Follow up on #1139

docs/examples/plot_output_onnx_single_probability.py

@@ -0,0 +1,144 @@
+"""
+Append onnx nodes to the converted model
+========================================
+
+This example show how to append some onnx nodes to the converted
+model to produce the desired output. In this case, it removes the second
+column of the output probabilies.
+
+To be completly accurate, most of the code was generated using a LLM
+and modified to accomodate with the latest changes.
+"""
+
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from skl2onnx import convert_sklearn
+from skl2onnx.common.data_types import FloatTensorType
+import onnx
+
+iris = load_iris()
+X, y = iris.data, iris.target
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+clr = LogisticRegression(max_iter=500)
+clr.fit(X_train, y_train)
+
+
+################################################
+# model_to_convert refers to the scikit-learn classifier to convert.
+model_to_convert = clr  # model to convert
+X_test = X_test[:1]  # data used to test or train, one row is enough
+
+################################################
+# Set the output filename for the modified ONNX model
+output_filename = "output_file.onnx"  # Replace with your desired output filename
+
+################################################
+# Step 1: Convert the model to ONNX format,
+# disabling the output of labels.
+# Define the input type for the ONNX model.
+# The input type is a float tensor with shape
+# [None, X_test.shape[1]], where None indicates that the
+# number of input samples can be flexible,
+# and X_test.shape[1] is the number of features for each input sample.
+# A "tensor" is essentially a multi-dimensional array,
+# commonly used in machine learning to represent data.
+# A "float tensor" specifically contains floating-point
+# numbers, which are numbers with decimals.
+initial_type = [("float_input", FloatTensorType([None, X_test.shape[1]]))]
+
+################################################
+# Convert the model to ONNX format.
+# - target_opset=18 specifies the version of ONNX operators to use.
+# - options={...} sets parameters for the conversion:
+#   - "zipmap": False ensures that the output is a raw array
+#   - of probabilities instead of a dictionary.
+#   - "output_class_labels": False ensures that the output
+#     contains only probabilities, not class labels.
+# ONNX (Open Neural Network Exchange) is an open format for
+# representing machine learning models.
+# It allows interoperability between different machine learning frameworks,
+# enabling the use of models across various platforms.
+onx = convert_sklearn(
+    model_to_convert,
+    initial_types=initial_type,
+    target_opset={"": 18, "ai.onnx.ml": 3},
+    options={
+        id(model_to_convert): {"zipmap": False, "output_class_labels": False}
+    },  # Ensures the output is only probabilities, not labels
+)
+
+################################################
+# Step 2: Load the ONNX model for further modifications if needed
+# Load the ONNX model from the serialized string representation.
+# An ONNX file is essentially a serialized representation of a machine learning
+# model that can be shared and used across different systems.
+onnx_model = onnx.load_model_from_string(onx.SerializeToString())
+
+################################################
+# Assuming the first output in this model should be the probability tensor
+# Extract the name of the output tensor representing the probabilities.
+# If there are multiple outputs, select the second one, otherwise, select the first.
+prob_output_name = (
+    onnx_model.graph.output[1].name
+    if len(onnx_model.graph.output) > 1
+    else onnx_model.graph.output[0].name
+)
+
+################################################
+# Add a Gather node to extract only the probability
+# of the positive class (index 1)
+# Create a tensor to specify the index to gather
+# (index 1), which represents the positive class.
+indices = onnx.helper.make_tensor(
+    "indices", onnx.TensorProto.INT64, (1,), [1]
+)  # Index 1 to gather positive class
+
+################################################
+# Create a "Gather" node in the ONNX graph to extract the probability of the positive class.
+# - inputs: [prob_output_name, "indices"] specify the inputs
+#   to this node (probability tensor and index tensor).
+# - outputs: ["positive_class_prob"] specify the name of the output of this node.
+# - axis=1 indicates gathering along the columns (features) of the probability tensor.
+# A "Gather" node is used to extract specific elements from a tensor.
+# Here, it extracts the probability for the positive class.
+gather_node = onnx.helper.make_node(
+    "Gather",
+    inputs=[prob_output_name, "indices"],
+    outputs=["positive_class_prob"],
+    axis=1,  # Gather along columns (axis 1)
+)
+
+################################################
+# Add the Gather node to the ONNX graph
+onnx_model.graph.node.append(gather_node)
+
+################################################
+# Add the tensor initializer for indices (needed for the Gather node)
+# Initializers in ONNX are used to define constant tensors that are used in the computation.
+onnx_model.graph.initializer.append(indices)
+
+################################################
+# Remove existing outputs and add only the new output for the positive class probability
+# Clear the existing output definitions to replace them with the new output.
+del onnx_model.graph.output[:]
+
+################################################
+# Define new output for the positive class probability
+# Create a new output tensor specification with the name "positive_class_prob".
+positive_class_output = onnx.helper.make_tensor_value_info(
+    "positive_class_prob", onnx.TensorProto.FLOAT, [None, 1]
+)
+onnx_model.graph.output.append(positive_class_output)
+
+################################################
+# Step 3: Save the modified ONNX model
+# Save the modified ONNX model to the specified output filename.
+# The resulting ONNX file can then be loaded and used in different environments
+# that support ONNX, such as inference servers or other machine learning frameworks.
+onnx.save(onnx_model, output_filename)
+
+
+################################################
+# The model can be printed as follows.
+print(onnx.printer.to_text(onnx_model))

docs/tutorial_4_advanced.rst

@@ -13,3 +13,4 @@ with issues and resolved issues.
     auto_tutorial/plot_ngrams
     auto_tutorial/plot_usparse_xgboost
     auto_tutorial/plot_woe_transformer
+    auto_tutorial/plot_output_onnx_single_probability