### cross-val: ####
parameters = {
'max_depth': [1000],
'max_samples': [.1],
'max_features': [min(10, n_features)],
'n_estimators': [50]
}
model = OneClassRF()
clf = grid_search.GridSearchCV(model, parameters, refit=False, cv=2)
clf.fit(X_train, y_train)
print('clf.best_params_', clf.best_params_)
model.set_params(**clf.best_params_)
print('OneClassRF processing...')
# weird: without CV but with same parameters, no error:
#model = OneClassRF(max_depth=1000, max_samples=.1, max_features=min(10, n_features), n_estimators=50, random_state=rng, n_jobs=-1) #commented since cross val
tstart = time()
### training only on normal data:
X_train = X_train[y_train == 0]
y_train = y_train[y_train == 0]
model.fit(X_train)
fit_time = time() - tstart
tstart = time()
scoring = model.predict(X_test) # the lower, the more normal
predict_time = time() - tstart
fpr, tpr, thresholds = roc_curve(y_test, scoring)
AUC = auc(fpr, tpr)
X_test = X[n_samples_train:, :]
y_train = y[:n_samples_train]
y_test = y[n_samples_train:]
### cross-val: ####
parameters = {'max_depth':[1000] , 'max_samples':[.1], 'max_features':[min(10, n_features)], 'n_estimators':[50]}
model = OneClassRF()
clf = grid_search.GridSearchCV(model, parameters, refit=False,cv=2)
clf.fit(X_train, y_train)
print('clf.best_params_', clf.best_params_)
model.set_params(**clf.best_params_)
print('OneClassRF processing...')
# weird: without CV but with same parameters, no error:
#model = OneClassRF(max_depth=1000, max_samples=.1, max_features=min(10, n_features), n_estimators=50, random_state=rng, n_jobs=-1) #commented since cross val
tstart = time()
### training only on normal data:
X_train = X_train[y_train==0]
y_train = y_train[y_train==0]
model.fit(X_train)
fit_time = time() - tstart
tstart = time()
scoring = model.predict(X_test) # the lower, the more normal
predict_time = time() - tstart
fpr, tpr, thresholds = roc_curve(y_test, scoring)
AUC = auc(fpr, tpr)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import OneClassRF
from sklearn.preprocessing import scale
rng = np.random.RandomState(42)
# Generate train data
X = 0.3 * rng.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
# fit the model
clf = OneClassRF(n_estimators=1, max_samples=1., max_depth=4, random_state=rng)
clf.fit(X_train)
# y_pred_train = -clf.decision_function(X_train)
# y_pred_test = -clf.decision_function(X_test)
# y_pred_outliers = -clf.decision_function(X_outliers)
# plot the line, the samples, and the nearest vectors to the plane
xx, yy = np.meshgrid(np.linspace(-5, 5, 200), np.linspace(-5, 5, 200))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
#Z = scale(Z)
Z = np.log(Z)
#plt.title("OneClassRF")
levels = np.linspace(Z.min(), Z.max(), 1000)
plt.contourf(xx, yy, Z, cmap=plt.cm.Blues, levels=levels)
plt.scatter(X_train[:, 0], X_train[:, 1], c='white')
plt.axis('tight')
