def setUp(self):
# setting up our random data-set
rng = np.random.RandomState(42)
# D1 = train machines; D2 = create COBRA; D3 = calibrate epsilon, alpha; D4 = testing
n_features = 20
D1, D2, D3, D4 = 200, 200, 200, 200
D = D1 + D2 + D3 + D4
X = rng.uniform(-1, 1, D * n_features).reshape(D, n_features)
Y = np.power(X[:, 1], 2) + np.power(X[:, 3], 3) + np.exp(X[:, 10])
# training data-set
X_train = X[:D1 + D2]
X_test = X[D1 + D2 + D3:D1 + D2 + D3 + D4]
# for testing
Y_train = Y[:D1 + D2]
Y_test = Y[D1 + D2 + D3:D1 + D2 + D3 + D4]
cobra = Cobra(random_state=0, epsilon=0.5)
cobra.fit(X_train, Y_train)
ewa = Ewa(random_state=0)
ewa.fit(X_train, Y_train)
kernel = KernelCobra(random_state=0)
kernel.fit(X_train, Y_train)
self.test_data = X_test
self.cobra = cobra
self.ewa = ewa
self.kernelcobra = kernel
def setUp(self):
# setting up our random data-set
rng = np.random.RandomState(42)
# D1 = train machines; D2 = create COBRA; D3 = calibrate epsilon, alpha; D4 = testing
n_features = 20
D1, D2, D3, D4 = 200, 200, 200, 200
D = D1 + D2 + D3 + D4
X = rng.uniform(-1, 1, D * n_features).reshape(D, n_features)
Y = np.power(X[:,1], 2) + np.power(X[:,3], 3) + np.exp(X[:,10])
# training data-set
X_train = X[:D1 + D2]
X_test = X[D1 + D2 + D3:D1 + D2 + D3 + D4]
# for testing
Y_train = Y[:D1 + D2]
Y_test = Y[D1 + D2 + D3:D1 + D2 + D3 + D4]
cobra = Cobra(random_state=0, epsilon=0.5)
cobra.fit(X_train, Y_train)
self.test_data = X_test
self.test_response = Y_test
self.cobra = cobra
self.cobra_vis = Visualisation(self.cobra, self.test_data[0:4], self.test_response[0:4])
self.indices, self.mse = self.cobra_vis.indice_info(self.test_data[0:4], self.test_response[0:4], epsilon=self.cobra.epsilon)
ewa = Ewa(random_state=0)
ewa.fit(X_train, Y_train)
self.ewa = ewa
self.ewa_vis = Visualisation(self.ewa, self.test_data[0:4], self.test_response[0:4])
def optimal_alpha_grid(self, X, y, line_points=200, info=False):
"""
Find the optimal epsilon and alpha for a single query point for the COBRA predictor.
Parameteres
-----------
X: array-like, [n_features]
Vector for which we want optimal alpha and epsilon values
y: float
Target value for query to compare.
line_points: integer, optional
Number of epsilon values to traverse the grid.
info: bool, optional
Returns MSE dictionary for each epsilon/alpha value
Returns
-------
MSE: dictionary mapping (alpha, epsilon) with mean squared errors
opt: optimal epislon/alpha combination
"""
# code to find maximum and minimum distance between predictions to create grid
a, size = sorted(self.aggregate.all_predictions_), len(
self.aggregate.all_predictions_)
res = [a[i + 1] - a[i] for i in range(size) if i + 1 < size]
emin = min(res)
emax = max(a) - min(a)
erange = np.linspace(emin, emax, line_points)
n_machines = np.arange(1, len(self.aggregate.estimators_) + 1)
MSE = {}
# looping over epsilon and alpha values
for epsilon in erange:
for num in n_machines:
machine = Cobra(random_state=self.random_state,
epsilon=epsilon)
machine.fit(self.aggregate.X_, self.aggregate.y_)
result = machine.predict(X.reshape(1, -1), alpha=num)
MSE[(num, epsilon)] = np.square(y - result)
if info:
return MSE
opt = min(MSE, key=MSE.get)
return opt, MSE[opt]
def optimal_epsilon(self, X, y, line_points=200, info=False):
"""
Find the optimal epsilon value for the COBRA predictor.
Parameteres
-----------
X: array-like, [n_features]
Vector for which we want for optimal epsilon.
y: float
Target value for query to compare.
line_points: integer, optional
Number of epsilon values to traverse the grid.
info: bool, optional
Returns MSE dictionary for each epsilon value.
Returns
-------
MSE: dictionary mapping epsilon with mean squared errors
opt: optimal epsilon value
"""
a, size = sorted(self.aggregate.all_predictions_), len(
self.aggregate.all_predictions_)
res = [a[i + 1] - a[i] for i in range(size) if i + 1 < size]
emin = min(res)
emax = max(a) - min(a)
erange = np.linspace(emin, emax, line_points)
MSE = {}
for epsilon in erange:
machine = Cobra(random_state=self.random_state, epsilon=epsilon)
machine.fit(self.aggregate.X_, self.aggregate.y_)
results = machine.predict(X)
MSE[epsilon] = (mean_squared_error(y, results))
if info:
return MSE
opt = min(MSE, key=MSE.get)
return opt, MSE[opt]
def set_up():
# setting up our random data-set
rng = np.random.RandomState(42)
# D1 = train machines; D2 = create COBRA; D3 = calibrate epsilon, alpha; D4 = testing
n_features = 20
D1, D2, D3, D4 = 200, 200, 200, 200
D = D1 + D2 + D3 + D4
X = rng.uniform(-1, 1, D * n_features).reshape(D, n_features)
Y = np.power(X[:, 1], 2) + np.power(X[:, 3], 3) + np.exp(X[:, 10])
# training data-set
X_train = X[:D1 + D2]
X_test = X[D1 + D2 + D3:D1 + D2 + D3 + D4]
# for testing
Y_train = Y[:D1 + D2]
Y_test = Y[D1 + D2 + D3:D1 + D2 + D3 + D4]
cobra = Cobra(random_state=0, epsilon=0.5)
cobra.fit(X_train, Y_train)
test_data = X_test
test_response = Y_test
cobra_vis = Visualisation(cobra, test_data[0:4], test_response[0:4])
return cobra_vis
def optimal_alpha(self, X, y, single=False, epsilon=None, info=False):
"""
Find the optimal alpha for testing data for the COBRA predictor.
Parameteres
-----------
X: array-like, [n_features]
Vector for which we want optimal alpha values
y: float
Target value for query to compare.
single: boolean, optional
Option to calculate optimal alpha for a single query point instead.
info: bool, optional
Returns MSE dictionary for each alpha value
epsilon: float, optional
fixed epsilon value to help determine optimal alpha.
Returns
-------
MSE: dictionary mapping alpha with mean squared errors
opt: optimal alpha combination
"""
if epsilon is None:
epsilon = self.aggregate.epsilon
MSE = {}
for alpha in range(1, len(self.aggregate.estimators_) + 1):
machine = Cobra(random_state=self.random_state, epsilon=epsilon)
machine.fit(self.aggregate.X_, self.aggregate.y_)
# for a single data point
if single:
result = machine.predict(X, alpha=alpha)
MSE[alpha] = np.square(y - result)
else:
results = machine.predict(X, alpha=alpha)
MSE[alpha] = (mean_squared_error(y, results))
if info:
return MSE
opt = min(MSE, key=MSE.get)
return opt, MSE[opt]
def define_cobra_model(train_names,
training_noise_kind,
patch_size=1,
optimi=True,
verbose=False):
"""
Train a cobra model for denoising task
INPUT :
train_names : list containing the name of images used to train the model
patch_size : use patch of size (2*patch_size+1)*(2*patch_size+1) as features
verbose : print or not information during the training
OUTPUT :
cobra : trained model
"""
#initial cobra parameters
Alpha = 4 #how many machines must agree
Epsilon = 0.2 # confidence parameter
print("Training cobra model...")
Xtrain, Xtrain1, Xtrain2, Ytrain = load_training_data(
train_names, training_noise_kind, patch_size)
cobra = Cobra(epsilon=Epsilon, machines=Alpha) # create a cobra machine
#cobra.fit(Xtrain, Ytrain, default=False, X_k=Xtrain1, X_l=Xtrain2, y_k=Ytrain, y_l=Ytrain) # fit the cobra machine with our data
cobra.fit(Xtrain, Ytrain) # fit the cobra machine with our data
print("Loading machines...")
cobra.load_machine('bilateral', machine('bilateral', 0, patch_size))
cobra.load_machine('nlmeans', machine('nlmeans', 1, patch_size))
cobra.load_machine('gauss', machine('gauss', 2, patch_size))
cobra.load_machine('median', machine('median', 3, patch_size))
cobra.load_machine('TVchambolle', machine('TVchambolle', 4, patch_size))
cobra.load_machine('richardson_lucy',
machine('richardson_lucy', 5, patch_size))
cobra.load_machine('inpainting', machine('inpainting', 6, patch_size))
cobra.load_machine('ksvd', machine('ksvd', 7, patch_size))
cobra.load_machine('lee', machine('lee', 8, patch_size))
# cobra.load_machine('bm3d', machine('bm3d', 9, patch_size))
print("Loading machine predictions...")
cobra.load_machine_predictions() #agregate
if verbose:
print(cobra.machine_predictions_)
if optimi:
print("Parameter optimisation")
cobra_diagnostics = Diagnostics(cobra, Xtrain, Ytrain)
Epsilon_opt, MSE = cobra_diagnostics.optimal_epsilon(Xtrain,
Ytrain,
line_points=100,
info=False)
Alpha_opt, MSE = cobra_diagnostics.optimal_alpha(Xtrain,
Ytrain,
epsilon=Epsilon_opt,
info=False)
if verbose:
print("epsilon = ", Epsilon_opt)
print("alpha = ", Alpha_opt)
print("Training cobra model again...")
cobra = Cobra(epsilon=Epsilon_opt, machines=Alpha_opt)
cobra.fit(Xtrain,
Ytrain,
default=False,
X_k=Xtrain1,
X_l=Xtrain2,
y_k=Ytrain,
y_l=Ytrain)
cobra.load_machine('bilateral', machine('bilateral', 0, patch_size))
cobra.load_machine('nlmeans', machine('nlmeans', 1, patch_size))
cobra.load_machine('gauss', machine('gauss', 2, patch_size))
cobra.load_machine('median', machine('median', 3, patch_size))
cobra.load_machine('TVchambolle', machine('TVchambolle', 4,
patch_size))
cobra.load_machine('richardson_lucy',
machine('richardson_lucy', 5, patch_size))
cobra.load_machine('inpainting', machine('inpainting', 6, patch_size))
cobra.load_machine('ksvd', machine('ksvd', 7, patch_size))
cobra.load_machine('lee', machine('lee', 8, patch_size))
# cobra.load_machine('bm3d', machine('bm3d', 9, patch_size))
cobra.load_machine_predictions()
if verbose:
print("Loading machine predictions...")
print(cobra.machine_predictions_)
return (cobra, Alpha, Epsilon)
def boxplot(self, reps=100, info=False):
"""
Plots boxplots of machines.
Parameters
----------
reps: int, optional
Number of times to repeat experiments for boxplot.
info: boolean, optional
Returns data
"""
if type(self.aggregate) is Cobra:
MSE = {k: [] for k, v in self.aggregate.machines_.items()}
MSE["COBRA"] = []
for i in range(0, reps):
cobra = Cobra(random_state=self.random_state, epsilon=self.aggregate.epsilon)
X, y = shuffle(self.aggregate.X_, self.aggregate.y_, random_state=self.aggregate.random_state)
cobra.fit(X, y, default=False)
cobra.split_data(shuffle_data=True)
for machine in self.aggregate.machines_:
self.aggregate.machines_[machine].fit(cobra.X_k_, cobra.y_k_)
cobra.load_machine(machine, self.aggregate.machines_[machine])
cobra.load_machine_predictions()
X_test, y_test = shuffle(self.X_test, self.y_test, random_state=self.aggregate.random_state)
for machine in cobra.machines_:
MSE[machine].append(mean_squared_error(y_test, cobra.machines_[machine].predict(X_test)))
MSE["COBRA"].append(mean_squared_error(y_test, cobra.predict(X_test)))
data, labels = [], []
for machine in MSE:
data.append(MSE[machine])
labels.append(machine)
if type(self.aggregate) is Ewa:
MSE = {k: [] for k, v in self.aggregate.machines_.items()}
MSE["EWA"] = []
for i in range(0, reps):
ewa = Ewa(random_state=self.random_state, beta=self.aggregate.beta)
X, y = shuffle(self.aggregate.X_, self.aggregate.y_, random_state=self.aggregate.random_state)
ewa.fit(X, y, default=False)
ewa.split_data(shuffle_data=True)
for machine in self.aggregate.machines_:
self.aggregate.machines_[machine].fit(ewa.X_k_, ewa.y_k_)
ewa.load_machine(machine, self.aggregate.machines_[machine])
ewa.load_machine_weights(self.aggregate.beta)
X_test, y_test = shuffle(self.X_test, self.y_test, random_state=self.aggregate.random_state)
for machine in ewa.machines_:
MSE[machine].append(mean_squared_error(y_test, ewa.machines_[machine].predict(X_test)))
MSE["EWA"].append(mean_squared_error(y_test, ewa.predict(X_test)))
data, labels = [], []
for machine in MSE:
data.append(MSE[machine])
labels.append(machine)
if type(self.aggregate) is ClassifierCobra:
errors = {k: [] for k, v in self.aggregate.machines_.items()}
errors["ClassifierCobra"] = []
for i in range(0, reps):
cc = ClassifierCobra(random_state=self.random_state)
X, y = shuffle(self.aggregate.X_, self.aggregate.y_, random_state=self.aggregate.random_state)
cc.fit(X, y, default=False)
cc.split_data(shuffle_data=True)
for machine in self.aggregate.machines_:
self.aggregate.machines_[machine].fit(cc.X_k_, cc.y_k_)
cc.load_machine(machine, self.aggregate.machines_[machine])
cc.load_machine_predictions()
X_test, y_test = shuffle(self.X_test, self.y_test, random_state=self.aggregate.random_state)
for machine in cc.machines_:
errors[machine].append(1 - accuracy_score(y_test, cc.machines_[machine].predict(X_test)))
errors["ClassifierCobra"].append(1 - accuracy_score(y_test, cc.predict(X_test)))
data, labels = [], []
for machine in errors:
data.append(errors[machine])
labels.append(machine)
plt.figure(figsize=(self.plot_size, self.plot_size))
plt.boxplot(data, labels=labels)
plt.show()
if info:
return data
D = D1 + D2 + D3 + D4 X = rng.uniform(-1, 1, D * n_features).reshape(D, n_features) # Y = np.power(X[:,1], 2) + np.power(X[:,3], 3) + np.exp(X[:,10]) Y = np.power(X[:,0], 2) + np.power(X[:,1], 3) # training data-set X_train = X[:D1 + D2] X_test = X[D1 + D2 + D3:D1 + D2 + D3 + D4] X_eps = X[D1 + D2:D1 + D2 + D3] # for testing Y_train = Y[:D1 + D2] Y_test = Y[D1 + D2 + D3:D1 + D2 + D3 + D4] Y_eps = Y[D1 + D2:D1 + D2 + D3] # set up our COBRA machine with the data cobra = Cobra(epsilon=0.5) cobra.fit(X_train, Y_train) ###################################################################### # Plotting COBRA # ~~~~~~~~~~~~~~ # # We use the visualisation class to plot our results, and for various # visualisations. # cobra_vis = Visualisation(cobra, X_test, Y_test) # to plot our machines, we need a linspace as input. This is the 'scale' to plot and should be the range of the results # since our data ranges from -1 to 1 it is such - and we space it out to a hundred points
X_eps = X[D1 + D2:D1 + D2 + D3] # for testing Y_train = Y[:D1 + D2] Y_test = Y[D1 + D2 + D3:D1 + D2 + D3 + D4] Y_eps = Y[D1 + D2:D1 + D2 + D3] ###################################################################### # Similar to other scikit-learn estimators, we set up our machine by # creating an object and then fitting it. Since we are not passing an # Epsilon value, we pass data to find an optimal epsilon value while # instantiating our object. The optimal epsilon is found through the # scikit-learn ``CVGridSearch``. The ``grid_points`` parameter decides how # many possible epsilon values must be traversed. # cobra = Cobra() cobra.set_epsilon(X_epsilon=X_eps, y_epsilon=Y_eps, grid_points=5) cobra.epsilon cobra.fit(X_train, Y_train) ###################################################################### # We now see if our object can fit into the scikit-learn pipeline and # GridSearch - and it can! # from sklearn.utils.estimator_checks import check_estimator # check_estimator(Cobra) #passes
def optimal_split(self,
X,
y,
split=None,
epsilon=None,
info=False,
graph=False):
"""
Find the optimal combination split (D_k, D_l) for fixed epsilon value for the COBRA predictor.
Parameteres
-----------
X: array-like, [n_features]
Vector for which we want for optimal split.
y: float
Target value for query to compare.
epsilon: float, optional.
fixed epsilon value to help determine optimal machines.
split: list, optional.
D_k, D_l break-up to calculate MSE
info: bool, optional.
Returns MSE dictionary for each split.
graph: bool, optional.
Plots graph of MSE vs split
Returns
-------
MSE: dictionary mapping split with mean squared errors
opt: optimal epsilon value
"""
if epsilon is None:
epsilon = self.aggregate.epsilon
if split is None:
split = [(0.20, 0.80), (0.40, 0.60), (0.50, 0.50), (0.60, 0.40),
(0.80, 0.20)]
MSE = {}
for k, l in split:
machine = Cobra(random_state=self.random_state, epsilon=epsilon)
machine.fit(self.aggregate.X_, self.aggregate.y_, default=False)
machine.split_data(int(k * len(self.aggregate.X_)),
int((k + l) * len(self.aggregate.X_)))
machine.load_default()
machine.load_machine_predictions()
results = machine.predict(X)
MSE[(k, l)] = (mean_squared_error(y, results))
if graph:
import matplotlib.pyplot as plt
ratio, mse = [], []
for value in split:
ratio.append(value[0])
mse.append(MSE[value])
plt.plot(ratio, mse)
if info:
return MSE
opt = min(MSE, key=MSE.get)
return opt, MSE[opt]
def optimal_machines(self, X, y, single=False, epsilon=None, info=False):
"""
Find the optimal combination of machines for testing data for the COBRA predictor.
Parameteres
-----------
X: array-like, [n_features]
Vector for which we want optimal machine combinations.
y: float
Target value for query to compare.
single: boolean, optional
Option to calculate optimal machine combinations for a single query point instead.
info: bool, optional
Returns MSE dictionary for each machine combination value
epsilon: float, optional
fixed epsilon value to help determine optimal machines.
Returns
-------
MSE: dictionary mapping machines with mean squared errors
opt: optimal machines combination
"""
if epsilon is None:
epsilon = self.aggregate.epsilon
n_machines = np.arange(1, len(self.aggregate.estimators_) + 1)
MSE = {}
for num in n_machines:
machine_names = self.aggregate.estimators_.keys()
use = list(itertools.combinations(machine_names, num))
for combination in use:
machine = Cobra(random_state=self.random_state,
epsilon=epsilon)
machine.fit(self.aggregate.X_,
self.aggregate.y_,
default=False)
machine.split_data()
machine.load_default(machine_list=combination)
machine.load_machine_predictions()
if single:
result = machine.predict(X.reshape(1, -1))
MSE[combination] = np.square(y - result)
else:
results = machine.predict(X)
MSE[combination] = (mean_squared_error(y, results))
if info:
return MSE
opt = min(MSE, key=MSE.get)
return opt, MSE[opt]
def cobraModelInit(trainNames, noiseType, imShape, patchSize=1, best=True):
"""
Initalise and train cobra mode
"""
print("Making training data ready")
trainingData, trainingData1, trainingData2, testingData = loadTrainingData(
trainNames, noiseType, patchSize)
denoisemethods = denoiseMethods()
epsilon = 0.2
machines = 3
cobra = Cobra(epsilon=epsilon, machines=machines)
print("Training model")
cobra.fit(trainingData, testingData)
for i, denoise in enumerate(denoisemethods):
cobra.load_machine(denoise, CobraMachine(denoise, patchSize))
cobra.load_machine_predictions()
# print("Predictions:", cobra.machine_predictions_)
if best:
print("Running Diagnostics")
cobra_diagnostics = Diagnostics(cobra,
trainingData,
testingData,
load_MSE=False)
print("epsilon")
epsilon, _ = cobra_diagnostics.optimal_epsilon(trainingData,
testingData,
line_points=100,
info=False)
print("machines")
machines, _ = cobra_diagnostics.optimal_alpha(trainingData,
testingData,
epsilon=epsilon,
info=False)
cobra = Cobra(epsilon=epsilon, machines=machines)
print("fit")
cobra.fit(trainingData,
testingData,
default=False,
X_k=trainingData1,
X_l=trainingData2,
y_k=testingData,
y_l=testingData)
for i, denoise in enumerate(denoisemethods):
cobra.load_machine(denoise, CobraMachine(denoise, patchSize))
cobra.load_machine_predictions()
# print("Predictions:", cobra.machine_predictions_)
return cobra, machines, epsilon
def optimal_machines_grid(self, X, y, line_points=200, info=False):
"""
Find the optimal epsilon and machine-combination for a single query point for the COBRA predictor.
Parameteres
-----------
X: array-like, [n_features]
Vector for which we want optimal machines and epsilon values
y: float
Target value for query to compare.
line_points: integer, optional
Number of epsilon values to traverse the grid.
info: bool, optional
Returns MSE dictionary for each epsilon/machine value.
Returns
-------
MSE: dictionary mapping (machine combination, epsilon) with mean squared errors
opt: optimal epislon/machine combination
"""
# code to find maximum and minimum distance between predictions to create grid
a, size = sorted(self.aggregate.all_predictions_), len(
self.aggregate.all_predictions_)
res = [a[i + 1] - a[i] for i in range(size) if i + 1 < size]
emin = min(res)
emax = max(a) - min(a)
erange = np.linspace(emin, emax, line_points)
n_machines = np.arange(1, len(self.aggregate.machines_) + 1)
MSE = {}
for epsilon in erange:
for num in n_machines:
machine_names = self.aggregate.machines_.keys()
use = list(itertools.combinations(machine_names, num))
for combination in use:
machine = Cobra(random_state=self.random_state,
epsilon=epsilon)
machine.fit(self.aggregate.X_,
self.aggregate.y_,
default=False)
machine.split_data()
machine.load_default(machine_list=combination)
machine.load_machine_predictions()
result = machine.predict(X.reshape(1, -1))
MSE[(combination, epsilon)] = np.square(y - result)
if info:
return MSE
opt = min(MSE, key=MSE.get)
return opt, MSE[opt]
def boxplot(self, reps=100, info=False, dataframe=None, kind="normal"):
"""
Plots boxplots of machines.
Parameters
----------
reps: int, optional
Number of times to repeat experiments for boxplot.
info: boolean, optional
Returns data
"""
kwargs = self.kwargs
if dataframe is None:
if type(self.aggregate) is Cobra:
MSE = {k: [] for k, v in self.estimators.items()}
MSE["Cobra"] = []
for i in range(0, reps):
cobra = Cobra(epsilon=self.aggregate.epsilon)
X, y = shuffle(self.aggregate.X_, self.aggregate.y_)
cobra.fit(X, y, default=False)
cobra.split_data(shuffle_data=True)
for machine in self.aggregate.estimators_:
self.aggregate.estimators_[machine].fit(cobra.X_k_, cobra.y_k_)
cobra.load_machine(machine, self.aggregate.estimators_[machine])
cobra.load_machine_predictions()
for machine in self.estimators:
if "Cobra" in machine:
self.estimators[machine].fit(X, y)
else:
self.estimators[machine].fit(cobra.X_k_, cobra.y_k_)
try:
if type(self.estimators[machine]) == KernelCobra:
preds = self.estimators[machine].predict(self.X_test, bandwidth=kwargs["bandwidth_kernel"])
else:
preds = self.estimators[machine].predict(self.X_test)
except KeyError:
preds = self.estimators[machine].predict(self.X_test)
MSE[machine].append(mean_squared_error(self.y_test, preds))
MSE["Cobra"].append(mean_squared_error(self.y_test, cobra.predict(self.X_test)))
try:
dataframe = pd.DataFrame(data=MSE)
except ValueError:
return MSE
if type(self.aggregate) is KernelCobra:
MSE = {k: [] for k, v in self.estimators.items()}
MSE["KernalCobra"] = []
for i in range(0, reps):
kernel = KernelCobra()
X, y = shuffle(self.aggregate.X_, self.aggregate.y_)
kernel.fit(X, y, default=False)
kernel.split_data(shuffle_data=True)
for machine in self.aggregate.estimators_:
self.aggregate.estimators_[machine].fit(kernel.X_k_, kernel.y_k_)
kernel.load_machine(machine, self.aggregate.estimators_[machine])
kernel.load_machine_predictions()
for machine in self.estimators:
if "Cobra" in machine:
self.estimators[machine].fit(X, y)
else:
self.estimators[machine].fit(cobra.X_k_, cobra.y_k_)
try:
if type(self.estimators[machine]) == KernelCobra:
preds = self.estimators[machine].predict(self.X_test, bandwidth=kwargs["bandwidth_kernel"])
else:
preds = self.estimators[machine].predict(self.X_test)
except KeyError:
preds = self.estimators[machine].predict(self.X_test)
MSE[machine].append(mean_squared_error(self.y_test, preds))
MSE["KernelCobra"].append(mean_squared_error(self.y_test, kernel.predict(self.X_test, bandwidth=kwargs[bandwidth_kernel])))
try:
dataframe = pd.DataFrame(data=MSE)
except ValueError:
return MSE
if type(self.aggregate) is Ewa:
MSE = {k: [] for k, v in self.aggregate.estimators_.items()}
MSE["EWA"] = []
for i in range(0, reps):
ewa = Ewa(random_state=self.random_state, beta=self.aggregate.beta)
X, y = shuffle(self.aggregate.X_, self.aggregate.y_, random_state=self.aggregate.random_state)
ewa.fit(X, y, default=False)
ewa.split_data(shuffle_data=True)
for machine in self.estimators:
self.aggregate.estimators_[machine].fit(ewa.X_k_, ewa.y_k_)
ewa.load_machine(machine, self.aggregate.estimators_[machine])
ewa.load_machine_weights(self.aggregate.beta)
X_test, y_test = shuffle(self.X_test, self.y_test, random_state=self.aggregate.random_state)
for machine in self.estimators:
if "EWA" in machine:
self.estimators[machine].fit(X, y)
else:
self.estimators[machine].fit(ewa.X_k_, ewa.y_k_)
try:
if type(self.estimators[machine]) == KernelCobra:
preds = self.estimators[machine].predict(self.X_test, bandwidth=kwargs["bandwidth_kernel"])
else:
preds = self.estimators[machine].predict(self.X_test)
except KeyError:
preds = self.estimators[machine].predict(self.X_test)
MSE[machine].append(mean_squared_error(y_test, preds))
MSE["EWA"].append(mean_squared_error(y_test, ewa.predict(X_test)))
try:
dataframe = pd.DataFrame(data=MSE)
except ValueError:
return MSE
if type(self.aggregate) is ClassifierCobra:
errors = {k: [] for k, v in self.aggregate.estimators_.items()}
errors["ClassifierCobra"] = []
for i in range(0, reps):
cc = ClassifierCobra(random_state=self.random_state)
X, y = shuffle(self.aggregate.X_, self.aggregate.y_, random_state=self.aggregate.random_state)
cc.fit(X, y, default=False)
cc.split_data(shuffle_data=True)
for machine in self.aggregate.estimators_:
self.aggregate.estimators_[machine].fit(cc.X_k_, cc.y_k_)
cc.load_machine(machine, self.aggregate.estimators_[machine])
cc.load_machine_predictions()
X_test, y_test = shuffle(self.X_test, self.y_test, random_state=self.aggregate.random_state)
for machine in self.estimators:
errors[machine].append(1 - accuracy_score(y_test, self.estimators[machine].predict(X_test)))
errors["ClassifierCobra"].append(1 - accuracy_score(y_test, cc.predict(X_test)))
try:
dataframe = pd.DataFrame(data=errors)
except ValueError:
return errors
# code for different boxplot styles using the python graph gallery tutorial:
# https://python-graph-gallery.com/39-hidden-data-under-boxplot/
sns.set(style="whitegrid")
if kind == "normal":
sns.boxplot(data=dataframe)
plt.title("Boxplot")
if kind == "violin":
sns.violinplot(data=dataframe)
plt.title("Violin Plot")
if kind == "jitterplot":
ax = sns.boxplot(data=dataframe)
ax = sns.stripplot(data=dataframe, color="orange", jitter=0.2, size=2.5)
plt.title("Boxplot with jitter", loc="left")
plt.ylabel("Mean Squared Errors")
plt.xlabel("Estimators")
plt.figure(figsize=(self.plot_size, self.plot_size))
plt.show()
if info:
return dataframe
X_train = X[:D1 + D2] X_test = X[D1 + D2 + D3:D1 + D2 + D3 + D4] X_eps = X[D1 + D2:D1 + D2 + D3] # for testing Y_train = Y[:D1 + D2] Y_test = Y[D1 + D2 + D3:D1 + D2 + D3 + D4] Y_eps = Y[D1 + D2:D1 + D2 + D3] ###################################################################### # Setting up COBRA # ~~~~~~~~~~~~~~~~ # # Let's up our COBRA machine with the data. # cobra = Cobra(random_state=0, epsilon=0.5) cobra.fit(X_train, Y_train, default=False) ###################################################################### # When we are fitting, we initialise COBRA with an epsilon value of # :math:`0.5` - this is because we are aware of the distribution and 0.5 # is a fair guess of what would be a "good" epsilon value, because the # data varies from :math:`-1` to :math:`1`. # # If we do not pass the :math:`\epsilon` parameter, we perform a CV on the # training data for an optimised epsilon. # # It can be noticed that the ``default`` parameter is set as false: this # is so we can walk you through what happens when COBRA is set-up, instead # of the deafult settings being used. #
