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]
print(str(location))
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=40,
cache_size=5000),
max_samples=4200,
n_estimators=10,
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)
for i in range(0, len(testY)):
output.write(str(location))
output.write(",")
output.write(str(testY[i]))
output.write(",")
output.write(str(prediction[i]))
output.write("\n")
output.close()