Walk through intermediate outputs

We reuse the example Convert a pipeline with ColumnTransformer and walk through intermediates outputs. It is very likely a converted model gives different outputs or fails due to a custom converter which is not correctly implemented. One option is to look into the output of every node of the ONNX graph.

Create and train a complex pipeline

We reuse the pipeline implemented in example Column Transformer with Mixed Types. There is one change because ONNX-ML Imputer does not handle string type. This cannot be part of the final ONNX pipeline and must be removed. Look for comment starting with --- below.

import onnx
import sklearn
import matplotlib.pyplot as plt
import os
from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
from skl2onnx.helpers.onnx_helper import select_model_inputs_outputs
from skl2onnx.helpers.onnx_helper import save_onnx_model
from skl2onnx.helpers.onnx_helper import enumerate_model_node_outputs
from skl2onnx.helpers.onnx_helper import load_onnx_model
import numpy
import onnxruntime as rt
from skl2onnx import convert_sklearn, __version__
import pprint
from skl2onnx.common.data_types import FloatTensorType, StringTensorType
from skl2onnx.common.data_types import Int64TensorType
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

titanic_url = ('https://raw.githubusercontent.com/amueller/'
               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')
data = pd.read_csv(titanic_url)
X = data.drop('survived', axis=1)
y = data['survived']

# SimpleImputer on string is not available
# for string in ONNX-ML specifications.
# So we do it beforehand.
for cat in ['embarked', 'sex', 'pclass']:
    X[cat].fillna('missing', inplace=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
    # --- SimpleImputer is not available for strings in ONNX-ML specifications.
    # ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features),
    ])

clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('classifier', LogisticRegression(solver='lbfgs'))])

clf.fit(X_train, y_train)

Out:

Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median')),
                                                                  ('scaler',
                                                                   StandardScaler())]),
                                                  ['age', 'fare']),
                                                 ('cat',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['embarked', 'sex',
                                                   'pclass'])])),
                ('classifier', LogisticRegression())])

Define the inputs of the ONNX graph

sklearn-onnx does not know the features used to train the model but it needs to know which feature has which name. We simply reuse the dataframe column definition.

print(X_train.dtypes)

Out:

pclass         int64
name          object
sex           object
age          float64
sibsp          int64
parch          int64
ticket        object
fare         float64
cabin         object
embarked      object
boat          object
body         float64
home.dest     object
dtype: object

After conversion.

def convert_dataframe_schema(df, drop=None):
    inputs = []
    for k, v in zip(df.columns, df.dtypes):
        if drop is not None and k in drop:
            continue
        if v == 'int64':
            t = Int64TensorType([None, 1])
        elif v == 'float64':
            t = FloatTensorType([None, 1])
        else:
            t = StringTensorType([None, 1])
        inputs.append((k, t))
    return inputs


inputs = convert_dataframe_schema(X_train)

pprint.pprint(inputs)

Out:

[('pclass', Int64TensorType(shape=[None, 1])),
 ('name', StringTensorType(shape=[None, 1])),
 ('sex', StringTensorType(shape=[None, 1])),
 ('age', FloatTensorType(shape=[None, 1])),
 ('sibsp', Int64TensorType(shape=[None, 1])),
 ('parch', Int64TensorType(shape=[None, 1])),
 ('ticket', StringTensorType(shape=[None, 1])),
 ('fare', FloatTensorType(shape=[None, 1])),
 ('cabin', StringTensorType(shape=[None, 1])),
 ('embarked', StringTensorType(shape=[None, 1])),
 ('boat', StringTensorType(shape=[None, 1])),
 ('body', FloatTensorType(shape=[None, 1])),
 ('home.dest', StringTensorType(shape=[None, 1]))]

Merging single column into vectors is not the most efficient way to compute the prediction. It could be done before converting the pipeline into a graph.

Convert the pipeline into ONNX

try:
    model_onnx = convert_sklearn(clf, 'pipeline_titanic', inputs,
                                 target_opset=12)
except Exception as e:
    print(e)

Out:

Isolated variables exist: {'parch', 'boat', 'sibsp', 'cabin', 'ticket', 'home_dest', 'body', 'name'}

scikit-learn does implicit conversions when it can. sklearn-onnx does not. The ONNX version of OneHotEncoder must be applied on columns of the same type.

X_train['pclass'] = X_train['pclass'].astype(str)
X_test['pclass'] = X_test['pclass'].astype(str)
white_list = numeric_features + categorical_features
to_drop = [c for c in X_train.columns if c not in white_list]
inputs = convert_dataframe_schema(X_train, to_drop)

model_onnx = convert_sklearn(clf, 'pipeline_titanic', inputs,
                             target_opset=12)


# And save.
with open("pipeline_titanic.onnx", "wb") as f:
    f.write(model_onnx.SerializeToString())

Out:

/home/dupre/github_xadupre/sklearn-onnx/docs/examples/plot_intermediate_outputs.py:143: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train['pclass'] = X_train['pclass'].astype(str)
/home/dupre/github_xadupre/sklearn-onnx/docs/examples/plot_intermediate_outputs.py:144: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test['pclass'] = X_test['pclass'].astype(str)

Compare the predictions

Final step, we need to ensure the converted model produces the same predictions, labels and probabilities. Let’s start with scikit-learn.

print("predict", clf.predict(X_test[:5]))
print("predict_proba", clf.predict_proba(X_test[:1]))

Out:

predict [0 1 0 1 0]
predict_proba [[0.74757949 0.25242051]]

Predictions with onnxruntime. We need to remove the dropped columns and to change the double vectors into float vectors as onnxruntime does not support double floats. onnxruntime does not accept dataframe. inputs must be given as a list of dictionary. Last detail, every column was described not really as a vector but as a matrix of one column which explains the last line with the reshape.

X_test2 = X_test.drop(to_drop, axis=1)
inputs = {c: X_test2[c].values for c in X_test2.columns}
for c in numeric_features:
    inputs[c] = inputs[c].astype(np.float32)
for k in inputs:
    inputs[k] = inputs[k].reshape((inputs[k].shape[0], 1))

We are ready to run onnxruntime.

sess = rt.InferenceSession("pipeline_titanic.onnx")
pred_onx = sess.run(None, inputs)
print("predict", pred_onx[0][:5])
print("predict_proba", pred_onx[1][:1])

Out:

predict [0 1 0 1 0]
predict_proba [{0: 0.9081845283508301, 1: 0.09181544184684753}]

Compute intermediate outputs

Unfortunately, there is actually no way to ask onnxruntime to retrieve the output of intermediate nodes. We need to modifies the ONNX before it is given to onnxruntime. Let’s see first the list of intermediate output.

model_onnx = load_onnx_model("pipeline_titanic.onnx")
for out in enumerate_model_node_outputs(model_onnx):
    print(out)

Out:

age_cast
fare_cast
merged_columns
variable
variable1
embarkedout
sexout
pclassout
concat_result
variable2
variable1_cast
variable2_cast
transformed_column
label
probability_tensor
probabilities
output_label
output_probability

Not that easy to tell which one is what as the ONNX has more operators than the original scikit-learn pipelines. The graph at Display the ONNX graph helps up to find the outputs of both numerical and textual pipeline: variable1, variable2. Let’s look into the numerical pipeline first.

num_onnx = select_model_inputs_outputs(model_onnx, 'variable1')
save_onnx_model(num_onnx, "pipeline_titanic_numerical.onnx")

Out:

b'\x08\x06\x12\x08skl2onnx\x1a\x051.7.1"\x07ai.onnx(\x002\x00:\xae\x04\n^\n\x08variable\x12\tvariable1\x1a\x06Scaler"\x06Scaler*\x15\n\x06offset=_\xe4\xe8A=\xc5\x1e\x03B\xa0\x01\x06*\x14\n\x05scale=/\x83\x9c==\xfb\xf1\xa2<\xa0\x01\x06:\nai.onnx.ml\n}\n\x0emerged_columns\x12\x08variable\x1a\x07Imputer"\x07Imputer*#\n\x14imputed_value_floats=\x00\x00\xe0A=gDgA\xa0\x01\x06*\x1e\n\x14replaced_value_float\x15\x00\x00\xc0\x7f\xa0\x01\x01:\nai.onnx.ml\nD\n\x08age_cast\n\tfare_cast\x12\x0emerged_columns\x1a\x06Concat"\x06Concat*\x0b\n\x04axis\x18\x01\xa0\x01\x02:\x00\n+\n\x04fare\x12\tfare_cast\x1a\x05Cast1"\x04Cast*\t\n\x02to\x18\x01\xa0\x01\x02:\x00\n(\n\x03age\x12\x08age_cast\x1a\x04Cast"\x04Cast*\t\n\x02to\x18\x01\xa0\x01\x02:\x00\x12\x10pipeline_titanic*\x1f\x08\x02\x10\x07:\x0b\xff\xff\xff\xff\xff\xff\xff\xff\xff\x01\tB\x0cshape_tensorZ\x16\n\x06pclass\x12\x0c\n\n\x08\x08\x12\x06\n\x00\n\x02\x08\x01Z\x13\n\x03sex\x12\x0c\n\n\x08\x08\x12\x06\n\x00\n\x02\x08\x01Z\x13\n\x03age\x12\x0c\n\n\x08\x01\x12\x06\n\x00\n\x02\x08\x01Z\x14\n\x04fare\x12\x0c\n\n\x08\x01\x12\x06\n\x00\n\x02\x08\x01Z\x18\n\x08embarked\x12\x0c\n\n\x08\x08\x12\x06\n\x00\n\x02\x08\x01b\x0b\n\tvariable1B\x02\x10\x0cB\x04\n\x00\x10\x0bB\x0e\n\nai.onnx.ml\x10\x01'

Let’s compute the numerical features.

sess = rt.InferenceSession("pipeline_titanic_numerical.onnx")
numX = sess.run(None, inputs)
print("numerical features", numX[0][:1])

Out:

numerical features [[-0.6198985  -0.49189988]]

We do the same for the textual features.

print(model_onnx)
text_onnx = select_model_inputs_outputs(model_onnx, 'variable2')
save_onnx_model(text_onnx, "pipeline_titanic_textual.onnx")
sess = rt.InferenceSession("pipeline_titanic_textual.onnx")
numT = sess.run(None, inputs)
print("textual features", numT[0][:1])

Out:

ir_version: 6
producer_name: "skl2onnx"
producer_version: "1.7.1"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
  node {
    input: "age"
    output: "age_cast"
    name: "Cast"
    op_type: "Cast"
    attribute {
      name: "to"
      i: 1
      type: INT
    }
    domain: ""
  }
  node {
    input: "fare"
    output: "fare_cast"
    name: "Cast1"
    op_type: "Cast"
    attribute {
      name: "to"
      i: 1
      type: INT
    }
    domain: ""
  }
  node {
    input: "age_cast"
    input: "fare_cast"
    output: "merged_columns"
    name: "Concat"
    op_type: "Concat"
    attribute {
      name: "axis"
      i: 1
      type: INT
    }
    domain: ""
  }
  node {
    input: "merged_columns"
    output: "variable"
    name: "Imputer"
    op_type: "Imputer"
    attribute {
      name: "imputed_value_floats"
      floats: 28.0
      floats: 14.45419979095459
      type: FLOATS
    }
    attribute {
      name: "replaced_value_float"
      f: nan
      type: FLOAT
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "variable"
    output: "variable1"
    name: "Scaler"
    op_type: "Scaler"
    attribute {
      name: "offset"
      floats: 29.111509323120117
      floats: 32.78004837036133
      type: FLOATS
    }
    attribute {
      name: "scale"
      floats: 0.07642208784818649
      floats: 0.01989077590405941
      type: FLOATS
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "embarked"
    output: "embarkedout"
    name: "OneHotEncoder"
    op_type: "OneHotEncoder"
    attribute {
      name: "cats_strings"
      strings: "C"
      strings: "Q"
      strings: "S"
      strings: "missing"
      type: STRINGS
    }
    attribute {
      name: "zeros"
      i: 1
      type: INT
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "sex"
    output: "sexout"
    name: "OneHotEncoder1"
    op_type: "OneHotEncoder"
    attribute {
      name: "cats_strings"
      strings: "female"
      strings: "male"
      type: STRINGS
    }
    attribute {
      name: "zeros"
      i: 1
      type: INT
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "pclass"
    output: "pclassout"
    name: "OneHotEncoder2"
    op_type: "OneHotEncoder"
    attribute {
      name: "cats_strings"
      strings: "1"
      strings: "2"
      strings: "3"
      type: STRINGS
    }
    attribute {
      name: "zeros"
      i: 1
      type: INT
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "embarkedout"
    input: "sexout"
    input: "pclassout"
    output: "concat_result"
    name: "Concat1"
    op_type: "Concat"
    attribute {
      name: "axis"
      i: 2
      type: INT
    }
    domain: ""
  }
  node {
    input: "concat_result"
    input: "shape_tensor"
    output: "variable2"
    name: "Reshape"
    op_type: "Reshape"
    domain: ""
  }
  node {
    input: "variable1"
    output: "variable1_cast"
    name: "Cast2"
    op_type: "Cast"
    attribute {
      name: "to"
      i: 1
      type: INT
    }
    domain: ""
  }
  node {
    input: "variable2"
    output: "variable2_cast"
    name: "Cast3"
    op_type: "Cast"
    attribute {
      name: "to"
      i: 1
      type: INT
    }
    domain: ""
  }
  node {
    input: "variable1_cast"
    input: "variable2_cast"
    output: "transformed_column"
    name: "Concat2"
    op_type: "Concat"
    attribute {
      name: "axis"
      i: 1
      type: INT
    }
    domain: ""
  }
  node {
    input: "transformed_column"
    output: "label"
    output: "probability_tensor"
    name: "LinearClassifier"
    op_type: "LinearClassifier"
    attribute {
      name: "classlabels_ints"
      ints: 0
      ints: 1
      type: INTS
    }
    attribute {
      name: "coefficients"
      floats: 0.4088466167449951
      floats: 0.048030976206064224
      floats: -0.3020475506782532
      floats: 0.1128229945898056
      floats: 0.4023498296737671
      floats: -0.21311041712760925
      floats: -1.2705841064453125
      floats: 1.2705990076065063
      floats: -1.084059238433838
      floats: -0.121848464012146
      floats: 1.2059226036071777
      floats: -0.4088466167449951
      floats: -0.048030976206064224
      floats: 0.3020475506782532
      floats: -0.1128229945898056
      floats: -0.4023498296737671
      floats: 0.21311041712760925
      floats: 1.2705841064453125
      floats: -1.2705990076065063
      floats: 1.084059238433838
      floats: 0.121848464012146
      floats: -1.2059226036071777
      type: FLOATS
    }
    attribute {
      name: "intercepts"
      floats: -0.3101347088813782
      floats: 0.3101347088813782
      type: FLOATS
    }
    attribute {
      name: "multi_class"
      i: 1
      type: INT
    }
    attribute {
      name: "post_transform"
      s: "LOGISTIC"
      type: STRING
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "probability_tensor"
    output: "probabilities"
    name: "Normalizer"
    op_type: "Normalizer"
    attribute {
      name: "norm"
      s: "L1"
      type: STRING
    }
    domain: "ai.onnx.ml"
  }
  node {
    input: "label"
    output: "output_label"
    name: "Cast4"
    op_type: "Cast"
    attribute {
      name: "to"
      i: 7
      type: INT
    }
    domain: ""
  }
  node {
    input: "probabilities"
    output: "output_probability"
    name: "ZipMap"
    op_type: "ZipMap"
    attribute {
      name: "classlabels_int64s"
      ints: 0
      ints: 1
      type: INTS
    }
    domain: "ai.onnx.ml"
  }
  name: "pipeline_titanic"
  initializer {
    dims: 2
    data_type: 7
    int64_data: -1
    int64_data: 9
    name: "shape_tensor"
  }
  input {
    name: "pclass"
    type {
      tensor_type {
        elem_type: 8
        shape {
          dim {
          }
          dim {
            dim_value: 1
          }
        }
      }
    }
  }
  input {
    name: "sex"
    type {
      tensor_type {
        elem_type: 8
        shape {
          dim {
          }
          dim {
            dim_value: 1
          }
        }
      }
    }
  }
  input {
    name: "age"
    type {
      tensor_type {
        elem_type: 1
        shape {
          dim {
          }
          dim {
            dim_value: 1
          }
        }
      }
    }
  }
  input {
    name: "fare"
    type {
      tensor_type {
        elem_type: 1
        shape {
          dim {
          }
          dim {
            dim_value: 1
          }
        }
      }
    }
  }
  input {
    name: "embarked"
    type {
      tensor_type {
        elem_type: 8
        shape {
          dim {
          }
          dim {
            dim_value: 1
          }
        }
      }
    }
  }
  output {
    name: "output_label"
    type {
      tensor_type {
        elem_type: 7
        shape {
          dim {
          }
        }
      }
    }
  }
  output {
    name: "output_probability"
    type {
      sequence_type {
        elem_type {
          map_type {
            key_type: 7
            value_type {
              tensor_type {
                elem_type: 1
              }
            }
          }
        }
      }
    }
  }
}
opset_import {
  domain: ""
  version: 11
}
opset_import {
  domain: "ai.onnx.ml"
  version: 1
}

textual features [[0. 0. 1. 0. 0. 1. 0. 0. 1.]]

Display the sub-ONNX graph

Finally, let’s see both subgraphs. First, numerical pipeline.

pydot_graph = GetPydotGraph(
    num_onnx.graph, name=num_onnx.graph.name, rankdir="TB",
    node_producer=GetOpNodeProducer(
        "docstring", color="yellow", fillcolor="yellow", style="filled"))
pydot_graph.write_dot("pipeline_titanic_num.dot")

os.system('dot -O -Gdpi=300 -Tpng pipeline_titanic_num.dot')

image = plt.imread("pipeline_titanic_num.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis('off')

Out:

(-0.5, 4393.5, 1033.5, -0.5)

Then textual pipeline.

pydot_graph = GetPydotGraph(
    text_onnx.graph, name=text_onnx.graph.name, rankdir="TB",
    node_producer=GetOpNodeProducer(
        "docstring", color="yellow", fillcolor="yellow", style="filled"))
pydot_graph.write_dot("pipeline_titanic_text.dot")

os.system('dot -O -Gdpi=300 -Tpng pipeline_titanic_text.dot')

image = plt.imread("pipeline_titanic_text.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis('off')

Out:

(-0.5, 6847.5, 1121.5, -0.5)

Versions used for this example

print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", __version__)

Out:

numpy: 1.19.1
scikit-learn: 0.23.2
onnx:  1.7.0
onnxruntime:  1.4.0
skl2onnx:  1.7.1