49 lines
1.1 KiB
Python
49 lines
1.1 KiB
Python
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import sqlite3
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conn = sqlite3.connect(r"nlpdata.db")\
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def create_dataset_ep(table):
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cursor = conn.cursor()
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sql = "select * from " + table + " LIMIT 20"
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cursor.execute(sql)
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conn.commit()
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dataset = []
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for row in cursor:
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eid = row[0]
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tag = row[1]
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content = row[2]
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if tag == "5" or tag == "4":
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dataset.append([eid, 2, content])
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print(eid, 2, content)
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elif tag == "1" or tag == "2":
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dataset.append([eid, 0, content])
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print(eid, 0, content)
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else:
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dataset.append([eid, 1, content])
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print(eid, 1, content)
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return dataset
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def create_dataset_pdt():
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conn_pdt = sqlite3.connect(r".\bptdata.db")
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cursor = conn_pdt.cursor()
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sql = "select * from " + "predict_data"
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cursor.execute(sql)
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conn_pdt.commit()
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dataset = []
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for row in cursor:
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stnid = row[0]
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text = row[1]
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dataset.append([stnid, 0, text])
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print(stnid, 0, text)
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return dataset
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if __name__ == '__main__':
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print(create_dataset_ep("amki_test"))
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