def display_model_analysis(self):
"""
Displays information about the model used : class name, library name, library version,
model parameters, ...
"""
print_md(f"**Model used :** {self.explainer.model.__class__.__name__}")
print_md(f"**Library :** {self.explainer.model.__class__.__module__}")
for name, module in sorted(sys.modules.items()):
if hasattr(module, '__version__') \
and self.explainer.model.__class__.__module__.split('.')[0] == module.__name__:
print_md(f"**Library version :** {module.__version__}")
print_md("**Model parameters :** ")
model_params = self.explainer.model.__dict__
table_template = template_env.get_template("double_table.html")
print_html(
table_template.render(
columns1=["Parameter key", "Parameter value"],
rows1=[{
"name": truncate_str(str(k), 50),
"value": truncate_str(str(v), 300)
} for k, v in list(model_params.items())[:len(model_params) //
2:]
], # Getting half of the parameters
columns2=["Parameter key", "Parameter value"],
rows2=[{
"name": truncate_str(str(k), 50),
"value": truncate_str(str(v), 300)
} for k, v in list(model_params.items())[len(model_params) //
2:]
] # Getting 2nd half of the parameters
))
print_md('---')
def test_truncate_str_3(self):
t = truncate_str("this is a test", 10)
assert t == "this is a..."
def test_truncate_str_2(self):
t = truncate_str("this is a test", 50)
assert t == "this is a test"
def test_truncate_str_1(self):
t = truncate_str(12)
assert t == 12
def make_skeleton(self):
"""
Describe the app skeleton (bootstrap grid) and initialize components containers
"""
self.skeleton['navbar'] = dbc.Container(
[
dbc.Row(
[
dbc.Col(html.A(
dbc.Row(
[
html.Img(src=self.logo, height="40px"),
html.H4("Shapash Monitor",
id="shapash_title"),
],
align="center",
),
href="https://github.com/MAIF/shapash",
target="_blank",
),
md=4,
align="left"),
dbc.Col(html.A(
dbc.Row(
[
html.H3(truncate_str(
self.explainer.title_story, maxlen=40),
id="shapash_title_story"),
],
align="center",
),
href="https://github.com/MAIF/shapash",
target="_blank",
),
md=4,
align="center"),
dbc.Col(
self.components['menu'],
md=4,
align='right',
)
],
style={
'padding': "5px 15px",
"verticalAlign": "middle"
},
)
],
fluid=True,
style={
'height': '50px',
'backgroundColor': self.bkg_color
},
)
self.skeleton['body'] = dbc.Container(
[
dbc.Row(
[
dbc.Col(
[
html.Div(
self.draw_component(
'graph', 'global_feature_importance'),
className="card",
id="card_global_feature_importance",
)
],
md=5,
align="center",
style={'padding': '0px 10px'},
),
dbc.Col(
[
html.Div(
self.draw_component('table', 'dataset'),
className="card",
id='card_dataset',
style={'cursor': 'pointer'},
)
],
md=7,
align="center",
style={'padding': '0px 10px'},
),
],
style={'padding': '15px 10px 0px 10px'},
),
dbc.Row(
[
dbc.Col(
[
html.Div(
self.draw_component(
'graph', 'feature_selector'),
className="card",
id='card_feature_selector',
)
],
md=5,
align="center",
style={'padding': '0px 10px'},
),
dbc.Col(
[
dbc.Row(
[
dbc.Col(
[
html.Div(
self.draw_component(
'graph',
'detail_feature'),
className="card",
id='card_detail_feature',
),
],
md=8,
align="center",
# style={'height': '27rem'}
),
dbc.Col(
[
html.Div(
self.draw_filter(),
className="card_filter",
id='card_filter',
),
],
md=4,
align="center",
),
], ),
],
md=7,
align="center",
style={'padding': '0px 10px'},
),
],
style={'padding': '15px 10px'},
),
],
className="mt-12",
fluid=True)
def generate_fig_univariate_categorical(
df_all: pd.DataFrame,
col: str,
hue: str,
nb_cat_max: int = 7,
) -> plt.Figure:
"""
Returns a matplotlib figure containing the distribution of a categorical feature.
If the feature is categorical and contains too many categories, the smallest
categories are grouped into a new 'Other' category so that the graph remains
readable.
Parameters
----------
df_all : pd.DataFrame
The input dataframe that contains the column of interest
col : str
The column of interest
hue : str
The column used to distinguish the values (ex. 'train' and 'test')
nb_cat_max : int
The number max of categories to be displayed. If the number of categories
is greater than nb_cat_max then groups smallest categories into a new
'Other' category
Returns
-------
matplotlib.pyplot.Figure
"""
df_cat = df_all.groupby([col, hue]).agg({col: 'count'})\
.rename(columns={col: "count"}).reset_index()
df_cat['Percent'] = df_cat['count'] * 100 / df_cat.groupby(
hue)['count'].transform('sum')
if pd.api.types.is_numeric_dtype(df_cat[col].dtype):
df_cat = df_cat.sort_values(col, ascending=True)
df_cat[col] = df_cat[col].astype(str)
nb_cat = df_cat.groupby([col]).agg({
'count': 'sum'
}).reset_index()[col].nunique()
if nb_cat > nb_cat_max:
df_cat = _merge_small_categories(df_cat=df_cat,
col=col,
hue=hue,
nb_cat_max=nb_cat_max)
fig, ax = plt.subplots(figsize=(7, 4))
sns.barplot(data=df_cat,
x='Percent',
y=col,
hue=hue,
palette=dict_color_palette,
ax=ax)
for p in ax.patches:
ax.annotate("{:.1f}%".format(np.nan_to_num(p.get_width(), nan=0)),
xy=(p.get_width(), p.get_y() + p.get_height() / 2),
xytext=(5, 0),
textcoords='offset points',
ha="left",
va="center")
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Removes plot borders
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
new_labels = [
truncate_str(i.get_text(), maxlen=45)
for i in ax.yaxis.get_ticklabels()
]
ax.yaxis.set_ticklabels(new_labels)
return fig