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test_regular.py
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"""
Test functions for regular module.
"""
import pytest
import numpy as np
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.gaussian_process import GaussianProcessRegressor, GaussianProcessClassifier
from sklearn.gaussian_process.kernels import Matern, WhiteKernel
from sklearn.base import clone
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initializers import GlorotUniform
from adapt.utils import make_classification_da, make_regression_da
from adapt.parameter_based import (RegularTransferLR,
RegularTransferLC,
RegularTransferNN,
RegularTransferGP)
np.random.seed(0)
Xs = np.concatenate((
np.random.randn(50)*0.1,
np.random.randn(50)*0.1 + 1.,
)).reshape(-1, 1)
Xt = (np.random.randn(100) * 0.1).reshape(-1, 1)
ys_reg = np.array([0.2 * x if x<0.5 else
10 for x in Xs.ravel()]).reshape(-1, 1)
yt_reg = np.array([0.2 * x if x<0.5 else
10 for x in Xt.ravel()]).reshape(-1, 1)
ys_classif = np.sign(np.array(
[x<0 if x<0.5 else x<1 for x in Xs.ravel()]
).astype(float) - 0.5).reshape(-1, 1)
yt_classif = np.sign(np.array(
[x<0 if x<0.5 else x<1 for x in Xt.ravel()]
).astype(float) - 0.5).reshape(-1, 1)
def _get_network(input_shape=(1,), output_shape=(1,)):
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Dense(np.prod(output_shape),
kernel_initializer=GlorotUniform(seed=0),
use_bias=True))
model.compile(loss="mse", optimizer=Adam(0.1))
return model
def test_setup():
lr = LinearRegression(fit_intercept=False)
lr.fit(Xs, ys_reg)
assert np.abs(lr.coef_[0][0] - 10) < 1
lr = LogisticRegression(penalty=None, solver='lbfgs')
lr.fit(Xs, ys_classif)
assert (lr.predict(Xt) == yt_classif.ravel()).sum() < 70
def test_regularlr_fit():
np.random.seed(0)
lr = LinearRegression(fit_intercept=False)
lr.fit(Xs, ys_reg)
model = RegularTransferLR(lr, lambda_=0.)
model.fit(Xt, yt_reg)
assert np.abs(model.estimator_.coef_[0] - 0.2) < 1
assert np.abs(model.predict(Xt) - yt_reg).sum() < 2
model = RegularTransferLR(lr, lambda_=1000000)
model.fit(Xt, yt_reg)
assert np.abs(model.estimator_.coef_[0] - 10) < 1
assert np.abs(model.estimator_.coef_[0] - lr.coef_[0]) < 0.001
model = RegularTransferLR(lr, lambda_=1.)
model.fit(Xt, yt_reg)
assert np.abs(model.estimator_.coef_[0] - 4) < 1
def test_regularlr_multioutput():
np.random.seed(0)
X = np.random.randn(100, 5)+2.
y = X[:, :2]
lr = LinearRegression()
lr.fit(X, y)
model = RegularTransferLR(lr, lambda_=1.)
model.fit(X, y)
assert np.abs(model.predict(X) - y).sum() < 2
assert np.all(model.coef_.shape == (2, 5))
assert np.all(model.intercept_.shape == (2,))
assert model.score(X, y) > 0.9
def test_regularlr_error():
np.random.seed(0)
Xs = np.random.randn(100, 5)
Xt = np.random.randn(100, 5)
ys = np.random.randn(100)
yt = np.random.randn(100)
lr = LinearRegression()
lr.fit(Xs, ys)
model = RegularTransferLR(lr, lambda_=1.)
model.fit(Xt, yt)
with pytest.raises(ValueError) as excinfo:
model.fit(np.random.randn(100, 4), yt)
assert "expected 5, got 4" in str(excinfo.value)
with pytest.raises(ValueError) as excinfo:
model.fit(Xt, np.random.randn(100, 2))
assert "expected 1, got 2" in str(excinfo.value)
def test_regularlc_fit():
np.random.seed(0)
lr = LogisticRegression(penalty=None, solver='lbfgs')
lr.fit(Xs, ys_classif)
model = RegularTransferLC(lr, lambda_=0)
model.fit(Xt, yt_classif)
assert (model.predict(Xt) == yt_classif.ravel()).sum() > 90
model = RegularTransferLC(lr, lambda_=100000000)
model.fit(Xt, yt_classif)
assert (model.predict(Xt) == yt_classif.ravel()).sum() < 70
assert np.abs(model.estimator_.coef_[0][0] - lr.coef_[0][0]) < 0.001
assert np.abs(model.estimator_.intercept_ - lr.intercept_[0]) < 0.001
model = RegularTransferLC(lr, lambda_=1.2)
model.fit(Xt, yt_classif)
assert (model.predict(Xt) == yt_classif.ravel()).sum() > 95
def test_regularlc_multiclass():
np.random.seed(0)
X = np.random.randn(100, 5)
y = np.zeros(len(X))
y[X[:, :2].sum(1)<0] = 1
y[X[:, 3:].sum(1)>0] = 2
lr = LogisticRegression(penalty=None, solver='lbfgs')
lr.fit(X, y)
model = RegularTransferLC(lr, lambda_=1.)
model.fit(X, y)
assert (model.predict(X) == y).sum() > 90
assert np.all(model.coef_.shape == (3, 5))
assert np.all(model.intercept_.shape == (3,))
assert model.score(X, y) > 0.9
def test_regularnn_fit():
tf.random.set_seed(0)
np.random.seed(0)
network = _get_network()
print(network.get_weights())
network.fit(Xs, ys_reg, epochs=100, batch_size=100, verbose=0)
print(network.get_weights())
model = RegularTransferNN(network, lambdas=0., optimizer=Adam(0.1), loss="mse")
model.fit(Xt, yt_reg, epochs=100, batch_size=100, verbose=0)
print(model.task_.get_weights())
# assert np.abs(network.predict(Xs) - ys_reg).sum() < 1
assert np.sum(np.abs(network.get_weights()[0] - model.task_.get_weights()[0])) > 4.
assert np.abs(model.predict(Xt) - yt_reg).sum() < 10
model = RegularTransferNN(network, lambdas=10000000., optimizer=Adam(0.1))
model.fit(Xt, yt_reg, epochs=100, batch_size=100, verbose=0)
assert np.sum(np.abs(network.get_weights()[0] - model.task_.get_weights()[0])) < 0.001
assert np.abs(model.predict(Xt) - yt_reg).sum() > 10
def test_regularnn_reg():
tf.random.set_seed(0)
np.random.seed(0)
network = _get_network()
network.fit(Xs, ys_reg, epochs=100, batch_size=100, verbose=0)
model = RegularTransferNN(network, regularizer="l1")
model.fit(Xt, yt_reg, epochs=100, batch_size=100, verbose=0)
with pytest.raises(ValueError) as excinfo:
model = RegularTransferNN(network, regularizer="l3")
assert "l1' or 'l2', got, l3" in str(excinfo.value)
def test_clone():
Xs = np.random.randn(100, 5)
ys = np.random.choice(2, 100)
lr = LinearRegression()
lr.fit(Xs, ys)
model = RegularTransferLR(lr, lambda_=1.)
model.fit(Xs, ys)
new_model = clone(model)
new_model.fit(Xs, ys)
new_model.predict(Xs);
assert model is not new_model
lr = LogisticRegression(penalty=None, solver='lbfgs')
lr.fit(Xs, ys)
model = RegularTransferLC(lr, lambda_=1.)
model.fit(Xs, ys)
new_model = clone(model)
new_model.fit(Xs, ys)
new_model.predict(Xs);
assert model is not new_model
def test_regulargp_reg():
Xs, ys, Xt, yt = make_regression_da()
kernel = Matern() + WhiteKernel()
src_model = GaussianProcessRegressor(kernel)
src_model.fit(Xs, ys)
score1 = src_model.score(Xt, yt)
tgt_model = RegularTransferGP(src_model, lambda_=1.)
tgt_model.fit(Xt[:3], yt[:3])
score2 = tgt_model.score(Xt, yt)
assert score1 < score2
def test_regulargp_classif():
Xs, ys, Xt, yt = make_classification_da()
kernel = Matern() + WhiteKernel()
src_model = GaussianProcessClassifier(kernel)
src_model.fit(Xs, ys)
score1 = src_model.score(Xt, yt)
tgt_model = RegularTransferGP(src_model, lambda_=1.)
tgt_model.fit(Xt[:3], yt[:3])
score2 = tgt_model.score(Xt, yt)
assert score1 < score2
def test_regulargp_multi_classif():
Xs, ys, Xt, yt = make_classification_da()
ys[:5] = 3
kernel = Matern() + WhiteKernel()
src_model = GaussianProcessClassifier(kernel)
src_model.fit(Xs, ys)
score1 = src_model.score(Xt, yt)
tgt_model = RegularTransferGP(src_model, lambda_=1.)
tgt_model.fit(Xt[:3], yt[:3])
score2 = tgt_model.score(Xt, yt)
assert score1 < score2