Smokers Health FA Taskrunner Workspace (#1621)

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* remove workspace

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

* code changes

Signed-off-by: yes <shailesh.tanwar@intel.com>

---------

Signed-off-by: yes <shailesh.tanwar@intel.com>

Author: tanwarsh

openfl-workspace/federated_analytics/smokers_health/.workspace

@@ -0,0 +1,94 @@
+# Federated Analytics: Smokers Health Example
+
+This workspace demonstrates how to use OpenFL for privacy-preserving analytics on the Smokers Health dataset. The setup enables distributed computation of health statistics (such as heart rate, cholesterol, and blood pressure) across multiple collaborators, without sharing raw data.
+
+## Instantiating a Workspace from Smokers Health Template
+To instantiate a workspace from the `federated_analytics/smokers_health` template, use the `fx workspace create` command. This will set up a new workspace with the required configuration and code.
+
+1. **Install dependencies:**
+```bash
+pip install virtualenv
+mkdir ~/openfl-smokers-health
+virtualenv ~/openfl-smokers-health/venv
+source ~/openfl-smokers-health/venv/bin/activate
+pip install openfl
+```
+
+2. **Create the Workspace Folder:**
+```bash
+cd ~/openfl-smokers-health
+fx workspace create --template federated_analytics/smokers_health --prefix fl_workspace
+cd ~/openfl-smokers-health/fl_workspace
+```
+
+## Directory Structure
+The workspace has the following structure:
+```
+smokers_health
+├── requirements.txt
+├── .workspace
+├── plan
+│   ├── plan.yaml
+│   ├── cols.yaml
+│   ├── data.yaml
+│   └── defaults/
+├── src
+│   ├── __init__.py
+│   ├── dataloader.py
+│   ├── taskrunner.py
+│   └── aggregate_health.py
+├── data/
+└── save/
+```
+
+### Directory Breakdown
+- **requirements.txt**: Lists all Python dependencies for the workspace.
+- **plan/**: Contains configuration files for the federation:
+    - `plan.yaml`: Main plan declaration.
+    - `cols.yaml`: List of authorized collaborators.
+    - `data.yaml`: Data path for each collaborator.
+    - `defaults/`: Default configuration values.
+- **src/**: Python modules for federated analytics:
+    - `dataloader.py`: Loads and shards the Smokers Health dataset, supports SQL queries.
+    - `taskrunner.py`: Groups data and computes mean health metrics by age, sex, and smoking status.
+    - `aggregatehealth.py`: Aggregates results from all collaborators.
+- **data/**: Place to store the downloaded and unzipped dataset.
+- **save/**: Stores aggregated results and analytics outputs.
+
+## Data Preparation
+The data loader will automatically download the Smokers Health dataset from Kaggle or a specified source. Make sure you have the required access or download the dataset manually if needed.
+
+## Defining the Data Loader
+The data loader supports SQL-like queries and can load data from CSV or other sources as configured. It shards the dataset among collaborators and provides query functionality for analytics tasks.
+
+## Defining the Task Runner
+The task runner groups the data by `age`, `sex`, and `current_smoker`, and computes the mean of `heart_rate`, `chol`, and `blood pressure (systolic/diastolic)`. The results are returned as numpy arrays for aggregation.
+
+## Running the Federation
+1. **Initialize the plan:**
+```bash
+fx plan initialize
+```
+2. **Set up the aggregator and collaborators:**
+```bash
+fx workspace certify
+fx aggregator generate-cert-request
+fx aggregator certify --silent
+
+fx collaborator create -n collaborator1 -d 1
+fx collaborator generate-cert-request -n collaborator1
+fx collaborator certify -n collaborator1 --silent
+
+fx collaborator create -n collaborator2 -d 2
+fx collaborator generate-cert-request -n collaborator2
+fx collaborator certify -n collaborator2 --silent
+```
+3. **Start the federation:**
+```bash
+fx aggregator start &
+fx collaborator start -n collaborator1 &
+fx collaborator start -n collaborator2 &
+```
+
+## License
+This project is licensed under the Apache License 2.0. See the LICENSE file for details.

openfl-workspace/federated_analytics/smokers_health/README.md

@@ -0,0 +1,4 @@
+# Copyright (C) 2025 Intel Corporation
+# Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.
+
+collaborators:

openfl-workspace/federated_analytics/smokers_health/plan/cols.yaml

@@ -0,0 +1,4 @@
+# Copyright (C) 2025 Intel Corporation
+# Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.
+
+# collaborator_name,data_directory_path

openfl-workspace/federated_analytics/smokers_health/plan/data.yaml

@@ -0,0 +1,45 @@
+aggregator:
+  defaults: plan/defaults/aggregator.yaml
+  template: openfl.component.Aggregator
+  settings:
+    last_state_path: save/result.json
+    rounds_to_train: 1
+
+collaborator:
+  defaults: plan/defaults/collaborator.yaml
+  template: openfl.component.Collaborator
+  settings:
+    use_delta_updates: false
+    opt_treatment: RESET
+
+data_loader:
+  defaults: plan/defaults/data_loader.yaml
+  template: src.dataloader.SmokersHealthDataLoader
+  settings:
+    collaborator_count: 2
+    data_group_name: smokers_health
+    batch_size: 150
+
+task_runner:
+  defaults: plan/defaults/task_runner.yaml
+  template: src.taskrunner.SmokersHealthAnalytics
+
+network:
+  defaults: plan/defaults/network.yaml
+
+assigner:
+  template: openfl.component.RandomGroupedAssigner
+  settings:
+    task_groups:
+      - name: analytics
+        percentage: 1.0
+        tasks:
+          - analytics
+
+tasks:
+  analytics:
+    function: analytics
+    aggregation_type:
+      template: src.aggregate_health.AggregateHealthMetrics
+    kwargs:
+      columns: ['age', 'sex', 'current_smoker', 'heart_rate', 'blood_pressure', 'cigs_per_day', 'chol']

openfl-workspace/federated_analytics/smokers_health/plan/plan.yaml

@@ -0,0 +1,35 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+from openfl.interface.aggregation_functions.core import AggregationFunction
+
+
+class AggregateHealthMetrics(AggregationFunction):
+    """Aggregation logic for Smokers Health analytics."""
+
+    def call(self, local_tensors, *_) -> dict:
+        """
+        Aggregates local tensors which contains mean of local health metrics such as
+        heart_rate_mean, cholesterol, systolic_blood_pressure, and
+        diastolic_blood_pressure which are grouped by age, sex and if they smoke or not.
+        Each tensor represents local metrics for these health parameters.
+
+        Args:
+            local_tensors (list): A list of objects, each containing a `tensor` attribute
+                      that represents local means for the health metrics.
+            *_: Additional arguments (unused).
+        Returns:
+            dict: A dictionary containing the aggregated means for each health metric.
+        Raises:
+            ValueError: If the input list `local_tensors` is empty, indicating
+            that there are no metrics to aggregate.
+        """
+
+        if not local_tensors:
+            raise ValueError("No local metrics to aggregate.")
+
+        agg_histogram = np.zeros_like(local_tensors[0].tensor)
+        for local_tensor in local_tensors:
+            agg_histogram += local_tensor.tensor / len(local_tensors)
+        return agg_histogram

openfl-workspace/federated_analytics/smokers_health/requirements.txt

@@ -0,0 +1,96 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from openfl.federated.data.loader import DataLoader
+import pandas as pd
+import os
+import subprocess
+
+
+class SmokersHealthDataLoader(DataLoader):
+    """Data Loader for Smokers Health Dataset."""
+
+    def __init__(self, batch_size, data_path, **kwargs):
+        super().__init__(**kwargs)
+
+        # If data_path is None, this is being used for model initialization only
+        if data_path is None:
+            return
+
+        # Load actual data if a data path is provided
+        try:
+            int(data_path)
+        except ValueError:
+            raise ValueError(
+                f"Expected '{data_path}' to be representable as `int`, "
+                "as it refers to the data shard number used by the collaborator."
+            )
+
+        # Download and prepare data
+        self._download_raw_data()
+        self.data_shard = self.load_data_shard(
+            shard_num=int(data_path), **kwargs
+        )
+
+    def _download_raw_data(self):
+        """
+        Downloads and extracts the raw data for the smokers' health dataset.
+        This method performs the following steps:
+        1. Downloads the dataset from the specified Kaggle URL using the `curl` command.
+        2. Saves the downloaded file as a ZIP archive in the `./data` directory.
+        3. Extracts the contents of the ZIP archive into the `data` directory.
+        """
+
+        download_path = os.path.expanduser('./data/smokers_health.zip')
+        subprocess.run(
+            [
+                'curl', '-L', '-o', download_path,
+                'https://www.kaggle.com/api/v1/datasets/download/jaceprater/smokers-health-data'
+            ],
+            check=True
+        )
+
+        # Unzip the downloaded file into the data directory
+        subprocess.run(['unzip', '-o', download_path, '-d', 'data'], check=True)
+
+    def load_data_shard(self, shard_num, **kwargs):
+        """
+        Loads data from a CSV file.
+        This method reads the data from a CSV file located at './data/smoking_health_data_final.csv'
+        and returns it as a pandas DataFrame.
+        Returns:
+            pd.DataFrame: The data loaded from the CSV file.
+        """
+        file_path = os.path.join('data', 'smoking_health_data_final.csv')
+        df = pd.read_csv(file_path)
+
+        # Split data into shards
+        shard_size = len(df) // shard_num
+        start_idx = shard_size * (shard_num - 1)
+        end_idx = start_idx + shard_size
+
+        return df.iloc[start_idx:end_idx]
+
+    def query(self, columns, **kwargs):
+        """
+        Query the data shard for the specified columns.
+        Args:
+            columns (list): A list of column names to query from the data shard.
+            **kwargs: Additional keyword arguments (currently not used).
+        Returns:
+            DataFrame: A DataFrame containing the data for the specified columns.
+        Raises:
+            ValueError: If the columns parameter is not a list.
+        """
+        if not isinstance(columns, list):
+            raise ValueError("Columns parameter must be a list")
+        return self.data_shard[columns]
+
+    def get_feature_shape(self):
+        """
+        This function is not required and is kept for compatibility.
+
+        Returns:
+            None
+        """
+        pass