class Simple:
def __init__(self, a, b, c, d):
self.model = TheilSenRegressor()
def update_a_b(self, x, y):
self.model.fit(x.reshape(-1, 1), y)
def set_c_d(self, c, d):
pass
def get_y(self, x):
return self.model.predict(x.reshape(-1, 1))
def get_likelihood(self, x, y):
return 1 / float(x.shape[0]) * np.sum(np.abs(y - self.get_y(x)))
def to_string(self):
return "a:{}, b:{}".format(self.model.coef_, self.model.intercept_)
def get_a_b(self):
return self.model.coef_, self.model.intercept_
@staticmethod
def var_to_weight(v):
return 1
@staticmethod
def get_c_d(x, r):
return None, None
def test_verbosity():
X, y, w, c = gen_toy_problem_1d()
# Check that Theil-Sen can be verbose
with no_stdout_stderr():
TheilSenRegressor(verbose=True, random_state=0).fit(X, y)
TheilSenRegressor(verbose=True, max_subpopulation=10,
random_state=0).fit(X, y)
def compute_quantal_size(scan):
""" Estimate the unit change in calcium response corresponding to a unit change in
pixel intensity (dubbed quantal size, lower is better).
Assumes images are stationary from one timestep to the next. Uses it to calculate a
measure of noise per bright intensity (which increases linearly given that imaging
noise is poisson), fits a line to it and uses the slope as the estimate.
:param np.array scan: 3-dimensional scan (image_height, image_width, num_frames).
:returns: int minimum pixel value in the scan (that appears a min number of times)
:returns: int maximum pixel value in the scan (that appears a min number of times)
:returns: np.array pixel intensities used for the estimation.
:returns: np.array noise variances used for the estimation.
:returns: float the estimated quantal size
:returns: float the estimated zero value
"""
# Set some params
num_frames = scan.shape[2]
min_count = num_frames * 0.1 # pixel values with fewer appearances will be ignored
max_acceptable_intensity = 3000 # pixel values higher than this will be ignored
# Make sure field is at least 32 bytes (int16 overflows if summed to itself)
scan = scan.astype(np.float32, copy=False)
# Create pixel values at each position in field
eps = 1e-4 # needed for np.round to not be biased towards even numbers (0.5 -> 1, 1.5 -> 2, 2.5 -> 3, etc.)
pixels = np.round((scan[:, :, :-1] + scan[:, :, 1:]) / 2 + eps)
pixels = pixels.astype(np.int16 if np.max(abs(pixels)) < 2 ** 15 else np.int32)
# Compute a good range of pixel values (common, not too bright values)
unique_pixels, counts = np.unique(pixels, return_counts=True)
min_intensity = min(unique_pixels[counts > min_count])
max_intensity = max(unique_pixels[counts > min_count])
max_acceptable_intensity = min(max_intensity, max_acceptable_intensity)
pixels_mask = np.logical_and(pixels >= min_intensity, pixels <= max_acceptable_intensity)
# Select pixels in good range
pixels = pixels[pixels_mask]
unique_pixels, counts = np.unique(pixels, return_counts=True)
# Compute noise variance
variances = ((scan[:, :, :-1] - scan[:, :, 1:]) ** 2 / 2)[pixels_mask]
pixels -= min_intensity
variance_sum = np.zeros(len(unique_pixels)) # sum of variances per pixel value
for i in range(0, len(pixels), int(1e8)): # chunk it for memory efficiency
variance_sum += np.bincount(pixels[i: i + int(1e8)], weights=variances[i: i + int(1e8)],
minlength=len(unique_pixels))[unique_pixels - min_intensity]
unique_variances = variance_sum / counts # average variance per intensity
# Compute quantal size (by fitting a linear regressor to predict the variance from intensity)
X = unique_pixels.reshape(-1, 1)
y = unique_variances
model = TheilSenRegressor() # robust regression
model.fit(X, y)
quantal_size = model.coef_[0]
zero_level = - model.intercept_ / model.coef_[0]
return (min_intensity, max_intensity, unique_pixels, unique_variances,
quantal_size, zero_level)
def compute_quantal_size(scan):
""" Estimate the unit change in calcium response corresponding to a unit change in
pixel intensity (dubbed quantal size, lower is better).
Assumes images are stationary from one timestep to the next. Uses it to calculate a
measure of noise per bright intensity (which increases linearly given that imaging
noise is poisson), fits a line to it and uses the slope as the estimate.
:param np.array scan: 3-dimensional scan (image_height, image_width, num_frames).
:returns: int minimum pixel value in the scan (that appears a min number of times)
:returns: int maximum pixel value in the scan (that appears a min number of times)
:returns: np.array pixel intensities used for the estimation.
:returns: np.array noise variances used for the estimation.
:returns: float the estimated quantal size
:returns: float the estimated zero value
"""
# Set some params
num_frames = scan.shape[2]
min_count = num_frames * 0.1 # pixel values with fewer appearances will be ignored
max_acceptable_intensity = 3000 # pixel values higher than this will be ignored
# Make sure field is at least 32 bytes (int16 overflows if summed to itself)
scan = scan.astype(np.float32, copy=False)
# Create pixel values at each position in field
eps = 1e-4 # needed for np.round to not be biased towards even numbers (0.5 -> 1, 1.5 -> 2, 2.5 -> 3, etc.)
pixels = np.round((scan[:, :, :-1] + scan[:, :, 1:]) / 2 + eps)
pixels = pixels.astype(np.int16 if np.max(abs(pixels)) < 2 ** 15 else np.int32)
# Compute a good range of pixel values (common, not too bright values)
unique_pixels, counts = np.unique(pixels, return_counts=True)
min_intensity = min(unique_pixels[counts > min_count])
max_intensity = max(unique_pixels[counts > min_count])
max_acceptable_intensity = min(max_intensity, max_acceptable_intensity)
pixels_mask = np.logical_and(pixels >= min_intensity, pixels <= max_acceptable_intensity)
# Select pixels in good range
pixels = pixels[pixels_mask]
unique_pixels, counts = np.unique(pixels, return_counts=True)
# Compute noise variance
variances = ((scan[:, :, :-1] - scan[:, :, 1:]) ** 2 / 2)[pixels_mask]
pixels -= min_intensity
variance_sum = np.zeros(len(unique_pixels)) # sum of variances per pixel value
for i in range(0, len(pixels), int(1e8)): # chunk it for memory efficiency
variance_sum += np.bincount(pixels[i: i + int(1e8)], weights=variances[i: i + int(1e8)],
minlength=len(unique_pixels))[unique_pixels - min_intensity]
unique_variances = variance_sum / counts # average variance per intensity
# Compute quantal size (by fitting a linear regressor to predict the variance from intensity)
X = unique_pixels.reshape(-1, 1)
y = unique_variances
model = TheilSenRegressor() # robust regression
model.fit(X, y)
quantal_size = model.coef_[0]
zero_level = - model.intercept_ / model.coef_[0]
return (min_intensity, max_intensity, unique_pixels, unique_variances,
quantal_size, zero_level)
def calculate_scaling_params(events, kmer_mean_levels):
events = pd.DataFrame(events)
events['pos'] = events['move'].cumsum()
jump_positions = events[events['move'] > 1]['pos']
jump_positions = set(jump_positions - 1) | set(jump_positions)
nonjump_positions = set(events['pos']) - jump_positions
if len(nonjump_positions) < MINIMUM_NONJUMP_POSITIONS:
return
statelevels = []
statelevels_jump = []
for pos, posevents in events.groupby('pos'):
state = posevents['model_state'].iloc[0]
if '_' in state:
continue
medlevel = posevents['mean'].median()
if pos in nonjump_positions:
statelevels.append([medlevel, kmer_mean_levels[state]])
else:
statelevels_jump.append([medlevel, kmer_mean_levels[state]])
statelevels_jump = np.array(statelevels_jump)
statelevels = np.array(statelevels)
regr = TheilSenRegressor(random_state=922)
regr.fit(statelevels[:, 0][:, np.newaxis], statelevels[:, 1])
return regr.coef_[0], regr.intercept_
def robust_cor(x, y):
if isinstance(x[0], list):
x = list(map(list, zip(*x)))
else:
x = np.array(x).reshape(-1, 1)
X = np.array(x)
Y = np.array(y)
theil_regr = TheilSenRegressor(random_state=42)
theil_regr.fit(X, Y)
y_pred = theil_regr.predict(X)
res = y_pred - y
tot_dev = y - np.mean(y)
SSres = np.dot(res, res)
SStot = np.dot(tot_dev, tot_dev)
adjR2 = 1 - (SSres / SStot) * (X.shape[0] - 1) / (X.shape[0] - X.shape[1] -
1)
sgn = np.sign(theil_regr.coef_)[0]
if adjR2 > 0:
corr_val = sgn * np.sqrt(adjR2)
else:
corr_val = 0
return [
corr_val, theil_regr.coef_, theil_regr.intercept_,
theil_regr.breakdown_
]
def test_checksubparams_n_subsamples_if_less_samples_than_features():
random_state = np.random.RandomState(0)
n_samples, n_features = 10, 20
X = random_state.normal(size=(n_samples, n_features))
y = random_state.normal(size=n_samples)
theil_sen = TheilSenRegressor(n_subsamples=9, random_state=0)
with pytest.raises(ValueError):
theil_sen.fit(X, y)
class Regressor(BaseEstimator):
def __init__(self):
self.regressorName="linear"
if self.regressorName=="rf":
self.clf= RandomForestRegressor(n_estimators=30, max_depth=63,max_features=50, n_jobs=-1)
elif self.regressorName=="gb":
self.clf= GradientBoostingRegressor(alpha=0.9, init=None,max_depth=3, learning_rate=0.2, loss='ls'
,max_features=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0
,n_estimators=2500,presort='auto', random_state=None, subsample=1.0, verbose=0,warm_start=True)
#self.clf =GridSearchCV(estimator=gb, param_grid=self.getParamGrid(),scoring='mean_squared_error',cv=3,n_jobs=-1)
#self.clf=gb
elif self.regressorName=="ridge":
self.clf = RidgeCV(alphas=(0.01, 0.1), fit_intercept=True, normalize=False, scoring=None, cv=5, gcv_mode=None, store_cv_values=False)
elif self.regressorName=="linear":
self.clf = LinearRegression()
elif self.regressorName=="lasso":
self.clf = LassoCV(cv=10)
elif self.regressorName=="svr":
self.clf = SVR(kernel='rbf',C=0.2, gamma=0.01)
elif self.regressorName=="knn":
self.clf = neighbors.KNeighborsRegressor(1, weights='distance',n_jobs=-1)
elif self.regressorName=="gauss":
self.clf = TheilSenRegressor()
def fit(self, X, y):
X=csc_matrix(X)
print "Training Algorithm"
self.clf.fit(X, y)
#print self.clf.best_estimator_
def predict(self, X):
X=csr_matrix(X)
print "Testing Algorithm"
return self.clf.predict(X)
def getRegressor(self):
return self.clf
def getRegressorName(self):
return self.regressorName
def getParamGrid(self):
if self.regressorName=="rf":
defaultGrid=[None]
maxDepthGrid=np.arange(10,70,7)
maxFeaturesGrid=["sqrt","log2",None]
maxTreesGrid=np.arange(10,100,10)
param_grid = {'max_features': defaultGrid}
elif self.regressorName == "gb":
#maxDepthGrid=np.arange(3,20,5)
learningRateGrid=np.arange(50,100,10)
#param_grid = {'max_depth': maxDepthGrid}
#param_grid={'loss':['ls', 'lad', 'huber', 'quantile']}
param_grid={'alpha':[0.9]}
return param_grid
class Regressor(BaseEstimator):
def __init__(self):
self.regressorName="gb"
if self.regressorName=="rf":
self.clf= RandomForestRegressor(n_estimators=400, max_depth=63,max_features=50, n_jobs=-1)
elif self.regressorName=="gb":
self.clf= GradientBoostingRegressor(alpha=0.9, init=None,max_depth=3, learning_rate=0.2, loss='ls'
,max_features=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0
,n_estimators=2500,presort='auto', random_state=None, subsample=1.0, verbose=0,warm_start=True)
#self.clf =GridSearchCV(estimator=gb, param_grid=self.getParamGrid(),scoring='mean_squared_error',cv=3,n_jobs=-1)
#self.clf=gb
elif self.regressorName=="ridge":
self.clf = RidgeCV(alphas=(0.01, 0.1), fit_intercept=True, normalize=False, scoring=None, cv=5, gcv_mode=None, store_cv_values=False)
elif self.regressorName=="linear":
self.clf = LinearRegression(alpha=0.01,max_iter=5000)
elif self.regressorName=="lasso":
self.clf = LassoCV(cv=10)
elif self.regressorName=="svr":
self.clf = SVR(kernel='rbf',C=0.2, gamma=0.01)
elif self.regressorName=="knn":
self.clf = neighbors.KNeighborsRegressor(1, weights='distance',n_jobs=-1)
elif self.regressorName=="gauss":
self.clf = TheilSenRegressor()
def fit(self, X, y):
#X=csc_matrix(X)
self.clf.fit(X, y)
#print self.clf.best_estimator_
def predict(self, X):
#X=csr_matrix(X)
return self.clf.predict(X)
def getRegressor(self):
return self.clf
def getRegressorName(self):
return self.regressorName
def getParamGrid(self):
if self.regressorName=="rf":
defaultGrid=[None]
maxDepthGrid=np.arange(10,70,7)
maxFeaturesGrid=["sqrt","log2",None]
maxTreesGrid=np.arange(10,100,10)
param_grid = {'max_features': defaultGrid}
elif self.regressorName == "gb":
#maxDepthGrid=np.arange(3,20,5)
learningRateGrid=np.arange(50,100,10)
#param_grid = {'max_depth': maxDepthGrid}
#param_grid={'loss':['ls', 'lad', 'huber', 'quantile']}
param_grid={'alpha':[0.9]}
return param_grid
def _fit_robust_line(shifts):
""" Use a robust linear regression algorithm to fit a line to the data."""
from sklearn.linear_model import TheilSenRegressor
X = np.arange(len(shifts)).reshape(-1, 1)
y = shifts
model = TheilSenRegressor() # robust regression
model.fit(X, y)
line = model.predict(X)
return line
def trainAlgo(self):
self.model = TheilSenRegressor(
fit_intercept=self.param['fit_intercept'],
copy_X=self.param['copy_X'],
max_subpopulation=self.param['max_subpopulation'],
n_subsamples=self.param['n_subsamples'],
max_iter=self.param['max_iter'],
tol=self.param['tol'],
random_state=self.param['random_state'],
verbose=self.param['verbose'],
)
self.model.fit(self.inputData['X'], self.outputData['Y'])
def test_theil_sen_1d_no_intercept():
X, y, w, c = gen_toy_problem_1d(intercept=False)
# Check that Least Squares fails
lstq = LinearRegression(fit_intercept=False).fit(X, y)
assert np.abs(lstq.coef_ - w - c) > 0.5
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(fit_intercept=False, random_state=0).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w + c, 1)
assert_almost_equal(theil_sen.intercept_, 0.0)
# non-regression test for #18104
theil_sen.score(X, y)
def createTheilSenRegressor(params):
info("Creating TheilSen Regressor", ind=4)
## Params
params = mergeParams(TheilSenRegressor(), params)
tuneParams = getTheilSenRegressorParams()
info("Without Parameters", ind=4)
## estimator
reg = TheilSenRegressor()
return {"estimator": reg, "params": tuneParams}
def theilsen_regress_predict(var):
"""
Input:-
var: 1-D array var
regressortype = LinearRegression, TheilSenRegressor
Output: regression coefficient
"""
regressor = TheilSenRegressor()
y = np.asarray(var).reshape(-1, 1)
X = np.arange(len(y)).reshape(-1, 1)
regressor.fit(X, y)
return regressor.predict(X)
def _fit_theil_sen_one_track(x_coords_metres, y_coords_metres,
valid_times_unix_sec):
"""Fits Theil-Sen model for one storm track.
P = number of points in track
:param x_coords_metres: length-P numpy array of x-coordinates.
:param y_coords_metres: length-P numpy array of y-coordinates.
:param valid_times_unix_sec: length-P numpy array of times.
:return: theil_sen_dict: Dictionary with the following keys.
theil_sen_dict['x_intercept_metres']: x-intercept.
theil_sen_dict['x_velocity_m_s01']: x-velocity (metres per second).
theil_sen_dict['y_intercept_metres']: y-intercept.
theil_sen_dict['y_velocity_m_s01']: y-velocity (metres per second).
"""
num_points = len(x_coords_metres)
valid_times_unix_sec = numpy.reshape(valid_times_unix_sec, (num_points, 1))
model_object_for_x = TheilSenRegressor(fit_intercept=True)
model_object_for_x.fit(valid_times_unix_sec, x_coords_metres)
model_object_for_y = TheilSenRegressor(fit_intercept=True)
model_object_for_y.fit(valid_times_unix_sec, y_coords_metres)
return {
X_INTERCEPT_KEY: model_object_for_x.intercept_,
X_VELOCITY_KEY: model_object_for_x.coef_,
Y_INTERCEPT_KEY: model_object_for_y.intercept_,
Y_VELOCITY_KEY: model_object_for_y.coef_
}
def test_less_samples_than_features():
random_state = np.random.RandomState(0)
n_samples, n_features = 10, 20
X = random_state.normal(size=(n_samples, n_features))
y = random_state.normal(size=n_samples)
# Check that Theil-Sen falls back to Least Squares if fit_intercept=False
theil_sen = TheilSenRegressor(fit_intercept=False, random_state=0).fit(X, y)
lstq = LinearRegression(fit_intercept=False).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, lstq.coef_, 12)
# Check fit_intercept=True case. This will not be equal to the Least
# Squares solution since the intercept is calculated differently.
theil_sen = TheilSenRegressor(fit_intercept=True, random_state=0).fit(X, y)
y_pred = theil_sen.predict(X)
assert_array_almost_equal(y_pred, y, 12)
class _TheilSenRegressorImpl:
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if y is not None:
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def __init__(self,
fit_intercept=True,
copy_X=True,
max_subpopulation=1e4,
n_subsamples=None,
max_iter=300,
tol=1.e-3,
random_state=None,
n_jobs=1,
verbose=False):
max_iter = int(max_iter)
_TheilSenRegressor.__init__(self, fit_intercept, copy_X,
max_subpopulation, n_subsamples, max_iter,
tol, random_state, n_jobs, verbose)
BaseWrapperReg.__init__(self)
def theilsen_regress_coeff(var, a):
"""
Input:-
var: 1-D array var
a: 1-D array index
regressortype = LinearRegression, TheilSenRegressor
Output: regression coefficient
"""
regressor = TheilSenRegressor()
y = np.asarray(var).reshape(-1, 1)
X = a.reshape(-1, 1)
regressor.fit(X, y)
return np.array([regressor.coef_])
def test_less_samples_than_features():
random_state = np.random.RandomState(0)
n_samples, n_features = 10, 20
X = random_state.normal(size=(n_samples, n_features))
y = random_state.normal(size=n_samples)
# Check that Theil-Sen falls back to Least Squares if fit_intercept=False
theil_sen = TheilSenRegressor(fit_intercept=False,
random_state=0).fit(X, y)
lstq = LinearRegression(fit_intercept=False).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, lstq.coef_, 12)
# Check fit_intercept=True case. This will not be equal to the Least
# Squares solution since the intercept is calculated differently.
theil_sen = TheilSenRegressor(fit_intercept=True, random_state=0).fit(X, y)
y_pred = theil_sen.predict(X)
assert_array_almost_equal(y_pred, y, 12)
def _regress_a(X, y, robust, n_jobs):
"""
Calculates the slope and intercept
"""
if robust:
model = TheilSenRegressor(n_jobs=n_jobs)
else:
model = LinearRegression(n_jobs=n_jobs)
model.fit(X, y)
slope_m = model.coef_[0]
intercept_b = model.intercept_
return slope_m, intercept_b
def get_best_degree(data):
degrees = range(1, 6)
errors = []
degrees = list(degrees)
for deg in degrees:
reg = Pipeline([
("quad", PolynomialFeatures(degree=deg)),
(
"linear",
TheilSenRegressor(max_subpopulation=50, max_iter=300),
),
])
numDims = np.size(data, 1)
X = data[:, 0:numDims - 1] # noqa
Y = data[:, numDims - 1]
reg.fit(X, Y)
out = reg.predict(X)
Sr = np.sum(np.square(Y - out))
errors.append(Sr)
min_degree = degrees[np.argmin(errors)]
return min_degree
def test_subsamples():
X, y, w, c = gen_toy_problem_4d()
theil_sen = TheilSenRegressor(n_subsamples=X.shape[0],
random_state=0).fit(X, y)
lstq = LinearRegression().fit(X, y)
# Check for exact the same results as Least Squares
assert_array_almost_equal(theil_sen.coef_, lstq.coef_, 9)
def log_log_robust_regression(cfs, y, kind=0):
assert y.shape[0] == 40
y = y.reshape(40, -1)
x = np.tile(cfs[:, np.newaxis], (1, y.shape[1]))
y = np.log(y).ravel()
x = np.log(x).ravel()[:, np.newaxis]
if kind == 0:
model = RANSACRegressor()
elif kind == 1:
model = TheilSenRegressor(n_jobs=-1)
elif kind == 2:
model = HuberRegressor()
else:
raise ValueError
model.fit(x, y)
yp = model.predict(x)
u = np.square(y - yp)
v = np.square(y - y.mean())
R2 = 1. - u / v
if kind == 0:
return model.estimator_.coef_, model.estimator_.intercept_, np.median(
R2)
elif kind in [1, 2]:
return model.coef_, model.intercept_, np.median(R2)
else:
raise ValueError
def show():
X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 4, 5, 0]
y = [0, 5, 9, 12, 13, 12, 9, 5, 0, 1, 0, 7]
X = list(map(lambda x: [x], X))
import pylab
pylab.scatter(X, y)
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
import numpy as np
from sklearn.linear_model import LinearRegression, TheilSenRegressor, HuberRegressor, RANSACRegressor
for regressor in [
[LinearRegression(), "linreg"],
[TheilSenRegressor(), "theil-sen"],
[HuberRegressor(), "huber"],
[RANSACRegressor(), "ransac"],
]:
model = make_pipeline(PolynomialFeatures(2), regressor[0])
model.fit(X, y)
print("")
print(regressor[1])
print(model.score(X, y))
test_x = np.linspace(-1, 10, 100)
test_y = []
for x in test_x:
test_y.append(model.predict([[x]])[0])
pylab.plot(test_x, test_y, label=regressor[1])
pylab.legend(loc="best")
pylab.show()
def underline_regression(x, y, method="ramp"):
start_params = guess(x, y)
if method == "ramp":
reg = minimize(asymmetric_ramp_loss,
x0=start_params,
args=(x, y),
bounds=((None, None), (0, None)),
method="Powell")
elif method == 'quadratic' or method == "parabolic":
reg = ParabolicRegressor.regress(x, y)
return reg
elif method == "squashed":
reg = minimize(squashed_loss,
x0=start_params,
jac=squashed_grad,
args=(x, y),
bounds=((None, None), (0, 1)),
method="L-BFGS-B")
elif method == "median":
y = y.reshape(-1, 1)
X = np.vstack((np.ones(y.shape).transpose(), x.reshape(-1,
1).transpose()))
reg = TheilSenRegressor(random_state=0).fit(X.transpose(), np.ravel(y))
offset = np.min(subtract_bg(y, x, [reg.coef_[0], reg.coef_[1]]))
return np.array([reg.coef_[0] + offset, reg.coef_[1]])
elif method == "huber":
reg = HubelRegressor.regress(x, y)
return reg
return (reg.x[0], reg.x[1])
def _cfunc_theilsen(x, y):
"""
Get Theil-Sen regression score for data set.
Args:
x: (list<float>) independent property (x-axis)
y: (list<float>) dependent property (y-axis)
Returns: (float) Theil-Sen score
"""
from sklearn.linear_model import TheilSenRegressor
r = TheilSenRegressor(random_state=21)
x_coeff = np.array(x)[:, np.newaxis]
r.fit(x_coeff, y)
return r.score(x_coeff, y)
def fit(self, smiles_list, logS_list):
X = []
y = []
for i, smiles in enumerate(smiles_list):
mol = Chem.MolFromSmiles(smiles)
(mw, logp, rotors, ap) = self._calc_esol_descriptors(mol)
X.append([mw, logp, rotors, ap])
y.append(logS_list[i])
if self.model == 'linear':
model = LinearRegression()
elif self.model == 'pls':
model = PLSRegression(n_components=2)
elif self.model == 'huber':
model = HuberRegressor(epsilon=1.5, alpha=2.0)
elif self.model == 'ts':
logging.debug(f'Model: {self.model}')
model = TheilSenRegressor()
else:
self.model = 'linear'
model = LinearRegression()
logging.debug(f'Model: {self.model}')
model.fit(X, y)
self._intercept = model.intercept_
self._coef["MW"] = model.coef_[0]
self._coef["LogP"] = model.coef_[1]
self._coef["RB"] = model.coef_[2]
self._coef["AP"] = model.coef_[3]
def regression(
data,
theilsen_max_iter=100,
order="auto",
threshold_multiplier=2,
):
if order == "auto":
order = get_best_degree(data)
elif not isinstance(order, int):
order = 1
reg = Pipeline([
("quad", PolynomialFeatures(degree=order)),
(
"linear",
TheilSenRegressor(max_subpopulation=50,
max_iter=theilsen_max_iter),
),
])
numDims = np.size(data, 1)
X = data[:, 0:numDims - 1] # noqa
Y = data[:, numDims - 1]
inlier_mask = np.ones(np.size(data, 0), dtype=bool)
mask_length = 0
threshold = 0
for _ in range(10):
if mask_length == sum(inlier_mask):
break
else:
mask_length = sum(inlier_mask)
inlier_mask = inlier_mask.astype(bool)
i_X = X[inlier_mask]
i_Y = Y[inlier_mask]
if i_X.shape[0] == 0:
inlier_mask = inlier_mask.astype(int)
break
reg.fit(i_X, i_Y)
ts = reg.predict(X)
residuals = abs(ts - Y)
inlier_residuals = abs(reg.predict(i_X) - i_Y)
threshold = np.median(inlier_residuals)
within = residuals < (threshold_multiplier * threshold)
inlier_mask = within.astype(int)
return reg, inlier_mask, threshold_multiplier * threshold, order
def fit(self, X, y, random_state=None):
"""
Train ENOLS on the given training set.
Parameters
----------
X: an input array of shape (n_sample, n_features)
y: an array of shape (n_sample,) containing the classes for the input examples
Return
------
self: the fitted model
"""
# use random instead of np.random to sample random numbers below
random = check_random_state(random_state)
estimators = [('lr', LinearRegression())]
if isinstance(self.sample_size, int):
self.sample_size = 'reservoir_sampling'
# add all the trained OLS models to this list
self.estimators_lr, self.estimators_TSR, self.estimators_enols = [], [], []
for i in range(self.n_estimators):
samples = sample_without_replacement(n_population=random.choice([50, 100]),
n_samples=random.choice([10, 20]),
random_state=random_state, method=self.sample_size)
X_train, y_train = [], []
for i in samples:
X_train.append(X[i]), y_train.append(y[i])
reg = LinearRegression()
reg.fit(np.array(X_train), np.array(y_train))
tsr = TheilSenRegressor()
tsr.fit(np.array(X_train), np.array(y_train))
enol = StackingRegressor(estimators=estimators, final_estimator=LinearRegression())
enol.fit(np.array(X_train), np.array(y_train))
self.estimators_lr.append(reg), self.estimators_TSR.append(tsr), self.estimators_enols.append(enol)
return self
def test_theil_sen_1d():
X, y, w, c = gen_toy_problem_1d()
# Check that Least Squares fails
lstq = LinearRegression().fit(X, y)
assert np.abs(lstq.coef_ - w) > 0.9
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(random_state=0).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w, 1)
assert_array_almost_equal(theil_sen.intercept_, c, 1)
def get_models():
models = list()
models.append(LinearRegression(fit_intercept=False))
models.append(HuberRegressor(fit_intercept=False))
#models.append(RANSACRegressor())#fit_intercept=False)) # Doesnt have option to not fit the intercept
models.append(
TheilSenRegressor(fit_intercept=False)
) # Strunggling a bit with this one as the output varies a lot given n_samples (if n_samples=1 then it returns the median of the ratio, if it equals the number of data points then it returns essentially the output of least square fitting)
return models
def test_theil_sen_2d():
X, y, w, c = gen_toy_problem_2d()
# Check that Least Squares fails
lstq = LinearRegression().fit(X, y)
assert norm(lstq.coef_ - w) > 1.0
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(max_subpopulation=1e3, random_state=0).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w, 1)
assert_array_almost_equal(theil_sen.intercept_, c, 1)
def __init__(self):
self.regressorName="gb"
if self.regressorName=="rf":
self.clf= RandomForestRegressor(n_estimators=400, max_depth=63,max_features=50, n_jobs=-1)
elif self.regressorName=="gb":
self.clf= GradientBoostingRegressor(alpha=0.9, init=None,max_depth=3, learning_rate=0.2, loss='ls'
,max_features=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0
,n_estimators=2500,presort='auto', random_state=None, subsample=1.0, verbose=0,warm_start=True)
#self.clf =GridSearchCV(estimator=gb, param_grid=self.getParamGrid(),scoring='mean_squared_error',cv=3,n_jobs=-1)
#self.clf=gb
elif self.regressorName=="ridge":
self.clf = RidgeCV(alphas=(0.01, 0.1), fit_intercept=True, normalize=False, scoring=None, cv=5, gcv_mode=None, store_cv_values=False)
elif self.regressorName=="linear":
self.clf = LinearRegression(alpha=0.01,max_iter=5000)
elif self.regressorName=="lasso":
self.clf = LassoCV(cv=10)
elif self.regressorName=="svr":
self.clf = SVR(kernel='rbf',C=0.2, gamma=0.01)
elif self.regressorName=="knn":
self.clf = neighbors.KNeighborsRegressor(1, weights='distance',n_jobs=-1)
elif self.regressorName=="gauss":
self.clf = TheilSenRegressor()
def fit_TheilSen(features_train, labels_train, features_pred): model = TheilSenRegressor() model.fit(features_train, labels_train) labels_pred = model.predict(features_pred) print "TheilSen - coefficient of determination R^2 of the prediction: ", model.score(features_train, labels_train) return labels_pred