Analyzer configuration

The same scheme follows for the analyzer. Parameters available for this component:

Parameter	Description	Type	Default
input	Input data in CSV format.	str
output_path	Output path.	str
prepare_data	Preprocessing configuration.	dict
plot	Plotting parameters.	dict
classification	Parameters for classification analyses, e.g., decision trees.	dict
feat_importance	Parameters for feature importance analyses, e.g., random forests.	dict

prepare_data parameters:

Parameter	Description	Type	Default
cols	Columns or dimensions to consider.	list
rows	Values of rows to filter.	dict
target	Dimension of interest, e.g. FLOPS.	str
norm	Normalization of values for the target dimension: minmax or zscore.	str
categories	Dictionary containing meta-information for the categories: num (number of categories to generate statically), grid_search (use KDE and perform grid searching for bandwidth and kernel parameters), mode (if normal, Silverman is used for KDE. If multimodal, Sheather-Jones is used).	dict

plots parameters:

Parameter	Description	Type	Default
sort	Dimension to use for sorting values.	str
type	Type of plot: relplot, scatterplot, lineplot or kdeplot.	str
format	Output format: png, pdf, eps, ps or svg.	str
x_axis	Dimension for the X axis.	str
y_axis	Dimension for the Y axis.	str
hue	Dimension to group by color.	str
size	Dimension to group by size.	str
log_scale	Apply logarithmic scale.	bool

Example

Imagine we have a dataset with 5 columns: DIM_0, DIM_1, DIM_2, DIM_3 and Cycles. We want to filter those values where DIM_1=="VALUE", and we want to measure the impact of the rest of the dimensions over Cycles. We also want to normalize the values and create categories as the target dimension is not discrete. We are plotting a KDE or density plot and a scatterplot. The system also performs a classification using a 4-depth decision tree. The possible configuration file could be as follows:

- kernel:
    input: input.csv
    output_path: .
    prepare_data:
        cols: DIM_0 DIM_2 DIM_3
        rows: { "DIM_1": "VALUE" }
        target: Cycles
        norm: True
        categories:
            enabled: True
            scaling_factor: "1"
    plot:
        density:
            enabled: True
            type: kdeplot
            format: pdf
            data: processed
            x_axis: tsc
            x_label: "Time Stamp Counter (TSC)"
            log_scale: False
            hue: tsc_cat
            cumulative: False
            hatches: True
            multiple: "stack"
            rugplot: True
            mark_centroids: True
        scatterplot:
            enabled: False
            type: scatterplot
            format: pdf
            data: processed
            x_axis: index
            y_axis: cycles
            palette: "crimson"
            hue: arch
    classification:
        type: decision_tree
        enabled: True
        config:
            max_depth: 4
            max_leaves: 10000
            proportion: True
            text_tree: False
            graph_tree: True
            orientation: "horizontal"
    feature_importance:
        type: random_forest
        enabled: True
        config:
            n_estimators: 50
            criterion: "gini"
            max_depth: 50
            n_jobs: -1
            random_state: 0

Refer to the examples/ for more elaborate examples.