def main():
## consider making the outputs of each of these modules a class instead?
print(
"\n------------------------------------\nSTART DATA PROCESSING MODULE\n------------------------------------"
)
filenames = sms_pre.readInFilenames()
[X, Y, numcategories, classOrderLs,
outputString] = sms_pre.inputDataFiles(filenames)
[
X_train, X_test, X_validate, X_cv_list, Y_train, Y_test, Y_validate,
Y_cv_list
] = sms_pre.manualTrainTestSplit(X, Y, N=10)
sms_pre.outputXYdatasets(dataset=X_train, writename="X_train.temp")
sms_pre.outputXYdatasets(dataset=X_validate, writename="X_validate.temp")
sms_pre.outputXYdatasets(dataset=X_test, writename="X_test.temp")
sms_pre.outputXYdatasets(dataset=Y_train, writename="Y_train.temp")
sms_pre.outputXYdatasets(dataset=Y_validate, writename="Y_validate.temp")
sms_pre.outputXYdatasets(dataset=Y_test, writename="Y_test.temp")
print("\nShapes of features:")
print("Full dataset X, Y")
print(X.shape, Y.shape)
print("\nTrain X, Y")
print(X_train.shape, Y_train.shape)
print("CV X, Y")
print(X_validate.shape, Y_validate.shape)
print("Test X, Y")
print(X_test.shape, Y_test.shape)
[X_train_scaled, X_test_scaled,
X_validate_scaled] = sms_pre.featureScaling(X_train, X_test, X_validate)
sms_pre.outputXYdatasets(dataset=X_train_scaled,
writename="X_train_scaled.temp")
sms_pre.outputXYdatasets(dataset=X_validate_scaled,
writename="X_validate_scaled.temp")
sms_pre.outputXYdatasets(dataset=X_test_scaled,
writename="X_test_scaled.temp")
print(
"\n---------------------------------------\nEND DATA PROCESSING MODULE\n---------------------------------------"
)
print(
"\n---------------------------------------\nSTART BUILDING NN MODULE\n---------------------------------------"
)
X_train = X_train_scaled
X_test = X_test_scaled
X_validate = X_validate_scaled
## BELOW FOR NEURAL NET
print("\nOptimizing Neural Network")
param_grid = [{
'alpha': [0, 0.01, 0.1, 1, 10],
#'alpha': [0.1,0.01],
#'activation': ['logistic'],
#'activation': ['identity','logistic','relu','tanh'],
'hidden_layer_sizes': [(100, 100, 100)],
#'hidden_layer_sizes': [(5,5,5),(25,25,25),(100,100,100)],
#'solver': ['sgd','lbfgs','adam'],
'solver': ['lbfgs'],
#'learning_rate': ['adaptive','constant','invscaling'],
'learning_rate': ['adaptive'],
#'learning_rate_init': [1e-1,1e-4,1e-7],
'learning_rate_init': [1e-4],
'shuffle': [True],
'verbose': [False],
'early_stopping': [False],
'max_iter': [1000],
'warm_start': [True]
}]
#clf = sms_bnn.makeSimpleNN(X_train,Y_train)
clf = sms_bnn.makeGridCVNN(X_train, Y_train, param_grid)
train_predicted = sms_bnn.summarizePredictScores(X_train,
Y_train,
clf,
label="Training")
validate_predicted = sms_bnn.summarizePredictScores(X_validate,
Y_validate,
clf,
label="Validation")
test_predicted = sms_bnn.summarizePredictScores(X_test,
Y_test,
clf,
label="Test")
print(
"\n---------------------------------------\nEND BUILDING NN MODULE\n---------------------------------------"
)
print(
"\n---------------------------------------\nSTART SUMMARIZE NN MODULE\n---------------------------------------"
)
#[test_predicted_merged,Y_test_merged] = sms_snn.mergeMultioutputToSingleoutput2(test_predicted,Y_test)
#[train_predicted_merged,Y_train_merged] = sms_snn.mergeMultioutputToSingleoutput2(train_predicted,Y_train)
#[validate_predicted_merged,Y_validate_merged] = sms_snn.mergeMultioutputToSingleoutput2(validate_predicted,Y_validate)
test_predicted_merged = sms_snn.mergeMultioutputToSingleoutput(
test_predicted)
train_predicted_merged = sms_snn.mergeMultioutputToSingleoutput(
train_predicted)
validate_predicted_merged = sms_snn.mergeMultioutputToSingleoutput(
validate_predicted)
Y_test_merged = sms_snn.mergeMultioutputToSingleoutput(Y_test)
Y_train_merged = sms_snn.mergeMultioutputToSingleoutput(Y_train)
Y_validate_merged = sms_snn.mergeMultioutputToSingleoutput(Y_validate)
sms_snn.summarizeMergedScores(Y_train_merged,
train_predicted_merged,
numcategories,
Y_train,
train_predicted,
label="Training")
sms_snn.summarizeMergedScores(Y_validate_merged,
validate_predicted_merged,
numcategories,
Y_validate,
validate_predicted,
label="Validation")
sms_snn.summarizeMergedScores(Y_test_merged,
test_predicted_merged,
numcategories,
Y_test,
test_predicted,
label="Test")
## SOURCE OF THIS: http://sdsawtelle.github.io/blog/output/week4-andrew-ng-machine-learning-with-python.html
print("\nOutput the following counts for train:")
print(classOrderLs)
counts_train = sms_snn.generateCountsForConfusionMatrix(
classOrderLs, Y_train_merged, train_predicted_merged)
print(counts_train)
df_train = pd.DataFrame(counts_train)
print("\nOutput the following counts for validate:")
print(classOrderLs)
counts_validate = sms_snn.generateCountsForConfusionMatrix(
classOrderLs, Y_validate_merged, validate_predicted_merged)
print(counts_validate)
df_validate = pd.DataFrame(counts_validate)
print("\nOutput the following counts for test:")
print(classOrderLs)
counts = sms_snn.generateCountsForConfusionMatrix(classOrderLs,
Y_test_merged,
test_predicted_merged)
print(counts)
df = pd.DataFrame(counts)
[true_positives, false_positives, true_negatives,
false_negatives] = sms_snn.trueFalsePosNegValues(classOrderLs, counts)
print("\n", classOrderLs)
print("TP:", true_positives)
print("FP:", false_positives)
print("TN:", true_negatives)
print("FN:", false_negatives)
sms_snn.outputCountsToFile(classOrderLs, df, outputString)
print(
"\n---------------------------------------\nEND SUMMARIZE NN MODULE\n---------------------------------------"
)
def main():
print(
"\n------------------------------------\nSTART DATA PROCESSING MODULE\n------------------------------------\n"
)
filenames = sms_pre.readInFilenames()
[X, Y, numcategories, classOrderLs,
outputString] = sms_pre.inputDataFiles(filenames)
[
X_train, X_test, X_validate, X_cv_list, Y_train, Y_test, Y_validate,
Y_cv_list
] = sms_pre.manualTrainTestSplit(X, Y, N=10)
sms_pre.outputXYdatasets(dataset=X_train, writename="X_train.temp")
sms_pre.outputXYdatasets(dataset=X_validate, writename="X_validate.temp")
sms_pre.outputXYdatasets(dataset=X_test, writename="X_test.temp")
sms_pre.outputXYdatasets(dataset=Y_train, writename="Y_train.temp")
sms_pre.outputXYdatasets(dataset=Y_validate, writename="Y_validate.temp")
sms_pre.outputXYdatasets(dataset=Y_test, writename="Y_test.temp")
print("\nShapes of features:")
print("Full dataset X, Y")
print(X.shape, Y.shape)
print("\nTrain X, Y")
print(X_train.shape, Y_train.shape)
print("CV X, Y")
print(X_validate.shape, Y_validate.shape)
print("Test X, Y")
print(X_test.shape, Y_test.shape)
[X_train_scaled, X_test_scaled,
X_validate_scaled] = sms_pre.featureScaling(X_train, X_test, X_validate)
sms_pre.outputXYdatasets(dataset=X_train_scaled,
writename="X_train_scaled.temp")
sms_pre.outputXYdatasets(dataset=X_validate_scaled,
writename="X_validate_scaled.temp")
sms_pre.outputXYdatasets(dataset=X_test_scaled,
writename="X_test_scaled.temp")
print(
"\n---------------------------------------\nEND DATA PROCESSING MODULE\n---------------------------------------\n"
)
print(
"\n---------------------------------------\nSTART BUILDING NN ALONE\n---------------------------------------\n"
)
X_train = X_train_scaled
X_test = X_test_scaled
X_validate = X_validate_scaled
## BELOW FOR NEURAL NET
print("\nOptimizing Neural Network")
param_grid = [{
'alpha': [0, 0.01, 0.1, 1, 10],
#'alpha': [0.1,0.01],
'activation': ['logistic'],
#'activation': ['identity','logistic','relu','tanh'],
'hidden_layer_sizes': [(100, 100, 100)],
#'hidden_layer_sizes': [(5,5,5),(25,25,25),(100,100,100)],
#'solver': ['sgd','lbfgs','adam'],
'solver': ['lbfgs'],
#'learning_rate': ['adaptive','constant','invscaling'],
'learning_rate': ['adaptive'],
#'learning_rate_init': [1e-1,1e-4,1e-7],
'learning_rate_init': [1e-4],
'shuffle': [True],
'verbose': [False],
'early_stopping': [False],
'max_iter': [1000],
'warm_start': [True]
}]
#clf = makeSimpleNN(X_train,Y_train)
clf = makeGridCVNN(X_train, Y_train, param_grid)
train_predicted = summarizePredictScores(X_train,
Y_train,
clf,
label="Training")
validate_predicted = summarizePredictScores(X_validate,
Y_validate,
clf,
label="Validation")
test_predicted = summarizePredictScores(X_test, Y_test, clf, label="Test")
print(
"\n---------------------------------------\nEND BUILDING NN ALONE\n---------------------------------------\n"
)