Synthe dataset icml

examples/plot_complex_data.py

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+"""
+==========================================
+Modeling competing risks on synthetic data
+==========================================
+
+This example introduces a synthetic data generation tool that can
+be helpful to study the relative performance and potential biases
+of predictive competing risks estimators on right-censored data.
+Some of the input features and their second order multiplicative
+interactions are statistically associated with the parameters of
+the distributions from which the competing events are sampled.
+
+"""
+
+# %%
+from sklearn.utils import check_random_state
+
+seed = 0
+rng = check_random_state(seed)
+
+
+# %%
+# In the following cell, we verify that the synthetic dataset is well defined.
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from hazardous.data._competing_weibull import compute_shape_and_scale
+
+
+X = rng.randn(10_000, 10)
+column_names = [f"feature_{i}" for i in range(X.shape[1])]
+X = pd.DataFrame(X, columns=column_names)
+
+SS_star = compute_shape_and_scale(
+    X,
+    features_rate=0.2,
+    n_events=3,
+    degree_interaction=2,
+    random_state=0,
+)
+
+fig, axes = plt.subplots(2, 3, figsize=(10, 7))
+for idx, col in enumerate(SS_star.columns):
+    sns.histplot(SS_star[col], ax=axes[idx // 3][idx % 3])
+
+# %%
+from hazardous.data._competing_weibull import make_synthetic_competing_weibull
+
+
+X, y_censored, y_uncensored = make_synthetic_competing_weibull(
+    n_samples=3_000,
+    base_scale=1_000,
+    n_features=10,
+    features_rate=0.5,
+    degree_interaction=2,
+    independent_censoring=False,
+    features_censoring_rate=0.2,
+    return_uncensored_data=True,
+    feature_rounding=3,
+    target_rounding=4,
+    censoring_relative_scale=4.0,
+    complex_features=True,
+    return_X_y=True,
+    random_state=seed,
+)
+
+print(y_censored["event"].value_counts().sort_index())
+
+sns.histplot(
+    y_censored,
+    x="duration",
+    hue="event",
+    multiple="stack",
+    palette="magma",
+)
+
+# %%
+from lifelines import AalenJohansenFitter
+
+
+calculate_variance = X.shape[0] <= 5_000
+aj = AalenJohansenFitter(calculate_variance=calculate_variance, seed=0)
+aj
+
+# %%
+from hazardous._gradient_boosting_incidence import GradientBoostingIncidence
+
+
+gb_incidence = GradientBoostingIncidence(
+    learning_rate=0.1,
+    n_iter=20,
+    max_leaf_nodes=15,
+    hard_zero_fraction=0.1,
+    min_samples_leaf=5,
+    loss="ibs",
+    show_progressbar=False,
+    random_state=seed,
+)
+gb_incidence
+
+# %%
+#
+# CIFs estimated on uncensored data
+# ---------------------------------
+#
+# Let's now estimate the CIFs on uncensored data and plot them against the
+# theoretical CIFs:
+
+import numpy as np
+from time import perf_counter
+
+
+def plot_cumulative_incidence_functions(
+    X,
+    y,
+    gb_incidence=None,
+    aj=None,
+    X_test=None,
+    y_test=None,
+    verbose=False,
+):
+    n_events = y["event"].max()
+    t_max = y["duration"].max()
+    _, axes = plt.subplots(figsize=(12, 4), ncols=n_events, sharey=True)
+
+    # Compute the estimate of the CIFs on a coarse grid.
+    coarse_timegrid = np.linspace(0, t_max, num=100)
+    censoring_fraction = (y["event"] == 0).mean()
+    plt.suptitle(
+        "Cause-specific cumulative incidence functions"
+        f" ({censoring_fraction:.1%} censoring)"
+    )
+
+    for event_id, ax in enumerate(axes, 1):
+        if gb_incidence is not None:
+            tic = perf_counter()
+            gb_incidence.set_params(event_of_interest=event_id)
+            gb_incidence.fit(X, y)
+            duration = perf_counter() - tic
+
+            if verbose:
+                print(f"GB Incidence for event {event_id} fit in {duration:.3f} s")
+
+            tic = perf_counter()
+            cifs_pred = gb_incidence.predict_cumulative_incidence(X, coarse_timegrid)
+            cif_mean = cifs_pred.mean(axis=0)
+            duration = perf_counter() - tic
+
+            if verbose:
+                print(
+                    f"GB Incidence for event {event_id} prediction in {duration:.3f} s"
+                )
+
+            if verbose:
+                brier_score_train = -gb_incidence.score(X, y)
+                print(f"Brier score on training data: {brier_score_train:.3f}")
+                if X_test is not None:
+                    brier_score_test = -gb_incidence.score(X_test, y_test)
+                    print(
+                        f"Brier score on testing data: {brier_score_test:.3f}",
+                    )
+            ax.plot(
+                coarse_timegrid,
+                cif_mean,
+                label="GradientBoostingIncidence",
+            )
+            ax.set(title=f"Event {event_id}")
+
+        if aj is not None:
+            tic = perf_counter()
+            aj.fit(y["duration"], y["event"], event_of_interest=event_id)
+            duration = perf_counter() - tic
+            if verbose:
+                print(f"Aalen-Johansen for event {event_id} fit in {duration:.3f} s")
+
+            aj.plot(label="Aalen-Johansen", ax=ax)
+            ax.set_xlim(0, 8_000)
+            ax.set_ylim(0, 0.5)
+
+        if event_id == 1:
+            ax.legend(loc="lower right")
+        else:
+            ax.legend().remove()
+
+        if verbose:
+            print("=" * 16, "\n")
+
+
+# %%
+from sklearn.model_selection import train_test_split
+
+
+X_train, X_test, y_train_c, y_test_c = train_test_split(
+    X, y_censored, test_size=0.3, random_state=seed
+)
+y_train_u = y_uncensored.loc[y_train_c.index]
+y_test_u = y_uncensored.loc[y_test_c.index]
+
+plot_cumulative_incidence_functions(
+    X_train,
+    y_train_u,
+    gb_incidence=gb_incidence,
+    aj=aj,
+    X_test=X_test,
+    y_test=y_test_u,
+)
+
+plot_cumulative_incidence_functions(
+    X_train,
+    y_train_c,
+    gb_incidence=gb_incidence,
+    aj=aj,
+    X_test=X_test,
+    y_test=y_test_c,
+)
+
+# %%