df
df['key1'] = (4, 4, 4, 6, 6, 6, 8, 8, 8, 8)
sns.pairplot(x_vars=["one"], y_vars=["two"], data=df, hue="key1", size=5)
sns.pairplot(vars=["one", "two", "three"], data=df, hue="key1", size=5)
#%%%
# Generate Data
num = 20
x, y = np.random.random((2, num))
labels = np.random.choice(['a', 'b', 'c'], num)
df = pd.DataFrame(dict(x=x, y=y, label=labels))
#Altair code
from altair import Chart
c = Chart(df)
c.mark_circle().encode(x='x', y='y', color='label')
#%%
import matplotlib.pyplot as plt
from mlxtend.plotting import category_scatter
df.columns
df.head()
fig = category_scatter(x='x',
y='y',
label_col='label',
data=df,
legend_loc='upper left')
df8b.columns
fig = category_scatter(x='marks1',
y='marks2',
def accuracy_vs_horizon_chart(
base: alt.Chart,
horizon_selection_brush: alt.Selection,
belief_horizon_unit: str,
intuitive_forecast_horizon: bool,
unique_belief_horizons,
) -> alt.LayerChart:
ha_chart = (
base.mark_circle()
.transform_joinaggregate(
on_the_fly_mae="mean(mae)",
on_the_fly_reference="mean(reference_value)",
groupby=["belief_horizon", "source"],
)
.transform_calculate(
on_the_fly_wape=alt.datum.on_the_fly_mae / alt.datum.on_the_fly_reference,
accuracy=1 - alt.datum.on_the_fly_wape,
)
.encode(
opacity=alt.condition(
selectors.time_selection_brush,
alt.Opacity(
"event_start:T", scale=alt.Scale(domain=(0.9999, 1)), legend=None
),
alt.value(0),
),
# Trick to be able to apply the selection filter for event_start (event_start must be a field in one of the encoding channels)
x=alt.X(
"belief_horizon:Q",
axis=alt.Axis(labelFlush=False),
scale=alt.Scale(
zero=False,
domain=(unique_belief_horizons[0], unique_belief_horizons[-1]),
),
title="%s (%s)"
% (
"Forecast horizon"
if intuitive_forecast_horizon
else "Belief horizon",
belief_horizon_unit,
),
),
y=alt.Y(
"accuracy:Q",
title="Accuracy (1-WAPE)",
axis=alt.Axis(format="%", minExtent=30),
scale={"domain": (0, 1)},
),
color=selectors.source_color_or(
selectors.idle_color,
selectors.horizon_hover_brush | horizon_selection_brush,
),
size=alt.value(100),
tooltip=[
alt.Tooltip("accuracy:Q", title="Accuracy (1-WAPE)", format=".1%"),
alt.Tooltip(
"belief_horizon:Q",
title="%s (%s)"
% (
"Forecast horizon"
if intuitive_forecast_horizon
else "Belief horizon",
belief_horizon_unit,
),
),
alt.Tooltip("source:O", title="Source"),
],
)
.properties(title="Model accuracy in the %s before" % belief_horizon_unit)
# .configure_title(offset=-270, orient="bottom")
)
ha_interpolation_chart = ha_chart.mark_line(interpolate="monotone").encode(
size=alt.value(1), color=alt.Color("source:N", legend=None)
)
return ha_interpolation_chart + ha_chart
