Copyright 2019 The TensorFlow Authors.¶
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Load CSV data¶
View on TensorFlow.org
Run in Google Colab
View source on GitHub
Download notebookThis tutorial provides examples of how to use CSV data with TensorFlow.
There are two main parts to this:
- Loading the data off disk
- Pre-processing it into a form suitable for training.
This tutorial focuses on the loading, and gives some quick examples of preprocessing. For a tutorial that focuses on the preprocessing aspect see the preprocessing layers guide and tutorial.
Setup¶
import pandas as pd
import numpy as np
# Make numpy values easier to read.
np.set_printoptions(precision=3, suppress=True)
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
In memory data¶
For any small CSV dataset the simplest way to train a TensorFlow model on it is to load it into memory as a pandas Dataframe or a NumPy array.
A relatively simple example is the abalone dataset.
- The dataset is small.
- All the input features are all limited-range floating point values.
Here is how to download the data into a Pandas DataFrame:
abalone_train = pd.read_csv(
"https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv",
names=["Length", "Diameter", "Height", "Whole weight", "Shucked weight",
"Viscera weight", "Shell weight", "Age"])
abalone_train.head()
The dataset contains a set of measurements of abalone, a type of sea snail.
“Abalone shell” (by Nicki Dugan Pogue, CC BY-SA 2.0)
The nominal task for this dataset is to predict the age from the other measurements, so separate the features and labels for training:
abalone_features = abalone_train.copy()
abalone_labels = abalone_features.pop('Age')
For this dataset you will treat all features identically. Pack the features into a single NumPy array.:
abalone_features = np.array(abalone_features)
abalone_features
Next make a regression model predict the age. Since there is only a single input tensor, a keras.Sequential model is sufficient here.
abalone_model = tf.keras.Sequential([
layers.Dense(64),
layers.Dense(1)
])
abalone_model.compile(loss = tf.losses.MeanSquaredError(),
optimizer = tf.optimizers.Adam())
To train that model, pass the features and labels to Model.fit:
abalone_model.fit(abalone_features, abalone_labels, epochs=10)
You have just seen the most basic way to train a model using CSV data. Next, you will learn how to apply preprocessing to normalize numeric columns.
Basic preprocessing¶
It's good practice to normalize the inputs to your model. The experimental.preprocessing layers provide a convenient way to build this normalization into your model.
The layer will precompute the mean and variance of each column, and use these to normalize the data.
First you create the layer:
normalize = preprocessing.Normalization()
Then you use the Normalization.adapt() method to adapt the normalization layer to your data.
Note: Only use your training data to .adapt() preprocessing layers. Do not use your validation or test data.
normalize.adapt(abalone_features)
Then use the normalization layer in your model:
norm_abalone_model = tf.keras.Sequential([
normalize,
layers.Dense(64),
layers.Dense(1)
])
norm_abalone_model.compile(loss = tf.losses.MeanSquaredError(),
optimizer = tf.optimizers.Adam())
norm_abalone_model.fit(abalone_features, abalone_labels, epochs=10)
Mixed data types¶
The "Titanic" dataset contains information about the passengers on the Titanic. The nominal task on this dataset is to predict who survived.
Image from Wikimedia
The raw data can easily be loaded as a Pandas DataFrame, but is not immediately usable as input to a TensorFlow model.
titanic = pd.read_csv("https://storage.googleapis.com/tf-datasets/titanic/train.csv")
titanic.head()
titanic_features = titanic.copy()
titanic_labels = titanic_features.pop('survived')
Because of the different data types and ranges you can't simply stack the features into NumPy array and pass it to a keras.Sequential model. Each column needs to be handled individually.
As one option, you could preprocess your data offline (using any tool you like) to convert categorical columns to numeric columns, then pass the processed output to your TensorFlow model. The disadvantage to that approach is that if you save and export your model the preprocessing is not saved with it. The experimental.preprocessing layers avoid this problem because they're part of the model.
In this example, you'll build a model that implements the preprocessing logic using Keras functional API. You could also do it by subclassing.
The functional API operates on "symbolic" tensors. Normal "eager" tensors have a value. In contrast these "symbolic" tensors do not. Instead they keep track of which operations are run on them, and build representation of the calculation, that you can run later. Here's a quick example:
# Create a symbolic input
input = tf.keras.Input(shape=(), dtype=tf.float32)
# Do a calculation using is
result = 2*input + 1
# the result doesn't have a value
result
calc = tf.keras.Model(inputs=input, outputs=result)
print(calc(1).numpy())
print(calc(2).numpy())
To build the preprocessing model, start by building a set of symbolic keras.Input objects, matching the names and data-types of the CSV columns.
inputs = {}
for name, column in titanic_features.items():
dtype = column.dtype
if dtype == object:
dtype = tf.string
else:
dtype = tf.float32
inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)
inputs
The first step in your preprocessing logic is to concatenate the numeric inputs together, and run them through a normalization layer:
numeric_inputs = {name:input for name,input in inputs.items()
if input.dtype==tf.float32}
x = layers.Concatenate()(list(numeric_inputs.values()))
norm = preprocessing.Normalization()
norm.adapt(np.array(titanic[numeric_inputs.keys()]))
all_numeric_inputs = norm(x)
all_numeric_inputs
Collect all the symbolic preprocessing results, to concatenate them later.
preprocessed_inputs = [all_numeric_inputs]
For the string inputs use the preprocessing.StringLookup function to map from strings to integer indices in a vocabulary. Next, use preprocessing.CategoryEncoding to convert the indexes into float32 data appropriate for the model.
The default settings for the preprocessing.CategoryEncoding layer create a one-hot vector for each input. A layers.Embedding would also work. See the preprocessing layers guide and tutorial for more on this topic.
for name, input in inputs.items():
if input.dtype == tf.float32:
continue
lookup = preprocessing.StringLookup(vocabulary=np.unique(titanic_features[name]))
one_hot = preprocessing.CategoryEncoding(max_tokens=lookup.vocab_size())
x = lookup(input)
x = one_hot(x)
preprocessed_inputs.append(x)
With the collection of inputs and processed_inputs, you can concatenate all the preprocessed inputs together, and build a model that handles the preprocessing:
preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)
titanic_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)
tf.keras.utils.plot_model(model = titanic_preprocessing , rankdir="LR", dpi=72, show_shapes=True)
This model just contains the input preprocessing. You can run it to see what it does to your data. Keras models don't automatically convert Pandas DataFrames because it's not clear if it should be converted to one tensor or to a dictionary of tensors. So convert it to a dictionary of tensors:
titanic_features_dict = {name: np.array(value)
for name, value in titanic_features.items()}
Slice out the first training example and pass it to this preprocessing model, you see the numeric features and string one-hots all concatenated together:
features_dict = {name:values[:1] for name, values in titanic_features_dict.items()}
titanic_preprocessing(features_dict)
Now build the model on top of this:
def titanic_model(preprocessing_head, inputs):
body = tf.keras.Sequential([
layers.Dense(64),
layers.Dense(1)
])
preprocessed_inputs = preprocessing_head(inputs)
result = body(preprocessed_inputs)
model = tf.keras.Model(inputs, result)
model.compile(loss=tf.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.optimizers.Adam())
return model
titanic_model = titanic_model(titanic_preprocessing, inputs)
When you train the model, pass the dictionary of features as x, and the label as y.
titanic_model.fit(x=titanic_features_dict, y=titanic_labels, epochs=10)
Since the preprocessing is part of the model, you can save the model and reload it somewhere else and get identical results:
titanic_model.save('test')
reloaded = tf.keras.models.load_model('test')
features_dict = {name:values[:1] for name, values in titanic_features_dict.items()}
before = titanic_model(features_dict)
after = reloaded(features_dict)
assert (before-after)<1e-3
print(before)
print(after)
Using tf.data¶
In the previous section you relied on the model's built-in data shuffling and batching while training the model.
If you need more control over the input data pipeline or need to use data that doesn't easily fit into memory: use tf.data.
For more examples see the tf.data guide.
On in memory data¶
As a first example of applying tf.data to CSV data consider the following code to manually slice up the dictionary of features from the previous section. For each index, it takes that index for each feature:
import itertools
def slices(features):
for i in itertools.count():
# For each feature take index `i`
example = {name:values[i] for name, values in features.items()}
yield example
Run this and print the first example:
for example in slices(titanic_features_dict):
for name, value in example.items():
print(f"{name:19s}: {value}")
break
The most basic tf.data.Dataset in memory data loader is the Dataset.from_tensor_slices constructor. This returns a tf.data.Dataset that implements a generalized version of the above slices function, in TensorFlow.
features_ds = tf.data.Dataset.from_tensor_slices(titanic_features_dict)
You can iterate over a tf.data.Dataset like any other python iterable:
for example in features_ds:
for name, value in example.items():
print(f"{name:19s}: {value}")
break
The from_tensor_slices function can handle any structure of nested dictionaries or tuples. The following code makes a dataset of (features_dict, labels) pairs:
titanic_ds = tf.data.Dataset.from_tensor_slices((titanic_features_dict, titanic_labels))
To train a model using this Dataset, you'll need to at least shuffle and batch the data.
titanic_batches = titanic_ds.shuffle(len(titanic_labels)).batch(32)
Instead of passing features and labels to Model.fit, you pass the dataset:
titanic_model.fit(titanic_batches, epochs=5)
From a single file¶
So far this tutorial has worked with in-memory data. tf.data is a highly scalable toolkit for building data pipelines, and provides a few functions for dealing loading CSV files.
titanic_file_path = tf.keras.utils.get_file("train.csv", "https://storage.googleapis.com/tf-datasets/titanic/train.csv")
Now read the CSV data from the file and create a tf.data.Dataset.
(For the full documentation, see tf.data.experimental.make_csv_dataset)
titanic_csv_ds = tf.data.experimental.make_csv_dataset(
titanic_file_path,
batch_size=5, # Artificially small to make examples easier to show.
label_name='survived',
num_epochs=1,
ignore_errors=True,)
This function includes many convenient features so the data is easy to work with. This includes:
- Using the column headers as dictionary keys.
- Automatically determining the type of each column.
for batch, label in titanic_csv_ds.take(1):
for key, value in batch.items():
print(f"{key:20s}: {value}")
print()
print(f"{'label':20s}: {label}")
Note: if you run the above cell twice it will produce different results. The default settings for make_csv_dataset include shuffle_buffer_size=1000, which is more than sufficient for this small dataset, but may not be for a real-world dataset.
It can also decompress the data on the fly. Here's a gzipped CSV file containing the metro interstate traffic dataset
Image from Wikimedia
traffic_volume_csv_gz = tf.keras.utils.get_file(
'Metro_Interstate_Traffic_Volume.csv.gz',
"https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz",
cache_dir='.', cache_subdir='traffic')
Set the compression_type argument to read directly from the compressed file:
traffic_volume_csv_gz_ds = tf.data.experimental.make_csv_dataset(
traffic_volume_csv_gz,
batch_size=256,
label_name='traffic_volume',
num_epochs=1,
compression_type="GZIP")
for batch, label in traffic_volume_csv_gz_ds.take(1):
for key, value in batch.items():
print(f"{key:20s}: {value[:5]}")
print()
print(f"{'label':20s}: {label[:5]}")
Note: If you need to parse those date-time strings in the tf.data pipeline you can use tfa.text.parse_time.
Caching¶
There is some overhead to parsing the csv data. For small models this can be the bottleneck in training.
Depending on your use case it may be a good idea to use Dataset.cache or data.experimental.snapshot so that the csv data is only parsed on the first epoch.
The main difference between the cache and snapshot methods is that cache files can only be used by the TensorFlow process that created them, but snapshot files can be read by other processes.
For example, iterating over the traffic_volume_csv_gz_ds 20 times, takes ~15 seconds without caching, or ~2s with caching.
%%time
for i, (batch, label) in enumerate(traffic_volume_csv_gz_ds.repeat(20)):
if i % 40 == 0:
print('.', end='')
print()
Note: Dataset.cache stores the data form the first epoch and replays it in order. So using .cache disables any shuffles earlier in the pipeline. Below the .shuffle is added back in after .cache.
%%time
caching = traffic_volume_csv_gz_ds.cache().shuffle(1000)
for i, (batch, label) in enumerate(caching.shuffle(1000).repeat(20)):
if i % 40 == 0:
print('.', end='')
print()
Note: snapshot files are meant for temporary storage of a dataset while in use. This is not a format for long term storage. The file format is considered an internal detail, and not guaranteed between TensorFlow versions.
%%time
snapshot = tf.data.experimental.snapshot('titanic.tfsnap')
snapshotting = traffic_volume_csv_gz_ds.apply(snapshot).shuffle(1000)
for i, (batch, label) in enumerate(snapshotting.shuffle(1000).repeat(20)):
if i % 40 == 0:
print('.', end='')
print()
If your data loading is slowed by loading csv files, and cache and snapshot are insufficient for your use case, consider re-encoding your data into a more streamlined format.
Multiple files¶
All the examples so far in this section could easily be done without tf.data. One place where tf.data can really simplify things is when dealing with collections of files.
For example, the character font images dataset is distributed as a collection of csv files, one per font.
Image by Willi Heidelbach from Pixabay
Download the dataset, and have a look at the files inside:
fonts_zip = tf.keras.utils.get_file(
'fonts.zip', "https://archive.ics.uci.edu/ml/machine-learning-databases/00417/fonts.zip",
cache_dir='.', cache_subdir='fonts',
extract=True)
import pathlib
font_csvs = sorted(str(p) for p in pathlib.Path('fonts').glob("*.csv"))
font_csvs[:10]
len(font_csvs)
When dealing with a bunch of files you can pass a glob-style file_pattern to the experimental.make_csv_dataset function. The order of the files is shuffled each iteration.
Use the num_parallel_reads argument to set how many files are read in parallel and interleaved together.
fonts_ds = tf.data.experimental.make_csv_dataset(
file_pattern = "fonts/*.csv",
batch_size=10, num_epochs=1,
num_parallel_reads=20,
shuffle_buffer_size=10000)
These csv files have the images flattened out into a single row. The column names are formatted r{row}c{column}. Here's the first batch:
for features in fonts_ds.take(1):
for i, (name, value) in enumerate(features.items()):
if i>15:
break
print(f"{name:20s}: {value}")
print('...')
print(f"[total: {len(features)} features]")
Optional: Packing fields¶
You probably don't want to work with each pixel in separate columns like this. Before trying to use this dataset be sure to pack the pixels into an image-tensor.
Here is code that parses the column names to build images for each example:
import re
def make_images(features):
image = [None]*400
new_feats = {}
for name, value in features.items():
match = re.match('r(\d+)c(\d+)', name)
if match:
image[int(match.group(1))*20+int(match.group(2))] = value
else:
new_feats[name] = value
image = tf.stack(image, axis=0)
image = tf.reshape(image, [20, 20, -1])
new_feats['image'] = image
return new_feats
Apply that function to each batch in the dataset:
fonts_image_ds = fonts_ds.map(make_images)
for features in fonts_image_ds.take(1):
break
Plot the resulting images:
from matplotlib import pyplot as plt
plt.figure(figsize=(6,6), dpi=120)
for n in range(9):
plt.subplot(3,3,n+1)
plt.imshow(features['image'][..., n])
plt.title(chr(features['m_label'][n]))
plt.axis('off')
Lower level functions¶
So far this tutorial has focused on the highest level utilities for reading csv data. There are other two APIs that may be helpful for advanced users if your use-case doesn't fit the basic patterns.
tf.io.decode_csv- a function for parsing lines of text into a list of CSV column tensors.tf.data.experimental.CsvDataset- a lower level csv dataset constructor.
This section recreates functionality provided by make_csv_dataset, to demonstrate how this lower level functionality can be used.
tf.io.decode_csv¶
This function decodes a string, or list of strings into a list of columns.
Unlike make_csv_dataset this function does not try to guess column data-types. You specify the column types by providing a list of record_defaults containing a value of the correct type, for each column.
To read the Titanic data as strings using decode_csv you would say:
text = pathlib.Path(titanic_file_path).read_text()
lines = text.split('\n')[1:-1]
all_strings = [str()]*10
all_strings
features = tf.io.decode_csv(lines, record_defaults=all_strings)
for f in features:
print(f"type: {f.dtype.name}, shape: {f.shape}")
To parse them with their actual types, create a list of record_defaults of the corresponding types:
print(lines[0])
titanic_types = [int(), str(), float(), int(), int(), float(), str(), str(), str(), str()]
titanic_types
features = tf.io.decode_csv(lines, record_defaults=titanic_types)
for f in features:
print(f"type: {f.dtype.name}, shape: {f.shape}")
Note: it is more efficient to call decode_csv on large batches of lines than on individual lines of csv text.
tf.data.experimental.CsvDataset¶
The tf.data.experimental.CsvDataset class provides a minimal CSV Dataset interface without the convenience features of the make_csv_dataset function: column header parsing, column type-inference, automatic shuffling, file interleaving.
This constructor follows uses record_defaults the same way as io.parse_csv:
simple_titanic = tf.data.experimental.CsvDataset(titanic_file_path, record_defaults=titanic_types, header=True)
for example in simple_titanic.take(1):
print([e.numpy() for e in example])
The above code is basically equivalent to:
def decode_titanic_line(line):
return tf.io.decode_csv(line, titanic_types)
manual_titanic = (
# Load the lines of text
tf.data.TextLineDataset(titanic_file_path)
# Skip the header row.
.skip(1)
# Decode the line.
.map(decode_titanic_line)
)
for example in manual_titanic.take(1):
print([e.numpy() for e in example])
Multiple files¶
To parse the fonts dataset using experimental.CsvDataset, you first need to determine the column types for the record_defaults. Start by inspecting the first row of one file:
font_line = pathlib.Path(font_csvs[0]).read_text().splitlines()[1]
print(font_line)
Only the first two fields are strings, the rest are ints or floats, and you can get the total number of features by counting the commas:
num_font_features = font_line.count(',')+1
font_column_types = [str(), str()] + [float()]*(num_font_features-2)
The CsvDatasaet constructor can take a list of input files, but reads them sequentially. The first file in the list of CSVs is AGENCY.csv:
font_csvs[0]
So when you pass pass the list of files to CsvDataaset the records from AGENCY.csv are read first:
simple_font_ds = tf.data.experimental.CsvDataset(
font_csvs,
record_defaults=font_column_types,
header=True)
for row in simple_font_ds.take(10):
print(row[0].numpy())
To interleave multiple files, use Dataset.interleave.
Here's an initial dataset that contains the csv file names:
font_files = tf.data.Dataset.list_files("fonts/*.csv")
This shuffles the file names each epoch:
print('Epoch 1:')
for f in list(font_files)[:5]:
print(" ", f.numpy())
print(' ...')
print()
print('Epoch 2:')
for f in list(font_files)[:5]:
print(" ", f.numpy())
print(' ...')
The interleave method takes a map_func that creates a child-Dataset for each element of the parent-Dataset.
Here, you want to create a CsvDataset from each element of the dataset of files:
def make_font_csv_ds(path):
return tf.data.experimental.CsvDataset(
path,
record_defaults=font_column_types,
header=True)
The Dataset returned by interleave returns elements by cycling over a number of the child-Datasets. Note, below, how the dataset cycles over cycle_length)=3 three font files:
font_rows = font_files.interleave(make_font_csv_ds,
cycle_length=3)
fonts_dict = {'font_name':[], 'character':[]}
for row in font_rows.take(10):
fonts_dict['font_name'].append(row[0].numpy().decode())
fonts_dict['character'].append(chr(row[2].numpy()))
pd.DataFrame(fonts_dict)
Performance¶
Earlier, it was noted that io.decode_csv is more efficient when run on a batch of strings.
It is possible to take advantage of this fact, when using large batch sizes, to improve CSV loading performance (but try caching first).
With the built-in loader 20, 2048-example batches take about 17s.
BATCH_SIZE=2048
fonts_ds = tf.data.experimental.make_csv_dataset(
file_pattern = "fonts/*.csv",
batch_size=BATCH_SIZE, num_epochs=1,
num_parallel_reads=100)
%%time
for i,batch in enumerate(fonts_ds.take(20)):
print('.',end='')
print()
Passing batches of text lines todecode_csv runs faster, in about 5s:
fonts_files = tf.data.Dataset.list_files("fonts/*.csv")
fonts_lines = fonts_files.interleave(
lambda fname:tf.data.TextLineDataset(fname).skip(1),
cycle_length=100).batch(BATCH_SIZE)
fonts_fast = fonts_lines.map(lambda x: tf.io.decode_csv(x, record_defaults=font_column_types))
%%time
for i,batch in enumerate(fonts_fast.take(20)):
print('.',end='')
print()
For another example of increasing csv performance by using large batches see the overfit and underfit tutorial.
This sort of approach may work, but consider other options like cache and snapshot, or re-enncoding your data into a more streamlined format.