python/src/pywy/tests/test_dl.py

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import pytest

from typing import List
from pywy.dataquanta import WayangContext
from pywy.platforms.java import JavaPlugin
from pywy.platforms.spark import SparkPlugin
from pywy.platforms.tensorflow import TensorflowPlugin
from pywy.basic.model.ops import Mean, Cast, Eq, ArgMax, Input, Op, CrossEntropyLoss, Linear, Sigmoid
from pywy.basic.model.optimizer import GradientDescent
from pywy.basic.model.option import Option
from pywy.basic.model.models import DLModel


# TODO: fix this test by giving it proper test resources & fixing some type issues with lists.
@pytest.mark.skip(reason="no way of currently testing this, since we are missing implementations for proper test resources & types in types.py")
def test_dl_tensorflow():
    l1 = Linear(4, 64, True)
    s1 = Sigmoid()
    l2 = Linear(64, 3, True)

    s1.with_ops(l1.with_ops(Input(Input.Type.FEATURES)))
    l2.with_ops(s1)

    model = DLModel(l2)

    criterion = CrossEntropyLoss(3)
    criterion.with_ops(
        Input(Input.Type.PREDICTED),
        Input(Input.Type.LABEL, Op.DType.INT32)
    )
    acc = Mean(0)
    acc.with_ops(
        Cast(Op.DType.FLOAT32).with_ops(
            Eq().with_ops(
                ArgMax(1).with_ops(
                    Input(Input.Type.PREDICTED)
                ),
                Input(Input.Type.LABEL, Op.DType.INT32)
            )
        )
    )

    optimizer = GradientDescent(0.02)
    option = Option(criterion, optimizer, 6, 100)

    floats: List[List[int]] = [[5.1, 3.5, 1.4, 0.2]]

    ints: List[List[int]] = [[0, 0, 1, 1, 2, 2]]

    ctx = WayangContext() \
        .register({JavaPlugin, SparkPlugin, TensorflowPlugin})
    trainXSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
    trainYSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
    testXSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])

    data_quanta = trainXSource.dlTraining(model, option, trainYSource, List[List[float]], List[List[float]]) \
        .predict(testXSource, List[List[float]], List[List[float]]) \
        .map(lambda x: "Test", List[List[float]], str) \
        .store_textfile("file:///var/www/html/data/wordcount-out-python.txt", List[float])
    
    assert data_quanta is not None