class ExtraTreesRegressorImpl():
def __init__(self, n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
self._hyperparams = {
'n_estimators': n_estimators,
'criterion': criterion,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'min_weight_fraction_leaf': min_weight_fraction_leaf,
'max_features': max_features,
'max_leaf_nodes': max_leaf_nodes,
'min_impurity_decrease': min_impurity_decrease,
'min_impurity_split': min_impurity_split,
'bootstrap': bootstrap,
'oob_score': oob_score,
'n_jobs': n_jobs,
'random_state': random_state,
'verbose': verbose,
'warm_start': warm_start}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
class ExtremelyRandomizeTreeEstimator(Estimator):
def __init__(self):
self.estimator = ExtraTreesRegressor(n_estimators=30)
self.initialized = False
def __call__(self, state, action):
if self.initialized:
x = np.array(state + [action[0], action[1]]).reshape(1, -1)
return self.estimator.predict(x)[0]
else:
return 0
def train(self, train_in, train_out):
self.initialized = True
train_in_formatted = np.array(train_in)
self.estimator.fit(train_in_formatted, train_out)
print("Train classifier...")
clf = ExtraTreesRegressor(n_estimators=200, n_jobs=5, random_state=randomstate)
clf.fit(X_train, y_train)
# save classifier for further use
dump(clf, clfpath)
print("Training complete...")
# clf = load(clfpath)
# VALIDATION SET
# load validation data
validationfeatures = pd.read_csv(
"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/validationfeat_normalized.csv",
index_col="ID")
y_pred_validation = clf.predict(validationfeatures)
pred_validation_df = pd.DataFrame(data=zip(validationfeatures.index.values,
y_pred_validation),
columns=["ID", "Prediction"])
pred_validation_df.to_csv(os.path.join(outpath, "validationprediction.csv"),
header=False,
index=False)
# TESTING SET
# load test data
testfeatures = pd.read_csv(
"/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/BRATS/BraTS2020/testingfeat_normalized_NEW.csv",
index_col="BraTS20ID")
y_pred_test = clf.predict(testfeatures)
pred_test_df = pd.DataFrame(data=zip(testfeatures.index.values, y_pred_test),