def main(path, predict_column):
df = pd.read_csv(path)
columns = df.columns.values
X = [
df[column].values for column in columns
if column != predict_column and column != 'Name'
]
y = df[predict_column].values
swtch = Switcher()
sk_X = []
for column in X:
converted = np.array(swtch.convert_to(column))
sk_X.append(converted)
sk_X = np.array(sk_X).T
sk_y = np.array(swtch.convert_to(y)).T
decisions = ID3()
entropy = decisions.fit(X, y)
print(f'Entropies:\t\t\t{entropy}')
sk_decisions = DecisionTreeClassifier(random_state=1370)
sk_decisions.fit(sk_X, sk_y)
print(f'SkLearn Tree Decision accuracy:\t{sk_decisions.score(sk_X, sk_y)}')
#print(sk_decisions.predict(['pycharm', 'Java', 'tea']))
return 0
def main(path, predict_column):
df = pd.read_csv(path)
columns = df.columns.values
X = [
df[column].values for column in columns
if column != predict_column and column != 'Name'
]
y = df[predict_column].values
decisions = ID3()
fitted = decisions.fit(X, y)
print(f'Entropy of {predict_column}:\t\t\t{fitted[1]}\n')
print(f'Information Gains of Xs\n')
headers = ' '.join(
map(
str,
list(
filter(lambda x: x != predict_column and x != "Name",
df.columns.values)))).replace(' ', '\t | ')
print(f'{headers}')
values = ' '.join(map(lambda x: "{0:.8f}".format(x),
fitted[0])).replace(' ', '\t | ')
print(values)
print('\n\n')
swtch = Switcher()
sk_X = []
for column in X:
converted = np.array(swtch.convert_to(column))
sk_X.append(converted)
sk_X = np.array(sk_X).T
sk_y = np.array(swtch.convert_to(y)).T
x_train, x_test, y_train, y_test = train_test_split(sk_X,
sk_y,
test_size=0.3)
sk_decisions = DecisionTreeClassifier(random_state=1370)
sk_decisions.fit(x_train, y_train)
print(
f'SkLearn Tree Decision accuracy:\t{sk_decisions.score(x_train, y_train)}'
)
print(f'SkLearn Tree Decision prediction: {sk_decisions.predict(x_test)}')
return 0
