def evalTrainStationTestStation(trainStation, testStation, features):
trainX, _, trainY, _ = splitDataForXValidation(set([trainStation]), set(), "location", dataByStation[trainStation], features, "target")
_, testX2, _, testY2 = splitDataForXValidation(set(), set([testStation]), "location", dataByStation[testStation], features, "target")
model = RandomForestRegressor(max_depth=10, n_estimators = 60, n_jobs = -1, random_state=42)
model.fit(trainX, trainY)
prediction = model.predict(testX2)
rmse = rmseEval(testY2, prediction)[1]
print("Training on station " + str(trainStation) + ", applying on station " + str(testStation) + ": rmse: " + str(rmse))
return rmse
'bank_holiday', 'hour', 'month', 'day_of_week', 'building_count', 'length',
'natural_area'
]
dataDict = {}
rmseDict = {}
for location in all_stations:
print("stations " + str(location))
trainStations = set(
float(station) for station in all_stations if station != location)
testStations = set([float(location)])
trainX, testX, trainY, testY = splitDataForXValidation(
trainStations, testStations, "location", data, features_TW, "target")
print("\tTW #train: " + str(len(trainY)) + ", #test:" + str(len(testY)))
model = RandomForestRegressor(min_samples_leaf=9,
n_estimators=59,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
prediction = model.predict(testX)
rmse = rmseEval(testY, prediction)[1]
print("\trmse: " + str(rmse))
dataDict[str(location) + "_obs"] = testY
ae = []
for i in range(0, len(testY)):
ae.append(abs(testY[i] - prediction[i]))
dataDict[str(location) + "_ae_tw"] = ae
rmseDict[str(location) + "_ae_tw"] = rmse
columns = []
loadData(DATA_FILE, ["timestamp"], data, columns)
for iteration in range(0, 5):
print("iter_" + str(iteration))
trainStations = []
testStations = []
for i in range(0, 5):
if i == iteration:
testStations = testStations + locations_grouped[i]
else:
trainStations = trainStations + locations_grouped[i]
print("\ttrainStations: " + str(trainStations))
print("\ttestStations: " + str(testStations))
trainStationSet = set(s for s in trainStations)
testStationSet = set(s for s in testStations)
trainX, testX, trainY, testY = splitDataForXValidation(
trainStationSet, testStationSet, "location", data, columns, "target")
print("\t#trainX: " + str(len(trainX)) + ", #testX:" + str(len(testX)))
print("\t#trainY: " + str(len(trainY)) + ", #testY:" + str(len(testY)))
model = RandomForestRegressor(max_depth=10,
n_estimators=30,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
prediction = model.predict(testX)
rmse = rmseEval(testY, prediction)[1]
print("\trmse: " + str(rmse))