Note
Click here to download the full example code
TfIdfVectorizer with ONNX#
This example is inspired from the following example: Column Transformer with Heterogeneous Data Sources which builds a pipeline to classify text.
Train a pipeline with TfidfVectorizer#
It replicates the same pipeline taken from scikit-learn documentation but reduces it to the part ONNX actually supports without implementing a custom converter. Let’s get the data.
import matplotlib.pyplot as plt
import os
from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
import numpy
import onnxruntime as rt
from skl2onnx.common.data_types import StringTensorType
from skl2onnx import convert_sklearn
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.datasets import fetch_20newsgroups
try:
from sklearn.datasets._twenty_newsgroups import (
strip_newsgroup_footer, strip_newsgroup_quoting)
except ImportError:
# scikit-learn < 0.24
from sklearn.datasets.twenty_newsgroups import (
strip_newsgroup_footer, strip_newsgroup_quoting)
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
# limit the list of categories to make running this example faster.
categories = ['alt.atheism', 'talk.religion.misc']
train = fetch_20newsgroups(random_state=1,
subset='train',
categories=categories,
)
test = fetch_20newsgroups(random_state=1,
subset='test',
categories=categories,
)
The first transform extract two fields from the data. We take it out form the pipeline and assume the data is defined by two text columns.
class SubjectBodyExtractor(BaseEstimator, TransformerMixin):
"""Extract the subject & body from a usenet post in a single pass.
Takes a sequence of strings and produces a dict of sequences. Keys are
`subject` and `body`.
"""
def fit(self, x, y=None):
return self
def transform(self, posts):
# construct object dtype array with two columns
# first column = 'subject' and second column = 'body'
features = np.empty(shape=(len(posts), 2), dtype=object)
for i, text in enumerate(posts):
headers, _, bod = text.partition('\n\n')
bod = strip_newsgroup_footer(bod)
bod = strip_newsgroup_quoting(bod)
features[i, 1] = bod
prefix = 'Subject:'
sub = ''
for line in headers.split('\n'):
if line.startswith(prefix):
sub = line[len(prefix):]
break
features[i, 0] = sub
return features
train_data = SubjectBodyExtractor().fit_transform(train.data)
test_data = SubjectBodyExtractor().fit_transform(test.data)
The pipeline is almost the same except we remove the custom features.
pipeline = Pipeline([
('union', ColumnTransformer(
[
('subject', TfidfVectorizer(min_df=50, max_features=500), 0),
('body_bow', Pipeline([
('tfidf', TfidfVectorizer()),
('best', TruncatedSVD(n_components=50)),
]), 1),
# Removed from the original example as
# it requires a custom converter.
# ('body_stats', Pipeline([
# ('stats', TextStats()), # returns a list of dicts
# ('vect', DictVectorizer()), # list of dicts -> feature matrix
# ]), 1),
],
transformer_weights={
'subject': 0.8,
'body_bow': 0.5,
# 'body_stats': 1.0,
}
)),
# Use a LogisticRegression classifier on the combined features.
# Instead of LinearSVC (not fully ready in onnxruntime).
('logreg', LogisticRegression()),
])
pipeline.fit(train_data, train.target)
print(classification_report(pipeline.predict(test_data), test.target))
precision recall f1-score support
0 0.69 0.78 0.73 285
1 0.75 0.66 0.70 285
accuracy 0.72 570
macro avg 0.72 0.72 0.71 570
weighted avg 0.72 0.72 0.71 570
ONNX conversion#
It is difficult to replicate the exact same tokenizer behaviour if the tokeniser comes from space, gensim or nltk. The default one used by scikit-learn uses regular expressions and is currently being implementing. The current implementation only considers a list of separators which can is defined in variable seps.
And save.
Predictions with onnxruntime.
sess = rt.InferenceSession("pipeline_tfidf.onnx")
print('---', train_data[0])
inputs = {'input': train_data[:1]}
pred_onx = sess.run(None, inputs)
print("predict", pred_onx[0])
print("predict_proba", pred_onx[1])
--- [" Re: Jews can't hide from keith@cco."
'Deletions...\n\nSo, you consider the german poster\'s remark anti-semitic? Perhaps you\nimply that anyone in Germany who doesn\'t agree with israely policy in a\nnazi? Pray tell, how does it even qualify as "casual anti-semitism"? \nIf the term doesn\'t apply, why then bring it up?\n\nYour own bigotry is shining through. \n-- ']
predict [1]
predict_proba [{0: 0.4384377896785736, 1: 0.561562180519104}]
With scikit-learn:
print(pipeline.predict(train_data[:1]))
print(pipeline.predict_proba(train_data[:1]))
[0]
[[0.71903792 0.28096208]]
There are discrepencies for this model because the tokenization is not exactly the same. This is a work in progress.
Display the ONNX graph#
Finally, let’s see the graph converted with sklearn-onnx.
pydot_graph = GetPydotGraph(
model_onnx.graph, name=model_onnx.graph.name,
rankdir="TB", node_producer=GetOpNodeProducer("docstring",
color="yellow",
fillcolor="yellow",
style="filled"))
pydot_graph.write_dot("pipeline_tfidf.dot")
os.system('dot -O -Gdpi=300 -Tpng pipeline_tfidf.dot')
image = plt.imread("pipeline_tfidf.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis('off')

(-0.5, 4939.5, 11475.5, -0.5)
Total running time of the script: ( 0 minutes 10.979 seconds)