From 06075bde4fb15ad01109ce63fe6815553f8dbeaa Mon Sep 17 00:00:00 2001 From: Andrei Stoian <95410270+andrei-stoian-zama@users.noreply.github.com> Date: Thu, 13 Jun 2024 14:31:13 +0200 Subject: [PATCH] fix: community links in the doc (#728) --- README.md | 2 +- UPGRADING.md | 2 +- docs/README.md | 3 +- docs/SUMMARY.md | 1 - docs/developer/debug_support_submit_issues.md | 2 +- docs/getting-started/README.md | 7 +-- docs/references/pandas.md | 61 ------------------- 7 files changed, 6 insertions(+), 72 deletions(-) delete mode 100644 docs/references/pandas.md diff --git a/README.md b/README.md index 0ad1560f6..063c284f1 100644 --- a/README.md +++ b/README.md @@ -277,7 +277,7 @@ This software is distributed under the **BSD-3-Clause-Clear** license. Read [thi ## Support - + diff --git a/UPGRADING.md b/UPGRADING.md index 9a579cf4a..4cc9717cc 100644 --- a/UPGRADING.md +++ b/UPGRADING.md @@ -1,6 +1,6 @@ # Upgrading Concrete ML In Your Project -This guide aims to help developers who are upgrading from older versions of Concrete ML. Although we cannot cover everything, we have compiled the most important points to assist you in your transition. If you encounter any issues, please do not hesitate to reach out to https://community.zama.ai or ask on the fhe.org Discord. +This guide aims to help developers who are upgrading from older versions of Concrete ML. Although we cannot cover everything, we have compiled the most important points to assist you in your transition. If you encounter any issues, please reach out through our [community channels](https://zama.ai/community-channels). ## Upgrading to 1.0.0 diff --git a/docs/README.md b/docs/README.md index 6f38f50de..73bb83795 100644 --- a/docs/README.md +++ b/docs/README.md @@ -49,8 +49,7 @@ Refer to the API, review product architecture, and access additional resources f Ask technical questions and discuss with the community. Our team of experts usually answers within 24 hours in working days. -- [Community forum](https://community.zama.ai/c/concrete-ml/8) -- [Discord channel](https://discord.fhe.org/) +- [Community channels](https://zama.ai/community-channels) ### Developers diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md index 8ab620656..9ea518028 100644 --- a/docs/SUMMARY.md +++ b/docs/SUMMARY.md @@ -42,7 +42,6 @@ ## References - [API](references/api/README.md) -- [Pandas support](references/pandas.md) ## Explanations diff --git a/docs/developer/debug_support_submit_issues.md b/docs/developer/debug_support_submit_issues.md index 3c6e458d5..8a0cc3309 100644 --- a/docs/developer/debug_support_submit_issues.md +++ b/docs/developer/debug_support_submit_issues.md @@ -6,7 +6,7 @@ Before opening an issue or asking for support, please read this documentation to Furthermore, undefined behavior may occur if the input-set, which is internally used by the compilation core to set bit-widths of some intermediate data, is not sufficiently representative of the future user inputs. With all the inputs in the input-set, it appears that intermediate data can be represented as an n-bit integer. But, for a particular computation, this same intermediate data needs additional bits to be represented. The FHE execution for this computation will result in an incorrect output, as typically occurs in integer overflows in classical programs. -If you didn't find an answer, you can ask a question on the [Zama forum](https://community.zama.ai) or in the FHE.org [Discord](https://discord.fhe.org). +If you didn't find an answer, you can ask a question through the [community channels](https://zama.ai/community-channels). ## Submitting an issue diff --git a/docs/getting-started/README.md b/docs/getting-started/README.md index e039397d6..f70d25db6 100644 --- a/docs/getting-started/README.md +++ b/docs/getting-started/README.md @@ -107,12 +107,9 @@ If you have built awesome projects using Concrete ML, feel free to let us know a ## Additional resources -- [Dedicated Concrete ML community support](https://community.zama.ai/c/concrete-ml/8) +- [Community channels](https://zama.ai/community-channels) - [Zama's blog](https://www.zama.ai/blog) -- [FHE.org community](https://fhe.org) ## Support -- Support forum: [https://community.zama.ai](https://community.zama.ai) (we answer in less than 24 hours). -- Live discussion on the FHE.org Discord server: [https://discord.fhe.org](https://discord.fhe.org) (inside the #**concrete** channel). -- Do you have a question about Zama? Write us on [Twitter](https://twitter.com/zama_fhe) or send us an email at: **hello@zama.ai** +- [Community channels](https://zama.ai/community-channels) (we answer in less than 24 hours). diff --git a/docs/references/pandas.md b/docs/references/pandas.md deleted file mode 100644 index 4acf4937b..000000000 --- a/docs/references/pandas.md +++ /dev/null @@ -1,61 +0,0 @@ -# Pandas - -Concrete ML fully supports Pandas, allowing built-in models such as linear and tree-based models to use Pandas dataframes and series just as they would be used with NumPy arrays. - -The table below summarizes current compatibility: - -| Methods | Support Pandas dataframe | -| :----------------------: | :----------------------: | -| fit | ✓ | -| compile | ✓ | -| predict (fhe="simulate") | ✓ | -| predict (fhe="execute") | ✓ | - -## Example - -The following example considers a `LogisticRegression` model on a simple classification problem. -A more advanced example can be found in the [Titanic use case notebook](../../use_case_examples/titanic/KaggleTitanic.ipynb), which considers a `XGBClassifier`. - -```python -import numpy as np -import pandas as pd -from concrete.ml.sklearn import LogisticRegression -from sklearn.datasets import make_classification -from sklearn.model_selection import train_test_split - -# Create the data-set as a Pandas dataframe -X, y = make_classification( - n_samples=250, - n_features=30, - n_redundant=0, - random_state=2, -) -X, y = pd.DataFrame(X), pd.DataFrame(y) - -# Retrieve train and test sets -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42) - -# Instantiate the model -model = LogisticRegression(n_bits=8) - -# Fit the model -model.fit(X_train, y_train) - -# Evaluate the model on the test set in clear -y_pred_clear = model.predict(X_test) - -# Compile the model -model.compile(X_train) - -# Perform the inference in FHE -y_pred_fhe = model.predict(X_test, fhe="execute") - -# Assert that FHE predictions are the same as the clear predictions -print( - f"{(y_pred_fhe == y_pred_clear).sum()} " - f"examples over {len(y_pred_fhe)} have an FHE inference equal to the clear inference." -) - -# Output: - # 100 examples over 100 have an FHE inference equal to the clear inference. -```