def testPandasFeedFnBatchTwoWithOneEpoch(self): if not HAS_PANDAS: return array1 = np.arange(32, 37) array2 = np.arange(64, 69) df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 101)) placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"] aff = ff._PandasFeedFn(placeholders, df, batch_size=2, num_epochs=1) expected = { "index_placeholder": [96, 97], "a_placeholder": [32, 33], "b_placeholder": [64, 65] } actual = aff() self.assertEqual(expected, vals_to_list(actual)) expected = { "index_placeholder": [98, 99], "a_placeholder": [34, 35], "b_placeholder": [66, 67] } actual = aff() self.assertEqual(expected, vals_to_list(actual)) expected = { "index_placeholder": [100], "a_placeholder": [36], "b_placeholder": [68] } actual = aff() self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchOneHundred(self): array1 = np.arange(32, 64) array2 = np.arange(64, 96) df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 128)) placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"] aff = ff._PandasFeedFn(placeholders, df, 100) expected = { "index_placeholder": list(range(96, 128)) * 3 + list(range(96, 100)), "a_placeholder": list(range(32, 64)) * 3 + list(range(32, 36)), "b_placeholder": list(range(64, 96)) * 3 + list(range(64, 68)) } actual = aff() self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchOne(self): array1 = np.arange(32, 64) array2 = np.arange(64, 96) df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 128)) placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"] aff = ff._PandasFeedFn(placeholders, df, 1) # cycle around a couple times for x in range(0, 100): i = x % 32 expected = {"index_placeholder": [i + 96], "a_placeholder": [32 + i], "b_placeholder": [64 + i]} actual = aff() self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchFive(self): array1 = np.arange(32, 64) array2 = np.arange(64, 96) df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 128)) placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"] aff = ff._PandasFeedFn(placeholders, df, 5) # cycle around a couple times for _ in range(0, 101, 2): aff() expected = {"index_placeholder": [127, 96, 97, 98, 99], "a_placeholder": [63, 32, 33, 34, 35], "b_placeholder": [95, 64, 65, 66, 67]} actual = aff() self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchOneHundredWithSmallDataArrayAndMultipleEpochs(self): if not HAS_PANDAS: return array1 = np.arange(32, 34) array2 = np.arange(64, 66) df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 98)) placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"] aff = ff._PandasFeedFn(placeholders, df, batch_size=100, num_epochs=2) expected = { "index_placeholder": [96, 97, 96, 97], "a_placeholder": [32, 33, 32, 33], "b_placeholder": [64, 65, 64, 65] } actual = aff() self.assertEqual(expected, vals_to_list(actual))