mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 05:51:17 +08:00
3097dcc995
* fix(security): enforce RestrictedUnpickler for load_instance to prevent unsafe pickle deserialization * fix: lint error
109 lines
2.6 KiB
Python
109 lines
2.6 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
|
|
from qlib.utils.pickle_utils import restricted_pickle_load
|
|
|
|
sns.set(color_codes=True)
|
|
plt.rcParams["font.sans-serif"] = "SimHei"
|
|
plt.rcParams["axes.unicode_minus"] = False
|
|
from tqdm.auto import tqdm
|
|
|
|
# tqdm.pandas() # for progress_apply
|
|
# %matplotlib inline
|
|
# %load_ext autoreload
|
|
|
|
|
|
# # Meta Input
|
|
|
|
# +
|
|
with open("./internal_data_s20.pkl", "rb") as f:
|
|
data = restricted_pickle_load(f)
|
|
|
|
data.data_ic_df.columns.names = ["start_date", "end_date"]
|
|
|
|
data_sim = data.data_ic_df.droplevel(axis=1, level="end_date")
|
|
|
|
data_sim.index.name = "test datetime"
|
|
# -
|
|
|
|
plt.figure(figsize=(40, 20))
|
|
sns.heatmap(data_sim)
|
|
|
|
plt.figure(figsize=(40, 20))
|
|
sns.heatmap(data_sim.rolling(20).mean())
|
|
|
|
# # Meta Model
|
|
|
|
from qlib import auto_init
|
|
|
|
auto_init()
|
|
from qlib.workflow import R
|
|
|
|
exp = R.get_exp(experiment_name="DDG-DA")
|
|
meta_rec = exp.list_recorders(rtype="list", max_results=1)[0]
|
|
meta_m = meta_rec.load_object("model")
|
|
|
|
pd.DataFrame(meta_m.tn.twm.linear.weight.detach().numpy()).T[0].plot()
|
|
|
|
pd.DataFrame(meta_m.tn.twm.linear.weight.detach().numpy()).T[0].rolling(5).mean().plot()
|
|
|
|
# # Meta Output
|
|
|
|
# +
|
|
with open("./tasks_s20.pkl", "rb") as f:
|
|
tasks = restricted_pickle_load(f)
|
|
|
|
task_df = {}
|
|
for t in tasks:
|
|
test_seg = t["dataset"]["kwargs"]["segments"]["test"]
|
|
if None not in test_seg:
|
|
# The last rolling is skipped.
|
|
task_df[test_seg] = t["reweighter"].time_weight
|
|
task_df = pd.concat(task_df)
|
|
|
|
task_df.index.names = ["OS_start", "OS_end", "IS_start", "IS_end"]
|
|
task_df = task_df.droplevel(["OS_end", "IS_end"])
|
|
task_df = task_df.unstack("OS_start")
|
|
# -
|
|
|
|
plt.figure(figsize=(40, 20))
|
|
sns.heatmap(task_df.T)
|
|
|
|
plt.figure(figsize=(40, 20))
|
|
sns.heatmap(task_df.rolling(10).mean().T)
|
|
|
|
# # Sub Models
|
|
#
|
|
# NOTE:
|
|
# - this section assumes that the model is Linear model!!
|
|
# - Other models does not support this analysis
|
|
|
|
exp = R.get_exp(experiment_name="rolling_ds")
|
|
|
|
|
|
def show_linear_weight(exp):
|
|
coef_df = {}
|
|
for r in exp.list_recorders("list"):
|
|
t = r.load_object("task")
|
|
if None in t["dataset"]["kwargs"]["segments"]["test"]:
|
|
continue
|
|
m = r.load_object("params.pkl")
|
|
coef_df[t["dataset"]["kwargs"]["segments"]["test"]] = pd.Series(m.coef_)
|
|
|
|
coef_df = pd.concat(coef_df)
|
|
|
|
coef_df.index.names = ["test_start", "test_end", "coef_idx"]
|
|
|
|
coef_df = coef_df.droplevel("test_end").unstack("coef_idx").T
|
|
|
|
plt.figure(figsize=(40, 20))
|
|
sns.heatmap(coef_df)
|
|
plt.show()
|
|
|
|
|
|
show_linear_weight(R.get_exp(experiment_name="rolling_ds"))
|
|
|
|
show_linear_weight(R.get_exp(experiment_name="rolling_models"))
|