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svm_multiclass.py
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svm_multiclass.py
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# ==============================================================================
# Copyright 2014 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# daal4py multi-class SVM example for shared memory systems
from pathlib import Path
import numpy as np
from readcsv import pd_read_csv
import daal4py as d4p
def main(readcsv=pd_read_csv):
nFeatures = 20
nClasses = 5
# read training data from file
# with nFeatures features per observation and 1 class label
data_path = Path(__file__).parent / "data" / "batch"
train_file = data_path / "svm_multi_class_train_dense.csv"
train_data = readcsv(train_file, range(nFeatures))
train_labels = readcsv(train_file, range(nFeatures, nFeatures + 1))
# Create and configure algorithm object
algorithm = d4p.multi_class_classifier_training(
nClasses=nClasses,
training=d4p.svm_training(method="thunder"),
prediction=d4p.svm_prediction(),
)
# Pass data to training. Training result provides model
train_result = algorithm.compute(train_data, train_labels)
assert train_result.model.NumberOfFeatures == nFeatures
assert isinstance(train_result.model.TwoClassClassifierModel(0), d4p.svm_model)
# Now the prediction stage
# Read data
pred_file = data_path / "svm_multi_class_test_dense.csv"
pred_data = readcsv(pred_file, range(nFeatures))
pred_labels = readcsv(pred_file, range(nFeatures, nFeatures + 1))
# Create an algorithm object to predict multi-class SVM values
algorithm = d4p.multi_class_classifier_prediction(
nClasses,
training=d4p.svm_training(method="thunder"),
prediction=d4p.svm_prediction(),
)
# Pass data to prediction. Prediction result provides prediction
pred_result = algorithm.compute(pred_data, train_result.model)
assert pred_result.prediction.shape == (train_data.shape[0], 1)
return (pred_result, pred_labels)
if __name__ == "__main__":
(pred_res, pred_labels) = main()
print(
"\nSVM classification results (first 20 observations):\n",
pred_res.prediction[0:20],
)
print("\nGround truth (first 20 observations):\n", pred_labels[0:20])
print("All looks good!")