#=============================================================================== # Copyright 2014-2021 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 SVM example for shared memory systems import daal4py as d4p import numpy as np # let's try to use pandas' fast csv reader try: import pandas def read_csv(f, c, t=np.float64): return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t) except ImportError: # fall back to numpy loadtxt def read_csv(f, c, t=np.float64): return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2) def main(readcsv=read_csv, method='defaultDense'): # input data file infile = "./data/batch/svm_two_class_train_dense.csv" testfile = "./data/batch/svm_two_class_test_dense.csv" # Configure a SVM object to use rbf kernel (and adjusting cachesize) kern = d4p.kernel_function_linear() # need an object that lives when creating train_algo train_algo = d4p.svm_training(method='thunder', kernel=kern, cacheSize=600000000) # Read data. Let's use features per observation data = readcsv(infile, range(20)) labels = readcsv(infile, range(20, 21)) train_result = train_algo.compute(data, labels) # Now let's do some prediction predict_algo = d4p.svm_prediction(kernel=kern) # read test data (with same #features) pdata = readcsv(testfile, range(20)) plabels = readcsv(testfile, range(20, 21)) # now predict using the model from the training above predict_result = predict_algo.compute(pdata, train_result.model) # Prediction result provides prediction assert(predict_result.prediction.shape == (pdata.shape[0], 1)) # result of classification decision_result = predict_result.prediction predict_labels = np.where(decision_result >= 0, 1, -1) return (decision_result, predict_labels, plabels) if __name__ == "__main__": (decision_function, predict_labels, plabels) = main() print( "\nSVM classification decision function (first 20 observations):\n", decision_function[0:20] ) print("\nSVM classification results (first 20 observations):\n", predict_labels[0:20]) print("\nGround truth (first 20 observations):\n", plabels[0:20]) print('All looks good!')