def test_tweedie_convergence(max_depth, split_criterion):
np.random.seed(33)
bootstrap = None
max_features = 1.0
n_estimators = 1
min_impurity_decrease = 1e-5
n_datapoints = 1000
tweedie = {
"poisson": {
"power": 1,
"gen": np.random.poisson,
"args": [0.01]
},
"gamma": {
"power": 2,
"gen": np.random.gamma,
"args": [2.0]
},
"inverse_gaussian": {
"power": 3,
"gen": np.random.wald,
"args": [0.1, 2.0]
}
}
# generating random dataset with tweedie distribution
X = np.random.random((n_datapoints, 4)).astype(np.float32)
y = tweedie[split_criterion]["gen"](*tweedie[split_criterion]["args"],
size=n_datapoints).astype(np.float32)
tweedie_preds = curfr(split_criterion=split_criterion,
max_depth=max_depth,
n_estimators=n_estimators,
bootstrap=bootstrap,
max_features=max_features,
min_impurity_decrease=min_impurity_decrease).fit(
X, y).predict(X)
mse_preds = curfr(split_criterion=2,
max_depth=max_depth,
n_estimators=n_estimators,
bootstrap=bootstrap,
max_features=max_features,
min_impurity_decrease=min_impurity_decrease).fit(
X, y).predict(X)
# y should not be non-positive for mean_poisson_deviance
mask = mse_preds > 0
mse_tweedie_deviance = mean_tweedie_deviance(
y[mask], mse_preds[mask], power=tweedie[split_criterion]["power"])
tweedie_tweedie_deviance = mean_tweedie_deviance(
y[mask], tweedie_preds[mask], power=tweedie[split_criterion]["power"])
# model trained on tweedie data with
# tweedie criterion must perform better on tweedie loss
assert mse_tweedie_deviance >= tweedie_tweedie_deviance
def test_tweedie_deviance_continuity():
n_samples = 100
y_true = np.random.RandomState(0).rand(n_samples) + 0.1
y_pred = np.random.RandomState(1).rand(n_samples) + 0.1
assert_allclose(
mean_tweedie_deviance(y_true, y_pred, power=0 - 1e-10),
mean_tweedie_deviance(y_true, y_pred, power=0),
)
# Ws we get closer to the limit, with 1e-12 difference the absolute
# tolerance to pass the below check increases. There are likely
# numerical precision issues on the edges of different definition
# regions.
assert_allclose(
mean_tweedie_deviance(y_true, y_pred, power=1 + 1e-10),
mean_tweedie_deviance(y_true, y_pred, power=1),
atol=1e-6,
)
assert_allclose(
mean_tweedie_deviance(y_true, y_pred, power=2 - 1e-10),
mean_tweedie_deviance(y_true, y_pred, power=2),
atol=1e-6,
)
assert_allclose(
mean_tweedie_deviance(y_true, y_pred, power=2 + 1e-10),
mean_tweedie_deviance(y_true, y_pred, power=2),
atol=1e-6,
)
def evaluate_forecast(self):
n = min(len(self.validation_data), len(self.forecasts))
y_forecast = self.forecasts[:n]
y_actual = self.validation_data.tail(n)["close"]
mean_abs_err = learn.mean_absolute_error(y_actual, y_forecast)
mean_sq_err = learn.mean_squared_error(y_actual, y_forecast)
mean_sq_lg_err = learn.mean_squared_log_error(y_actual, y_forecast)
mean_abs_percent_err = learn.mean_absolute_percentage_error(
y_actual, y_forecast)
median_abs_err = learn.median_absolute_error(y_actual, y_forecast)
mean_gamma_dev = learn.mean_gamma_deviance(y_actual, y_forecast)
mean_poisson_dev = learn.mean_poisson_deviance(y_actual, y_forecast)
mean_tweedie_dev = learn.mean_tweedie_deviance(y_actual, y_forecast)
explained_variance = learn.explained_variance_score(
y_actual, y_forecast)
max_residual = learn.max_error(y_actual, y_forecast)
coeff_determination = learn.r2_score(y_actual, y_forecast)
metrics = {
"Mean Squared Error (MSE)": mean_sq_err,
"Mean Absolute Error (MAE)": mean_abs_err,
"Mean Squared Logarithmic Error (MSLE)": mean_sq_lg_err,
"Mean Absolute Percentage Error (MAPE)": mean_abs_percent_err,
"Median Absolute Error (MedAE)": median_abs_err,
"Mean Gamma Deviance": mean_gamma_dev,
"Mean Poisson Deviance": mean_poisson_dev,
"Mean Tweedie Deviance Error": mean_tweedie_dev,
"Explained Variance Regression Score": explained_variance,
"Max Residual Error": max_residual,
"Coefficient of Determination": coeff_determination
}
self.metrics = metrics
def mtd(self) -> float:
"""
Mean tweedie deviance error metric for regression problems
:return: float
Mean-Tweedie-Deviance-Error Score
"""
return mean_tweedie_deviance(y_true=self.obs,
y_pred=self.pred,
sample_weight=None)
def validate(self, X_test, y_test):
if not self._is_fitted:
raise Exception("Model is not fitted.")
scores = {}
scores_exp = {}
y_pred = self.model.predict(X_test)
scores["r2"] = r2_score(y_test, y_pred)
scores["mse"] = mean_tweedie_deviance(y_test, y_pred, power=0)
scores["poisson_deviance"] = mean_tweedie_deviance(y_test,
y_pred,
power=1)
scores["gamma_deviance"] = mean_tweedie_deviance(y_test,
y_pred,
power=2)
self.test_scores = scores
scores_exp["r2"] = r2_score(np.exp(y_test), np.exp(y_pred))
scores_exp["mse"] = mean_tweedie_deviance(np.exp(y_test),
np.exp(y_pred),
power=0)
scores_exp["poisson_deviance"] = mean_tweedie_deviance(np.exp(y_test),
np.exp(y_pred),
power=1)
scores_exp["gamma_deviance"] = mean_tweedie_deviance(np.exp(y_test),
np.exp(y_pred),
power=2)
self.test_scores_exp = scores_exp
return self.test_scores, self.test_scores_exp
def test_regression_metrics(n_samples=50):
y_true = np.arange(n_samples)
y_pred = y_true + 1
assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
assert_almost_equal(mean_squared_log_error(y_true, y_pred),
mean_squared_error(np.log(1 + y_true),
np.log(1 + y_pred)))
assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
assert_almost_equal(max_error(y_true, y_pred), 1.)
assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=0),
mean_squared_error(y_true, y_pred))
# Tweedie deviance needs positive y_pred, except for p=0,
# p>=2 needs positive y_true
# results evaluated by sympy
y_true = np.arange(1, 1 + n_samples)
y_pred = 2 * y_true
n = n_samples
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=-1),
5/12 * n * (n**2 + 2 * n + 1))
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=1),
(n + 1) * (1 - np.log(2)))
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=2),
2 * np.log(2) - 1)
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3/2),
((6 * np.sqrt(2) - 8) / n) * np.sqrt(y_true).sum())
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3),
np.sum(1 / y_true) / (4 * n))
def test_regression_metrics(n_samples=50):
y_true = np.arange(n_samples)
y_pred = y_true + 1
y_pred_2 = y_true - 1
assert_almost_equal(mean_squared_error(y_true, y_pred), 1.0)
assert_almost_equal(
mean_squared_log_error(y_true, y_pred),
mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred)),
)
assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.0)
assert_almost_equal(mean_pinball_loss(y_true, y_pred), 0.5)
assert_almost_equal(mean_pinball_loss(y_true, y_pred_2), 0.5)
assert_almost_equal(mean_pinball_loss(y_true, y_pred, alpha=0.4), 0.6)
assert_almost_equal(mean_pinball_loss(y_true, y_pred_2, alpha=0.4), 0.4)
assert_almost_equal(median_absolute_error(y_true, y_pred), 1.0)
mape = mean_absolute_percentage_error(y_true, y_pred)
assert np.isfinite(mape)
assert mape > 1e6
assert_almost_equal(max_error(y_true, y_pred), 1.0)
assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
assert_almost_equal(explained_variance_score(y_true, y_pred), 1.0)
assert_almost_equal(
mean_tweedie_deviance(y_true, y_pred, power=0),
mean_squared_error(y_true, y_pred),
)
assert_almost_equal(d2_tweedie_score(y_true, y_pred, power=0),
r2_score(y_true, y_pred))
# Tweedie deviance needs positive y_pred, except for p=0,
# p>=2 needs positive y_true
# results evaluated by sympy
y_true = np.arange(1, 1 + n_samples)
y_pred = 2 * y_true
n = n_samples
assert_almost_equal(
mean_tweedie_deviance(y_true, y_pred, power=-1),
5 / 12 * n * (n**2 + 2 * n + 1),
)
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=1),
(n + 1) * (1 - np.log(2)))
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=2),
2 * np.log(2) - 1)
assert_almost_equal(
mean_tweedie_deviance(y_true, y_pred, power=3 / 2),
((6 * np.sqrt(2) - 8) / n) * np.sqrt(y_true).sum(),
)
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3),
np.sum(1 / y_true) / (4 * n))
dev_mean = 2 * np.mean(xlogy(y_true, 2 * y_true / (n + 1)))
assert_almost_equal(
d2_tweedie_score(y_true, y_pred, power=1),
1 - (n + 1) * (1 - np.log(2)) / dev_mean,
)
dev_mean = 2 * np.log((n + 1) / 2) - 2 / n * np.log(factorial(n))
assert_almost_equal(d2_tweedie_score(y_true, y_pred, power=2),
1 - (2 * np.log(2) - 1) / dev_mean)
def mean_tweedie_deviance(y_true: np.ndarray,
y_pred: np.ndarray,
*,
sample_weight: Optional[np.ndarray] = None,
power: float = 0) -> float:
"""Mean Tweedie deviance regression loss.
Read more in the :ref:`User Guide <mean_tweedie_deviance>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
power : float, default=0
Tweedie power parameter. Either power <= 0 or power >= 1.
The higher `p` the less weight is given to extreme
deviations between true and predicted targets.
- power < 0: Extreme stable distribution. Requires: y_pred > 0.
- power = 0 : Normal distribution, output corresponds to
mean_squared_error. y_true and y_pred can be any real numbers.
- power = 1 : Poisson distribution. Requires: y_true >= 0 and
y_pred > 0.
- 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0
and y_pred > 0.
- power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
- power = 3 : Inverse Gaussian distribution. Requires: y_true > 0
and y_pred > 0.
- otherwise : Positive stable distribution. Requires: y_true > 0
and y_pred > 0.
Returns
-------
loss : float
A non-negative floating point value (the best value is 0.0).
"""
y_type, y_true, y_pred, sample_weight, multioutput = _check_reg_targets(
y_true, y_pred, sample_weight)
if sample_weight is not None:
check_consistent_length(y_true, y_pred, sample_weight)
else:
check_consistent_length(y_true, y_pred)
return sklearn_metrics.mean_tweedie_deviance(y_true=y_true,
y_pred=y_pred,
sample_weight=sample_weight,
power=power)
def set_metrics(y_pred, y_true, dict):
try:
dict["max_error"] = mets.max_error(y_true, y_pred)
except:
pass
try:
dict["explained_variance_score"] = mets.explained_variance_score(y_true, y_pred)
except:
pass
try:
dict["mean_absolute_error"] = mets.mean_absolute_error(y_true, y_pred)
except:
pass
try:
dict["mean_squared_error"] = mets.mean_squared_error(y_true, y_pred)
except:
pass
try:
dict["mean_squared_log_error"] = mets.mean_squared_log_error(y_true, y_pred)
except:
pass
try:
dict["median_absolute_error"] = mets.median_absolute_error(y_true, y_pred)
except:
pass
try:
dict["r2_score"] = mets.r2_score(y_true, y_pred)
except:
pass
try:
dict["mean_poisson_deviance"] = mets.mean_poisson_deviance(y_true, y_pred)
except:
pass
try:
dict["mean_gamma_deviance"] = mets.mean_gamma_deviance(y_true, y_pred)
except:
pass
try:
dict["mean_tweedie_deviance"] = mets.mean_tweedie_deviance(y_true, y_pred)
except:
pass
return dict
def test_regression_metrics_at_limits():
# Single-sample case
# Note: for r2 and d2_tweedie see also test_regression_single_sample
assert_almost_equal(mean_squared_error([0.0], [0.0]), 0.0)
assert_almost_equal(mean_squared_error([0.0], [0.0], squared=False), 0.0)
assert_almost_equal(mean_squared_log_error([0.0], [0.0]), 0.0)
assert_almost_equal(mean_absolute_error([0.0], [0.0]), 0.0)
assert_almost_equal(mean_pinball_loss([0.0], [0.0]), 0.0)
assert_almost_equal(mean_absolute_percentage_error([0.0], [0.0]), 0.0)
assert_almost_equal(median_absolute_error([0.0], [0.0]), 0.0)
assert_almost_equal(max_error([0.0], [0.0]), 0.0)
assert_almost_equal(explained_variance_score([0.0], [0.0]), 1.0)
# Perfect cases
assert_almost_equal(r2_score([0.0, 1], [0.0, 1]), 1.0)
assert_almost_equal(d2_pinball_score([0.0, 1], [0.0, 1]), 1.0)
# Non-finite cases
# R² and explained variance have a fix by default for non-finite cases
for s in (r2_score, explained_variance_score):
assert_almost_equal(s([0, 0], [1, -1]), 0.0)
assert_almost_equal(s([0, 0], [1, -1], force_finite=False), -np.inf)
assert_almost_equal(s([1, 1], [1, 1]), 1.0)
assert_almost_equal(s([1, 1], [1, 1], force_finite=False), np.nan)
msg = ("Mean Squared Logarithmic Error cannot be used when targets "
"contain negative values.")
with pytest.raises(ValueError, match=msg):
mean_squared_log_error([-1.0], [-1.0])
msg = ("Mean Squared Logarithmic Error cannot be used when targets "
"contain negative values.")
with pytest.raises(ValueError, match=msg):
mean_squared_log_error([1.0, 2.0, 3.0], [1.0, -2.0, 3.0])
msg = ("Mean Squared Logarithmic Error cannot be used when targets "
"contain negative values.")
with pytest.raises(ValueError, match=msg):
mean_squared_log_error([1.0, -2.0, 3.0], [1.0, 2.0, 3.0])
# Tweedie deviance error
power = -1.2
assert_allclose(mean_tweedie_deviance([0], [1.0], power=power),
2 / (2 - power),
rtol=1e-3)
msg = "can only be used on strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.0], [0.0], power=power)
with pytest.raises(ValueError, match=msg):
d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
assert_almost_equal(mean_tweedie_deviance([0.0], [0.0], power=0), 0.0, 2)
power = 1.0
msg = "only be used on non-negative y and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.0], [0.0], power=power)
with pytest.raises(ValueError, match=msg):
d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
power = 1.5
assert_allclose(mean_tweedie_deviance([0.0], [1.0], power=power),
2 / (2 - power))
msg = "only be used on non-negative y and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.0], [0.0], power=power)
with pytest.raises(ValueError, match=msg):
d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
power = 2.0
assert_allclose(mean_tweedie_deviance([1.0], [1.0], power=power),
0.00,
atol=1e-8)
msg = "can only be used on strictly positive y and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.0], [0.0], power=power)
with pytest.raises(ValueError, match=msg):
d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
power = 3.0
assert_allclose(mean_tweedie_deviance([1.0], [1.0], power=power),
0.00,
atol=1e-8)
msg = "can only be used on strictly positive y and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.0], [0.0], power=power)
with pytest.raises(ValueError, match=msg):
d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
power = 0.5
with pytest.raises(ValueError,
match="is only defined for power<=0 and power>=1"):
mean_tweedie_deviance([0.0], [0.0], power=power)
with pytest.raises(ValueError,
match="is only defined for power<=0 and power>=1"):
d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
def score(self,
actual: np.array,
predicted: np.array,
sample_weight: typing.Optional[np.array] = None,
labels: typing.Optional[np.array] = None) -> float:
"""
:param actual: Ground truth (correct) target values.
:param predicted: Estimated target values
:param sample_weight: weights
:param labels: not used
power: default=1.5 sent to function via toml dictionary
Tweedie power parameter. Either power <= 0 or power >= 1.
To non-default power parameter use recipe_dict add via toml config DAI option. Example:
recipe_dict = "{'power':2.0}"
Multiple parameters example (first param is for demo only):
validate_meta_learner=false\nrecipe_dict = "{'power':2.0}"
The higher p the less weight is given to extreme deviations between true and predicted targets.
power < 0: Extreme stable distribution. Requires: y_pred > 0.
power = 0 : Normal distribution, output corresponds to mean_squared_error. y_true and y_pred can be any real numbers.
power = 1 : Poisson distribution. Requires: y_true >= 0 and y_pred > 0.
1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0 and y_pred > 0.
power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
power = 3 : Inverse Gaussian distribution. Requires: y_true > 0 and y_pred > 0.
otherwise : Positive stable distribution. Requires: y_true > 0 and y_pred > 0.
:return: score
"""
try:
"""Initialize logger to print additional info in case of invalid inputs(exception is raised) and to enable debug prints"""
logger = self.logger
from h2oaicore.systemutils import loggerinfo
# loggerinfo(logger, "Start TW Deviance Scorer.......")
# loggerinfo(logger, 'Actual:%s' % str(actual))
# loggerinfo(logger, 'Predicted:%s' % str(predicted))
# loggerinfo(logger, 'Sample W:%s' % str(sample_weight))
from sklearn.metrics import mean_tweedie_deviance
if config.recipe_dict is not None:
power = config.recipe_dict.get('power', 1.5)
else:
power = 1.5
# loggerinfo(logger, 'Power:%s' % str(power))
if sample_weight is not None:
'''Check if any element of the sample_weight array is nan'''
if np.isnan(np.sum(sample_weight)):
loggerinfo(logger, 'Sample Weight:%s' % str(sample_weight))
loggerinfo(
logger, 'Sample Weight Nan values index:%s' %
str(np.argwhere(np.isnan(sample_weight))))
raise RuntimeError(
'Error during Tweedie Deviance score calculation. Invalid sample weight values. Expecting only non-nan values'
)
if 0 < power < 1:
loggerinfo(logger, 'Power:%s' % str(power))
loggerinfo(
logger,
"""Invalid power value. Power should be one of the following: \n
power < 0: Extreme stable distribution. Requires: y_pred > 0.
power = 0 : Normal distribution, output corresponds to mean_squared_error. y_true and y_pred can be any real numbers.
power = 1 : Poisson distribution. Requires: y_true >= 0 and y_pred > 0.
1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0 and y_pred > 0.
power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
power = 3 : Inverse Gaussian distribution. Requires: y_true > 0 and y_pred > 0.
otherwise : Positive stable distribution. Requires: y_true > 0 and y_pred > 0."""
)
raise RuntimeError(
'Error during Tweedie Deviance score calculation. Invalid power value.'
)
actual = actual.astype('float64')
predicted = predicted.astype('float64')
'''Safety mechanizm in case predictions or actuals are zero'''
epsilon = 1E-8
actual += epsilon
predicted += epsilon
if power == 0:
'''No need to validate sign of actual or predicted'''
pass
elif power < 0:
if (predicted <= 0).any():
loggerinfo(logger, 'Predicted:%s' % str(predicted))
loggerinfo(
logger, 'Invalid Predicted:%s' %
str(predicted[predicted <= 0]))
raise RuntimeError(
'Power <0. Error during Tweedie Deviance score calculation. Invalid predicted values. Expecting only positive values'
)
elif 1 <= power < 2:
if (actual < 0).any():
loggerinfo(logger, 'Actual:%s' % str(actual))
loggerinfo(
logger,
'Non-positive Actuals:%s' % str(actual[actual < 0]))
raise RuntimeError(
'1 <= power < 2. Error during Tweedie Deviance score calculation. Invalid actuals values. Expecting zero or positive values'
)
if (predicted <= 0).any() or np.isnan(np.sum(predicted)):
loggerinfo(logger, 'Predicted:%s' % str(predicted))
loggerinfo(
logger, 'Invalid Predicted:%s' %
str(predicted[predicted <= 0]))
raise RuntimeError(
'1 <= power < 2. Error during Tweedie Deviance score calculation. Invalid predicted values. Expecting only positive values'
)
elif power >= 2:
if (actual <= 0).any():
loggerinfo(logger, 'Actual:%s' % str(actual))
loggerinfo(
logger,
'Non-positive Actuals:%s' % str(actual[actual <= 0]))
raise RuntimeError(
'power >= 2. Error during Tweedie Deviance score calculation. Invalid actuals values. Expecting zero or positive values'
)
if (predicted <= 0).any() or np.isnan(np.sum(predicted)):
loggerinfo(logger, 'Predicted:%s' % str(predicted))
loggerinfo(
logger, 'Invalid Predicted:%s' %
str(predicted[predicted <= 0]))
raise RuntimeError(
'power >= 2. Error during Tweedie Deviance score calculation. Invalid predicted values. Expecting only positive values'
)
'''Check if any element of the arrays is nan'''
if np.isnan(np.sum(actual)):
loggerinfo(logger, 'Actual:%s' % str(actual))
loggerinfo(
logger,
'Nan values index:%s' % str(np.argwhere(np.isnan(actual))))
raise RuntimeError(
'Error during Tweedie Deviance score calculation. Invalid actuals values. Expecting only non-nan values'
)
if np.isnan(np.sum(predicted)):
loggerinfo(logger, 'Predicted:%s' % str(predicted))
loggerinfo(
logger, 'Nan values index:%s' %
str(np.argwhere(np.isnan(predicted))))
raise RuntimeError(
'Error during Tweedie Deviance score calculation. Invalid predicted values. Expecting only non-nan values'
)
score = mean_tweedie_deviance(actual,
predicted,
sample_weight=sample_weight,
power=power)
'''Validate that score is non-negative and is not infinity or Nan'''
if score >= 0 and score < float("inf"):
pass
else:
loggerinfo(logger, 'Invalid calculated score:%s' % str(score))
raise RuntimeError(
'Error during Tweedie Deviance score calculation. Invalid calculated score:%s. \
Score should be non-negative and less than infinity. Nan is not valid'
% str(score))
except Exception as e:
'''Print error message into DAI log file'''
loggerinfo(
logger,
'Error during Tweedie Deviance score calculation. Exception raised: %s'
% str(e))
raise
return score
def test_regression_metrics_at_limits():
assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(max_error([0.], [0.]), 0.00, 2)
assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
assert_raises_regex(
ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.", mean_squared_log_error,
[-1.], [-1.])
assert_raises_regex(
ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.", mean_squared_log_error,
[1., 2., 3.], [1., -2., 3.])
assert_raises_regex(
ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.", mean_squared_log_error,
[1., -2., 3.], [1., 2., 3.])
# Tweedie deviance error
p = -1.2
assert_allclose(mean_tweedie_deviance([0], [1.], p=p),
2. / (2. - p),
rtol=1e-3)
with pytest.raises(ValueError,
match="can only be used on strictly positive y_pred."):
mean_tweedie_deviance([0.], [0.], p=p)
assert_almost_equal(mean_tweedie_deviance([0.], [0.], p=0), 0.00, 2)
msg = "only be used on non-negative y_true and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=1.0)
p = 1.5
assert_allclose(mean_tweedie_deviance([0.], [1.], p=p), 2. / (2. - p))
msg = "only be used on non-negative y_true and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=p)
p = 2.
assert_allclose(mean_tweedie_deviance([1.], [1.], p=p), 0.00, atol=1e-8)
msg = "can only be used on strictly positive y_true and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=p)
p = 3.
assert_allclose(mean_tweedie_deviance([1.], [1.], p=p), 0.00, atol=1e-8)
msg = "can only be used on strictly positive y_true and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=p)
with pytest.raises(ValueError,
match="deviance is only defined for p<=0 and p>=1."):
mean_tweedie_deviance([0.], [0.], p=0.5)
def cross_validate(self, X, y, k=None):
if not self._is_fitted:
raise Exception("Model is not fitted.")
if self.gs_model is None:
raise Exception("Cross validation requires a grid search model.")
if k == None:
k = int(X.shape[0] / 2) if X.shape[0] < 3 else 3
print(f"Cross validating on '{k}' folds")
scores = {}
scores["r2"] = []
scores["mse"] = []
scores["poisson_deviance"] = []
scores["gamma_deviance"] = []
scores_exp = {}
scores_exp["r2"] = []
scores_exp["mse"] = []
scores_exp["poisson_deviance"] = []
scores_exp["gamma_deviance"] = []
kf = KFold(k)
for train_index, test_index in kf.split(X):
clf_model = RandomForestQuantileRegressor(
**self.gs_model.best_params_)
clf_model.fit(X[train_index, :], y[train_index])
y_pred = clf_model.predict(X[test_index, :])
y_test = y[test_index]
if test_index.__len__() > 1:
r2 = r2_score(y_test, y_pred)
scores["r2"].append(r2)
r2_exp = r2_score(np.exp(y_test), np.exp(y_pred))
scores_exp["r2"].append(r2_exp)
mse = mean_tweedie_deviance(y_test, y_pred, power=0)
poisson_deviance = mean_tweedie_deviance(y_test, y_pred, power=1)
gamma_deviance = mean_tweedie_deviance(y_test, y_pred, power=2)
scores["mse"].append(mse)
scores["poisson_deviance"].append(poisson_deviance)
scores["gamma_deviance"].append(gamma_deviance)
mse_exp = mean_tweedie_deviance(np.exp(y_test),
np.exp(y_pred),
power=0)
poisson_deviance_exp = mean_tweedie_deviance(np.exp(y_test),
np.exp(y_pred),
power=1)
gamma_deviance_exp = mean_tweedie_deviance(np.exp(y_test),
np.exp(y_pred),
power=2)
scores_exp["mse"].append(mse_exp)
scores_exp["poisson_deviance"].append(poisson_deviance_exp)
scores_exp["gamma_deviance"].append(gamma_deviance_exp)
final_scores = {}
final_scores["r2_mean"] = np.mean(
[0 if s < 0 else s for s in scores["r2"]])
final_scores["r2_std"] = np.std(
[0 if s < 0 else s for s in scores["r2"]])
final_scores["mse_mean"] = np.mean(scores["mse"])
final_scores["mse_std"] = np.std(scores["mse"])
final_scores["poisson_deviance_mean"] = np.mean(
scores["poisson_deviance"])
final_scores["poisson_deviance_std"] = np.std(
scores["poisson_deviance"])
final_scores["gamma_deviance_mean"] = np.mean(scores["gamma_deviance"])
final_scores["gamma_deviance_std"] = np.std(scores["gamma_deviance"])
self.cross_validation_scores = final_scores
final_scores_exp = {}
final_scores_exp["r2_mean"] = np.mean(
[0 if s < 0 else s for s in scores_exp["r2"]])
final_scores_exp["r2_std"] = np.std(
[0 if s < 0 else s for s in scores_exp["r2"]])
final_scores_exp["mse_mean"] = np.mean(scores_exp["mse"])
final_scores_exp["mse_std"] = np.std(scores_exp["mse"])
final_scores_exp["poisson_deviance_mean"] = np.mean(
scores_exp["poisson_deviance"])
final_scores_exp["poisson_deviance_std"] = np.std(
scores_exp["poisson_deviance"])
final_scores_exp["gamma_deviance_mean"] = np.mean(
scores_exp["gamma_deviance"])
final_scores_exp["gamma_deviance_std"] = np.std(
scores_exp["gamma_deviance"])
self.cross_validation_scores_exp = final_scores_exp
return self.cross_validation_scores, self.cross_validation_scores_exp
def tweedie_eval(y_pred, y_true):
y_true = y_true.get_label()
y_true = rollup(y_true.reshape(NUM_ITEMS, -1)).flatten()
y_pred = rollup(y_pred.reshape(NUM_ITEMS, -1)).flatten()
loss = mean_tweedie_deviance(y_true, y_pred, power=1.5)
return "tweedie_eval", loss, False
def _sk_deviance(preds: Tensor, targets: Tensor, power: float):
sk_preds = preds.view(-1).numpy()
sk_target = targets.view(-1).numpy()
return mean_tweedie_deviance(sk_target, sk_preds, power=power)
def test_regression_metrics_at_limits():
assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_squared_error([0.], [0.], squared=False), 0.00, 2)
assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(max_error([0.], [0.]), 0.00, 2)
assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
"contain negative values.")
with pytest.raises(ValueError, match=err_msg):
mean_squared_log_error([-1.], [-1.])
err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
"contain negative values.")
with pytest.raises(ValueError, match=err_msg):
mean_squared_log_error([1., 2., 3.], [1., -2., 3.])
err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
"contain negative values.")
with pytest.raises(ValueError, match=err_msg):
mean_squared_log_error([1., -2., 3.], [1., 2., 3.])
# Tweedie deviance error
power = -1.2
assert_allclose(mean_tweedie_deviance([0], [1.], power=power),
2 / (2 - power), rtol=1e-3)
with pytest.raises(ValueError,
match="can only be used on strictly positive y_pred."):
mean_tweedie_deviance([0.], [0.], power=power)
assert_almost_equal(mean_tweedie_deviance([0.], [0.], power=0), 0.00, 2)
msg = "only be used on non-negative y_true and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], power=1.0)
power = 1.5
assert_allclose(mean_tweedie_deviance([0.], [1.], power=power),
2 / (2 - power))
msg = "only be used on non-negative y_true and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], power=power)
power = 2.
assert_allclose(mean_tweedie_deviance([1.], [1.], power=power), 0.00,
atol=1e-8)
msg = "can only be used on strictly positive y_true and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], power=power)
power = 3.
assert_allclose(mean_tweedie_deviance([1.], [1.], power=power),
0.00, atol=1e-8)
msg = "can only be used on strictly positive y_true and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], power=power)
with pytest.raises(ValueError,
match="is only defined for power<=0 and power>=1"):
mean_tweedie_deviance([0.], [0.], power=0.5)
def _mean_tweedie_deviance(y_true, y_pred):
from sklearn.metrics import mean_tweedie_deviance
return mean_tweedie_deviance(y_true, y_pred)
