psi_quick_start.md

PSI Quick Start

1. install fate_client with extra package fate

python -m pip install -U pip && python -m pip install fate_client[fate,fate_flow]==2.2.0

after installing packages successfully, initialize fate_flow service and fate_client

mkdir fate_workspace
fate_flow init --ip 127.0.0.1 --port 9380 --home $(pwd)/fate_workspace
pipeline init --ip 127.0.0.1 --port 9380

fate_flow start
fate_flow status # make sure fate_flow service is started

2. download example data

wget https://github.com/raw/wiki/FederatedAI/FATE/example/data/breast_hetero_guest.csv && \
wget https://github.com/raw/wiki/FederatedAI/FATE/example/data/breast_hetero_host.csv

3. transform example data to dataframe using in fate

import os
from fate_client.pipeline import FateFlowPipeline


base_path = os.path.abspath(os.path.join(__file__, os.path.pardir))
guest_data_path = os.path.join(base_path, "breast_hetero_guest.csv")
host_data_path = os.path.join(base_path, "breast_hetero_host.csv")

data_pipeline = FateFlowPipeline().set_parties(local="0")
guest_meta = {
    "delimiter": ",", "dtype": "float64", "label_type": "int64","label_name": "y", "match_id_name": "id"
}
host_meta = {
    "delimiter": ",", "input_format": "dense", "match_id_name": "id"
}
data_pipeline.transform_local_file_to_dataframe(file=guest_data_path, namespace="experiment", name="breast_hetero_guest",
                                                meta=guest_meta, head=True, extend_sid=True)
data_pipeline.transform_local_file_to_dataframe(file=host_data_path, namespace="experiment", name="breast_hetero_host",
                                                meta=host_meta, head=True, extend_sid=True)

4. run psi

from fate_client.pipeline.components.fate import (
    Reader,
    PSI
)
from fate_client.pipeline import FateFlowPipeline


# create pipeline for training
pipeline = FateFlowPipeline().set_parties(guest="9999", host="10000")

# create reader task_desc
reader_0 = Reader("reader_0")
reader_0.guest.task_parameters(namespace="experiment", name="breast_hetero_guest")
reader_0.hosts[0].task_parameters(namespace="experiment", name="breast_hetero_host")

# create psi component_desc
psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])

# add training task
pipeline.add_tasks([reader_0, psi_0])

# compile and train
pipeline.compile()
pipeline.fit()

5. More tutorials More pipeline api guides can be found in this link