Visualization

visualize_chemspace

visualize_chemspace(X: np.ndarray, y: Optional[Union[List[np.ndarray], np.ndarray]] = None, labels: Optional[List[str]] = None, n_cols: int = 2, fig_base_size: float = 8, w_h_ratio: float = 0.5, dpi: int = 150, seaborn_theme: Optional[str] = 'whitegrid', plot_kwargs: dict = None, umap_kwargs: dict = None)

Plot the coverage in chemical space. Also, color based on the target values.

Parameters:

Name	Type	Description	Default
X	ndarray	Array the molecular features.	required
y	Optional[Union[List[ndarray], ndarray]]	A list of arrays with the target values.	None
labels	Optional[List[str]]	Optional list of labels for each set of features.	None
n_cols	int	Number of columns in the subplots.	2
fig_base_size	float	Base size of the plots.	8
w_h_ratio	float	Width/height ratio.	0.5
dpi	int	DPI value of the figure.	150
seaborn_theme	Optional[str]	Seaborn theme.	'whitegrid'
plot_kwargs	dict	seaborn plot arguments.	None
umap_kwargs	dict	Keyword arguments for the UMAP algorithm.	None

visualize_continuous_distribution

visualize_continuous_distribution(data: np.ndarray, log_scale: bool = False, bins: Optional[Sequence[float]] = None)

KDE plot the distribution of the column in data with colored sections under the KDE curve.

Parameters:

Name	Type	Description	Default
data	ndarray	A 1D numpy array with the values to plot the distribution for.	required
log_scale	bool	Whether to plot the x-axis in log scale.	False
bins	Optional[Sequence[float]]	The bin boundaries to color the area under the KDE curve.	None

visualize_distribution_with_outliers

visualize_distribution_with_outliers(values: np.ndarray, is_outlier: Optional[List[bool]] = None, title: str = 'Probability Plot')

Visualize the distribution of the data and highlight the potential outliers.

Parameters:

Name	Type	Description	Default
values	ndarray	Values for visulization.	required
is_outlier	Optional[List[bool]]	List of outlier flag.	None
title	str	Title of plot	'Probability Plot'