def test_ovr_pipeline():
# Test with pipeline of length one
# This test is needed because the multiclass estimators may fail to detect
# the presence of predict_proba or decision_function.
clf = Pipeline([("tree", DecisionTreeClassifier())])
ovr_pipe = OneVsRestClassifier(clf)
ovr_pipe.fit(iris.data, iris.target)
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_array_equal(ovr.predict(iris.data), ovr_pipe.predict(iris.data))
def _make_estimators(X_train, y_train, y_ml_train):
# Make estimators that make sense to test various scoring methods
sensible_regr = DecisionTreeRegressor(random_state=0)
# some of the regressions scorers require strictly positive input.
sensible_regr.fit(X_train, y_train + 1)
sensible_clf = DecisionTreeClassifier(random_state=0)
sensible_clf.fit(X_train, y_train)
sensible_ml_clf = DecisionTreeClassifier(random_state=0)
sensible_ml_clf.fit(X_train, y_ml_train)
return dict([(name, sensible_regr) for name in REGRESSION_SCORERS] +
[(name, sensible_clf)
for name in CLF_SCORERS] + [(name, sensible_clf)
for name in CLUSTER_SCORERS] +
[(name, sensible_ml_clf) for name in MULTILABEL_ONLY_SCORERS])
def test_thresholded_scorers():
# Test scorers that take thresholds.
X, y = make_blobs(random_state=0, centers=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = LogisticRegression(random_state=0)
clf.fit(X_train, y_train)
score1 = get_scorer('roc_auc')(clf, X_test, y_test)
score2 = roc_auc_score(y_test, clf.decision_function(X_test))
score3 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
assert_almost_equal(score1, score2)
assert_almost_equal(score1, score3)
logscore = get_scorer('neg_log_loss')(clf, X_test, y_test)
logloss = log_loss(y_test, clf.predict_proba(X_test))
assert_almost_equal(-logscore, logloss)
# same for an estimator without decision_function
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
score1 = get_scorer('roc_auc')(clf, X_test, y_test)
score2 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
assert_almost_equal(score1, score2)
# test with a regressor (no decision_function)
reg = DecisionTreeRegressor()
reg.fit(X_train, y_train)
score1 = get_scorer('roc_auc')(reg, X_test, y_test)
score2 = roc_auc_score(y_test, reg.predict(X_test))
assert_almost_equal(score1, score2)
# Test that an exception is raised on more than two classes
X, y = make_blobs(random_state=0, centers=3)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf.fit(X_train, y_train)
with pytest.raises(ValueError, match="multiclass format is not supported"):
get_scorer('roc_auc')(clf, X_test, y_test)
# test error is raised with a single class present in model
# (predict_proba shape is not suitable for binary auc)
X, y = make_blobs(random_state=0, centers=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = DecisionTreeClassifier()
clf.fit(X_train, np.zeros_like(y_train))
with pytest.raises(ValueError, match="need classifier with two classes"):
get_scorer('roc_auc')(clf, X_test, y_test)
# for proba scorers
with pytest.raises(ValueError, match="need classifier with two classes"):
get_scorer('neg_log_loss')(clf, X_test, y_test)
def test_set_random_state():
lda = LinearDiscriminantAnalysis()
tree = DecisionTreeClassifier()
# Linear Discriminant Analysis doesn't have random state: smoke test
set_random_state(lda, 3)
set_random_state(tree, 3)
assert tree.random_state == 3
def test_plot_roc_curve_error_non_binary(pyplot, data):
X, y = data
clf = DecisionTreeClassifier()
clf.fit(X, y)
msg = "Estimator should solve a binary classification problem"
with pytest.raises(ValueError, match=msg):
plot_roc_curve(clf, X, y)
def test_ovr_coef_exceptions():
# Not fitted exception!
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
# lambda is needed because we don't want coef_ to be evaluated right away
assert_raises(ValueError, lambda x: ovr.coef_, None)
# Doesn't have coef_ exception!
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_raises(AttributeError, lambda x: ovr.coef_, None)
def test_pickle_version_warning_is_not_raised_with_matching_version():
iris = datasets.load_iris()
tree = DecisionTreeClassifier().fit(iris.data, iris.target)
tree_pickle = pickle.dumps(tree)
assert b"version" in tree_pickle
tree_restored = assert_no_warnings(pickle.loads, tree_pickle)
# test that we can predict with the restored decision tree classifier
score_of_original = tree.score(iris.data, iris.target)
score_of_restored = tree_restored.score(iris.data, iris.target)
assert score_of_original == score_of_restored
def test_thresholded_scorers_multilabel_indicator_data():
# Test that the scorer work with multilabel-indicator format
# for multilabel and multi-output multi-class classifier
X, y = make_multilabel_classification(allow_unlabeled=False,
random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# Multi-output multi-class predict_proba
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
y_proba = clf.predict_proba(X_test)
score1 = get_scorer('roc_auc')(clf, X_test, y_test)
score2 = roc_auc_score(y_test, np.vstack([p[:, -1] for p in y_proba]).T)
assert_almost_equal(score1, score2)
# Multi-output multi-class decision_function
# TODO Is there any yet?
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
clf._predict_proba = clf.predict_proba
clf.predict_proba = None
clf.decision_function = lambda X: [p[:, 1] for p in clf._predict_proba(X)]
y_proba = clf.decision_function(X_test)
score1 = get_scorer('roc_auc')(clf, X_test, y_test)
score2 = roc_auc_score(y_test, np.vstack([p for p in y_proba]).T)
assert_almost_equal(score1, score2)
# Multilabel predict_proba
clf = OneVsRestClassifier(DecisionTreeClassifier())
clf.fit(X_train, y_train)
score1 = get_scorer('roc_auc')(clf, X_test, y_test)
score2 = roc_auc_score(y_test, clf.predict_proba(X_test))
assert_almost_equal(score1, score2)
# Multilabel decision function
clf = OneVsRestClassifier(LinearSVC(random_state=0))
clf.fit(X_train, y_train)
score1 = get_scorer('roc_auc')(clf, X_test, y_test)
score2 = roc_auc_score(y_test, clf.decision_function(X_test))
assert_almost_equal(score1, score2)
def test_raises_on_score_list():
# Test that when a list of scores is returned, we raise proper errors.
X, y = make_blobs(random_state=0)
f1_scorer_no_average = make_scorer(f1_score, average=None)
clf = DecisionTreeClassifier()
assert_raises(ValueError,
cross_val_score,
clf,
X,
y,
scoring=f1_scorer_no_average)
grid_search = GridSearchCV(clf,
scoring=f1_scorer_no_average,
param_grid={'max_depth': [1, 2]})
assert_raises(ValueError, grid_search.fit, X, y)
def bench_scikit_tree_classifier(X, Y):
"""Benchmark with scikit-learn decision tree classifier"""
from mrex.tree import DecisionTreeClassifier
gc.collect()
# start time
tstart = datetime.now()
clf = DecisionTreeClassifier()
clf.fit(X, Y).predict(X)
delta = (datetime.now() - tstart)
# stop time
scikit_classifier_results.append(delta.seconds +
delta.microseconds / mu_second)
def test_export_text_errors():
clf = DecisionTreeClassifier(max_depth=2, random_state=0)
clf.fit(X, y)
err_msg = "max_depth bust be >= 0, given -1"
with pytest.raises(ValueError, match=err_msg):
export_text(clf, max_depth=-1)
err_msg = "feature_names must contain 2 elements, got 1"
with pytest.raises(ValueError, match=err_msg):
export_text(clf, feature_names=['a'])
err_msg = "decimals must be >= 0, given -1"
with pytest.raises(ValueError, match=err_msg):
export_text(clf, decimals=-1)
err_msg = "spacing must be > 0, given 0"
with pytest.raises(ValueError, match=err_msg):
export_text(clf, spacing=0)
def test_plot_tree_gini(pyplot):
# mostly smoke tests
# Check correctness of export_graphviz for criterion = gini
clf = DecisionTreeClassifier(max_depth=3,
min_samples_split=2,
criterion="gini",
random_state=2)
clf.fit(X, y)
# Test export code
feature_names = ['first feat', 'sepal_width']
nodes = plot_tree(clf, feature_names=feature_names)
assert len(nodes) == 3
assert nodes[0].get_text() == ("first feat <= 0.0\ngini = 0.5\n"
"samples = 6\nvalue = [3, 3]")
assert nodes[1].get_text() == "gini = 0.0\nsamples = 3\nvalue = [3, 0]"
assert nodes[2].get_text() == "gini = 0.0\nsamples = 3\nvalue = [0, 3]"
def test_gridsearch():
# Check that base trees can be grid-searched.
# AdaBoost classification
boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
parameters = {
'n_estimators': (1, 2),
'base_estimator__max_depth': (1, 2),
'algorithm': ('SAMME', 'SAMME.R')
}
clf = GridSearchCV(boost, parameters)
clf.fit(iris.data, iris.target)
# AdaBoost regression
boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(),
random_state=0)
parameters = {'n_estimators': (1, 2), 'base_estimator__max_depth': (1, 2)}
clf = GridSearchCV(boost, parameters)
clf.fit(boston.data, boston.target)
def test_precision():
rng_reg = RandomState(2)
rng_clf = RandomState(8)
for X, y, clf in zip(
(rng_reg.random_sample((5, 2)),
rng_clf.random_sample((1000, 4))),
(rng_reg.random_sample((5, )),
rng_clf.randint(2, size=(1000, ))),
(DecisionTreeRegressor(criterion="friedman_mse", random_state=0,
max_depth=1),
DecisionTreeClassifier(max_depth=1, random_state=0))):
clf.fit(X, y)
for precision in (4, 3):
dot_data = export_graphviz(clf, out_file=None, precision=precision,
proportion=True)
# With the current random state, the impurity and the threshold
# will have the number of precision set in the export_graphviz
# function. We will check the number of precision with a strict
# equality. The value reported will have only 2 precision and
# therefore, only a less equal comparison will be done.
# check value
for finding in finditer(r"value = \d+\.\d+", dot_data):
assert (
len(search(r"\.\d+", finding.group()).group()) <=
precision + 1)
# check impurity
if is_classifier(clf):
pattern = r"gini = \d+\.\d+"
else:
pattern = r"friedman_mse = \d+\.\d+"
# check impurity
for finding in finditer(pattern, dot_data):
assert (len(search(r"\.\d+", finding.group()).group()) ==
precision + 1)
# check threshold
for finding in finditer(r"<= \d+\.\d+", dot_data):
assert (len(search(r"\.\d+", finding.group()).group()) ==
precision + 1)
def test_score_sample_weight():
rng = np.random.RandomState(0)
# test both ClassifierMixin and RegressorMixin
estimators = [
DecisionTreeClassifier(max_depth=2),
DecisionTreeRegressor(max_depth=2)
]
sets = [datasets.load_iris(), datasets.load_boston()]
for est, ds in zip(estimators, sets):
est.fit(ds.data, ds.target)
# generate random sample weights
sample_weight = rng.randint(1, 10, size=len(ds.target))
# check that the score with and without sample weights are different
assert (est.score(ds.data, ds.target) != est.score(
ds.data, ds.target,
sample_weight=sample_weight)), ("Unweighted and weighted scores "
"are unexpectedly equal")
def test_graphviz_errors():
# Check for errors of export_graphviz
clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2)
# Check not-fitted decision tree error
out = StringIO()
with pytest.raises(NotFittedError):
export_graphviz(clf, out)
clf.fit(X, y)
# Check if it errors when length of feature_names
# mismatches with number of features
message = ("Length of feature_names, "
"1 does not match number of features, 2")
with pytest.raises(ValueError, match=message):
export_graphviz(clf, None, feature_names=["a"])
message = ("Length of feature_names, "
"3 does not match number of features, 2")
with pytest.raises(ValueError, match=message):
export_graphviz(clf, None, feature_names=["a", "b", "c"])
# Check error when argument is not an estimator
message = "is not an estimator instance"
with pytest.raises(TypeError, match=message):
export_graphviz(clf.fit(X, y).tree_)
# Check class_names error
out = StringIO()
with pytest.raises(IndexError):
export_graphviz(clf, out, class_names=[])
# Check precision error
out = StringIO()
with pytest.raises(ValueError, match="should be greater or equal"):
export_graphviz(clf, out, precision=-1)
with pytest.raises(ValueError, match="should be an integer"):
export_graphviz(clf, out, precision="1")
X = X / 255
# Create train-test split (as [Joachims, 2006])
print("Creating train-test split...")
n_train = 60000
X_train = X[:n_train]
y_train = y[:n_train]
X_test = X[n_train:]
y_test = y[n_train:]
return X_train, X_test, y_train, y_test
ESTIMATORS = {
"dummy": DummyClassifier(),
'CART': DecisionTreeClassifier(),
'ExtraTrees': ExtraTreesClassifier(),
'RandomForest': RandomForestClassifier(),
'Nystroem-SVM': make_pipeline(
Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
'SampledRBF-SVM': make_pipeline(
RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)),
'LogisticRegression-SAG': LogisticRegression(solver='sag', tol=1e-1,
C=1e4),
'LogisticRegression-SAGA': LogisticRegression(solver='saga', tol=1e-1,
C=1e4),
'MultilayerPerceptron': MLPClassifier(
hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
solver='sgd', learning_rate_init=0.2, momentum=0.9, verbose=1,
tol=1e-4, random_state=1),
'MLP-adam': MLPClassifier(
# Standardize first 10 features (the numerical ones)
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
mean[10:] = 0.0
std[10:] = 1.0
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
return X_train, X_test, y_train, y_test
ESTIMATORS = {
'GBRT': GradientBoostingClassifier(n_estimators=250),
'ExtraTrees': ExtraTreesClassifier(n_estimators=20),
'RandomForest': RandomForestClassifier(n_estimators=20),
'CART': DecisionTreeClassifier(min_samples_split=5),
'SGD': SGDClassifier(alpha=0.001),
'GaussianNB': GaussianNB(),
'liblinear': LinearSVC(loss="l2",
penalty="l2",
C=1000,
dual=False,
tol=1e-3),
'SAG': LogisticRegression(solver='sag', max_iter=2, C=1000)
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--classifiers',
nargs="+",
choices=ESTIMATORS,
# Parameters
n_classes = 3
plot_colors = "ryb"
plot_step = 0.02
# Load data
iris = load_iris()
for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2,
3]]):
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Train
clf = DecisionTreeClassifier().fit(X, y)
# Plot the decision boundary
plt.subplot(2, 3, pairidx + 1)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)
plt.xlabel(iris.feature_names[pair[0]])
###############################################################################
# Total impurity of leaves vs effective alphas of pruned tree
# ---------------------------------------------------------------
# Minimal cost complexity pruning recursively finds the node with the "weakest
# link". The weakest link is characterized by an effective alpha, where the
# nodes with the smallest effective alpha are pruned first. To get an idea of
# what values of ``ccp_alpha`` could be appropriate, scikit-learn provides
# :func:`DecisionTreeClassifier.cost_complexity_pruning_path` that returns the
# effective alphas and the corresponding total leaf impurities at each step of
# the pruning process. As alpha increases, more of the tree is pruned, which
# increases the total impurity of its leaves.
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = DecisionTreeClassifier(random_state=0)
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
###############################################################################
# In the following plot, the maximum effective alpha value is removed, because
# it is the trivial tree with only one node.
fig, ax = plt.subplots()
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
###############################################################################
# Next, we train a decision tree using the effective alphas. The last value
# in ``ccp_alphas`` is the alpha value that prunes the whole tree,
from mrex.discriminant_analysis import QuadraticDiscriminantAnalysis
h = .02 # step size in the mesh
names = [
"Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
"Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "Naive Bayes",
"QDA"
]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
GaussianProcessClassifier(1.0 * RBF(1.0)),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
MLPClassifier(alpha=1, max_iter=1000),
AdaBoostClassifier(),
GaussianNB(),
QuadraticDiscriminantAnalysis()
]
X, y = make_classification(n_features=2,
n_redundant=0,
n_informative=2,
random_state=1,
n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
import numpy as np
import matplotlib.pyplot as plt
from mrex import datasets
from mrex.tree import DecisionTreeClassifier
from mrex.neighbors import KNeighborsClassifier
from mrex.svm import SVC
from mrex.ensemble import VotingClassifier
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0, 2]]
y = iris.target
# Training classifiers
clf1 = DecisionTreeClassifier(max_depth=4)
clf2 = KNeighborsClassifier(n_neighbors=7)
clf3 = SVC(gamma=.1, kernel='rbf', probability=True)
eclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2),
('svc', clf3)],
voting='soft',
weights=[2, 1, 2])
clf1.fit(X, y)
clf2.fit(X, y)
clf3.fit(X, y)
eclf.fit(X, y)
# Plotting decision regions
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
# Running ``GridSearchCV`` using multiple evaluation metrics
# ----------------------------------------------------------
#
X, y = make_hastie_10_2(n_samples=8000, random_state=42)
# The scorers can be either be one of the predefined metric strings or a scorer
# callable, like the one returned by make_scorer
scoring = {'AUC': 'roc_auc', 'Accuracy': make_scorer(accuracy_score)}
# Setting refit='AUC', refits an estimator on the whole dataset with the
# parameter setting that has the best cross-validated AUC score.
# That estimator is made available at ``gs.best_estimator_`` along with
# parameters like ``gs.best_score_``, ``gs.best_params_`` and
# ``gs.best_index_``
gs = GridSearchCV(DecisionTreeClassifier(random_state=42),
param_grid={'min_samples_split': range(2, 403, 10)},
scoring=scoring,
refit='AUC',
return_train_score=True)
gs.fit(X, y)
results = gs.cv_results_
###############################################################################
# Plotting the result
# -------------------
plt.figure(figsize=(13, 13))
plt.title("GridSearchCV evaluating using multiple scorers simultaneously",
fontsize=16)
def test_set_params_updates_valid_params():
# Check that set_params tries to set SVC().C, not
# DecisionTreeClassifier().C
gscv = GridSearchCV(DecisionTreeClassifier(), {})
gscv.set_params(estimator=SVC(), estimator__C=42.0)
assert gscv.estimator.C == 42.0
from mrex.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
n_estimators = 30
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),
n_estimators=n_estimators)]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X1, y1 = make_gaussian_quantiles(cov=2.,
n_samples=200,
n_features=2,
n_classes=2,
random_state=1)
X2, y2 = make_gaussian_quantiles(mean=(3, 3),
cov=1.5,
n_samples=300,
n_features=2,
n_classes=2,
random_state=1)
X = np.concatenate((X1, X2))
y = np.concatenate((y1, -y2 + 1))
# Create and fit an AdaBoosted decision tree
bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),
algorithm="SAMME",
n_estimators=200)
bdt.fit(X, y)
plot_colors = "br"
plot_step = 0.02
class_names = "AB"
plt.figure(figsize=(10, 5))
# Plot the decision boundaries
plt.subplot(121)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
from mrex import datasets
from mrex.tree import DecisionTreeClassifier
from mrex.metrics import zero_one_loss
from mrex.ensemble import AdaBoostClassifier
n_estimators = 400
# A learning rate of 1. may not be optimal for both SAMME and SAMME.R
learning_rate = 1.
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
X_test, y_test = X[2000:], y[2000:]
X_train, y_train = X[:2000], y[:2000]
dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
dt_stump.fit(X_train, y_train)
dt_stump_err = 1.0 - dt_stump.score(X_test, y_test)
dt = DecisionTreeClassifier(max_depth=9, min_samples_leaf=1)
dt.fit(X_train, y_train)
dt_err = 1.0 - dt.score(X_test, y_test)
ada_discrete = AdaBoostClassifier(base_estimator=dt_stump,
learning_rate=learning_rate,
n_estimators=n_estimators,
algorithm="SAMME")
ada_discrete.fit(X_train, y_train)
ada_real = AdaBoostClassifier(base_estimator=dt_stump,
learning_rate=learning_rate,
[('lr', LinearRegression()),
('rf', RandomForestRegressor(n_estimators=5))]))]
)
@pytest.mark.parametrize("drop", [None, 'drop'])
def test_none_estimator_with_weights(X, y, voter, drop):
# check that an estimator can be set to None and passing some weight
# regression test for
# https://github.com/scikit-learn/scikit-learn/issues/13777
voter.fit(X, y, sample_weight=np.ones(y.shape))
voter.set_params(lr=drop)
voter.fit(X, y, sample_weight=np.ones(y.shape))
y_pred = voter.predict(X)
assert y_pred.shape == y.shape
@pytest.mark.parametrize(
"estimator",
[VotingRegressor(
estimators=[('lr', LinearRegression()),
('tree', DecisionTreeRegressor(random_state=0))]),
VotingClassifier(
estimators=[('lr', LogisticRegression(random_state=0)),
('tree', DecisionTreeClassifier(random_state=0))])],
ids=['VotingRegressor', 'VotingClassifier']
)
def test_check_estimators_voting_estimator(estimator):
# FIXME: to be removed when meta-estimators can be specified themselves
# their testing parameters (for required parameters).
check_estimator(estimator)
check_no_attributes_set_in_init(estimator.__class__.__name__, estimator)
from mrex.datasets import make_gaussian_quantiles
from mrex.ensemble import AdaBoostClassifier
from mrex.metrics import accuracy_score
from mrex.tree import DecisionTreeClassifier
X, y = make_gaussian_quantiles(n_samples=13000,
n_features=10,
n_classes=3,
random_state=1)
n_split = 3000
X_train, X_test = X[:n_split], X[n_split:]
y_train, y_test = y[:n_split], y[n_split:]
bdt_real = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),
n_estimators=600,
learning_rate=1)
bdt_discrete = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),
n_estimators=600,
learning_rate=1.5,
algorithm="SAMME")
bdt_real.fit(X_train, y_train)
bdt_discrete.fit(X_train, y_train)
real_test_errors = []
discrete_test_errors = []
for real_test_predict, discrete_train_predict in zip(
- the rules that were used to predict a sample; - the decision path shared by a group of samples. """ import numpy as np from mrex.model_selection import train_test_split from mrex.datasets import load_iris from mrex.tree import DecisionTreeClassifier iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) estimator = DecisionTreeClassifier(max_leaf_nodes=3, random_state=0) estimator.fit(X_train, y_train) # The decision estimator has an attribute called tree_ which stores the entire # tree structure and allows access to low level attributes. The binary tree # tree_ is represented as a number of parallel arrays. The i-th element of each # array holds information about the node `i`. Node 0 is the tree's root. NOTE: # Some of the arrays only apply to either leaves or split nodes, resp. In this # case the values of nodes of the other type are arbitrary! # # Among those arrays, we have: # - left_child, id of the left child of the node # - right_child, id of the right child of the node # - feature, feature used for splitting the node # - threshold, threshold value at the node #