def main():
'''
Use this script to run experiments and fine-tune the algoritms
'''
# Load the dataset
file_name = sys.argv[1]
dirname = os.path.dirname(__file__)
file_name = os.path.join(dirname, file_name)
d = DataSet(file_name)
d.loadDataSet()
# Remove useless features (not numeric + bad regressors).
to_remove = [
d.data_set[0].index('Index'),
d.data_set[0].index('First Name'),
d.data_set[0].index('Last Name'),
d.data_set[0].index('Birthday'),
d.data_set[0].index('Best Hand'),
d.data_set[0].index('Hogwarts House'),
# Tests 7/10/18
d.data_set[0].index('Arithmancy'),
d.data_set[0].index('Defense Against the Dark Arts'),
d.data_set[0].index('Divination'),
d.data_set[0].index('Muggle Studies'),
d.data_set[0].index('History of Magic'),
d.data_set[0].index('Transfiguration'),
d.data_set[0].index('Potions'),
d.data_set[0].index('Care of Magical Creatures'),
d.data_set[0].index('Charms'),
d.data_set[0].index('Flying'),
]
X = np.array([[
d.data_set[i][j] for j in range(len(d.data_set[0]))
if j not in to_remove
] for i in range(len(d.data_set))])
X = convert_to_float(X[1:, ])
y_col_nb = d.data_set[0].index('Hogwarts House')
y = np.array(d.extractColumn(y_col_nb)[1:])
# Impute missing values
m = MeanImputation(X)
m.train()
m.transform()
# Scale the variables
sc = Scaling(X)
sc.train()
sc.transform()
# Split the dataset in a training and testing set
sp = SplitTrainTest(X, y)
sp.Split()
X_train = sp.X_train
y_train = sp.y_train
X_test = sp.X_test
y_test = sp.y_test
# Train a logistic regression model
l = LogisticRegression(X=X_train, y=y_train)
l.train()
# Compute the confusion matrix over the training set
y_predicted = l.predict()
cm1 = ConfusionMatrix(y_train, y_predicted)
cm1.getMatrix()
print('\n\n')
print(
'**************** Confusion Matrix on the training set ****************'
)
print('\n')
cm1.Print()
# Compute the confusion matrix over the testing set
y_predicted = l.predict(X_test)
cm2 = ConfusionMatrix(y_test, y_predicted, cm1.unique_labels)
cm2.getMatrix()
print('\n\n')
print(
'**************** Confusion Matrix on the testing set ****************'
)
print('\n')
cm2.Print()
def main():
file_name = sys.argv[1]
dirname = os.path.dirname(__file__)
file_name = os.path.join(dirname, file_name)
d = DataSet(file_name)
d.loadDataSet()
to_remove = [
d.data_set[0].index('Index'),
d.data_set[0].index('First Name'),
d.data_set[0].index('Last Name'),
d.data_set[0].index('Birthday'),
d.data_set[0].index('Best Hand'),
d.data_set[0].index('Hogwarts House'),
# Tests 7/10/18
d.data_set[0].index('Arithmancy'),
d.data_set[0].index('Defense Against the Dark Arts'),
d.data_set[0].index('Divination'),
d.data_set[0].index('Muggle Studies'),
d.data_set[0].index('History of Magic'),
d.data_set[0].index('Transfiguration'),
d.data_set[0].index('Potions'),
d.data_set[0].index('Care of Magical Creatures'),
d.data_set[0].index('Charms'),
d.data_set[0].index('Flying'),
]
X = np.array([[
d.data_set[i][j] for j in range(len(d.data_set[0]))
if j not in to_remove
] for i in range(len(d.data_set))])
X = convert_to_float(X[1:, ])
y_col_nb = d.data_set[0].index('Hogwarts House')
y = np.array(d.extractColumn(y_col_nb)[1:])
m = MeanImputation(X)
m.train()
m.transform()
sc = Scaling(X)
sc.train()
sc.transform()
sp = SplitTrainTest(X, y)
sp.Split()
X_train = sp.X_train
y_train = sp.y_train
X_test = sp.X_test
y_test = sp.y_test
l = LogisticRegression(X=X_train,
y=y_train,
optimizer='sgd',
optimizer_params={
'alpha': 0.5,
'n': 5,
'batch_size': 16
})
l.train()
y_predicted = l.predict()
cm1 = ConfusionMatrix(y_train, y_predicted)
cm1.getMatrix()
print('\n\n')
print(
'**************** Confusion Matrix on the training set ****************'
)
print('\n')
cm1.Print()
y_predicted = l.predict(X_test)
cm2 = ConfusionMatrix(y_test, y_predicted, cm1.unique_labels)
cm2.getMatrix()
print('\n\n')
print(
'**************** Confusion Matrix on the testing set ****************'
)
print('\n')
cm2.Print()
