def get_pred_surface(self, rh, X_scaled, conf_list: list,
contour_step_size):
"""fit epm on the scaled input dimension and
return data to plot a contour plot of the empirical performance
Parameters
----------
rh: RunHistory
runhistory
X_scaled: np.array
configurations in scaled 2dim
conf_list: list
list of Configuration objects
Returns
-------
contour_data: (np.array, np.array, np.array)
x, y, Z for contour plots
"""
# use PCA to reduce features to also at most 2 dims
scen = copy.deepcopy(self.scenario) # pca changes feats
if scen.feature_array.shape[1] > 2:
self.logger.debug(
"Use PCA to reduce features to from %d dim to 2 dim",
scen.feature_array.shape[1])
# perform PCA
insts = scen.feature_dict.keys()
feature_array = np.array([scen.feature_dict[i] for i in insts])
feature_array = StandardScaler().fit_transform(feature_array)
feature_array = PCA(n_components=2).fit_transform(feature_array)
# inject in scenario-object
scen.feature_array = feature_array
scen.feature_dict = dict([(inst, feature_array[idx, :])
for idx, inst in enumerate(insts)])
scen.n_features = 2
# convert the data to train EPM on 2-dim featurespace (for contour-data)
self.logger.debug("Convert data for epm.")
X, y, types = convert_data_for_epm(scenario=scen,
runhistory=rh,
logger=self.logger)
types = np.array(np.zeros((2 + scen.feature_array.shape[1])),
dtype=np.uint)
num_params = len(scen.cs.get_hyperparameters())
# impute missing values in configs and insert MDS'ed (2dim) configs to the right positions
conf_dict = {}
for idx, c in enumerate(conf_list):
conf_list[idx] = impute_inactive_values(c)
conf_dict[str(conf_list[idx].get_array())] = X_scaled[idx, :]
X_trans = []
for x in X:
x_scaled_conf = conf_dict[str(x[:num_params])]
# append scaled config + pca'ed features (total of 4 values) per config/feature-sample
X_trans.append(
np.concatenate((x_scaled_conf, x[num_params:]), axis=0))
X_trans = np.array(X_trans)
self.logger.debug("Train random forest for contour-plot.")
bounds = np.array([(0, np.nan), (0, np.nan)], dtype=object)
model = RandomForestWithInstances(types=types,
bounds=bounds,
instance_features=np.array(
scen.feature_array),
ratio_features=1.0)
start = time.time()
model.train(X_trans, y)
self.logger.debug("Fitting random forest took %f time",
time.time() - start)
x_min, x_max = X_scaled[:, 0].min() - 1, X_scaled[:, 0].max() + 1
y_min, y_max = X_scaled[:, 1].min() - 1, X_scaled[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, contour_step_size),
np.arange(y_min, y_max, contour_step_size))
self.logger.debug("x_min: %f, x_max: %f, y_min: %f, y_max: %f", x_min,
x_max, y_min, y_max)
self.logger.debug(
"Predict on %d samples in grid to get surface (step-size: %f)",
np.c_[xx.ravel(), yy.ravel()].shape[0], contour_step_size)
start = time.time()
Z, _ = model.predict_marginalized_over_instances(np.c_[xx.ravel(),
yy.ravel()])
Z = Z.reshape(xx.shape)
self.logger.debug("Predicting random forest took %f time",
time.time() - start)
return xx, yy, Z
def get_pred_surface(self, rh, X_scaled, conf_list: list,
contour_step_size):
"""fit epm on the scaled input dimension and
return data to plot a contour plot of the empirical performance
Parameters
----------
rh: RunHistory
runhistory
X_scaled: np.array
configurations in scaled 2dim
conf_list: list
list of Configuration objects
contour_step_size: float
step-size for contour
Returns
-------
contour_data: (np.array, np.array, np.array)
x, y, Z for contour plots
"""
# use PCA to reduce features to also at most 2 dims
scen = copy.deepcopy(self.scenario) # pca changes feats
if scen.feature_array.shape[1] > 2:
self.logger.debug(
"Use PCA to reduce features to from %d dim to 2 dim",
scen.feature_array.shape[1])
# perform PCA
insts = scen.feature_dict.keys()
feature_array = np.array([scen.feature_dict[i] for i in insts])
feature_array = StandardScaler().fit_transform(feature_array)
feature_array = PCA(n_components=2).fit_transform(feature_array)
# inject in scenario-object
scen.feature_array = feature_array
scen.feature_dict = dict([(inst, feature_array[idx, :])
for idx, inst in enumerate(insts)])
scen.n_features = 2
# convert the data to train EPM on 2-dim featurespace (for contour-data)
self.logger.debug("Convert data for epm.")
X, y, types = convert_data_for_epm(scenario=scen,
runhistory=rh,
impute_inactive_parameters=True,
logger=self.logger)
types = np.array(np.zeros((2 + scen.feature_array.shape[1])),
dtype=np.uint)
num_params = len(scen.cs.get_hyperparameters())
# impute missing values in configs and insert MDS'ed (2dim) configs to the right positions
conf_dict = {}
# Remove forbidden clauses (this is necessary to enable the impute_inactive_values-method, see #226)
cs_no_forbidden = copy.deepcopy(conf_list[0].configuration_space)
cs_no_forbidden.forbidden_clauses = []
for idx, c in enumerate(conf_list):
c.configuration_space = cs_no_forbidden
conf_list[idx] = impute_inactive_values(c)
conf_dict[str(conf_list[idx].get_array())] = X_scaled[idx, :]
# Debug compare elements:
c1, c2 = {str(z) for z in X}, {str(z) for z in conf_dict.keys()}
self.logger.debug(
"{} elements not in both sets, {} elements in both sets, X (len {}) and conf_dict (len {}) "
"(might be a problem related to forbidden clauses?)".format(
len(c1 ^ c2), len(c1 & c2), len(c1 ^ c2), len(c1), len(c2)))
# self.logger.debug("Elements: {}".format(str(c1 ^ c2)))
X_trans = [
] # X_trans is the same as X but with reduced 2-dim features (so shape is (N, 2) instead of (N, M))
for x in X:
x_scaled_conf = conf_dict[str(x[:num_params])]
# append scaled config + pca'ed features (total of 4 values) per config/feature-sample
X_trans.append(
np.concatenate((x_scaled_conf, x[num_params:]), axis=0))
X_trans = np.array(X_trans)
self.logger.debug(
"Train random forest for contour-plot. Shape of X: {}, shape of X_trans: {}"
.format(X.shape, X_trans.shape))
self.logger.debug("Faking configspace to be able to train rf...")
# We need to fake config-space bypass imputation of inactive values in random forest implementation
fake_cs = ConfigurationSpace(name="fake-cs-for-configurator-footprint")
bounds = np.array([(0, np.nan), (0, np.nan)], dtype=object)
model = RandomForestWithInstances(fake_cs,
types,
bounds,
seed=self.rng.randint(MAXINT),
instance_features=np.array(
scen.feature_array),
ratio_features=1.0)
start = time.time()
model.train(X_trans, y)
self.logger.debug("Fitting random forest took %f time",
time.time() - start)
x_min, x_max = X_scaled[:, 0].min() - 1, X_scaled[:, 0].max() + 1
y_min, y_max = X_scaled[:, 1].min() - 1, X_scaled[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, contour_step_size),
np.arange(y_min, y_max, contour_step_size))
self.logger.debug("x_min: %f, x_max: %f, y_min: %f, y_max: %f", x_min,
x_max, y_min, y_max)
self.logger.debug(
"Predict on %d samples in grid to get surface (step-size: %f)",
np.c_[xx.ravel(), yy.ravel()].shape[0], contour_step_size)
start = time.time()
Z, _ = model.predict_marginalized_over_instances(np.c_[xx.ravel(),
yy.ravel()])
Z = Z.reshape(xx.shape)
self.logger.debug("Predicting random forest took %f time",
time.time() - start)
return xx, yy, Z