Intel DAAL AI加速 ——传统决策树和随机森林

# file: dt_cls_dense_batch.py
#===============================================================================
# Copyright 2014-2018 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel's prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.
#===============================================================================

## <a name="DAAL-EXAMPLE-PY-DT_CLS_DENSE_BATCH"></a>
## \example dt_cls_dense_batch.py

import os
import sys

from daal.algorithms.decision_tree.classification import prediction, training
from daal.algorithms import classifier
from daal.data_management import (
FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable
)
utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
sys.path.insert(0, utils_folder)
from utils import printNumericTables

DAAL_PREFIX = os.path.join('..', 'data')

# Input data set parameters
trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'decision_tree_train.csv')
pruneDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'decision_tree_prune.csv')
testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'decision_tree_test.csv')

nFeatures = 5
nClasses = 5

# Model object for the decision tree classification algorithm
model = None
predictionResult = None
testGroundTruth = None

def trainModel():
global model

# Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
trainDataSource = FileDataSource(
trainDatasetFileName,
DataSourceIface.notAllocateNumericTable,
DataSourceIface.doDictionaryFromContext
)

# Create Numeric Tables for training data and labels
trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
mergedData = MergedNumericTable(trainData, trainGroundTruth)

# Retrieve the data from the input file
trainDataSource.loadDataBlock(mergedData)

# Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
pruneDataSource = FileDataSource(
pruneDatasetFileName,
DataSourceIface.notAllocateNumericTable,
DataSourceIface.doDictionaryFromContext
)

# Create Numeric Tables for pruning data and labels
pruneData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
pruneGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
pruneMergedData = MergedNumericTable(pruneData, pruneGroundTruth)

# Retrieve the data from the input file
pruneDataSource.loadDataBlock(pruneMergedData)

# Create an algorithm object to train the decision tree classification model
algorithm = training.Batch(nClasses)

# Pass the training data set and dependent values to the algorithm
algorithm.input.set(classifier.training.data, trainData)
algorithm.input.set(classifier.training.labels, trainGroundTruth)
algorithm.input.setTable(training.dataForPruning, pruneData)
algorithm.input.setTable(training.labelsForPruning, pruneGroundTruth)

# Train the decision tree classification model and retrieve the results of the training algorithm
trainingResult = algorithm.compute()
model = trainingResult.get(classifier.training.model)

def testModel():
global testGroundTruth, predictionResult

# Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
testDataSource = FileDataSource(
testDatasetFileName,
DataSourceIface.notAllocateNumericTable,
DataSourceIface.doDictionaryFromContext
)

# Create Numeric Tables for testing data and labels
testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
mergedData = MergedNumericTable(testData, testGroundTruth)

# Retrieve the data from input file
testDataSource.loadDataBlock(mergedData)

# Create algorithm objects for decision tree classification prediction with the default method
algorithm = prediction.Batch()

# Pass the testing data set and trained model to the algorithm
#print("Number of columns: {}".format(testData.getNumberOfColumns()))
algorithm.input.setTable(classifier.prediction.data,  testData)
algorithm.input.setModel(classifier.prediction.model, model)

# Compute prediction results and retrieve algorithm results
# (Result class from classifier.prediction)
predictionResult = algorithm.compute()

def printResults():

printNumericTables(
testGroundTruth,
predictionResult.get(classifier.prediction.prediction),
"Ground truth", "Classification results",
"Decision tree classification results (first 20 observations):",
20, flt64=False
)

if __name__ == "__main__":

trainModel()
testModel()
printResults()

随机森林的：

# file: df_cls_dense_batch.py
#===============================================================================
# Copyright 2014-2018 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel's prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.
#===============================================================================

## <a name="DAAL-EXAMPLE-PY-DF_CLS_DENSE_BATCH"></a>
## \example df_cls_dense_batch.py

import os
import sys

from daal.algorithms import decision_forest
from daal.algorithms.decision_forest.classification import prediction, training
from daal.algorithms import classifier
from daal.data_management import (
FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable,
MergedNumericTable, features
)

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
sys.path.insert(0, utils_folder)
from utils import printNumericTable, printNumericTables

DAAL_PREFIX = os.path.join('..', 'data')

# Input data set parameters
trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_classification_train.csv')
testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_classification_test.csv')

nFeatures = 3
nClasses = 5

# Decision forest parameters
nTrees = 10
minObservationsInLeafNode = 8

# Model object for the decision forest classification algorithm
model = None
predictionResult = None
testGroundTruth = None

def trainModel():
global model

# Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
trainDataSource = FileDataSource(
trainDatasetFileName,
DataSourceIface.notAllocateNumericTable,
DataSourceIface.doDictionaryFromContext
)

# Create Numeric Tables for training data and labels
trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
mergedData = MergedNumericTable(trainData, trainGroundTruth)

# Retrieve the data from the input file
trainDataSource.loadDataBlock(mergedData)

#  Get the dictionary and update it with additional information about data
dict = trainData.getDictionary()

#  Add a feature type to the dictionary
dict[0].featureType = features.DAAL_CONTINUOUS
dict[1].featureType = features.DAAL_CONTINUOUS
dict[2].featureType = features.DAAL_CATEGORICAL

# Create an algorithm object to train the decision forest classification model
algorithm = training.Batch(nClasses)
algorithm.parameter.nTrees = nTrees
algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode
algorithm.parameter.featuresPerNode = nFeatures
algorithm.parameter.varImportance = decision_forest.training.MDI
algorithm.parameter.resultsToCompute = decision_forest.training.computeOutOfBagError

# Pass the training data set and dependent values to the algorithm
algorithm.input.set(classifier.training.data, trainData)
algorithm.input.set(classifier.training.labels, trainGroundTruth)

# Train the decision forest classification model and retrieve the results of the training algorithm
trainingResult = algorithm.compute()
model = trainingResult.get(classifier.training.model)
printNumericTable(trainingResult.getTable(training.variableImportance), "Variable importance results: ")
printNumericTable(trainingResult.getTable(training.outOfBagError), "OOB error: ")

def testModel():
global testGroundTruth, predictionResult

# Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
testDataSource = FileDataSource(
testDatasetFileName,
DataSourceIface.notAllocateNumericTable,
DataSourceIface.doDictionaryFromContext
)

# Create Numeric Tables for testing data and labels
testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
mergedData = MergedNumericTable(testData, testGroundTruth)

# Retrieve the data from input file
testDataSource.loadDataBlock(mergedData)

#  Get the dictionary and update it with additional information about data
dict = testData.getDictionary()

#  Add a feature type to the dictionary
dict[0].featureType = features.DAAL_CONTINUOUS
dict[1].featureType = features.DAAL_CONTINUOUS
dict[2].featureType = features.DAAL_CATEGORICAL

# Create algorithm objects for decision forest classification prediction with the default method
algorithm = prediction.Batch(nClasses)

# Pass the testing data set and trained model to the algorithm
algorithm.input.setTable(classifier.prediction.data,  testData)
algorithm.input.setModel(classifier.prediction.model, model)

# Compute prediction results and retrieve algorithm results
# (Result class from classifier.prediction)
predictionResult = algorithm.compute()

def printResults():
printNumericTable(predictionResult.get(classifier.prediction.prediction),"Decision forest prediction results (first 10 rows):",10)
printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);

if __name__ == "__main__":

trainModel()
testModel()
printResults()