From 999bb1698bf75facd7057d97f224695ad32a98d8 Mon Sep 17 00:00:00 2001 From: FBruzzesi Date: Sun, 28 Jul 2024 17:24:37 +0200 Subject: [PATCH 1/4] refactor all **kwargs into kwargs --- sklego/meta/_shrinkage_utils.py | 2 +- sklego/meta/decay_estimator.py | 4 ++-- sklego/meta/grouped_predictor.py | 2 +- sklego/meta/hierarchical_predictor.py | 4 ++-- sklego/meta/ordinal_classification.py | 6 ++++-- 5 files changed, 10 insertions(+), 8 deletions(-) diff --git a/sklego/meta/_shrinkage_utils.py b/sklego/meta/_shrinkage_utils.py index 741299f6..757eeee4 100644 --- a/sklego/meta/_shrinkage_utils.py +++ b/sklego/meta/_shrinkage_utils.py @@ -212,7 +212,7 @@ def _fit_shrinkage_factors(self, frame, groups, most_granular_only=False): } shrinkage_factors = { - grp_value: self.shrinkage_function_(counts_, **self.shrinkage_kwargs) + grp_value: self.shrinkage_function_(counts_, **(self.shrinkage_kwargs or {})) for grp_value, counts_ in hierarchical_counts.items() } diff --git a/sklego/meta/decay_estimator.py b/sklego/meta/decay_estimator.py index eeb55bb2..b454c132 100644 --- a/sklego/meta/decay_estimator.py +++ b/sklego/meta/decay_estimator.py @@ -87,7 +87,7 @@ class DecayEstimator(BaseEstimator, MetaEstimatorMixin): _required_parameters = ["model"] - def __init__(self, model, decay_func="exponential", check_input=False, **decay_kwargs): + def __init__(self, model, decay_func="exponential", check_input=False, decay_kwargs=None): self.model = model self.decay_func = decay_func self.check_input = check_input @@ -128,7 +128,7 @@ def fit(self, X, y): else: raise ValueError(f"`decay_func` should be one of {self._ALLOWED_DECAYS.keys()} or a callable") - self.weights_ = self.decay_func_(X, y, **self.decay_kwargs) + self.weights_ = self.decay_func_(X, y, **(self.decay_kwargs or {})) self.estimator_ = clone(self.model) try: diff --git a/sklego/meta/grouped_predictor.py b/sklego/meta/grouped_predictor.py index 1a9d1b13..a61f331f 100644 --- a/sklego/meta/grouped_predictor.py +++ b/sklego/meta/grouped_predictor.py @@ -99,7 +99,7 @@ def __init__( shrinkage=None, use_global_model=True, check_X=True, - **shrinkage_kwargs, + shrinkage_kwargs=None, ): self.estimator = estimator self.groups = groups diff --git a/sklego/meta/hierarchical_predictor.py b/sklego/meta/hierarchical_predictor.py index 57a65613..f7a53274 100644 --- a/sklego/meta/hierarchical_predictor.py +++ b/sklego/meta/hierarchical_predictor.py @@ -135,7 +135,7 @@ class HierarchicalPredictor(ShrinkageMixin, MetaEstimatorMixin, BaseEstimator): check_X : bool, default=True Whether to validate `X` to be non-empty 2D array of finite values and attempt to cast `X` to float. If disabled, the model/pipeline is expected to handle e.g. missing, non-numeric, or non-finite values. - **shrinkage_kwargs : dict + shrinkage_kwargs : dict Keyword arguments to the shrinkage function Attributes @@ -210,7 +210,7 @@ def __init__( fallback_method="parent", n_jobs=None, check_X=True, - **shrinkage_kwargs, + shrinkage_kwargs=None, ): self.estimator = estimator self.groups = groups diff --git a/sklego/meta/ordinal_classification.py b/sklego/meta/ordinal_classification.py index 4299b86b..a08a4e92 100644 --- a/sklego/meta/ordinal_classification.py +++ b/sklego/meta/ordinal_classification.py @@ -95,7 +95,7 @@ class OrdinalClassifier(MultiOutputMixin, ClassifierMixin, MetaEstimatorMixin, B is_multiclass = True - def __init__(self, estimator, *, n_jobs=None, use_calibration=False, **calibration_kwargs): + def __init__(self, estimator, *, n_jobs=None, use_calibration=False, calibration_kwargs=None): self.estimator = estimator self.n_jobs = n_jobs self.use_calibration = use_calibration @@ -218,7 +218,9 @@ def _fit_binary_estimator(self, X, y, y_label): """ y_bin = (y <= y_label).astype(int) if self.use_calibration: - return CalibratedClassifierCV(estimator=clone(self.estimator), **self.calibration_kwargs).fit(X, y_bin) + return CalibratedClassifierCV(estimator=clone(self.estimator), **(self.calibration_kwargs or {})).fit( + X, y_bin + ) else: return clone(self.estimator).fit(X, y_bin) From 9afe9ace409693ec605e71ac61705959b9f16ea4 Mon Sep 17 00:00:00 2001 From: FBruzzesi Date: Sun, 28 Jul 2024 17:24:49 +0200 Subject: [PATCH 2/4] refactor tests --- tests/test_meta/test_decay_estimator.py | 2 +- tests/test_meta/test_grouped_predictor.py | 22 +++++----- .../test_meta/test_hierarchical_predictor.py | 42 +++++++++++-------- tests/test_pandas_utils/test_pandas_utils.py | 2 + 4 files changed, 39 insertions(+), 29 deletions(-) diff --git a/tests/test_meta/test_decay_estimator.py b/tests/test_meta/test_decay_estimator.py index fabf8061..a0d6fc5e 100644 --- a/tests/test_meta/test_decay_estimator.py +++ b/tests/test_meta/test_decay_estimator.py @@ -53,7 +53,7 @@ def test_decay_weight(mod, is_clf, decay_func, decay_kwargs): if is_clf: y = (y < 0).astype(int) - mod = DecayEstimator(mod, decay_func=decay_func, **decay_kwargs).fit(X, y) + mod = DecayEstimator(mod, decay_func=decay_func, decay_kwargs=decay_kwargs).fit(X, y) assert np.logical_and(mod.weights_ >= 0, mod.weights_ <= 1).all() assert np.all(mod.weights_[:-1] <= mod.weights_[1:]) diff --git a/tests/test_meta/test_grouped_predictor.py b/tests/test_meta/test_grouped_predictor.py index 5d20a23a..21606a46 100644 --- a/tests/test_meta/test_grouped_predictor.py +++ b/tests/test_meta/test_grouped_predictor.py @@ -249,7 +249,7 @@ def test_constant_shrinkage(shrinkage_data): ["Planet", "Country", "City"], shrinkage="constant", use_global_model=False, - alpha=0.1, + shrinkage_kwargs={"alpha": 0.1}, ) shrinkage_factors = np.array([0.01, 0.09, 0.9]) @@ -304,7 +304,7 @@ def test_min_n_obs_shrinkage(shrinkage_data): ["Planet", "Country", "City"], shrinkage="min_n_obs", use_global_model=False, - min_n_obs=2, + shrinkage_kwargs={"min_n_obs": 2}, ) shrink_est.fit(X, y) @@ -327,7 +327,7 @@ def test_min_n_obs_shrinkage_too_little_obs(shrinkage_data): ["Planet", "Country", "City"], shrinkage="min_n_obs", use_global_model=False, - min_n_obs=too_big_n_obs, + shrinkage_kwargs={"min_n_obs": too_big_n_obs}, ) with pytest.raises(ValueError) as e: @@ -459,7 +459,7 @@ def test_global_model_shrinkage(shrinkage_data): ["Planet", "Country", "City"], shrinkage="min_n_obs", use_global_model=False, - min_n_obs=2, + shrinkage_kwargs={"min_n_obs": 2}, ) shrink_est_with_global = GroupedPredictor( @@ -467,7 +467,7 @@ def test_global_model_shrinkage(shrinkage_data): ["Country", "City"], shrinkage="min_n_obs", use_global_model=True, - min_n_obs=2, + shrinkage_kwargs={"min_n_obs": 2}, ) shrink_est_without_global.fit(X, y) @@ -490,7 +490,7 @@ def test_shrinkage_single_group(shrinkage_data): "Country", shrinkage="constant", use_global_model=True, - alpha=0.1, + shrinkage_kwargs={"alpha": 0.1}, ) shrinkage_factors = np.array([0.1, 0.9]) @@ -519,7 +519,7 @@ def test_shrinkage_single_group_no_global(shrinkage_data): "Country", shrinkage="constant", use_global_model=False, - alpha=0.1, + shrinkage_kwargs={"alpha": 0.1}, ) shrink_est.fit(X, y) @@ -548,7 +548,9 @@ def test_unseen_groups_shrinkage(shrinkage_data): X, y = df.drop(columns="Target"), df["Target"] - shrink_est = GroupedPredictor(DummyRegressor(), ["Planet", "Country", "City"], shrinkage="constant", alpha=0.1) + shrink_est = GroupedPredictor( + DummyRegressor(), ["Planet", "Country", "City"], shrinkage="constant", shrinkage_kwargs={"alpha": 0.1} + ) shrink_est.fit(X, y) @@ -569,7 +571,7 @@ def test_predict_missing_group_column(shrinkage_data): ["Planet", "Country", "City"], shrinkage="constant", use_global_model=False, - alpha=0.1, + shrinkage_kwargs={"alpha": 0.1}, ) shrink_est.fit(X, y) @@ -592,7 +594,7 @@ def test_predict_missing_value_column(shrinkage_data): ["Planet", "Country", "City"], shrinkage="constant", use_global_model=False, - alpha=0.1, + shrinkage_kwargs={"alpha": 0.1}, ) shrink_est.fit(X, y) diff --git a/tests/test_meta/test_hierarchical_predictor.py b/tests/test_meta/test_hierarchical_predictor.py index ff5c6cd2..94bd0161 100644 --- a/tests/test_meta/test_hierarchical_predictor.py +++ b/tests/test_meta/test_hierarchical_predictor.py @@ -116,24 +116,28 @@ def make_hierarchical_dummy(frame_func): ) @pytest.mark.parametrize("fallback_method", ["raise", "parent"]) @pytest.mark.parametrize( - "shrinkage", + ("shrinkage", "kwargs"), [ - {"shrinkage": None}, - {"shrinkage": "equal"}, - {"shrinkage": "relative"}, - {"shrinkage": "min_n_obs", "min_n_obs": 10}, - {"shrinkage": "constant", "alpha": 0.5}, + (None, None), + ("equal", None), + ("relative", None), + ("min_n_obs", {"min_n_obs": 10}), + ("constant", {"alpha": 0.5}), ], ) -def test_fit_predict(meta_cls, base_estimator, task, fallback_method, shrinkage): +def test_fit_predict(meta_cls, base_estimator, task, fallback_method, shrinkage, kwargs): """Tests that the model can be fit and predict with different configurations of fallback and shrinkage methods if X to predict contains same groups as X used to fit. """ X, y, groups = make_hierarchical_dataset(task, frame_func=frame_funcs[randint(0, 1)]) - meta_model = meta_cls(estimator=base_estimator, groups=groups, fallback_method=fallback_method, **shrinkage).fit( - X, y - ) + meta_model = meta_cls( + estimator=base_estimator, + groups=groups, + fallback_method=fallback_method, + shrinkage=shrinkage, + shrinkage_kwargs=kwargs, + ).fit(X, y) assert meta_model.estimators_ is not None assert meta_model.predict(X) is not None @@ -173,23 +177,25 @@ def test_fallback(meta_cls, base_estimator, task, fallback_method, context): ], ) @pytest.mark.parametrize( - "shrinkage", + ("shrinkage", "kwargs"), [ - {"shrinkage": None}, - {"shrinkage": "equal"}, - {"shrinkage": "relative"}, - {"shrinkage": "min_n_obs", "min_n_obs": 10}, - {"shrinkage": "constant", "alpha": 0.5}, + (None, None), + ("equal", None), + ("relative", None), + ("min_n_obs", {"min_n_obs": 10}), + ("constant", {"alpha": 0.5}), ], ) -def test_shrinkage(meta_cls, base_estimator, task, metric, shrinkage): +def test_shrinkage(meta_cls, base_estimator, task, metric, shrinkage, kwargs): """Tests that the model performance is better than the base estimator when predicting with different shrinkage methods. """ X, y, groups = make_hierarchical_dataset(task, frame_func=frame_funcs[randint(0, 1)]) X_ = nw.from_native(X).drop(groups).pipe(nw.to_native) - meta_model = meta_cls(estimator=clone(base_estimator), groups=groups, **shrinkage).fit(X, y) + meta_model = meta_cls( + estimator=clone(base_estimator), groups=groups, shrinkage=shrinkage, shrinkage_kwargs=kwargs + ).fit(X, y) base_model = clone(base_estimator).fit(X_, y) assert metric(y, base_model.predict(X_)) <= metric(y, meta_model.predict(X)) diff --git a/tests/test_pandas_utils/test_pandas_utils.py b/tests/test_pandas_utils/test_pandas_utils.py index bff49ff0..19c8ce98 100644 --- a/tests/test_pandas_utils/test_pandas_utils.py +++ b/tests/test_pandas_utils/test_pandas_utils.py @@ -48,9 +48,11 @@ def test_add_lags_correct_df(data, frame_func): ans = add_lags(test_df, "X1", -1) if isinstance(ans, pl.LazyFrame): ans = ans.collect() + print("HERE\n", ans) if isinstance(expected, pl.LazyFrame): expected = expected.collect() assert [x for x in ans.columns] == [x for x in expected.columns] + assert (ans.to_numpy() == expected.to_numpy()).all() From 372f7c3e8e80e7b2afe9e3d00706722fd3f59ea3 Mon Sep 17 00:00:00 2001 From: FBruzzesi Date: Sun, 28 Jul 2024 22:51:36 +0200 Subject: [PATCH 3/4] rerun meta-models --- docs/_scripts/meta-models.py | 25 +- docs/_static/meta-models/baseline-model.png | Bin 50368 -> 50499 bytes .../meta-models/confusion-balanced-grid.html | 418 +++++++++++++++++- .../confusion-balancer-results.png | Bin 42268 -> 42268 bytes docs/_static/meta-models/decay-functions.png | Bin 54124 -> 54124 bytes docs/_static/meta-models/decay-model.png | Bin 51177 -> 57924 bytes .../meta-models/grouped-dummy-model.png | Bin 52345 -> 52345 bytes docs/_static/meta-models/grouped-model.png | Bin 50109 -> 50109 bytes .../_static/meta-models/grouped-transform.png | Bin 128982 -> 128982 bytes docs/_static/meta-models/make-blobs.png | Bin 125394 -> 125394 bytes docs/_static/meta-models/ordinal_data.md | 2 +- .../outlier-classifier-stacking.html | 410 ++++++++++++++++- .../meta-models/outlier-classifier.html | 410 ++++++++++++++++- docs/_static/meta-models/skewed-data.png | Bin 69932 -> 67675 bytes docs/_static/meta-models/threshold-chart.png | Bin 65062 -> 63536 bytes docs/_static/meta-models/ts-data.png | Bin 41317 -> 41317 bytes 16 files changed, 1244 insertions(+), 21 deletions(-) diff --git a/docs/_scripts/meta-models.py b/docs/_scripts/meta-models.py index b7134ebe..9069c8ed 100644 --- a/docs/_scripts/meta-models.py +++ b/docs/_scripts/meta-models.py @@ -132,7 +132,7 @@ def plot_model(model): ("datagrab", FeatureUnion([ ("discrete", Pipeline([ ("grab", ColumnSelector("diet")), - ("encode", OneHotEncoder(categories="auto", sparse=False)) + ("encode", OneHotEncoder(categories="auto")) ])), ("continuous", Pipeline([ ("grab", ColumnSelector("time")), @@ -265,8 +265,15 @@ def plot_model(model): mod1 = (GroupedPredictor(DummyRegressor(), groups=["m"]) .fit(df[["m"]], df["yt"])) -mod2 = (GroupedPredictor(DecayEstimator(DummyRegressor(), decay_func="exponential", decay_rate=0.9), groups=["m"]) - .fit(df[["index", "m"]], df["yt"])) +mod2 = (GroupedPredictor( + estimator=DecayEstimator( + model=DummyRegressor(), + decay_func="exponential", + decay_kwargs={"decay_rate": 0.9} + ), + groups=["m"] + ).fit(df[["index", "m"]], df["yt"]) +) plt.figure(figsize=(12, 3)) plt.plot(df["yt"], alpha=0.5); @@ -494,12 +501,16 @@ def false_negatives(mod, x, y): from sklearn.linear_model import LogisticRegression from sklego.meta import OrdinalClassifier -ord_clf = OrdinalClassifier(LogisticRegression(), n_jobs=-1, use_calibration=False) -_ = ord_clf.fit(X, y) -ord_clf.predict_proba(X[0]) +ord_clf = OrdinalClassifier( + LogisticRegression(), + n_jobs=-1, + use_calibration=False, + ).fit(X, y) + +ord_clf.predict_proba(X[:1]) # --8<-- [end:ordinal-classifier] -print(ord_clf.predict_proba(X[0])) +print(ord_clf.predict_proba(X[:1])) # --8<-- [start:ordinal-classifier-with-calibration] from 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GridSearchCV(cv=5,
+
GridSearchCV(cv=5,
              estimator=ConfusionBalancer(alpha=1.0,
                                          estimator=LogisticRegression(max_iter=1000)),
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@@ -10,9 +414,9 @@
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-                      'negatives': <function false_negatives at 0x7f33dfb60c10>,
-                      'positives': <function false_positives at 0x7f33dfb61fc0>})
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LogisticRegression()
\ No newline at end of file diff --git a/docs/_static/meta-models/outlier-classifier.html b/docs/_static/meta-models/outlier-classifier.html index 263609f1..d9284d52 100644 --- a/docs/_static/meta-models/outlier-classifier.html +++ b/docs/_static/meta-models/outlier-classifier.html @@ -1,3 +1,407 @@ -
OutlierClassifier(model=IsolationForest(contamination=0.01, n_estimators=1000,
-                                        random_state=0))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\ No newline at end of file +
OutlierClassifier(model=IsolationForest(contamination=0.01, n_estimators=1000,
+                                        random_state=0))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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b/docs/_static/meta-models/ts-data.png index dab2adc9a80194018941db677a1bb18856b46157..36a9e87ed1ec373adbcf0d6964a1c3fdeb0ed4af 100644 GIT binary patch delta 45 zcmaEQi0SDerU`BemU@Od3K=CO1;tkS`nicE1v&X8Ihjd%`9 Date: Sun, 28 Jul 2024 22:51:46 +0200 Subject: [PATCH 4/4] print --- tests/test_pandas_utils/test_pandas_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_pandas_utils/test_pandas_utils.py b/tests/test_pandas_utils/test_pandas_utils.py index 19c8ce98..dd4bbc58 100644 --- a/tests/test_pandas_utils/test_pandas_utils.py +++ b/tests/test_pandas_utils/test_pandas_utils.py @@ -48,7 +48,6 @@ def test_add_lags_correct_df(data, frame_func): ans = add_lags(test_df, "X1", -1) if isinstance(ans, pl.LazyFrame): ans = ans.collect() - print("HERE\n", ans) if isinstance(expected, pl.LazyFrame): expected = expected.collect() assert [x for x in ans.columns] == [x for x in expected.columns]
StackingClassifier(estimators=[('anomaly',
+