# Base Imports
import numpy as np
import seaborn as sns
# utils
from holisticai.utils import get_colors, mat_to_binary, normalize_tensor
from holisticai.utils._validation import _recommender_checks
from matplotlib import pyplot as plt
[docs]
def long_tail_plot(mat_pred, top=None, thresh=0.5, normalize=False, ax=None, size=None, title=None):
"""
Long Tail Plot.
Description
-----------
This function plots the counts in the predictions
for all items.
Parameters
----------
mat_pred : matrix-like
Matrix with shape (num_users, num_items). A recommender
score (binary or soft pred) for each user,item pair.
top (optional) : int
If not None, the number of items that are shown to each user.
thresh (optional) : float
Threshold indicating value at which
a given item is shown to user (if top is None).
normalize (optional) : bool
If True, normalises the data matrix to [0,1] range.
ax (optional) : matplotlib axes
Pre-existing axes for the plot
size (optional) : (int, int)
Size of the figure
title (optional) : str
Title of the figure
Returns
-------
matplotlib ax
"""
# input checks and coerce
_, _, mat_pred, _, top, thresh, normalize = _recommender_checks(
group_a=None,
group_b=None,
mat_pred=mat_pred,
mat_true=None,
top=top,
thresh=thresh,
normalize=normalize,
)
# normalize
if normalize:
mat_pred = normalize_tensor(mat_pred)
# make binary (ie shown / not shown)
binary_mat_pred = mat_to_binary(mat_pred, top=top, thresh=thresh)
# item counts and sort
item_counts = list(binary_mat_pred.sum(axis=0))
item_counts_sorted = sorted(item_counts, reverse=True)
# setup
sns.set_theme()
if ax is None:
fig, ax = plt.subplots(figsize=size)
# chart
ax.set_xlabel("Items (sorted by popularity)")
ax.set_ylabel("Item Count")
hai_color = get_colors(1, extended_colors=False)
ax.plot(
range(len(item_counts_sorted)),
item_counts_sorted,
linewidth=2,
color=hai_color[0],
)
if title is not None:
ax.set_title(title)
else:
ax.set_title("Long Tail Plot")
return ax
[docs]
def exposure_diff_plot(
group_a,
group_b,
mat_pred,
top=None,
thresh=0.5,
normalize=False,
ax=None,
size=None,
title=None,
):
"""
Exposure Difference plot.
Description
-----------
This function plots the difference in the exposure
distributions between group_a and group_b.
Parameters
----------
group_a : array-like
Group membership vector (binary)
group_b : array-like
Group membership vector (binary)
mat_pred : matrix-like
Matrix with shape (num_users, num_items). A recommender
score (binary or soft pred) for each user,item pair.
top (optional) : int
If not None, the number of items that are shown to each user.
thresh (optional) : float
Threshold in (0,1) range indicating value at which
a given item is shown to user (if top is None).
normalize (optional) : bool
If True, normalises the data matrix to [0,1] range.
ax (optional) : matplotlib axes
Pre-existing axes for the plot
size (optional) : (int, int)
Size of the figure
title (optional) : str
Title of the figure
Returns
-------
matplotlib ax
"""
# input checks and coerce
group_a, group_b, mat_pred, _, top, thresh, normalize = _recommender_checks(
group_a=group_a,
group_b=group_b,
mat_pred=mat_pred,
mat_true=None,
top=top,
thresh=thresh,
normalize=normalize,
)
# normalise
if normalize:
mat_pred = normalize_tensor(mat_pred)
# make binary (ie shown / not shown)
binary_mat_pred = mat_to_binary(mat_pred, top=top, thresh=thresh)
# Split by group
mat_pred_a = binary_mat_pred[group_a == 1]
mat_pred_b = binary_mat_pred[group_b == 1]
# Get the item exposure distribution for min
item_count_a = np.nansum(mat_pred_a, axis=0)
item_dist_a = item_count_a / item_count_a.sum()
# Get the item exposure distribution for maj
item_count_b = np.nansum(mat_pred_b, axis=0)
item_dist_b = item_count_b / item_count_b.sum()
# take difference
item_dist_diff = item_dist_a - item_dist_b
# sort
item_dist_diff_sorted = sorted(item_dist_diff, reverse=True)
item_dist_diff_sorted = list(item_dist_diff_sorted)
# setup
sns.set_theme()
if ax is None:
fig, ax = plt.subplots(figsize=size)
# chart
ax.set_xlabel("Items (sorted by exposure difference)")
ax.set_ylabel("$Exposure_a - Exposure_b$")
hai_color = get_colors(1, extended_colors=False)
ax.plot(
range(len(item_dist_diff_sorted)),
item_dist_diff_sorted,
linewidth=2,
color=hai_color[0],
)
if title is not None:
ax.set_title(title)
else:
ax.set_title("Exposure Difference Plot")
return ax
[docs]
def exposure_ratio_plot(
group_a,
group_b,
mat_pred,
top=None,
thresh=0.5,
normalize=False,
ax=None,
size=None,
title=None,
):
"""
Exposure Ratio plot.
Description
-----------
This function plots the ratio in the exposure
distributions between group_a and group_b.
Parameters
----------
group_a : array-like
Group membership vector (binary)
group_b : array-like
Group membership vector (binary)
mat_pred : matrix-like
Matrix with shape (num_users, num_items). A recommender
score (binary or soft pred) for each user,item pair.
top (optional) : int
If not None, the number of items that are shown to each user.
thresh (optional) : float
Threshold in (0,1) range indicating value at which
a given item is shown to user (if top is None).
normalize (optional) : bool
If True, normalises the data matrix to [0,1] range.
ax (optional) : matplotlib axes
Pre-existing axes for the plot
size (optional) : (int, int)
Size of the figure
title (optional) : str
Title of the figure
Returns
-------
matplotlib ax
"""
# input checks and coerce
group_a, group_b, mat_pred, _, top, thresh, normalize = _recommender_checks(
group_a=group_a,
group_b=group_b,
mat_pred=mat_pred,
mat_true=None,
top=top,
thresh=thresh,
normalize=normalize,
)
# normalise
if normalize:
mat_pred = normalize_tensor(mat_pred)
# make binary (ie shown / not shown)
binary_mat_pred = mat_to_binary(mat_pred, top=top, thresh=thresh)
# Split by group
mat_pred_a = binary_mat_pred[group_a == 1]
mat_pred_b = binary_mat_pred[group_b == 1]
# Get the item exposure distribution for min
item_count_a = np.nansum(mat_pred_a, axis=0)
item_dist_a = item_count_a / item_count_a.sum()
# Get the item exposure distribution for maj
item_count_b = np.nansum(mat_pred_b, axis=0)
item_dist_b = item_count_b / item_count_b.sum()
# take ratio
item_dist_rat = item_dist_a / item_dist_b
# sort by absolute value
item_dist_rat_sorted = sorted(item_dist_rat, reverse=True)
item_dist_rat_sorted = list(item_dist_rat_sorted)
# setup
sns.set_theme()
if ax is None:
fig, ax = plt.subplots(figsize=size)
# chart
ax.set_xlabel("Items (sorted by exposure ratio)")
ax.set_ylabel("$Exposure_a/Exposure_b$")
hai_color = get_colors(1, extended_colors=False)
ax.plot(
range(len(item_dist_rat_sorted)),
item_dist_rat_sorted,
linewidth=2,
color=hai_color[0],
)
if title is not None:
ax.set_title(title)
else:
ax.set_title("Exposure Ratio Plot")
return ax
