docker-python/tests/test_fastai.py at master · AI-For-Rural/docker-python · GitHub

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import unittest

import fastai

import pandas as pd

import torch

from fastai.tabular import *

from fastai.core import partition

from fastai.torch_core import tensor

class TestFastAI(unittest.TestCase):

def test_partition(self):

result = partition([1,2,3,4,5], 2)

self.assertEqual(3, len(result))

def test_has_version(self):

self.assertGreater(len(fastai.__version__), 1)

# based on https://github.com/fastai/fastai/blob/master/tests/test_torch_core.py#L17

def test_torch_tensor(self):

a = tensor([1, 2, 3])

b = torch.tensor([1, 2, 3])

self.assertTrue(torch.all(a == b))

def test_tabular(self):

df = pd.read_csv("/input/tests/data/train.csv")

procs = [FillMissing, Categorify, Normalize]

valid_idx = range(len(df)-5, len(df))

dep_var = "label"

cont_names = []

for i in range(784):

cont_names.append("pixel" + str(i))

data = (TabularList.from_df(df, path="", cont_names=cont_names, cat_names=[], procs=procs)

.split_by_idx(valid_idx)

.label_from_df(cols=dep_var)

.databunch())

learn = tabular_learner(data, layers=[200, 100])

learn.fit(epochs=1)

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