Merge pull request #39 from Raul9595/master

Add Parallel Coordinates plot

docs/api/plots.rst

@@ -7,6 +7,7 @@ Plots
 .. autosummary::
    plot_fitness_evolution
    plot_search_space
+   plot_parallel_coordinates
 
 .. automodule:: sklearn_genetic.plots
    :members:

docs/release_notes.rst

@@ -14,6 +14,7 @@ Features:
 
 * Added the :class:`~sklearn_genetic.callbacks.TimerStopping` callback to stop
   the iterations after a total (threshold) fitting time has been elapsed.
+* Added new parallel coordinates plot in  :func:`~sklearn_genetic.plots.plot_parallel_coordinates`.
 * Now if one or more callbacks decides to stop the algorithm, it will print
   its class name to know which callbacks were responsible of the stopping.
 * Added support for extra methods coming from scikit-learn's BaseSearchCV, it is

sklearn_genetic/plots.py

@@ -1,16 +1,20 @@
 import logging
 
+logger = logging.getLogger(__name__)  # noqa
+
 # Check if seaborn is installed as an extra requirement
 try:
     import seaborn as sns
 except ModuleNotFoundError:  # noqa
-    logger = logging.getLogger(__name__)  # noqa
     logger.error(
         "seaborn not found, pip install seaborn to use plots functions"
     )  # noqa
+import pandas as pd
+import numpy as np
 
 from .utils import logbook_to_pandas
 from .parameters import Metrics
+from .space import Categorical
 
 """
 This module contains some useful function to explore the results of the optimization routines
@@ -89,3 +93,69 @@ def plot_search_space(estimator, height=2, s=25, features: list = None):
     )
     g = g.map_diag(sns.kdeplot, shade=True, palette="crest", alpha=0.2, color="red")
     return g
+
+
+def noise(score):
+    """
+    Parameters
+    ----------
+    score: Series
+        The `score` column from the logbook data of :class:`~sklearn_genetic.GASearchCV`
+
+    Returns
+    -------
+    Noise to be added to each element of the score to avoid non-unique bin edges
+
+    """
+    score_len = len(score)
+    score_std = score.std()
+    noise_ratio = 1e7
+    noise = (np.random.random(score_len) * score_std / noise_ratio) - (
+        score_std / 2 * noise_ratio
+    )
+    return noise
+
+
+def plot_parallel_coordinates(estimator, features: list = None):
+    """
+    Parameters
+    ----------
+    estimator: estimator object
+        A fitted estimator from :class:`~sklearn_genetic.GASearchCV`
+    features: list, default=None
+        Subset of features to plot, if ``None`` it plots all the features by default
+
+    Returns
+    -------
+    Parallel Coordinates plot of the non-categorical values
+
+    """
+
+    df = logbook_to_pandas(estimator.logbook)
+    param_grid = estimator.space.param_grid
+    score = df["score"]
+    if features:
+        non_categorical_features = []
+        for feature in features:
+            if not isinstance(param_grid[feature], Categorical):
+                non_categorical_features.append(feature)
+            else:
+                logger.warning(
+                    "`%s` is Categorical variable! It was dropped from the plot feature list",
+                    feature,
+                )
+        stats = df[non_categorical_features]
+    else:
+        non_categorical_variables = []
+        for variable, var_type in param_grid.items():
+            if not isinstance(var_type, Categorical):
+                non_categorical_variables.append(variable)
+        non_categorical_variables.append("score")
+        stats = df[non_categorical_variables]
+
+    stats["score_quartile"] = pd.qcut(score + noise(score), 4, labels=[1, 2, 3, 4])
+    g = pd.plotting.parallel_coordinates(
+        stats, "score_quartile", color=("#8E8E8D", "#4ECDC4", "#C7F464", "#FF0000")
+    )
+
+    return g

sklearn_genetic/tests/test_plots.py

@@ -4,7 +4,7 @@
 from sklearn.tree import DecisionTreeRegressor
 
 from .. import GASearchCV
-from ..plots import plot_fitness_evolution, plot_search_space
+from ..plots import plot_fitness_evolution, plot_search_space, plot_parallel_coordinates
 from ..space import Integer, Categorical, Continuous
 
 
@@ -61,3 +61,10 @@ def test_plot_space():
     plot = plot_search_space(
         evolved_estimator, features=["ccp_alpha", "max_depth", "min_samples_split"]
     )
+
+
+def test_plot_parallel():
+    plot = plot_parallel_coordinates(evolved_estimator)
+    plot = plot_parallel_coordinates(
+        evolved_estimator, features=["ccp_alpha", "criterion"]
+    )