MNT rename functions and add tests (#31)

Author: joaopfonseca

docs/api.rst

@@ -38,20 +38,6 @@ This is the full API documentation of the `mlresearch` package.
     data_augmentation.GeometricSMOTE
     data_augmentation.OverSamplingAugmentation
 
-:mod:`mlresearch.preprocessing`
--------------------------------
-
-.. automodule:: mlresearch.preprocessing
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: mlresearch
-
-.. autosummary::
-   :toctree: _generated/
-   :template: class.rst
-
-    preprocessing.PipelineEncoder
 
 :mod:`mlresearch.datasets`
 --------------------------
@@ -72,6 +58,25 @@ This is the full API documentation of the `mlresearch` package.
     datasets.MultiClassDatasets
     datasets.RemoteSensingDatasets
 
+:mod:`mlresearch.latex`
+-----------------------
+
+.. automodule:: mlresearch.latex
+    :no-members:
+    :no-inherited-members:
+
+.. currentmodule:: mlresearch
+
+.. autosummary::
+    :toctree: _generated/
+    :template: function.rst
+
+    latex.format_table
+    latex.make_bold
+    latex.make_mean_sem_table
+    latex.export_longtable
+
+
 :mod:`mlresearch.metrics`
 -------------------------
 
@@ -97,23 +102,20 @@ This is the full API documentation of the `mlresearch` package.
 
     metrics.ALScorer
 
-:mod:`mlresearch.latex`
------------------------
+:mod:`mlresearch.preprocessing`
+-------------------------------
 
-.. automodule:: mlresearch.latex
-    :no-members:
-    :no-inherited-members:
+.. automodule:: mlresearch.preprocessing
+   :no-members:
+   :no-inherited-members:
 
 .. currentmodule:: mlresearch
 
 .. autosummary::
-    :toctree: _generated/
-    :template: function.rst
+   :toctree: _generated/
+   :template: class.rst
 
-    latex.format_table
-    latex.make_bold
-    latex.make_mean_sem_table
-    latex.export_longtable
+    preprocessing.PipelineEncoder
 
 :mod:`mlresearch.utils`
 -----------------------
@@ -133,6 +135,6 @@ This is the full API documentation of the `mlresearch` package.
     utils.load_datasets
     utils.check_pipelines
     utils.check_pipelines_wrapper
-    utils.load_plt_sns_configs
-    utils.val_to_color
+    utils.set_matplotlib_style
+    utils.feature_to_color
     utils.generate_paths

mlresearch/utils/__init__.py

@@ -5,7 +5,7 @@
 from ._image import image_to_dataframe, dataframe_to_image
 from ._data import load_datasets
 from ._check_pipelines import check_pipelines, check_pipelines_wrapper
-from ._visualization import load_plt_sns_configs, val_to_color
+from ._visualization import set_matplotlib_style, feature_to_color
 from ._utils import generate_paths
 
 __all__ = [
@@ -14,7 +14,7 @@
     "load_datasets",
     "check_pipelines",
     "check_pipelines_wrapper",
-    "load_plt_sns_configs",
-    "val_to_color",
+    "set_matplotlib_style",
+    "feature_to_color",
     "generate_paths",
 ]

mlresearch/utils/_visualization.py

@@ -38,38 +38,20 @@ def _optional_import(module: str) -> types.ModuleType:
     return module_
 
 
-def load_plt_sns_configs(font_size=8, **rcparams):
+def set_matplotlib_style(font_size=8, **rcparams):
     """
-    Load LaTeX style configurations for Matplotlib Visualizations.
+    Load LaTeX-style configurations for Matplotlib Visualizations.
     """
     plt = _optional_import("matplotlib.pyplot")
 
     # Replicates the rcParams of seaborn's "whitegrid" style and a few extra
     # configurations I like
+    plt.style.use("seaborn-v0_8-whitegrid")
     base_style = {
-        # Whitegrid
-        "axes.axisbelow": True,
-        "axes.edgecolor": ".8",
-        "axes.grid": True,
-        "axes.labelcolor": ".15",
-        "font.sans-serif": [
-            "Arial",
-            "DejaVu Sans",
-            "Liberation Sans",
-            "Bitstream Vera Sans",
-            "sans-serif",
-        ],
-        "grid.color": ".8",
-        "image.cmap": "rocket",
-        "lines.solid_capstyle": "round",
-        "patch.edgecolor": "w",
-        "patch.force_edgecolor": True,
-        "text.color": ".15",
-        "xtick.bottom": False,
-        "xtick.color": ".15",
-        "ytick.color": ".15",
-        "ytick.left": False,
-        # Extras
+        # "patch.edgecolor": "w",
+        # "patch.force_edgecolor": True,
+        # "xtick.bottom": False,
+        # "ytick.left": False,
         "font.family": "serif",
         # Use 10pt font in plots, to match 10pt font in document
         "axes.labelsize": (10 / 8) * font_size,
@@ -84,7 +66,6 @@ def load_plt_sns_configs(font_size=8, **rcparams):
         "figure.subplot.bottom": 0.12,
         "figure.subplot.top": 0.944,
         "figure.subplot.wspace": 0.071,
-        "figure.subplot.hspace": 0.2,
     }
     plt.rcParams.update(base_style)
 
@@ -104,13 +85,13 @@ def load_plt_sns_configs(font_size=8, **rcparams):
     plt.rcParams.update(rcparams)
 
 
-def val_to_color(col, cmap="RdYlBu_r"):
+def feature_to_color(col, cmap="RdYlBu_r"):
     """
     Converts a column of values to hex-type colors.
 
     Parameters
     ----------
-    col : array-like of shape (n_samples,)
+    col : {list, array-like} of shape (n_samples,)
         Values to convert to hex-type color code
 
     cmap : str or `~matplotlib.colors.Colormap`
@@ -124,6 +105,9 @@ def val_to_color(col, cmap="RdYlBu_r"):
     colors = _optional_import("matplotlib.colors")
     cm = _optional_import("matplotlib.cm")
 
+    if type(col) == list:
+        col = np.array(col)
+
     norm = colors.Normalize(vmin=col.min(), vmax=col.max(), clip=True)
     mapper = cm.ScalarMappable(norm=norm, cmap=cmap)
     rgba = mapper.to_rgba(col)

mlresearch/utils/tests/test_visualization.py

@@ -0,0 +1,21 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from .._visualization import set_matplotlib_style, feature_to_color
+
+
+def test_set_matplotlib_style():
+    default_params = dict(plt.rcParams)
+    set_matplotlib_style(12)
+    new_params = dict(plt.rcParams)
+    changed_params = [
+        key for key in new_params.keys() if new_params[key] != default_params[key]
+    ]
+    assert len(changed_params) > 1
+
+
+def test_feature_to_color():
+    colors = feature_to_color(np.array([1, 2, 3, 4, 5]))
+    colors2 = feature_to_color([1, 2, 3, 4, 5])
+    assert (colors == colors2).all()
+    assert colors.size == np.unique(colors).size