Plot enhancement (microsoft#1390)

* 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>

qlib/contrib/report/analysis_model/analysis_model_performance.py

@@ -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 @@
 
 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"}
+
+    def _corr_series(x, method):
+        return x["label"].corr(x["score"], method=method)
 
-    _index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6)
-    _monthly_ic = ic.groupby(_index).mean()
+    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
 

qlib/contrib/report/analysis_position/risk_analysis.py

@@ -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,)
 

qlib/contrib/report/analysis_position/score_ic.py

@@ -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:

qlib/contrib/report/utils.py

@@ -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()]

setup.py

@@ -44,7 +44,7 @@ def get_version(rel_path: str) -> str:
 # 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",