def eval_one(min_samples_leaf, n_estimators):
log("min_samples_leaf: " + str(min_samples_leaf) + ", n_estimators: " +
str(n_estimators))
all_observations = []
all_pred_ALL = []
for group in range(0, len(groups)):
trainStations = []
for i in range(0, len(groups)):
if i != group:
trainStations.extend(groups[i])
testStations = groups[group]
train_station_set = set([float(s) for s in trainStations])
test_station_set = set([float(s) for s in testStations])
trainX, testX, trainY, testY = splitDataForXValidation(
train_station_set, test_station_set, "location", data,
all_features, "target")
model = RandomForestRegressor(min_samples_leaf=min_samples_leaf,
n_estimators=n_estimators,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
prediction_ALL = model.predict(testX)
rmse = rmseEval(testY, prediction_ALL)[1]
log("\tALL rmse: " + str(rmse))
all_observations.extend(testY)
all_pred_ALL.extend(prediction_ALL)
rmse = rmseEval(all_observations, all_pred_ALL)[1]
log("\tALL rmse:" + str(rmse))
return rmse
def doPrediction(locations, data, columns, features, columns2, outputFileName):
predictionData = {}
for c in columns2:
predictionData[c] = []
# modelling
for location in locations:
trainX, testX, trainY, testY, dataY = splitDataForXValidation(
location, "location", data, features, columns, "target")
print("\tT+W #train: " + str(len(trainY)) + ", #test:" +
str(len(testY)))
model = RandomForestRegressor(min_samples_leaf=2,
n_estimators=650,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
prediction = model.predict(testX)
rmse = rmseEval(testY, prediction)[1]
print("\trmse: " + str(rmse))
for c in columns2:
if c == 'prediction':
predictionData[c].extend(prediction)
else:
predictionData[c].extend(dataY[c])
for c in predictionData:
print("\t" + c + " -> #" + str(len(predictionData[c])))
rmse = rmseEval(predictionData['target'], predictionData['prediction'])[1]
print("overall RMSE: " + str(rmse))
print("Writing out results...")
output = open(outputFileName, 'w')
output.write(','.join([str(x) for x in columns2]))
output.write("\n")
for i in range(0, len(predictionData['target'])):
output.write(str(predictionData[columns2[0]][i]))
for j in range(1, len(columns2)):
output.write(",")
output.write(str(predictionData[columns2[j]][i]))
output.write("\n")
output.close()
print("Done...")
def eval_one(step):
if step in cached_results:
return cached_results[step]
eval_features = []
for i in range(0, len(all_features)):
if step[i]:
eval_features.append(all_features[i])
all_predictions = []
all_observations = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(location, "location", data, eval_features, "target")
model = RandomForestRegressor(min_samples_leaf = 2, random_state=42, n_estimators=650, n_jobs=-1)
model.fit(trainX, trainY)
predictions = model.predict(testX)
all_observations.extend(testY)
all_predictions.extend(predictions)
rmse = rmseEval(all_observations, all_predictions)[1]
cached_results[step] = rmse
# save down the cached result
cache_output = open(CACHE_FILE, "a")
step_list = [str(s) for s in step]
step_str = ",".join(step_list)
cache_output.write(str(rmse) + ";" + step_str + "\n")
cache_output.close()
return rmse
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 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
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]
def rf(week, timestampWeekCategory, stationNames, ospmData2013, ospmData2014,
data2013, data2014):
columns = []
for c in data2013:
columns.append(c)
columns.remove("location")
columns.remove("timestamp")
columns.remove("target")
X = []
y = []
for i in range(0, len(data2013["target"])):
timestamp = str(int(data2013["timestamp"][i]))
weekC = timestampWeekCategory[timestamp]
if int(weekC) >= week:
y.append(data2013["target"][i])
x = []
for c in columns:
x.append(data2013[c][i])
X.append(x)
model = RandomForestRegressor(min_samples_leaf=9,
n_estimators=59,
n_jobs=-1,
random_state=42)
model.fit(X, y)
# print(str(len(X)))
X = []
y = []
for i in range(0, len(data2014["target"])):
y.append(data2014["target"][i])
x = []
for c in columns:
x.append(data2014[c][i])
X.append(x)
prediction = model.predict(X)
rmse = rmseEval(y, prediction)
return rmse
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 eval_one(features):
all_predictions = []
all_observations = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, features, "target")
model = RandomForestRegressor(min_samples_leaf=2,
random_state=42,
n_estimators=650,
n_jobs=-1)
model.fit(trainX, trainY)
predictions = model.predict(testX)
all_observations.extend(testY)
all_predictions.extend(predictions)
rmse = rmseEval(all_observations, all_predictions)[1]
log("\tRMSE: " + str(rmse))
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 evaluateFeatures(vector, features, data):
featureToUse = []
for i in range(len(vector)):
if vector[i] == 1:
featureToUse.append(features[i])
combinedRmse = []
# modelling
for location in locationValues:
trainX, testX, trainY, testY = splitDataForXValidation2(
location, "location", data, featureToUse, "target")
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)
combinedRmse.append(rmse[1])
# calculate avg rmse
avgRmse = 0.0
for rmse in combinedRmse:
avgRmse = avgRmse + rmse
avgRmse = avgRmse / len(combinedRmse)
return avgRmse
allPredictionsTW = []
allPredictionsTWA = []
for location in locations:
location2s = [l for l in locations if l != location]
log("Location: " + str(location) + ", location2: " + str(location2s))
# tw_4stations
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, tw_features, "target")
allObservations.extend(testY)
model = create_model()
model.fit(trainX, trainY)
predictionTW = model.predict(testX)
rmse = rmseEval(testY, predictionTW)[1]
log("\tTW:" + str(rmse))
allPredictionsTW.extend(predictionTW)
# tw_4stations
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, twa_features, "target")
model = create_model()
model.fit(trainX, trainY)
predictionTWA = model.predict(testX)
rmse = rmseEval(testY, predictionTWA)[1]
log("\tTWA:" + str(rmse))
allPredictionsTWA.extend(predictionTWA)
#combination
classifier_X = []
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)
def eval_one(step):
if step in cached_results:
return cached_results[step]
eval_features = []
for i in range(0, len(all_features)):
if step[i]:
eval_features.append(all_features[i])
all_observations = []
all_pred_combined = []
for group in range(0, len(groups)):
train_stations, test_stations = generate_train_test_station_list(
group, groups)
train_station_set = set([float(s) for s in train_stations])
test_station_set = set([float(s) for s in test_stations])
train_lower = [
float(train_stations[i]) for i in range(0, len(train_stations))
if i < (len(train_stations) / 2.0)
]
train_lower_set = set(train_lower)
train_upper = [
float(train_stations[i]) for i in range(0, len(train_stations))
if i >= (len(train_stations) / 2.0)
]
train_upper_set = set(train_upper)
test_lower = [
float(test_stations[i]) for i in range(0, len(test_stations))
if i < (len(test_stations) / 2.0)
]
# tw_lower
trainX, testX, trainY, testY = splitDataForXValidation(
train_lower_set, test_station_set, "location", data, tw_features,
"target")
model = create_model()
model.fit(trainX, trainY)
prediction_lower = model.predict(testX)
# tw_upper
trainX, testX, trainY, testY = splitDataForXValidation(
train_upper_set, test_station_set, "location", data, tw_features,
"target")
model = create_model()
model.fit(trainX, trainY)
prediction_upper = model.predict(testX)
trainX, testX, trainY, testY, train_location, test_location = splitDataForXValidationWithLocation(
train_station_set, test_station_set, "location", data,
eval_features, "target")
train_label = generate_label(train_location, train_lower)
test_label = generate_label(test_location, test_lower)
model = create_classifier_model()
model.fit(trainX, train_label)
prediction_label = model.predict(testX)
pred_combined = generate_combined_prediction(prediction_label,
prediction_lower,
prediction_upper)
all_pred_combined.extend(pred_combined)
all_observations.extend(testY)
rmse = rmseEval(all_observations, all_pred_combined)[1]
cached_results[step] = rmse
# save down the cached result
cache_output = open(CACHE_FILE, "a")
step_list = [str(s) for s in step]
step_str = ",".join(step_list)
cache_output.write(str(rmse) + ";" + step_str + "\n")
cache_output.close()
return rmse
log("\ttrainStations: " + str(trainStations))
log("\ttestStations: " + str(testStations))
train_station_set = set([float(s) for s in trainStations])
test_station_set = set([float(s) for s in testStations])
trainX, testX, trainY, testY = splitDataForXValidation(
train_station_set, test_station_set, "location", data, tw_features,
"target")
model = RandomForestRegressor(min_samples_leaf=29,
n_estimators=64,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
prediction_TW = model.predict(testX)
rmse = rmseEval(testY, prediction_TW)[1]
log("\tTW rmse: " + str(rmse))
all_observations.extend(testY)
all_pred_TW.extend(prediction_TW)
trainX, testX, trainY, testY = splitDataForXValidation(
train_station_set, test_station_set, "location", data, twa_features,
"target")
model = RandomForestRegressor(min_samples_leaf=29,
n_estimators=64,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
prediction_TWA = model.predict(testX)
rmse = rmseEval(testY, prediction_TWA)[1]
log("\tTWA rmse: " + str(rmse))
errorsTimestamps = {}
eData = {}
for m in models:
errors = {}
for l in locations:
errors[l] = 0
errorsTimestamps[m] = set()
records = len(data[m]['target'])
print("Overall:")
print("\t" + "#records: " + str(records))
rmse = rmseEval(data[m]['target'], data[m]['prediction'])[1]
print("\t" + "rmse: " + str(rmse))
absoluteError = ae(data[m]['target'], data[m]['prediction'])
absoluteError.sort()
eData[m] = absoluteError
# error without records have ae > 20.0
data2 = {}
for c in columns[m]:
data2[c] = []
for i in range(0, records):
if abs(data[m]['target'][i] - data[m]['prediction'][i]) < 20.0:
for c in columns[m]:
trainStationList = [s for s in all_stations if float(s) in trainStations]
log(output_log, "\ttrainStationList:" + str(trainStationList))
testStations = set(float(station) for station in testStationList)
trainX, testX, trainY, testY = splitDataForXValidation(
trainStations, testStations, "location", data, features_TW, "target")
log(output_log,
"\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_TW = model.predict(testX)
rmse = rmseEval(testY, prediction_TW)[1]
log(output_log, "\trmse: " + str(rmse))
obs.extend(testY)
all_pred_TW.extend(prediction_TW)
trainX, testX, trainY, testY, trainLocation, testLocation = splitDataForXValidationWithLocation(
trainStations, testStations, "location", data, columns, "target")
train_lower = [
float(trainStationList[i]) for i in range(0, len(trainStationList))
if i < (len(trainStationList) / 2.0)
]
train_upper = [
float(trainStationList[i]) for i in range(0, len(trainStationList))
if i >= (len(trainStationList) / 2.0)
]
predictionsNormal[method].append(p)
observationsNormal[method].append(o)
rmseLevels = {}
maeLevels = {}
rLevels = {}
fac2Levels = {}
nmseLevels = {}
fbLevels = {}
rsLevels = {}
mgLevels = {}
vgLevels = {}
for method in methods:
print("Method: " + method)
rmse = rmseEval(observations[method], predictions[method])[1]
print("\trmse: " + str(rmse))
mae = maeEval(observations[method], predictions[method])[1]
print("\tmae: " + str(mae))
r = correlationEval(observations[method], predictions[method])[1]
print("\tr: " + str(r))
print("\tr2: " +
str(rsquaredEval(observations[method], predictions[method])[1]))
print("\tr2: " + str(r2_score(observations[method], predictions[method])))
fac2 = fac2Eval(observations[method], predictions[method])
print("\tfac2: " + str(fac2))
print("\tmg: " + str(mgEval(observations[method], predictions[method])))
nmse = nmse_from_paper(observations[method], predictions[method])
print("\tnmse: " + str(nmse))
fb = fbEval(observations[method], predictions[method])[1]
print("\tfb: " + str(fb))
def doEval(landuse, topo, traffic_static, traffic_dynamic, weather, time,
output):
if landuse == False and topo == False and traffic_dynamic == False and traffic_static == False and weather == False and time == False:
return
groupName = "lu"
if landuse == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "to"
if topo == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "ts"
if traffic_static == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "td"
if traffic_dynamic == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "we"
if weather == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "ti"
if time == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
print("Group: " + groupName)
features = []
if landuse:
features.append('leisure_area')
features.append('landuse_area')
if topo:
features.append('buildings_number')
features.append('buildings_area')
if traffic_static:
features.append('lane_length')
features.append('length')
if traffic_dynamic:
features.append('traffic_length_car')
features.append('traffic_length_lgv')
features.append('traffic_length_hgv')
if weather:
features.append('winddirection')
features.append('windspeed')
features.append('temperature')
features.append('rain')
features.append('pressure')
if time:
features.append('hour')
features.append('day_of_week')
features.append('month')
features.append('bank_holiday')
features.append('race_day')
all_obs = []
all_prediction = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, features, "target")
model = RandomForestRegressor(min_samples_leaf=2,
random_state=42,
n_estimators=650,
n_jobs=-1)
model.fit(trainX, trainY)
prediction = model.predict(testX)
all_obs.extend(testY)
all_prediction.extend(prediction)
rmse = rmseEval(all_obs, all_prediction)[1]
output.write(str(groupName) + "," + str(rmse) + "\n")
output.flush()
def doEval(landuse, topo, traffic_static, traffic_dynamic, weather, time,
output):
if landuse == False and topo == False and traffic_dynamic == False and traffic_static == False and weather == False and time == False:
return
groupName = "lu"
if landuse == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "to"
if topo == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "ts"
if traffic_static == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "td"
if traffic_dynamic == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "we"
if weather == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
groupName = groupName + "ti"
if time == True:
groupName = groupName + "1"
else:
groupName = groupName + "0"
print("Group: " + groupName)
columnsToSkip = ['timestamp']
if landuse == False:
columnsToSkip.append('leisure_area')
columnsToSkip.append('landuse_area')
if topo == False:
columnsToSkip.append('buildings_number')
columnsToSkip.append('buildings_area')
if traffic_static == False:
columnsToSkip.append('lane_length')
columnsToSkip.append('length')
if traffic_dynamic == False:
columnsToSkip.append('atc')
if weather == False:
columnsToSkip.append('winddirection')
columnsToSkip.append('windspeed')
columnsToSkip.append('temperature')
columnsToSkip.append('rain')
columnsToSkip.append('pressure')
if time == False:
columnsToSkip.append('hour')
columnsToSkip.append('day_of_week')
columnsToSkip.append('month')
columnsToSkip.append('bank_holiday')
columnsToSkip.append('race_day')
columns = []
data = {}
loadData(DATA_FILE, columnsToSkip, data, columns)
# modelling
for location in locations:
print("Location: " + str(location))
trainX, testX, trainY, testY = splitDataForXValidation1(
location, "location", data, columns, "target")
print("\tRFR #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))
output.write(str(groupName) + "," + str(rmse) + "\n")
def evalColumns(columns):
# log("Evaluating " + str([all_columns[i] for i in range(0, len(all_columns)) if columns[i]]))
overallY = []
overallPred = []
for location in locations:
trainX = loadX(INPUT_DIRECTORY + "z_" + str(int(location)) + "_trainX.csv", all_features)
trainY = loadSingleColumnsFile(INPUT_DIRECTORY + "z_" + str(int(location)) + "_trainY.csv")
trainPreds = []
for tag in top4tags:
p = loadSingleColumnsFile(INPUT_DIRECTORY + "z_" + str(int(location)) + "_trainPred_" + tag + ".csv")
for i in range(0, len(p)):
trainX[i].append(p[i])
trainPreds.append(p)
labelY = []
for i in range(0, len(trainY)):
bestAbs = abs(trainY[i] - trainPreds[0][i])
bestIndex = 0
for j in range(0, len(top4tags)):
modelAbs = abs(trainY[i] - trainPreds[j][i])
if modelAbs < bestAbs:
bestAbs = modelAbs
bestIndex = j
labelY.append(bestIndex)
# reduce trainX
reducedTrainX = []
for d in trainX:
reducedD = []
for i in range(0, len(all_columns)):
if columns[i]:
reducedD.append(d[i])
reducedTrainX.append(reducedD)
model = RandomForestClassifier(random_state=42, n_estimators=100, max_depth=15)
model.fit(reducedTrainX, labelY)
testX = loadX(INPUT_DIRECTORY + "z_" + str(int(location)) + "_testX.csv", all_features)
testY = loadSingleColumnsFile(INPUT_DIRECTORY + "z_" + str(int(location)) + "_testY.csv")
testPreds = []
for tag in top4tags:
p = loadSingleColumnsFile(INPUT_DIRECTORY + "z_" + str(int(location)) + "_testPred_" + tag + ".csv")
for i in range(0, len(p)):
testX[i].append(p[i])
testPreds.append(p)
reducedTestX = []
for d in testX:
reducedD = []
for i in range(0, len(all_columns)):
if columns[i]:
reducedD.append(d[i])
reducedTestX.append(reducedD)
testPredY = model.predict(reducedTestX)
prediction = []
for i in range(0, len(testPredY)):
p = testPreds[testPredY[i]][i]
prediction.append(p)
overallY = overallY + testY
overallPred = overallPred + prediction
rmse = rmseEval(overallPred, overallY)[1]
return rmse
print("\ttrainStationList:" + str(trainStationList))
trainStationList = [s for s in all_stations if float(s) in trainStations]
print("\ttrainStationList:" + str(trainStationList))
testStations = set(float(station) for station in testStationList)
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))
#
# trainX, testX, trainY, testY = splitDataForXValidation(trainStations, testStations, "location", data, features_TWA, "target")
# print("\tTWA #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))
#
# trainX, testX, trainY, testY = splitDataForXValidation(trainStations, testStations, "location", data, features_ALL, "target")
# print("\tALL #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)
def eval_one(step):
eval_features = []
for i in range(0, len(all_features)):
if step[i]:
eval_features.append(all_features[i])
all_observations = []
all_pred_combined = []
all_label = []
all_pred_label = []
for group in range(0, len(groups)):
train_stations, test_stations = generate_train_test_station_list(group, groups)
train_station_set = set([float(s) for s in train_stations])
test_station_set = set([float(s) for s in test_stations])
trainX, testX, trainY, testY = splitDataForXValidation(train_station_set, test_station_set, "location", data, tw_features, "target")
model = RandomForestRegressor(min_samples_leaf = 29, n_estimators = 64, n_jobs = -1, random_state=42)
model.fit(trainX, trainY)
prediction_TW = model.predict(testX)
rmse = rmseEval(testY, prediction_TW)[1]
all_observations.extend(testY)
trainX, testX, trainY, testY = splitDataForXValidation(train_station_set, test_station_set, "location", data, twa_features, "target")
model = RandomForestRegressor(min_samples_leaf = 29, n_estimators = 64, n_jobs = -1, random_state=42)
model.fit(trainX, trainY)
prediction_TWA = model.predict(testX)
rmse = rmseEval(testY, prediction_TWA)[1]
group2s = [groups[i] for i in range(0, len(groups)) if i != group]
#combination
classifier_X = []
classifier_Y = []
for group2 in range(0, len(group2s)):
train_stations, test_stations = generate_train_test_station_list(group2, group2s)
train_station_set = set([float(s) for s in train_stations])
test_station_set = set([float(s) for s in test_stations])
trainX, testX, trainY, testY = splitDataForXValidation(train_station_set, test_station_set, "location", data, tw_features, "target")
model = RandomForestRegressor(min_samples_leaf = 29, n_estimators = 64, n_jobs = -1, random_state=42)
model.fit(trainX, trainY)
prediction_3groups_TW = model.predict(testX)
trainX, testX, trainY, testY = splitDataForXValidation(train_station_set, test_station_set, "location", data, twa_features, "target")
model = RandomForestRegressor(min_samples_leaf = 29, n_estimators = 64, n_jobs = -1, random_state=42)
model.fit(trainX, trainY)
prediction_3groups_TWA = model.predict(testX)
trainX, testX, trainY, testY = splitDataForXValidation(train_station_set, test_station_set, "location", data, eval_features, "target")
classifier_X.extend(testX)
label = generate_label(testY, prediction_3groups_TW, prediction_3groups_TWA)
classifier_Y.extend(label)
train_stations, test_stations = generate_train_test_station_list(group, groups)
train_station_set = set([float(s) for s in train_stations])
test_station_set = set([float(s) for s in test_stations])
model = create_classifier_model()
model.fit(classifier_X, classifier_Y)
_, testX, _, testY = splitDataForXValidation(train_station_set, test_station_set, "location", data, eval_features, "target")
classifier_prediction = model.predict(testX)
test_label = generate_label(testY, prediction_TW, prediction_TWA)
all_label.extend(test_label)
all_pred_label.extend(classifier_prediction)
combined_prediction = generate_combined_prediction(classifier_prediction, prediction_TW, prediction_TWA)
rmse = rmseEval(testY, combined_prediction)[1]
all_pred_combined.extend(combined_prediction)
rmse = rmseEval(all_observations, all_pred_combined)[1]
accuracy = calculate_accuracy(all_label, all_pred_label)
return rmse, accuracy
def evalColumns(columns):
overallY = []
overallPred = []
for location in locations:
location2s = [l for l in locations if l != location]
print("Location: " + str(location) + ", location2: " + str(location2s))
# generating testPreds
testPreds = {}
for datagroup in topDatagroups:
tag, features = getTagAndFeatures(datagroup)
trainX, testX, trainY, testY = splitDataForXValidation(location, "location", data, features, "target")
model = RandomForestRegressor(min_samples_leaf = 9, n_estimators = 59, n_jobs = -1, random_state=42)
model.fit(trainX, trainY)
prediction = model.predict(testX)
testPreds[tag] = prediction
trainPreds = defaultdict(list)
for datagroup in topDatagroups:
tag, features = getTagAndFeatures(datagroup)
print("\ttag: " + str(tag) + ", features: " + str(features))
for location2 in location2s:
trainX1, trainX2, trainY1, trainY2, testX, testY = splitDataForXValidationSampled2(location, location2, "location", data, features, "target")
model = RandomForestRegressor(min_samples_leaf = 9, n_estimators = 59, n_jobs = -1, random_state=42)
model.fit(trainX1, trainY1)
train1Prediction = model.predict(trainX1)
train2Prediction = model.predict(trainX2)
testPrediction = model.predict(testX)
train1Rmse = str(rmseEval(trainY1, train1Prediction)[1])
train2Rmse = str(rmseEval(trainY2, train2Prediction)[1])
testRmse = str(rmseEval(testY, testPrediction)[1])
print("\t\ttrain1 rmse: " + train1Rmse)
print("\t\ttrain2 rmse: " + train2Rmse)
print("\t\ttest rmse: " + testRmse)
for x in train2Prediction:
trainPreds[tag].append(x)
# get combined train2y
combinedTrain2Y = []
for location2 in location2s:
trainX1, trainX2, trainY1, trainY2, testX, testY = splitDataForXValidationSampled2(location, location2, "location", data, all_features, "target")
combinedTrain2Y = combinedTrain2Y + trainY2
# calculate labels
labelTrain2Y = []
for i in range(0, len(combinedTrain2Y)):
bestModel = 0
bestAbs = abs(combinedTrain2Y[i] - trainPreds[topTags[0]][i])
for j in range(0, len(topTags)):
tag = topTags[j]
modelAbs = abs(combinedTrain2Y[i] - trainPreds[tag][i])
if modelAbs < bestAbs:
bestAbs = modelAbs
bestModel = j
labelTrain2Y.append(bestModel)
# generating testX
_, testX, _, _ = splitDataForXValidation(location, "location", data, all_features, "target")
# trainX2
tX2 = []
for location2 in location2s:
_, trainX2, _, _, _, _ = splitDataForXValidationSampled2(location, location2, "location", data, all_features, "target")
for row in trainX2:
tX2.append(row)
for tag in topTags:
for i in range(0, len(trainPreds[tag])):
tX2[i].append(trainPreds[tag][i])
reducedTrainX2 = []
for d in tX2:
reducedD = []
for i in range(0, len(all_columns)):
if columns[i]:
reducedD.append(d[i])
reducedTrainX2.append(reducedD)
model = RandomForestClassifier(random_state=42, n_estimators=100, max_depth=15)
model.fit(reducedTrainX2, labelTrain2Y)
for tag in topTags:
for i in range(0, len(testPreds[tag])):
testX[i].append(testPreds[tag][i])
reducedTestX = []
for d in testX:
reducedD = []
for i in range(0, len(all_columns)):
if columns[i]:
reducedD.append(d[i])
reducedTestX.append(reducedD)
pred = model.predict(reducedTestX)
finalPrediction = []
for i in range(0, len(testY)):
p = testPreds[topTags[pred[i]]][i]
finalPrediction.append(p)
rmse = str(rmseEval(testY, finalPrediction)[1])
print("\tRMSE: " + str(rmse))
for x in testY:
overallY.append(x)
for x in finalPrediction:
overallPred.append(x)
rmse = rmseEval(overallPred, overallY)[1]
return rmse
testY[i] -
predData[tag][str(location)][str(int(testTimestamp[i]))])
if tagAbs < bestAbs:
bestModel = tag
bestAbs = tagAbs
locationBestCounter[bestModel] = locationBestCounter[bestModel] + 1
twPred = predData["TW"][str(location)][str(int(testTimestamp[i]))]
twPredictions.append(twPred)
bestPred = predData[bestModel][str(location)][str(int(
testTimestamp[i]))]
bestPredictions.append(bestPred)
# print(str(locationBestCounter))
rmse = rmseEval(testY, twPredictions)[1]
print("\tTW rmse: " + str(rmse))
rmse = rmseEval(testY, bestPredictions)[1]
print("\tBest rmse: " + str(rmse))
for tag in tags:
bestCounter[tag] = bestCounter[tag] + locationBestCounter[tag]
print("BestCounter:")
orderedBestCounter = []
for tag in tags:
orderedBestCounter.append((bestCounter[tag], tag))
orderedBestCounter.sort(reverse=True)
for t in orderedBestCounter:
print("\t" + t[1] + ": " + str(t[0]))
def eval_one(step):
eval_features = []
for i in range(0, len(all_features)):
if step[i]:
eval_features.append(all_features[i])
all_observations = []
all_pred_combined = []
Y = []
P = []
for group in range(0, len(groups)):
train_stations, test_stations = generate_train_test_station_list(
group, groups)
train_station_set = set([float(s) for s in train_stations])
test_station_set = set([float(s) for s in test_stations])
train_lower = [
float(train_stations[i]) for i in range(0, len(train_stations))
if i < (len(train_stations) / 2.0)
]
train_lower_set = set(train_lower)
train_upper = [
float(train_stations[i]) for i in range(0, len(train_stations))
if i >= (len(train_stations) / 2.0)
]
train_upper_set = set(train_upper)
test_lower = [
float(test_stations[i]) for i in range(0, len(test_stations))
if i < (len(test_stations) / 2.0)
]
# tw_lower
trainX, testX, trainY, testY = splitDataForXValidation(
train_lower_set, test_station_set, "location", data, tw_features,
"target")
model = create_model()
model.fit(trainX, trainY)
prediction_lower = model.predict(testX)
# tw_upper
trainX, testX, trainY, testY = splitDataForXValidation(
train_upper_set, test_station_set, "location", data, tw_features,
"target")
model = create_model()
model.fit(trainX, trainY)
prediction_upper = model.predict(testX)
trainX, testX, trainY, testY, train_location, test_location = splitDataForXValidationWithLocation(
train_station_set, test_station_set, "location", data,
eval_features, "target")
train_label = generate_label(train_location, train_lower)
test_label = generate_label(test_location, test_lower)
model = create_classifier_model()
model.fit(trainX, train_label)
prediction_label = model.predict(testX)
pred_combined = generate_combined_prediction(prediction_label,
prediction_lower,
prediction_upper)
all_pred_combined.extend(pred_combined)
all_observations.extend(testY)
Y.extend(test_label)
P.extend(prediction_label)
rmse = rmseEval(all_observations, all_pred_combined)[1]
accuracy = accuracy_score(Y, P)
return rmse, accuracy
timestampData2.append(str(int(v)))
# modelling
for location in locations:
trainX, testX, trainY, testY, trainTimestamp, testTimestamp = splitDataForXValidation(
location, "location", data, featureTW, "target", timestampData)
print("\tT+W (on data without ATC) #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))
for i in range(0, len(testY)):
timestamp = testTimestamp[i]
value = prediction[i]
TWpredictionData[str(location)][timestamp] = value
trainX, testX, trainY, testY, trainTimestamp, testTimestamp = splitDataForXValidation(
location, "location", data2, featureTWAtc, "target", timestampData2)
print("\tT+W+Atc #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)
'winddirection', 'windspeed', 'temperature', 'rain', 'pressure'
]
for location in locations:
print("location: " + str(location))
# save down trainX, trainY, testX, testY
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, columns, "target")
print("\t#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)
testPrediction = model.predict(testX)
testRmse = str(rmseEval(testY, testPrediction)[1])
print("\tRFR+All rmse: " + str(testRmse))
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, columnsTW, "target")
print("\t#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)
testPrediction = model.predict(testX)
testRmse = str(rmseEval(testY, testPrediction)[1])
print("\tRFR+TW rmse: " + str(testRmse))
for sr in [0.95, 0.9, 0.85, 0.8, 0.75, 0.7]:
all_features, trainX2)
writeOutData(OUTPUT_DIRECTORY + "z_" + str(int(location)) + "_testX.csv",
all_features, testX)
writeOutData(OUTPUT_DIRECTORY + "z_" + str(int(location)) + "_trainY.csv",
["target"], trainY2)
writeOutData(OUTPUT_DIRECTORY + "z_" + str(int(location)) + "_testY.csv",
["target"], testY)
for dataGroup in generateAllDataGroups():
tag, features = getTagAndFeatures(dataGroup)
trainX1, trainX2, trainY1, trainY2, testX, testY = splitDataForXValidationSampled(
location, "location", sampleRate, 42, data, features, "target")
model = RandomForestRegressor(min_samples_leaf=9,
n_estimators=59,
n_jobs=-1,
random_state=42)
model.fit(trainX1, trainY1)
trainPrediction = model.predict(trainX2)
testPrediction = model.predict(testX)
trainRmse = str(rmseEval(trainY2, trainPrediction)[1])
testRmse = str(rmseEval(testY, testPrediction)[1])
print("\t" + tag + ": #train: " + str(len(trainY2)) + ", #test:" +
str(len(testY)) + ", trainRMSE: " + trainRmse + ", testRMSE: " +
testRmse)
writeOutData(
OUTPUT_DIRECTORY + "z_" + str(int(location)) + "_trainPred_" +
tag + ".csv", ["trainPred_" + tag], trainPrediction)
writeOutData(
OUTPUT_DIRECTORY + "z_" + str(int(location)) + "_testPred_" + tag +
".csv", ["testPred_" + tag], testPrediction)
output.write(
"location,timestamp,obs,pred_TW,pred_TWA,pred_combined,combined_uses_tw_twa\n"
)
output_log = open(OUTPUT_LOG_FILE, 'w')
for location in locations:
trainX, testX, trainY, testY, trainTimestamp, testTimestamp = splitDataForXValidation(
location, "location", data, tw_features, "target", timestampData)
model = RandomForestRegressor(min_samples_leaf=9,
n_estimators=59,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
testPredictionTW = model.predict(testX)
rmse = str(rmseEval(testY, testPredictionTW)[1])
log(output_log, "\tTW rmse: " + rmse)
for x in testY:
allObs.append(x)
for x in testPredictionTW:
allPredictionTW.append(x)
trainX, testX, trainY, testY, trainTimestamp, testTimestamp = splitDataForXValidation(
location, "location", data, twa_features, "target", timestampData)
model = RandomForestRegressor(min_samples_leaf=9,
n_estimators=59,
n_jobs=-1,
random_state=42)
model.fit(trainX, trainY)
testPredictionTWA = model.predict(testX)
rmse = str(rmseEval(testY, testPredictionTWA)[1])
log(output_log, "\tTWA rmse: " + rmse)
