cocos/test/manual/README.md

Manual tests

CLI

Throughout the tests, we assume that our current working directory is the root of the agent repository, both on the host machine and in the VM.

Python requirements

Do this both on the host machine and in the VM.

apt update
apt install python3-pip
pip3 install pandas scikit-learn

Agent-CLI interaction

Agent is started automatically in the VM when launched but requires configuration and manifest to be passed by manager. Alternatively you can pass configuration using this simplified script

Open console on the host, and run

export AGENT_GRPC_URL=localhost:7002
export MANAGER_GRPC_URL=localhost:7001

# Retieve Attestation
go run cmd/cli/main.go attestation get '<report_data>'

# Validate Attestation
go run cmd/cli/main.go attestation validate '<attesation>' '<report_data>'

# Run the CLI program with algorithm input
go run cmd/cli/main.go algo test/manual/algo/lin_reg.py Algorithm1 AlgorithmProvider1
# 2023/09/21 10:43:53 Uploading algorithm binary: test/manual/algo/lin_reg.py

# Run the CLI program with dataset input
go run cmd/cli/main.go data test/manual/data/iris.csv Dataset1 Provider1
go run cmd/cli/main.go data test/manual/data/iris.csv Dataset2 Provider2
# 2023/09/21 10:45:25 Uploading dataset CSV: test/manual/data/iris.csv

# Run the CLI program to fetch computation result
go run cmd/cli/main.go result Consumer1
# 2023/09/21 10:45:39 Retrieving computation result file
# 2023/09/21 10:45:40 Computation result retrieved and saved successfully!

Now there is a result.bin file in the current working directory. The file holds the trained logistic regression model. To test the model, run

python3 test/manual/algo/lin_reg_test.py test/manual/data/iris.csv result.bin

You should get an output (truncated for the sake of brevity):

   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
1   2            4.9           3.0            1.4           0.2  Iris-setosa
2   3            4.7           3.2            1.3           0.2  Iris-setosa
3   4            4.6           3.1            1.5           0.2  Iris-setosa
4   5            5.0           3.6            1.4           0.2  Iris-setosa
Precision, Recall, Confusion matrix, in training

                 precision    recall  f1-score   support

    Iris-setosa      1.000     1.000     1.000        21
Iris-versicolor      0.923     0.889     0.906        27
 Iris-virginica      0.893     0.926     0.909        27

       accuracy                          0.933        75
      macro avg      0.939     0.938     0.938        75
   weighted avg      0.934     0.933     0.933        75