Module ethik.image_classification_explainer
Expand source code
import itertools
import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from . import base_explainer
from .cache_explainer import CacheExplainer
from .warnings import ConstantWarning
def images_to_dataframe(images):
img_shape = images[0].shape
return pd.DataFrame(
data=images.reshape(len(images), -1),
columns=itertools.product(*[np.arange(n) for n in img_shape]),
)
class ImageClassificationExplainer(CacheExplainer):
"""An explainer specially suited for image classification.
This has exactly the same API as `Explainer`, but expects to be provided with an array of
images instead of a tabular dataset.
TODO: add a note about n_taus being 2
Parameters:
alpha (float): A `float` between `0` and `0.5` which indicates by how close the `Explainer`
should look at extreme values of a distribution. The closer to zero, the more so
extreme values will be accounted for. The default is `0.05` which means that all values
beyond the 5th and 95th quantiles are ignored.
max_iterations (int): The number of iterations used when applying the Newton step
of the optimization procedure. Default is `5`.
tol (float): The bottom threshold for the gradient of the optimization
procedure. When reached, the procedure stops. Otherwise, a warning
is raised about the fact that the optimization did not converge.
Default is `1e-4`.
n_jobs (int): The number of jobs to use for parallel computations. See
`joblib.Parallel()`. Default is `-1`.
memoize (bool): Indicates whether or not memoization should be used or not. If `True`, then
intermediate results will be stored in order to avoid recomputing results that can be
reused by successively called methods. For example, if you call `plot_influence` followed by
`plot_influence_ranking` and `memoize` is `True`, then the intermediate results required by
`plot_influence` will be reused for `plot_influence_ranking`. Memoization is turned on by
default because computations are time-consuming for images.
verbose (bool): Whether or not to show progress bars during
computations. Default is `True`.
"""
def __init__(
self,
alpha=0.05,
max_iterations=10,
tol=1e-4,
n_jobs=-1,
memoize=True,
verbose=True,
):
super().__init__(
alpha=alpha,
n_taus=2,
max_iterations=max_iterations,
tol=tol,
n_jobs=n_jobs,
memoize=memoize,
verbose=verbose,
)
def _set_image_shape(self, images):
self.img_shape = images[0].shape
if self.img_shape[-1] == 1:
self.img_shape = self.img_shape[:-1]
def explain_influence(self, X_test, y_pred):
"""Compute the influence of the model for the features in `X_test`.
Args:
X_test (np.array): An array of images, i.e. a 3d numpy array of
dimension `(n_images, n_rows, n_cols)`.
y_pred (pd.DataFrame or pd.Series): The model predictions
for the samples in `X_test`. For binary classification and regression,
`pd.Series` is expected. For multi-label classification, a
pandas dataframe with one column per label is
expected. The values can either be probabilities or `0/1`
(for a one-hot-encoded output).
Returns:
pd.DataFrame:
See `ethik.explainer.Explainer.explain_influence()`.
"""
self._set_image_shape(images=X_test)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=ConstantWarning)
return super().explain_influence(
X_test=images_to_dataframe(X_test), y_pred=y_pred
)
def explain_performance(self, X_test, y_test, y_pred, metric):
"""Compute the change in model's performance for the features in `X_test`.
Args:
X_test (np.array): An array of images, i.e. a 3d numpy array of
dimension `(n_images, n_rows, n_cols)`.
y_test (pd.DataFrame or pd.Series): The true values
for the samples in `X_test`. For binary classification and regression,
a `pd.Series` is expected. For multi-label classification,
a pandas dataframe with one column per label is
expected. The values can either be probabilities or `0/1`
(for a one-hot-encoded output).
y_pred (pd.DataFrame or pd.Series): The model predictions
for the samples in `X_test`. The format is the same as `y_test`.
metric (callable): A scikit-learn-like metric
`f(y_true, y_pred, sample_weight=None)`. The metric must be able
to handle the `y` data. For instance, for `sklearn.metrics.accuracy_score()`,
"the set of labels predicted for a sample must exactly match the
corresponding set of labels in `y_true`".
Returns:
pd.DataFrame:
See `ethik.explainer.Explainer.explain_performance()`.
"""
self._set_image_shape(images=X_test)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=ConstantWarning)
return super().explain_performance(
X_test=images_to_dataframe(X_test),
y_test=y_test,
y_pred=y_pred,
metric=metric,
)
def _get_fig_size(self, cell_width, n_rows, n_cols):
if cell_width is None:
cell_width = 800 / n_cols
im_height, im_width = self.img_shape
ratio = im_height / im_width
cell_height = ratio * cell_width
return n_cols * cell_width, n_rows * cell_height
def plot_influence(self, X_test, y_pred, n_cols=3, cell_width=None):
"""Plot the influence of the model for the features in `X_test`.
Args:
X_test (pd.DataFrame or np.array): See `ImageClassificationExplainer.explain_influence()`.
y_pred (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_influence()`.
n_cols (int): The number of classes to render per row. Default is `3`.
cell_width (int, optional): The width of each cell in pixels.
Returns:
plotly.graph_objs.Figure:
A Plotly figure. It shows automatically in notebook cells but you
can also call the `.show()` method to plot multiple charts in the
same cell.
"""
influences = self.explain_influence(X_test=X_test, y_pred=y_pred)
z_values = {}
for label, group in influences.groupby("label"):
diffs = (
group.query("tau == 1")["influence"]
- group.query("tau == -1")["influence"].values
)
diffs = diffs.to_numpy().reshape(self.img_shape)
z_values[label] = diffs
n_plots = len(z_values)
labels = sorted(z_values)
n_rows = n_plots // n_cols + 1
fig = make_subplots(
rows=n_rows,
cols=n_cols,
subplot_titles=list(map(str, labels)),
shared_xaxes="all",
shared_yaxes="all",
horizontal_spacing=0.2 / n_cols,
vertical_spacing=0.2 / n_rows,
)
# We want all the heatmaps to share the same scale
zmin = min(np.min(z) for z in z_values.values())
zmax = max(np.max(z) for z in z_values.values())
# We want to make sure that 0 is at the center of the scale
zmin, zmax = min(zmin, -zmax), max(zmax, -zmin)
im_height, im_width = self.img_shape
colorbar_width = 30
colorbar_ticks_width = 27
colorbar_x = 1.02
for i, label in enumerate(labels):
fig.add_trace(
go.Heatmap(
z=z_values[label][::-1],
x=list(range(im_width)),
y=list(range(im_height)),
zmin=zmin,
zmax=zmax,
colorscale="RdBu",
zsmooth="best",
showscale=(i == 0),
name=label,
hoverinfo="x+y+z",
reversescale=True,
colorbar=dict(
thicknessmode="pixels",
thickness=colorbar_width,
xpad=0,
x=colorbar_x,
),
),
row=i // n_cols + 1,
col=i % n_cols + 1,
)
for i in range(n_plots):
fig.update_layout(
{
f"xaxis{i+1}": dict(visible=False),
f"yaxis{i+1}": dict(scaleanchor=f"x{i+1}", visible=False),
}
)
width, height = self._get_fig_size(cell_width, n_rows, n_cols)
width += (colorbar_x - 1) * width + colorbar_width + colorbar_ticks_width
fig.update_layout(
margin=dict(t=20, l=20, b=20, r=20),
width=width,
height=height,
autosize=False,
)
return fig
def plot_performance(self, X_test, y_test, y_pred, metric):
"""Plot the performance of the model for the features in `X_test`.
Args:
X_test (pd.DataFrame or np.array): See `ImageClassificationExplainer.explain_performance()`.
y_test (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_performance()`.
y_pred (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_performance()`.
metric (callable): See `ImageClassificationExplainer.explain_performance()`.
Returns:
plotly.graph_objs.Figure:
A Plotly figure. It shows automatically in notebook cells but you
can also call the `.show()` method to plot multiple charts in the
same cell.
TODO: explain what is represented on the image.
"""
perf = self.explain_performance(
X_test=X_test, y_test=y_test, y_pred=y_pred, metric=metric
)
metric_name = self.get_metric_name(metric)
mask = perf["label"] == perf["label"].iloc[0]
diffs = (
perf[mask].query(f"tau == 1")[metric_name].to_numpy()
- perf[mask].query(f"tau == -1")[metric_name].to_numpy()
)
diffs = diffs.reshape(self.img_shape)
# We want to make sure that 0 is at the center of the scale
zmin, zmax = diffs.min(), diffs.max()
zmin, zmax = min(zmin, -zmax), max(zmax, -zmin)
height, width = self.img_shape
fig = go.Figure()
fig.add_trace(
go.Heatmap(
z=diffs[::-1],
x=list(range(width)),
y=list(range(height)),
zmin=zmin,
zmax=zmax,
colorscale="RdBu",
zsmooth="best",
showscale=True,
hoverinfo="x+y+z",
reversescale=True,
)
)
fig_width = 500
fig.update_layout(
margin=dict(t=20, l=20, b=20),
width=fig_width,
height=fig_width * height / width,
xaxis=dict(visible=False),
yaxis=dict(visible=False, scaleanchor="x", scaleratio=height / width),
)
return figFunctions
def images_to_dataframe(images)-
Expand source code
def images_to_dataframe(images): img_shape = images[0].shape return pd.DataFrame( data=images.reshape(len(images), -1), columns=itertools.product(*[np.arange(n) for n in img_shape]), )
Classes
class ImageClassificationExplainer (alpha=0.05, max_iterations=10, tol=0.0001, n_jobs=-1, memoize=True, verbose=True)-
An explainer specially suited for image classification.
This has exactly the same API as
Explainer, but expects to be provided with an array of images instead of a tabular dataset.TODO: add a note about n_taus being 2
Parameters
alpha:float- A
floatbetween0and0.5which indicates by how close theExplainershould look at extreme values of a distribution. The closer to zero, the more so extreme values will be accounted for. The default is0.05which means that all values beyond the 5th and 95th quantiles are ignored. max_iterations:int- The number of iterations used when applying the Newton step
of the optimization procedure. Default is
5. tol:float- The bottom threshold for the gradient of the optimization
procedure. When reached, the procedure stops. Otherwise, a warning
is raised about the fact that the optimization did not converge.
Default is
1e-4. n_jobs:int- The number of jobs to use for parallel computations. See
joblib.Parallel(). Default is-1. memoize:bool- Indicates whether or not memoization should be used or not. If
True, then intermediate results will be stored in order to avoid recomputing results that can be reused by successively called methods. For example, if you callplot_influencefollowed byplot_influence_rankingandmemoizeisTrue, then the intermediate results required byplot_influencewill be reused forplot_influence_ranking. Memoization is turned on by default because computations are time-consuming for images. verbose:bool- Whether or not to show progress bars during
computations. Default is
True.
Expand source code
class ImageClassificationExplainer(CacheExplainer): """An explainer specially suited for image classification. This has exactly the same API as `Explainer`, but expects to be provided with an array of images instead of a tabular dataset. TODO: add a note about n_taus being 2 Parameters: alpha (float): A `float` between `0` and `0.5` which indicates by how close the `Explainer` should look at extreme values of a distribution. The closer to zero, the more so extreme values will be accounted for. The default is `0.05` which means that all values beyond the 5th and 95th quantiles are ignored. max_iterations (int): The number of iterations used when applying the Newton step of the optimization procedure. Default is `5`. tol (float): The bottom threshold for the gradient of the optimization procedure. When reached, the procedure stops. Otherwise, a warning is raised about the fact that the optimization did not converge. Default is `1e-4`. n_jobs (int): The number of jobs to use for parallel computations. See `joblib.Parallel()`. Default is `-1`. memoize (bool): Indicates whether or not memoization should be used or not. If `True`, then intermediate results will be stored in order to avoid recomputing results that can be reused by successively called methods. For example, if you call `plot_influence` followed by `plot_influence_ranking` and `memoize` is `True`, then the intermediate results required by `plot_influence` will be reused for `plot_influence_ranking`. Memoization is turned on by default because computations are time-consuming for images. verbose (bool): Whether or not to show progress bars during computations. Default is `True`. """ def __init__( self, alpha=0.05, max_iterations=10, tol=1e-4, n_jobs=-1, memoize=True, verbose=True, ): super().__init__( alpha=alpha, n_taus=2, max_iterations=max_iterations, tol=tol, n_jobs=n_jobs, memoize=memoize, verbose=verbose, ) def _set_image_shape(self, images): self.img_shape = images[0].shape if self.img_shape[-1] == 1: self.img_shape = self.img_shape[:-1] def explain_influence(self, X_test, y_pred): """Compute the influence of the model for the features in `X_test`. Args: X_test (np.array): An array of images, i.e. a 3d numpy array of dimension `(n_images, n_rows, n_cols)`. y_pred (pd.DataFrame or pd.Series): The model predictions for the samples in `X_test`. For binary classification and regression, `pd.Series` is expected. For multi-label classification, a pandas dataframe with one column per label is expected. The values can either be probabilities or `0/1` (for a one-hot-encoded output). Returns: pd.DataFrame: See `ethik.explainer.Explainer.explain_influence()`. """ self._set_image_shape(images=X_test) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConstantWarning) return super().explain_influence( X_test=images_to_dataframe(X_test), y_pred=y_pred ) def explain_performance(self, X_test, y_test, y_pred, metric): """Compute the change in model's performance for the features in `X_test`. Args: X_test (np.array): An array of images, i.e. a 3d numpy array of dimension `(n_images, n_rows, n_cols)`. y_test (pd.DataFrame or pd.Series): The true values for the samples in `X_test`. For binary classification and regression, a `pd.Series` is expected. For multi-label classification, a pandas dataframe with one column per label is expected. The values can either be probabilities or `0/1` (for a one-hot-encoded output). y_pred (pd.DataFrame or pd.Series): The model predictions for the samples in `X_test`. The format is the same as `y_test`. metric (callable): A scikit-learn-like metric `f(y_true, y_pred, sample_weight=None)`. The metric must be able to handle the `y` data. For instance, for `sklearn.metrics.accuracy_score()`, "the set of labels predicted for a sample must exactly match the corresponding set of labels in `y_true`". Returns: pd.DataFrame: See `ethik.explainer.Explainer.explain_performance()`. """ self._set_image_shape(images=X_test) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConstantWarning) return super().explain_performance( X_test=images_to_dataframe(X_test), y_test=y_test, y_pred=y_pred, metric=metric, ) def _get_fig_size(self, cell_width, n_rows, n_cols): if cell_width is None: cell_width = 800 / n_cols im_height, im_width = self.img_shape ratio = im_height / im_width cell_height = ratio * cell_width return n_cols * cell_width, n_rows * cell_height def plot_influence(self, X_test, y_pred, n_cols=3, cell_width=None): """Plot the influence of the model for the features in `X_test`. Args: X_test (pd.DataFrame or np.array): See `ImageClassificationExplainer.explain_influence()`. y_pred (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_influence()`. n_cols (int): The number of classes to render per row. Default is `3`. cell_width (int, optional): The width of each cell in pixels. Returns: plotly.graph_objs.Figure: A Plotly figure. It shows automatically in notebook cells but you can also call the `.show()` method to plot multiple charts in the same cell. """ influences = self.explain_influence(X_test=X_test, y_pred=y_pred) z_values = {} for label, group in influences.groupby("label"): diffs = ( group.query("tau == 1")["influence"] - group.query("tau == -1")["influence"].values ) diffs = diffs.to_numpy().reshape(self.img_shape) z_values[label] = diffs n_plots = len(z_values) labels = sorted(z_values) n_rows = n_plots // n_cols + 1 fig = make_subplots( rows=n_rows, cols=n_cols, subplot_titles=list(map(str, labels)), shared_xaxes="all", shared_yaxes="all", horizontal_spacing=0.2 / n_cols, vertical_spacing=0.2 / n_rows, ) # We want all the heatmaps to share the same scale zmin = min(np.min(z) for z in z_values.values()) zmax = max(np.max(z) for z in z_values.values()) # We want to make sure that 0 is at the center of the scale zmin, zmax = min(zmin, -zmax), max(zmax, -zmin) im_height, im_width = self.img_shape colorbar_width = 30 colorbar_ticks_width = 27 colorbar_x = 1.02 for i, label in enumerate(labels): fig.add_trace( go.Heatmap( z=z_values[label][::-1], x=list(range(im_width)), y=list(range(im_height)), zmin=zmin, zmax=zmax, colorscale="RdBu", zsmooth="best", showscale=(i == 0), name=label, hoverinfo="x+y+z", reversescale=True, colorbar=dict( thicknessmode="pixels", thickness=colorbar_width, xpad=0, x=colorbar_x, ), ), row=i // n_cols + 1, col=i % n_cols + 1, ) for i in range(n_plots): fig.update_layout( { f"xaxis{i+1}": dict(visible=False), f"yaxis{i+1}": dict(scaleanchor=f"x{i+1}", visible=False), } ) width, height = self._get_fig_size(cell_width, n_rows, n_cols) width += (colorbar_x - 1) * width + colorbar_width + colorbar_ticks_width fig.update_layout( margin=dict(t=20, l=20, b=20, r=20), width=width, height=height, autosize=False, ) return fig def plot_performance(self, X_test, y_test, y_pred, metric): """Plot the performance of the model for the features in `X_test`. Args: X_test (pd.DataFrame or np.array): See `ImageClassificationExplainer.explain_performance()`. y_test (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_performance()`. y_pred (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_performance()`. metric (callable): See `ImageClassificationExplainer.explain_performance()`. Returns: plotly.graph_objs.Figure: A Plotly figure. It shows automatically in notebook cells but you can also call the `.show()` method to plot multiple charts in the same cell. TODO: explain what is represented on the image. """ perf = self.explain_performance( X_test=X_test, y_test=y_test, y_pred=y_pred, metric=metric ) metric_name = self.get_metric_name(metric) mask = perf["label"] == perf["label"].iloc[0] diffs = ( perf[mask].query(f"tau == 1")[metric_name].to_numpy() - perf[mask].query(f"tau == -1")[metric_name].to_numpy() ) diffs = diffs.reshape(self.img_shape) # We want to make sure that 0 is at the center of the scale zmin, zmax = diffs.min(), diffs.max() zmin, zmax = min(zmin, -zmax), max(zmax, -zmin) height, width = self.img_shape fig = go.Figure() fig.add_trace( go.Heatmap( z=diffs[::-1], x=list(range(width)), y=list(range(height)), zmin=zmin, zmax=zmax, colorscale="RdBu", zsmooth="best", showscale=True, hoverinfo="x+y+z", reversescale=True, ) ) fig_width = 500 fig.update_layout( margin=dict(t=20, l=20, b=20), width=fig_width, height=fig_width * height / width, xaxis=dict(visible=False), yaxis=dict(visible=False, scaleanchor="x", scaleratio=height / width), ) return figAncestors
Methods
def explain_influence(self, X_test, y_pred)-
Compute the influence of the model for the features in
X_test.Args
X_test:np.array- An array of images, i.e. a 3d numpy array of
dimension
(n_images, n_rows, n_cols). y_pred:pd.DataFrameorpd.Series- The model predictions
for the samples in
X_test. For binary classification and regression,pd.Seriesis expected. For multi-label classification, a pandas dataframe with one column per label is expected. The values can either be probabilities or0/1(for a one-hot-encoded output).
Returns
pd.DataFrame:- See
ethik.explainer.Explainer.explain_influence().
Expand source code
def explain_influence(self, X_test, y_pred): """Compute the influence of the model for the features in `X_test`. Args: X_test (np.array): An array of images, i.e. a 3d numpy array of dimension `(n_images, n_rows, n_cols)`. y_pred (pd.DataFrame or pd.Series): The model predictions for the samples in `X_test`. For binary classification and regression, `pd.Series` is expected. For multi-label classification, a pandas dataframe with one column per label is expected. The values can either be probabilities or `0/1` (for a one-hot-encoded output). Returns: pd.DataFrame: See `ethik.explainer.Explainer.explain_influence()`. """ self._set_image_shape(images=X_test) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConstantWarning) return super().explain_influence( X_test=images_to_dataframe(X_test), y_pred=y_pred ) def explain_performance(self, X_test, y_test, y_pred, metric)-
Compute the change in model's performance for the features in
X_test.Args
X_test:np.array- An array of images, i.e. a 3d numpy array of
dimension
(n_images, n_rows, n_cols). y_test:pd.DataFrameorpd.Series- The true values
for the samples in
X_test. For binary classification and regression, apd.Seriesis expected. For multi-label classification, a pandas dataframe with one column per label is expected. The values can either be probabilities or0/1(for a one-hot-encoded output). y_pred:pd.DataFrameorpd.Series- The model predictions
for the samples in
X_test. The format is the same asy_test. metric:callable- A scikit-learn-like metric
f(y_true, y_pred, sample_weight=None). The metric must be able to handle theydata. For instance, forsklearn.metrics.accuracy_score(), "the set of labels predicted for a sample must exactly match the corresponding set of labels iny_true".
Returns
pd.DataFrame:- See
ethik.explainer.Explainer.explain_performance().
Expand source code
def explain_performance(self, X_test, y_test, y_pred, metric): """Compute the change in model's performance for the features in `X_test`. Args: X_test (np.array): An array of images, i.e. a 3d numpy array of dimension `(n_images, n_rows, n_cols)`. y_test (pd.DataFrame or pd.Series): The true values for the samples in `X_test`. For binary classification and regression, a `pd.Series` is expected. For multi-label classification, a pandas dataframe with one column per label is expected. The values can either be probabilities or `0/1` (for a one-hot-encoded output). y_pred (pd.DataFrame or pd.Series): The model predictions for the samples in `X_test`. The format is the same as `y_test`. metric (callable): A scikit-learn-like metric `f(y_true, y_pred, sample_weight=None)`. The metric must be able to handle the `y` data. For instance, for `sklearn.metrics.accuracy_score()`, "the set of labels predicted for a sample must exactly match the corresponding set of labels in `y_true`". Returns: pd.DataFrame: See `ethik.explainer.Explainer.explain_performance()`. """ self._set_image_shape(images=X_test) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConstantWarning) return super().explain_performance( X_test=images_to_dataframe(X_test), y_test=y_test, y_pred=y_pred, metric=metric, ) def plot_influence(self, X_test, y_pred, n_cols=3, cell_width=None)-
Plot the influence of the model for the features in
X_test.Args
X_test:pd.DataFrameornp.array- See
ImageClassificationExplainer.explain_influence(). y_pred:pd.DataFrameorpd.Series- See
ImageClassificationExplainer.explain_influence(). n_cols:int- The number of classes to render per row. Default is
3. cell_width:int, optional- The width of each cell in pixels.
Returns
plotly.graph_objs.Figure:- A Plotly figure. It shows automatically in notebook cells but you
can also call the
.show()method to plot multiple charts in the same cell.
Expand source code
def plot_influence(self, X_test, y_pred, n_cols=3, cell_width=None): """Plot the influence of the model for the features in `X_test`. Args: X_test (pd.DataFrame or np.array): See `ImageClassificationExplainer.explain_influence()`. y_pred (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_influence()`. n_cols (int): The number of classes to render per row. Default is `3`. cell_width (int, optional): The width of each cell in pixels. Returns: plotly.graph_objs.Figure: A Plotly figure. It shows automatically in notebook cells but you can also call the `.show()` method to plot multiple charts in the same cell. """ influences = self.explain_influence(X_test=X_test, y_pred=y_pred) z_values = {} for label, group in influences.groupby("label"): diffs = ( group.query("tau == 1")["influence"] - group.query("tau == -1")["influence"].values ) diffs = diffs.to_numpy().reshape(self.img_shape) z_values[label] = diffs n_plots = len(z_values) labels = sorted(z_values) n_rows = n_plots // n_cols + 1 fig = make_subplots( rows=n_rows, cols=n_cols, subplot_titles=list(map(str, labels)), shared_xaxes="all", shared_yaxes="all", horizontal_spacing=0.2 / n_cols, vertical_spacing=0.2 / n_rows, ) # We want all the heatmaps to share the same scale zmin = min(np.min(z) for z in z_values.values()) zmax = max(np.max(z) for z in z_values.values()) # We want to make sure that 0 is at the center of the scale zmin, zmax = min(zmin, -zmax), max(zmax, -zmin) im_height, im_width = self.img_shape colorbar_width = 30 colorbar_ticks_width = 27 colorbar_x = 1.02 for i, label in enumerate(labels): fig.add_trace( go.Heatmap( z=z_values[label][::-1], x=list(range(im_width)), y=list(range(im_height)), zmin=zmin, zmax=zmax, colorscale="RdBu", zsmooth="best", showscale=(i == 0), name=label, hoverinfo="x+y+z", reversescale=True, colorbar=dict( thicknessmode="pixels", thickness=colorbar_width, xpad=0, x=colorbar_x, ), ), row=i // n_cols + 1, col=i % n_cols + 1, ) for i in range(n_plots): fig.update_layout( { f"xaxis{i+1}": dict(visible=False), f"yaxis{i+1}": dict(scaleanchor=f"x{i+1}", visible=False), } ) width, height = self._get_fig_size(cell_width, n_rows, n_cols) width += (colorbar_x - 1) * width + colorbar_width + colorbar_ticks_width fig.update_layout( margin=dict(t=20, l=20, b=20, r=20), width=width, height=height, autosize=False, ) return fig def plot_performance(self, X_test, y_test, y_pred, metric)-
Plot the performance of the model for the features in
X_test.Args
X_test:pd.DataFrameornp.array- See
ImageClassificationExplainer.explain_performance(). y_test:pd.DataFrameorpd.Series- See
ImageClassificationExplainer.explain_performance(). y_pred:pd.DataFrameorpd.Series- See
ImageClassificationExplainer.explain_performance(). metric:callable- See
ImageClassificationExplainer.explain_performance().
Returns
plotly.graph_objs.Figure:-
A Plotly figure. It shows automatically in notebook cells but you can also call the
.show()method to plot multiple charts in the same cell.TODO: explain what is represented on the image.
Expand source code
def plot_performance(self, X_test, y_test, y_pred, metric): """Plot the performance of the model for the features in `X_test`. Args: X_test (pd.DataFrame or np.array): See `ImageClassificationExplainer.explain_performance()`. y_test (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_performance()`. y_pred (pd.DataFrame or pd.Series): See `ImageClassificationExplainer.explain_performance()`. metric (callable): See `ImageClassificationExplainer.explain_performance()`. Returns: plotly.graph_objs.Figure: A Plotly figure. It shows automatically in notebook cells but you can also call the `.show()` method to plot multiple charts in the same cell. TODO: explain what is represented on the image. """ perf = self.explain_performance( X_test=X_test, y_test=y_test, y_pred=y_pred, metric=metric ) metric_name = self.get_metric_name(metric) mask = perf["label"] == perf["label"].iloc[0] diffs = ( perf[mask].query(f"tau == 1")[metric_name].to_numpy() - perf[mask].query(f"tau == -1")[metric_name].to_numpy() ) diffs = diffs.reshape(self.img_shape) # We want to make sure that 0 is at the center of the scale zmin, zmax = diffs.min(), diffs.max() zmin, zmax = min(zmin, -zmax), max(zmax, -zmin) height, width = self.img_shape fig = go.Figure() fig.add_trace( go.Heatmap( z=diffs[::-1], x=list(range(width)), y=list(range(height)), zmin=zmin, zmax=zmax, colorscale="RdBu", zsmooth="best", showscale=True, hoverinfo="x+y+z", reversescale=True, ) ) fig_width = 500 fig.update_layout( margin=dict(t=20, l=20, b=20), width=fig_width, height=fig_width * height / width, xaxis=dict(visible=False), yaxis=dict(visible=False, scaleanchor="x", scaleratio=height / width), ) return fig
Inherited members
CacheExplainer:CAT_COL_SEPcompare_influencecompare_performancecompute_distributionscompute_weightsget_metric_nameplot_cumulative_weightsplot_distributionsplot_influence_2dplot_influence_comparisonplot_influence_rankingplot_performance_2dplot_performance_comparisonplot_performance_rankingplot_weight_distributionrank_by_influencerank_by_performance