def _plot_with_data_bar(source: alt.Chart,
line: alt.Chart,
type_: ValueType,
width: Optional[int] = None,
height: Optional[int] = None) -> alt.Chart:
width = 800 if width is None else width
height = 400 if height is None else height
# TODO: Add validation that the x and y series names are correct
# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection(type='single', nearest=True, on='mouseover', fields=['days'],
empty='none')
# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors = alt.Chart(source).mark_point().encode(
x='days:Q',
opacity=alt.value(0),
).add_selection(
nearest
)
# Draw points on the line, and highlight based on selection
points = line.mark_point().encode(
opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)
# Draw text labels near the points, and highlight based on selection
text = line.mark_text(align='left', dx=5, dy=10).encode(
text=alt.condition(nearest, f'{type_.value}:Q', alt.value(' '), format=".0f")
)
# Draw a rule at the location of the selection
rules = alt.Chart(source).mark_rule(color='gray').encode(
x='days:Q',
).transform_filter(
nearest
)
# Put the five layers into a chart and bind the data
chart = alt.layer(
line, selectors, points, rules, text
).properties(
width=width, height=height
)
return chart
def confusion_matrix(df=None, truth=None, pred=None, mapping=None):
if df is None:
df = pd.DataFrame({'truth': truth, 'pred': pred})
truth = 'truth'
pred = 'pred'
threshold = len(df)
if mapping:
assert isinstance(mapping, dict), 'mapping should be a dictionary'
df[truth] = df[truth].map(lambda x: mapping[x])
df[pred] = df[pred].map(lambda x: mapping[x])
sz = 450 if len(df[truth].unique()) > 4 else 250
base = Chart(df, height=sz, width=sz).transform_aggregate(
num_vals='count()', groupby=[truth, pred]).transform_calculate(
rev_num_vals='-(datum.num_vals) + max(datum.num_vals)', ).encode(
alt.Y(f'{truth}:O', scale=alt.Scale(paddingInner=0)),
alt.X(f'{pred}:O', scale=alt.Scale(paddingInner=0)),
)
hm = base.mark_rect().encode(color=alt.Color(
'num_vals:Q', scale=alt.Scale(scheme="lightorange"), legend=None))
tx = base.mark_text(baseline='middle').encode(
text='num_vals:Q',
# color=alt.Color(alt.value('gray'))
# color='rev_num_vals:Q'
# color=alt.Color(
# 'num_vals:Q', scale=alt.Scale(scheme="redyellowgreen"),
# )
color=alt.condition(alt.datum.num_vals > threshold, alt.value('black'),
alt.value('black')))
try:
from sklearn.metrics import classification_report
print(classification_report(df[truth], df[pred]))
except:
logger.info('Skipping Report')
return hm + tx
