def resolution_estimate(raw_data, n_spectra=25):
slopes = []
intercepts = []
for i in range(n_spectra):
mzs, intensities = read_random_spectrum(raw_data)
peak_positions = np.array(gradient(mzs, intensities)[-1])
intensities_at_peaks = intensities[peak_positions]
high_intensity_threshold = np.percentile(intensities_at_peaks, 40)
peak_positions = peak_positions[intensities[peak_positions] > high_intensity_threshold]
resolutions = []
for i, peak_pos in enumerate(peak_positions):
resolutions.append(resolution_at_peak(peak_pos, mzs, intensities))
resolutions = np.array(resolutions)
mzs = mzs[peak_positions]
mzs = mzs[resolutions > 0]
resolutions = resolutions[resolutions > 0]
ransac = RANSACRegressor()
ransac.fit(np.log(mzs).reshape((-1,1)), np.log(resolutions).reshape((-1,1)))
slope = ransac.estimator_.coef_[0][0]
intercept = ransac.estimator_.intercept_[0]
slopes.append(slope)
intercepts.append(intercept)
slope = np.median(slopes)
intercept = np.median(intercepts)
return lambda mz: np.exp(intercept + slope * np.log(mz))
def ransac_fit(X, y):
'''
一个强健的fit
:return:
'''
from sklearn.linear_model import RANSACRegressor
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
residual_metric=lambda x: np.sum(np.abs(x), axis=1),
residual_threshold=5.0,
random_state=0)
ransac.fit(X, y)
# 输出斜率|截距等数据
print('Slope: %.3f' % ransac.estimator_.coef_[0])
print('Intercept: %.3f' % ransac.estimator_.intercept_)
# plot
inlier_mask = ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
line_X = np.arange(3, 10, 1)
line_y_ransac = ransac.predict(line_X[:, np.newaxis])
plt.scatter(X[inlier_mask], y[inlier_mask],
c='blue', marker='o', label='Inliers')
plt.scatter(X[outlier_mask], y[outlier_mask],
c='lightgreen', marker='s', label='Outliers')
plt.plot(line_X, line_y_ransac, color='red')
plt.xlabel('Average number of rooms [RM]')
plt.ylabel('Price in $1000\'s [MEDV]')
plt.legend(loc='upper left')
plt.show()
def train_RANSACRegressionModel(
X,
y,
base_estimator=None,
min_samples=None,
residual_threshold=None,
is_data_valid=None,
is_model_valid=None,
max_trials=100,
stop_n_inliers=inf,
stop_score=inf,
stop_probability=0.99,
residual_metric=None,
random_state=None,
):
"""
Train a RANSAC regression model
"""
model = RANSACRegressor(
base_estimator=base_estimator,
min_samples=min_samples,
residual_threshold=residual_threshold,
is_data_valid=is_data_valid,
is_model_valid=is_model_valid,
max_trials=max_trials,
stop_n_inliers=stop_n_inliers,
stop_score=stop_score,
stop_probability=stop_probability,
residual_metric=residual_metric,
random_state=random_state,
)
model = model.fit(X, y)
return model
def identify_linear_outliers(pts, win_size=7):
# this runs a sliding window across the trace, performing a RANSAC regression
# for each window. A point is considered an outlier if the moving-RANSAC
# never considers it an inlier.
regressor = RANSACRegressor()
x = np.arange(win_size, dtype=np.float64)
x = np.expand_dims(x, axis=1)
inlier_count = np.zeros_like(pts)
npts = len(pts)
for i in range(npts-win_size+1):
y = pts[i:i+win_size]
# RANSAC of this section of the trace
try:
regressor.fit(x, y)
inlier_inds = regressor.inlier_mask_
except ValueError: # no consensus -- (almost) all the points were bad
inlier_inds = []
# accumulate the number of times each point was an inlier
for j, inlier in enumerate(inlier_inds):
if inlier:
inlier_count[i+j] += 1
# Note: the following line will always consider the first and last points outliers!
# However, I don't think this will matter for downstream analysis. -BK
outlier_mask = np.logical_or(inlier_count < 2, pts == 0)
# outlier_inds = np.where(outlier_mask)[0]
#
# # points that are exactly zero are always considered outliers
# outlier_inds = np.append(outlier_inds, np.where(pts==0)[0])
return outlier_mask
def get_outliers_by_ransac(self, table, column_indexes):
'''
Get outliers using RANSAC regression, which deals better with large outliers in the y direction,
and faster than Huber when the number of samples is very large.
RANSAC outpus perfect precision (100%) but far from perfect recall (could be 50% - 60%) in our experiments.
'''
X = table[ :, column_indexes[ :-1]].astype(float)
X = utils.enforce_columns(X)
y = table[ :, column_indexes[-1]].astype(float)
# preprocessing doesn't make any difference for RANSAC in our experiments
#x = preprocessing.minmax_scale(x)
#y = preprocessing.minmax_scale(y)
model_ransac = RANSACRegressor(LinearRegression())
model_ransac.fit(X, y)
inlier_mask = model_ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
outliers = [idx for idx, val in enumerate(outlier_mask) if val]
residuals = abs(model_ransac.predict(X) - y)
confidences = preprocessing.minmax_scale(residuals[outliers])*0.09+0.9
return (outliers, confidences)
def test_ransac_stop_n_inliers():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, stop_n_inliers=2,
random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.n_trials_, 1)
def test_ransac_sparse_csc():
X_sparse = sparse.csc_matrix(X)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0)
ransac_estimator.fit(X_sparse, y)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_predict():
X = np.arange(100)[:, None]
y = np.zeros((100,))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=0.5, random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.predict(X), np.zeros(100))
def test_ransac_score():
X = np.arange(100)[:, None]
y = np.zeros((100,))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=0.5, random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.score(X[2:], y[2:]), 1)
assert_less(ransac_estimator.score(X[:2], y[:2]), 1)
def test_ransac_default_residual_threshold():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, random_state=0)
# Estimate parameters of corrupted data
ransac_estimator.fit(X, y)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def fit_plane(points):
'''
fit a plane through a list of 3d points and return a, b, c, d that represents the plane as ax+by+cz+d=0
'''
X = [[p[0], p[1]] for p in points]
X = np.matrix(X)
y = [p[2] for p in points]
model = RANSACRegressor(LinearRegression())
model.fit(X, y)
d = list(model.estimator_.intercept_.flatten())[0]
a, b = list(model.estimator_.coef_.flatten())
c = -1
return a, b, c, d
def test_ransac_max_trials():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, max_trials=0,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, max_trials=11,
random_state=0)
assert getattr(ransac_estimator, 'n_trials_', None) is None
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.n_trials_, 2)
def regression_information(dem, bilinear_interpolation_results):
dem_shape = dem.shape
# print dem_shape
dem = dem.flatten()
bilinear_interpolation_results = bilinear_interpolation_results.flatten()
alt_data = np.column_stack((dem, bilinear_interpolation_results))
alt_data = alt_data[np.where(alt_data[:, 0] > 0)]
RANSAC_lr = RANSACRegressor(LinearRegression())
RANSAC_lr.fit(alt_data[:, 0:1], alt_data[:, 1])
predict_result = RANSAC_lr.predict(alt_data[:, 0:1]).transpose()[0]
# print predict_result
# print predict_result.shape
residual = bilinear_interpolation_results - predict_result
residual = np.reshape(residual, dem_shape)
return RANSAC_lr, residual
def test_ransac_multi_dimensional_targets():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0)
# 3-D target values
yyy = np.column_stack([y, y, y])
# Estimate parameters of corrupted data
ransac_estimator.fit(X, yyy)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def _ransac_regression(pts, regressor):
ransac = RANSACRegressor(regressor)
x = np.array([a['peak_size'] for a in pts])
y = np.array([b['relative_peak_height'] for b in pts])
X = x[:, np.newaxis]
ransac.fit(X, y)
inlier_mask = ransac.inlier_mask_
ransac_mse = mean_squared_error(y[inlier_mask], ransac.predict(X[inlier_mask])) ** .5
ransac_r2 = r2_score(y[inlier_mask], ransac.predict(X[inlier_mask]))
return {
'intercept': ransac.estimator_.intercept_,
'r_squared': ransac_r2,
'slope': ransac.estimator_.coef_[0],
'sd': ransac_mse
}
def test_ransac_none_estimator():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0)
ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0)
ransac_estimator.fit(X, y)
ransac_none_estimator.fit(X, y)
assert_array_almost_equal(ransac_estimator.predict(X), ransac_none_estimator.predict(X))
def test_ransac_fit_sample_weight():
ransac_estimator = RANSACRegressor(random_state=0)
n_samples = y.shape[0]
weights = np.ones(n_samples)
ransac_estimator.fit(X, y, weights)
# sanity check
assert_equal(ransac_estimator.inlier_mask_.shape[0], n_samples)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
# check that mask is correct
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
# check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
# X = X1 repeated n1 times, X2 repeated n2 times and so forth
random_state = check_random_state(0)
X_ = random_state.randint(0, 200, [10, 1])
y_ = np.ndarray.flatten(0.2 * X_ + 2)
sample_weight = random_state.randint(0, 10, 10)
outlier_X = random_state.randint(0, 1000, [1, 1])
outlier_weight = random_state.randint(0, 10, 1)
outlier_y = random_state.randint(-1000, 0, 1)
X_flat = np.append(np.repeat(X_, sample_weight, axis=0),
np.repeat(outlier_X, outlier_weight, axis=0), axis=0)
y_flat = np.ndarray.flatten(np.append(np.repeat(y_, sample_weight, axis=0),
np.repeat(outlier_y, outlier_weight, axis=0),
axis=0))
ransac_estimator.fit(X_flat, y_flat)
ref_coef_ = ransac_estimator.estimator_.coef_
sample_weight = np.append(sample_weight, outlier_weight)
X_ = np.append(X_, outlier_X, axis=0)
y_ = np.append(y_, outlier_y)
ransac_estimator.fit(X_, y_, sample_weight)
assert_almost_equal(ransac_estimator.estimator_.coef_, ref_coef_)
# check that if base_estimator.fit doesn't support
# sample_weight, raises error
base_estimator = Lasso()
ransac_estimator = RANSACRegressor(base_estimator)
assert_raises(ValueError, ransac_estimator.fit, X, y, weights)
def fit(self, angs, pts):
print(angs.shape)
print(pts.shape)
model1 = RANSACRegressor(LinearRegression())
model2 = RANSACRegressor(LinearRegression())
model1.fit(angs[:,[0]], pts[:,0])
model2.fit(angs[:,[2]], pts[:,1])
self.m1, self.b1 = float(model1.estimator_.coef_), model1.estimator_.intercept_
self.m2, self.b2 = float(model2.estimator_.coef_), model2.estimator_.intercept_
print('Coefficients :')
print(self.m1, self.b1, self.m2, self.b2)
def test_ransac_max_trials():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, max_trials=0,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
# there is a 1e-9 chance it will take these many trials. No good reason
# 1e-2 isn't enough, can still happen
# 2 is the what ransac defines as min_samples = X.shape[1] + 1
max_trials = _dynamic_max_trials(
len(X) - len(outliers), X.shape[0], 2, 1 - 1e-9)
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2)
for i in range(50):
ransac_estimator.set_params(min_samples=2, random_state=i)
ransac_estimator.fit(X, y)
assert_less(ransac_estimator.n_trials_, max_trials + 1)
pd.DataFrame(data).describe() from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression, RANSACRegressor from sklearn.metrics import mean_squared_error x_tr, x_te, y_tr, y_te = train_test_split(data, target, test_size=0.2) lr = LinearRegression() lr.fit(x_tr, y_tr) mean_squared_error(lr.predict(x_tr), y_tr) mean_squared_error(lr.predict(x_te), y_te) Rr = RANSACRegressor() Rr.fit(x_tr, y_tr) mean_squared_error(Rr.predict(x_tr), y_tr) mean_squared_error(Rr.predict(x_te), y_te) from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor dr = DecisionTreeRegressor(max_features='sqrt') dr.fit(x_tr, y_tr) mean_squared_error(dr.predict(x_tr), y_tr) mean_squared_error(dr.predict(x_te), y_te)
def test_ransac_residual_loss():
loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1)
loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1)
loss_mono = lambda y_true, y_pred : np.abs(y_true - y_pred)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
loss=loss_multi1)
ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
loss=loss_multi2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator1.predict(X))
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.loss = loss_mono
ransac_estimator2.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
loss="squared_loss")
ransac_estimator3.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
X = df[['RM']].values
y = df['MEDV'].values
from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
sc_y = StandardScaler()
X_std = sc_x.fit_transform(X)
y_std = sc_y.fit_transform(y)
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RANSACRegressor
import numpy as np
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
residual_metric=lambda x: np.sum(np.abs(x), axis=1),
residual_threshold=5.0,
random_state=0)
ransac.fit(X, y)
inlier_mask = ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
line_X = np.arange(3, 10, 1)
line_y_ransac = ransac.predict(line_X[:, np.newaxis])
plt.scatter(X[inlier_mask],
y[inlier_mask],
c='blue',
marker='o',
label='Inliers')
def test_ransac_residual_loss():
loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1)
loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1)
loss_mono = lambda y_true, y_pred : np.abs(y_true - y_pred)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
loss=loss_multi1)
ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
loss=loss_multi2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator1.predict(X))
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.loss = loss_mono
ransac_estimator2.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
loss="squared_loss")
ransac_estimator3.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
y_test = np.sin(X_test)
X_test = X_test[:, np.newaxis]
y_errors = y.copy()
y_errors[::3] = 3
X_errors = X.copy()
X_errors[::3] = 3
y_errors_large = y.copy()
y_errors_large[::3] = 10
X_errors_large = X.copy()
X_errors_large[::3] = 10
estimators = [('OLS', LinearRegression()),
('Theil-Sen', TheilSenRegressor(random_state=42)),
('RANSAC', RANSACRegressor(random_state=42)),
('HuberRegressor', HuberRegressor())]
colors = {
'OLS': 'turquoise',
'Theil-Sen': 'gold',
'RANSAC': 'lightgreen',
'HuberRegressor': 'black'
}
linestyle = {
'OLS': '-',
'Theil-Sen': '-.',
'RANSAC': '--',
'HuberRegressor': '--'
}
lw = 3
def ransac_regressor(self):
x_train, x_test, y_train, y_test = self.preprocessing()
model = RANSACRegressor()
y_pred = model.fit(x_train, y_train).predict(x_test)
self.printing(y_test, y_pred, 'RANSAC')
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import RANSACRegressor
from boston_dataset import BostonDataset
boston = BostonDataset()
RANSAC = RANSACRegressor()
df = boston.df
# Feature matrix, target vector
X = df['RM'].values.reshape(-1, 1)
y = df['MEDV'].values
# Fit model
RANSAC.fit(X, y)
# [boolean]
inlier_mask = RANSAC.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
line_X = np.arange(3, 10, 1)
# [3, 4, 5, 6, 7, 8, 9] rooms
line_y_ransac = RANSAC.predict(line_X.reshape(-1, 1))
def test():
# print(inlier_mask)
t_pca = [t0_pca, t1_pca]
min_curvature = np.empty([2, 3])
max_curvature = np.empty([2, 3])
saddle = np.empty([2, 3])
for objIndex in [0, 1]:
print "Time: {}".format(timer() - start)
print "Fit polynomial"
if regression_method.lower() in TS_SPECIFIERS:
model = Pipeline([('poly', PolynomialFeatures(degree=order)),
('regr', TheilSenRegressor())])
elif regression_method.lower() in RANSAC_SPECIFIERS:
model = Pipeline([('poly', PolynomialFeatures(degree=order)),
('regr', RANSACRegressor())])
else:
model = Pipeline([('poly', PolynomialFeatures(degree=order)),
('regr', LinearRegression(fit_intercept=False))])
# z as a response to x, y (coords in pca, z-3rd component)
model = model.fit(p_pca[objIndex][:, :2], p_pca[objIndex][:, 2])
# coefficients of the polynomial
if regression_method.lower() in RANSAC_SPECIFIERS:
C = model.named_steps['regr'].estimator_.coef_
else:
C = model.named_steps['regr'].coef_
print "Coefficients: "
print C
可以用在非线性或线性数据集上，具体算法过程看官网吧
2、TheilSen，广义中位数评估
适合小数据，适合处理在X上的中等异常，如果特征数增加到一定程度时并没有好过最小二乘
能接受最大X的29%被破坏
3、Huber，大于线性损失的被认为是异常值的样本
如果样本数>>特征数，则最快
如果啥都不懂就用RANSAC....
'''
rg_1 = RANSACRegressor(base_estimator=None,
min_samples=None,
residual_threshold=None,
is_data_valid=None,
is_model_valid=None,
max_trials=100,
stop_n_inliers=inf,
stop_score=inf,
stop_probability=0.99,
residual_metric=None,
loss='absolute_loss',
random_state=None)
rg_2 = TheilSenRegressor(fit_intercept=True,
copy_X=True,
max_subpopulation=10000.0,
n_subsamples=None,
max_iter=300,
tol=0.001,
random_state=None,
n_jobs=1,
verbose=False)
rg_3 = HuberRegressor(epsilon=1.35,
def get_algorithm(self):
'''
Inputs:
algorithm (string) - Name of the regressor to run. Follows Sklearn naming conventions.
Available keys: ARDRegression | AdaBoostRegressor | BaggingRegressor | BayesianRidge | CCA
DecisionTreeRegressor | ElasticNet | ExtraTreeRegressor
ExtraTreesRegressor | GaussianProcessRegressor | GradientBoostingRegressor
HuberRegressor | KNeighborsRegressor | KernelRidge | Lars | Lasso
LassoLars | LinearRegression | LinearSVR | MLPRegressor | NuSVR |
OrthogonalMatchingPursuit | PLSCanonical | PLSRegression |
PassiveAggressiveRegressor | RANSACRegressor | RandomForestRegressor |
Ridge | SGDRegressor | SVR | TheilSenRegressor | TransformedTargetRegressor
Currently not supporting: ElasticNetCV | LarsCV | LassoCV | LassoLarsCV | LassoLarsIC |
MultiTaskElasticNet | MultiTaskElasticNetCV | MultiTaskLasso | MultiTaskLassoCV |
OrthogonalMatchingPursuitCV | RidgeCV | RadiusNeighborsRegressor
Outputs:
Notes:
Scoring Metrics: https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter
'''
if (self.algorithmName == "ARDRegression"): algorithm = ARDRegression()
elif (self.algorithmName == "AdaBoostRegressor"):
algorithm = AdaBoostRegressor()
elif (self.algorithmName == "BaggingRegressor"):
algorithm = BaggingRegressor()
elif (self.algorithmName == "BayesianRidge"):
algorithm = BayesianRidge()
elif (self.algorithmName == "CCA"):
algorithm = CCA()
elif (self.algorithmName == "DecisionTreeRegressor"):
algorithm = DecisionTreeRegressor()
elif (self.algorithmName == "ElasticNet"):
algorithm = ElasticNet()
elif (self.algorithmName == "ExtraTreeRegressor"):
algorithm = ExtraTreeRegressor()
elif (self.algorithmName == "ExtraTreesRegressor"):
algorithm = ExtraTreesRegressor()
elif (self.algorithmName == "GaussianProcessRegressor"):
algorithm = GaussianProcessRegressor()
elif (self.algorithmName == "GradientBoostingRegressor"):
algorithm = GradientBoostingRegressor()
elif (self.algorithmName == "HuberRegressor"):
algorithm = HuberRegressor()
elif (self.algorithmName == "KNeighborsRegressor"):
algorithm = KNeighborsRegressor()
elif (self.algorithmName == "KernelRidge"):
algorithm = KernelRidge()
elif (self.algorithmName == "Lars"):
algorithm = Lars()
elif (self.algorithmName == "Lasso"):
algorithm = Lasso()
elif (self.algorithmName == "LassoLars"):
algorithm = LassoLars()
elif (self.algorithmName == "LinearRegression"):
algorithm = LinearRegression()
elif (self.algorithmName == "LinearSVR"):
algorithm = LinearSVR()
elif (self.algorithmName == "MLPRegressor"):
algorithm = MLPRegressor()
elif (self.algorithmName == "NuSVR"):
algorithm = NuSVR()
elif (self.algorithmName == "OrthogonalMatchingPursuit"):
algorithm = OrthogonalMatchingPursuit()
elif (self.algorithmName == "PLSCanonical"):
algorithm = PLSCanonical()
elif (self.algorithmName == "PLSRegression"):
algorithm = PLSRegression()
elif (self.algorithmName == "PassiveAggressiveRegressor"):
algorithm = PassiveAggressiveRegressor()
elif (self.algorithmName == "RANSACRegressor"):
algorithm = RANSACRegressor()
elif (self.algorithmName == "RandomForestRegressor"):
algorithm = RandomForestRegressor()
elif (self.algorithmName == "Ridge"):
algorithm = Ridge()
elif (self.algorithmName == "SGDRegressor"):
algorithm = SGDRegressor()
elif (self.algorithmName == "SVR"):
algorithm = SVR()
elif (self.algorithmName == "TheilSenRegressor"):
algorithm = TheilSenRegressor()
elif (self.algorithmName == "TransformedTargetRegressor"):
algorithm = TransformedTargetRegressor()
else:
return None
return algorithm
def get_model_from_name(model_name, training_params=None, is_hp_search=False):
global keras_imported
# For Keras
epochs = 1000
# if os.environ.get('is_test_suite', 0) == 'True' and model_name[:12] == 'DeepLearning':
# print('Heard that this is the test suite. Limiting number of epochs, which will increase training speed dramatically at the expense of model accuracy')
# epochs = 100
all_model_params = {
'LogisticRegression': {},
'RandomForestClassifier': {
'n_jobs': -2,
'n_estimators': 30
},
'ExtraTreesClassifier': {
'n_jobs': -1
},
'AdaBoostClassifier': {},
'SGDClassifier': {
'n_jobs': -1
},
'Perceptron': {
'n_jobs': -1
},
'LinearSVC': {
'dual': False
},
'LinearRegression': {
'n_jobs': -2
},
'RandomForestRegressor': {
'n_jobs': -2,
'n_estimators': 30
},
'LinearSVR': {
'dual': False,
'loss': 'squared_epsilon_insensitive'
},
'ExtraTreesRegressor': {
'n_jobs': -1
},
'MiniBatchKMeans': {
'n_clusters': 8
},
'GradientBoostingRegressor': {
'learning_rate': 0.1,
'warm_start': True
},
'GradientBoostingClassifier': {
'learning_rate': 0.1,
'warm_start': True
},
'SGDRegressor': {
'shuffle': False
},
'PassiveAggressiveRegressor': {
'shuffle': False
},
'AdaBoostRegressor': {},
'LGBMRegressor': {
'n_estimators': 2000,
'learning_rate': 0.15,
'num_leaves': 8,
'lambda_l2': 0.001,
'histogram_pool_size': 16384
},
'LGBMClassifier': {
'n_estimators': 2000,
'learning_rate': 0.15,
'num_leaves': 8,
'lambda_l2': 0.001,
'histogram_pool_size': 16384
},
'DeepLearningRegressor': {
'epochs': epochs,
'batch_size': 50,
'verbose': 2
},
'DeepLearningClassifier': {
'epochs': epochs,
'batch_size': 50,
'verbose': 2
},
'CatBoostRegressor': {},
'CatBoostClassifier': {}
}
# if os.environ.get('is_test_suite', 0) == 'True':
# all_model_params
model_params = all_model_params.get(model_name, None)
if model_params is None:
model_params = {}
if is_hp_search == True:
if model_name[:12] == 'DeepLearning':
model_params['epochs'] = 50
if model_name[:4] == 'LGBM':
model_params['n_estimators'] = 500
if training_params is not None:
print('Now using the model training_params that you passed in:')
print(training_params)
# Overwrite our stock params with what the user passes in (i.e., if the user wants 10,000 trees, we will let them do it)
model_params.update(training_params)
print(
'After overwriting our defaults with your values, here are the final params that will be used to initialize the model:'
)
print(model_params)
model_map = {
# Classifiers
'LogisticRegression': LogisticRegression(),
'RandomForestClassifier': RandomForestClassifier(),
'RidgeClassifier': RidgeClassifier(),
'GradientBoostingClassifier': GradientBoostingClassifier(),
'ExtraTreesClassifier': ExtraTreesClassifier(),
'AdaBoostClassifier': AdaBoostClassifier(),
'LinearSVC': LinearSVC(),
# Regressors
'LinearRegression': LinearRegression(),
'RandomForestRegressor': RandomForestRegressor(),
'Ridge': Ridge(),
'LinearSVR': LinearSVR(),
'ExtraTreesRegressor': ExtraTreesRegressor(),
'AdaBoostRegressor': AdaBoostRegressor(),
'RANSACRegressor': RANSACRegressor(),
'GradientBoostingRegressor': GradientBoostingRegressor(),
'Lasso': Lasso(),
'ElasticNet': ElasticNet(),
'LassoLars': LassoLars(),
'OrthogonalMatchingPursuit': OrthogonalMatchingPursuit(),
'BayesianRidge': BayesianRidge(),
'ARDRegression': ARDRegression(),
# Clustering
'MiniBatchKMeans': MiniBatchKMeans(),
}
try:
model_map['SGDClassifier'] = SGDClassifier(max_iter=1000, tol=0.001)
model_map['Perceptron'] = Perceptron(max_iter=1000, tol=0.001)
model_map['PassiveAggressiveClassifier'] = PassiveAggressiveClassifier(
max_iter=1000, tol=0.001)
model_map['SGDRegressor'] = SGDRegressor(max_iter=1000, tol=0.001)
model_map['PassiveAggressiveRegressor'] = PassiveAggressiveRegressor(
max_iter=1000, tol=0.001)
except TypeError:
model_map['SGDClassifier'] = SGDClassifier()
model_map['Perceptron'] = Perceptron()
model_map['PassiveAggressiveClassifier'] = PassiveAggressiveClassifier(
)
model_map['SGDRegressor'] = SGDRegressor()
model_map['PassiveAggressiveRegressor'] = PassiveAggressiveRegressor()
if xgb_installed:
model_map['XGBClassifier'] = XGBClassifier()
model_map['XGBRegressor'] = XGBRegressor()
if lgb_installed:
model_map['LGBMRegressor'] = LGBMRegressor()
model_map['LGBMClassifier'] = LGBMClassifier()
if catboost_installed:
model_map['CatBoostRegressor'] = CatBoostRegressor(
calc_feature_importance=True)
model_map['CatBoostClassifier'] = CatBoostClassifier(
calc_feature_importance=True)
if model_name[:12] == 'DeepLearning':
if keras_imported == False:
# Suppress some level of logs if TF is installed (but allow it to not be installed, and use Theano instead)
try:
os.environ['TF_CPP_MIN_VLOG_LEVEL'] = '3'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow import logging
logging.set_verbosity(logging.INFO)
except:
pass
global maxnorm
global Dense, Dropout
global LeakyReLU, PReLU, ThresholdedReLU, ELU
global Sequential
global keras_load_model
global regularizers, optimizers
global Activation
global KerasRegressor, KerasClassifier
from keras.constraints import maxnorm
from keras.layers import Activation, Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU, PReLU, ThresholdedReLU, ELU
from keras.models import Sequential
from keras.models import load_model as keras_load_model
from keras import regularizers, optimizers
from keras.wrappers.scikit_learn import KerasRegressor, KerasClassifier
keras_imported = True
model_map['DeepLearningClassifier'] = KerasClassifier(
build_fn=make_deep_learning_classifier)
model_map['DeepLearningRegressor'] = KerasRegressor(
build_fn=make_deep_learning_model)
try:
model_without_params = model_map[model_name]
except KeyError as e:
print(
'It appears you are trying to use a library that is not available when we try to import it, or using a value for model_names that we do not recognize'
)
raise (e)
if os.environ.get('is_test_suite', False) == 'True':
if 'n_jobs' in model_params:
model_params['n_jobs'] = 1
model_with_params = model_without_params.set_params(**model_params)
return model_with_params
def fit_linreg_robust(x,
y,
mask=None,
intercept=False,
r2=True,
est_method='rlm'):
"""Apply robust linear regression of y w.r.t x.
Arguments
---------
x: :class:`~numpy.ndarray` or sparse `csr_matrix`
A vector of independent variables.
y: :class:`~numpy.ndarray` or sparse `csr_matrix`
A vector of dependent variables.
intercept: bool
If using steady state assumption for fitting, then:
True -- the linear regression is performed with an unfixed intercept;
False -- the linear regresssion is performed with a fixed zero intercept.
est_method: str (default: `rlm`)
The linear regression estimation method that will be used.
Returns
-------
k: float
The estimated slope.
b: float
The estimated intercept.
r2: float
Coefficient of determination or r square calculated with the extreme data points.
all_r2: float
The r2 calculated using all data points.
"""
x = x.A if issparse(x) else x
y = y.A if issparse(y) else y
_mask = np.logical_and(~np.isnan(x), ~np.isnan(y))
if mask is not None:
_mask &= mask
xx = x[_mask]
yy = y[_mask]
try:
if est_method.lower() == 'rlm':
xx_ = sm.add_constant(xx) if intercept else xx
res = sm.RLM(yy, xx_).fit()
k, b = res.params[::-1] if intercept else (res.params[0], 0)
elif est_method.lower() == 'ransac':
reg = RANSACRegressor(LinearRegression(fit_intercept=intercept),
random_state=0)
reg.fit(xx.reshape(-1, 1), yy.reshape(-1, 1))
k, b = reg.estimator_.coef_[0, 0], (reg.estimator_.intercept_[0]
if intercept else 0)
else:
raise ImportError(
f"estimation method {est_method} is not implemented. "
f"Currently supported linear regression methods include `rlm` and `ransac`."
)
except:
if intercept:
ym = np.mean(yy)
xm = np.mean(xx)
cov = np.mean(xx * yy) - xm * ym
var_x = np.mean(xx * xx) - xm * xm
k = cov / var_x
b = ym - k * xm
# # assume b is always positive
# if b < 0:
# k, b = np.mean(xx * yy) / np.mean(xx * xx), 0
else:
# use uncentered cov and var_x
cov = np.mean(xx * yy)
var_x = np.mean(xx * xx)
k = cov / var_x
b = 0
if r2:
SS_tot_n, all_SS_tot_n = np.var(yy), np.var(y)
SS_res_n, all_SS_res_n = (
np.mean((yy - k * xx - b)**2),
np.mean((y - k * x - b)**2),
)
r2, all_r2 = 1 - SS_res_n / SS_tot_n, 1 - all_SS_res_n / all_SS_tot_n
return k, b, r2, all_r2
else:
return k, b
def main():
regression_name=sys.argv[1]
datapath=sys.argv[2]
if(datapath=='housing.data.txt'):
df = pd.read_csv('housing.data.txt',
header=None,
sep='\s+')
df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',
'NOX', 'RM', 'AGE', 'DIS', 'RAD',
'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
X=df.iloc[:,:-1]
y=df['MEDV'].values
else:
df=pd.read_csv('all_breakdown.csv')
df=df.fillna(0)
X=df.iloc[:,1:-1]
y=df['WIND TOTAL'].values
y2d=y[ : , np . newaxis ] #change one dimensional array to two dimensions
sc_x = StandardScaler ( )
sc_y = StandardScaler ( )
sc_x.fit (X)
sc_y.fit (y)
x_std = sc_x.transform(X)
y_std = sc_y.transform(y2d).flatten()
X_train, X_test, y_train, y_test = train_test_split(x_std, y_std, test_size=0.3, random_state=0)
if (regression_name=="Linear"):
model = LinearRegression ()
model.fit (X_train, y_train)
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
print('Linear Regression')
print ( 'Slope : %.3f ' %model.coef_[0])
print ( 'Intercept : %.3f' %model.intercept_)
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)));
elif (regression_name=="RANSAC"):
ransac = RANSACRegressor(LinearRegression() , max_trials=100, min_samples=50, loss='absolute_loss' , residual_threshold=5.0, random_state=1)
ransac. fit (X_train,y_train)
print('RANSAC Regressor')
print ( 'Slope : %.3f ' %ransac.estimator_.coef_[0])
print ( 'Intercept : %.3f' %ransac.estimator_.intercept_)
# print( 'Score of the prediction: %.3f' %ransac.score(X_test,y_test))
y_train_pred = ransac.predict(X_train)
y_test_pred = ransac.predict(X_test)
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)));
elif (regression_name=="Ridge"):
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
y_train_pred = ridge.predict(X_train)
y_test_pred = ridge.predict(X_test)
print('Ridge Regularization')
print ('Slope : %.3f'%ridge.coef_[0])
print ('Intercept : %.3f' %ridge.intercept_)
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)));
elif (regression_name=="Lasso"):
lasso = Lasso(alpha=1.0)
lasso.fit(X_train, y_train)
y_train_pred = lasso.predict(X_train)
y_test_pred = lasso.predict(X_test)
print('Lasso Regularization')
print ( 'Slope : %.3f ' %lasso.coef_[0])
print ( 'Intercept : %.3f' %lasso.intercept_ )
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)));
elif (regression_name=="Nonlinear"):
tree = DecisionTreeRegressor(max_depth=3)
tree. fit (X_train,y_train)
y_test_pred = tree . predict (X_test)
y_train_pred = tree.predict(X_train)
print('Non linear Regression - Decision Tree Regressor')
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)));
elif (regression_name=="Normal"):
if(datapath=='housing.data.txt'):
onevec = np.ones((X_train.shape[0]) ) #this generates a 1−dimensional array
onevec = onevec [ : , np . newaxis ] # changes the 1−dimensional array to 2−dimensional array
Xb = np.hstack((onevec,X_train)) # Xb is a 2−dimensional array
w = np.zeros(X_train.shape[1])
z = np.linalg.inv(np.dot(Xb.T,Xb))
w = np.dot(z, np.dot(Xb.T,y_train))
print('Normal Equation Solution')
print('Slope: %.3f' %w[1])
print ( 'Intercept : %.3f' %w[0]);
yhat = np.dot(Xb,w.T)
print('MSE train: %.3f,' %mean_squared_error(y_train, yhat))
else:
print('Not Applicable');
else:
print ("No regression available with the given name");
print("--- Time taken is %s seconds ---" % (time.time() - start_time))
def fit_RANSAC(features_train, labels_train, features_pred): model = RANSACRegressor() model.fit(features_train, labels_train) labels_pred = model.predict(features_pred) print "RANSAC - coefficient of determination R^2 of the prediction: ", model.score(features_train, labels_train) return labels_pred
#
from sklearn.linear_model import TheilSenRegressor
X = np.reshape(star.temp.array,(-1,1))
y = star.light
reg = TheilSenRegressor(random_state=0).fit(X,y)
plt.scatter(star.temp, star.light)
plt.plot(xr, reg.intercept_ + reg.coef_*xr,'k-')
#
from sklearn.linear_model import RANSACRegressor
reg = RANSACRegressor().fit(X,y)
i = reg.inlier_mask_
plt.scatter(star.temp[i], star.light[i])
plt.scatter(star.temp[~i], star.light[~i],marker='x')
plt.plot(xr, reg.estimator_.intercept_ + reg.estimator_.coef_*xr,
'k-')
# ## Exercises
# ## Packages Used
import sys
import matplotlib
import statsmodels as sm
import seaborn as sns
def test_ransac_residual_metric():
residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1)
residual_metric2 = lambda dy: np.sum(dy ** 2, axis=1)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
residual_metric=residual_metric1)
ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
residual_metric=residual_metric2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_equal(ransac_estimator0.predict(X), ransac_estimator1.predict(X))
assert_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.fit(X, y)
assert_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X))
def get_ransac(self, x, y): # RANSAC регрессор
ransac = RANSACRegressor(LinearRegression(), residual_threshold=5)
ransac.fit(x, y)
return ransac
ax.plot(xvals, xvals, 'k--')
ax.set_xlim([-125, 60])
ax.set_ylim([-125, 60])
ax.set_aspect('equal')
ax.text(0.5,
1.05,
'y = {0:0.3f}x + {1:0.3f}'.format(perform_dstBiot['Slope'],
perform_dstBiot['Intercept']),
horizontalalignment='center',
transform=ax.transAxes)
ax.set_xlabel('Sym-H (Observed) [nT]')
ax.set_ylabel('Sym-H (Modeled) [nT]')
if False:
from sklearn.linear_model import TheilSenRegressor, RANSACRegressor
#tsreg = TheilSenRegressor(random_state=77)
reg = RANSACRegressor(random_state=77)
reg.fit(kyotodata['sym-h'][:, np.newaxis], data['dstBiot'][:,
np.newaxis])
y_pred = reg.predict(xvals[:, np.newaxis])
ax.plot(xvals, y_pred, 'm-', label='RANSAC')
ax.legend()
plt.tight_layout()
plt.savefig('Jan2005_DstCompare_scatter_new.png')
#now write metrics to log
logging.info('=================')
logging.info('===PERFORMANCE===')
logging.info('=================')
logging.info('N_points: {}\n'.format(len(data['dstBiot'])))
if useBiot:
from sklearn import datasets
california = datasets.california_housing.fetch_california_housing()
X, y = california.data, california.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression, RANSACRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.svm import SVR
from sklearn.svm import LinearSVR
regressors = [
LinearRegression(),
RANSACRegressor(),
KNeighborsRegressor(),
KNeighborsRegressor(n_neighbors=9, metric='manhattan'),
SVR(),
LinearSVR(),
SVR(kernel='linear'
), # Cf. LinearSVR: much slower, might be better or worse:
GaussianProcessRegressor(),
]
from sklearn.metrics import explained_variance_score
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
from time import time
for model in regressors:
def main():
parser = argparse.ArgumentParser(
description=
'Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs')
parser.add_argument('--ROOT_PATH', default='datasets/airborne_lidar')
parser.add_argument('--dataset', default='airborne_lidar')
# parameters
parser.add_argument(
'--compute_geof',
default=1,
type=int,
help='compute hand-crafted features of the local geometry')
parser.add_argument(
'--k_nn_local',
default=20,
type=int,
help='number of neighbors to describe the local geometry')
parser.add_argument('--k_nn_adj',
default=5,
type=int,
help='number of neighbors for the adjacency graph')
parser.add_argument('--voxel_width',
default=0.03,
type=float,
help='voxel size when subsampling (in m)')
parser.add_argument('--plane_model',
default=1,
type=int,
help='uses a simple plane model to derive elevation')
parser.add_argument(
'--use_voronoi',
default=0.0,
type=float,
help=
'uses the Voronoi graph in combination to knn to build the adjacency graph, '
'useful for sparse aquisitions. If 0., do not use voronoi. '
'If >0, then is the upper length limit for an edge to be kept. ')
parser.add_argument('--ver_batch',
default=5000000,
type=int,
help='batch size for reading large files')
args = parser.parse_args()
# path to data
if args.ROOT_PATH[-1] == '/':
root = args.ROOT_PATH
else:
root = args.ROOT_PATH + '/'
if not os.path.exists(root + 'features_supervision'):
os.makedirs(root + 'features_supervision')
# list of subfolders to be processed
if args.dataset == 'airborne_lidar':
folders = ["trn/", "val/", "tst/"]
n_labels = 4
else:
raise ValueError('%s is an unknown data set' % args.dataset)
pruning = args.voxel_width > 0
# ------------------------------------------------------------------------------
for folder in folders:
print("=================\n " + folder + "\n=================")
data_folder = root + folder
output_folder = root + "features_supervision/" + folder
if not os.path.isdir(data_folder):
raise ValueError(f"{folder} does not exist")
if not os.path.isdir(output_folder):
os.mkdir(output_folder)
if args.dataset == 'airborne_lidar':
files = glob.glob(data_folder + "*.las")
if len(files) == 0:
raise ValueError(f"{folder} is empty")
n_files = len(files)
i_file = 0
for file in files:
file_name = os.path.splitext(os.path.basename(file))[0]
data_file = f"{data_folder}{file_name}.las"
str_file = f"{output_folder}{file_name}.h5"
i_file = i_file + 1
print(f"{i_file} / {n_files}---> {file_name}")
if os.path.isfile(str_file):
print(
" graph structure already computed - delete for update..."
)
else:
# --- build the geometric feature file h5 file ---
print(" computing graph structure...")
# --- read the data files and compute the labels---
if args.dataset == 'airborne_lidar':
xyz, nb_return, intensity, label = read_airborne_lidar_format(
data_file)
if args.dataset == 's3dis':
xyz, rgb, labels, objects = read_s3dis_format(data_file)
if pruning:
n_objects = int(objects.max() + 1)
xyz, rgb, labels, objects = libply_c.prune(
xyz, args.voxel_width, rgb, labels, objects,
n_labels, n_objects)
# hard_labels = labels.argmax(axis=1)
objects = objects[:, 1:].argmax(axis=1) + 1
else:
# hard_labels = labels
objects = objects
elif args.dataset == 'sema3d':
has_labels = (os.path.isfile(label_file))
if has_labels:
xyz, rgb, labels = read_semantic3d_format(
data_file, n_labels, label_file, args.voxel_width,
args.ver_batch)
else:
xyz, rgb = read_semantic3d_format(
data_file, 0, '', args.voxel_width, args.ver_batch)
labels = np.array([0])
objects = np.array([0])
is_transition = np.array(False)
elif args.dataset == 'vkitti':
xyz, rgb, labels = read_vkitti_format(data_file)
if pruning:
xyz, rgb, labels, o = libply_c.prune(
xyz.astype('f4'), args.voxel_width,
rgb.astype('uint8'), labels.astype('uint8'),
np.zeros(1, dtype='uint8'), n_labels, 0)
# ---compute nn graph-------
n_ver = xyz.shape[0]
print("computing NN structure")
graph_nn, local_neighbors = compute_graph_nn_2(
xyz,
args.k_nn_adj,
args.k_nn_local,
voronoi=args.use_voronoi)
if args.dataset == 's3dis':
is_transition = objects[graph_nn["source"]] != objects[
graph_nn["target"]]
elif args.dataset == 'sema3d' and has_labels:
# sema has no object, we make them ourselves with label inpainting
hard_labels = np.argmax(labels[:, 1:], 1) + 1
no_labels = (labels[:, 1:].sum(1) == 0).nonzero()
hard_labels[no_labels] = 0
is_transition = hard_labels[graph_nn["source"]] != hard_labels[graph_nn["target"]] * (hard_labels[graph_nn["source"]] != 0) \
* (hard_labels[graph_nn["target"]] != 0)
edg_source = graph_nn["source"][(
is_transition == 0).nonzero()].astype('uint32')
edg_target = graph_nn["target"][(
is_transition == 0).nonzero()].astype('uint32')
edge_weight = np.ones_like(edg_source).astype('f4')
node_weight = np.ones((n_ver, ), dtype='f4')
node_weight[no_labels] = 0
print("Inpainting labels")
dump, objects = libcp.cutpursuit2(
np.array(hard_labels).reshape((n_ver, 1)).astype('f4'),
edg_source, edg_target, edge_weight, node_weight, 0.01)
is_transition = objects[graph_nn["source"]] != objects[
graph_nn["target"]]
elif args.dataset == 'vkitti':
# we define the objects as the constant connected components of the labels
hard_labels = np.argmax(labels, 1)
is_transition = hard_labels[
graph_nn["source"]] != hard_labels[graph_nn["target"]]
dump, objects = libply_c.connected_comp(
n_ver, graph_nn["source"].astype('uint32'),
graph_nn["target"].astype('uint32'),
(is_transition == 0).astype('uint8'), 0)
if args.compute_geof:
geof = libply_c.compute_geof(
xyz, local_neighbors,
args.k_nn_local).astype('float32')
geof[:, 3] = 2. * geof[:, 3]
else:
geof = 0
if args.plane_model: # use a simple palne model to the compute elevation
low_points = (xyz[:, 2] - xyz[:, 2].min() <
0.5).nonzero()[0]
reg = RANSACRegressor(random_state=0).fit(
xyz[low_points, :2], xyz[low_points, 2])
elevation = xyz[:, 2] - reg.predict(xyz[:, :2])
else:
elevation = xyz[:, 2] - xyz[:, 2].min()
# compute the xy normalized position
ma, mi = np.max(xyz[:, :2], axis=0,
keepdims=True), np.min(xyz[:, :2],
axis=0,
keepdims=True)
xyn = (xyz[:, :2] - mi) / (ma - mi + 1e-8) # global position
write_structure(
str_file, xyz, rgb, graph_nn,
local_neighbors.reshape([n_ver, args.k_nn_local]),
is_transition, labels, objects, geof, elevation, xyn)
def fit(self, idle_engine_speed, on_engine, temperature_derivatives,
temperatures, *args):
"""
Calibrates an engine temperature regression model to predict engine
temperatures.
This model returns the delta temperature function of temperature
(previous), acceleration, and power at the wheel.
:param idle_engine_speed:
Engine speed idle median and std [RPM].
:type idle_engine_speed: (float, float)
:param on_engine:
If the engine is on [-].
:type on_engine: numpy.array
:param temperature_derivatives:
Derivative temperature vector [°C].
:type temperature_derivatives: numpy.array
:param temperatures:
Temperature vector [°C].
:type temperatures: numpy.array
:return:
The calibrated engine temperature regression model.
:rtype: ThermalModel
"""
spl = _build_samples(temperature_derivatives, temperatures, *args)
self.thermostat = self._identify_thermostat(spl, idle_engine_speed)
spl = _filter_samples(spl, on_engine, self.thermostat)
opt = {
'random_state': 0,
'max_depth': 2,
'n_estimators': int(min(300, 0.25 * (len(spl) - 1))),
'loss': 'huber',
'alpha': 0.99
}
model = RANSACRegressor(base_estimator=self.base_model(**opt),
random_state=0,
min_samples=0.85,
max_trials=10)
model = Pipeline([('feature_selection',
_SelectFromModel(model,
'0.8*median',
in_mask=(0, 2))),
('classification', model)])
model.fit(spl[:, :-1], spl[:, -1])
self.model = model.steps[-1][-1]
self.mask = np.where(model.steps[0][-1]._get_support_mask())[0]
self.min_temp = spl[:, 0].min()
spl = spl[:co2_utl.argmax(self.thermostat <= spl[:, 0])]
if not spl.any():
self.min_temp = -float('inf')
return self
spl = spl[:co2_utl.argmax(np.percentile(spl[:, 0], 60) <= spl[:, 0])]
opt = {
'random_state': 0,
'max_depth': 2,
'n_estimators': int(min(300, 0.25 * (len(spl) - 1))),
'loss': 'huber',
'alpha': 0.99
}
model = self.base_model(**opt)
model = Pipeline([('feature_selection',
_SelectFromModel(model, '0.8*median',
in_mask=(1, ))),
('classification', model)])
model.fit(spl[:, 1:-1], spl[:, -1])
self.cold = model.steps[-1][-1]
self.mask_cold = np.where(
model.steps[0][-1]._get_support_mask())[0] + 1
return self
def test_ransac_min_n_samples():
base_estimator = LinearRegression()
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2. / X.shape[0],
residual_threshold=5, random_state=0)
ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1,
residual_threshold=5, random_state=0)
ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2,
residual_threshold=5, random_state=0)
ransac_estimator5 = RANSACRegressor(base_estimator, min_samples=2.0,
residual_threshold=5, random_state=0)
ransac_estimator6 = RANSACRegressor(base_estimator,
residual_threshold=5, random_state=0)
ransac_estimator7 = RANSACRegressor(base_estimator,
min_samples=X.shape[0] + 1,
residual_threshold=5, random_state=0)
ransac_estimator1.fit(X, y)
ransac_estimator2.fit(X, y)
ransac_estimator5.fit(X, y)
ransac_estimator6.fit(X, y)
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator2.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator5.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator6.predict(X))
assert_raises(ValueError, ransac_estimator3.fit, X, y)
assert_raises(ValueError, ransac_estimator4.fit, X, y)
assert_raises(ValueError, ransac_estimator7.fit, X, y)
def main():
# Checks for correct number of arguments
if len(sys.argv) != 3:
print(
'usage: ./troll_identifier.py [TRAIN DATASET] [TEST/DEV DATASET]')
sys.sys.exit()
# set up dataset
data_train = pd.read_csv(sys.argv[1])
data_test = pd.read_csv(sys.argv[2])
print('train:\n{}\n'.format(sys.argv[1]))
print('test:\n{}\n'.format(sys.argv[2]))
if 'small' in sys.argv[1]:
size = 'small'
elif 'medium' in sys.argv[1]:
size = 'medium'
else:
size = 'large'
x_train = data_train.drop(
[data_train.columns[0], data_train.columns[1], data_train.columns[-1]],
axis=1).apply(pd.to_numeric, errors='ignore')
y_train = pd.Series(data_train.iloc[:, -1])
x_test = data_test.drop(
[data_test.columns[0], data_test.columns[1], data_test.columns[-1]],
axis=1).apply(pd.to_numeric, errors='ignore')
y_test = pd.Series(data_test.iloc[:, -1])
# type = input('type: [1: supervised, 2: semi-supervised, 3: unsupervised] ')
# if type == 1:
parameter = None
method = input('select a method: {}: '.format(methods))
if method == 1:
classifier = input('select a classifier: {}: '.format(classifiers))
if classifier == 1:
parameter = input('criterion: [1: gini, 2: entropy] ')
if parameter == 1:
model = DecisionTreeClassifier(criterion='gini')
parameter = 'gini'
elif parameter == 2:
model = DecisionTreeClassifier(criterion='entropy')
parameter = 'entropy'
else:
print('no criterion chosen')
sys.exit()
elif classifier == 2:
model = ExtraTreeClassifier()
elif classifier == 3:
model = ExtraTreesClassifier()
elif classifier == 4:
parameter = input('n: [1: 1, 2: 3: 3: 5] ')
if parameter == 1:
model = KNeighborsClassifier(n_neighbors=1)
parameter = '1'
elif parameter == 2:
model = KNeighborsClassifier(n_neighbors=3)
parameter = '3'
elif parameter == 3:
model = KNeighborsClassifier(n_neighbors=5)
parameter = '5'
else:
print('no n chosen')
sys.exit()
elif classifier == 5:
parameter = input(
'version: [1: gaussian, 2: bernoulli, 3: multinomial, 4: complement] '
)
if parameter == 1:
model = GaussianNB()
parameter = 'gaussian'
elif parameter == 2:
model = BernoulliNB()
parameter = 'bernoulli'
elif parameter == 3:
model = MultinomialNB()
parameter = 'multinomial'
elif parameter == 4:
model = ComplementNB()
parameter = 'complement'
else:
print('no version chosen')
sys.exit()
elif classifier == 6:
model = RadiusNeighborsClassifier(radius=1.0)
elif classifier == 7:
model = RandomForestClassifier(n_estimators=50, random_state=1)
elif classifier == 8:
model = LinearSVC(multi_class='crammer_singer') #multi_class='ovr'
elif classifier == 9:
model = GradientBoostingClassifier()
elif classifier == 10:
model = GaussianProcessClassifier(multi_class='one_vs_one')
elif classifier == 11:
model = SGDClassifier()
elif classifier == 12:
model = PassiveAggressiveClassifier()
elif classifier == 13:
model = NearestCentroid()
elif classifier == 14:
model = Perceptron(tol=1e-3, random_state=0)
elif classifier == 15:
model = MLPClassifier()
elif classifier == 16:
model = AdaBoostClassifier(n_estimators=50)
elif classifier == 17:
parameter = input(
'strategy: [1: stratified, 2: most frequent, 3: prior, 4: uniform, 5: constant] '
)
if parameter == 1:
model = DummyClassifier(strategy='stratified')
parameter = 'stratified'
elif parameter == 2:
model = DummyClassifier(strategy='most_frequent')
parameter = 'most frequent'
elif parameter == 3:
model = DummyClassifier(strategy='prior')
parameter = 'prior'
elif parameter == 4:
model = DummyClassifier(strategy='uniform')
parameter = 'uniform'
elif parameter == 5:
model = DummyClassifier(strategy='constant')
parameter = 'constant'
else:
print('no strategy selected')
sys.exit()
else:
print('no classifier chosen')
sys.exit()
import time
# Starts timer
start = time.clock()
# train the model using the training sets and check score
model.fit(x_train, y_train)
model.score(x_train, y_train)
# predict output
predictions = pd.Series(model.predict(x_test))
report = classification_report(
y_test,
predictions,
target_names=['RightTroll', 'LeftTroll', 'Other'])
confusion = confusion_matrix(
y_test, predictions, labels=["RightTroll", "LeftTroll", "Other"])
if (parameter != None):
filename = '{},{},{},{}.txt'.format(size, methods[method],
classifiers[classifier],
parameter)
else:
filename = '{},{},{}.txt'.format(size, methods[method],
classifiers[classifier])
# Prints the time taken
end = time.clock()
time = str(end - start)
with open(filename, 'w') as output:
output.write('method:\n{}\n\n'.format(methods[method]))
output.write('classifier:\n{}\n\n'.format(classifiers[classifier]))
output.write('accuracy:\n{:.2f}%\n\n'.format(
100 * accuracy_score(y_test, predictions)))
output.write('report:\n{}\n\n'.format(report))
output.write('confusion:\n{}\n\n'.format(confusion))
output.write('time:\n{}s\n\n'.format(time))
output.write('data:\n{:10}\t{:10}\t{:10}\n'.format(
'actual', 'predict', 'match?'))
for i in range(len(predictions)):
output.write('{:10}\t{:10}\t{:10}\n'.format(
y_train[i], predictions[i], y_test[i] == predictions[i]))
print('\nmethod:\n{}\n'.format(methods[method]))
print('classifier:\n{}\n'.format(classifiers[classifier]))
print('accuracy:\n{:.2f}%\n'.format(
100 * accuracy_score(y_test, predictions)))
print('report:\n{}\n'.format(report))
print('confusion:\n{}\n'.format(confusion))
print('time: {}s\n'.format(time))
elif method == 2:
# transform into binary classification problem
# y_train = y_train.apply(lambda x: 0 if x == 'Other' else 1)
# y_test = y_test.apply(lambda x: 0 if x == 'Other' else 1)
# transform string labels into integers
le = LabelEncoder()
le.fit(
y_train
) # print(le.transform(['LeftTroll', 'Other', 'Other', 'RightTroll'])), print(le.inverse_transform([0, 1, 2, 1]))
print(le.classes_)
y_train = le.transform(y_train)
y_test = le.transform(y_test)
regressor = input('select a regressor: {}: '.format(regressors))
if regressor == 1:
print(method, regressor)
model = LinearDiscriminantAnalysis()
elif regressor == 2:
print(method, regressor)
model = LogisticRegression(solver='lbfgs',
multi_class='multinomial') #'newton-cg'
elif regressor == 3:
print(method, regressor)
model = RidgeClassifier()
elif regressor == 4:
print(method, regressor)
model = QuadraticDiscriminantAnalysis()
elif regressor == 5:
model = OneVsRestClassifier(LinearRegression())
elif regressor == 6:
model = OneVsRestClassifier(DecisionTreeRegressor())
elif regressor == 7:
print(method, regressor)
model = OneVsRestClassifier(Lasso(alpha=0.1))
elif regressor == 8:
print(method, regressor)
model = OneVsRestClassifier(MultiTaskLasso(alpha=0.1))
elif regressor == 9:
print(method, regressor)
model = OneVsRestClassifier(ElasticNet(random_state=0))
elif regressor == 10:
print(method, regressor)
model = OneVsRestClassifier(MultiTaskElasticNet(random_state=0))
elif regressor == 11:
print(method, regressor)
model = OneVsRestClassifier(Lars(n_nonzero_coefs=1))
elif regressor == 12:
print(method, regressor)
model = OneVsRestClassifier(LassoLars(alpha=.1))
elif regressor == 13:
print(method, regressor)
model = OneVsRestClassifier(OrthogonalMatchingPursuit())
elif regressor == 14:
print(method, regressor)
model = OneVsRestClassifier(BayesianRidge())
elif regressor == 15:
print(method, regressor)
model = OneVsRestClassifier(ARDRegression())
elif regressor == 16:
print(method, regressor)
model = OneVsRestClassifier(TheilSenRegressor(random_state=0))
elif regressor == 17:
print(method, regressor)
model = OneVsRestClassifier(HuberRegressor())
elif regressor == 18:
print(method, regressor)
model = OneVsRestClassifier(RANSACRegressor(random_state=0))
else:
print('no regressor chosen')
sys.exit()
import time
# Starts timer
start = time.clock()
# train the model using the training sets and check score
model.fit(x_train, y_train)
model.score(x_train, y_train)
# y_train = le.inverse_transform(y_train)
# y_test = le.inverse_transform(y_test)
# print('coefficient:', model.coef_)
# print('intercept:', model.intercept_)
# predict output
predictions = pd.Series(model.predict(x_test))
if (parameter != None):
filename = '{},{},{},{}.txt'.format(size, methods[method],
regressors[regressor],
parameter)
else:
filename = '{},{},{}.txt'.format(size, methods[method],
regressors[regressor])
# Prints the time taken
end = time.clock()
time = str(end - start)
with open(filename, 'w') as output:
output.write('method:\n{}\n\n'.format(methods[method]))
output.write('regressor:\n{}\n\n'.format(regressors[regressor]))
output.write('accuracy:\n{:.2f}%\n\n'.format(
100 * accuracy_score(y_test, predictions)))
output.write('time:\n{}s\n\n'.format(time))
output.write('data:\n{:10}\t{:10}\t{:10}\n'.format(
'actual', 'predict', 'match?'))
for i in range(len(predictions)):
output.write('{:10}\t{:10}\t{:10}\n'.format(
y_train[i], predictions[i], y_test[i] == predictions[i]))
print('\nmethod:\n{}\n'.format(methods[method]))
print('regressor:\n{}\n'.format(regressors[regressor]))
print('accuracy:\n{:.2f}%\n'.format(
100 * accuracy_score(y_test, predictions)))
print('time: {}s\n'.format(time))
else:
print('no method chosen')
sys.exit()
# No. of samples: 506
# No. of explanatory variables: 13
df = pd.read_csv('housing.data.txt', header=None, sep='\s+')
df.columns = [
'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
'PTRATIO', 'B', 'LSTAT', 'MEDV'
]
#print(df.head())
X = df[['RM']].values
y = df['MEDV'].values
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
loss='absolute_loss',
residual_threshold=5.0,
random_state=0)
ransac.fit(X, y)
# plotting inliers and outliers together with the linear fit
inlier_mask = ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
line_X = np.arange(3, 10, 1)
line_y_ransac = ransac.predict(line_X[:, np.newaxis])
plt.scatter(X[inlier_mask],
y[inlier_mask],
c='steelblue',
edgecolor='white',
marker='o',
label='Inliers')
def run(self, trainingDasaset, plotting):
dataset = trainingDasaset
accuracy = 0
y = dataset['int_rate']
X = dataset.drop(columns=[
'int_rate',
])
if plotting == True:
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
clf = RANSACRegressor(random_state=42)
#clf=self.gridSearch(clf,X_train, y_train)
clf.fit(X_train, y_train)
print(
"###################################RANSACRegressor#############################"
)
accuracy = clf.score(X_test, y_test)
#pred = clf.predict(X_test)
#accuracy = np.sqrt(metrics.mean_squared_error(y_test,pred))
print("score:" + str(accuracy))
else:
clf = RANSACRegressor(random_state=42)
#clf=self.gridSearch(clf,X_train, y_train)
clf.fit(X, y)
testData = pd.read_csv(
"./SiameseNeuralNetworkProject/MachineLearningAlgorithmSuite/CleanedData/SiameseTrainingData.csv"
)
predictions = clf.predict(testData)
np.savetxt(
"./SiameseNeuralNetworkProject/MachineLearningAlgorithmSuite/OutputFiles/RANSACRegressorPredictions.csv",
predictions,
delimiter=",")
testData = pd.read_csv(
"./SiameseNeuralNetworkProject/MachineLearningAlgorithmSuite/CleanedData/OverallTestingData.csv"
)
predictions = clf.predict(testData)
np.savetxt(
"./SiameseNeuralNetworkProject/MachineLearningAlgorithmSuite/OutputFiles/RANSACRegressorPredictionsTestData.csv",
predictions,
delimiter=",")
return accuracy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.metrics import classification_report, confusion_matrix
#loading the dataset
train = pd.read_csv("C:/Users/HP/Desktop/train (1).csv")
test = pd.read_csv("C:/Users/HP/Desktop/test (2).csv")
train = train.dropna()
test = test.dropna()
train.head()
X_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, 1].values)
X_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, 1].values)
#RANSAC Regressor
from sklearn.linear_model import RANSACRegressor
model = RANSACRegressor()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = model.score(X_test, y_test)
plt.plot(X_train, model.predict(X_train), color='r')
plt.show()
print(accuracy)
print(accuracy)
def get_base_model():
return {'ransac_regressor': RANSACRegressor()}
import cv2
from sklearn.linear_model import (
LinearRegression, TheilSenRegressor, RANSACRegressor, HuberRegressor)
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
np.random.seed(42)
poly = PolynomialFeatures(2)
reg = LinearRegression(PolynomialFeatures(2))
ransac_estimator = RANSACRegressor(reg, min_samples=3,random_state=42)
RANSACRegressor()
regressor = HuberRegressor()
model = make_pipeline(poly, regressor)
x=np.linspace(0, 5, 200)
y=x*x
model.fit(x.reshape(-1, 1), y)
ransac_estimator.fit(x.reshape(-1, 1), y)
#egressor.warm_start = True
#egressor.fit(x.reshape(-1, 1),y)
#rint('oi')
x0 = np.zeros(3)
def fun(x,t, y):
return x[2] + t*x[1]+ t*t*x[0] -y
# Kernel ridge.
('KernelRidge', lambda: KernelRidge()),
# Linear.
# Way too slow.
#('ARDRegression', lambda: ARDRegression()),
('HuberRegressor', lambda: HuberRegressor()),
('LinearRegression', lambda: LinearRegression()),
# ValueError: Unknown label type: 'continuous'
#('LogisticRegression', lambda: LogisticRegression()),
# ValueError: Unknown label type: 'continuous'
#('LogisticRegressionCV', lambda: LogisticRegressionCV()),
('PassiveAggressiveRegressor', lambda: PassiveAggressiveRegressor()),
# ValueError: Unknown label type: 'continuous'
#('RandomizedLogisticRegression', lambda: RandomizedLogisticRegression()),
('RANSACRegressor', lambda: RANSACRegressor()),
('SGDRegressor', lambda: SGDRegressor()),
# Way too slow.
#('TheilSenRegressor', lambda: TheilSenRegressor()),
# Neighbors.
('KNeighborsRegressor', lambda: KNeighborsRegressor()),
# Predicts Nan, infinity or too large of value.
#('RadiusNeighborsRegressor', lambda: RadiusNeighborsRegressor()),
# Neural network.
# Increase max_iter to avoid Warning about non-convergence within max_iter.
('MLPRegressor', lambda: MLPRegressor(max_iter=1000)),
# Support vector machine.
('SVR', lambda: SVR()),
def get_model_obj(modelType, n_clusters=None, **kwargs):
if modelType == 'knn':
from sklearn.neighbors import KNeighborsClassifier
# 6 seems to give the best trade-off between accuracy and precision
knn = KNeighborsClassifier(n_neighbors=6, **kwargs)
return knn
elif modelType == 'gaussianNB':
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB(**kwargs)
return gnb
elif modelType == 'multinomialNB':
from sklearn.naive_bayes import MultinomialNB
# TODO: figure out how to configure binomial distribution
mnb = MultinomialNB(**kwargs)
return mnb
elif modelType == 'bernoulliNB':
from sklearn.naive_bayes import BernoulliNB
bnb = BernoulliNB(**kwargs)
return bnb
elif modelType == 'randomForest':
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(random_state=234, **kwargs)
return rfc
elif modelType == 'svm':
from sklearn.svm import SVC
svc = SVC(random_state=0, probability=True, **kwargs)
return svc
elif modelType == 'LinearRegression':
#assert column, "Column name required for building a linear model"
#assert dataframe[column].shape == target.shape
from sklearn import linear_model
l_reg = linear_model.LinearRegression(**kwargs)
return l_reg
elif modelType == 'RidgeRegression':
from sklearn.linear_model import Ridge
if not kwargs:
kwargs = {'alpha': 0.5}
ridge_reg = Ridge(**kwargs)
return ridge_reg
elif modelType == 'RidgeRegressionCV':
from sklearn import linear_model
if not kwargs:
kwargs = {'alphas': [0.1, 1.0, 10.0]}
ridge_cv_reg = linear_model.RidgeCV(**kwargs)
return ridge_cv_reg
elif modelType == 'LassoRegression':
from sklearn import linear_model
if not kwargs:
kwargs = {'alpha': 0.1}
lasso_reg = linear_model.Lasso(**kwargs)
return lasso_reg
elif modelType == 'ElasticNetRegression':
from sklearn.metrics import r2_score
from sklearn import linear_model
if not kwargs:
kwargs = {'alpha': 0.1, 'l1_ratio': 0.7}
enet_reg = linear_model.ElasticNet(**kwargs)
return enet_reg
elif modelType == 'LogisticRegression':
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression(random_state=123, **kwargs)
return log_reg
elif modelType == 'RANSACRegression':
from sklearn.linear_model import LinearRegression, RANSACRegressor
ransac_model = RANSACRegressor(LinearRegression())
return ransac_model
elif modelType == 'kde':
from sklearn.neighbors.kde import KernelDensity
kde = KernelDensity(kernel='gaussian', bandwidth=0.2, **kwargs)
return kde
elif modelType == 'AR':
import statsmodels.api as sm
# fit an AR model and forecast
ar_fitted = sm.tsa.AR(dataframe).fit(maxlag=9,
method='mle',
disp=-1,
**kwargs)
#ts_forecast = ar_fitted.predict(start='2008', end='2050')
return ar_fitted
elif modelType == 'SARIMAX':
mod = sm.tsa.statespace.SARIMAX(df.riders,
trend='n',
order=(0, 1, 0),
seasonal_order=(1, 1, 1, 12),
**kwargs)
return mod
elif modelType == 'sgd':
# Online classifiers http://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_comparison.html
from sklearn.linear_model import SGDClassifier
sgd = SGDClassifier(**kwargs)
return sgd
elif modelType == 'perceptron':
from sklearn.linear_model import Perceptron
perceptron = Perceptron(**kwargs)
return perceptron
elif modelType == 'xgboost':
import xgboost as xgb
xgbm = xgb.XGBClassifier(**kwargs)
return xgbm
elif modelType == 'baseNN':
from keras.models import Sequential
from keras.layers import Dense
# create model
model = Sequential()
assert args.get('inputParams', None)
assert args.get('outputParams', None)
model.add(Dense(inputParams))
model.add(Dense(outputParams))
if args.get('compileParams'):
# Compile model
model.compile(
compileParams
) # loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
elif modelType == 'lightGBMRegression':
from pylightgbm.models import GBMRegressor
lgbm_lreg = GBMRegressor(num_iterations=100,
early_stopping_round=10,
num_leaves=10,
min_data_in_leaf=10)
return lgbm_lreg
elif modelType == 'lightGBMBinaryClass':
from pylightgbm.models import GBMClassifier
lgbm_bc = GBMClassifier(metric='binary_error', min_data_in_leaf=1)
return lgbm_bc
# Clustering models
elif modelType == 'KMeans':
assert n_clusters, "Number of clusters argument mandatory"
cluster_callable = KMeans
# seed of 10 for reproducibility.
clusterer = cluster_callable(n_clusters=n_clusters, random_state=10)
return clusterer
elif modelType == 'dbscan':
if not n_clusters:
logging.warn(
"Number of clusters irrelevant for cluster type : %s" %
(modelType))
cluster_callable = DBSCAN
clusterer = cluster_callable(eps=0.5)
return clusterer
elif modelType == 'affinity_prop':
if not n_clusters:
logging.warn(
"Number of clusters irrelevant for cluster type : %s" %
(modelType))
clusterer = AffinityPropagation(damping=.9, preference=-200)
return clusterer
elif modelType == 'spectral':
assert n_clusters, "Number of clusters argument mandatory"
clusterer = SpectralClustering(n_clusters=n_clusters,
eigen_solver='arpack',
affinity="nearest_neighbors")
return clusterer
elif modelType == 'birch':
if not n_clusters:
logging.warn(
"Number of clusters irrelevant for cluster type : %s" %
(modelType))
clusterer = Birch(n_clusters=2)
return clusterer
elif modelType == 'agglomerativeCluster':
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(dataframe,
n_neighbors=10,
include_self=False)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
clusterer = AgglomerativeClustering(n_clusters=cluster,
linkage='ward',
connectivity=connectivity)
return clusterer
elif modelType == 'meanShift':
# estimate bandwidth for mean shift
bandwidth = cluster.estimate_bandwidth(dataframe, quantile=0.3)
clusterer = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
return clusterer
elif modelType == 'gmm':
from sklearn import mixture
gmm = mixture.GaussianMixture(n_components=5, covariance_type='full')
return gmm
elif modelType == 'dgmm':
from sklearn import mixture
dgmm = mixture.BayesianGaussianMixture(n_components=5,
covariance_type='full')
return dgmm
else:
raise 'Unknown model type: see utils.py for available'
#enr_sts_scores = enr.predict(xt[:, np.newaxis]) enr.fit(x, y) enr_sts_scores = enr.predict(xt) # Passive Aggressive Regression print 'passive aggressive' par = PassiveAggressiveRegressor() par.fit(x, y) par_sts_scores = par.predict(xt) #par.fit(x[:, np.newaxis], y) #par_sts_scores = par.predict(xt[:, np.newaxis]) # RANSAC Regression print 'ransac' ransac = RANSACRegressor() #ransac.fit(x[:, np.newaxis], y) #ransac_sts_scores = ransac.predict(xt[:, np.newaxis]) ransac.fit(x, y) ransac_sts_scores = ransac.predict(xt) # Logistic Regression print 'logistic' lgr = LogisticRegression() #lgr.fit(x[:, np.newaxis], y) #lgr_sts_scores = lgr.predict(xt[:, np.newaxis]) lgr.fit(x, y) lgr_sts_scores = lgr.predict(xt)
def test_ransac_min_n_samples():
base_estimator = LinearRegression()
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2. / X.shape[0],
residual_threshold=5, random_state=0)
ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1,
residual_threshold=5, random_state=0)
ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2,
residual_threshold=5, random_state=0)
ransac_estimator5 = RANSACRegressor(base_estimator, min_samples=2.0,
residual_threshold=5, random_state=0)
ransac_estimator6 = RANSACRegressor(base_estimator,
residual_threshold=5, random_state=0)
ransac_estimator7 = RANSACRegressor(base_estimator,
min_samples=X.shape[0] + 1,
residual_threshold=5, random_state=0)
ransac_estimator1.fit(X, y)
ransac_estimator2.fit(X, y)
ransac_estimator5.fit(X, y)
ransac_estimator6.fit(X, y)
assert_equal(ransac_estimator1.predict(X), ransac_estimator2.predict(X))
assert_equal(ransac_estimator1.predict(X), ransac_estimator5.predict(X))
assert_equal(ransac_estimator1.predict(X), ransac_estimator6.predict(X))
assert_raises(ValueError, ransac_estimator3.fit, X, y)
assert_raises(ValueError, ransac_estimator4.fit, X, y)
assert_raises(ValueError, ransac_estimator7.fit, X, y)
m = 'meteor'
if m == 'asiya':
x = np.loadtxt('x.asiya.train')
y = np.loadtxt('y.asiya.train')
elif m == 'meteor':
x = np.loadtxt('x.meteor.train')[:,np.newaxis]
y = np.loadtxt('y.meteor.train')
x_test = np.loadtxt('x.meteor.test')[:,np.newaxis]
regressors = {'lr':LinearRegression(),
'br':BayesianRidge(compute_score=True),
'enr':ElasticNet(),
'par':PassiveAggressiveRegressor(),
'ransac':RANSACRegressor(),
'lgr':LogisticRegression(),
'svr_rbf':SVR(kernel='rbf', C=1e3, gamma=0.1)}
#'svr_lin':SVR(kernel='linear', C=1e3)}
#'svr_poly':SVR(kernel='poly', C=1e3, degree=2)}
def build_regressors(num):
rgs = regressors[num]
rgs.fit(x, y)
with open(num+'.'+m+'.pk', 'wb') as fid:
pickle.dump(rgs, fid)
'''
x = x_test
lr = pickle.load(open("lr."+m+'.pk', 'rb'))
br = pickle.load(open("br."+m+'.pk', 'rb'))
class RansacClass:
"""
Name : RANSACRegressor
Attribute : None
Method : predict, predict_by_cv, save_model
"""
def __init__(self):
# 알고리즘 이름
self._name = 'ransac'
# 기본 경로
self._f_path = os.path.abspath(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
os.pardir))
# 경고 메시지 삭제
warnings.filterwarnings('ignore')
# 원본 데이터 로드
data = pd.read_csv(self._f_path +
"/regression/resource/regression_sample.csv",
sep=",",
encoding="utf-8")
# 학습 및 테스트 데이터 분리
self._x = (data["year"] <= 2017)
self._y = (data["year"] >= 2018)
# 학습 데이터 분리
self._x_train, self._y_train = self.preprocessing(data[self._x])
# 테스트 데이터 분리
self._x_test, self._y_test = self.preprocessing(data[self._y])
# 모델 선언
self._model = RANSACRegressor()
# 모델 학습
self._model.fit(self._x_train, self._y_train)
# 데이터 전처리
def preprocessing(self, data):
# 학습
x = []
# 레이블
y = []
# 기준점(7일)
base_interval = 7
# 기온
temps = list(data["temperature"])
for i in range(len(temps)):
if i < base_interval:
continue
y.append(temps[i])
xa = []
for p in range(base_interval):
d = i + p - base_interval
xa.append(temps[d])
x.append(xa)
return x, y
# 일반 예측
def predict(self, save_img=False, show_chart=False):
# 예측
y_pred = self._model.predict(self._x_test)
# 스코어 정보
score = r2_score(self._y_test, y_pred)
# 리포트 확인
if hasattr(self._model, 'coef_') and hasattr(self._model,
'intercept_'):
print(f'Coef = {self._model.coef_}')
print(f'intercept = {self._model.intercept_}')
print(f'Score = {score}')
# 이미지 저장 여부
if save_img:
self.save_chart_image(y_pred, show_chart)
# 예측 값 & 스코어
return [list(y_pred), score]
# CV 예측(Cross Validation)
def predict_by_cv(self):
# Regression 알고리즘은 실 프로젝트 상황에 맞게 Cross Validation 구현
return False
# GridSearchCV 예측
def predict_by_gs(self):
pass
# 모델 저장 및 갱신
def save_model(self, renew=False):
# 모델 저장
if not renew:
# 처음 저장
joblib.dump(self._model,
self._f_path + f'/model/{self._name}_rg.pkl')
else:
# 기존 모델 대체
if os.path.isfile(self._f_path + f'/model/{self._name}_rg.pkl'):
os.rename(
self._f_path + f'/model/{self._name}_rg.pkl',
self._f_path +
f'/model/{str(self._name) + str(time.time())}_rg.pkl')
joblib.dump(self._model,
self._f_path + f'/model/{self._name}_rg.pkl')
# 회귀 차트 저장
def save_chart_image(self, data, show_chart):
# 사이즈
plt.figure(figsize=(15, 10), dpi=100)
# 레이블
plt.plot(self._y_test, c='r')
# 예측 값
plt.plot(data, c='b')
# 이미지로 저장
plt.savefig('./chart_images/tenki-kion-lr.png')
# 차트 확인(Optional)
if show_chart:
plt.show()
def __del__(self):
del self._x_train, self._x_test, self._y_train, self._y_test, self._x, self._y, self._model
def test_ransac_residual_metric():
residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1)
residual_metric2 = lambda dy: np.sum(dy**2, axis=1)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
residual_metric=residual_metric1)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
residual_metric=residual_metric2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_equal(ransac_estimator0.predict(X), ransac_estimator1.predict(X))
assert_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.fit(X, y)
assert_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X))
z = np.linalg.inv(np.dot(Xb.T, Xb))
w = np.dot(z, np.dot(Xb.T, y))
print('Slope: %.3f' % w[1])
print('Intercept: %.3f' % w[0])
# # Fitting a robust regression model using RANSAC
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
loss='absolute_loss',
residual_threshold=5.0,
random_state=0)
ransac.fit(X, y)
inlier_mask = ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
line_X = np.arange(3, 10, 1)
line_y_ransac = ransac.predict(line_X[:, np.newaxis])
plt.scatter(X[inlier_mask], y[inlier_mask],
c='steelblue', edgecolor='white',
marker='o', label='Inliers')
plt.scatter(X[outlier_mask], y[outlier_mask],
def get_model_from_name(model_name, training_params=None):
# For Keras
epochs = 250
if os.environ.get('is_test_suite',
0) == 'True' and model_name[:12] == 'DeepLearning':
print(
'Heard that this is the test suite. Limiting number of epochs, which will increase training speed dramatically at the expense of model accuracy'
)
epochs = 30
all_model_params = {
'LogisticRegression': {
'n_jobs': -2
},
'RandomForestClassifier': {
'n_jobs': -2
},
'ExtraTreesClassifier': {
'n_jobs': -1
},
'AdaBoostClassifier': {
'n_estimators': 10
},
'SGDClassifier': {
'n_jobs': -1
},
'Perceptron': {
'n_jobs': -1
},
'LinearRegression': {
'n_jobs': -2
},
'RandomForestRegressor': {
'n_jobs': -2
},
'ExtraTreesRegressor': {
'n_jobs': -1
},
'MiniBatchKMeans': {
'n_clusters': 8
},
'GradientBoostingRegressor': {
'presort': False
},
'SGDRegressor': {
'shuffle': False
},
'PassiveAggressiveRegressor': {
'shuffle': False
},
'AdaBoostRegressor': {
'n_estimators': 10
},
'XGBRegressor': {
'nthread': -1,
'n_estimators': 200
},
'XGBClassifier': {
'nthread': -1,
'n_estimators': 200
},
'LGBMRegressor': {},
'LGBMClassifier': {},
'DeepLearningRegressor': {
'epochs': epochs,
'batch_size': 50,
'verbose': 2
},
'DeepLearningClassifier': {
'epochs': epochs,
'batch_size': 50,
'verbose': 2
}
}
model_params = all_model_params.get(model_name, None)
if model_params is None:
model_params = {}
if training_params is not None:
print('Now using the model training_params that you passed in:')
print(training_params)
# Overwrite our stock params with what the user passes in (i.e., if the user wants 10,000 trees, we will let them do it)
model_params.update(training_params)
print(
'After overwriting our defaults with your values, here are the final params that will be used to initialize the model:'
)
print(model_params)
model_map = {
# Classifiers
'LogisticRegression': LogisticRegression(),
'RandomForestClassifier': RandomForestClassifier(),
'RidgeClassifier': RidgeClassifier(),
'GradientBoostingClassifier': GradientBoostingClassifier(),
'ExtraTreesClassifier': ExtraTreesClassifier(),
'AdaBoostClassifier': AdaBoostClassifier(),
'SGDClassifier': SGDClassifier(),
'Perceptron': Perceptron(),
'PassiveAggressiveClassifier': PassiveAggressiveClassifier(),
# Regressors
'LinearRegression': LinearRegression(),
'RandomForestRegressor': RandomForestRegressor(),
'Ridge': Ridge(),
'ExtraTreesRegressor': ExtraTreesRegressor(),
'AdaBoostRegressor': AdaBoostRegressor(),
'RANSACRegressor': RANSACRegressor(),
'GradientBoostingRegressor': GradientBoostingRegressor(),
'Lasso': Lasso(),
'ElasticNet': ElasticNet(),
'LassoLars': LassoLars(),
'OrthogonalMatchingPursuit': OrthogonalMatchingPursuit(),
'BayesianRidge': BayesianRidge(),
'ARDRegression': ARDRegression(),
'SGDRegressor': SGDRegressor(),
'PassiveAggressiveRegressor': PassiveAggressiveRegressor(),
# Clustering
'MiniBatchKMeans': MiniBatchKMeans()
}
if xgb_installed:
model_map['XGBClassifier'] = XGBClassifier()
model_map['XGBRegressor'] = XGBRegressor()
if lgb_installed:
model_map['LGBMRegressor'] = LGBMRegressor()
model_map['LGBMClassifier'] = LGBMClassifier()
if keras_installed:
model_map['DeepLearningClassifier'] = KerasClassifier(
build_fn=make_deep_learning_classifier)
model_map['DeepLearningRegressor'] = KerasRegressor(
build_fn=make_deep_learning_model)
try:
model_without_params = model_map[model_name]
except KeyError as e:
print(
'It appears you are trying to use a library that is not available when we try to import it, or using a value for model_names that we do not recognize'
)
raise (e)
model_with_params = model_without_params.set_params(**model_params)
return model_with_params
