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32c3070b73
* Run ddg-da successfully * Support include valid; More parameters * Support L2 reg & visualization * Blackformat * Enable fill_method * Support specify handler & optim dataset * Fix Pylint
108 lines
2.5 KiB
Python
108 lines
2.5 KiB
Python
import pickle
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.set(color_codes=True)
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plt.rcParams["font.sans-serif"] = "SimHei"
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plt.rcParams["axes.unicode_minus"] = False
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from tqdm.auto import tqdm
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# tqdm.pandas() # for progress_apply
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# %matplotlib inline
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# %load_ext autoreload
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# # Meta Input
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# +
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with open("./internal_data_s20.pkl", "rb") as f:
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data = pickle.load(f)
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data.data_ic_df.columns.names = ["start_date", "end_date"]
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data_sim = data.data_ic_df.droplevel(axis=1, level="end_date")
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data_sim.index.name = "test datetime"
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# -
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plt.figure(figsize=(40, 20))
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sns.heatmap(data_sim)
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plt.figure(figsize=(40, 20))
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sns.heatmap(data_sim.rolling(20).mean())
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# # Meta Model
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from qlib import auto_init
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auto_init()
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from qlib.workflow import R
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exp = R.get_exp(experiment_name="DDG-DA")
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meta_rec = exp.list_recorders(rtype="list", max_results=1)[0]
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meta_m = meta_rec.load_object("model")
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pd.DataFrame(meta_m.tn.twm.linear.weight.detach().numpy()).T[0].plot()
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pd.DataFrame(meta_m.tn.twm.linear.weight.detach().numpy()).T[0].rolling(5).mean().plot()
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# # Meta Output
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# +
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with open("./tasks_s20.pkl", "rb") as f:
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tasks = pickle.load(f)
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task_df = {}
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for t in tasks:
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test_seg = t["dataset"]["kwargs"]["segments"]["test"]
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if None not in test_seg:
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# The last rolling is skipped.
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task_df[test_seg] = t["reweighter"].time_weight
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task_df = pd.concat(task_df)
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task_df.index.names = ["OS_start", "OS_end", "IS_start", "IS_end"]
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task_df = task_df.droplevel(["OS_end", "IS_end"])
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task_df = task_df.unstack("OS_start")
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# -
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plt.figure(figsize=(40, 20))
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sns.heatmap(task_df.T)
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plt.figure(figsize=(40, 20))
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sns.heatmap(task_df.rolling(10).mean().T)
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# # Sub Models
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#
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# NOTE:
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# - this section assumes that the model is Linear model!!
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# - Other models does not support this analysis
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exp = R.get_exp(experiment_name="rolling_ds")
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def show_linear_weight(exp):
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coef_df = {}
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for r in exp.list_recorders("list"):
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t = r.load_object("task")
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if None in t["dataset"]["kwargs"]["segments"]["test"]:
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continue
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m = r.load_object("params.pkl")
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coef_df[t["dataset"]["kwargs"]["segments"]["test"]] = pd.Series(m.coef_)
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coef_df = pd.concat(coef_df)
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coef_df.index.names = ["test_start", "test_end", "coef_idx"]
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coef_df = coef_df.droplevel("test_end").unstack("coef_idx").T
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plt.figure(figsize=(40, 20))
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sns.heatmap(coef_df)
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plt.show()
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show_linear_weight(R.get_exp(experiment_name="rolling_ds"))
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show_linear_weight(R.get_exp(experiment_name="rolling_models"))
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