def evalOne(parameters):
all_obs = []
all_pred = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, all_features, "target")
if "depth" in parameters:
model = RandomForestRegressor(
max_depth=parameters["depth"],
random_state=42,
n_estimators=parameters["n_estimators"],
n_jobs=-1)
elif "leaf" in parameters:
model = RandomForestRegressor(
min_samples_leaf=parameters["leaf"],
random_state=42,
n_estimators=parameters["n_estimators"],
n_jobs=-1)
elif "max_leaf" in parameters:
model = RandomForestRegressor(
max_leaf_nodes=parameters["max_leaf"],
random_state=42,
n_estimators=parameters["n_estimators"],
n_jobs=-1)
model.fit(trainX, trainY)
prediction = model.predict(testX)
all_obs.extend(testY)
all_pred.extend(prediction)
return rmseEval(all_obs, all_pred)[1]
def evalOne(parameters):
all_obs = []
all_pred = []
# all_obs_train = []
# all_pred_train = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(location, "location", data, all_features, "target")
model = KNeighborsRegressor(weights = parameters["weights"], n_neighbors = parameters["neighbors"], p = parameters["p"])
model.fit(trainX, trainY)
# train_prediction = model.predict(trainX)
prediction = model.predict(testX)
all_obs.extend(testY)
all_pred.extend(prediction)
# all_obs_train.extend(trainY)
# all_pred_train.extend(train_prediction)
return rmseEval(all_obs, all_pred)[1]
def evalOne(parameters):
all_obs = []
all_pred = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(location, "location", data, all_features, "target")
normalizer_X = StandardScaler()
trainX = normalizer_X.fit_transform(trainX)
testX = normalizer_X.transform(testX)
normalizer_Y = StandardScaler()
trainY = normalizer_Y.fit_transform(trainY)
testY = normalizer_Y.transform(testY)
model = BaggingRegressor(base_estimator=SVR(kernel='rbf', C=parameters["C"], cache_size=5000), max_samples=parameters["max_samples"],n_estimators=parameters["n_estimators"], verbose=0, n_jobs=-1)
model.fit(trainX, trainY)
prediction = model.predict(testX)
prediction = normalizer_Y.inverse_transform(prediction)
testY = normalizer_Y.inverse_transform(testY)
all_obs.extend(testY)
all_pred.extend(prediction)
return rmseEval(all_obs, all_pred)[1]
def evalOne(parameters):
all_obs = []
all_pred = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, all_features, "target")
normalizer_X = StandardScaler()
trainX = normalizer_X.fit_transform(trainX)
testX = normalizer_X.transform(testX)
normalizer_Y = StandardScaler()
trainY = normalizer_Y.fit_transform(trainY)
testY = normalizer_Y.transform(testY)
layers = []
for _ in range(0, parameters["hidden_layers"]):
layers.append(
Layer(parameters["hidden_type"],
units=parameters["hidden_neurons"]))
layers.append(Layer("Linear"))
model = Regressor(layers=layers,
learning_rate=parameters["learning_rate"],
n_iter=parameters["iteration"],
random_state=42)
X = np.array(trainX)
y = np.array(trainY)
model.fit(X, y)
model.fit(trainX, trainY)
prediction = model.predict(testX)
prediction = normalizer_Y.inverse_transform(prediction)
testY = normalizer_Y.inverse_transform(testY)
print("location: " + str(location) + " -> " +
str(rmseEval(prediction, testY)[1]))
all_obs.extend(testY)
all_pred.extend(prediction)
return rmseEval(all_obs, all_pred)[1]
columns = []
loadData("/data/york_hour_2013.csv", ["timestamp", "atc"], data, columns)
print(str(columns))
all_features = deepcopy(columns)
all_features.remove("target")
all_features.remove("location")
# remove to decrease rmse from 10000000.0...
all_features.remove('buildings_area')
all_features.remove('leisure_area')
all_obs = []
all_pred = []
for location in locations:
print("Location: " + str(location))
trainX, testX, trainY, testY = splitDataForXValidation(location, "location", data, all_features, "target")
model = linear_model.LinearRegression(True, True, True, -1)
model.fit(trainX, trainY)
prediction = model.predict(testX)
rmse = str(rmseEval(testY, prediction)[1])
print("\tRmse:" + rmse)
all_obs.extend(testY)
all_pred.extend(prediction)
print("Overall:")
rmse = str(rmseEval(all_obs, all_pred)[1])
print("Rmse:" + rmse)