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Plot enhancement (#1390)

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* horizontally put the bar figures

* 1) use rangebreaks to handle gaps in datetime axis instead of make them string; 2) allow simultaneously plot rankic in ic_figure

* pylint improvement

* fix black lint

* better axis formatting

* default not show gaps

* resolve doc built error

* fix pylint

* Update qlib/contrib/report/analysis_model/analysis_model_performance.py

More detailed description

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* Update qlib/contrib/report/analysis_model/analysis_model_performance.py

for Python backward compatibility

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* add doc string

* fix black

* 1) limit numpy version as numba support for 1.24+ has not been released; 2) no need to use custom numba version for pytest.

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
YQ Tsui
2022-12-31 07:58:41 +08:00
committed by GitHub
parent 756bd0f65b
commit 2f5ce3dc01
5 changed files with 95 additions and 43 deletions
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94
qlib/contrib/report/analysis_model/analysis_model_performance.py
View File

@@ -1,5 +1,6 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from functools import partial
import pandas as pd
@@ -10,7 +11,11 @@ import matplotlib.pyplot as plt
from scipy import stats
from typing import Sequence
from qlib.typehint import Literal
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
from ..utils import guess_plotly_rangebreaks
def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int = 5, **kwargs) -> tuple:
@@ -48,12 +53,13 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int
t_df["long-average"] = t_df["Group1"] - pred_label.groupby(level="datetime")["label"].mean()
t_df = t_df.dropna(how="all") # for days which does not contain label
# FIXME: support HIGH-FREQ
t_df.index = t_df.index.strftime("%Y-%m-%d")
# Cumulative Return By Group
group_scatter_figure = ScatterGraph(
t_df.cumsum(),
layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)),
layout=dict(
title="Cumulative Return",
xaxis=dict(tickangle=45, rangebreaks=kwargs.get("rangebreaks", guess_plotly_rangebreaks(t_df.index))),
),
).figure
t_df = t_df.loc[:, ["long-short", "long-average"]]
@@ -110,22 +116,36 @@ def _plot_qq(data: pd.Series = None, dist=stats.norm) -> go.Figure:
return fig
def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> tuple:
def _pred_ic(
pred_label: pd.DataFrame = None, methods: Sequence[Literal["IC", "Rank IC"]] = ("IC", "Rank IC"), **kwargs
) -> tuple:
"""
:param pred_label:
:param rank:
:param pred_label: pd.DataFrame
must contain one column of realized return with name `label` and one column of predicted score names `score`.
:param methods: Sequence[Literal["IC", "Rank IC"]]
IC series to plot.
IC is sectional pearson correlation between label and score
Rank IC is the spearman correlation between label and score
For the Monthly IC, IC histogram, IC Q-Q plot. Only the first type of IC will be plotted.
:return:
"""
if rank:
ic = pred_label.groupby(level="datetime").apply(
lambda x: x["label"].rank(pct=True).corr(x["score"].rank(pct=True))
)
else:
ic = pred_label.groupby(level="datetime").apply(lambda x: x["label"].corr(x["score"]))
_methods_mapping = {"IC": "pearson", "Rank IC": "spearman"}
_index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6)
_monthly_ic = ic.groupby(_index).mean()
def _corr_series(x, method):
return x["label"].corr(x["score"], method=method)
ic_df = pd.concat(
[
pred_label.groupby(level="datetime").apply(partial(_corr_series, method=_methods_mapping[m])).rename(m)
for m in methods
],
axis=1,
)
_ic = ic_df.iloc(axis=1)[0]
_index = _ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6)
_monthly_ic = _ic.groupby(_index).mean()
_monthly_ic.index = pd.MultiIndex.from_arrays(
[_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)],
names=["year", "month"],
@@ -148,27 +168,27 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t
_monthly_ic = _monthly_ic.reindex(fill_index)
_ic_df = ic.to_frame("ic")
ic_bar_figure = ic_figure(_ic_df, kwargs.get("show_nature_day", True))
ic_bar_figure = ic_figure(ic_df, kwargs.get("show_nature_day", False))
ic_heatmap_figure = HeatmapGraph(
_monthly_ic.unstack(),
layout=dict(title="Monthly IC", yaxis=dict(tickformat=",d")),
layout=dict(title="Monthly IC", xaxis=dict(dtick=1), yaxis=dict(tickformat="04d", dtick=1)),
graph_kwargs=dict(xtype="array", ytype="array"),
).figure
dist = stats.norm
_qqplot_fig = _plot_qq(ic, dist)
_qqplot_fig = _plot_qq(_ic, dist)
if isinstance(dist, stats.norm.__class__):
dist_name = "Normal"
else:
dist_name = "Unknown"
_ic_df = _ic.to_frame("IC")
_bin_size = ((_ic_df.max() - _ic_df.min()) / 20).min()
_sub_graph_data = [
(
"ic",
"IC",
dict(
row=1,
col=1,
@@ -202,12 +222,13 @@ def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple:
pred = pred_label.copy()
pred["score_last"] = pred.groupby(level="instrument")["score"].shift(lag)
ac = pred.groupby(level="datetime").apply(lambda x: x["score"].rank(pct=True).corr(x["score_last"].rank(pct=True)))
# FIXME: support HIGH-FREQ
_df = ac.to_frame("value")
_df.index = _df.index.strftime("%Y-%m-%d")
ac_figure = ScatterGraph(
_df,
layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)),
layout=dict(
title="Auto Correlation",
xaxis=dict(tickangle=45, rangebreaks=kwargs.get("rangebreaks", guess_plotly_rangebreaks(_df.index))),
),
).figure
return (ac_figure,)
@@ -233,32 +254,33 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple:
"Bottom": bottom,
}
)
# FIXME: support HIGH-FREQ
r_df.index = r_df.index.strftime("%Y-%m-%d")
turnover_figure = ScatterGraph(
r_df,
layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)),
layout=dict(
title="Top-Bottom Turnover",
xaxis=dict(tickangle=45, rangebreaks=kwargs.get("rangebreaks", guess_plotly_rangebreaks(r_df.index))),
),
).figure
return (turnover_figure,)
def ic_figure(ic_df: pd.DataFrame, show_nature_day=True, **kwargs) -> go.Figure:
"""IC figure
r"""IC figure
:param ic_df: ic DataFrame
:param show_nature_day: whether to display the abscissa of non-trading day
:param \*\*kwargs: contains some parameters to control plot style in plotly. Currently, supports
- `rangebreaks`: https://plotly.com/python/time-series/#Hiding-Weekends-and-Holidays
:return: plotly.graph_objs.Figure
"""
if show_nature_day:
date_index = pd.date_range(ic_df.index.min(), ic_df.index.max())
ic_df = ic_df.reindex(date_index)
# FIXME: support HIGH-FREQ
ic_df.index = ic_df.index.strftime("%Y-%m-%d")
ic_bar_figure = BarGraph(
ic_df,
layout=dict(
title="Information Coefficient (IC)",
xaxis=dict(type="category", tickangle=45),
xaxis=dict(tickangle=45, rangebreaks=kwargs.get("rangebreaks", guess_plotly_rangebreaks(ic_df.index))),
),
).figure
return ic_bar_figure
@@ -272,9 +294,10 @@ def model_performance_graph(
rank=False,
graph_names: list = ["group_return", "pred_ic", "pred_autocorr"],
show_notebook: bool = True,
show_nature_day=True,
show_nature_day: bool = False,
**kwargs,
) -> [list, tuple]:
"""Model performance
r"""Model performance
:param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score, label]**.
It is usually same as the label of model training(e.g. "Ref($close, -2)/Ref($close, -1) - 1").
@@ -297,17 +320,14 @@ def model_performance_graph(
:param graph_names: graph names; default ['cumulative_return', 'pred_ic', 'pred_autocorr', 'pred_turnover'].
:param show_notebook: whether to display graphics in notebook, the default is `True`.
:param show_nature_day: whether to display the abscissa of non-trading day.
:param \*\*kwargs: contains some parameters to control plot style in plotly. Currently, supports
- `rangebreaks`: https://plotly.com/python/time-series/#Hiding-Weekends-and-Holidays
:return: if show_notebook is True, display in notebook; else return `plotly.graph_objs.Figure` list.
"""
figure_list = []
for graph_name in graph_names:
fun_res = eval(f"_{graph_name}")(
pred_label=pred_label,
lag=lag,
N=N,
reverse=reverse,
rank=rank,
show_nature_day=show_nature_day,
pred_label=pred_label, lag=lag, N=N, reverse=reverse, rank=rank, show_nature_day=show_nature_day, **kwargs
)
figure_list += fun_res

2
qlib/contrib/report/analysis_position/risk_analysis.py
View File

@@ -119,7 +119,7 @@ def _get_risk_analysis_figure(analysis_df: pd.DataFrame) -> Iterable[py.Figure]:
_figure = SubplotsGraph(
_get_all_risk_analysis(analysis_df),
kind_map=dict(kind="BarGraph", kwargs={}),
subplots_kwargs={"rows": 4, "cols": 1},
subplots_kwargs={"rows": 1, "cols": 4},
).figure
return (_figure,)

11
qlib/contrib/report/analysis_position/score_ic.py
View File

@@ -4,6 +4,7 @@
import pandas as pd
from ..graph import ScatterGraph
from ..utils import guess_plotly_rangebreaks
def _get_score_ic(pred_label: pd.DataFrame):
@@ -19,7 +20,7 @@ def _get_score_ic(pred_label: pd.DataFrame):
return pd.DataFrame({"ic": _ic, "rank_ic": _rank_ic})
def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True) -> [list, tuple]:
def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True, **kwargs) -> [list, tuple]:
"""score IC
Example:
@@ -53,11 +54,13 @@ def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True) -> [lis
:return: if show_notebook is True, display in notebook; else return **plotly.graph_objs.Figure** list.
"""
_ic_df = _get_score_ic(pred_label)
# FIXME: support HIGH-FREQ
_ic_df.index = _ic_df.index.strftime("%Y-%m-%d")
_figure = ScatterGraph(
_ic_df,
layout=dict(title="Score IC", xaxis=dict(type="category", tickangle=45)),
layout=dict(
title="Score IC",
xaxis=dict(tickangle=45, rangebreaks=kwargs.get("rangebreaks", guess_plotly_rangebreaks(_ic_df.index))),
),
graph_kwargs={"mode": "lines+markers"},
).figure
if show_notebook:

29
qlib/contrib/report/utils.py
View File

@@ -1,6 +1,7 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import matplotlib.pyplot as plt
import pandas as pd
def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None, sharex=False, sharey=False):
@@ -43,3 +44,31 @@ def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None
res = res.item()
yield res
plt.show()
def guess_plotly_rangebreaks(dt_index: pd.DatetimeIndex):
"""
This function `guesses` the rangebreaks required to remove gaps in datetime index.
It basically calculates the difference between a `continuous` datetime index and index given.
For more details on `rangebreaks` params in plotly, see
https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-rangebreaks
Parameters
----------
dt_index: pd.DatetimeIndex
The datetimes of the data.
Returns
-------
the `rangebreaks` to be passed into plotly axis.
"""
dt_idx = dt_index.sort_values()
gaps = dt_idx[1:] - dt_idx[:-1]
min_gap = gaps.min()
gaps_to_break = {}
for gap, d in zip(gaps, dt_idx[:-1]):
if gap > min_gap:
gaps_to_break.setdefault(gap - min_gap, []).append(d + min_gap)
return [dict(values=v, dvalue=int(k.total_seconds() * 1000)) for k, v in gaps_to_break.items()]

2
setup.py
View File

@@ -44,7 +44,7 @@ if not _CYTHON_INSTALLED:
# What packages are required for this module to be executed?
# `estimator` may depend on other packages. In order to reduce dependencies, it is not written here.
REQUIRED = [
"numpy>=1.12.0",
"numpy>=1.12.0, <1.24",
"pandas>=0.25.1",
"scipy>=1.0.0",
"requests>=2.18.0",