diff options
Diffstat (limited to 'tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py')
| -rw-r--r-- | tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py | 103 |
1 files changed, 50 insertions, 53 deletions
diff --git a/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py b/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py index c164a12b1d..09f19ad274 100644 --- a/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py +++ b/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py @@ -63,57 +63,54 @@ def _assert_df_equals_dict(expected_df, actual_dict): actual_dict[col])) -def _make_test_csv(): - f = tempfile.NamedTemporaryFile( - dir=test.get_temp_dir(), delete=False, mode="w") - w = csv.writer(f) - w.writerow(["int", "float", "bool", "string"]) - for _ in range(100): - intvalue = np.random.randint(-10, 10) - floatvalue = np.random.rand() - boolvalue = int(np.random.rand() > 0.3) - stringvalue = "S: %.4f" % np.random.rand() - - row = [intvalue, floatvalue, boolvalue, stringvalue] - w.writerow(row) - f.close() - return f.name - - -def _make_test_csv_sparse(): - f = tempfile.NamedTemporaryFile( - dir=test.get_temp_dir(), delete=False, mode="w") - w = csv.writer(f) - w.writerow(["int", "float", "bool", "string"]) - for _ in range(100): - # leave columns empty; these will be read as default value (e.g. 0 or NaN) - intvalue = np.random.randint(-10, 10) if np.random.rand() > 0.5 else "" - floatvalue = np.random.rand() if np.random.rand() > 0.5 else "" - boolvalue = int(np.random.rand() > 0.3) if np.random.rand() > 0.5 else "" - stringvalue = (("S: %.4f" % np.random.rand()) if np.random.rand() > 0.5 else - "") - - row = [intvalue, floatvalue, boolvalue, stringvalue] - w.writerow(row) - f.close() - return f.name - - -def _make_test_tfrecord(): - f = tempfile.NamedTemporaryFile(dir=test.get_temp_dir(), delete=False) - w = tf_record.TFRecordWriter(f.name) - for i in range(100): - ex = example_pb2.Example() - ex.features.feature["var_len_int"].int64_list.value.extend(range((i % 3))) - ex.features.feature["fixed_len_float"].float_list.value.extend( - [float(i), 2 * float(i)]) - w.write(ex.SerializeToString()) - return f.name - - class TensorFlowDataFrameTestCase(test.TestCase): """Tests for `TensorFlowDataFrame`.""" + def _make_test_csv(self): + f = tempfile.NamedTemporaryFile( + dir=self.get_temp_dir(), delete=False, mode="w") + w = csv.writer(f) + w.writerow(["int", "float", "bool", "string"]) + for _ in range(100): + intvalue = np.random.randint(-10, 10) + floatvalue = np.random.rand() + boolvalue = int(np.random.rand() > 0.3) + stringvalue = "S: %.4f" % np.random.rand() + + row = [intvalue, floatvalue, boolvalue, stringvalue] + w.writerow(row) + f.close() + return f.name + + def _make_test_csv_sparse(self): + f = tempfile.NamedTemporaryFile( + dir=self.get_temp_dir(), delete=False, mode="w") + w = csv.writer(f) + w.writerow(["int", "float", "bool", "string"]) + for _ in range(100): + # leave columns empty; these will be read as default value (e.g. 0 or NaN) + intvalue = np.random.randint(-10, 10) if np.random.rand() > 0.5 else "" + floatvalue = np.random.rand() if np.random.rand() > 0.5 else "" + boolvalue = int(np.random.rand() > 0.3) if np.random.rand() > 0.5 else "" + stringvalue = (("S: %.4f" % np.random.rand()) if np.random.rand() > 0.5 else + "") + + row = [intvalue, floatvalue, boolvalue, stringvalue] + w.writerow(row) + f.close() + return f.name + + def _make_test_tfrecord(self): + f = tempfile.NamedTemporaryFile(dir=self.get_temp_dir(), delete=False) + w = tf_record.TFRecordWriter(f.name) + for i in range(100): + ex = example_pb2.Example() + ex.features.feature["var_len_int"].int64_list.value.extend(range((i % 3))) + ex.features.feature["fixed_len_float"].float_list.value.extend( + [float(i), 2 * float(i)]) + w.write(ex.SerializeToString()) + return f.name + def _assert_pandas_equals_tensorflow(self, pandas_df, tensorflow_df, num_batches, batch_size): self.assertItemsEqual( @@ -190,7 +187,7 @@ class TensorFlowDataFrameTestCase(test.TestCase): batch_size = 8 enqueue_size = 7 - data_path = _make_test_csv() + data_path = self._make_test_csv() default_values = [0, 0.0, 0, ""] pandas_df = pd.read_csv(data_path) @@ -211,7 +208,7 @@ class TensorFlowDataFrameTestCase(test.TestCase): num_epochs = 17 expected_num_batches = (num_epochs * 100) // batch_size - data_path = _make_test_csv() + data_path = self._make_test_csv() default_values = [0, 0.0, 0, ""] tensorflow_df = df.TensorFlowDataFrame.from_csv( @@ -234,7 +231,7 @@ class TensorFlowDataFrameTestCase(test.TestCase): num_batches = 100 batch_size = 8 - data_path = _make_test_csv_sparse() + data_path = self._make_test_csv_sparse() feature_spec = { "int": parsing_ops.FixedLenFeature(None, dtypes.int16, np.nan), "float": parsing_ops.VarLenFeature(dtypes.float16), @@ -270,7 +267,7 @@ class TensorFlowDataFrameTestCase(test.TestCase): enqueue_size = 11 batch_size = 13 - data_path = _make_test_tfrecord() + data_path = self._make_test_tfrecord() features = { "fixed_len_float": parsing_ops.FixedLenFeature( @@ -318,7 +315,7 @@ class TensorFlowDataFrameTestCase(test.TestCase): num_epochs = 17 expected_num_batches = (num_epochs * 100) // batch_size - data_path = _make_test_csv() + data_path = self._make_test_csv() default_values = [0, 0.0, 0, ""] tensorflow_df = df.TensorFlowDataFrame.from_csv( |