While using this as a model for spam classification, I'd like to add an additional feature of the Subject plus the body.
I have all of my features in a pandas dataframe. For example, the subject is df['Subject'], the body is df['body_text'] and the spam/ham label is df['ham/spam']
I receive the following error:
TypeError: 'FeatureUnion' object is not iterable
How can I use both df['Subject'] and df['body_text'] as features all while running them through the pipeline function?
from sklearn.pipeline import FeatureUnion
features = df[['Subject', 'body_text']].values
combined_2 = FeatureUnion(list(features))
pipeline = Pipeline([
('count_vectorizer', CountVectorizer(ngram_range=(1, 2))),
('tfidf_transformer', TfidfTransformer()),
('classifier', MultinomialNB())])
pipeline.fit(combined_2, df['ham/spam'])
k_fold = KFold(n=len(df), n_folds=6)
scores = []
confusion = numpy.array([[0, 0], [0, 0]])
for train_indices, test_indices in k_fold:
train_text = combined_2.iloc[train_indices]
train_y = df.iloc[test_indices]['ham/spam'].values
test_text = combined_2.iloc[test_indices]
test_y = df.iloc[test_indices]['ham/spam'].values
pipeline.fit(train_text, train_y)
predictions = pipeline.predict(test_text)
prediction_prob = pipeline.predict_proba(test_text)
confusion += confusion_matrix(test_y, predictions)
score = f1_score(test_y, predictions, pos_label='spam')
scores.append(score)
FeatureUnion was not meant to be used that way. It instead takes two feature extractors / vectorizers and applies them to the input. It does not take data in the constructor the way it is shown.
CountVectorizer is expecting a sequence of strings. The easiest way to provide it with that is to concatenate the strings together. That would pass both the text in both columns to the same CountVectorizer.
combined_2 = df['Subject'] + ' ' + df['body_text']
An alternative method would be to run CountVectorizer and optionally TfidfTransformer individually on each column, and then stack the results.
import scipy.sparse as sp
subject_vectorizer = CountVectorizer(...)
subject_vectors = subject_vectorizer.fit_transform(df['Subject'])
body_vectorizer = CountVectorizer(...)
body_vectors = body_vectorizer.fit_transform(df['body_text'])
combined_2 = sp.hstack([subject_vectors, body_vectors], format='csr')
A third option is to implement your own transformer that would extract a dataframe column.
class DataFrameColumnExtracter(TransformerMixin):
def __init__(self, column):
self.column = column
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
return X[self.column]
In that case you could use FeatureUnion on two pipelines, each containing your custom transformer, then CountVectorizer.
subj_pipe = make_pipeline(
DataFrameColumnExtracter('Subject'),
CountVectorizer()
)
body_pipe = make_pipeline(
DataFrameColumnExtracter('body_text'),
CountVectorizer()
)
feature_union = make_union(subj_pipe, body_pipe)
This feature union of pipelines will take the dataframe and each pipeline will process its column. It will produce the concatenation of term count matrices from the two columns given.
sparse_matrix_of_counts = feature_union.fit_transform(df)
This feature union can also be added as the first step in a larger pipeline.
Related
I have thousands of json.gz files, each with a variety of information about scientific papers. For each file, I have to extract the relevant information - e.g. title and labels - to make a dataset, then transform it to a tf.dataset. However, it is poorly efficient since I cannot filter the subjects directly or shuffle them in a single step.
I would like to read them using tf.dataset.interleave in order to shuffle them, but also to filter them according to specific labels.
Here is how I'm doing it up to now.
import tensorflow as tf
import pandas as pd
#For relevant feature extraction
def load_file(file):
#with gzip.open(bytes.decode(file), 'r') as fin: # 4. gzip
with gzip.open(file, 'r') as fin:
json_bytes = fin.read()
json_str = json_bytes.decode('utf-8') # 2. string (i.e. JSON)
bb = json.loads(json_str)
bb = pd.json_normalize(bb, 'items', ['indexed', ['title', 'publisher', 'type','indexed.date-parts', 'subject']],
errors='ignore')
bb.dropna(subset=['title', 'publisher', 'type','indexed.date-parts', 'subject'], inplace=True)
bb.subject = bb.subject.apply(lambda x: int(themes[list(set(x) & set(list(themes.keys())))[0]]) if len(list(set(x) & set(list(themes.keys()))))>0 else len(list(themes.keys()))+1)
bb.title = bb.title.str.join('').values
#bb['author'] = bb['author'].apply(lambda x: '; '.join([', '.join([i['given'], i['family']]) for i in x]))
bb['indexed.date-parts'] = bb['indexed.date-parts'].apply(lambda tpl: datetime.datetime.strptime('-'.join(str(x) for x in tpl[0]), '%Y-%m-%d').strftime('%Y-%m-%d'))
#bb = bb.sample(n=32, replace=True)
#return bb.title.str.join('').values, bb.subject.str.join(', ').values
return dict(bb[['title', 'publisher', 'type','indexed.date-parts', 'subject' ]])
file_list = ['file_2021_01/10625.json.gz',
'file_2021_01/23897.json.gz',
'file_2021_01/12169.json.gz',
'file_2021_01/427.json.gz',...]
filenames = tf.data.Dataset.list_files(file_list, shuffle=True)
dataset = filenames.apply(
tf.data.experimental.parallel_interleave(
lambda x: tf.data.Dataset.from_tensor_slices(tf.numpy_function(load_file, [x], (tf.int64))), cycle_length=1))
However, it results it a error:
InternalError: Unsupported object type dict
[[{{node PyFunc}}]] [Op:IteratorGetNext]
Thanks
I want to concatenate two tensors checkerboard-ly in tensorflow2, like examples showed below:
example 1:
a = [[1,1],[1,1]]
b = [[0,0],[0,0]]
concated_a_and_b = [[1,0,1,0],[0,1,0,1]]
example 2:
a = [[1,1,1],[1,1,1],[1,1,1]]
b = [[0,0,0],[0,0,0],[0,0,0]]
concated_a_and_b = [[1,0,1,0,1,0],[0,1,0,1,0,1],[1,0,1,0,1,0]]
Is there a decent way in tensorflow2 to concatenate them like this?
A bit of background for this:
I first split a tensor c with a checkerboard mask into two halves a and b. A after some transformation I have to concat them back into oringnal shape and order.
What I mean by checkerboard-ly:
Step 1: Generate a matrix with alternated values
You can do this by first concatenating into [1, 0] pairs, and then by applying a final reshape.
Step 2: Reverse some rows
I split the matrix into two parts, reverse the second part and then rebuild the full matrix by picking alternatively from the first and second part
Code sample:
import math
import numpy as np
import tensorflow as tf
a = tf.ones(shape=(3, 4))
b = tf.zeros(shape=(3, 4))
x = tf.expand_dims(a, axis=-1)
y = tf.expand_dims(b, axis=-1)
paired_ones_zeros = tf.concat([x, y], axis=-1)
alternated_values = tf.reshape(paired_ones_zeros, [-1, a.shape[1] + b.shape[1]])
num_samples = alternated_values.shape[0]
middle = math.ceil(num_samples / 2)
is_num_samples_odd = middle * 2 != num_samples
# Gather first part of the matrix, don't do anything to it
first_elements = tf.gather_nd(alternated_values, [[index] for index in range(middle)])
# Gather second part of the matrix and reverse its elements
second_elements = tf.reverse(tf.gather_nd(alternated_values, [[index] for index in range(middle, num_samples)]), axis=[1])
# Pick alternatively between first and second part of the matrix
indices = np.concatenate([[[index], [index + middle]] for index in range(middle)], axis=0)
if is_num_samples_odd:
indices = indices[:-1]
output = tf.gather_nd(
tf.concat([first_elements, second_elements], axis=0),
indices
)
print(output)
I know this is not a decent way as it will affect time and space complexity. But it solves the above problem
def concat(tf1, tf2):
result = []
for (index, (tf_item1, tf_item2)) in enumerate(zip(tf1, tf2)):
item = []
for (subitem1, subitem2) in zip(tf_item1, tf_item2):
if index % 2 == 0:
item.append(subitem1)
item.append(subitem2)
else:
item.append(subitem2)
item.append(subitem1)
concated_a_and_b.append(item)
return concated_a_and_b
I am working on a multi-class classification task using my own images.
filenames = [] # a list of filenames
labels = [] # a list of labels corresponding to the filenames
full_ds = tf.data.Dataset.from_tensor_slices((filenames, labels))
This full dataset will be shuffled and split into train, valid and test dataset
full_ds_size = len(filenames)
full_ds = full_ds.shuffle(buffer_size=full_ds_size*2, seed=128) # seed is used for reproducibility
train_ds_size = int(0.64 * full_ds_size)
valid_ds_size = int(0.16 * full_ds_size)
train_ds = full_ds.take(train_ds_size)
remaining = full_ds.skip(train_ds_size)
valid_ds = remaining.take(valid_ds_size)
test_ds = remaining.skip(valid_ds_size)
Now I am struggling to understand how each class is distributed in train_ds, valid_ds and test_ds. An ugly solution is to iterate all the element in the dataset and count the occurrence of each class. Is there any better way to solve it?
My ugly solution:
def get_class_distribution(dataset):
class_distribution = {}
for element in dataset.as_numpy_iterator():
label = element[1]
if label in class_distribution.keys():
class_distribution[label] += 1
else:
class_distribution[label] = 0
# sort dict by key
class_distribution = collections.OrderedDict(sorted(class_distribution.items()))
return class_distribution
train_ds_class_dist = get_class_distribution(train_ds)
valid_ds_class_dist = get_class_distribution(valid_ds)
test_ds_class_dist = get_class_distribution(test_ds)
print(train_ds_class_dist)
print(valid_ds_class_dist)
print(test_ds_class_dist)
The answer below assumes:
there are five classes.
labels are integers from 0 to 4.
It can be modified to suit your needs.
Define a counter function:
def count_class(counts, batch, num_classes=5):
labels = batch['label']
for i in range(num_classes):
cc = tf.cast(labels == i, tf.int32)
counts[i] += tf.reduce_sum(cc)
return counts
Use the reduce operation:
initial_state = dict((i, 0) for i in range(5))
counts = train_ds.reduce(initial_state=initial_state,
reduce_func=count_class)
print([(k, v.numpy()) for k, v in counts.items()])
A solution inspired by user650654 's answer, only using TensorFlow primitives (with tf.unique_with_counts instead of for loop):
In theory, this should have better performance and scale better to large datasets, batches or class count.
num_classes = 5
#tf.function
def count_class(counts, batch):
y, _, c = tf.unique_with_counts(batch[1])
return tf.tensor_scatter_nd_add(counts, tf.expand_dims(y, axis=1), c)
counts = train_ds.reduce(
initial_state=tf.zeros(num_classes, tf.int32),
reduce_func=count_class)
print(counts.numpy())
Similar and simpler version with numpy that actually had better performances for my simple use-case:
count = np.zeros(num_classes, dtype=np.int32)
for _, labels in train_ds:
y, _, c = tf.unique_with_counts(labels)
count[y.numpy()] += c.numpy()
print(count)
Does anyone know how to split a dataset created by the dataset API (tf.data.Dataset) in Tensorflow into Test and Train?
Assuming you have all_dataset variable of tf.data.Dataset type:
test_dataset = all_dataset.take(1000)
train_dataset = all_dataset.skip(1000)
Test dataset now has first 1000 elements and the rest goes for training.
You may use Dataset.take() and Dataset.skip():
train_size = int(0.7 * DATASET_SIZE)
val_size = int(0.15 * DATASET_SIZE)
test_size = int(0.15 * DATASET_SIZE)
full_dataset = tf.data.TFRecordDataset(FLAGS.input_file)
full_dataset = full_dataset.shuffle()
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.skip(val_size)
test_dataset = test_dataset.take(test_size)
For more generality, I gave an example using a 70/15/15 train/val/test split but if you don't need a test or a val set, just ignore the last 2 lines.
Take:
Creates a Dataset with at most count elements from this dataset.
Skip:
Creates a Dataset that skips count elements from this dataset.
You may also want to look into Dataset.shard():
Creates a Dataset that includes only 1/num_shards of this dataset.
Disclaimer I stumbled upon this question after answering this one so I thought I'd spread the love
Most of the answers here use take() and skip(), which requires knowing the size of your dataset before hand. This isn't always possible, or is difficult/intensive to ascertain.
Instead what you can do is to essentially slice the dataset up so that 1 every N records becomes a validation record.
To accomplish this, lets start with a simple dataset of 0-9:
dataset = tf.data.Dataset.range(10)
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Now for our example, we're going to slice it so that we have a 3/1 train/validation split. Meaning 3 records will go to training, then 1 record to validation, then repeat.
split = 3
dataset_train = dataset.window(split, split + 1).flat_map(lambda ds: ds)
# [0, 1, 2, 4, 5, 6, 8, 9]
dataset_validation = dataset.skip(split).window(1, split + 1).flat_map(lambda ds: ds)
# [3, 7]
So the first dataset.window(split, split + 1) says to grab split number (3) of elements, then advance split + 1 elements, and repeat. That + 1 effectively skips the 1 element we're going to use in our validation dataset.
The flat_map(lambda ds: ds) is because window() returns the results in batches, which we don't want. So we flatten it back out.
Then for the validation data we first skip(split), which skips over the first split number (3) of elements that were grabbed in the first training window, so we start our iteration on the 4th element. The window(1, split + 1) then grabs 1 element, advances split + 1 (4), and repeats.
Note on nested datasets:
The above example works well for simple datasets, but flat_map() will generate an error if the dataset is nested. To address this, you can swap out the flat_map() with a more complicated version that can handle both simple and nested datasets:
.flat_map(lambda *ds: ds[0] if len(ds) == 1 else tf.data.Dataset.zip(ds))
#ted's answer will cause some overlap. Try this.
train_ds_size = int(0.64 * full_ds_size)
valid_ds_size = int(0.16 * full_ds_size)
train_ds = full_ds.take(train_ds_size)
remaining = full_ds.skip(train_ds_size)
valid_ds = remaining.take(valid_ds_size)
test_ds = remaining.skip(valid_ds_size)
use code below to test.
tf.enable_eager_execution()
dataset = tf.data.Dataset.range(100)
train_size = 20
valid_size = 30
test_size = 50
train = dataset.take(train_size)
remaining = dataset.skip(train_size)
valid = remaining.take(valid_size)
test = remaining.skip(valid_size)
for i in train:
print(i)
for i in valid:
print(i)
for i in test:
print(i)
Now Tensorflow doesn't contain any tools for that.
You could use sklearn.model_selection.train_test_split to generate train/eval/test dataset, then create tf.data.Dataset respectively.
You can use shard:
dataset = dataset.shuffle() # optional
trainset = dataset.shard(2, 0)
testset = dataset.shard(2, 1)
See:
https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard
The upcoming TensorFlow 2.10.0 will have a tf.keras.utils.split_dataset function, see the rc3 release notes:
Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
In case size of the dataset is known:
from typing import Tuple
import tensorflow as tf
def split_dataset(dataset: tf.data.Dataset,
dataset_size: int,
train_ratio: float,
validation_ratio: float) -> Tuple[tf.data.Dataset, tf.data.Dataset, tf.data.Dataset]:
assert (train_ratio + validation_ratio) < 1
train_count = int(dataset_size * train_ratio)
validation_count = int(dataset_size * validation_ratio)
test_count = dataset_size - (train_count + validation_count)
dataset = dataset.shuffle(dataset_size)
train_dataset = dataset.take(train_count)
validation_dataset = dataset.skip(train_count).take(validation_count)
test_dataset = dataset.skip(validation_count + train_count).take(test_count)
return train_dataset, validation_dataset, test_dataset
Example:
size_of_ds = 1001
train_ratio = 0.6
val_ratio = 0.2
ds = tf.data.Dataset.from_tensor_slices(list(range(size_of_ds)))
train_ds, val_ds, test_ds = split_dataset(ds, size_of_ds, train_ratio, val_ratio)
A robust way to split dataset into two parts is to first deterministically map every item in the dataset into a bucket with, for example, tf.strings.to_hash_bucket_fast. Then you can split the dataset into two by filtering by the bucket. If you split your data into five buckets, you get 80-20 split assuming that the split is even.
As an example, assume that your dataset contains dictionaries with key filename. We split the data into five buckets based on this key. With this add_fold function, we add the key "fold" in the dictionaries:
def add_fold(buckets: int):
def add_(sample, label):
fold = tf.strings.to_hash_bucket(sample["filename"], num_buckets=buckets)
return {**sample, "fold": fold}, label
return add_
dataset = dataset.map(add_fold(buckets=5))
Now we can split the dataset into two disjoint datasets with Dataset.filter:
def pick_fold(fold: int):
def filter_fn(sample, _):
return tf.math.equal(sample["fold"], fold)
return filter_fn
def skip_fold(fold: int):
def filter_fn(sample, _):
return tf.math.not_equal(sample["fold"], fold)
return filter_fn
train_dataset = dataset.filter(skip_fold(0))
val_dataset = dataset.filter(pick_fold(0))
The key that you use for hashing should be one that captures the correlations in the dataset. For example, if your samples collected by the same person are correlated and you want all samples with the same collector end up in the same bucket (and the same split), you should use the collector name or ID as the hashing column.
Of course, you can skip the part with dataset.map and do the hashing and filtering in one filter function. Here's a full example:
dataset = tf.data.Dataset.from_tensor_slices([f"value-{i}" for i in range(10000)])
def to_bucket(sample):
return tf.strings.to_hash_bucket_fast(sample, 5)
def filter_train_fn(sample):
return tf.math.not_equal(to_bucket(sample), 0)
def filter_val_fn(sample):
return tf.math.logical_not(filter_train_fn(sample))
train_ds = dataset.filter(filter_train_fn)
val_ds = dataset.filter(filter_val_fn)
print(f"Length of training set: {len(list(train_ds.as_numpy_iterator()))}")
print(f"Length of validation set: {len(list(val_ds.as_numpy_iterator()))}")
This prints:
Length of training set: 7995
Length of validation set: 2005
Can't comment, but above answer has overlap and is incorrect. Set BUFFER_SIZE to DATASET_SIZE for perfect shuffle. Try different sized val/test size to verify. Answer should be:
DATASET_SIZE = tf.data.experimental.cardinality(full_dataset).numpy()
train_size = int(0.7 * DATASET_SIZE)
val_size = int(0.15 * DATASET_SIZE)
test_size = int(0.15 * DATASET_SIZE)
full_dataset = full_dataset.shuffle(BUFFER_SIZE)
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.take(val_size)
test_dataset = test_dataset.skip(val_size)
I frequently use tf.add_to_collection to have Tensorflow automatically serialize intermediary results into a checkpoint. I find this the most convenient way to later fetch pointers to interesting tensors when a model was restored from a checkpoint. However, I realized that RNN state tuples cannot easily be added to a graph collection. Consider the following dummy example in TF 1.3:
import tensorflow as tf
import numpy as np
in_ = tf.placeholder(tf.float32, shape=[None, 5, 1])
batch_size = tf.shape(in_)[0]
cell1 = tf.nn.rnn_cell.BasicLSTMCell(num_units=128)
cell2 = tf.nn.rnn_cell.BasicLSTMCell(num_units=256)
cell = tf.nn.rnn_cell.MultiRNNCell([cell1, cell2])
outputs, last_state = tf.nn.dynamic_rnn(cell=cell,
inputs=in_,
initial_state=cell.zero_state(batch_size, dtype=tf.float32))
tf.add_to_collection('states', last_state)
loss = tf.reduce_mean(in_ - outputs)
loss_s = tf.summary.scalar('loss', loss)
writer = tf.summary.FileWriter('.', tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
l, s = sess.run([loss, loss_s], feed_dict={in_: np.ones([1, 5, 1])})
writer.add_summary(s)
This will produce the following warning:
WARNING:tensorflow:Error encountered when serializing states.
Type is unsupported, or the types of the items don't match field type in CollectionDef.
'tuple' object has no attribute 'name'
It seems that the serialization cannot handle tuples, and of course the last_state variable is a tuple. May be one could loop through the tuple and add each element individually to the collection, but that seems too complicated. What's a better way of handling this? In the end, I would like to access last_state again when the model is restored, ideally without needing access to the original code that created the model.
Actually, looping through every element of the state is not too complicated, and straight-forward to implement:
def add_to_collection_rnn_state(name, rnn_state):
for layer in rnn_state:
tf.add_to_collection(name, layer.c)
tf.add_to_collection(name, layer.h)
And then to load it:
def get_collection_rnn_state(name):
layers = []
coll = tf.get_collection(name)
for i in range(0, len(coll), 2):
state = tf.nn.rnn_cell.LSTMStateTuple(coll[i], coll[i+1])
layers.append(state)
return tuple(layers)
Note that this assumes that one collection only stores on state, i.e. use a different collection for every state you want to store, e.g. like this:
add_to_collection_rnn_state('states', last_state)
add_to_collection_rnn_state('init_state', init_state)
Edit
As pointed out correctly in the comments, the proposed solution only works for LSTMCells (that are represented as tuples as well). A more general solution that can handle GRU cells or potentially custom cells and mixes thereof, could look like this:
import tensorflow as tf
import numpy as np
def add_to_collection_rnn_state(name, rnn_state):
# store the name of each cell type in a different collection
coll_of_names = name + '__names__'
for layer in rnn_state:
n = layer.__class__.__name__
tf.add_to_collection(coll_of_names, n)
try:
for l in layer:
tf.add_to_collection(name, l)
except TypeError:
# layer is not iterable so just add it directly
tf.add_to_collection(name, layer)
def get_collection_rnn_state(name):
layers = []
coll = tf.get_collection(name)
coll_of_names = tf.get_collection(name + '__names__')
idx = 0
for n in coll_of_names:
if 'LSTMStateTuple' in n:
state = tf.nn.rnn_cell.LSTMStateTuple(coll[idx], coll[idx+1])
idx += 2
else: # add more cell types here
state = coll[idx]
idx += 1
layers.append(state)
return tuple(layers)
in_ = tf.placeholder(tf.float32, shape=[None, 5, 1])
batch_size = tf.shape(in_)[0]
cell1 = tf.nn.rnn_cell.BasicLSTMCell(num_units=128)
cell2 = tf.nn.rnn_cell.GRUCell(num_units=256)
cell3 = tf.nn.rnn_cell.BasicRNNCell(num_units=256)
cell = tf.nn.rnn_cell.MultiRNNCell([cell1, cell2, cell3])
outputs, last_state = tf.nn.dynamic_rnn(cell=cell,
inputs=in_,
initial_state=cell.zero_state(batch_size, dtype=tf.float32))
add_to_collection_rnn_state('last_state', last_state)
last_state_r = get_collection_rnn_state('last_state')
Comparing last_state and last_state_r reveals that both are identical (which they should be). Note that I am using a different collection to store the names because tensorflow can only serialize a collection when all elements in the collection are of the same type. E.g. mixing strings with Tensors in the same collection does not work.