema Definition
+
+Concrete ML's encrypted `DataFrame` operations support a specific set of data types:
+
+- **Integer**:  Integers are supported within a specific range determined by the encryption scheme's quantization parameters. Default range is 1 to 15. 0 being used for the `NaN`. Values outside this range will cause a `ValueError` to be raised during the pre-processing stage.
+- **Quantized Float**: Floating-point numbers are quantized to integers within the supported range. This is achieved by computing a scale and zero point for each column, which are used to map the floating-point numbers to the quantized integer space.
+- **String Enum**: String columns are mapped to integers starting from 1. This mapping is stored and later used for de-quantization. If the number of unique strings exceeds 15, a `ValueError` is raised.
+
+## Supported Operations on Encrypted Data-frames
+
+> **Outsourced execution**: The merge operation on Encrypted DataFrames can be **securely** performed on a third-party server. This means that the server can execute the merge without ever having access to the unencrypted data. The server only requires the encrypted DataFrames.
+
+Encrypted DataFrames support a subset of operations that are available for pandas DataFrames. The following operations are currently supported:
+
+- `merge`: left or right join two data-frames
+
+<!--pytest-codeblocks:cont-->
+
+```python
+df_right = """index,day,time,size
+2,Thur,Lunch,2
+5,Sat,Dinner,3
+9,Sun,Dinner,2"""
+
+# Encrypt the DataFrame
+df_encrypted2 = client.encrypt_from_pandas(pandas.read_csv(StringIO(df_right)))
+
+df_encrypted_merged = df_encrypted.merge(df_encrypted2, how="left", on="index")
+```
+
+## Serialization of Encrypted Data-frames
+
+Encrypted `DataFrame` objects can be serialized to a file format for storage or transfer. When serialized, they contain the encrypted data and [evaluation keys](../getting-started/concepts.md#cryptography-concepts) necessary to perform computations.
+
+> **Security**: Serialized data-frames do not contain any secret keys. The data-frames can be exchanged with any third-party without any risk.
+
+### Saving and loading Data-frames
+
+To save or load an encrypted `DataFrame` from a file, use the following commands:
+
+<!--pytest-codeblocks:cont-->
+
+```python
+from concrete.ml.pandas import load_encrypted_dataframe
+
+# Save
+df_encrypted_merged.save("df_encrypted_merged")
+
+# Load
+df_encrypted_merged = load_encrypted_dataframe("df_encrypted_merged")
+
+# Decrypt the DataFrame
+df_decrypted = client.decrypt_to_pandas(df_encrypted)
+```
+
+## Error Handling
+
+The library is designed to raise specific errors when encountering issues during the pre-processing and post-processing stages:
+
+- `ValueError`: Raised when a column contains values outside the allowed range for integers, when there are too many unique strings, or when encountering an unsupported data type. Raised also when an operation is attempted on a data type that is not supported by the operation.
+
+## Example Workflow
+
+An example workflow where two clients encrypt two `DataFrame` objects, perform a merge operation on the server side, and then decrypt the results is available in the notebook [encrypted_pandas.ipynb](../advanced_examples/EncryptedPandas.ipynb).
+
+## Current Limitations
+
+While this API offers a new secure way to work on remotely stored and encrypted data, it has some strong limitations at the moment:
+
+- **Precision of Values**: The precision for numerical values is limited to 4 bits.
+- **Supported Operations**: The `merge` operation is the only one available.
+- **Index Handling**: Index values are not preserved; users should move any relevant data from the index to a dedicated new column before encrypting.
+- **Integer Range**: The range of integers that can be encrypted is between 1 and 15.
+- **Uniqueness for `merge`**: The `merge` operation requires that the columns to merge on contain unique values. Currently this means that data-frames are limited to 15 rows.
+- **Metadata Security**: Column names and the mapping of strings to integers are not encrypted and are sent to the server in clear text.

docs/getting-started/README.md

@@ -2,7 +2,11 @@
 
 <figure><img src="../.gitbook/assets/doc_header_CML.png" alt=""><figcaption></figcaption></figure>
 
-Concrete ML is an open source, privacy-preserving, machine learning framework based on Fully Homomorphic Encryption (FHE). It enables data scientists without any prior knowledge of cryptography to automatically turn machine learning models into their FHE equivalent, using familiar APIs from scikit-learn and PyTorch (see how it looks for [linear models](../built-in-models/linear.md), [tree-based models](../built-in-models/tree.md), and [neural networks](../built-in-models/neural-networks.md)). Concrete ML supports converting models for inference with FHE but can also [train some models](../built-in-models/training.md) on encrypted data.
+Concrete ML is an open source, privacy-preserving, machine learning framework based on Fully Homomorphic Encryption (FHE). It enables data scientists without any prior knowledge of cryptography to:
+
+- automatically turn machine learning models into their FHE equivalent, using familiar APIs from scikit-learn and PyTorch (see how this works for [linear models](../built-in-models/linear.md), [tree-based models](../built-in-models/tree.md), and [neural networks](../built-in-models/neural-networks.md)).
+- [train models](../built-in-models/training.md) on encrypted data.
+- [pre-process encrypted data](../built-in-models/encrypted_dataframe.md) through a data-frame paradigm
 
 Fully Homomorphic Encryption is an encryption technique that allows computing directly on encrypted data, without needing to decrypt it. With FHE, you can build private-by-design applications without compromising on features. You can learn more about FHE in [this introduction](https://www.zama.ai/post/tfhe-deep-dive-part-1) or by joining the [FHE.org](https://fhe.org) community.
 

docs/getting-started/cloud.md

@@ -1,8 +1,8 @@
-# Inference in the cloud
+# Working in the cloud
 
-Concrete ML models can be easily deployed in a client/server setting, enabling the creation of privacy-preserving services in the cloud.
+Concrete ML models and data-frames can be easily deployed in a client/server setting, enabling the creation of privacy-preserving services in the cloud.
 
-As seen in the [concepts section](concepts.md), once compiled to FHE, a Concrete ML model generates machine code that performs the inference on private data. _Secret_ encryption keys are needed so that the user can securely encrypt their data and decrypt the inference result. An _evaluation_ key is also needed for the server to securely process the user's encrypted data.
+As seen in the [concepts section](concepts.md), once compiled to FHE, a Concrete ML model or data-frame generates machine code that execute prediction, training or pre-processing on encrypted data. _Secret_ encryption keys are needed so that the user can securely encrypt their data and decrypt the execution result. An _evaluation_ key is also needed for the server to securely process the user's encrypted data.
 
 Keys are generated by the user _once_ for each service they use, based on the model the service provides and its cryptographic parameters.
 
@@ -12,10 +12,10 @@ The overall communications protocol to enable cloud deployment of machine learni
 
 The steps detailed above are:
 
-1. The model developer deploys the compiled machine learning model to the server. This model includes the cryptographic parameters. The server is now ready to provide private inference.
+1. The model developer deploys the compiled machine learning model to the server. This model includes the cryptographic parameters. The server is now ready to provide private inference. Crypto-graphic parameters and compiled programs for data-frames are included directly in Concrete ML.
 1. The client requests the cryptographic parameters (also called "client specs"). Once it receives them from the server, the _secret_ and _evaluation_ keys are generated.
-1. The client sends the _evaluation_ key to the server. The server is now ready to accept requests from this client. The client sends their encrypted data.
-1. The server uses the _evaluation_ key to securely run inference on the user's data and sends back the encrypted result.
+1. The client sends the _evaluation_ key to the server. The server is now ready to accept requests from this client. The client sends their encrypted data. Serialized data-frames include client evaluation keys.
+1. The server uses the _evaluation_ key to securely run prediction, training and pre-processing on the user's data and sends back the encrypted result.
 1. The client now decrypts the result and can send back new requests.
 
-For more information on how to implement this basic secure inference protocol, refer to the [Production Deployment section](../guides/client_server.md) and to the [client/server example](../advanced_examples/ClientServer.ipynb).
+For more information on how to implement this basic secure inference protocol, refer to the [Production Deployment section](../guides/client_server.md) and to the [client/server example](../advanced_examples/ClientServer.ipynb). For information on training on encrypted data, see [the corresponding section](../built-in-models/training.md).