def testPandasFeeding(self):
if not HAS_PANDAS:
return
batch_size = 3
iterations = 1000
index = np.arange(100, 132)
a = np.arange(32)
b = np.arange(32, 64)
dataframe = pd.DataFrame({"a": a, "b": b}, index=index)
pandas_source = in_memory_source.PandasSource(dataframe, batch_size)
pandas_columns = pandas_source()
cache = {}
with tf.Graph().as_default():
pandas_tensors = [col.build(cache) for col in pandas_columns]
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(iterations):
indices = [
j % dataframe.shape[0]
for j in range(batch_size * i, batch_size * (i + 1))
]
expected_df_indices = dataframe.index[indices]
expected_rows = dataframe.iloc[indices]
actual_value = sess.run(pandas_tensors)
np.testing.assert_array_equal(expected_df_indices,
actual_value[0])
for col_num, col in enumerate(dataframe.columns):
np.testing.assert_array_equal(
expected_rows[col].values,
actual_value[col_num + 1])
coord.request_stop()
coord.join(threads)
def from_pandas(cls,
pandas_dataframe,
batch_size=None,
shuffle=True,
queue_capacity=None,
min_after_dequeue=None,
seed=None):
"""Create a `tf.learn.DataFrame` from a `pandas.DataFrame`.
Args:
pandas_dataframe: `pandas.DataFrame` that serves as a data source.
batch_size: desired batch size.
shuffle: whether records should be shuffled. Defaults to true.
queue_capacity: capacity of the queue that will store parsed `Example`s
min_after_dequeue: minimum number of elements that can be left by a
dequeue operation. Only used if `shuffle` is true.
seed: passed to random shuffle operations. Only used if `shuffle` is true.
Returns:
A `tf.learn.DataFrame` that contains batches drawn from the given
`pandas_dataframe`.
"""
pandas_source = in_memory_source.PandasSource(pandas_dataframe, batch_size,
queue_capacity, shuffle,
min_after_dequeue, seed)
dataframe = cls()
dataframe.assign(**(pandas_source()._asdict()))
return dataframe
def from_pandas(cls,
pandas_dataframe,
num_threads=None,
enqueue_size=None,
batch_size=None,
queue_capacity=None,
min_after_dequeue=None,
shuffle=True,
seed=None,
data_name="pandas_data"):
"""Create a `tf.learn.DataFrame` from a `pandas.DataFrame`.
Args:
pandas_dataframe: `pandas.DataFrame` that serves as a data source.
num_threads: the number of threads to use for enqueueing.
enqueue_size: the number of rows to enqueue per step.
batch_size: desired batch size.
queue_capacity: capacity of the queue that will store parsed `Example`s
min_after_dequeue: minimum number of elements that can be left by a
dequeue operation. Only used if `shuffle` is true.
shuffle: whether records should be shuffled. Defaults to true.
seed: passed to random shuffle operations. Only used if `shuffle` is true.
data_name: a scope name identifying the data.
Returns:
A `tf.learn.DataFrame` that contains batches drawn from the given
`pandas_dataframe`.
"""
pandas_source = in_memory_source.PandasSource(
pandas_dataframe,
num_threads=num_threads,
enqueue_size=enqueue_size,
batch_size=batch_size,
queue_capacity=queue_capacity,
shuffle=shuffle,
min_after_dequeue=min_after_dequeue,
seed=seed,
data_name=data_name)
dataframe = cls()
dataframe.assign(**(pandas_source()._asdict()))
return dataframe