#!/usr/bin/env python3
from typing import List, Tuple
from sklearn import linear_model
import tqdm
import scipy
import sqlite3
import argparse
import numpy as np
class matrix_builds_inputpnames:
independent_variables: scipy.sparse.coo_array
dependent_variable: np.ndarray
def __init__(self, db: str) -> None:
row: List[int] = []
col: List[int] = []
data: List[int] = []
con = sqlite3.connect(db)
cur = con.cursor()
builds = self.table_fetch(cur, '''
SELECT ROWID, drv_id, duration FROM builds_cleaned
''')
self.dependent_variable = np.array([ [ row[1] ] for row in tqdm.tqdm(builds) ])
for build in tqdm.tqdm(builds):
inputpnames = self.table_fetch(cur, '''
SELECT drv_id, pname_id FROM input_pnames
WHERE input_pnames.drv_id = ?
''', (build[1],) )
for inputpname in inputpnames:
row.append(build[0] - 1)
col.append(inputpname[1])
data.append(1)
np_row = np.array(row)
np_col = np.array(col)
np_data = np.array(data)
self.independent_variables = scipy.sparse.coo_array((np_data, (np_row, np_col)))
con.commit()
con.close()
@staticmethod
def table_fetch(cur: sqlite3.Cursor, query: str, args: Tuple = ()) -> List:
query_exec = cur.execute(query, args)
return query_exec.fetchall()
def args_get():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--db', required=True, help="path to sqlite database containing input_pnames and builds")
parser.add_argument('-t', '--test', type=int, required=True, help="row to test against")
return parser.parse_args()
if __name__ == '__main__':
args = args_get()
matrix = matrix_builds_inputpnames(args.db)
regr = linear_model.LinearRegression()
regr.fit(matrix.independent_variables, matrix.dependent_variable)
pred = regr.predict([matrix.independent_variables.toarray()[args.test]])
print(f'prediction : {pred[0][0]}')
print(f'og duration : {matrix.dependent_variable[args.test][0]}')
