chore: import upstream snapshot with attribution

tests/tensorflow/iris_data_utils.py

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+# From https://github.com/tensorflow/models/blob/master/samples/core/get_started/iris_data.py
+# This file is the example used by TensorFlow to get users started. This code is used for testing.
+import pandas as pd
+import tensorflow as tf
+
+TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
+TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
+
+CSV_COLUMN_NAMES = ["SepalLength", "SepalWidth", "PetalLength", "PetalWidth", "Species"]
+SPECIES = ["Setosa", "Versicolor", "Virginica"]
+
+
+def maybe_download():
+    train_path = tf.keras.utils.get_file(TRAIN_URL.split("/")[-1], TRAIN_URL)
+    test_path = tf.keras.utils.get_file(TEST_URL.split("/")[-1], TEST_URL)
+
+    return train_path, test_path
+
+
+def load_data(y_name="Species"):
+    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
+    train_path, test_path = maybe_download()
+
+    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
+    train_y = train.pop(y_name)
+    train_x = train
+
+    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
+    test_y = test.pop(y_name)
+    test_x = test
+
+    return (train_x, train_y), (test_x, test_y)
+
+
+def train_input_fn(features, labels, batch_size):
+    """An input function for training"""
+    # Convert the inputs to a Dataset.
+    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
+
+    # Shuffle, repeat, and batch the examples.
+    return dataset.shuffle(1000).repeat().batch(batch_size)
+
+
+def eval_input_fn(features, labels, batch_size):
+    """An input function for evaluation or prediction"""
+    features = dict(features)
+
+    # Use only features when labels are null.
+    inputs = features if labels is None else (features, labels)
+
+    # Convert the inputs to a Dataset.
+    dataset = tf.data.Dataset.from_tensor_slices(inputs)
+
+    # Batch the examples
+    assert batch_size is not None, "batch_size must not be None"
+    return dataset.batch(batch_size)
+
+
+# The remainder of this file contains a simple example of a csv parser,
+#     implemented using the `Dataset` class.
+
+# `tf.parse_csv` sets the types of the outputs to match the examples given in
+#     the `record_defaults` argument.
+CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]]
+
+
+def _parse_line(line):
+    # Decode the line into its fields
+    fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
+
+    # Pack the result into a dictionary
+    features = dict(zip(CSV_COLUMN_NAMES, fields))
+
+    # Separate the label from the features
+    label = features.pop("Species")
+
+    return features, label
+
+
+def csv_input_fn(csv_path, batch_size):
+    # Create a dataset containing the text lines.
+    dataset = tf.data.TextLineDataset(csv_path).skip(1)
+
+    # Parse each line.
+    dataset = dataset.map(_parse_line)
+
+    # Shuffle, repeat, and batch the examples.
+    return dataset.shuffle(1000).repeat().batch(batch_size)