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37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import random
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import pandas as pd
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from ...data import D
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from ..model.base import Model
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class ScoreFileModel(Model):
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"""
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This model will load a score file, and return score at date exists in score file.
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"""
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def __init__(self, score_path):
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pred_test = pd.read_csv(score_path, index_col=[0, 1], parse_dates=True, infer_datetime_format=True)
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self.pred = pred_test
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def get_data_with_date(self, date, **kwargs):
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score = self.pred.loc(axis=0)[:, date] # (stock_id, trade_date) multi_index, score in pdate
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score_series = score.reset_index(level="datetime", drop=True)[
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"score"
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] # pd.Series ; index:stock_id, data: score
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return score_series
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def predict(self, x_test, **kwargs):
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return x_test
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def score(self, x_test, **kwargs):
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return
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def fit(self, x_train, y_train, x_valid, y_valid, w_train=None, w_valid=None, **kwargs):
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return
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def save(self, fname, **kwargs):
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return
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