本文整理汇总了Python中windml.datasets.nrel.NREL.get_target方法的典型用法代码示例。如果您正苦于以下问题:Python NREL.get_target方法的具体用法?Python NREL.get_target怎么用?Python NREL.get_target使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类windml.datasets.nrel.NREL
的用法示例。
在下文中一共展示了NREL.get_target方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_mreg_interpolation_multi
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
def test_mreg_interpolation_multi(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
count_neighbors = len(neighbors)
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 10, 'variant' : 'uniform'}
processed = 0
missed = {k : count_neighbors for k in indices}
exclude = []
damaged_nseries = []
for neighbor in neighbors:
nseries = neighbor.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(nseries, percentage=.50, exclude=exclude)
for index in indices:
if(index not in missed.keys()):
missed[index] = count_neighbors
missed[index] -= 1
if(missed[index] == 1):
exclude.append(index) # exclude in next iterations
damaged_nseries.append(damaged)
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=damaged_nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
示例2: compute_mse
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
def compute_mse(regressor, horizon):
# get wind park and corresponding target.
windpark = NREL().get_windpark(NREL.park_id['tehachapi'], 3, 2004, 2005)
target = windpark.get_target()
# use power mapping for pattern-label mapping.
feature_window = 3
mapping = PowerMapping()
X = mapping.get_features_park(windpark, feature_window, horizon)
y = mapping.get_labels_turbine(target, feature_window, horizon)
# train roughly for the year 2004, test for 2005.
train_to = int(math.floor(len(X) * 0.5))
test_to = len(X)
train_step, test_step = 25, 25
X_train=X[:train_to:train_step]
y_train=y[:train_to:train_step]
X_test=X[train_to:test_to:test_step]
y_test=y[train_to:test_to:test_step]
if(regressor == 'svr'):
reg = SVR(kernel='rbf', epsilon=0.1, C = 100.0,\
gamma = 0.0001).fit(X_train,y_train)
mse = mean_squared_error(reg.predict(X_test),y_test)
elif(regressor == 'knn'):
reg = KNeighborsRegressor(10, 'uniform').fit(X_train,y_train)
mse = mean_squared_error(reg.predict(X_test),y_test)
return mse
示例3: compute_mse
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
def compute_mse(regressor, param):
# get wind park and corresponding target. forecast is for the target
# turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
# use power mapping for pattern-label mapping. Feature window length
# is 3 time steps and time horizon (forecast) is 3 time steps.
feature_window = 6
horizon = 3
mapping = PowerMapping()
X = mapping.get_features_park(windpark, feature_window, horizon)
Y = mapping.get_labels_turbine(target, feature_window, horizon)
# train roughly for the year 2004.
train_to = int(math.floor(len(X) * 0.5))
# test roughly for the year 2005.
test_to = len(X)
# train and test only every fifth pattern, for performance.
train_step, test_step = 5, 5
if(regressor == 'rf'):
# random forest regressor
reg = RandomForestRegressor(n_estimators=param, criterion='mse')
reg = reg.fit(X[0:train_to:train_step], Y[0:train_to:train_step])
y_hat = reg.predict(X[train_to:test_to:test_step])
elif(regressor == 'knn'):
# TODO the regressor does not need to be newly trained in
# the case of KNN
reg = KNeighborsRegressor(param, 'uniform')
# fitting the pattern-label pairs
reg = reg.fit(X[0:train_to:train_step], Y[0:train_to:train_step])
y_hat = reg.predict(X[train_to:test_to:test_step])
else:
raise Exception("No regressor set.")
# naive is also known as persistence model.
naive_hat = zeros(len(y_hat), dtype = float32)
for i in range(0, len(y_hat)):
# naive label is the label as horizon time steps before.
# we have to consider to use only the fifth label here, too.
naive_hat[i] = Y[train_to + (i * test_step) - horizon]
# computing the mean squared errors of Linear and naive prediction.
mse_y_hat, mse_naive_hat = 0, 0
for i in range(0, len(y_hat)):
y = Y[train_to + (i * test_step)]
mse_y_hat += (y_hat[i] - y) ** 2
mse_naive_hat += (naive_hat[i] - y) ** 2
mse_y_hat /= float(len(y_hat))
mse_naive_hat /= float(len(y_hat))
return mse_y_hat, mse_naive_hat
示例4: test_backward_copy_interpolation
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
def test_backward_copy_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 10, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
t_hat = BackwardCopy().interpolate(measurements, timestep=timestep)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(measurements.shape[0] == t_hat.shape[0])
assert(len(after_misses) < 1)
示例5: test_mreg_interpolation
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
def test_mreg_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 10, 'variant' : 'uniform'}
nseries = [t.get_measurements()[300:500] for t in neighbors]
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
示例6: test_topological_interpolation
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
def test_topological_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 10, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = NMARDestroyer().destroy(measurements, percentage=.80,\
min_length=10, max_length=100)
tloc = (target.longitude, target.latitude)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:500] for t in neighbors]
nlocs = [(t.longitude, t.latitude) for t in neighbors]
t_hat = TopologicInterpolation().interpolate(\
damaged, method="topologic",\
timestep=timestep, location=tloc,\
neighbor_series = nseries,\
neighbor_locations = nlocs)
misses = MissingDataFinder().find(t_hat, timestep)
assert(measurements.shape[0] == t_hat.shape[0])
assert(len(misses) < 1)
示例7: NREL
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_target [as 别名]
# Stefan Oehmcke <[email protected]>
# License: BSD 3 clause
from __future__ import print_function
import math
import matplotlib.pyplot as plt
from numpy import zeros, float32
from windml.datasets.nrel import NREL
from windml.mapping.power_mapping import PowerMapping
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error
# get windpark and corresponding target. forecast is for the target turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004, 2005)
target = windpark.get_target()
# use power mapping for pattern-label mapping.
feature_window, horizon = 3, 3
mapping = PowerMapping()
X = mapping.get_features_park(windpark, feature_window, horizon)
y = mapping.get_labels_turbine(target, feature_window, horizon)
# train roughly for the year 2004, test roughly for the year 2005.
train_to, test_to = int(math.floor(len(X) * 0.5)), len(X)
# train and test only every fifth pattern, for performance.
train_step, test_step = 5, 5
X_train = X[:train_to:train_step]
y_train = y[:train_to:train_step]
X_test = X[train_to:test_to:test_step]
y_test = y[train_to:test_to:test_step]