def test_all_file(self):
options = {
'df': pd.read_csv("benchmark.csv", sep=';'),
'label_column': "Joga",
'n_trees': 5,
'bootstrap_size': 10
}
tr = RandomForest()
model = tr.train(options)
for _, row in options['df'].iterrows():
target_label = row["Joga"]
predicted = model.predict(row.drop("Joga"))
self.assertEqual(target_label, predicted)
def test_benchmark(self):
options = {
'df': pd.read_csv("benchmark.csv", sep=';'),
'label_column': "Joga",
'n_trees': 5,
'bootstrap_size': 10
}
tr = RandomForest()
model = tr.train(options)
inf_data = pd.Series(
["Ensolarado", "Quente", "Normal", "Verdadeiro"],
index=["Tempo", "Temperatura", "Umidade", "Ventoso"],
name="InferenceData")
self.assertEqual(model.predict(inf_data), 'Sim')
# decision tree
tree = DecisionTree(5, train_data.shape[0])
tree.train(train_data, train_label)
res = tree.predict(validation_data)
score = 0
for i in range(len(res)):
if res[i] == validation_label[i]:
score += 1
score /= len(res)
print(score)
# random forest
forest = RandomForest(100,5,train_data.shape[0],6)
forest.train(train_data, train_label)
res = forest.predict(validation_data)
score = 0
for i in range(len(res)):
if res[i] == validation_label[i]:
score += 1
score /= len(res)
print(score)
# write to csv
# with open('titanic_prediction.csv', 'wt') as f:
# writer = csv.writer(f, delimiter=',')
# writer.writerow(['Id', 'Category'])
# for i, cat in enumerate(res):
tree.train(train_data, train_label)
res = tree.predict(validation_data[:1,:])
score = 0
for i in range(len(res)):
if res[i] == validation_label[i]:
score += 1
score /= len(res)
print(score)
# random forest
rf = RandomForest(10,10,train_data.shape[0],train_data.shape[1])
rf.train(train_data,train_label)
res = rf.predict(validation_data)
score = 0
for i in range(len(res)):
if res[i] == validation_label[i]:
score += 1
score /= len(res)
print(score)
# with open('titanic_prediction.csv', 'wt') as f:
# writer = csv.writer(f, delimiter=',')
# writer.writerow(['Id', 'Category'])
# for i, cat in enumerate(res):
# writer.writerow([str(i + 1), str(cat)])