gjo-calibration/plotting.py

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Python
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2021-05-31 18:59:24 +00:00
import numpy as np
import plotly.graph_objects as go
from calibration import calibration_curve
def plotly_calibration(y_true, y_pred, n_bins, strategy="quantile"):
fraction_of_positives, mean_predicted_value, counts = calibration_curve(
y_true, y_pred, n_bins=n_bins, strategy=strategy
)
error_y = np.sqrt((fraction_of_positives) * (1 - fraction_of_positives) / counts)
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=mean_predicted_value,
y=fraction_of_positives,
customdata=counts,
mode="markers",
error_y=dict(
type="data",
array=error_y,
thickness=1.5,
width=3,
),
hovertemplate="<br>".join(
[
"x: %{x:.3f}",
"y: %{y:.3f}",
"N: %{customdata}",
"<extra></extra>",
]
),
showlegend=False,
)
)
fig.add_shape(
type="line",
x0=0,
y0=0,
x1=1,
y1=1,
line=dict(
color="LightSeaGreen",
width=2,
dash="dot",
),
opacity=0.5,
)
fig.update_layout(
width=800,
height=800,
title="Calibration plot",
xaxis_title="Mean predicted value",
yaxis_title="Fraction of positives (± std)",
)
fig.update_xaxes(
range=[-0.05, 1.05],
constrain="domain",
)
fig.update_yaxes(
range=[-0.05, 1.05],
constrain="domain",
scaleanchor="x",
scaleratio=1,
)
return fig
def plotly_calibration_odds(y_true, y_pred, n_bins, strategy="quantile"):
y_pred = np.clip(y_pred, 0.005, 0.995) # clipping to avoid undefined odds
y_true = np.clip(y_true, 1e-3, 1 - 1e-3)
fraction_of_positives, mean_predicted_value, counts = calibration_curve(
y_true, y_pred, n_bins=n_bins, strategy=strategy
)
error_y = np.sqrt((fraction_of_positives) * (1 - fraction_of_positives) / counts)
fig = go.Figure()
transform = lambda x: np.log2(1 / (1 - x) - 1) # 66.6% → 2^{1}:1 → 1
customdata = np.dstack(
[
counts,
[
f"{2**x:.1f} : 1" if x > 0 else f"1 : {2**-x:.1f}"
for x in transform(mean_predicted_value)
],
[
f"{2**x:.1f} : 1" if x > 0 else f"1 : {2**-x:.1f}"
for x in transform(fraction_of_positives)
],
]
).squeeze()
fig.add_trace(
go.Scatter(
x=transform(mean_predicted_value),
y=transform(fraction_of_positives),
customdata=customdata,
mode="markers",
error_y=dict(
type="data",
symmetric=False,
array=transform(fraction_of_positives + error_y)
- transform(fraction_of_positives),
arrayminus=transform(fraction_of_positives)
- transform(fraction_of_positives - error_y),
thickness=1.5,
width=3,
),
hovertemplate="<br>".join(
[
"x: %{customdata[1]}",
"y: %{customdata[2]}",
"N: %{customdata[0]}",
"<extra></extra>",
]
),
showlegend=False,
)
)
fig.add_shape(
type="line",
x0=-8,
y0=-8,
x1=8,
y1=8,
line=dict(
color="LightSeaGreen",
width=2,
dash="dot",
),
opacity=0.5,
)
fig.update_layout(
width=800,
height=800,
title="Calibration plot in terms of odds",
xaxis_title="Mean predicted value",
yaxis_title="Fraction of positives (± std)",
)
fig.update_xaxes(
range=[-8, 8],
constrain="domain",
tickmode="array",
tickvals=list(range(-10, 10)),
ticktext=[
f"{2**x} : 1" if x > 0 else f"1 : {2**-x}" for x in list(range(-10, 10))
],
)
fig.update_yaxes(
range=[-8, 8],
constrain="domain",
scaleanchor="x",
scaleratio=1,
tickvals=list(range(-10, 10)),
ticktext=[
f"{2**x} : 1" if x > 0 else f"1 : {2**-x}" for x in list(range(-10, 10))
],
)
return fig