def range_train():
sample_sizes = [1e6]
for size in sample_sizes:
exp = Experiment("leagueoflegends", size, 75)
model, losses, iterations = exp.regular_train()
exp.predict(model, 1000, "s", True)
plt.title("Losses for size " + str(size))
plt.plot(iterations, losses)
plt.xlabel("Iteration")
plt.ylabel("Loss")
if not os.path.exists("plots/" + str(size) + ".png"):
plt.savefig("plots/" + str(size) + ".png")
else:
count = 1
while os.path.exists("plots/" + str(size) + ".png"):
count += 1
plt.savefig("plots/" + str(size) + ".png")
print("#" * 80)
print("FINAL LOSS FOR SIZE ", size, losses[len(losses) - 1])
print("#" * 80)
plt.clf()
def test_predict(self):
# Test with dataset type: sklearn DataBunch
experiment = Experiment('exp1', models=['rf', 'dt'], exp_type='classification')
iris = load_iris()
experiment.train(iris.data[:120], iris.target[:120])
predictions = experiment.predict(iris.data[120:])
actual = {
'rf':
[2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
'dt':
[2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}
self.assertEqual( predictions, actual )
# Test with dataset type: pandas DataFrame
experiment = Experiment('exp1', models=['rf', 'dt'], exp_type='classification')
iris_df = load_iris(as_frame=True).frame
X = iris_df.loc[:, iris_df.columns != 'target']
y = iris_df['target']
experiment.train(X.iloc[:120], y.iloc[:120])
predictions = experiment.predict(X.iloc[120:])
actual = {
'rf':
[2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
'dt':
[2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}
self.assertEqual( predictions, actual )
def custom():
filename = "../kelvin"
exp = Experiment(subreddit=None,
sample_size=0,
percentile=0,
custom=True,
custom_file=filename)
model, losses, iterations = exp.regular_train(epochs=5)
exp.predict(model, 100, "s", False)
def single_complete_train():
exp = Experiment("leagueoflegends", None, 90)
model, losses, iterations = exp.regular_train(epochs=1)
exp.predict(model, 1000, "s", True)
plt.title("Losses for size " + str(None))
plt.plot(iterations, losses)
plt.xlabel("Iteration")
plt.ylabel("Loss")