Is TfIdfVectorizer safe to use?

[geofforigin8]

Is TfIdfVectorizer safe to use?

[justinchuby]

cc @xadupre @gramalingam

[xadupre]

mlprodict is archived because its main functionality, a python runtime for onnx called OnnxInference, was moved into onnx package as ReferenceEvaluator. I did not write an example about the trick I put in place to avoid ambiguities but it is maintained in unit tests: https://github.com/onnx/sklearn-onnx/blob/main/tests/test_sklearn_text.py#L11. This issue is not mentioned in onnx documentation because the ambiguity comes from scikit-learn and not onnx. This case usually don't happen unless the n-grams contain spaces. In that case, the converter gets confused.

[geofforigin8]

Thank you for the quick reply! Using the test cases as a model, I wrote a sample program and I was not having success when using any of the min_df, max_df or max_features parameters.

It looks like convert_sklearn_text_vectorizer is expecting stop_words_ to be a set of string but when min_df et al are set, it is being set to a set of tuples, so as a result running to_onnx on it will fail with an error like this:
TypeError: One stop word is not a string ('is', 'this') in stop_words={('is', 'this'), ...

[geofforigin8]

I've sort of fixed my problem by setting stop_words to an empty set and moving on. However, I'm still seeing some discrepancies and tracked it down to what looks like single-char tokens not being skipped by ONNX?

to reproduce:

import numpy as np 
import skl2onnx
from skl2onnx.sklapi import TraceableTfidfVectorizer, TraceableCountVectorizer
from skl2onnx import update_registered_converter
from skl2onnx.shape_calculators.text_vectorizer import (
    calculate_sklearn_text_vectorizer_output_shapes,
)
from skl2onnx.operator_converters.text_vectoriser import convert_sklearn_text_vectorizer
from skl2onnx.operator_converters.tfidf_vectoriser import convert_sklearn_tfidf_vectoriser
from skl2onnx.common.data_types import StringTensorType
from numpy.testing import assert_almost_equal
from onnxruntime import InferenceSession


update_registered_converter(
    TraceableCountVectorizer,
    "Skl2onnxTraceableCountVectorizer",
    calculate_sklearn_text_vectorizer_output_shapes,
    convert_sklearn_text_vectorizer,
    options={
        "tokenexp": None,
        "separators": None,
        "nan": [True, False],
        "keep_empty_string": [True, False],
        "locale": None,
    },
)

update_registered_converter(
    TraceableTfidfVectorizer,
    "Skl2onnxTraceableTfidfVectorizer",
    calculate_sklearn_text_vectorizer_output_shapes,
    convert_sklearn_tfidf_vectoriser,
    options={
        "tokenexp": None,
        "separators": None,
        "nan": [True, False],
        "keep_empty_string": [True, False],
        "locale": None,
    },
)

corpus = np.array([
        "This is the first document.",
        "This document is the second document.",
        "And this is the third one.",
        "Is this the first document?",
        "first Z document", # uncomment this and the last assertion fails because ONNX still sees the "Z" token when it shouldn't?
        "",
    ]
)

FEATURES = 12

vect = TraceableTfidfVectorizer(ngram_range=(1,2), max_features=FEATURES)
vect.fit(corpus)

print('vocabulary:', vect.vocabulary_)
print('stop_words:', vect.stop_words_)

A = vect.transform(corpus).todense()

vect.stop_words_ = set() # should be safe right?

B = vect.transform(corpus).todense()
assert_almost_equal(A,B)  # make sure vectorizer still does what we expect

model_onnx = skl2onnx.to_onnx(
    vect,
    "TfidfVectorizer",
    initial_types=[("input", StringTensorType([None, 1]))],
    target_opset=12,
)

sess = InferenceSession(model_onnx.SerializeToString(), providers=['CPUExecutionProvider'])

inputs = {"input": corpus.reshape((-1, 1))}

out = sess.run(None, inputs)[0]
assert out.shape == (len(corpus), FEATURES)
assert_almost_equal(out.ravel(), A.A1)

sklearn gives the same vector for both strings as expected:
vect.transform(["first document", "first Z document"]).todense()

matrix([[0.        , 0.51822427, 0.60474937, 0.60474937, 0.        ,
         0.        , 0.        , 0.        , 0.        , 0.        ,
         0.        , 0.        ],
        [0.        , 0.51822427, 0.60474937, 0.60474937, 0.        ,
         0.        , 0.        , 0.        , 0.        , 0.        ,
         0.        , 0.        ]])

And the model from onnx is is only matching the results on the first string:
sess.run(None, {'input': [["first document"], ["first Z document"]]})[0]

array([[0.        , 0.5182243 , 0.6047494 , 0.6047494 , 0.        ,
        0.        , 0.        , 0.        , 0.        , 0.        ,
        0.        , 0.        ],
       [0.        , 0.65069556, 0.7593387 , 0.        , 0.        ,
        0.        , 0.        , 0.        , 0.        , 0.        ,
        0.        , 0.        ]], dtype=float32)

[gramalingam]

Hi, I haven't understood the details here. ONNX is a specification. The issue could either be in the conversion side or it could be a bug in the onnxruntime implementation. If it is the latter, I would recommend filing an issue in the onnxruntime repo. Do you know?

Xavier might have a better idea (he is off this week).

[geofforigin8]

Nope, I did not know. I don't have a full image of all the related projects, ownership and relations between them. Thanks for the link though, I thought I found a related issue reported there but, I don't think that's it.

I was able to resolve my issue by explicitly passing "tokenexp": r"\b\w\w+\b" in the options. I don't think this substitution is correct:
https://github.com/onnx/sklearn-onnx/blob/d2029c1a9752f62a63fc5c4447b4d9fe75e8fe39/skl2onnx/operator_converters/text_vectoriser.py#L238

It should be something like [a-zA-Z0-9_]{2,} or \b\w\w+\b. Is the reason for this replacement because the onnx runtime chokes on the (?u) that sklearn has in it's default for token_pattern?