def test_icp_classification_tree(self):
# -----------------------------------------------------------------------------
# Setup training, calibration and test indices
# -----------------------------------------------------------------------------
data = load_iris()
idx = np.random.permutation(data.target.size)
train = idx[:int(idx.size / 3)]
calibrate = idx[int(idx.size / 3):int(2 * idx.size / 3)]
test = idx[int(2 * idx.size / 3):]
# -----------------------------------------------------------------------------
# Train and calibrate
# -----------------------------------------------------------------------------
icp = IcpClassifier(
ClassifierNc(ClassifierAdapter(DecisionTreeClassifier()),
MarginErrFunc()))
icp.fit(data.data[train, :], data.target[train])
icp.calibrate(data.data[calibrate, :], data.target[calibrate])
# -----------------------------------------------------------------------------
# Predict
# -----------------------------------------------------------------------------
prediction = icp.predict(data.data[test, :], significance=0.1)
header = np.array(["c0", "c1", "c2", "Truth"])
table = np.vstack([prediction.T, data.target[test]]).T
df = pd.DataFrame(np.vstack([header, table]))
print(df)
def ccp_predict(self, data_lbld, data_unlbld, new_lbld):
# Create SMOTE instance for class rebalancing
smote = SMOTE(random_state=self.random_state)
# Create instance of classifier
classifier_y = self.classifiers['classifier_y']
parameters_y = self.clf_parameters['classifier_y']
clf = classifier_y.set_params(**parameters_y)
X = data_lbld.iloc[:, :-2]
y = data_lbld.iloc[:, -1]
X_new = new_lbld.iloc[:, :-2]
y_new = new_lbld.iloc[:, -1]
X = X.append(X_new, sort=False)
y = y.append(y_new)
X_unlbld = data_unlbld.iloc[:, :-2]
sss = StratifiedKFold(n_splits=5, random_state=self.random_state)
sss.get_n_splits(X, y)
p_values = []
for train_index, calib_index in sss.split(X, y):
X_train, X_calib = X.iloc[train_index], X.iloc[calib_index]
y_train, y_calib = y.iloc[train_index], y.iloc[calib_index]
if self.rebalancing_parameters['SMOTE_y']:
X_train, y_train = smote.fit_resample(X_train, y_train)
clf.fit(X_train[:, :-1], y_train, sample_weight=X_train[:, -1])
else:
clf.fit(X_train.iloc[:, :-1],
y_train,
sample_weight=X_train.iloc[:, -1])
nc = NcFactory.create_nc(clf, MarginErrFunc())
icp = IcpClassifier(nc)
if self.rebalancing_parameters['SMOTE_y']:
icp.fit(X_train[:, :-1], y_train)
else:
icp.fit(X_train.iloc[:, :-1].values, y_train)
icp.calibrate(X_calib.iloc[:, :-1].values, y_calib)
# Predict confidences for validation sample and unlabeled sample
p_values.append(
icp.predict(X_unlbld.iloc[:, :-1].values, significance=None))
mean_p_values = np.array(p_values).mean(axis=0)
ccp_predictions = pd.DataFrame(mean_p_values,
columns=['mean_p_0', 'mean_p_1'])
ccp_predictions["credibility"] = [
row.max() for _, row in ccp_predictions.iterrows()
]
ccp_predictions["confidence"] = [
1 - row.min() for _, row in ccp_predictions.iterrows()
]
ccp_predictions.index = X_unlbld.index
return ccp_predictions
icp.fit(X[train, :], y[train])
icp.calibrate(X[calibrate, :], y[calibrate])
ccp = CrossConformalClassifier(
IcpClassifier(
ProbEstClassifierNc(DecisionTreeClassifier(), inverse_probability)))
ccp.fit(X[train, :], y[train])
acp = AggregatedCp(
IcpClassifier(
ProbEstClassifierNc(DecisionTreeClassifier(), inverse_probability)),
CrossSampler())
acp.fit(X[train, :], y[train])
# -----------------------------------------------------------------------------
# Predict
# -----------------------------------------------------------------------------
print('# Inductive')
prediction = icp.predict(X[test, :], significance=0.1)
for pred, actual in zip(prediction[:5], y[test]):
print(pred, actual)
print('\n# Cross')
prediction = ccp.predict(X[test, :], significance=0.1)
for pred, actual in zip(prediction[:5], y[test]):
print(pred, actual)
print('\n# Aggre')
prediction = acp.predict(X[test, :], significance=0.1)
for pred, actual in zip(prediction[:5], y[test]):
print(pred, actual)
data.loc[data['target'] < 0, 'target'] = 0
labels = data['id']
ll = len(labels)
target = data['target'].values
test = data.drop(['id'], axis=1)
test = test.drop(['target'], axis=1).values
f = open(modelfile, mode='rb')
nmodels_built = cloudpickle.load(f)
print("Models built:", nmodels_built)
if nmodels > nmodels_built:
print(
"More models ordered (", nmodels,
") than the file contains. Setting the number of models to built models."
)
nmodels = nmodels_built
for xx in range(1, nmodels + 1):
print("Predicting from model", xx)
modelfile2 = infile + "_nonconf" + "_" + str(xx) + ".model"
num = [xx] * ll
icp_norm = cloudpickle.load(f)
predicted = icp_norm.predict(test)
predicted0 = [x[0] for x in predicted]
predicted1 = [x[1] for x in predicted]
writeOutListsApp(outfile,
[labels, predicted0, predicted1, target, num])
f.close()
print(" - finished\n")
from sklearn.datasets import load_iris from nonconformist.icp import IcpClassifier from nonconformist.nc import ProbEstClassifierNc, margin # ----------------------------------------------------------------------------- # Setup training, calibration and test indices # ----------------------------------------------------------------------------- data = load_iris() idx = np.random.permutation(data.target.size) train = idx[: int(idx.size / 3)] calibrate = idx[int(idx.size / 3) : int(2 * idx.size / 3)] test = idx[int(2 * idx.size / 3) :] # ----------------------------------------------------------------------------- # Train and calibrate # ----------------------------------------------------------------------------- icp = IcpClassifier(ProbEstClassifierNc(DecisionTreeClassifier(), margin)) icp.fit(data.data[train, :], data.target[train]) icp.calibrate(data.data[calibrate, :], data.target[calibrate]) # ----------------------------------------------------------------------------- # Predict # ----------------------------------------------------------------------------- prediction = icp.predict(data.data[test, :], significance=0.1) header = np.array(["c0", "c1", "c2", "Truth"]) table = np.vstack([prediction.T, data.target[test]]).T df = pd.DataFrame(np.vstack([header, table])) print(df)
def split_data(data, n_train, n_test):
n_train = n_train * len(data) // (n_train + n_test)
n_test = len(data) - n_train
ind = np.random.permutation(len(data))
return data[ind[:n_train]], data[ind[n_train:n_train + n_test]]
#data = Orange.data.Table("../data/usps.tab")
data = Orange.data.Table("iris")
for sig in np.linspace(0.0, 0.4, 11):
errs, szs = [], []
for rep in range(10):
#train, test = split_data(data, 7200, 2098)
train, test = split_data(data, 2, 1)
train, calib = split_data(train, 2, 1)
#icp = IcpClassifier(ProbEstClassifierNc(DecisionTreeClassifier(), margin))
icp = IcpClassifier(ProbEstClassifierNc(LogisticRegression(), margin))
#icp = ICP()
icp.fit(train.X, train.Y)
icp.calibrate(calib.X, calib.Y)
pred = icp.predict(test.X, significance=sig)
acc = sum(p[y] for p, y in zip(pred, test.Y)) / len(pred)
err = 1 - acc
sz = sum(sum(p) for p in pred) / len(pred)
errs.append(err)
szs.append(sz)
print(sig, np.mean(errs), np.mean(szs))
from nonconformist.base import ClassifierAdapter
from nonconformist.icp import IcpClassifier
from nonconformist.nc import ClassifierNc, MarginErrFunc
# -----------------------------------------------------------------------------
# Setup training, calibration and test indices
# -----------------------------------------------------------------------------
data = load_iris()
idx = np.random.permutation(data.target.size)
train = idx[:int(idx.size / 3)]
calibrate = idx[int(idx.size / 3):int(2 * idx.size / 3)]
test = idx[int(2 * idx.size / 3):]
# -----------------------------------------------------------------------------
# Train and calibrate
# -----------------------------------------------------------------------------
icp = IcpClassifier(
ClassifierNc(ClassifierAdapter(DecisionTreeClassifier()), MarginErrFunc()))
icp.fit(data.data[train, :], data.target[train])
icp.calibrate(data.data[calibrate, :], data.target[calibrate])
# -----------------------------------------------------------------------------
# Predict
# -----------------------------------------------------------------------------
prediction = icp.predict(data.data[test, :], significance=0.1)
header = np.array(["c0", "c1", "c2", "Truth"])
table = np.vstack([prediction.T, data.target[test]]).T
df = pd.DataFrame(np.vstack([header, table]))
print(df)
