Add E2E test for Pandas

.github/workflows/e2e.yml

@@ -104,6 +104,41 @@ jobs:
           ./test.sh
 
 
+  pandas:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 10
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.8.16
+      - name: Install
+        run: |
+          python -m pip install -U pip==22.3.1
+          python -m pip install -U setuptools==65.6.3
+          python -m pip install poetry==1.3.2
+          poetry config virtualenvs.create false
+      - name: Install dependencies
+        run: |
+          cd e2e/pandas
+          python -m poetry install
+      - name: Cache Datasets
+        uses: actions/cache@v2
+        with:
+          path: "./e2e/pandas/data"
+          key: pandas-datasets
+      - name: Download Datasets
+        run: |
+          cd e2e/pandas
+          mkdir -p data
+          python -c "from sklearn.datasets import load_iris; load_iris(as_frame=True)['data'].to_csv('./data/client.csv')"
+      - name: Run edge client test
+        run: |
+          cd e2e/pandas
+          ./test.sh
+
+
   jax:
     runs-on: ubuntu-22.04
     timeout-minutes: 10

e2e/pandas/README.md

@@ -0,0 +1,3 @@
+# Flower with Pandas testing
+
+This directory is used for testing Flower with Pandas by computing an historgram of some simple Iris dataset.

e2e/pandas/client.py

@@ -0,0 +1,41 @@
+import warnings
+from typing import Dict, List, Tuple
+
+import numpy as np
+import pandas as pd
+
+import flwr as fl
+
+
+df = pd.read_csv("./data/client.csv")
+
+column_names = ["sepal length (cm)", "sepal width (cm)"]
+
+
+def compute_hist(df: pd.DataFrame, col_name: str) -> np.ndarray:
+    freqs, _ = np.histogram(df[col_name])
+    return freqs
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def fit(
+        self, parameters: List[np.ndarray], config: Dict[str, str]
+    ) -> Tuple[List[np.ndarray], int, Dict]:
+        hist_list = []
+        # Execute query locally
+        for c in column_names:
+            hist = compute_hist(df, c)
+            hist_list.append(hist)
+        return (
+            hist_list,
+            len(df),
+            {},
+        )
+
+if __name__ == "__main__":
+    # Start Flower client
+    fl.client.start_numpy_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient(),
+    )

e2e/pandas/pyproject.toml

@@ -0,0 +1,17 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "quickstart_pandas"
+version = "0.1.0"
+description = "Pandas Federated Analytics Quickstart with Flower"
+authors = ["Ragy Haddad <ragy202@gmail.com>"]
+maintainers = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+flwr = { path = "../../", develop = true, extras = ["simulation"] }
+numpy = "^1.21.0"
+pandas = "^2.0.0"
+scikit-learn = "^1.1.1"

e2e/pandas/server.py

@@ -0,0 +1,34 @@
+import flwr as fl
+
+from strategy import FedAnalytics
+
+# Start Flower server
+hist = fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=1),
+    strategy=FedAnalytics(),
+)
+assert hist.metrics_centralized["Aggregated histograms"][1][1] == [
+    "Length:",
+    "18",
+    "46",
+    "28",
+    "54",
+    "32",
+    "52",
+    "36",
+    "12",
+    "10",
+    "12",
+    "Width:",
+    "8",
+    "14",
+    "44",
+    "48",
+    "74",
+    "62",
+    "20",
+    "22",
+    "4",
+    "4",
+]

e2e/pandas/strategy.py

@@ -0,0 +1,78 @@
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+
+from flwr.common import (
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy import Strategy
+
+
+class FedAnalytics(Strategy):
+    def __init__(
+        self, compute_fns: List[Callable] = None, col_names: List[str] = None
+    ) -> None:
+        super().__init__()
+
+    def initialize_parameters(
+        self, client_manager: Optional[ClientManager] = None
+    ) -> Optional[Parameters]:
+        return None
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        config = {}
+        fit_ins = FitIns(parameters, config)
+        clients = client_manager.sample(num_clients=2, min_num_clients=2)
+        return [(client, fit_ins) for client in clients]
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        # Get results from fit
+        # Convert results
+        values_aggregated = [
+            (parameters_to_ndarrays(fit_res.parameters)) for _, fit_res in results
+        ]
+        length_agg_hist = 0
+        width_agg_hist = 0
+        for val in values_aggregated:
+            length_agg_hist += val[0]
+            width_agg_hist += val[1]
+
+        ndarr = np.concatenate(
+            (["Length:"], length_agg_hist, ["Width:"], width_agg_hist)
+        )
+        return ndarrays_to_parameters(ndarr), {}
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        agg_hist = [arr.item() for arr in parameters_to_ndarrays(parameters)]
+        return 0, {"Aggregated histograms": agg_hist}
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        pass
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        pass

e2e/pandas/test.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+bash ../test.sh