80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
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# Iris_SVCClassifier.py
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# The code demonstrates the process of training SVC model on the Iris dataset, exporting it to ONNX format, and making predictions using the ONNX model.
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# It also evaluates the accuracy of both the original model and the ONNX model.
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# Copyright 2023, MetaQuotes Ltd.
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# https://www.mql5.com
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# import necessary libraries
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from sklearn import datasets
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from sklearn.svm import SVC
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from sklearn.metrics import accuracy_score, classification_report
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from skl2onnx import convert_sklearn
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from skl2onnx.common.data_types import FloatTensorType
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import onnxruntime as ort
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import numpy as np
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from sys import argv
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# define the path for saving the model
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data_path = argv[0]
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last_index = data_path.rfind("\\") + 1
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data_path = data_path[0:last_index]
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# load the Iris dataset
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# create an SVC Classifier model with a linear kernel
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svc_model = SVC(kernel='linear', C=1.0)
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# train the model on the entire dataset
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svc_model.fit(X, y)
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# predict classes for the entire dataset
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y_pred = svc_model.predict(X)
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# evaluate the model's accuracy
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accuracy = accuracy_score(y, y_pred)
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print("Accuracy of SVC Classifier model:", accuracy)
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# display the classification report
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print("\nClassification Report:\n", classification_report(y, y_pred))
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# define the input data type
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initial_type = [('float_input', FloatTensorType([None, X.shape[1]]))]
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# export the model to ONNX format with float data type
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onnx_model = convert_sklearn(svc_model, initial_types=initial_type, target_opset=12)
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# save the model to a file
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onnx_filename = data_path +"..\\models\\svc_iris.onnx"
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with open(onnx_filename, "wb") as f:
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f.write(onnx_model.SerializeToString())
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# print model path
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print(f"Model saved to {onnx_filename}")
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# load the ONNX model and make predictions
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onnx_session = ort.InferenceSession(onnx_filename)
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input_name = onnx_session.get_inputs()[0].name
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output_name = onnx_session.get_outputs()[0].name
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# display information about input tensors in ONNX
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print("\nInformation about input tensors in ONNX:")
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for i, input_tensor in enumerate(onnx_session.get_inputs()):
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print(f"{i + 1}. Name: {input_tensor.name}, Data Type: {input_tensor.type}, Shape: {input_tensor.shape}")
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# display information about output tensors in ONNX
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print("\nInformation about output tensors in ONNX:")
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for i, output_tensor in enumerate(onnx_session.get_outputs()):
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print(f"{i + 1}. Name: {output_tensor.name}, Data Type: {output_tensor.type}, Shape: {output_tensor.shape}")
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# convert data to floating-point format (float32)
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X_float32 = X.astype(np.float32)
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# predict classes for the entire dataset using ONNX
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y_pred_onnx = onnx_session.run([output_name], {input_name: X_float32})[0]
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# evaluate the accuracy of the ONNX model
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accuracy_onnx = accuracy_score(y, y_pred_onnx)
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print("\nAccuracy of SVC Classifier model in ONNX format:", accuracy_onnx)
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