diff --git a/examples/bayesian_quantification.py b/examples/bayesian_quantification.py
index 3bca084..06c928c 100644
--- a/examples/bayesian_quantification.py
+++ b/examples/bayesian_quantification.py
@@ -20,30 +20,21 @@ Due to a low sample size and the fact that classes 2 and 3 are hard to distingui
it is hard to estimate the proportions accurately, what is visible by looking at the posterior samples,
showing large uncertainty.
"""
-from dataclasses import dataclass
import numpy as np
import matplotlib.pyplot as plt
+import quapy as qp
from sklearn.ensemble import RandomForestClassifier
from quapy.method.aggregative import BayesianCC, ACC, PACC
-from quapy.data import LabelledCollection
+from quapy.data import LabelledCollection, Dataset
+
FIGURE_PATH = "bayesian_quantification.pdf"
-@dataclass
-class SimulatedData:
- """Auxiliary class to keep the training and test data sets."""
- n_classes: int
- X_train: np.ndarray
- Y_train: np.ndarray
- X_test: np.ndarray
- Y_test: np.ndarray
-
-
-def simulate_data(rng) -> SimulatedData:
+def simulate_data(rng) -> Dataset:
"""Generates a simulated data set with three classes."""
# Number of examples of each class in both data sets
@@ -53,44 +44,33 @@ def simulate_data(rng) -> SimulatedData:
# Mean vectors and shared covariance of P(X|Y) distributions
mus = [np.zeros(2), np.array([1, 1.5]), np.array([1.5, 1])]
cov = np.eye(2)
-
+
+ def gen_Xy(centers, sizes):
+ X = np.concatenate([rng.multivariate_normal(mu_i, cov, size_i) for mu_i, size_i in zip(centers, sizes)])
+ y = np.concatenate([[i] * n for i, n in enumerate(sizes)])
+ return X, y
+
# Generate the features accordingly
- X_train = np.concatenate([
- rng.multivariate_normal(mus[i], cov, size=n_train[i])
- for i in range(3)
- ])
+ train = LabelledCollection(*gen_Xy(centers=mus, sizes=n_train))
+ test = LabelledCollection(*gen_Xy(centers=mus, sizes=n_test))
- X_test = np.concatenate([
- rng.multivariate_normal(mus[i], cov, size=n_test[i])
- for i in range(3)
- ])
-
- Y_train = np.concatenate([[i] * n for i, n in enumerate(n_train)])
- Y_test = np.concatenate([[i] * n for i, n in enumerate(n_test)])
-
- return SimulatedData(
- n_classes=3,
- X_train=X_train,
- X_test=X_test,
- Y_train=Y_train,
- Y_test=Y_test,
- )
+ return Dataset(training=train, test=test)
-def plot_simulated_data(axs, data: SimulatedData) -> None:
+def plot_simulated_data(axs, data: Dataset) -> None:
"""Plots a simulated data set.
-
- Args:
- axs: a list of three `plt.Axes` objects, on which the samples will be plotted.
- data: the simulated data set.
+
+ :param axs: a list of three `plt.Axes` objects, on which the samples will be plotted.
+ :param data: the simulated data set.
"""
+ train, test = data.train_test
xlim = (
- -0.3 + min(data.X_train[:, 0].min(), data.X_test[:, 0].min()),
- 0.3 + max(data.X_train[:, 0].max(), data.X_test[:, 0].max())
+ -0.3 + min(train.X[:, 0].min(), test.X[:, 0].min()),
+ 0.3 + max(train.X[:, 0].max(), test.X[:, 0].max())
)
ylim = (
- -0.3 + min(data.X_train[:, 1].min(), data.X_test[:, 1].min()),
- 0.3 + max(data.X_train[:, 1].max(), data.X_test[:, 1].max())
+ -0.3 + min(train.X[:, 1].min(), test.X[:, 1].min()),
+ 0.3 + max(train.X[:, 1].max(), test.X[:, 1].max())
)
for ax in axs:
@@ -105,63 +85,23 @@ def plot_simulated_data(axs, data: SimulatedData) -> None:
ax = axs[0]
ax.set_title("Training set")
for i in range(data.n_classes):
- ax.scatter(data.X_train[data.Y_train == i, 0], data.X_train[data.Y_train == i, 1], c=f"C{i}", s=3, rasterized=True)
+ ax.scatter(train.X[train.y == i, 0], train.X[train.y == i, 1], c=f"C{i}", s=3, rasterized=True)
ax = axs[1]
ax.set_title("Test set\n(with labels)")
for i in range(data.n_classes):
- ax.scatter(data.X_test[data.Y_test == i, 0], data.X_test[data.Y_test == i, 1], c=f"C{i}", s=3, rasterized=True)
+ ax.scatter(test.X[test.y == i, 0], test.X[test.y == i, 1], c=f"C{i}", s=3, rasterized=True)
ax = axs[2]
ax.set_title("Test set\n(as observed)")
- ax.scatter(data.X_test[:, 0], data.X_test[:, 1], c="C5", s=3, rasterized=True)
+ ax.scatter(test.X[:, 0], test.X[:, 1], c="C5", s=3, rasterized=True)
-def get_random_forest() -> RandomForestClassifier:
- """An auxiliary factory method to generate a random forest."""
- return RandomForestClassifier(n_estimators=10, random_state=5)
-
-
-def train_and_plot_bayesian_quantification(ax: plt.Axes, training: LabelledCollection, test: np.ndarray, n_classes: int) -> None:
- """Fits Bayesian quantification and plots posterior mean as well as individual samples"""
- quantifier = BayesianCC(classifier=get_random_forest())
- quantifier.fit(training)
-
- # Obtain mean prediction
- mean_prediction = quantifier.quantify(test)
- x_ax = np.arange(n_classes)
- ax.plot(x_ax, mean_prediction, c="salmon", linewidth=2, linestyle=":", label="Bayesian")
-
- # Obtain individual samples
- samples = quantifier.get_prevalence_samples()
- for sample in samples[::5, :]:
- ax.plot(x_ax, sample, c="salmon", alpha=0.1, linewidth=0.3, rasterized=True)
-
-
-def _get_estimate(estimator_class, training: LabelledCollection, test: np.ndarray) -> None:
- """Auxiliary method for running ACC and PACC."""
- estimator = estimator_class(get_random_forest())
- estimator.fit(training)
- return estimator.quantify(test)
-
-
-def train_and_plot_acc(ax: plt.Axes, training: LabelledCollection, test: np.ndarray, n_classes: int) -> None:
- estimate = _get_estimate(ACC, training, test)
- ax.plot(np.arange(n_classes), estimate, c="darkblue", linewidth=2, linestyle=":", label="ACC")
-
-
-def train_and_plot_pacc(ax: plt.Axes, training: LabelledCollection, test: np.ndarray, n_classes: int) -> None:
- estimate = _get_estimate(PACC, training, test)
- ax.plot(np.arange(n_classes), estimate, c="limegreen", linewidth=2, linestyle=":", label="PACC")
-
-
-def plot_true_proportions(ax: plt.Axes, test_labels: np.ndarray, n_classes: int) -> None:
+def plot_true_proportions(ax: plt.Axes, test_prevalence: np.ndarray) -> None:
"""Plots the true proportions."""
- counts = np.bincount(test_labels, minlength=n_classes)
- proportion = counts / counts.sum()
-
+ n_classes = len(test_prevalence)
x_ax = np.arange(n_classes)
- ax.plot(x_ax, proportion, c="black", linewidth=2, label="True")
+ ax.plot(x_ax, test_prevalence, c="black", linewidth=2, label="True")
ax.set_xlabel("Class")
ax.set_ylabel("Prevalence")
@@ -171,11 +111,59 @@ def plot_true_proportions(ax: plt.Axes, test_labels: np.ndarray, n_classes: int)
ax.set_ylim(-0.01, 1.01)
+def get_random_forest() -> RandomForestClassifier:
+ """An auxiliary factory method to generate a random forest."""
+ return RandomForestClassifier(n_estimators=10, random_state=5)
+
+
+def _get_estimate(estimator_class, training: LabelledCollection, test: np.ndarray) -> None:
+ """Auxiliary method for running ACC and PACC."""
+ estimator = estimator_class(get_random_forest())
+ estimator.fit(training)
+ return estimator.quantify(test)
+
+
+def train_and_plot_bayesian_quantification(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
+ """Fits Bayesian quantification and plots posterior mean as well as individual samples"""
+ print('training model Bayesian CC...', end='')
+ quantifier = BayesianCC(classifier=get_random_forest())
+ quantifier.fit(training)
+
+ # Obtain mean prediction
+ mean_prediction = quantifier.quantify(test.X)
+ mae = qp.error.mae(test.prevalence(), mean_prediction)
+ x_ax = np.arange(training.n_classes)
+ ax.plot(x_ax, mean_prediction, c="salmon", linewidth=2, linestyle=":", label="Bayesian")
+
+ # Obtain individual samples
+ samples = quantifier.get_prevalence_samples()
+ for sample in samples[::5, :]:
+ ax.plot(x_ax, sample, c="salmon", alpha=0.1, linewidth=0.3, rasterized=True)
+ print(f'MAE={mae:.4f} [done]')
+
+
+def train_and_plot_acc(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
+ print('training model ACC...', end='')
+ estimate = _get_estimate(ACC, training, test.X)
+ mae = qp.error.mae(test.prevalence(), estimate)
+ ax.plot(np.arange(training.n_classes), estimate, c="darkblue", linewidth=2, linestyle=":", label="ACC")
+ print(f'MAE={mae:.4f} [done]')
+
+
+def train_and_plot_pacc(ax: plt.Axes, training: LabelledCollection, test: LabelledCollection) -> None:
+ print('training model PACC...', end='')
+ estimate = _get_estimate(PACC, training, test.X)
+ mae = qp.error.mae(test.prevalence(), estimate)
+ ax.plot(np.arange(training.n_classes), estimate, c="limegreen", linewidth=2, linestyle=":", label="PACC")
+ print(f'MAE={mae:.4f} [done]')
+
def main() -> None:
# --- Simulate data ---
+ print('generating simulated data')
rng = np.random.default_rng(42)
data = simulate_data(rng)
+ training, test = data.train_test
# --- Plot simulated data ---
fig, axs = plt.subplots(1, 4, figsize=(13, 3), dpi=300)
@@ -185,17 +173,19 @@ def main() -> None:
# --- Plot quantification results ---
ax = axs[3]
- plot_true_proportions(ax, test_labels=data.Y_test, n_classes=data.n_classes)
-
- training = LabelledCollection(data.X_train, data.Y_train)
- train_and_plot_acc(ax, training=training, test=data.X_test, n_classes=data.n_classes)
- train_and_plot_pacc(ax, training=training, test=data.X_test, n_classes=data.n_classes)
- train_and_plot_bayesian_quantification(ax=ax, training=training, test=data.X_test, n_classes=data.n_classes)
+ plot_true_proportions(ax, test_prevalence=test.prevalence())
+
+ train_and_plot_acc(ax, training=training, test=test)
+ train_and_plot_pacc(ax, training=training, test=test)
+ train_and_plot_bayesian_quantification(ax=ax, training=training, test=test)
+ print('[done]')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False)
+ print(f'saving plot in path {FIGURE_PATH}...', end='')
fig.tight_layout()
fig.savefig(FIGURE_PATH)
+ print('[done]')
if __name__ == '__main__':