Dataframe as an input

A pipeline usually ingests data as a matrix. It may be converted in a matrix if all the data share the same type. But data held in a dataframe have usually multiple types, float, integer or string for categories. ONNX also supports that case.

A dataset with categories

from mlinsights.plotting import pipeline2dot
import numpy
import pprint
from mlprodict.onnx_conv import guess_schema_from_data
from onnxruntime import InferenceSession
from pyquickhelper.helpgen.graphviz_helper import plot_graphviz
from mlprodict.onnxrt import OnnxInference
from mlprodict.onnx_conv import to_onnx as to_onnx_ext
from skl2onnx import to_onnx
from pandas import DataFrame
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import RandomForestClassifier


data = DataFrame([
    dict(CAT1='a', CAT2='c', num1=0.5, num2=0.6, y=0),
    dict(CAT1='b', CAT2='d', num1=0.4, num2=0.8, y=1),
    dict(CAT1='a', CAT2='d', num1=0.5, num2=0.56, y=0),
    dict(CAT1='a', CAT2='d', num1=0.55, num2=0.56, y=1),
    dict(CAT1='a', CAT2='c', num1=0.35, num2=0.86, y=0),
    dict(CAT1='a', CAT2='c', num1=0.5, num2=0.68, y=1),
])

cat_cols = ['CAT1', 'CAT2']
train_data = data.drop('y', axis=1)


categorical_transformer = Pipeline([
    ('onehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
    transformers=[
        ('cat', categorical_transformer, cat_cols)],
    remainder='passthrough')
pipe = Pipeline([('preprocess', preprocessor),
                 ('rf', RandomForestClassifier())])
pipe.fit(train_data, data['y'])

Pipeline(steps=[('preprocess',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('cat',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore',
                                                                                 sparse=False))]),
                                                  ['CAT1', 'CAT2'])])),
                ('rf', RandomForestClassifier())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Display.

dot = pipeline2dot(pipe, train_data)
ax = plot_graphviz(dot)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)

Conversion to ONNX

Function to_onnx does not handle dataframes.

try:
    onx = to_onnx(pipe, train_data[:1])
except NotImplementedError as e:
    print(e)

But it possible to use an extended one.

onx = to_onnx_ext(
    pipe, train_data[:1],
    options={RandomForestClassifier: {'zipmap': False}})

Graph

oinf = OnnxInference(onx)
ax = plot_graphviz(oinf.to_dot())
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)

Prediction with ONNX

onnxruntime does not support dataframes.

sess = InferenceSession(onx.SerializeToString(),
                        providers=['CPUExecutionProvider'])
try:
    sess.run(None, train_data)
except Exception as e:
    print(e)

run(): incompatible function arguments. The following argument types are supported:
    1. (self: onnxruntime.capi.onnxruntime_pybind11_state.InferenceSession, arg0: List[str], arg1: Dict[str, object], arg2: onnxruntime.capi.onnxruntime_pybind11_state.RunOptions) -> List[object]

Invoked with: <onnxruntime.capi.onnxruntime_pybind11_state.InferenceSession object at 0x7faf8eea20b0>, ['label', 'probabilities'],   CAT1 CAT2  num1  num2
0    a    c  0.50  0.60
1    b    d  0.40  0.80
2    a    d  0.50  0.56
3    a    d  0.55  0.56
4    a    c  0.35  0.86
5    a    c  0.50  0.68, None

Let’s use a shortcut

oinf = OnnxInference(onx)
got = oinf.run(train_data)
print(pipe.predict(train_data))
print(got['label'])

[0 1 0 1 0 1]
[0 1 0 1 0 1]

And probilities.

print(pipe.predict_proba(train_data))
print(got['probabilities'])

[[0.78 0.22]
 [0.26 0.74]
 [0.69 0.31]
 [0.28 0.72]
 [0.73 0.27]
 [0.27 0.73]]
[[0.78       0.22      ]
 [0.2600001  0.7399999 ]
 [0.69000006 0.30999997]
 [0.28000015 0.71999985]
 [0.73       0.26999995]
 [0.2700001  0.7299999 ]]

It looks ok. Let’s dig into the details to directly use onnxruntime.

Unhide conversion logic with a dataframe

A dataframe can be seen as a set of columns with different types. That’s what ONNX should see: a list of inputs, the input name is the column name, the input type is the column type.

init = guess_schema_from_data(train_data)

pprint.pprint(init)

[('CAT1', StringTensorType(shape=[None, 1])),
 ('CAT2', StringTensorType(shape=[None, 1])),
 ('num1', DoubleTensorType(shape=[None, 1])),
 ('num2', DoubleTensorType(shape=[None, 1]))]

Let’s use float instead.

for c in train_data.columns:
    if c not in cat_cols:
        train_data[c] = train_data[c].astype(numpy.float32)

init = guess_schema_from_data(train_data)
pprint.pprint(init)

[('CAT1', StringTensorType(shape=[None, 1])),
 ('CAT2', StringTensorType(shape=[None, 1])),
 ('num1', FloatTensorType(shape=[None, 1])),
 ('num2', FloatTensorType(shape=[None, 1]))]

Let’s convert with skl2onnx only.

onx2 = to_onnx(
    pipe, initial_types=init,
    options={RandomForestClassifier: {'zipmap': False}})

Let’s run it with onnxruntime. We need to convert the dataframe into a dictionary where column names become keys, and column values become values.

inputs = {c: train_data[c].values.reshape((-1, 1))
          for c in train_data.columns}
pprint.pprint(inputs)

{'CAT1': array([['a'],
       ['b'],
       ['a'],
       ['a'],
       ['a'],
       ['a']], dtype=object),
 'CAT2': array([['c'],
       ['d'],
       ['d'],
       ['d'],
       ['c'],
       ['c']], dtype=object),
 'num1': array([[0.5 ],
       [0.4 ],
       [0.5 ],
       [0.55],
       [0.35],
       [0.5 ]], dtype=float32),
 'num2': array([[0.6 ],
       [0.8 ],
       [0.56],
       [0.56],
       [0.86],
       [0.68]], dtype=float32)}

Inference.

sess2 = InferenceSession(onx2.SerializeToString(),
                         providers=['CPUExecutionProvider'])

got2 = sess2.run(None, inputs)

print(pipe.predict(train_data))
print(got2[0])

[0 1 0 1 0 1]
[0 1 0 1 0 1]

And probilities.

print(pipe.predict_proba(train_data))
print(got2[1])

[[0.78 0.22]
 [0.26 0.74]
 [0.69 0.31]
 [0.28 0.72]
 [0.73 0.27]
 [0.27 0.73]]
[[0.78       0.22000003]
 [0.2600004  0.7399996 ]
 [0.69000006 0.30999997]
 [0.2800004  0.7199996 ]
 [0.73       0.27      ]
 [0.2700004  0.7299996 ]]