def test_receptive_field():
"""Test model prep and fitting."""
from sklearn.linear_model import Ridge
# Make sure estimator pulling works
mod = Ridge()
# Test the receptive field model
# Define parameters for the model and simulate inputs + weights
tmin, tmax = 0., 10.
n_feats = 3
X = rng.randn(n_feats, 10000)
w = rng.randn(int((tmax - tmin) + 1) * n_feats)
# Delay inputs and cut off first 4 values since they'll be cut in the fit
X_del = np.vstack(_delay_time_series(X, tmin, tmax, 1., axis=-1))
y = np.dot(w, X_del)
X = np.rollaxis(X, -1, 0) # time to first dimension
# Fit the model and test values
feature_names = ['feature_%i' % ii for ii in [0, 1, 2]]
rf = ReceptiveField(tmin, tmax, 1, feature_names, estimator=mod)
rf.fit(X, y)
assert_array_equal(rf.delays_, np.arange(tmin, tmax + 1))
y_pred = rf.predict(X)
assert_array_almost_equal(y[rf.keep_samples_],
y_pred.squeeze()[rf.keep_samples_], 2)
scores = rf.score(X, y)
assert_true(scores > .99)
assert_array_almost_equal(rf.coef_.reshape(-1, order='F'), w, 2)
# Make sure different input shapes work
rf.fit(X[:, np.newaxis:, ], y[:, np.newaxis])
rf.fit(X, y[:, np.newaxis])
assert_raises(ValueError, rf.fit, X[..., np.newaxis], y)
assert_raises(ValueError, rf.fit, X[:, 0], y)
assert_raises(ValueError, rf.fit, X[..., np.newaxis],
np.tile(y[..., np.newaxis], [2, 1, 1]))
# stim features must match length of input data
assert_raises(ValueError, rf.fit, X[:, :1], y)
# auto-naming features
rf = ReceptiveField(tmin, tmax, 1, estimator=mod)
rf.fit(X, y)
assert_equal(rf.feature_names, ['feature_%s' % ii for ii in [0, 1, 2]])
# X/y same n timepoints
assert_raises(ValueError, rf.fit, X, y[:-2])
# Float becomes ridge
rf = ReceptiveField(tmin, tmax, 1, ['one', 'two', 'three'],
estimator=0)
str(rf) # repr works before fit
rf.fit(X, y)
assert_true(isinstance(rf.estimator_, TimeDelayingRidge))
str(rf) # repr works after fit
rf = ReceptiveField(tmin, tmax, 1, ['one'], estimator=0)
rf.fit(X[:, [0]], y)
str(rf) # repr with one feature
# Should only accept estimators or floats
rf = ReceptiveField(tmin, tmax, 1, estimator='foo')
assert_raises(ValueError, rf.fit, X, y)
rf = ReceptiveField(tmin, tmax, 1, estimator=np.array([1, 2, 3]))
assert_raises(ValueError, rf.fit, X, y)
# tmin must be <= tmax
rf = ReceptiveField(5, 4, 1)
assert_raises(ValueError, rf.fit, X, y)
# scorers
for key, val in _SCORERS.items():
rf = ReceptiveField(tmin, tmax, 1, ['one'],
estimator=0, scoring=key)
rf.fit(X[:, [0]], y)
y_pred = rf.predict(X[:, [0]])
assert_array_almost_equal(val(y[:, np.newaxis], y_pred),
rf.score(X[:, [0]], y), 4)
# Need 2D input
assert_raises(ValueError, _SCORERS['corrcoef'], y.squeeze(), y_pred)
# Need correct scorers
rf = ReceptiveField(tmin, tmax, 1., scoring='foo')
assert_raises(ValueError, rf.fit, X, y)
def test_receptive_field():
"""Test model prep and fitting."""
from sklearn.linear_model import Ridge
# Make sure estimator pulling works
mod = Ridge()
# Test the receptive field model
# Define parameters for the model and simulate inputs + weights
tmin, tmax = -10., 0
n_feats = 3
X = rng.randn(10000, n_feats)
w = rng.randn(int((tmax - tmin) + 1) * n_feats)
# Delay inputs and cut off first 4 values since they'll be cut in the fit
X_del = np.concatenate(_delay_time_series(X, tmin, tmax,
1.).transpose(2, 0, 1),
axis=1)
y = np.dot(X_del, w)
# Fit the model and test values
feature_names = ['feature_%i' % ii for ii in [0, 1, 2]]
rf = ReceptiveField(tmin, tmax, 1, feature_names, estimator=mod)
rf.fit(X, y)
assert_array_equal(rf.delays_, np.arange(tmin, tmax + 1))
y_pred = rf.predict(X)
assert_allclose(y[rf.valid_samples_], y_pred[rf.valid_samples_], atol=1e-2)
scores = rf.score(X, y)
assert_true(scores > .99)
assert_allclose(rf.coef_.T.ravel(), w, atol=1e-2)
# Make sure different input shapes work
rf.fit(X[:, np.newaxis:, ], y[:, np.newaxis])
rf.fit(X, y[:, np.newaxis])
assert_raises(ValueError, rf.fit, X[..., np.newaxis], y)
assert_raises(ValueError, rf.fit, X[:, 0], y)
assert_raises(ValueError, rf.fit, X[..., np.newaxis],
np.tile(y[..., np.newaxis], [2, 1, 1]))
# stim features must match length of input data
assert_raises(ValueError, rf.fit, X[:, :1], y)
# auto-naming features
rf = ReceptiveField(tmin, tmax, 1, estimator=mod)
rf.fit(X, y)
assert_equal(rf.feature_names, ['feature_%s' % ii for ii in [0, 1, 2]])
# X/y same n timepoints
assert_raises(ValueError, rf.fit, X, y[:-2])
# Float becomes ridge
rf = ReceptiveField(tmin, tmax, 1, ['one', 'two', 'three'], estimator=0)
str(rf) # repr works before fit
rf.fit(X, y)
assert_true(isinstance(rf.estimator_, TimeDelayingRidge))
str(rf) # repr works after fit
rf = ReceptiveField(tmin, tmax, 1, ['one'], estimator=0)
rf.fit(X[:, [0]], y)
str(rf) # repr with one feature
# Should only accept estimators or floats
rf = ReceptiveField(tmin, tmax, 1, estimator='foo')
assert_raises(ValueError, rf.fit, X, y)
rf = ReceptiveField(tmin, tmax, 1, estimator=np.array([1, 2, 3]))
assert_raises(ValueError, rf.fit, X, y)
# tmin must be <= tmax
rf = ReceptiveField(5, 4, 1)
assert_raises(ValueError, rf.fit, X, y)
# scorers
for key, val in _SCORERS.items():
rf = ReceptiveField(tmin, tmax, 1, ['one'], estimator=0, scoring=key)
rf.fit(X[:, [0]], y)
y_pred = rf.predict(X[:, [0]]).T.ravel()[:, np.newaxis]
assert_allclose(val(y[:, np.newaxis], y_pred),
rf.score(X[:, [0]], y),
rtol=1e-2)
# Need 2D input
assert_raises(ValueError, _SCORERS['corrcoef'], y.ravel(), y_pred)
# Need correct scorers
rf = ReceptiveField(tmin, tmax, 1., scoring='foo')
assert_raises(ValueError, rf.fit, X, y)
def test_receptive_field(n_jobs):
"""Test model prep and fitting."""
from sklearn.linear_model import Ridge
# Make sure estimator pulling works
mod = Ridge()
rng = np.random.RandomState(1337)
# Test the receptive field model
# Define parameters for the model and simulate inputs + weights
tmin, tmax = -10., 0
n_feats = 3
rng = np.random.RandomState(0)
X = rng.randn(10000, n_feats)
w = rng.randn(int((tmax - tmin) + 1) * n_feats)
# Delay inputs and cut off first 4 values since they'll be cut in the fit
X_del = np.concatenate(
_delay_time_series(X, tmin, tmax, 1.).transpose(2, 0, 1), axis=1)
y = np.dot(X_del, w)
# Fit the model and test values
feature_names = ['feature_%i' % ii for ii in [0, 1, 2]]
rf = ReceptiveField(tmin, tmax, 1, feature_names, estimator=mod,
patterns=True)
rf.fit(X, y)
assert_array_equal(rf.delays_, np.arange(tmin, tmax + 1))
y_pred = rf.predict(X)
assert_allclose(y[rf.valid_samples_], y_pred[rf.valid_samples_], atol=1e-2)
scores = rf.score(X, y)
assert scores > .99
assert_allclose(rf.coef_.T.ravel(), w, atol=1e-3)
# Make sure different input shapes work
rf.fit(X[:, np.newaxis:, ], y[:, np.newaxis])
rf.fit(X, y[:, np.newaxis])
pytest.raises(ValueError, rf.fit, X[..., np.newaxis], y)
pytest.raises(ValueError, rf.fit, X[:, 0], y)
pytest.raises(ValueError, rf.fit, X[..., np.newaxis],
np.tile(y[..., np.newaxis], [2, 1, 1]))
# stim features must match length of input data
pytest.raises(ValueError, rf.fit, X[:, :1], y)
# auto-naming features
feature_names = ['feature_%s' % ii for ii in [0, 1, 2]]
rf = ReceptiveField(tmin, tmax, 1, estimator=mod,
feature_names=feature_names)
assert_equal(rf.feature_names, feature_names)
rf = ReceptiveField(tmin, tmax, 1, estimator=mod)
rf.fit(X, y)
assert_equal(rf.feature_names, None)
# X/y same n timepoints
pytest.raises(ValueError, rf.fit, X, y[:-2])
# Float becomes ridge
rf = ReceptiveField(tmin, tmax, 1, ['one', 'two', 'three'],
estimator=0, patterns=True)
str(rf) # repr works before fit
rf.fit(X, y)
assert isinstance(rf.estimator_, TimeDelayingRidge)
str(rf) # repr works after fit
rf = ReceptiveField(tmin, tmax, 1, ['one'], estimator=0, patterns=True)
rf.fit(X[:, [0]], y)
str(rf) # repr with one feature
# Should only accept estimators or floats
rf = ReceptiveField(tmin, tmax, 1, estimator='foo', patterns=True)
pytest.raises(ValueError, rf.fit, X, y)
rf = ReceptiveField(tmin, tmax, 1, estimator=np.array([1, 2, 3]))
pytest.raises(ValueError, rf.fit, X, y)
# tmin must be <= tmax
rf = ReceptiveField(5, 4, 1, patterns=True)
pytest.raises(ValueError, rf.fit, X, y)
# scorers
for key, val in _SCORERS.items():
rf = ReceptiveField(tmin, tmax, 1, ['one'],
estimator=0, scoring=key, patterns=True)
rf.fit(X[:, [0]], y)
y_pred = rf.predict(X[:, [0]]).T.ravel()[:, np.newaxis]
assert_allclose(val(y[:, np.newaxis], y_pred,
multioutput='raw_values'),
rf.score(X[:, [0]], y), rtol=1e-2)
# Need 2D input
pytest.raises(ValueError, _SCORERS['corrcoef'], y.ravel(), y_pred,
multioutput='raw_values')
# Need correct scorers
rf = ReceptiveField(tmin, tmax, 1., scoring='foo')
pytest.raises(ValueError, rf.fit, X, y)
def test_receptive_field_basic(n_jobs):
"""Test model prep and fitting."""
from sklearn.linear_model import Ridge
# Make sure estimator pulling works
mod = Ridge()
rng = np.random.RandomState(1337)
# Test the receptive field model
# Define parameters for the model and simulate inputs + weights
tmin, tmax = -10., 0
n_feats = 3
rng = np.random.RandomState(0)
X = rng.randn(10000, n_feats)
w = rng.randn(int((tmax - tmin) + 1) * n_feats)
# Delay inputs and cut off first 4 values since they'll be cut in the fit
X_del = np.concatenate(
_delay_time_series(X, tmin, tmax, 1.).transpose(2, 0, 1), axis=1)
y = np.dot(X_del, w)
# Fit the model and test values
feature_names = ['feature_%i' % ii for ii in [0, 1, 2]]
rf = ReceptiveField(tmin, tmax, 1, feature_names, estimator=mod,
patterns=True)
rf.fit(X, y)
assert_array_equal(rf.delays_, np.arange(tmin, tmax + 1))
y_pred = rf.predict(X)
assert_allclose(y[rf.valid_samples_], y_pred[rf.valid_samples_], atol=1e-2)
scores = rf.score(X, y)
assert scores > .99
assert_allclose(rf.coef_.T.ravel(), w, atol=1e-3)
# Make sure different input shapes work
rf.fit(X[:, np.newaxis:], y[:, np.newaxis])
rf.fit(X, y[:, np.newaxis])
with pytest.raises(ValueError, match='If X has 3 .* y must have 2 or 3'):
rf.fit(X[..., np.newaxis], y)
with pytest.raises(ValueError, match='X must be shape'):
rf.fit(X[:, 0], y)
with pytest.raises(ValueError, match='X and y do not have the same n_epo'):
rf.fit(X[:, np.newaxis], np.tile(y[:, np.newaxis, np.newaxis],
[1, 2, 1]))
with pytest.raises(ValueError, match='X and y do not have the same n_tim'):
rf.fit(X, y[:-2])
with pytest.raises(ValueError, match='n_features in X does not match'):
rf.fit(X[:, :1], y)
# auto-naming features
feature_names = ['feature_%s' % ii for ii in [0, 1, 2]]
rf = ReceptiveField(tmin, tmax, 1, estimator=mod,
feature_names=feature_names)
assert_equal(rf.feature_names, feature_names)
rf = ReceptiveField(tmin, tmax, 1, estimator=mod)
rf.fit(X, y)
assert_equal(rf.feature_names, None)
# Float becomes ridge
rf = ReceptiveField(tmin, tmax, 1, ['one', 'two', 'three'], estimator=0)
str(rf) # repr works before fit
rf.fit(X, y)
assert isinstance(rf.estimator_, TimeDelayingRidge)
str(rf) # repr works after fit
rf = ReceptiveField(tmin, tmax, 1, ['one'], estimator=0)
rf.fit(X[:, [0]], y)
str(rf) # repr with one feature
# Should only accept estimators or floats
with pytest.raises(ValueError, match='`estimator` must be a float or'):
ReceptiveField(tmin, tmax, 1, estimator='foo').fit(X, y)
with pytest.raises(ValueError, match='`estimator` must be a float or'):
ReceptiveField(tmin, tmax, 1, estimator=np.array([1, 2, 3])).fit(X, y)
with pytest.raises(ValueError, match='tmin .* must be at most tmax'):
ReceptiveField(5, 4, 1).fit(X, y)
# scorers
for key, val in _SCORERS.items():
rf = ReceptiveField(tmin, tmax, 1, ['one'],
estimator=0, scoring=key, patterns=True)
rf.fit(X[:, [0]], y)
y_pred = rf.predict(X[:, [0]]).T.ravel()[:, np.newaxis]
assert_allclose(val(y[:, np.newaxis], y_pred,
multioutput='raw_values'),
rf.score(X[:, [0]], y), rtol=1e-2)
with pytest.raises(ValueError, match='inputs must be shape'):
_SCORERS['corrcoef'](y.ravel(), y_pred, multioutput='raw_values')
# Need correct scorers
with pytest.raises(ValueError, match='scoring must be one of'):
ReceptiveField(tmin, tmax, 1., scoring='foo').fit(X, y)