def mean_model(features, solutions, verbose=0):
columns = solutions.columns
clf = DummyRegressor()
print('Training Model... ')
clf.fit(features, solutions)
print('Done Training')
return (clf, columns)
def test_regressor():
X = [[0]] * 4 # ignored
y = [1, 2, 1, 1]
reg = DummyRegressor()
reg.fit(X, y)
assert_array_equal(reg.predict(X), [5. / 4] * len(X))
def test_quantile_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
median = np.median(y_learn, axis=0).reshape((1, -1))
quantile_values = np.percentile(y_learn, axis=0, q=80).reshape((1, -1))
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor(strategy="quantile", quantile=0.5)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(
median, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
# Correctness oracle
est = DummyRegressor(strategy="quantile", quantile=0.8)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(
quantile_values, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
def the_mocked_model():
X, y = make_regression(n_samples=500, n_features=2)
model = DummyRegressor()
model.fit(X, y)
return model
def dumb_regressor_result(x_test, x_train, y_test, y_train):
"""
Dumb regressor, predict only the mean value for each target variable,
returns MAE and MSE metrics per each variable.
Args:
x_test: validation samples
x_train: training samples
y_test: validation target
y_train: training target
Returns:
dumb_metrics: list of metrics results after dumb regression
"""
dumb_reg = DummyRegressor()
fake_data = np.zeros((x_train.shape[0], 1))
fake_test = np.zeros((1, 1))
dumb_reg.fit(fake_data, y_train)
dumb_pred = dumb_reg.predict(fake_test)[0]
dumb_metrics = []
for i in range(dumb_pred.size):
dumb_pred_var = np.full((x_test.shape[0], 1), dumb_pred[i])
dumb_mse_var = mean_squared_error(y_test[:, i], dumb_pred_var)
dumb_mae_var = mean_absolute_error(y_test[:, i], dumb_pred_var)
dumb_metrics.append([dumb_mse_var, dumb_mae_var])
return dumb_metrics
def test_y_mean_attribute_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
# when strategy = 'mean'
est = DummyRegressor(strategy='mean')
est.fit(X, y)
assert_equal(est.y_mean_, np.mean(y))
def test_regressor():
X = [[0]] * 4 # ignored
y = [1, 2, 1, 1]
reg = DummyRegressor()
reg.fit(X, y)
assert_array_equal(reg.predict(X), [5. / 4] * len(X))
def test_weights_regressor():
"""Check weighted average regression prediction on boston dataset."""
reg1 = DummyRegressor(strategy='mean')
reg2 = DummyRegressor(strategy='median')
reg3 = DummyRegressor(strategy='quantile', quantile=.2)
ereg = VotingRegressor([('mean', reg1), ('median', reg2),
('quantile', reg3)], weights=[1, 2, 10])
X_r_train, X_r_test, y_r_train, y_r_test = \
train_test_split(X_r, y_r, test_size=.25)
reg1_pred = reg1.fit(X_r_train, y_r_train).predict(X_r_test)
reg2_pred = reg2.fit(X_r_train, y_r_train).predict(X_r_test)
reg3_pred = reg3.fit(X_r_train, y_r_train).predict(X_r_test)
ereg_pred = ereg.fit(X_r_train, y_r_train).predict(X_r_test)
avg = np.average(np.asarray([reg1_pred, reg2_pred, reg3_pred]), axis=0,
weights=[1, 2, 10])
assert_almost_equal(ereg_pred, avg, decimal=2)
ereg_weights_none = VotingRegressor([('mean', reg1), ('median', reg2),
('quantile', reg3)], weights=None)
ereg_weights_equal = VotingRegressor([('mean', reg1), ('median', reg2),
('quantile', reg3)],
weights=[1, 1, 1])
ereg_weights_none.fit(X_r_train, y_r_train)
ereg_weights_equal.fit(X_r_train, y_r_train)
ereg_none_pred = ereg_weights_none.predict(X_r_test)
ereg_equal_pred = ereg_weights_equal.predict(X_r_test)
assert_almost_equal(ereg_none_pred, ereg_equal_pred, decimal=2)
def train_classifier(): X_train = tfv.transform(video_captions_train) X_test = tfv.transform(video_captions_test) dummy = DummyRegressor(strategy="median") dummy.fit(X_train, Y_train) Y_pred_med = dummy.predict(X_test)
def test_constants_not_specified_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
est = DummyRegressor(strategy='constant')
with pytest.raises(TypeError):
est.fit(X, y)
def test_quantile_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
median = np.median(y_learn, axis=0).reshape((1, -1))
quantile_values = np.percentile(y_learn, axis=0, q=80).reshape((1, -1))
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor(strategy="quantile", quantile=0.5)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(median, y_learn, y_pred_learn, y_test,
y_pred_test)
_check_behavior_2d(est)
# Correctness oracle
est = DummyRegressor(strategy="quantile", quantile=0.8)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(quantile_values, y_learn, y_pred_learn, y_test,
y_pred_test)
_check_behavior_2d(est)
def train_intelligence(dataframe, text_column, classification_column):
vectorizer = TfidfVectorizer(lowercase=False)
bag_of_words = vectorizer.fit_transform(dataframe[text_column])
train, test, class_train, class_test = train_test_split(
bag_of_words,
dataframe[classification_column],
random_state=42,
test_size=0.25)
logistic_regression = LogisticRegression()
logistic_regression.fit(train, class_train)
pesos = pd.DataFrame(logistic_regression.coef_[0].T,
index=vectorizer.get_feature_names())
print(pesos.nlargest(10, 0))
print(pesos.nsmallest(10, 0))
filename = 'anton_brain.sav'
pickle.dump(logistic_regression, open(filename, 'wb'))
filename = 'anton_vectorizer.sav'
pickle.dump(vectorizer, open(filename, 'wb'))
###### baseline ######
baseline = DummyRegressor(strategy="mean")
baseline.fit(train, class_train)
print('Baseline Accuracy: ')
print(round(baseline.predict(class_test)[0] * 100, 2))
###### baseline ######
print('Algorithm Accuracy:')
print(round(logistic_regression.score(test, class_test) * 100, 2))
return
def test_weights_regressor():
"""Check weighted average regression prediction on diabetes dataset."""
reg1 = DummyRegressor(strategy="mean")
reg2 = DummyRegressor(strategy="median")
reg3 = DummyRegressor(strategy="quantile", quantile=0.2)
ereg = VotingRegressor(
[("mean", reg1), ("median", reg2), ("quantile", reg3)], weights=[1, 2, 10]
)
X_r_train, X_r_test, y_r_train, y_r_test = train_test_split(
X_r, y_r, test_size=0.25
)
reg1_pred = reg1.fit(X_r_train, y_r_train).predict(X_r_test)
reg2_pred = reg2.fit(X_r_train, y_r_train).predict(X_r_test)
reg3_pred = reg3.fit(X_r_train, y_r_train).predict(X_r_test)
ereg_pred = ereg.fit(X_r_train, y_r_train).predict(X_r_test)
avg = np.average(
np.asarray([reg1_pred, reg2_pred, reg3_pred]), axis=0, weights=[1, 2, 10]
)
assert_almost_equal(ereg_pred, avg, decimal=2)
ereg_weights_none = VotingRegressor(
[("mean", reg1), ("median", reg2), ("quantile", reg3)], weights=None
)
ereg_weights_equal = VotingRegressor(
[("mean", reg1), ("median", reg2), ("quantile", reg3)], weights=[1, 1, 1]
)
ereg_weights_none.fit(X_r_train, y_r_train)
ereg_weights_equal.fit(X_r_train, y_r_train)
ereg_none_pred = ereg_weights_none.predict(X_r_test)
ereg_equal_pred = ereg_weights_equal.predict(X_r_test)
assert_almost_equal(ereg_none_pred, ereg_equal_pred, decimal=2)
class MeanRegressor(BaseEstimator):
def __init__(self):
"""
Model predicting the mean value of the target label
"""
self.reg = DummyRegressor(strategy='mean')
def fit(self, X, y):
"""
Fits the model
:param X: input dataframe
:param y: labels dataframe
:return:
"""
X = X['user_id']
self.reg.fit(X, y)
def predict(self, X):
"""
Predicts the values with given input data
:param X: input dataframe
:return: list of predicted values
"""
X = X['user_id']
return self.reg.predict(X)
def test_y_mean_attribute_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
# when strategy = 'mean'
est = DummyRegressor(strategy='mean')
est.fit(X, y)
assert_equal(est.y_mean_, np.mean(y))
def test_unknown_strategey_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
est = DummyRegressor(strategy='gona')
with pytest.raises(ValueError):
est.fit(X, y)
class GradientBoosting:
def __init__(self,
n_estimators=100,
learning_rate=0.1,
subsamples=1.0,
max_features=None,
max_depth=3,
min_leaf_size=1,
init=None):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.subsamples = subsamples
self.max_features = max_features
self.max_depth = max_depth
self.min_leaf_size = min_leaf_size
if init is None:
self.init_model = DummyRegressor()
else:
self.init_model = init
self.estimators = []
self.weights = []
self.loss_by_iter = []
def fit(self, X, y):
if self.init_model == 'zeros':
pred = np.zeros(y.shape)
else:
self.init_model.fit(X, y)
pred = self.init_model.predict(X)
res = pred.copy()
self.loss_by_iter.append(mse(res, y))
for i in tqdm(range(self.n_estimators)):
grad = (y - res)
cur_estimator = CART(max_depth=self.max_depth,
min_leaf_size=self.min_leaf_size,
max_features=self.max_features)
if self.subsamples < 1.0:
sample_ids = np.arange(y.shape[0])
np.random.shuffle(sample_ids)
sample_ids = sample_ids[:int(y.shape[0] * self.subsamples)]
cur_estimator.fit(X[sample_ids], grad[sample_ids])
else:
cur_estimator.fit(X, grad)
self.estimators.append(cur_estimator)
pred = cur_estimator.predict(X)
# b = golden_section(res, pred, y)
b = 1
self.weights.append(b)
res += self.learning_rate * b * pred
self.loss_by_iter.append(mse(res, y))
def predict(self, X):
pred = self.init_model.predict(X)
for i in range(self.n_estimators):
pred += self.learning_rate * self.weights[i] * self.estimators[
i].predict(X)
return pred
def test_dummy_regressor_on_nan_value():
X = [[np.NaN]]
y = [1]
y_expected = [1]
clf = DummyRegressor()
clf.fit(X, y)
y_pred = clf.predict(X)
assert_array_equal(y_pred, y_expected)
def dummy_regressor(X, y, args={}):
"""
用于使用随机预测得到的结果，用于和其他模型进行对比
"""
from sklearn.dummy import DummyRegressor
clf = DummyRegressor(**args)
clf.fit(X, y)
return clf
def test_constants_not_specified_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
est = DummyRegressor(strategy="constant")
err_msg = "Constant target value has to be specified"
with pytest.raises(TypeError, match=err_msg):
est.fit(X, y)
def dummy_regressor(X_train, X_test, y_train, y_test):
dummy_clf = DummyRegressor()
dummy_clf.fit(X_train, y_train)
dummy_pred = dummy_clf.predict(X_test)
# Evaluate the root mean squared error
calculated_error = np.sqrt(mean_squared_error(y_test, dummy_pred))
return round(calculated_error,3)
def test_dummy_regressor_on_nan_value():
X = [[np.NaN]]
y = [1]
y_expected = [1]
clf = DummyRegressor()
clf.fit(X, y)
y_pred = clf.predict(X)
assert_array_equal(y_pred, y_expected)
def test_y_mean_attribute_regressor():
X = [[0]] * 5
y = [1, 2, 4, 6, 8]
# when strategy = 'mean'
est = DummyRegressor(strategy="mean")
est.fit(X, y)
assert est.constant_ == np.mean(y)
def test_dummy_regressor_on_3D_array():
X = np.array([[['foo']], [['bar']], [['baz']]])
y = np.array([2, 2, 2])
y_expected = np.array([2, 2, 2])
cls = DummyRegressor()
cls.fit(X, y)
y_pred = cls.predict(X)
assert_array_equal(y_pred, y_expected)
def test_constant_size_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X = random_state.randn(10, 10)
y = random_state.randn(10, 5)
est = DummyRegressor(strategy="constant", constant=[1, 2, 3, 4])
with pytest.raises(ValueError):
est.fit(X, y)
def test_dummy_regressor_on_3D_array():
X = np.array([[['foo']], [['bar']], [['baz']]])
y = np.array([2, 2, 2])
y_expected = np.array([2, 2, 2])
cls = DummyRegressor()
cls.fit(X, y)
y_pred = cls.predict(X)
assert_array_equal(y_pred, y_expected)
class Regressor(BaseEstimator):
def __init__(self):
self.clf = DummyRegressor()
def fit(self, X, y):
self.clf.fit(X, y)
def predict(self, X):
return self.clf.predict(X)
def execute(self):
from sklearn.dummy import DummyRegressor
dummy_regr = DummyRegressor(strategy="mean")
dummy_regr.fit(self.partitions.x_train, self.partitions.y_train)
y_pred = dummy_regr.predict(self.partitions.x_test)
self.y_pred = y_pred
return self.y_pred, self.partitions.y_test
def main():
X_train = pd.read_csv("data/X_train.csv")
y_train = pd.read_csv("data/y_train.csv")
dummy_reg = DummyRegressor()
dummy_reg.fit(X_train, y_train)
pickle.dump(dummy_reg, open("models/dummy_reg.pkl", 'wb'))
def dummy_regressor_accuracy(x,
y,
evaluator: Callable,
strategy: str = 'mean'):
dummy = DummyRegressor(strategy)
dummy.fit(x, y)
y_hat = dummy.predict(x)
print('DummyRegressor accuracy:', evaluator(y_hat, y))
return dummy
def test_median_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="median")
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
def test_scorer_sample_weight():
"""Test that scorers support sample_weight or raise sensible errors"""
# Unlike the metrics invariance test, in the scorer case it's harder
# to ensure that, on the classifier output, weighted and unweighted
# scores really should be unequal.
X, y = make_classification(random_state=0)
_, y_ml = make_multilabel_classification(n_samples=X.shape[0],
return_indicator=True,
random_state=0)
split = train_test_split(X, y, y_ml, random_state=0)
X_train, X_test, y_train, y_test, y_ml_train, y_ml_test = split
sample_weight = np.ones_like(y_test)
sample_weight[:10] = 0
# get sensible estimators for each metric
sensible_regr = DummyRegressor(strategy='median')
sensible_regr.fit(X_train, y_train)
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)
estimator = dict([(name, sensible_regr) for name in REGRESSION_SCORERS] +
[(name, sensible_clf) for name in CLF_SCORERS] +
[(name, sensible_ml_clf)
for name in MULTILABEL_ONLY_SCORERS])
for name, scorer in SCORERS.items():
if name in MULTILABEL_ONLY_SCORERS:
target = y_ml_test
else:
target = y_test
try:
weighted = scorer(estimator[name],
X_test,
target,
sample_weight=sample_weight)
ignored = scorer(estimator[name], X_test[10:], target[10:])
unweighted = scorer(estimator[name], X_test, target)
assert_not_equal(weighted,
unweighted,
msg="scorer {0} behaves identically when "
"called with sample weights: {1} vs "
"{2}".format(name, weighted, unweighted))
assert_almost_equal(weighted,
ignored,
err_msg="scorer {0} behaves differently when "
"ignoring samples and setting sample_weight to"
" 0: {1} vs {2}".format(
name, weighted, ignored))
except TypeError as e:
assert_true(
"sample_weight" in str(e),
"scorer {0} raises unhelpful exception when called "
"with sample weights: {1}".format(name, str(e)))
def train_dummy_regressors(features, target):
for strat in ['mean', 'median']:
dr = DummyRegressor(strategy=strat)
dr.fit(features, y=target.flatten())
dummy_score = (100 * dr.score(features, target))
print('{:.1f} % score for a dummy regressor using the {} stragety'.format(
dummy_score,
dr.get_params()['strategy']))
def test_constant_size_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X = random_state.randn(10, 10)
y = random_state.randn(10, 5)
est = DummyRegressor(strategy="constant", constant=[1, 2, 3, 4])
err_msg = r"Constant target value should have shape \(5, 1\)."
with pytest.raises(ValueError, match=err_msg):
est.fit(X, y)
def test_scorer_sample_weight():
# Test that scorers support sample_weight or raise sensible errors
# Unlike the metrics invariance test, in the scorer case it's harder
# to ensure that, on the classifier output, weighted and unweighted
# scores really should be unequal.
X, y = make_classification(random_state=0)
_, y_ml = make_multilabel_classification(n_samples=X.shape[0], random_state=0)
split = train_test_split(X, y, y_ml, random_state=0)
X_train, X_test, y_train, y_test, y_ml_train, y_ml_test = split
sample_weight = np.ones_like(y_test)
sample_weight[:10] = 0
# get sensible estimators for each metric
sensible_regr = DummyRegressor(strategy="median")
sensible_regr.fit(X_train, y_train)
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)
estimator = dict(
[(name, sensible_regr) for name in REGRESSION_SCORERS]
+ [(name, sensible_clf) for name in CLF_SCORERS]
+ [(name, sensible_ml_clf) for name in MULTILABEL_ONLY_SCORERS]
)
for name, scorer in SCORERS.items():
if name in MULTILABEL_ONLY_SCORERS:
target = y_ml_test
else:
target = y_test
try:
weighted = scorer(estimator[name], X_test, target, sample_weight=sample_weight)
ignored = scorer(estimator[name], X_test[10:], target[10:])
unweighted = scorer(estimator[name], X_test, target)
assert_not_equal(
weighted,
unweighted,
msg="scorer {0} behaves identically when "
"called with sample weights: {1} vs "
"{2}".format(name, weighted, unweighted),
)
assert_almost_equal(
weighted,
ignored,
err_msg="scorer {0} behaves differently when "
"ignoring samples and setting sample_weight to"
" 0: {1} vs {2}".format(name, weighted, ignored),
)
except TypeError as e:
assert_true(
"sample_weight" in str(e),
"scorer {0} raises unhelpful exception when called " "with sample weights: {1}".format(name, str(e)),
)
def test_median_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="median")
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
class DummyEstimator(BaseTesterEstimator):
def __init__(self):
self.regressor = DummyRegressor()
def fit(self, x, y):
self.regressor.fit(x, y)
def predict(self, x):
return self.regressor.predict(x)
def test_dummy_regressor_return_std():
X = [[0]] * 3 # ignored
y = np.array([2, 2, 2])
y_std_expected = np.array([0, 0, 0])
cls = DummyRegressor()
cls.fit(X, y)
y_pred_list = cls.predict(X, return_std=True)
# there should be two elements when return_std is True
assert len(y_pred_list) == 2
# the second element should be all zeros
assert_array_equal(y_pred_list[1], y_std_expected)
def test_quantile_invalid():
X = [[0]] * 5 # ignored
y = [0] * 5 # ignored
est = DummyRegressor(strategy="quantile", quantile=None)
err_msg = (
"When using `strategy='quantile', you have to specify the desired quantile"
)
with pytest.raises(ValueError, match=err_msg):
est.fit(X, y)
def test_dummy_regressor_return_std():
X = [[0]] * 3 # ignored
y = np.array([2, 2, 2])
y_std_expected = np.array([0, 0, 0])
cls = DummyRegressor()
cls.fit(X, y)
y_pred_list = cls.predict(X, return_std=True)
# there should be two elements when return_std is True
assert_equal(len(y_pred_list), 2)
# the second element should be all zeros
assert_array_equal(y_pred_list[1], y_std_expected)
def train_average_predictor(journeys: DataFrame, last: str,
target: str) -> Optional[Any]:
journeys = journeys[pd.notnull(journeys[target])
& pd.notnull(journeys[last])]
y = travel_times(journeys, [], last,
target).astype("int64") / 1_000_000_000
if len(y) > 0:
predictor = DummyRegressor(strategy="median")
predictor.fit(journeys, y)
return predictor
else:
return None
def baseline():
from sklearn.dummy import DummyRegressor
baseline = DummyRegressor(strategy='mean')
baseline.fit(X_train_scaled, y_train)
y_pred_train = baseline.predict(X_train_scaled)
#y_pred_train_round = np.round(y_pred_train)
y_pred_test = baseline.predict(X_test_scaled)
#y_pred_test_round = np.round(y_pred_test)
print(r2_score(y_test, y_pred_test))
#print (lm.score(X_test_scaled, y_test))
#plot_conf_mat(y_test, y_pred_round)
return scores_results(y_train, y_test, y_pred_train, y_pred_test)
def test_regressor_prediction_independent_of_X(strategy):
y = [0, 2, 1, 1]
X1 = [[0]] * 4
reg1 = DummyRegressor(strategy=strategy, constant=0, quantile=0.7)
reg1.fit(X1, y)
predictions1 = reg1.predict(X1)
X2 = [[1]] * 4
reg2 = DummyRegressor(strategy=strategy, constant=0, quantile=0.7)
reg2.fit(X2, y)
predictions2 = reg2.predict(X2)
assert_array_equal(predictions1, predictions2)
def _make_estimators(X_train, y_train, y_ml_train):
# Make estimators that make sense to test various scoring methods
sensible_regr = DummyRegressor(strategy='median')
sensible_regr.fit(X_train, y_train)
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_ml_clf) for name in MULTILABEL_ONLY_SCORERS]
)
def test_constant_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="constant", constant=[43])
reg.fit(X, y)
assert_array_equal(reg.predict(X), [43] * len(X))
reg = DummyRegressor(strategy="constant", constant=43)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [43] * len(X))
def test_multioutput_regressor():
X_learn = np.random.randn(10, 10)
y_learn = np.random.randn(10, 5)
mean = np.mean(y_learn, axis=0).reshape((1, -1))
X_test = np.random.randn(20, 10)
y_test = np.random.randn(20, 5)
# Correctness oracle
est = DummyRegressor()
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
assert_array_equal(np.tile(mean, (y_learn.shape[0], 1)), y_pred_learn)
assert_array_equal(np.tile(mean, (y_test.shape[0], 1)), y_pred_test)
_check_behavior_2d(est)
def test_mean_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
mean = np.mean(y_learn, axis=0).reshape((1, -1))
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor()
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(mean, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d(est)
def _minimize_simbo_general(fun,
x0, # only used to get number of features
args=(),
callback=None,
batch_size=100,
population_size=10000,
maxiter=10000,
scorer=None, # if no scorer given, scores are constant
selector=None, # only relevant is sampler is given
sampler=None):
n_iter = int(maxiter / batch_size)
assert n_iter > 0
dummy_generator = generative_models.DummyGenerator(len(x0))
if scorer is None:
scorer = DummyRegressor()
if sampler is None:
sampler = dummy_generator
if isinstance(selector, float) and 0 < selector < 1:
selector = percentile_selector(selector)
for i in range(n_iter):
if i == 0:
batch = dummy_generator.sample(batch_size)
else:
population = sampler.sample(population_size)
scores = scorer.predict(population)
batch_w_score = heapq.nsmallest(batch_size, zip(scores, population),
key=lambda x: x[0])
batch = [v for score, v in batch_w_score]
results = optimize_utils.score_multi(fun, batch, args, callback)
selected = selector(results, batch) if selector is not None else batch
scorer.fit(batch, results)
sampler.fit(selected)
best_fval, best_x = max(zip(results, batch), key=lambda x: x[0])
nfev = batch_size * n_iter
return optimize_utils.to_result(x=best_x, fun=best_fval,
niter=n_iter, nfev=nfev)
def test_scorer_sample_weight():
"""Test that scorers support sample_weight or raise sensible errors"""
# Unlike the metrics invariance test, in the scorer case it's harder
# to ensure that, on the classifier output, weighted and unweighted
# scores really should be unequal.
X, y = make_classification(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
sample_weight = np.ones_like(y_test)
sample_weight[:10] = 0
# get sensible estimators for each metric
sensible_regr = DummyRegressor(strategy='median')
sensible_regr.fit(X_train, y_train)
sensible_clf = DecisionTreeClassifier()
sensible_clf.fit(X_train, y_train)
estimator = dict([(name, sensible_regr)
for name in REGRESSION_SCORERS] +
[(name, sensible_clf)
for name in CLF_SCORERS])
for name, scorer in SCORERS.items():
try:
weighted = scorer(estimator[name], X_test, y_test,
sample_weight=sample_weight)
ignored = scorer(estimator[name], X_test[10:], y_test[10:])
unweighted = scorer(estimator[name], X_test, y_test)
assert_not_equal(weighted, unweighted,
"scorer {0} behaves identically when called with "
"sample weights: {1} vs {2}".format(name,
weighted,
unweighted))
assert_equal(weighted, ignored,
"scorer {0} behaves differently when ignoring "
"samples and setting sample_weight to 0: "
"{1} vs {2}".format(name, weighted, ignored))
except TypeError as e:
assert_true("sample_weight" in str(e),
"scorer {0} raises unhelpful exception when called "
"with sample weights: {1}".format(name, str(e)))
def test_constant_strategy_multioutput_regressor():
random_state = np.random.RandomState(seed=1)
X_learn = random_state.randn(10, 10)
y_learn = random_state.randn(10, 5)
# test with 2d array
constants = random_state.randn(5)
X_test = random_state.randn(20, 10)
y_test = random_state.randn(20, 5)
# Correctness oracle
est = DummyRegressor(strategy="constant", constant=constants)
est.fit(X_learn, y_learn)
y_pred_learn = est.predict(X_learn)
y_pred_test = est.predict(X_test)
_check_equality_regressor(constants, y_learn, y_pred_learn, y_test, y_pred_test)
_check_behavior_2d_for_constant(est)
def simplest(cube, y, cv):
""" just use the mean to impute the missing values
"""
from sklearn.dummy import DummyRegressor
clf = DummyRegressor()
X = cube.reshape(cube.shape[0], cube.shape[1] * cube.shape[2])
sse = np.zeros(y.shape[1])
for train, test in cv:
y_train, y_test = y[train], y[test]
y_predict = clf.fit(X[train], y[train]).predict(X[test])
sse += np.mean((y_predict - y_test) ** 2, 0)
return sse
def test_quantile_strategy_regressor():
random_state = np.random.RandomState(seed=1)
X = [[0]] * 5 # ignored
y = random_state.randn(5)
reg = DummyRegressor(strategy="quantile", quantile=0.5)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.median(y)] * len(X))
reg = DummyRegressor(strategy="quantile", quantile=0)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.min(y)] * len(X))
reg = DummyRegressor(strategy="quantile", quantile=1)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.max(y)] * len(X))
reg = DummyRegressor(strategy="quantile", quantile=0.3)
reg.fit(X, y)
assert_array_equal(reg.predict(X), [np.percentile(y, q=30)] * len(X))
def test_stacked_featurizer(self):
data = self.make_test_data()
data['y'] = [1, 2, 3]
# Test for a regressor
model = DummyRegressor()
model.fit(self.multi.featurize_many(data['x']), data['y'])
# Test the predictions
f = StackedFeaturizer(self.single, model)
self.assertEqual([2], f.featurize(data['x'][0]))
# Test the feature names
self.assertEqual(['prediction'], f.feature_labels())
f.name = 'ML'
self.assertEqual(['ML prediction'], f.feature_labels())
# Test classifier
model = DummyClassifier("prior")
data['y'] = [0, 0, 1]
model.fit(self.multi.featurize_many(data['x']), data['y'])
# Test the prediction
f.model = model
self.assertEqual([2. / 3], f.featurize(data['x'][0]))
# Test the feature labels
self.assertRaises(ValueError, f.feature_labels)
f.class_names = ['A', 'B']
self.assertEqual(['ML P(A)'], f.feature_labels())
# Test with three classes
data['y'] = [0, 2, 1]
model.fit(self.multi.featurize_many(data['x']), data['y'])
self.assertArrayAlmostEqual([1. / 3] * 2, f.featurize(data['x'][0]))
f.class_names = ['A', 'B', 'C']
self.assertEqual(['ML P(A)', 'ML P(B)'], f.feature_labels())
def main():
# read review data
print('parsing review data...')
reviews = parse_json('./yelp_dataset_challenge_academic_dataset/yelp_academic_dataset_review.json')
# use only reviews posted after 2008
valid_reviews = []
for review in reviews:
review_date = datetime.datetime.strptime(review['date'], '%Y-%m-%d')
if review_date.year < 2008:
continue
valid_reviews.append(review)
reviews = valid_reviews
# sample the data
# sample_num = len(reviews)
# print('sampling...', sample_num, 'out of', len(reviews))
# reviews = sample(reviews, sample_num)
# tokenize text for all reviews
print('tokenizing text for all reviews...')
texts = [review['text'] for review in reviews]
count_vect = CountVectorizer(max_features = 100)
X = count_vect.fit_transform(texts)
# transform from occurrence to frequency
print('converting occurrence to frequency...')
tfidf_transformer = TfidfTransformer()
X = tfidf_transformer.fit_transform(X)
# load the linear model for normalization
clf = joblib.load('./normalization/linear_model_for_normalization.pkl')
# get labels
print('calculating labels...')
y = []
for review in reviews:
review_date = datetime.datetime.strptime(review['date'], '%Y-%m-%d')
# normalize
normalizor = clf.predict(np.array([[review_date.year]]))[0][0]
review_quality = sum(review['votes'].values()) / normalizor
y.append(review_quality)
# splitting into train and test set
print('splitting into train and test set...')
train_len = int(X.shape[0] * 0.6)
X_train = X[:train_len, :]
y_train = y[:train_len]
X_test = X[train_len:, :]
y_test = y[train_len:]
print('train size:', X_train.shape)
print('test size:', X_test.shape)
# convert to polynomial features
# print('converting to polynomial features...')
# poly = PolynomialFeatures(2)
# X_train = poly.fit_transform(X_train.toarray())
# X_test = poly.fit_transform(X_test.toarray())
# print('train set: ', X_train.shape)
# print('test set: ', X_test.shape)
# scale the attributes to [0, 1]
print('standardizing the features...')
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
X_test = min_max_scaler.transform(X_test)
# training classifiers
print('training, predicting and evaluating...')
# Dummy Regression (baseline model)
print('\nDummy Regression:')
model = DummyRegressor(strategy='mean')
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# Linear Regression
print('\nLinear_regression: ')
model = LinearRegression()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# Ridge
print('\nRidge: ')
model = Ridge()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# passive aggresive
print('\nPoly: ')
model = PassiveAggressiveRegressor()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# AdaBoost
print('\nAdaBoost: ')
model = AdaBoostRegressor()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# Random Forest
print('\nRandom Forest:')
model = RandomForestRegressor()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
def main():
# load training and testing data set
print('parsing training set...')
X_train, y_train = parse('./data_set/train_set.csv')
print('parsing testing set...')
X_test, y_test = parse('./data_set/test_set.csv')
print('train set: ', X_train.shape)
print('test set: ', X_test.shape)
# The result turns out to be worse using non-linear polynomial regression
# convert to polynomial features
# print('converting to polynomial features...')
# poly = PolynomialFeatures(2)
# X_train = poly.fit_transform(X_train)
# X_test = poly.fit_transform(X_test)
# print('train set: ', X_train.shape)
# print('test set: ', X_test.shape)
# scale the attributes to [0, 1]
print('standardizing the features...')
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
X_test = min_max_scaler.transform(X_test)
# training classifiers
print('training, predicting and evaluating...')
# Dummy Regression (baseline model)
print('\nDummy Regression: (baseline)')
model = DummyRegressor(strategy='mean')
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# Linear Regression
print('\nLinear_regression: ')
model = LinearRegression()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# KNN Regression
# print('\nKNN Regression: ')
# model = KNeighborsRegressor()
# model.fit(X_train, y_train)
# y_pre = model.predict(X_test)
# print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
# print('r2_score: ', r2_score(y_test, y_pre))
# Neural Network - Bernoulli Restricted Boltzmann Machine (RBM)
# print('\nNeural Network - RBM: ')
# model = BernoulliRBM()
# model.fit(X_train, y_train)
# y_pre = model.predict(X_test)
# print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
# print('r2_score: ', r2_score(y_test, y_pre))
# AdaBoost
print('\nAdaBoost: ')
model = AdaBoostRegressor()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
# Random Forest
print('\nRandom Forest:')
model = RandomForestRegressor()
model.fit(X_train, y_train)
y_pre = model.predict(X_test)
print('mean absolute error: ', mean_absolute_error(y_test, y_pre))
print('r2_score: ', r2_score(y_test, y_pre))
def test_regressor_score_with_None(y, y_test):
reg = DummyRegressor()
reg.fit(None, y)
assert_equal(reg.score(None, y_test), 1.0)
X_fmri_train = X_fmri[train]
X_fmri_test = X_fmri[test]
X_meg_train = X_meg[train]
X_meg_test = X_meg[test]
y_train = y[train]
y_test = y[test]
X_train = np.hstack([X_fmri_train,X_meg_train])
X_test = np.hstack([X_fmri_test,X_meg_test])
pls.fit(X_train, y_train)
pred = pls.predict(X_test)
mae += mean_absolute_error(y_test, pred)
dumb.fit(X_train, y_train)
dumb_pred = dumb.predict(X_test)
dumb_mae += mean_absolute_error(y_test,dumb_pred)
if within:
pls.fit(X_fmri_train, y_train)
pred = pls.predict(X_fmri_test)
fmri_mae += mean_absolute_error(y_test, pred)
pls.fit(X_meg_train, y_train)
pred = pls.predict(X_meg_test)
meg_mae += mean_absolute_error(y_test, pred)
comp_scores.append(mae/nfolds)
dumb_scores.append(dumb_mae/nfolds)
fmri_scores.append(fmri_mae/nfolds)
pls = PLSRegression(n_components=ncomp)
dumb = DummyRegressor(strategy='mean')
mae = 0
dumb_mae = 0
for oidx, (train, test) in enumerate(cv):
X_fmri_train = X_fmri[train]
X_fmri_test = X_fmri[test]
X_meg_train = X_meg[train]
X_meg_test = X_meg[test]
pls.fit(X_fmri_train, X_meg_train)
pred = pls.predict(X_fmri_test)
mae += mean_absolute_error(X_meg_test, pred)
dumb.fit(X_fmri_train, X_meg_train)
dumb_pred = dumb.predict(X_fmri_test)
dumb_mae += mean_absolute_error(X_meg_test,dumb_pred)
comp_scores.append(mae/nfolds)
dumb_scores.append(dumb_mae/nfolds)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.plot(max_comps,comp_scores,max_comps,dumb_scores)
t_str = seed + str(band)
plt.title(t_str)
plt.savefig(home+'/tmp/meg_fmri_%s_%s.png'%(seed,band[0]))