.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_intermediate_outputs.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_intermediate_outputs.py: Walk through intermediate outputs ================================= We reuse the example :ref:`example-complex-pipeline` 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. .. GENERATED FROM PYTHON SOURCE LINES 28-103 .. code-block:: Python import skl2onnx 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 import pprint from skl2onnx.common.data_types import ( FloatTensorType, StringTensorType, 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) .. raw:: html 
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())])
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.


.. GENERATED FROM PYTHON SOURCE LINES 104-110 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. .. GENERATED FROM PYTHON SOURCE LINES 110-112 .. code-block:: Python print(X_train.dtypes) .. rst-class:: sphx-glr-script-out .. code-block:: none 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 .. GENERATED FROM PYTHON SOURCE LINES 113-114 After conversion. .. GENERATED FROM PYTHON SOURCE LINES 114-135 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none [('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]))] .. GENERATED FROM PYTHON SOURCE LINES 136-139 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. .. GENERATED FROM PYTHON SOURCE LINES 141-143 Convert the pipeline into ONNX ++++++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 143-149 .. code-block:: Python try: model_onnx = convert_sklearn(clf, "pipeline_titanic", inputs, target_opset=12) except Exception as e: print(e) .. GENERATED FROM PYTHON SOURCE LINES 150-153 *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. .. GENERATED FROM PYTHON SOURCE LINES 153-167 .. code-block:: Python 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()) .. GENERATED FROM PYTHON SOURCE LINES 168-174 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*. .. GENERATED FROM PYTHON SOURCE LINES 174-178 .. code-block:: Python print("predict", clf.predict(X_test[:5])) print("predict_proba", clf.predict_proba(X_test[:1])) .. rst-class:: sphx-glr-script-out .. code-block:: none predict [0 0 0 0 1] predict_proba [[0.88600265 0.11399735]] .. GENERATED FROM PYTHON SOURCE LINES 179-188 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*. .. GENERATED FROM PYTHON SOURCE LINES 188-196 .. code-block:: Python 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)) .. GENERATED FROM PYTHON SOURCE LINES 197-198 We are ready to run *onnxruntime*. .. GENERATED FROM PYTHON SOURCE LINES 198-205 .. code-block:: Python sess = rt.InferenceSession("pipeline_titanic.onnx", providers=["CPUExecutionProvider"]) pred_onx = sess.run(None, inputs) print("predict", pred_onx[0][:5]) print("predict_proba", pred_onx[1][:1]) .. rst-class:: sphx-glr-script-out .. code-block:: none predict [0 0 0 0 1] predict_proba [{0: 0.9543983340263367, 1: 0.04560166597366333}] .. GENERATED FROM PYTHON SOURCE LINES 206-213 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. .. GENERATED FROM PYTHON SOURCE LINES 213-218 .. code-block:: Python model_onnx = load_onnx_model("pipeline_titanic.onnx") for out in enumerate_model_node_outputs(model_onnx): print(out) .. rst-class:: sphx-glr-script-out .. code-block:: none merged_columns embarkedout sexout pclassout concat_result variable variable2 variable1 transformed_column label probabilities output_label output_probability .. GENERATED FROM PYTHON SOURCE LINES 219-225 Not that easy to tell which one is what as the *ONNX* has more operators than the original *scikit-learn* pipelines. The graph at :ref:`l-plot-complex-pipeline-graph` helps up to find the outputs of both numerical and textual pipeline: *variable1*, *variable2*. Let's look into the numerical pipeline first. .. GENERATED FROM PYTHON SOURCE LINES 225-229 .. code-block:: Python num_onnx = select_model_inputs_outputs(model_onnx, "variable1") save_onnx_model(num_onnx, "pipeline_titanic_numerical.onnx") .. rst-class:: sphx-glr-script-out .. code-block:: none b'\x08\x07\x12\x08skl2onnx\x1a\x061.18.0"\x07ai.onnx(\x002\x00:\xcd\x03\n:\n\x03age\n\x04fare\x12\x0emerged_columns\x1a\x06Concat"\x06Concat*\x0b\n\x04axis\x18\x01\xa0\x01\x02:\x00\n}\n\x0emerged_columns\x12\x08variable\x1a\x07Imputer"\x07Imputer*#\n\x14imputed_value_floats=\x00\x00\xe0A=2UgA\xa0\x01\x06*\x1e\n\x14replaced_value_float\x15\x00\x00\xc0\x7f\xa0\x01\x01:\nai.onnx.ml\n^\n\x08variable\x12\tvariable1\x1a\x06Scaler"\x06Scaler*\x15\n\x06offset=f\x13\xecA=\xc3\xad\x02B\xa0\x01\x06*\x14\n\x05scale=h7\x9f==\x1a\xbc\xa7<\xa0\x01\x06:\nai.onnx.ml\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\x04\n\x00\x10\x0bB\x0e\n\nai.onnx.ml\x10\x01' .. GENERATED FROM PYTHON SOURCE LINES 230-231 Let's compute the numerical features. .. GENERATED FROM PYTHON SOURCE LINES 231-238 .. code-block:: Python sess = rt.InferenceSession( "pipeline_titanic_numerical.onnx", providers=["CPUExecutionProvider"] ) numX = sess.run(None, inputs) print("numerical features", numX[0][:1]) .. rst-class:: sphx-glr-script-out .. code-block:: none numerical features [[ 1.6707252 -0.52448833]] .. GENERATED FROM PYTHON SOURCE LINES 239-240 We do the same for the textual features. .. GENERATED FROM PYTHON SOURCE LINES 240-250 .. code-block:: Python 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", providers=["CPUExecutionProvider"] ) numT = sess.run(None, inputs) print("textual features", numT[0][:1]) .. rst-class:: sphx-glr-script-out .. code-block:: none ir_version: 7 producer_name: "skl2onnx" producer_version: "1.18.0" domain: "ai.onnx" model_version: 0 doc_string: "" graph { node { input: "age" input: "fare" output: "merged_columns" name: "Concat" op_type: "Concat" attribute { name: "axis" i: 1 type: INT } domain: "" } 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: -1 type: INT } domain: "" } node { input: "merged_columns" output: "variable" name: "Imputer" op_type: "Imputer" attribute { name: "imputed_value_floats" floats: 28 floats: 14.4583 type: FLOATS } attribute { name: "replaced_value_float" f: nan type: FLOAT } domain: "ai.onnx.ml" } node { input: "concat_result" input: "shape_tensor" output: "variable2" name: "Reshape" op_type: "Reshape" domain: "" } node { input: "variable" output: "variable1" name: "Scaler" op_type: "Scaler" attribute { name: "offset" floats: 29.5094719 floats: 32.6696892 type: FLOATS } attribute { name: "scale" floats: 0.0777424 floats: 0.020475436 type: FLOATS } domain: "ai.onnx.ml" } node { input: "variable1" input: "variable2" output: "transformed_column" name: "Concat2" op_type: "Concat" attribute { name: "axis" i: 1 type: INT } domain: "" } node { input: "transformed_column" output: "label" output: "probabilities" name: "LinearClassifier" op_type: "LinearClassifier" attribute { name: "classlabels_ints" ints: 0 ints: 1 type: INTS } attribute { name: "coefficients" floats: 0.414287567 floats: 0.0486155599 floats: -0.254193634 floats: 0.0872347131 floats: 0.349803418 floats: -0.259518 floats: -1.27854633 floats: 1.20187283 floats: -1.06555367 floats: -0.00171175227 floats: 0.990591884 floats: -0.414287567 floats: -0.0486155599 floats: 0.254193634 floats: -0.0872347131 floats: -0.349803418 floats: 0.259518 floats: 1.27854633 floats: -1.20187283 floats: 1.06555367 floats: 0.00171175227 floats: -0.990591884 type: FLOATS } attribute { name: "intercepts" floats: -0.1677939 floats: 0.1677939 type: FLOATS } attribute { name: "multi_class" i: 0 type: INT } attribute { name: "post_transform" s: "LOGISTIC" type: STRING } domain: "ai.onnx.ml" } node { input: "label" output: "output_label" name: "Cast" 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.]] .. GENERATED FROM PYTHON SOURCE LINES 251-255 Display the sub-ONNX graph ++++++++++++++++++++++++++ Finally, let's see both subgraphs. First, numerical pipeline. .. GENERATED FROM PYTHON SOURCE LINES 255-273 .. code-block:: Python 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") .. image-sg:: /auto_examples/images/sphx_glr_plot_intermediate_outputs_001.png :alt: plot intermediate outputs :srcset: /auto_examples/images/sphx_glr_plot_intermediate_outputs_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (np.float64(-0.5), np.float64(1229.5), np.float64(2558.5), np.float64(-0.5)) .. GENERATED FROM PYTHON SOURCE LINES 274-275 Then textual pipeline. .. GENERATED FROM PYTHON SOURCE LINES 275-293 .. code-block:: Python 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") .. image-sg:: /auto_examples/images/sphx_glr_plot_intermediate_outputs_002.png :alt: plot intermediate outputs :srcset: /auto_examples/images/sphx_glr_plot_intermediate_outputs_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (np.float64(-0.5), np.float64(5630.5), np.float64(2735.5), np.float64(-0.5)) .. GENERATED FROM PYTHON SOURCE LINES 294-295 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 295-301 .. code-block:: Python print("numpy:", numpy.__version__) print("scikit-learn:", sklearn.__version__) print("onnx: ", onnx.__version__) print("onnxruntime: ", rt.__version__) print("skl2onnx: ", skl2onnx.__version__) .. rst-class:: sphx-glr-script-out .. code-block:: none numpy: 2.2.0 scikit-learn: 1.6.0 onnx: 1.18.0 onnxruntime: 1.21.0+cu126 skl2onnx: 1.18.0 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.980 seconds) .. _sphx_glr_download_auto_examples_plot_intermediate_outputs.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_intermediate_outputs.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_intermediate_outputs.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_intermediate_outputs.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_