-
Notifications
You must be signed in to change notification settings - Fork 19
Expand file tree
/
Copy path_continuous.py
More file actions
274 lines (239 loc) · 7.64 KB
/
_continuous.py
File metadata and controls
274 lines (239 loc) · 7.64 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
"""Continuous DiD visualization functions (dose-response curves)."""
from typing import TYPE_CHECKING, Any, Optional, Tuple
import pandas as pd
if TYPE_CHECKING:
from diff_diff.continuous_did_results import ContinuousDiDResults, DoseResponseCurve
def plot_dose_response(
results: Optional["ContinuousDiDResults"] = None,
*,
curve: Optional["DoseResponseCurve"] = None,
data: Optional[pd.DataFrame] = None,
target: str = "att",
alpha: float = 0.05,
figsize: Tuple[float, float] = (10, 6),
title: Optional[str] = None,
xlabel: str = "Dose",
ylabel: str = "Treatment Effect",
color: str = "#2563eb",
ci_color: Optional[str] = None,
show_zero_line: bool = True,
ax: Optional[Any] = None,
show: bool = True,
backend: str = "matplotlib",
) -> Any:
"""
Plot dose-response curve from Continuous DiD estimation.
Visualizes how the treatment effect varies with the treatment dose
(intensity), with confidence bands.
Parameters
----------
results : ContinuousDiDResults, optional
Results from ContinuousDiD estimator. Extracts the dose-response
curve based on ``target``.
curve : DoseResponseCurve, optional
A DoseResponseCurve object directly.
data : pd.DataFrame, optional
DataFrame with columns ``dose``, ``effect``, ``se`` (and optionally
``conf_int_lower``, ``conf_int_upper``).
target : str, default="att"
Which dose-response curve: ``"att"`` or ``"acrt"``.
alpha : float, default=0.05
Significance level for confidence intervals (used with DataFrame input).
figsize : tuple, default=(10, 6)
Figure size (width, height) in inches.
title : str, optional
Plot title. Auto-generated if None.
xlabel : str, default="Dose"
X-axis label.
ylabel : str, default="Treatment Effect"
Y-axis label.
color : str, default="#2563eb"
Color for the line.
ci_color : str, optional
Color for confidence band. Defaults to ``color`` with transparency.
show_zero_line : bool, default=True
Whether to show a horizontal line at y=0.
ax : matplotlib.axes.Axes, optional
Axes to plot on. If None, creates new figure.
show : bool, default=True
Whether to call plt.show() at the end.
backend : str, default="matplotlib"
Plotting backend: ``"matplotlib"`` or ``"plotly"``.
Returns
-------
matplotlib.axes.Axes or plotly.graph_objects.Figure
The axes object (matplotlib) or figure (plotly).
"""
from scipy import stats as scipy_stats
# Extract dose-response data
if sum(x is not None for x in (results, curve, data)) != 1:
raise ValueError("Provide exactly one of 'results', 'curve', or 'data'.")
if results is not None:
if target == "att":
curve = results.dose_response_att
elif target == "acrt":
curve = results.dose_response_acrt
else:
raise ValueError(f"target must be 'att' or 'acrt', got '{target}'")
if curve is not None:
# Infer target from curve when passed directly (not via results)
if results is None and hasattr(curve, "target") and curve.target:
target = curve.target
dose_grid = curve.dose_grid
effects = curve.effects
ci_lower = curve.conf_int_lower
ci_upper = curve.conf_int_upper
elif data is not None:
if "dose" not in data.columns or "effect" not in data.columns:
raise ValueError("DataFrame must have 'dose' and 'effect' columns")
dose_grid = data["dose"].values
effects = data["effect"].values
if "conf_int_lower" in data.columns and "conf_int_upper" in data.columns:
ci_lower = data["conf_int_lower"].values
ci_upper = data["conf_int_upper"].values
elif "se" in data.columns:
z = scipy_stats.norm.ppf(1 - alpha / 2)
ci_lower = effects - z * data["se"].values
ci_upper = effects + z * data["se"].values
else:
ci_lower = None
ci_upper = None
else:
raise ValueError("Must provide 'results', 'curve', or 'data'.")
# Auto-generate title
if title is None:
if target == "att":
title = "ATT Dose-Response Curve"
else:
title = "ACRT Dose-Response Curve"
if backend == "plotly":
return _render_dose_response_plotly(
dose_grid=dose_grid,
effects=effects,
ci_lower=ci_lower,
ci_upper=ci_upper,
title=title,
xlabel=xlabel,
ylabel=ylabel,
color=color,
ci_color=ci_color,
show_zero_line=show_zero_line,
show=show,
)
return _render_dose_response_mpl(
dose_grid=dose_grid,
effects=effects,
ci_lower=ci_lower,
ci_upper=ci_upper,
figsize=figsize,
title=title,
xlabel=xlabel,
ylabel=ylabel,
color=color,
ci_color=ci_color,
show_zero_line=show_zero_line,
ax=ax,
show=show,
)
def _render_dose_response_mpl(
*,
dose_grid,
effects,
ci_lower,
ci_upper,
figsize,
title,
xlabel,
ylabel,
color,
ci_color,
show_zero_line,
ax,
show,
):
"""Render dose-response curve with matplotlib."""
from diff_diff.visualization._common import _require_matplotlib
plt = _require_matplotlib()
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
# Zero line
if show_zero_line:
ax.axhline(y=0, color="gray", linestyle="--", linewidth=1, alpha=0.5)
# Confidence band
if ci_lower is not None and ci_upper is not None:
band_color = ci_color or color
ax.fill_between(
dose_grid,
ci_lower,
ci_upper,
alpha=0.15,
color=band_color,
label="95% CI",
)
# Effect line
ax.plot(dose_grid, effects, color=color, linewidth=2, label="Effect")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
ax.legend(loc="best")
ax.grid(True, alpha=0.3)
fig.tight_layout()
if show:
plt.show()
return ax
def _render_dose_response_plotly(
*,
dose_grid,
effects,
ci_lower,
ci_upper,
title,
xlabel,
ylabel,
color,
ci_color,
show_zero_line,
show,
):
"""Render dose-response curve with plotly."""
from diff_diff.visualization._common import (
_color_to_rgba,
_plotly_default_layout,
_require_plotly,
)
go = _require_plotly()
fig = go.Figure()
# Zero line
if show_zero_line:
fig.add_hline(y=0, line_dash="dash", line_color="gray", line_width=1, opacity=0.5)
# Confidence band
if ci_lower is not None and ci_upper is not None:
band_color = ci_color or color
dose_list = list(dose_grid)
fig.add_trace(
go.Scatter(
x=dose_list + dose_list[::-1],
y=list(ci_upper) + list(ci_lower)[::-1],
fill="toself",
fillcolor=_color_to_rgba(band_color, 0.15),
line=dict(color="rgba(0,0,0,0)"),
name="95% CI",
hoverinfo="skip",
)
)
# Effect line
fig.add_trace(
go.Scatter(
x=list(dose_grid),
y=list(effects),
mode="lines",
line=dict(color=color, width=2),
name="Effect",
)
)
_plotly_default_layout(fig, title=title, xlabel=xlabel, ylabel=ylabel)
if show:
fig.show()
return fig