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src/a/s/astroML-0.2/book_figures/chapter9/fig_rrlyrae_qda.py   astroML(Download)
 
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-0.2/book_figures/chapter9/fig_rrlyrae_naivebayes.py   astroML(Download)
from sklearn.naive_bayes import GaussianNB
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-0.2/book_figures/chapter9/fig_rrlyrae_logreg.py   astroML(Download)
from sklearn.linear_model import LogisticRegression
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-0.2/book_figures/chapter9/fig_rrlyrae_lda.py   astroML(Download)
 
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-0.2/book_figures/chapter9/fig_rrlyrae_knn.py   astroML(Download)
from sklearn.neighbors import KNeighborsClassifier
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-0.2/book_figures/chapter9/fig_rrlyrae_decisiontree.py   astroML(Download)
from sklearn.tree import DecisionTreeClassifier
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
N_tot = len(y)

src/a/s/astroML-0.2/book_figures/chapter9/fig_ROC_curve.py   astroML(Download)
from sklearn.metrics import precision_recall_curve, roc_curve
 
from astroML.utils import split_samples, completeness_contamination
from astroML.datasets import fetch_rrlyrae_combined
 
X, y = fetch_rrlyrae_combined()
y = y.astype(int)
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-HEAD/book_figures/chapter9/fig_rrlyrae_qda.py   astroML(Download)
 
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-HEAD/book_figures/chapter9/fig_rrlyrae_naivebayes.py   astroML(Download)
from sklearn.naive_bayes import GaussianNB
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

src/a/s/astroML-HEAD/book_figures/chapter9/fig_rrlyrae_logreg.py   astroML(Download)
from sklearn.linear_model import LogisticRegression
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
 
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]]  # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
                                                     random_state=0)
 

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