def test_fetch_drug_protein():
dataset = fetch_drug_interaction(tmpdir)
assert_equal(dataset.data.shape, (1862, 660))
assert_equal(dataset.target.shape, (1862, 1554))
assert_equal(len(dataset.feature_names), 660)
assert_equal(len(dataset.target_names), 1554)
dataset = fetch_protein_interaction(tmpdir)
assert_equal(dataset.data.shape, (1554, 876))
assert_equal(dataset.target.shape, (1554, 1862))
assert_equal(len(dataset.feature_names), 876)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.base import clone
from sklearn.cross_validation import train_test_split
from sklearn.random_projection import SparseRandomProjection
from sklearn.metrics import label_ranking_average_precision_score as lrap_score
from random_output_trees.datasets import fetch_drug_interaction
from random_output_trees.ensemble import RandomForestClassifier
random_state = np.random.RandomState(0)
# Let's load a multilabel dataset
dataset = fetch_drug_interaction()
X = dataset.data
y = dataset.target # y.shape = (1862, 1554)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5,
random_state=0)
n_outputs = y.shape[1]
def benchmark(base_estimator, random_state=None, n_iter=3):
scores = []
times = []
for iter_ in range(n_iter):
estimator = clone(base_estimator)
estimator.set_params(random_state=random_state)
time_start = time()
import numpy as np
import matplotlib.pyplot as plt
from sklearn.base import clone
from sklearn.cross_validation import train_test_split
from sklearn.random_projection import SparseRandomProjection
from sklearn.metrics import label_ranking_average_precision_score as lrap_score
from random_output_trees.datasets import fetch_drug_interaction
from random_output_trees.ensemble import RandomForestClassifier
random_state = np.random.RandomState(0)
# Let's load a multilabel dataset
dataset = fetch_drug_interaction()
X = dataset.data
y = dataset.target # y.shape = (1862, 1554)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.5,
random_state=0)
n_outputs = y.shape[1]
def benchmark(base_estimator, random_state=None, n_iter=3):
scores = []
times = []
for iter_ in range(n_iter):
estimator = clone(base_estimator)
estimator.set_params(random_state=random_state)
