Python grid_search.GridSearchCV类代码示例

def test_grid_search():
    """Test that the best estimator contains the right value for foo_param"""
    clf = MockClassifier()
    cross_validation = GridSearchCV(clf, {'foo_param': [1, 2, 3]})
    # make sure it selects the smallest parameter in case of ties
    assert_equal(cross_validation.fit(X, y).best_estimator.foo_param, 2)

    for i, foo_i in enumerate([1, 2, 3]):
        assert cross_validation.grid_scores_[i][0] == {'foo_param' : foo_i}

def do_grid_search(X,Y, gs_params):
    """ Given data (X,Y) will perform a grid search on g_params
        for a LogisticRegression called logreg
        """
    lrpipe = Pipeline([
        ('logreg',  LogisticRegression()  )
        ])
    gs = GridSearchCV( lrpipe, gs_params , n_jobs=-1)
    #print gs
    gs = gs.fit(X,Y)

    best_parameters, score = max(gs.grid_scores_, key=lambda x: x[1])
    logger.info("best_parameters: " +str( best_parameters ) )
    logger.info("expected score: "+str( score ) )

    return best_parameters

def train_svm_crossvalidated(X, y, tuned_parameters={'kernel': ['rbf'], 'gamma': 2.0**np.arange(-15,3), 'C': 2.0**np.arange(-5, 15)}):
    """
    Performs grid search with stratified K-fold cross validation on observations X with 
    true labels y and returns an optimal SVM, clf
    """

    k_fold = _size_dependent_k_split(np.size(X,0))

    clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=recall_score)
    clf.fit(X, y, cv=StratifiedKFold(y, k_fold))

    y_true, y_pred = y, clf.predict(X)

    #print "Classification report for the best estimator: "
    #print clf.best_estimator
    print "Tuned with optimal value: %0.3f" % recall_score(y_true, y_pred)
    
    return clf

def do_grid_search(X,Y, gs_params=None):
    """ Given data (X,Y) will perform a grid search on g_params
        for a LogisticRegression called logreg
        """
    svpipe = Pipeline([
        ('rbfsvm',  SVC()  )
        ])
    if not gs_params: 
        gs_params = {
                'rbfsvm__C': (1.5, 2, 5, 10, 20),
                'rbfsvm__gamma': (0.01, 0.1, 0.3, 0.6, 1, 1.5, 2, 5 ) ,
                }
    gs = GridSearchCV( svpipe, gs_params , n_jobs=-1)
    #print gs
    gs = gs.fit(X,Y)

    best_parameters, score = max(gs.grid_scores_, key=lambda x: x[1])
    logger.info("best_parameters: " +str( best_parameters ) )
    logger.info("expected score: "+str( score ) )

    return best_parameters

    def ParameterGridSearch(self, callback = None, nValidation = 5):
        '''
        Grid search for the best C and gamma parameters for the RBF Kernel.
        The efficiency of the parameters is evaluated using nValidation-fold
        cross-validation of the training data.
    
        As this process is time consuming and parallelizable, a number of
        threads equal to the number of cores in the computer is used for the
        calculations
        '''
        from scikits.learn.grid_search import GridSearchCV
        from scikits.learn.metrics import precision_score
        from scikits.learn.cross_val import StratifiedKFold
        # 
        # XXX: program crashes with >1 worker when running cpa.py
        #      No crash when running from classifier.py. Why?
        #
        n_workers = 1
        #try:
            #from multiprocessing import cpu_count
            #n_workers = cpu_count()
        #except:
            #n_workers = 1

        # Define the parameter ranges for C and gamma and perform a grid search for the optimal setting
        parameters = {'C': 2**np.arange(-5,11,2, dtype=float),
                      'gamma': 2**np.arange(3,-11,-2, dtype=float)}                
        clf = GridSearchCV(SVC(kernel='rbf'), parameters, n_jobs=n_workers, score_func=precision_score)
        clf.fit(self.svm_train_values, self.svm_train_labels, 
                cv=StratifiedKFold(self.svm_train_labels, nValidation))

        # Pick the best parameters as the ones with the maximum cross-validation rate
        bestParameters = max(clf.grid_scores_, key=lambda a: a[1])
        bestC = bestParameters[0]['C']
        bestGamma = bestParameters[0]['gamma']
        logging.info('Optimal values: C=%s g=%s rate=%s'%
                     (bestC, bestGamma, bestParameters[1]))
        return bestC, bestGamma

def test_dense_vectorizer_pipeline_grid_selection():
    # raw documents
    data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS
    # simulate iterables
    train_data = iter(data[1:-1])
    test_data = iter([data[0], data[-1]])

    # label junk food as -1, the others as +1
    y = np.ones(len(data))
    y[:6] = -1
    y_train = y[1:-1]
    y_test = np.array([y[0],y[-1]])

    pipeline = Pipeline([('vect', CountVectorizer()),
                         ('svc', DenseLinearSVC())])

    parameters = {
        'vect__analyzer': (WordNGramAnalyzer(min_n=1, max_n=1),
                           WordNGramAnalyzer(min_n=1, max_n=2)),
        'svc__loss'  : ('l1', 'l2')
    }


    # find the best parameters for both the feature extraction and the
    # classifier
    grid_search = GridSearchCV(pipeline, parameters, n_jobs=1)

    # cross-validation doesn't work if the length of the data is not known,
    # hence use lists instead of iterators
    pred = grid_search.fit(list(train_data), y_train).predict(list(test_data))
    assert_array_equal(pred, y_test)

    # on this toy dataset bigram representation which is used in the last of the
    # grid_search is considered the best estimator since they all converge to
    # 100% accurracy models
    assert_equal(grid_search.best_score, 1.0)
    best_vectorizer = grid_search.best_estimator.named_steps['vect']
    assert_equal(best_vectorizer.analyzer.max_n, 2)

def test_grid_search_sparse_score_func():
    X_, y_ = test_dataset_classif(n_samples=200, n_features=100, seed=0)

    clf = LinearSVC()
    cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
    cv.fit(X_[:180], y_[:180])
    y_pred = cv.predict(X_[180:])
    C = cv.best_estimator.C

    X_ = sp.csr_matrix(X_)
    clf = SparseLinearSVC()
    cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
    cv.fit(X_[:180], y_[:180])
    y_pred2 = cv.predict(X_[180:])
    C2 = cv.best_estimator.C

    assert_array_equal(y_pred, y_pred2)
    assert_equal(C, C2)

def test_grid_search_sparse():
    """Test that grid search works with both dense and sparse matrices"""
    X_, y_ = test_dataset_classif(n_samples=200, n_features=100, seed=0)

    clf = LinearSVC()
    cv = GridSearchCV(clf, {'C':[0.1, 1.0]})
    cv.fit(X_[:180], y_[:180])
    y_pred = cv.predict(X_[180:])
    C = cv.best_estimator.C

    X_ = sp.csr_matrix(X_)
    clf = SparseLinearSVC()
    cv = GridSearchCV(clf, {'C':[0.1, 1.0]})
    cv.fit(X_[:180], y_[:180])
    y_pred2 = cv.predict(X_[180:])
    C2 = cv.best_estimator.C

    assert np.mean(y_pred == y_pred2) >= .9
    assert_equal(C, C2)

def test_grid_search_sparse_score_func():
    X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)

    clf = LinearSVC()
    cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
    # XXX: set refit to False due to a random bug when True (default)
    cv.set_params(refit=False).fit(X_[:180], y_[:180])
    y_pred = cv.predict(X_[180:])
    C = cv.best_estimator.C

    X_ = sp.csr_matrix(X_)
    clf = SparseLinearSVC()
    cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
    # XXX: set refit to False due to a random bug when True (default)
    cv.set_params(refit=False).fit(X_[:180], y_[:180])
    y_pred2 = cv.predict(X_[180:])
    C2 = cv.best_estimator.C

    assert_array_equal(y_pred, y_pred2)
    assert_equal(C, C2)

# Data attributes
targets = [0, 1, 2]
target_names = ["covered", "no alternance", "uncovered"]
target_colors = "rgb"
    
# Classification settings
pipeline = Pipeline([
    ('extr', InfinitivesExtractor()),
    ('svc', LinearSVC(multi_class=True))
])
parameters = {
    'extr__count': (True,False),
    'extr__n': (3, 4, 5, 6),
    'svc__C': (1e-1, 1e-2, 1e9)
}
grid_search = GridSearchCV(pipeline, parameters)

print "Loading data..."
X, y = load_data()
print "Searching for the best model..."
t0 = time()
grid_search.fit(X, y)
print "Done in %0.3f" % (time() - t0)
print "Best score: %0.3f" % grid_search.best_score
clf = grid_search.best_estimator
print clf
yp = clf.predict(X)
print classification_report(y, yp, targets, target_names)

#pl.figure()
#pl.title("Classification rate for 3-fold stratified CV")

# split the dataset in two equal part respecting label proportions
train, test = iter(StratifiedKFold(y, 2)).next()

################################################################################
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

scores = [
    ('precision', precision_score),
    ('recall', recall_score),
]

for score_name, score_func in scores:
    clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func)
    clf.fit(X[train], y[train], cv=StratifiedKFold(y[train], 5))
    y_true, y_pred = y[test], clf.predict(X[test])

    print "Classification report for the best estimator: "
    print clf.best_estimator
    print "Tuned for '%s' with optimal value: %0.3f" % (
        score_name, score_func(y_true, y_pred))
    print classification_report(y_true, y_pred)
    print "Grid scores:"
    pprint(clf.grid_scores_)
    print

# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality

n_components = 150
print "Extracting the top %d eigenfaces" % n_components
pca_sl = RandomizedPCA(n_components=n_components, whiten=True)
pca_sl.fit(X_train)
#components, mean = pca.pca(X_train, n_components)

#print "PCA components shape", pca.components_.T.shape 
#eigenfaces = pca.components_.T.reshape((-1, 64, 64))

# project the input data on the eigenfaces orthonormal basis
X_train_pca = pca_sl.transform(X_train)
#X_train_pca = pca.transform(X_train, mean, components)

################################################################################
# Train a SVM classification model

print "Fitting the classifier to the training set"
param_grid = {
    'C': [1, 5, 10, 50, 100],
    'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
    }
clf = GridSearchCV(SVC(kernel='rbf'), param_grid,
                   fit_params={'class_weight': 'auto'})

clf = clf.fit(X_train_pca, y_train)
print "Best estimator found by grid search:"
print clf.best_estimator

y_test = dataset.target[split:]

# Build a vectorizer / classifier pipeline using the previous analyzer
pipeline = Pipeline([
    ('vect', CountVectorizer(max_features=100000)),
    ('tfidf', TfidfTransformer()),
    ('clf', LinearSVC(C=1000)),
])

parameters = {
    'vect__analyzer__max_n': (1, 2),
    'vect__max_df': (.95,),
}

# Fit the pipeline on the training set using grid search for the parameters
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1)
grid_search.fit(docs_train[:200], y_train[:200])

# Refit the best parameter set on the complete training set
clf = grid_search.best_estimator.fit(docs_train, y_train)

# Predict the outcome on the testing set
y_predicted = clf.predict(docs_test)

# Print the classification report
print metrics.classification_report(y_test, y_predicted,
                                    class_names=dataset.target_names)

# Plot the confusion matrix
cm = metrics.confusion_matrix(y_test, y_predicted)
print cm

print "Extracting the top %d eigenfaces" % n_components
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)

eigenfaces = pca.components_.T.reshape((n_components, 64, 64))

# project the input data on the eigenfaces orthonormal basis
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)


# Train a SVM classification model

print "Fitting the classifier to the training set"
param_grid = {"C": [1, 5, 10, 100], "gamma": [0.0001, 0.001, 0.01, 0.1]}
clf = GridSearchCV(SVC(kernel="rbf"), param_grid, fit_params={"class_weight": "auto"}, n_jobs=-1)
clf = clf.fit(X_train_pca, y_train)
print "Best estimator found by grid search:"
print clf.best_estimator


# Quantitative evaluation of the model quality on the test set

y_pred = clf.predict(X_test_pca)
print classification_report(y_test, y_pred, labels=selected_target, target_names=target_names[selected_target])

print confusion_matrix(y_test, y_pred, labels=selected_target)


# Qualitative evaluation of the predictions using matplotlib

def test_GridSearch():
    clf = MockClassifier()
    cross_validation = GridSearchCV(clf, {'foo_param':[1, 2, 3]})
    assert_equal(cross_validation.fit(X, y).best_estimator.foo_param, 2)