def __Accuracy(dataDict, parameterDict):
train_X = dataDict['train_X']
train_Y = dataDict['train_Y']
cross_X = dataDict['cross_X']
cross_Y = dataDict['cross_Y']
criterion = parameterDict['criterion']
max_features = parameterDict['max_features']
max_depth = parameterDict['max_depth']
min_samples_split = parameterDict['min_samples_split']
min_samples_leaf = parameterDict['min_samples_leaf']
clf = DecisionTreeClassifierMultiClass(splitter='best', random_state=1,
criterion=criterion, max_features=max_features, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf)
model = clf.fit(train_X, train_Y) # All features must be float.
accuracy = clf.score(cross_X, cross_Y) # Score=Accuracy=(TP+TN)/(TP+TN+FP+FN)=%Correct
return accuracy
def Execute(context):
timeStamp = TimeStamp()
connectionSQL = ConnectionSQL(context)
criterion = 'gini'
max_features = None
max_depth = 14
min_samples_split=6
min_samples_leaf=6
n_jobs=-1
# model parameter ranges - used for searching for optimial parameter settings
criterionList = ['gini', 'entropy']
max_featuresList = [7, 8, 9, 10, 11, 12, 13, 14, None]
max_depthList = [6, 8, 10, 11, 12, 13, 14, None]
min_samples_splitList = [3, 5, 6, 7, 8, 9]
min_samples_leafList = [3, 4, 5, 6, 7]
parameterRangeDict = {'criterion':criterionList, 'max_features':max_featuresList, 'max_depth':max_depthList, 'min_samples_split':min_samples_splitList, 'min_samples_leaf':min_samples_leafList }
timeStamp1 = TimeStamp('Loading X, Y')
# 0=DataID, 1=P_A, 2=P_B, 3=P_C, 4=P_D, 5=P_E, 6=P_F, 7=P_G, 8=State, ... 27=Cost
# Load Training Data and Cross Validation Data
train_X, train_Y, train_DataID = connectionSQL.GetFeaturesAndResultsFromCache(featuresColumns=__featuresColumns, dataType="Train", modelName=__modelName, preProcessDataFrame=__PreProcessDataFrame, viewName=__viewName)
train_DataID = None
# Load Cross Validation Data
cross_X, cross_Y, cross_DataID = connectionSQL.GetFeaturesAndResultsFromCache(featuresColumns=__featuresColumns, dataType="Cross", modelName=__modelName, preProcessDataFrame=__PreProcessDataFrame, viewName=__viewName)
cross_DataID = None
print ' Elaspe=' + timeStamp1.Elaspse
print ''
if (__optimiseParameters):
timeStamp1 = TimeStamp('Optimise parameters')
# package paramaters
dataDict = {'train_X':train_X, 'train_Y':train_Y, 'cross_X':cross_X, 'cross_Y':cross_Y}
# Optimal Paramater selection
#parameterDictMax = __OptimalParamaterSelection(dataDict=dataDict, accuracyFunct=__Accuracy, parameterRangeDict=parameterRangeDict)
optimiseModelParameters = OptimiseModelParameters(modelName=__modelName)
optimiseModelParameters.Percent = 0.2 # 20%
parameterDictMax, accuracyMax = optimiseModelParameters.ExecuteMoneCarlo(dataDict=dataDict, accuracyFunct=__Accuracy, parameterRangeDict=parameterRangeDict)
criterion = parameterDictMax['criterion']
max_features = parameterDictMax['max_features']
max_depth = parameterDictMax['max_depth']
min_samples_split = parameterDictMax['min_samples_split']
min_samples_leaf = parameterDictMax['min_samples_leaf']
print 'Elaspe=' + timeStamp1.Elaspse
print ''
timeStamp1 = TimeStamp('Fit model')
print __modelName + ' criterion=' + criterion + ' max_features=' + str(max_features) + ' max_depth=' + str(max_depth) + ' min_samples_split=' + str(min_samples_split) + ' min_samples_leaf=' + str(min_samples_leaf)
clf = DecisionTreeClassifierMultiClass(splitter='best', random_state=1,
criterion=criterion, max_features=max_features, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf)
model = clf.fit(train_X, train_Y) # All features must be float.
print 'Elaspe=' + timeStamp1.Elaspse
print ''
# Training error reports
SharedLibrary.TrainingError(dataType='Training', modelName=__modelName, clf=clf, model=model, dfX=train_X, dfY=train_Y)
SharedLibrary.TrainingError(dataType='CrossVal', modelName=__modelName, clf=clf, model=model, dfX=cross_X, dfY=cross_Y)
# Cross validation report
SharedLibrary.CrossValidation(modelName=__modelName, clf=clf, dfX=train_X, dfY=train_Y, n_fold=5)
# UPDATE DRV_Predict with all data predictions.
timeStamp1 = TimeStamp('Update_DRV_Predict')
all_X, all_Y, all_DataID = connectionSQL.GetFeaturesAndResultsFromCache(featuresColumns=__featuresColumns, dataType="ALL", modelName=__modelName, preProcessDataFrame=__PreProcessDataFrame, viewName=__viewName)
all_Y = None
connectionSQL.Update_DRV_Predict(modelName=__modelName, model=model, all_X=all_X, all_DataID=all_DataID)
print 'Elaspe=' + timeStamp1.Elaspse
print ''
# Feature analysis summary
featureImportances = enumerate(clf.feature_importances_)
featureImportanceHistogram = np.array([(importance,train_X.columns[i]) for (i,importance) in featureImportances if importance > 0.01])
print __modelName + ' Elaspe=' + timeStamp.Elaspse
return __modelName, featureImportanceHistogram
