scikit-learn · lorentzenchr · May 9, 2022 · May 9, 2022 · May 11, 2022 · May 11, 2022
diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
@@ -318,6 +318,13 @@ Changelog
 :mod:`sklearn.linear_model`
 ...........................
 
+- |Enhancement| :class:`linear_model.GammaRegressor`,
+  :class:`linear_model.PoissonRegressor` and :class:`linear_model.TweedieRegressor` got
+  a `solver` parameter with the two new solvers `solver="newton-cholesky"` and
+  `solver="newton-qr-cholesky"`. Those are 2nd order (Newton) optimisation routines
+  that may reach higher precision in less time than the already available `"lbfgs"`.
+  :pr:`23314` by :user:`Christian Lorentzen <lorentzenchr>`.
+
 - |Enhancement| :class:`linear_model.GammaRegressor`,
   :class:`linear_model.PoissonRegressor` and :class:`linear_model.TweedieRegressor`
   can reach higher precision with the lbfgs solver, in particular when `tol` is set

diff --git a/examples/linear_model/plot_poisson_regression_non_normal_loss.py b/examples/linear_model/plot_poisson_regression_non_normal_loss.py
@@ -110,7 +110,11 @@
 linear_model_preprocessor = ColumnTransformer(
     [
         ("passthrough_numeric", "passthrough", ["BonusMalus"]),
-        ("binned_numeric", KBinsDiscretizer(n_bins=10), ["VehAge", "DrivAge"]),
+        (
+            "binned_numeric",
+            KBinsDiscretizer(n_bins=10, subsample=int(2e5), random_state=0),
+            ["VehAge", "DrivAge"],
+        ),
         ("log_scaled_numeric", log_scale_transformer, ["Density"]),
         (
             "onehot_categorical",
@@ -247,7 +251,7 @@ def score_estimator(estimator, df_test):
 poisson_glm = Pipeline(
     [
         ("preprocessor", linear_model_preprocessor),
-        ("regressor", PoissonRegressor(alpha=1e-12, max_iter=300)),
+        ("regressor", PoissonRegressor(alpha=1e-12, solver="newton-cholesky")),
     ]
 )
 poisson_glm.fit(

diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py
@@ -56,12 +56,12 @@
 from sklearn.metrics import mean_squared_error
 
 
-def load_mtpl2(n_samples=100000):
+def load_mtpl2(n_samples=None):
     """Fetch the French Motor Third-Party Liability Claims dataset.
 
     Parameters
     ----------
-    n_samples: int, default=100000
+    n_samples: int, default=None
       number of samples to select (for faster run time). Full dataset has
       678013 samples.
     """
@@ -215,7 +215,7 @@ def score_estimator(
 from sklearn.compose import ColumnTransformer
 
 
-df = load_mtpl2(n_samples=60000)
+df = load_mtpl2()
 
 # Note: filter out claims with zero amount, as the severity model
 # requires strictly positive target values.
@@ -233,7 +233,11 @@ def score_estimator(
 
 column_trans = ColumnTransformer(
     [
-        ("binned_numeric", KBinsDiscretizer(n_bins=10), ["VehAge", "DrivAge"]),
+        (
+            "binned_numeric",
+            KBinsDiscretizer(n_bins=10, subsample=int(2e5), random_state=0),
+            ["VehAge", "DrivAge"],
+        ),
         (
             "onehot_categorical",
             OneHotEncoder(),
@@ -276,10 +280,26 @@ def score_estimator(
 
 df_train, df_test, X_train, X_test = train_test_split(df, X, random_state=0)
 
+# %%
+#
+# Let us keep in mind that despite the seemingly large number of data points in
+# this dataset, the number of evaluation points where the claim amount is
+# non-zero is quite small:
+len(df_test)
+
+# %%
+len(df_test[df_test["ClaimAmount"] > 0])
+
+# %%
+#
+# As a consequence, we expect a significant variability in our
+# evaluation upon random resampling of the train test split.
+#
 # The parameters of the model are estimated by minimizing the Poisson deviance
-# on the training set via a quasi-Newton solver: l-BFGS. Some of the features
-# are collinear, we use a weak penalization to avoid numerical issues.
-glm_freq = PoissonRegressor(alpha=1e-3, max_iter=400)
+# on the training set via a Newton solver. Some of the features are collinear
+# (e.g. because we did not drop any categorical level in the `OneHotEncoder`),
+# we use a weak L2 penalization to avoid numerical issues.
+glm_freq = PoissonRegressor(alpha=1e-4, solver="newton-cholesky")
 glm_freq.fit(X_train, df_train["Frequency"], sample_weight=df_train["Exposure"])
 
 scores = score_estimator(
@@ -295,6 +315,12 @@ def score_estimator(
 print(scores)
 
 # %%
+#
+# Note that the score measured on the test set is surprisingly better than on
+# the training set. This might be specific to this random train-test split.
+# Proper cross-validation could help us to assess the sampling variability of
+# these results.
+#
 # We can visually compare observed and predicted values, aggregated by the
 # drivers age (``DrivAge``), vehicle age (``VehAge``) and the insurance
 # bonus/malus (``BonusMalus``).
@@ -374,7 +400,7 @@ def score_estimator(
 mask_train = df_train["ClaimAmount"] > 0
 mask_test = df_test["ClaimAmount"] > 0
 
-glm_sev = GammaRegressor(alpha=10.0, max_iter=10000)
+glm_sev = GammaRegressor(alpha=10.0, solver="newton-cholesky")
 
 glm_sev.fit(
     X_train[mask_train.values],
@@ -395,13 +421,43 @@ def score_estimator(
 print(scores)
 
 # %%
-# Here, the scores for the test data call for caution as they are
-# significantly worse than for the training data indicating an overfit despite
-# the strong regularization.
 #
-# Note that the resulting model is the average claim amount per claim. As
-# such, it is conditional on having at least one claim, and cannot be used to
-# predict the average claim amount per policy in general.
+# Those metric values are not necessarily easy to interpret. It can be
+# insightful to compare them with a model that does not use any input
+# features and always predicts a constant value, i.e. the average claim
+# amount, in the same setting:
+
+from sklearn.dummy import DummyRegressor
+
+dummy_sev = DummyRegressor(strategy="mean")
+dummy_sev.fit(
+    X_train[mask_train.values],
+    df_train.loc[mask_train, "AvgClaimAmount"],
+    sample_weight=df_train.loc[mask_train, "ClaimNb"],
+)
+
+scores = score_estimator(
+    dummy_sev,
+    X_train[mask_train.values],
+    X_test[mask_test.values],
+    df_train[mask_train],
+    df_test[mask_test],
+    target="AvgClaimAmount",
+    weights="ClaimNb",
+)
+print("Evaluation of a mean predictor on target AvgClaimAmount")
+print(scores)
+
+# %%
+#
+# We conlude that the claim amount is very challenging to predict. Still, the
+# Gamma regressor is able to leverage some information from the input features
+# to slighly improve upon the mean baseline in terms of D².
+#
+# Note that the resulting model is the average claim amount per claim. As such,
+# it is conditional on having at least one claim, and cannot be used to predict
+# the average claim amount per policy. For this, it needs to be combined with
+# a claims frequency model.
 
 print(
     "Mean AvgClaim Amount per policy:              %.2f "
@@ -415,7 +471,10 @@ def score_estimator(
     "Predicted Mean AvgClaim Amount | NbClaim > 0: %.2f"
     % glm_sev.predict(X_train).mean()
 )
-
+print(
+    "Predicted Mean AvgClaim Amount (dummy) | NbClaim > 0: %.2f"
+    % dummy_sev.predict(X_train).mean()
+)
 
 # %%
 # We can visually compare observed and predicted values, aggregated for
@@ -481,7 +540,7 @@ def score_estimator(
 from sklearn.linear_model import TweedieRegressor
 
 
-glm_pure_premium = TweedieRegressor(power=1.9, alpha=0.1, max_iter=10000)
+glm_pure_premium = TweedieRegressor(power=1.9, alpha=0.1, solver="newton-cholesky")
 glm_pure_premium.fit(
     X_train, df_train["PurePremium"], sample_weight=df_train["Exposure"]
 )