add styler to evaluate

README.md

@@ -127,7 +127,7 @@ or via `conda`:
 ⚡ Usage
 -------
 
-[![SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/tvdboom/ATOM/blob/master/examples/getting_started.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1H8pL-iAICeaKqWQxWsb6fN9zPNZK722s#scrollTo=LrtjgDQFvU2z&forceEdit=true&sandboxMode=true)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tvdboom/ATOM/HEAD)
 
 ATOM contains a variety of classes and functions to perform data cleaning,
@@ -186,9 +186,9 @@ atom.run(models=["LDA", "AdaB"], metric="auc", n_trials=10)
 And lastly, analyze the results.
 
 ```python
-atom.evaluate()
+print(atom.evaluate())
 
-atom.plot_lift()
+atom.plot_roc()
 ```
 
 <br><br>

atom/basemodel.py

@@ -322,11 +322,11 @@ def _est_class(self) -> type[Predictor]:
 
         # Try engine, else import from the default module
         try:
-            module = import_module(f"{self.engine.estimator}.{module.split('.', 1)[1:]}")
+            mod = import_module(f"{self.engine.estimator}.{module.split('.', 1)[1]}")
         except (ModuleNotFoundError, AttributeError):
-            module = import_module(module)
+            mod = import_module(module)
 
-        return getattr(module, est_name)
+        return self._wrap_class(getattr(mod, est_name))
 
     @property
     def _shap(self) -> ShapExplanation:

atom/baserunner.py

@@ -1022,7 +1022,8 @@ def evaluate(
         rows: RowSelector = "test",
         *,
         threshold: FloatZeroToOneExc | Sequence[FloatZeroToOneExc] = 0.5,
-    ) -> pd.DataFrame:
+        as_frame: Bool = False,
+    ) -> Any | pd.DataFrame:
         """Get all models' scores for the provided metrics.
 
         Parameters
@@ -1048,15 +1049,27 @@ def evaluate(
             The same threshold per target column is applied to all
             models.
 
+        as_frame: bool, default=False
+            Whether to return the scores as a pd.DataFrame. If False, a
+            `pandas.io.formats.style.Styler` object is returned, which
+            has a `_repr_html_` method defined, so it is rendered
+            automatically in a notebook. The highest score per metric
+            is highlighted.
+
         Returns
         -------
-        pd.DataFrame
+        [Styler][] or pd.DataFrame
             Scores of the models.
 
         """
         check_is_fitted(self)
 
-        return pd.DataFrame([m.evaluate(metric, rows, threshold=threshold) for m in self._models])
+        df = pd.DataFrame([m.evaluate(metric, rows, threshold=threshold) for m in self._models])
+
+        if len(self._models) == 1 or as_frame:
+            return df
+        else:
+            return df.style.highlight_max(props="background-color: lightgreen")
 
     @composed(crash, beartype)
     def export_pipeline(self, model: str | Model | None = None) -> Pipeline:

atom/models/custom.py

@@ -5,6 +5,7 @@
 
 """
 
+from functools import cached_property
 from typing import Any
 
 from atom.basemodel import BaseModel
@@ -17,10 +18,10 @@ class CustomModel(BaseModel):
     def __init__(self, **kwargs):
         # Assign the estimator and store the provided parameters
         if callable(est := kwargs.pop("estimator")):
-            self._est = self._wrap_class(est)
+            self._est = est
             self._params = {}
         else:
-            self._est = self._wrap_class(est.__class__)
+            self._est = est.__class__
             self._params = est.get_params()
 
         if hasattr(est, "name"):
@@ -55,10 +56,10 @@ def fullname(self) -> str:
         """Return the estimator's class name."""
         return self._est_class.__name__
 
-    @property
+    @cached_property
     def _est_class(self) -> type[Predictor]:
         """Return the estimator's class."""
-        return self._est
+        return self._wrap_class(self._est)
 
     def _get_est(self, params: dict[str, Any]) -> Predictor:
         """Get the model's estimator with unpacked parameters.

atom/plots/dataplot.py

@@ -235,7 +235,7 @@ def plot_ccf(
         plot_interval: Bool = False,
         title: str | dict[str, Any] | None = None,
         legend: Legend | dict[str, Any] | None = "upper right",
-        figsize: tuple[IntLargerZero, IntLargerZero] = (900, 600),
+        figsize: tuple[IntLargerZero, IntLargerZero] | None = None,
         filename: str | Path | None = None,
         display: Bool | None = True,
     ) -> go.Figure | None:
@@ -1010,20 +1010,21 @@ def plot_fft(
     ) -> go.Figure | None:
         """Plot the fourier transformation of a time series.
 
-        A Fast Fourier Transformper (FFT) plot visualizes the frequency
+        A Fast Fourier Transformer (FFT) plot visualizes the frequency
         domain representation of a signal by transforming it from the
         time domain to the frequency domain using the FFT algorithm.
         The x-axis shows the frequencies, normalized to the
-        [Nyquist frequency][], and the y-axis shows the power spectral
-        density or squared amplitude per frequency unit on a logarithmic
-        scale. This plot is only available for [forecast][time-series]
-        tasks.
+        [Nyquist frequency][nyquist], and the y-axis shows the power
+        spectral density or squared amplitude per frequency unit on a
+        logarithmic scale. This plot is only available for
+        [forecast][time-series] tasks.
 
         !!! tip
             - If the plot peaks at f~0, it can indicate the wandering
-              behavior characteristic of a [random walk][] that needs
-              to be differentiated. It could also be indicative of a
-              stationary [ARMA][] process with a high positive phi value.
+              behavior characteristic of a [random walk][random_walk]
+              that needs to be differentiated. It could also be indicative
+              of a stationary [ARMA][] process with a high positive phi
+              value.
             - Peaking at a frequency and its multiples is indicative of
               seasonality. The lowest frequency in this case is called
               the fundamental frequency, and the inverse of this
@@ -1631,16 +1632,17 @@ def plot_periodogram(
         series analysis for identifying dominant frequencies, periodic
         patterns, and overall spectral characteristics of the data.
         The x-axis shows the frequencies, normalized to the
-        [Nyquist frequency][], and the y-axis shows the power spectral
-        density or squared amplitude per frequency unit on a logarithmic
-        scale. This plot is only available for [forecast][time-series]
-        tasks.
+        [Nyquist frequency][nyquist], and the y-axis shows the power
+        spectral density or squared amplitude per frequency unit on a
+        logarithmic scale. This plot is only available for
+        [forecast][time-series] tasks.
 
         !!! tip
             - If the plot peaks at f~0, it can indicate the wandering
-              behavior characteristic of a [random walk][] that needs
-              to be differentiated. It could also be indicative of a
-              stationary [ARMA][] process with a high positive phi value.
+              behavior characteristic of a [random walk][random_walk]
+              that needs to be differentiated. It could also be indicative
+              of a stationary [ARMA][] process with a high positive phi
+              value.
             - Peaking at a frequency and its multiples is indicative of
               seasonality. The lowest frequency in this case is called
               the fundamental frequency, and the inverse of this

atom/plots/predictionplot.py

@@ -738,10 +738,9 @@ def plot_errors(
 
         Plot the actual targets from a set against the predicted values
         generated by the regressor. A linear fit is made on the data.
-        The gray, intersected line shows the identity line. This plot
-        can be useful to detect noise or heteroscedasticity along a
-        range of the target domain. This plot is unavailable for
-        classification tasks.
+        This plot can be useful to detect noise or heteroscedasticity
+        along a range of the target domain. This plot is unavailable
+        for classification tasks.
 
         Parameters
         ----------

docs_sources/changelog/v6.x.x.md

@@ -34,6 +34,8 @@
 * Transformations only on `y` are now accepted, e.g., `atom.scale(columns=-1)`.
 * The [Imputer][] class has many more strategies for numerical columns designed
   for time series.
+* The [evaluate][atomclassifier-evaluate] method highlights the highest score
+  per metric.
 * Full support for [pandas nullable dtypes](https://pandas.pydata.org/docs/user_guide/integer_na.html).
 * The dataset can now be provided as callable.
 * The [FeatureExtractor][] class can extract features from the dataset's index.

docs_sources/getting_started.md

@@ -62,7 +62,7 @@ package files for all versions published on PyPI.
 
 ## Usage
 
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tvdboom/ATOM/blob/master/examples/getting_started.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1H8pL-iAICeaKqWQxWsb6fN9zPNZK722s#scrollTo=LrtjgDQFvU2z&forceEdit=true&sandboxMode=true)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tvdboom/ATOM/HEAD)
 
 ATOM contains a variety of classes and functions to perform data cleaning,
@@ -152,5 +152,5 @@ atom.run(models=["LR", "LDA"], metric="auc", n_trials=6)  # hide
 
 print(atom.evaluate())
 
-atom.plot_lift()
+atom.plot_roc()
 ```

docs_sources/scripts/autodocs.py

@@ -77,6 +77,7 @@
     registry="https://www.mlflow.org/docs/latest/model-registry.html",
     ray="https://docs.ray.io/en/latest/cluster/getting-started.html",
     # BaseRunner
+    styler="https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.html",
     stackingclassifier="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html",
     stackingregressor="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingRegressor.html",
     stackingforecaster="https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.compose.StackingForecaster.html",
@@ -254,7 +255,7 @@
     update_traces="https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html#plotly.graph_objects.Figure.update_traces",
     fanova="https://optuna.readthedocs.io/en/stable/reference/generated/optuna.importance.FanovaImportanceEvaluator.html",
     kde="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html",
-    nyquist_frequency="https://en.wikipedia.org/wiki/Nyquist_frequency",
+    nyquist="https://en.wikipedia.org/wiki/Nyquist_frequency",
     random_walk="https://en.wikipedia.org/wiki/Random_walk",
     arma="https://en.wikipedia.org/wiki/Autoregressive_moving-average_model",
     wordcloud="https://amueller.github.io/word_cloud/generated/wordcloud.WordCloud.html",

docs_sources/user_guide/accelerating.md

@@ -46,7 +46,7 @@ regardless of the engine parameter.
     one to use, the first one is used by default.
 
 !!! example
-    [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tvdboom/ATOM/blob/master/examples/accelerating_cuml.ipynb)
+    [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gbTMqTt5sDuP3kBLy1-_U6Z2uZaSm43O?authuser=0#scrollTo=FEB9_7R7Wq4h&forceEdit=true&sandboxMode=true)
 
     Train a model on a GPU yourself using Google Colab. Just click on the
     badge above and run the notebook! Make sure to choose the GPU runtime

tests/test_baserunner.py

@@ -864,8 +864,15 @@ def test_evaluate(metric):
     """Assert that the evaluate method works."""
     atom = ATOMClassifier(X_bin, y_bin, random_state=1)
     pytest.raises(NotFittedError, atom.evaluate)
-    atom.run(["Tree", "SVM"])
-    assert isinstance(atom.evaluate(metric), pd.DataFrame)
+    atom.run("Tree")
+    assert isinstance(atom.evaluate(metric, as_frame=True), pd.DataFrame)
+
+
+def test_evaluate_returns_styler():
+    """Assert that the evaluate method returns a pandas styler."""
+    atom = ATOMClassifier(X_bin, y_bin, random_state=1)
+    atom.run(["Tree", "LR"])
+    assert isinstance(atom.evaluate(), pd.io.formats.style.Styler)
 
 
 def test_export_pipeline_same_transformer():

tests/test_models.py

@@ -145,6 +145,7 @@ def test_models_sklearnex_classification():
         n_trials=2,
         est_params={"LR": {"max_iter": 5}, "RF": {"n_estimators": 5}},
     )
+    assert all(m.estimator.__module__.startswith(("daal4py", "sklearnex")) for m in atom._models)
 
 
 @pytest.mark.skipif(machine() not in ("x86_64", "AMD64"), reason="Only x86 support.")
@@ -156,6 +157,7 @@ def test_models_sklearnex_regression():
         n_trials=2,
         est_params={"RF": {"n_estimators": 5}},
     )
+    assert all(m.estimator.__module__.startswith(("daal4py", "sklearnex")) for m in atom._models)
 
 
 @patch.dict(