5. Putting it all together¶
5.1. Pipelining¶
We have seen that some estimators can transform data, and some estimators can predict variables. We can create combined estimators:
>>> from scikits.learn import linear_model, decomposition, datasets
>>> logistic = linear_model.LogisticRegression()
>>> pca = decomposition.PCA()
>>> from scikits.learn.pipeline import Pipeline
>>> pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])
>>> digits = datasets.load_digits()
>>> X_digits = digits.data
>>> y_digits = digits.target
>>> pca.fit(X_digits, y_digits)
PCA(copy=True, n_components=None, whiten=False)
>>> pl.plot(pca.explained_variance_)
[<matplotlib.lines.Line2D object at ...>]
Parameters of pipelines can be set using ‘__’ separated parameter names:
>>> pipe._set_params(pca__n_components=30)
Pipeline(steps=[('pca', PCA(copy=True, n_components=30, whiten=False)), ('logistic', LogisticRegression(C=1.0, dual=False, fit_intercept=True, intercept_scaling=1,
penalty='l2', tol=0.0001))])
>>> pca.n_components
30
>>> from scikits.learn.grid_search import GridSearchCV
>>> n_components = [10, 15, 20, 30, 40, 50, 64]
>>> Cs = np.logspace(-4, 4, 16)
>>> estimator = GridSearchCV(pipe,
... dict(pca__n_components=n_components,
... logistic__C=Cs),
... n_jobs=-1)
>>> estimator.fit(X_digits, y_digits)
GridSearchCV(cv=None,...
5.2. Face recognition with eigenfaces¶
The dataset used in this example is a preprocessed excerpt of the “Labeled Faces in the Wild”, aka LFW:
 |
 |
| Prediction | Eigenfaces |
Expected results for the top 5 most represented people in the dataset:
precision recall f1-score support
Gerhard_Schroeder 0.91 0.75 0.82 28
Donald_Rumsfeld 0.84 0.82 0.83 33
Tony_Blair 0.65 0.82 0.73 34
Colin_Powell 0.78 0.88 0.83 58
George_W_Bush 0.93 0.86 0.90 129
avg / total 0.86 0.84 0.85 282
5.3. Open problem: stock market structure¶
Can we predict the variation in stock prices for Google?