Add data analysis feature for report (#918)

* Add data analysis feature for report

* better display

qlib/contrib/eva/alpha.py

@@ -4,8 +4,10 @@ Here is a batch of evaluation functions.
 The interface should be redesigned carefully in the future.
 """
 import pandas as pd
-
 from typing import Tuple
+from qlib import get_module_logger
+from qlib.utils.paral import complex_parallel, DelayedDict
+from joblib import Parallel, delayed
 
 
 def calc_long_short_prec(
@@ -61,32 +63,6 @@ def calc_long_short_prec(
     return (l_dom.groupby(date_col).sum() / l_c), (s_dom.groupby(date_col).sum() / s_c)
 
 
-def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> Tuple[pd.Series, pd.Series]:
-    """calc_ic.
-
-    Parameters
-    ----------
-    pred :
-        pred
-    label :
-        label
-    date_col :
-        date_col
-
-    Returns
-    -------
-    (pd.Series, pd.Series)
-        ic and rank ic
-    """
-    df = pd.DataFrame({"pred": pred, "label": label})
-    ic = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"]))
-    ric = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"], method="spearman"))
-    if dropna:
-        return ic.dropna(), ric.dropna()
-    else:
-        return ic, ric
-
-
 def calc_long_short_return(
     pred: pd.Series,
     label: pd.Series,
@@ -127,3 +103,105 @@ def calc_long_short_return(
     r_short = group.apply(lambda x: x.nsmallest(N(x), columns="pred").label.mean())
     r_avg = group.label.mean()
     return (r_long - r_short) / 2, r_avg
+
+
+def pred_autocorr(pred: pd.Series, lag=1, inst_col="instrument", date_col="datetime"):
+    """pred_autocorr.
+
+    Limitation:
+    - If the datetime is not sequential densely, the correlation will be calulated based on adjacent dates. (some users may expected NaN)
+
+    :param pred: pd.Series with following format
+                instrument  datetime
+                SH600000    2016-01-04   -0.000403
+                            2016-01-05   -0.000753
+                            2016-01-06   -0.021801
+                            2016-01-07   -0.065230
+                            2016-01-08   -0.062465
+    :type pred: pd.Series
+    :param lag:
+    """
+    if isinstance(pred, pd.DataFrame):
+        pred = pred.iloc[:, 0]
+        get_module_logger("pred_autocorr").warning("Only the first column in {pred.columns} of `pred` is kept")
+    pred_ustk = pred.sort_index().unstack(inst_col)
+    corr_s = {}
+    for (idx, cur), (_, prev) in zip(pred_ustk.iterrows(), pred_ustk.shift(lag).iterrows()):
+        corr_s[idx] = cur.corr(prev)
+    corr_s = pd.Series(corr_s).sort_index()
+    return corr_s
+
+
+def pred_autocorr_all(pred_dict, n_jobs=-1, **kwargs):
+    """
+    calculate auto correlation for pred_dict
+
+    Parameters
+    ----------
+    pred_dict : dict
+        A dict like {<method_name>:  <prediction>}
+    kwargs :
+        all these arguments will be passed into pred_autocorr
+    """
+    ac_dict = {}
+    for k, pred in pred_dict.items():
+        ac_dict[k] = delayed(pred_autocorr)(pred, **kwargs)
+    return complex_parallel(Parallel(n_jobs=n_jobs, verbose=10), ac_dict)
+
+
+def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> (pd.Series, pd.Series):
+    """calc_ic.
+
+    Parameters
+    ----------
+    pred :
+        pred
+    label :
+        label
+    date_col :
+        date_col
+
+    Returns
+    -------
+    (pd.Series, pd.Series)
+        ic and rank ic
+    """
+    df = pd.DataFrame({"pred": pred, "label": label})
+    ic = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"]))
+    ric = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"], method="spearman"))
+    if dropna:
+        return ic.dropna(), ric.dropna()
+    else:
+        return ic, ric
+
+
+def calc_all_ic(pred_dict_all, label, date_col="datetime", dropna=False, n_jobs=-1):
+    """calc_all_ic.
+
+    Parameters
+    ----------
+    pred_dict_all :
+        A dict like {<method_name>:  <prediction>}
+    label:
+        A pd.Series of label values
+
+    Returns
+    -------
+    {'Q2+IND_z': {'ic': <ic series like>
+                          2016-01-04   -0.057407
+                          ...
+                          2020-05-28    0.183470
+                          2020-05-29    0.171393
+                  'ric': <rank ic series like>
+                          2016-01-04   -0.040888
+                          ...
+                          2020-05-28    0.236665
+                          2020-05-29    0.183886
+                  }
+    ...}
+    """
+    pred_all_ics = {}
+    for k, pred in pred_dict_all.items():
+        pred_all_ics[k] = DelayedDict(["ic", "ric"], delayed(calc_ic)(pred, label, date_col=date_col, dropna=dropna))
+    pred_all_ics = complex_parallel(Parallel(n_jobs=n_jobs, verbose=10), pred_all_ics)
+    return pred_all_ics