Commit 2297d48
| Original file line number | Diff line number | Diff line change | |
|---|---|---|---|
@@ -1,21 +1,12 @@ | |||
| 1 | 1 | import unittest | |
| 2 | 2 | ||
| 3 | 3 | import fastai | |
| 4 | - import pandas as pd | ||
| 5 | - import torch | ||
| 6 | 4 | ||
| 7 | - from fastai.tabular import * | ||
| 8 | - from fastai.core import partition | ||
| 9 | - from fastai.torch_core import tensor | ||
| 5 | + from fastai.tabular.all import * | ||
| 10 | 6 | ||
| 11 | 7 | class TestFastAI(unittest.TestCase): | |
| 12 | - def test_partition(self): | ||
| 13 | - result = partition([1,2,3,4,5], 2) | ||
| 14 | - | ||
| 15 | - self.assertEqual(3, len(result)) | ||
| 16 | - | ||
| 17 | 8 | def test_has_version(self): | |
| 18 | - self.assertGreater(len(fastai.__version__), 1) | ||
| 9 | + self.assertGreater(len(fastai.__version__), 2) | ||
| 19 | 10 | ||
| 20 | 11 | # based on https://github.com/fastai/fastai/blob/master/tests/test_torch_core.py#L17 | |
| 21 | 12 | def test_torch_tensor(self): | |
@@ -25,18 +16,12 @@ def test_torch_tensor(self): | |||
| 25 | 16 | self.assertTrue(torch.all(a == b)) | |
| 26 | 17 | ||
| 27 | 18 | def test_tabular(self): | |
| 28 | - df = pd.read_csv("/input/tests/data/train.csv") | ||
| 29 | - procs = [FillMissing, Categorify, Normalize] | ||
| 30 | - | ||
| 31 | - valid_idx = range(len(df)-5, len(df)) | ||
| 32 | - dep_var = "label" | ||
| 33 | - cont_names = [] | ||
| 34 | - for i in range(784): | ||
| 35 | - cont_names.append("pixel" + str(i)) | ||
| 36 | - | ||
| 37 | - data = (TabularList.from_df(df, path="", cont_names=cont_names, cat_names=[], procs=procs) | ||
| 38 | - .split_by_idx(valid_idx) | ||
| 39 | - .label_from_df(cols=dep_var) | ||
| 40 | - .databunch()) | ||
| 41 | - learn = tabular_learner(data, layers=[200, 100]) | ||
| 42 | - learn.fit(epochs=1) | ||
| 19 | + dls = TabularDataLoaders.from_csv( | ||
| 20 | + "/input/tests/data/train.csv", | ||
| 21 | + cont_names=["pixel"+str(i) for i in range(784)], | ||
| 22 | + y_names='label', | ||
| 23 | + procs=[FillMissing, Categorify, Normalize]) | ||
| 24 | + learn = tabular_learner(dls, layers=[200, 100]) | ||
| 25 | + learn.fit_one_cycle(n_epoch=1) | ||
| 26 | + | ||
| 27 | + self.assertGreater(learn.smooth_loss, 0) | ||
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