#=============================================================================== # 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 Gradient Bossting Classification 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=None, 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=None, t=np.float64): return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2, dtype=t) def main(readcsv=read_csv, method='defaultDense'): nFeatures = 3 nClasses = 5 maxIterations = 200 minObservationsInLeafNode = 8 # input data file infile = "./data/batch/df_classification_train.csv" testfile = "./data/batch/df_classification_test.csv" # Configure a training object (5 classes) train_algo = d4p.gbt_classification_training( nClasses=nClasses, maxIterations=maxIterations, minObservationsInLeafNode=minObservationsInLeafNode, featuresPerNode=nFeatures, varImportance='weight|totalCover|cover|totalGain|gain' ) # Read data. Let's use 3 features per observation data = readcsv(infile, range(3), t=np.float32) labels = readcsv(infile, range(3, 4), t=np.float32) train_result = train_algo.compute(data, labels) # Now let's do some prediction # previous version has different interface predict_algo = d4p.gbt_classification_prediction( nClasses=nClasses, resultsToEvaluate="computeClassLabels|computeClassProbabilities" ) # read test data (with same #features) pdata = readcsv(testfile, range(3), t=np.float32) # now predict using the model from the training above predict_result = predict_algo.compute(pdata, train_result.model) # Prediction result provides prediction plabels = readcsv(testfile, range(3, 4), t=np.float32) assert np.count_nonzero(predict_result.prediction - plabels) / pdata.shape[0] < 0.022 return (train_result, predict_result, plabels) if __name__ == "__main__": (train_result, predict_result, plabels) = main() print( "\nGradient boosted trees prediction results (first 10 rows):\n", predict_result.prediction[0:10] ) print("\nGround truth (first 10 rows):\n", plabels[0:10]) print( "\nGradient boosted trees prediction probabilities (first 10 rows):\n", predict_result.probabilities[0:10] ) print("\nvariableImportanceByWeight:\n", train_result.variableImportanceByWeight) print( "\nvariableImportanceByTotalCover:\n", train_result.variableImportanceByTotalCover ) print("\nvariableImportanceByCover:\n", train_result.variableImportanceByCover) print( "\nvariableImportanceByTotalGain:\n", train_result.variableImportanceByTotalGain ) print("\nvariableImportanceByGain:\n", train_result.variableImportanceByGain) print('All looks good!')