ItemToItemVisualApp

class rectools.visuals.visual_app.ItemToItemVisualApp(data_storage: AppDataStorage, auto_display: bool = True, formatters: Optional[Dict[str, Callable]] = None, rows_limit: int = 20, min_width: int = 50)[source]

Bases: VisualAppBase

Jupyter widgets app for item-to-item recommendations visualization and models comparison. Do not create instances of this class directly. Use ItemToItemVisualApp.construct method instead.

Inherited-members

Parameters

  • data_storage (AppDataStorage) –

  • auto_display (bool) –

  • formatters (Optional[Dict[str, Callable]]) –

  • rows_limit (int) –

  • min_width (int) –

Methods

construct(reco, item_data[, selected_items, ...])

Construct visualization widgets for item-to-item recommendations.

display()

Display full VisualApp widget

load(folder_name[, auto_display, ...])

Create widgets from data that was processed and saved earlier.

save(folder_name[, overwrite])

Save stored data to re-create widgets when necessary.
classmethod construct(reco: Union[DataFrame, Dict[Hashable, DataFrame]], item_data: DataFrame, selected_items: Optional[Dict[Hashable, Hashable]] = None, n_random_items: int = 0, auto_display: bool = True, formatters: Optional[Dict[str, Callable]] = None, rows_limit: int = 20, min_width: int = 100) → ItemToItemVisualApp[source]

Construct visualization widgets for item-to-item recommendations.

This will process raw data and create Jupyter widgets for visual analysis and comparison of different models. Created app outputs both target item data and recommended items data from different models for all of the selected items.

Model names for comparison will be listed from the reco dictionary keys or reco dataframe Columns.Model values depending on the format provided.

Target items for comparison can be predefined or random. For predefined items pass selected_items dict with item “names” as keys and item ids as values. For random target items pass n_random_items number greater then 0. You must specify at least one of the above or provide both.

Optionally provide formatters to process dataframe columns values to desired html outputs.

Parameters

  • reco (tp.Union[pd.DataFrame, TablesDict]) –

    Recommendations from different models in a form of a pd.DataFrame or a dict. In the dict form model names are supposed to be dict keys, and recommendations from different models are supposed to be pd.DataFrames as dict values. In the DataFrame form all recommendations must be specified in one DataFrame with Columns.Model column to separate different models. Other required columns for both forms are:

    • Columns.TargetItem - target item id

    • Columns.Item - recommended item id

    • Any other columns that you wish to display in widgets (e.g. rank or score)

    The original order of the rows will be preserved. Keep in mind to sort the rows correctly before visualizing. The most intuitive way is to sort by rank in ascending order.

  • item_data (pd.DataFrame) –

    Data for items that is used for visualisation in both interactions and recommendations widgets. Supposed to be in form of a pandas DataFrame with columns:

    • Columns.Item - item id

    • Any other columns with item data (e.g. name, category, popularity, image link)

  • selected_items (tp.Optional[tp.Dict[tp.Hashable, ExternalId]], default None) – Predefined items that will be displayed in widgets. Item names must be specified as keys of the dict and item ids as values of the dict. Must be provided if n_random_items = 0.

  • n_random_items (int, default 0) – Number of random items to add for visualization from target items in recommendation tables. Must be greater then 0 if selected_items are not specified.

  • auto_display (bool, optional, default True) – Display widgets right after initialization.

  • formatters (tp.Optional[tp.Dict[str, tp.Callable]], optional, default None) – Formatter functions to apply to columns elements in the sections of interactions and recommendations. Keys of the dict must be columns names (item_data, interactions and recommendations columns can be specified here). Values bust be functions that will be applied to corresponding columns elements. The result of each function must be a unicode string that represents html code. Formatters can be used to format text, create links and display images with html.

  • rows_limit (int, optional, default 20) – Maximum number of rows to display in the sections of interactions and recommendations.

  • min_width (int, optional, default 100) – Minimum column width in pixels for dataframe columns in widgets output. Must be greater then 10.

Return type

ItemToItemVisualApp

Examples

Providing reco as TablesDict

>>> reco = {
...     "model_1": pd.DataFrame({Columns.TargetItem: [1, 2], Columns.Item: [3, 4], Columns.Score: [0.99, 0.9]}),
...     "model_2": pd.DataFrame({Columns.TargetItem: [1, 2], Columns.Item: [5, 6], Columns.Rank: [1, 1]})
... }
>>>
>>> item_data = pd.DataFrame({
...     Columns.Item: [3, 4, 5, 6, 1, 2],
...     "feature_1": ["one", "two", "three", "five", "one", "two"]
... })
>>>
>>> selected_items = {"item_one": 1}
>>>
>>> app = ItemToItemVisualApp.construct(
...     reco=reco,
...     item_data=item_data,
...     selected_items=selected_items,
...     auto_display=False
... )

Providing reco as pd.DataFrame and adding formatters

>>> reco = pd.DataFrame({
...     Columns.TargetItem: [1, 2, 1, 2],
...     Columns.Item: [3, 4, 5, 6],
...     Columns.Model: ["model_1", "model_1", "model_2", "model_2"]
... })
>>>
>>> item_data = pd.DataFrame({
...     Columns.Item: [3, 4, 5, 6, 1, 2],
...     "feature_1": ["one", "two", "three", "five", "one", "two"]
... })
>>>
>>> selected_items = {"item_one": 1}
>>> formatters = {"item_id": lambda x: f"<b>{x}</b>"}
>>>
>>> app = ItemToItemVisualApp.construct(
...     reco=reco,
...     item_data=item_data,
...     selected_items=selected_items,
...     formatters=formatters,
...     auto_display=False
... )