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LOFexample.py
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LOFexample.py
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# To add a new cell, type '#%%'
# To add a new markdown cell, type '#%% [markdown]'
#%% Change working directory from the workspace root to the ipynb file location.
# Turn this addition off with the DataScience.changeDirOnImportExport setting
# ms-python.python added
#%%
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import LocalOutlierFactor
#%%
from lensutils import read_data as read
from lensutils import SingleLens as slf
#%%
data = read('./single/',t_range=(7940,8040))
#%%
fitter = slf(data,[1.927,7984.64, 9.964])
#%% [markdown]
# Example with one one data source
#%%
data_key = 'KMT-C31-'
t = data[data_key][0]
obs = data[data_key][1]
err = data[data_key][2]
t, _, _ = fitter.data[data_key]
coeffs, _ = fitter.linear_fit(data_key,fitter.magnification(t))
fx = coeffs[0]+coeffs[1]*fitter.magnification(t)
obsl = (obs - coeffs[0])/coeffs[1] # This gives only magnification from baseline 1
err1 = err/coeffs[1]
plt.plot(t,obsl,'.')
#%%
plt.errorbar(t,obsl,err1 , fmt = '.')
#%%
mag = fitter.magnification(data['KMT-C31-'][0])
chi = (mag-obsl)**2/err1**2
#%% [markdown]
# Trying a 3D plot of the data
#%%
from mpl_toolkits.mplot3d import axes3d, Axes3D
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(mag,obsl,chi,c='r')
plt.show()
#%%
X = np.hstack((mag.reshape(len(mag),1),obsl.reshape(len(obsl),1),chi.reshape(len(chi),1)))
#%%
# fit the model for outlier detection (default)
clf = LocalOutlierFactor(n_neighbors=20,metric='chebyshev', contamination=0.1,)
# use fit_predict to compute the predicted labels of the training samples
# (when LOF is used for outlier detection, the estimator has no predict,
# decision_function and score_samples methods).
#%%
y_pred = clf.fit_predict(X)
X_scores = clf.negative_outlier_factor_
plt.title("Local Outlier Factor (LOF)")
plt.scatter(t, X[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(t, X[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
#plt.scatter(X[y_pred==1, 0], X[y_pred==1, 1],marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.scatter(t, X[:, 1], color='k', s=3., label='Data points')
plt.scatter(t[y_pred!=1], X[y_pred!=1, 1], s=100, edgecolors='r',
facecolors='none', label='Outlier scores')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
#legend.legendHandles[0]._sizes = [10]
#legend.legendHandles[1]._sizes = [20]
plt.show()
#%% [markdown]
# The following plots is form the view of the linear relation from the data and the model.
#%%
mag = fitter.magnification(data['KMT-C31-'][0])
#%%
plt.plot(mag,obsl,'.')
#%%
Y = np.hstack((mag.reshape(len(mag),1),obsl.reshape(len(obsl),1)))
#%%
# fit the model for outlier detection (default)
clf2 = LocalOutlierFactor(n_neighbors=20,metric='chebyshev', contamination=0.1,)
# use fit_predict to compute the predicted labels of the training samples
# (when LOF is used for outlier detection, the estimator has no predict,
# decision_function and score_samples methods).
#%%
y_pred = clf2.fit_predict(Y)
X_scores = clf2.negative_outlier_factor_
plt.title("Local Outlier Factor (LOF)")
plt.scatter(X[:, 0], X[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(X[:, 0], X[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
#plt.scatter(X[y_pred==1, 0], X[y_pred==1, 1],marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.scatter(X[:, 0], X[:, 1], color='k', s=3., label='Data points')
plt.scatter(X[y_pred != 1, 0], X[y_pred != 1, 1], s=100,marker='x', c='r' ,label='Outlier scores')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
#legend.legendHandles[0]._sizes = [10]
#legend.legendHandles[1]._sizes = [20]
plt.show()
#%% [markdown]
# #The Following uses all the datasets for the single example
#%%
t_vec = []
obs_vc = []
err_vec = []
for data_key in data.keys():
print(data_key)
t, _, err = fitter.data[data_key]
obs = data[data_key][1]
coeffs, _ = fitter.linear_fit(data_key,fitter.magnification(t))
obsl = (obs - coeffs[0])/coeffs[1]
err1 = err/coeffs[1]
t_vec = np.append(t_vec,t)
obs_vc = np.append(obs_vc,obsl)
err_vec = np.append(err_vec,err1)
#%%
obs_vc = obs_vc[t_vec.argsort()]
err_vec = err_vec[t_vec.argsort()]
t_vec = np.sort(t_vec)
#%%
plt.errorbar(t_vec,obs_vc,err_vec,fmt='.')
#%%
magl = fitter.magnification(t_vec)
chi2 = (magl-obs_vc)**2/err_vec**2
#%%
plt.plot(t_vec,obs_vc,'.')
plt.plot(t_vec,magl,'--')
#%%
plt.plot(magl,obs_vc,'.')
#%%
Z = np.hstack((magl.reshape(len(magl),1),obs_vc.reshape(len(obs_vc),1),chi2.reshape(len(chi2),1)))
#%%
# fit the model for outlier detection (default)
clf2 = LocalOutlierFactor(n_neighbors=20,metric='chebyshev', contamination=0.01,)
# use fit_predict to compute the predicted labels of the training samples
# (when LOF is used for outlier detection, the estimator has no predict,
# decision_function and score_samples methods).
#%%
y_pred = clf2.fit_predict(Z)
X_scores = clf2.negative_outlier_factor_
plt.title("Local Outlier Factor (LOF)")
plt.scatter(t_vec, Z[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(t_vec, Z[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.plot(t_vec,magl,'--')
#plt.vlines(t_vec[X_scores==X_scores.max()], 1 , 1.05)
plt.vlines(t_vec[X_scores==X_scores.min()], 1 , 1.01367504)
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
plt.scatter(Z[:, 0], Z[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(Z[:, 0], Z[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.plot(magl,obs_vc,'--')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
#plt.scatter(X[y_pred==1, 0], X[y_pred==1, 1],marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.scatter(t_vec, Z[:, 1], color='k', s=3., label='Data points')
plt.scatter(t_vec[y_pred!=1], Z[y_pred!=1, 1], s=50,marker='o',edgecolors='r',facecolors='none' ,label='Outlier scores')
plt.plot(t_vec,magl, label='Model')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
#legend.legendHandles[0]._sizes = [10]
#legend.legendHandles[1]._sizes = [20]
plt.show()
#%% [markdown]
# This is the firs binary example, extreme case.
#%%
bindata = read('./bin1/',t_range=(7800,7950),max_uncertainty=1)
#%%
binfitter = slf(bindata,[4.366,7884.99, 6.197])
#%%
t_vec = []
obs_vc = []
err_vec = []
for data_key in bindata.keys():
print(data_key)
t, _, err = binfitter.data[data_key]
obs = bindata[data_key][1]
coeffs, _ = binfitter.linear_fit(data_key,binfitter.magnification(t))
obsl = (obs - coeffs[0])/coeffs[1]
err1 = err/coeffs[1]
t_vec = np.append(t_vec,t)
obs_vc = np.append(obs_vc,obsl)
err_vec = np.append(err_vec,err1)
obs_vc = obs_vc[t_vec.argsort()]
err_vec = err_vec[t_vec.argsort()]
t_vec = np.sort(t_vec)
magl = binfitter.magnification(t_vec)
chi2 = (magl-obs_vc)**2/err_vec**2
plt.plot(t_vec,obs_vc,'.')
plt.plot(t_vec,magl,'--')
#%%
Z = np.hstack((magl.reshape(len(magl),1),obs_vc.reshape(len(obs_vc),1),chi2.reshape(len(chi2),1)))
#%%
# fit the model for outlier detection (default)
clf3 = LocalOutlierFactor(n_neighbors=20,metric='chebyshev', contamination=0.1,)
# use fit_predict to compute the predicted labels of the training samples
# (when LOF is used for outlier detection, the estimator has no predict,
# decision_function and score_samples methods).
#%%
y_pred = clf2.fit_predict(Z)
X_scores = clf2.negative_outlier_factor_
plt.title("Local Outlier Factor (LOF)")
plt.scatter(t_vec, Z[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(t_vec, Z[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.plot(t_vec,magl,'--')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
plt.scatter(Z[:, 0], Z[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(Z[:, 0], Z[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.plot(magl,obs_vc,'--')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
#plt.scatter(X[y_pred==1, 0], X[y_pred==1, 1],marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.scatter(t_vec, Z[:, 1], color='k', s=3., label='Data points')
plt.scatter(t_vec[y_pred!=1], Z[y_pred!=1, 1], s=50,marker='o',edgecolors='r',facecolors='none' ,label='Outlier scores')
plt.plot(t_vec,magl, label='Model')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
#legend.legendHandles[0]._sizes = [10]
#legend.legendHandles[1]._sizes = [20]
plt.show()
#%% [markdown]
# Second example of anomalous fit. This case is very close to a good fit.
#%%
bindata2 = read('./bin2/',t_range=(7875,8025),max_uncertainty=1)
#%%
binfitter2 = slf(bindata2,[0.27,7947.29, 20.33])
#%%
t_vec = []
obs_vc = []
err_vec = []
for data_key in bindata2.keys():
print(data_key)
t, _, err = binfitter2.data[data_key]
obs = bindata2[data_key][1]
coeffs, _ = binfitter2.linear_fit(data_key,binfitter2.magnification(t))
obsl = (obs - coeffs[0])/coeffs[1]
err1 = err/coeffs[1]
t_vec = np.append(t_vec,t)
obs_vc = np.append(obs_vc,obsl)
err_vec = np.append(err_vec,err1)
obs_vc = obs_vc[t_vec.argsort()]
err_vec = err_vec[t_vec.argsort()]
t_vec = np.sort(t_vec)
magl = binfitter2.magnification(t_vec)
chi2 = (magl-obs_vc)**2/err_vec**2
plt.errorbar(t_vec,obs_vc,err_vec, fmt='.')
plt.plot(t_vec,magl,'--')
#%%
Z = np.hstack((magl.reshape(len(magl),1),obs_vc.reshape(len(obs_vc),1),chi2.reshape(len(chi2),1)))
#%%
# fit the model for outlier detection (default)
clf4 = LocalOutlierFactor(n_neighbors=20,metric='chebyshev', contamination=0.1)
# use fit_predict to compute the predicted labels of the training samples
# (when LOF is used for outlier detection, the estimator has no predict,
# decision_function and score_samples methods).
#%%
y_pred = clf4.fit_predict(Z)
X_scores = clf4.negative_outlier_factor_
plt.title("Local Outlier Factor (LOF)")
plt.scatter(t_vec, Z[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(t_vec, Z[:, 1], s=1000 * radius, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.plot(t_vec,magl,'--')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
legend.legendHandles[0]._sizes = [10]
legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
#plt.scatter(Z[:, 0], Z[:, 1], color='k', s=3., label='Data points')
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
plt.scatter(Z[y_pred!=1, 0], Z[y_pred!=1, 1], s=50, edgecolors='r',
facecolors='none', label='Outlier scores')
#plt.scatter(X[:, 0], X[:, 1], y_pred,marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.errorbar(magl,obs_vc,fmt='.')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
#legend = plt.legend(loc='upper left')
#legend.legendHandles[0]._sizes = [10]
#legend.legendHandles[1]._sizes = [20]
plt.show()
#%%
plt.title("Local Outlier Factor (LOF)")
# plot circles with radius proportional to the outlier scores
radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
#plt.scatter(X[y_pred==1, 0], X[y_pred==1, 1],marker='+', edgecolors='r',
# facecolors='none', label='Outlier scores')
plt.scatter(t_vec, Z[:, 1], color='k', s=3., label='Data points')
plt.scatter(t_vec[y_pred!=1], Z[y_pred!=1, 1], s=50,marker='o',edgecolors='r',facecolors='none' ,label='Outlier scores')
plt.plot(t_vec,magl,'--')
plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
legend = plt.legend(loc='upper left')
#legend.legendHandles[0]._sizes = [10]
#legend.legendHandles[1]._sizes = [20]
plt.show()