Module statkit.decision
Evaluate models using decision curve analysis.
Expand source code
"""Evaluate models using decision curve analysis."""
from typing import Literal, Optional, Union
from matplotlib import pyplot as plt
from numpy import array, divide, linspace, ones_like, r_, zeros_like
from numpy.typing import NDArray
from pandas import Series
from sklearn.metrics import confusion_matrix
from sklearn.utils import column_or_1d
def _binary_classification_thresholds(y_true, y_proba, thresholds):
"""Compute false and true positives for given probability thresholds."""
y_true = column_or_1d(y_true)
y_proba = column_or_1d(y_proba)
matrices = []
for t in thresholds:
if t < 1:
y_pred = (y_proba > t).astype(int)
else:
y_pred = (y_proba >= t).astype(int)
cm = confusion_matrix(y_true, y_pred)
matrices.append(cm)
matrices = array(matrices)
tns = matrices[:, 0, 0]
fps = matrices[:, 0, 1]
fns = matrices[:, 1, 0]
tps = matrices[:, 1, 1]
return tns, fps, fns, tps
def net_benefit(
y_true: Union[Series, NDArray],
y_pred: Union[Series, NDArray],
thresholds=100,
action: bool = True,
):
"""Net benefit of taking an action using a model's predictions.
Args:
y_true: Binary ground truth label (1: positive, 0: negative class).
y_pred: Probability of positive class label.
thresholds: When an array, evaluate net benefit at these coordinates (the
probability thresholds). When an int, the number of x coordinates.
action: When `True` (`False`), estimate net benefit of taking (not taking) an
action/intervention/treatment.
Returns:
thresholds: Probability threshold of prediction a positive class.
benefit: The net benefit corresponding to the thresholds.
References:
[1]: Vickers-Elkin. "Decision curve analysis: a novel method for evaluating
prediction models." Medical Decision Making 26.6 (2006): 565-574.
[2]: Rousson-Zumbrunn. "Decision curve analysis revisited: overall net
benefit, relationships to ROC curve analysis, and application to case-control
studies." BMC medical informatics and decision making 11.1 (2011): 1–9.
"""
if set(y_true.astype(int)) != {0, 1}:
raise ValueError(
"Decision curve analysis only supports binary classification (with labels 1 and 0)."
)
if isinstance(thresholds, int):
thresholds = linspace(0, 1, num=thresholds)
N = len(y_true)
tns, fps, fns, tps = _binary_classification_thresholds(y_true, y_pred, thresholds)
if action:
loss_over_profit = divide(
thresholds, 1 - thresholds, where=thresholds < 1, out=zeros_like(thresholds)
)
benefit = tps / N - fps / N * loss_over_profit
else:
# Invert 0<-->1 so that true positives are true negatives, and false positives
# are false negatives.
profit_over_loss = divide(
1 - thresholds,
thresholds,
where=thresholds != 0,
out=zeros_like(thresholds),
)
benefit = tns / N - fns / N * profit_over_loss
return thresholds, benefit
def net_benefit_oracle(y_true, action: bool = True) -> float:
"""Net benefit of omniscient strategy, i.e., a hypothetical perfect predictor."""
if action:
return y_true.mean()
return 1 - y_true.mean()
def net_benefit_action(y_true, thresholds, action: bool = True):
"""Net benefit of always doing an action/intervention/treatment.
Args:
action: When `False`, invert positive label in `y_true`.
"""
if action:
loss_over_profit = divide(
thresholds,
1 - thresholds,
where=thresholds < 1,
out=1e9 * ones_like(thresholds),
)
return y_true.mean() - (1 - y_true.mean()) * loss_over_profit
profit_over_loss = divide(
1 - thresholds,
thresholds,
where=thresholds != 0,
out=1e9 * ones_like(thresholds),
)
return 1 - y_true.mean() - y_true.mean() * profit_over_loss
def overall_net_benefit(y_true, y_pred, n_thresholds: int = 100):
"""Net benefit combining both taking and not-taking action."""
thresholds_action, benefit_action = net_benefit(
y_true, y_pred, n_thresholds, action=True
)
_, benefit_no_action = net_benefit(y_true, y_pred, thresholds_action, action=False)
return thresholds_action, benefit_action + benefit_no_action
class NetBenefitDisplay:
"""Net benefit decision curve analysis visualisation.
Args:
threshold_probability: Probability to dichotomise the predicted probability
of the model.
net_benefit: Net benefit of taking an action as a function of
`threshold_probability`.
oracle: The (constant) net benefit of a perfect predictor.
benefit_type: Literal["action", "noop", "overall"] = "action",
Example:
```python
from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from statkit.decision import NetBenefitDisplay
centers = [[0, 0], [1, 1]]
X_train, y_train = make_blobs(
centers=centers, cluster_std=1, n_samples=20, random_state=5
)
X_test, y_test = make_blobs(
centers=centers, cluster_std=1, n_samples=20, random_state=1005
)
clf = LogisticRegression(random_state=5).fit(X_train, y_train)
y_pred_base = clf.predict_proba(X_test)[:, 1]
NetBenefitDisplay.from_predictions(y_test, y_pred_base, name='Logistic Regression')
```
"""
def __init__(
self,
threshold_probability,
net_benefit,
net_benefit_action=None,
net_benefit_noop=None,
benefit_type: Literal["action", "noop", "overall"] = "action",
oracle: Optional[float] = None,
estimator_name: Optional[str] = None,
):
self.threshold_probability = threshold_probability
self.net_benefit = net_benefit
self.net_benefit_action = net_benefit_action
self.net_benefit_noop = net_benefit_noop
self.benefit_type = benefit_type
self.estimator_name = estimator_name
self.oracle = oracle
def plot(self, show_references: bool = True, ax=None):
"""
Args:
show_references: Show oracle (requires prevalence) and no
action/treatment/intervention reference curves.
ax: Optional axes object to plot on. If `None`, a new figure and axes is
created.
"""
if ax is None:
_, ax = plt.subplots()
self.ax_ = ax
self.figure_ = ax.figure
ax.plot(
self.threshold_probability, self.net_benefit, "-", label=self.estimator_name
)
if show_references:
ax.plot(
self.threshold_probability,
self.net_benefit_action,
":",
label="Always act",
)
ax.plot(
self.threshold_probability,
self.net_benefit_noop,
"-.",
label="Never act",
)
if self.oracle is not None:
ax.plot([0, 1], [self.oracle, self.oracle], "--", label="Oracle")
ylabel = "Net benefit"
if self.benefit_type == "action":
ylabel = "Net benefit (action)"
if self.benefit_type == "noop":
ylabel = "Net benefit (no-action)"
if self.benefit_type == "overall":
ylabel = "Overall net benefit"
ax.set(xlabel="Threshold probability", ylabel=ylabel)
margin = 0.05
ax.set_ylim([-margin, 1 + margin])
ax.legend(loc="upper right", frameon=False)
return self
@classmethod
def from_predictions(
cls,
y_true,
y_pred,
benefit_type: Literal["action", "noop", "overall"] = "action",
thresholds: int = 100,
name: Optional[str] = None,
show_references: bool = True,
ax=None,
):
"""Make a net benefit plot from true and predicted labels.
Args:
y_true: Binary ground truth label (1: positive, 0: negative class).
y_pred: Predicted class labels.
benefit_type: Type of net benefit curve. `"action"`: net benefit of
treatment/intervention/action; `"noop`: net benefit of no
treatment/intervention/action; `"overall"`: overall net benefit (see
`overall_net_benefit`).
show_references: Show oracle (requires prevalence) and no
action/treatment/intervention reference curves.
thresholds: When an array, evaluate net benefit at these coordinates (the
probability thresholds). When an int, the number of x coordinates.
ax: Optional axes object to plot on. If `None`, a new figure and axes is
created.
"""
if benefit_type == "action":
oracle = net_benefit_oracle(y_true, action=True)
thresholds, benefit = net_benefit(y_true, y_pred, thresholds)
benefit_action = net_benefit_action(y_true, thresholds, action=True)
benefit_noop = zeros_like(benefit)
elif benefit_type == "noop":
oracle = net_benefit_oracle(y_true, action=False)
thresholds, benefit = net_benefit(y_true, y_pred, thresholds, action=False)
benefit_noop = net_benefit_action(y_true, thresholds, action=False)
benefit_action = zeros_like(benefit)
elif benefit_type == "overall":
oracle = 1.0
thresholds, benefit = overall_net_benefit(y_true, y_pred, thresholds)
benefit_action = net_benefit_action(y_true, thresholds, action=True)
benefit_noop = net_benefit_action(y_true, thresholds, action=False)
return cls(
thresholds,
benefit,
benefit_action,
benefit_noop,
benefit_type,
oracle,
estimator_name=name,
).plot(ax=ax, show_references=show_references)Functions
def net_benefit(y_true: Union[pandas.core.series.Series, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]], y_pred: Union[pandas.core.series.Series, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]], thresholds=100, action: bool = True)-
Net benefit of taking an action using a model's predictions.
Args
y_true- Binary ground truth label (1: positive, 0: negative class).
y_pred- Probability of positive class label.
thresholds- When an array, evaluate net benefit at these coordinates (the probability thresholds). When an int, the number of x coordinates.
action- When
True(False), estimate net benefit of taking (not taking) an action/intervention/treatment.
Returns
thresholds- Probability threshold of prediction a positive class.
benefit- The net benefit corresponding to the thresholds.
References
[1]: Vickers-Elkin. "Decision curve analysis: a novel method for evaluating prediction models." Medical Decision Making 26.6 (2006): 565-574. [2]: Rousson-Zumbrunn. "Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies." BMC medical informatics and decision making 11.1 (2011): 1–9.
Expand source code
def net_benefit( y_true: Union[Series, NDArray], y_pred: Union[Series, NDArray], thresholds=100, action: bool = True, ): """Net benefit of taking an action using a model's predictions. Args: y_true: Binary ground truth label (1: positive, 0: negative class). y_pred: Probability of positive class label. thresholds: When an array, evaluate net benefit at these coordinates (the probability thresholds). When an int, the number of x coordinates. action: When `True` (`False`), estimate net benefit of taking (not taking) an action/intervention/treatment. Returns: thresholds: Probability threshold of prediction a positive class. benefit: The net benefit corresponding to the thresholds. References: [1]: Vickers-Elkin. "Decision curve analysis: a novel method for evaluating prediction models." Medical Decision Making 26.6 (2006): 565-574. [2]: Rousson-Zumbrunn. "Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies." BMC medical informatics and decision making 11.1 (2011): 1–9. """ if set(y_true.astype(int)) != {0, 1}: raise ValueError( "Decision curve analysis only supports binary classification (with labels 1 and 0)." ) if isinstance(thresholds, int): thresholds = linspace(0, 1, num=thresholds) N = len(y_true) tns, fps, fns, tps = _binary_classification_thresholds(y_true, y_pred, thresholds) if action: loss_over_profit = divide( thresholds, 1 - thresholds, where=thresholds < 1, out=zeros_like(thresholds) ) benefit = tps / N - fps / N * loss_over_profit else: # Invert 0<-->1 so that true positives are true negatives, and false positives # are false negatives. profit_over_loss = divide( 1 - thresholds, thresholds, where=thresholds != 0, out=zeros_like(thresholds), ) benefit = tns / N - fns / N * profit_over_loss return thresholds, benefit def net_benefit_action(y_true, thresholds, action: bool = True)-
Net benefit of always doing an action/intervention/treatment.
Args
action- When
False, invert positive label iny_true.
Expand source code
def net_benefit_action(y_true, thresholds, action: bool = True): """Net benefit of always doing an action/intervention/treatment. Args: action: When `False`, invert positive label in `y_true`. """ if action: loss_over_profit = divide( thresholds, 1 - thresholds, where=thresholds < 1, out=1e9 * ones_like(thresholds), ) return y_true.mean() - (1 - y_true.mean()) * loss_over_profit profit_over_loss = divide( 1 - thresholds, thresholds, where=thresholds != 0, out=1e9 * ones_like(thresholds), ) return 1 - y_true.mean() - y_true.mean() * profit_over_loss def net_benefit_oracle(y_true, action: bool = True) ‑> float-
Net benefit of omniscient strategy, i.e., a hypothetical perfect predictor.
Expand source code
def net_benefit_oracle(y_true, action: bool = True) -> float: """Net benefit of omniscient strategy, i.e., a hypothetical perfect predictor.""" if action: return y_true.mean() return 1 - y_true.mean() def overall_net_benefit(y_true, y_pred, n_thresholds: int = 100)-
Net benefit combining both taking and not-taking action.
Expand source code
def overall_net_benefit(y_true, y_pred, n_thresholds: int = 100): """Net benefit combining both taking and not-taking action.""" thresholds_action, benefit_action = net_benefit( y_true, y_pred, n_thresholds, action=True ) _, benefit_no_action = net_benefit(y_true, y_pred, thresholds_action, action=False) return thresholds_action, benefit_action + benefit_no_action
Classes
class NetBenefitDisplay (threshold_probability, net_benefit, net_benefit_action=None, net_benefit_noop=None, benefit_type: Literal['action', 'noop', 'overall'] = 'action', oracle: Optional[float] = None, estimator_name: Optional[str] = None)-
Net benefit decision curve analysis visualisation.
Args
threshold_probability- Probability to dichotomise the predicted probability of the model.
net_benefit- Net benefit of taking an action as a function of
threshold_probability. oracle- The (constant) net benefit of a perfect predictor.
benefit_type- Literal["action", "noop", "overall"] = "action",
Example
from sklearn.datasets import make_blobs from sklearn.linear_model import LogisticRegression from statkit.decision import NetBenefitDisplay centers = [[0, 0], [1, 1]] X_train, y_train = make_blobs( centers=centers, cluster_std=1, n_samples=20, random_state=5 ) X_test, y_test = make_blobs( centers=centers, cluster_std=1, n_samples=20, random_state=1005 ) clf = LogisticRegression(random_state=5).fit(X_train, y_train) y_pred_base = clf.predict_proba(X_test)[:, 1] NetBenefitDisplay.from_predictions(y_test, y_pred_base, name='Logistic Regression')Expand source code
class NetBenefitDisplay: """Net benefit decision curve analysis visualisation. Args: threshold_probability: Probability to dichotomise the predicted probability of the model. net_benefit: Net benefit of taking an action as a function of `threshold_probability`. oracle: The (constant) net benefit of a perfect predictor. benefit_type: Literal["action", "noop", "overall"] = "action", Example: ```python from sklearn.datasets import make_blobs from sklearn.linear_model import LogisticRegression from statkit.decision import NetBenefitDisplay centers = [[0, 0], [1, 1]] X_train, y_train = make_blobs( centers=centers, cluster_std=1, n_samples=20, random_state=5 ) X_test, y_test = make_blobs( centers=centers, cluster_std=1, n_samples=20, random_state=1005 ) clf = LogisticRegression(random_state=5).fit(X_train, y_train) y_pred_base = clf.predict_proba(X_test)[:, 1] NetBenefitDisplay.from_predictions(y_test, y_pred_base, name='Logistic Regression') ``` """ def __init__( self, threshold_probability, net_benefit, net_benefit_action=None, net_benefit_noop=None, benefit_type: Literal["action", "noop", "overall"] = "action", oracle: Optional[float] = None, estimator_name: Optional[str] = None, ): self.threshold_probability = threshold_probability self.net_benefit = net_benefit self.net_benefit_action = net_benefit_action self.net_benefit_noop = net_benefit_noop self.benefit_type = benefit_type self.estimator_name = estimator_name self.oracle = oracle def plot(self, show_references: bool = True, ax=None): """ Args: show_references: Show oracle (requires prevalence) and no action/treatment/intervention reference curves. ax: Optional axes object to plot on. If `None`, a new figure and axes is created. """ if ax is None: _, ax = plt.subplots() self.ax_ = ax self.figure_ = ax.figure ax.plot( self.threshold_probability, self.net_benefit, "-", label=self.estimator_name ) if show_references: ax.plot( self.threshold_probability, self.net_benefit_action, ":", label="Always act", ) ax.plot( self.threshold_probability, self.net_benefit_noop, "-.", label="Never act", ) if self.oracle is not None: ax.plot([0, 1], [self.oracle, self.oracle], "--", label="Oracle") ylabel = "Net benefit" if self.benefit_type == "action": ylabel = "Net benefit (action)" if self.benefit_type == "noop": ylabel = "Net benefit (no-action)" if self.benefit_type == "overall": ylabel = "Overall net benefit" ax.set(xlabel="Threshold probability", ylabel=ylabel) margin = 0.05 ax.set_ylim([-margin, 1 + margin]) ax.legend(loc="upper right", frameon=False) return self @classmethod def from_predictions( cls, y_true, y_pred, benefit_type: Literal["action", "noop", "overall"] = "action", thresholds: int = 100, name: Optional[str] = None, show_references: bool = True, ax=None, ): """Make a net benefit plot from true and predicted labels. Args: y_true: Binary ground truth label (1: positive, 0: negative class). y_pred: Predicted class labels. benefit_type: Type of net benefit curve. `"action"`: net benefit of treatment/intervention/action; `"noop`: net benefit of no treatment/intervention/action; `"overall"`: overall net benefit (see `overall_net_benefit`). show_references: Show oracle (requires prevalence) and no action/treatment/intervention reference curves. thresholds: When an array, evaluate net benefit at these coordinates (the probability thresholds). When an int, the number of x coordinates. ax: Optional axes object to plot on. If `None`, a new figure and axes is created. """ if benefit_type == "action": oracle = net_benefit_oracle(y_true, action=True) thresholds, benefit = net_benefit(y_true, y_pred, thresholds) benefit_action = net_benefit_action(y_true, thresholds, action=True) benefit_noop = zeros_like(benefit) elif benefit_type == "noop": oracle = net_benefit_oracle(y_true, action=False) thresholds, benefit = net_benefit(y_true, y_pred, thresholds, action=False) benefit_noop = net_benefit_action(y_true, thresholds, action=False) benefit_action = zeros_like(benefit) elif benefit_type == "overall": oracle = 1.0 thresholds, benefit = overall_net_benefit(y_true, y_pred, thresholds) benefit_action = net_benefit_action(y_true, thresholds, action=True) benefit_noop = net_benefit_action(y_true, thresholds, action=False) return cls( thresholds, benefit, benefit_action, benefit_noop, benefit_type, oracle, estimator_name=name, ).plot(ax=ax, show_references=show_references)Static methods
def from_predictions(y_true, y_pred, benefit_type: Literal['action', 'noop', 'overall'] = 'action', thresholds: int = 100, name: Optional[str] = None, show_references: bool = True, ax=None)-
Make a net benefit plot from true and predicted labels.
Args
y_true- Binary ground truth label (1: positive, 0: negative class).
y_pred- Predicted class labels.
benefit_type- Type of net benefit curve.
"action": net benefit of treatment/intervention/action;"noop: net benefit of no treatment/intervention/action;"overall": overall net benefit (seeoverall_net_benefit()). show_references- Show oracle (requires prevalence) and no action/treatment/intervention reference curves.
thresholds- When an array, evaluate net benefit at these coordinates (the probability thresholds). When an int, the number of x coordinates.
ax- Optional axes object to plot on. If
None, a new figure and axes is created.
Expand source code
@classmethod def from_predictions( cls, y_true, y_pred, benefit_type: Literal["action", "noop", "overall"] = "action", thresholds: int = 100, name: Optional[str] = None, show_references: bool = True, ax=None, ): """Make a net benefit plot from true and predicted labels. Args: y_true: Binary ground truth label (1: positive, 0: negative class). y_pred: Predicted class labels. benefit_type: Type of net benefit curve. `"action"`: net benefit of treatment/intervention/action; `"noop`: net benefit of no treatment/intervention/action; `"overall"`: overall net benefit (see `overall_net_benefit`). show_references: Show oracle (requires prevalence) and no action/treatment/intervention reference curves. thresholds: When an array, evaluate net benefit at these coordinates (the probability thresholds). When an int, the number of x coordinates. ax: Optional axes object to plot on. If `None`, a new figure and axes is created. """ if benefit_type == "action": oracle = net_benefit_oracle(y_true, action=True) thresholds, benefit = net_benefit(y_true, y_pred, thresholds) benefit_action = net_benefit_action(y_true, thresholds, action=True) benefit_noop = zeros_like(benefit) elif benefit_type == "noop": oracle = net_benefit_oracle(y_true, action=False) thresholds, benefit = net_benefit(y_true, y_pred, thresholds, action=False) benefit_noop = net_benefit_action(y_true, thresholds, action=False) benefit_action = zeros_like(benefit) elif benefit_type == "overall": oracle = 1.0 thresholds, benefit = overall_net_benefit(y_true, y_pred, thresholds) benefit_action = net_benefit_action(y_true, thresholds, action=True) benefit_noop = net_benefit_action(y_true, thresholds, action=False) return cls( thresholds, benefit, benefit_action, benefit_noop, benefit_type, oracle, estimator_name=name, ).plot(ax=ax, show_references=show_references)
Methods
def plot(self, show_references: bool = True, ax=None)-
Args
show_references- Show oracle (requires prevalence) and no action/treatment/intervention reference curves.
ax- Optional axes object to plot on. If
None, a new figure and axes is created.
Expand source code
def plot(self, show_references: bool = True, ax=None): """ Args: show_references: Show oracle (requires prevalence) and no action/treatment/intervention reference curves. ax: Optional axes object to plot on. If `None`, a new figure and axes is created. """ if ax is None: _, ax = plt.subplots() self.ax_ = ax self.figure_ = ax.figure ax.plot( self.threshold_probability, self.net_benefit, "-", label=self.estimator_name ) if show_references: ax.plot( self.threshold_probability, self.net_benefit_action, ":", label="Always act", ) ax.plot( self.threshold_probability, self.net_benefit_noop, "-.", label="Never act", ) if self.oracle is not None: ax.plot([0, 1], [self.oracle, self.oracle], "--", label="Oracle") ylabel = "Net benefit" if self.benefit_type == "action": ylabel = "Net benefit (action)" if self.benefit_type == "noop": ylabel = "Net benefit (no-action)" if self.benefit_type == "overall": ylabel = "Overall net benefit" ax.set(xlabel="Threshold probability", ylabel=ylabel) margin = 0.05 ax.set_ylim([-margin, 1 + margin]) ax.legend(loc="upper right", frameon=False) return self