├── datasets
    ├── __init__.py
    ├── assistment
    │   ├── concept_map.json
    │   ├── metadata.json
    │   ├── test_triplets.csv
    │   ├── train_triplets.csv
    │   └── triplets.csv
    └── data_prep.py
├── models
    ├── irt
    │   └── checkpoint.pt
    └── ncd
    │   └── checkpoint.pt
├── pyat
    ├── __init__.py
    ├── driver.py
    ├── model
    │   ├── __init__.py
    │   ├── abstract_model.py
    │   └── irt_model.py
    ├── strategy
    │   ├── __init__.py
    │   ├── abstract_strategy.py
    │   └── maat_strategy.py
    └── utils
    │   ├── __init__.py
    │   ├── _utils.py
    │   └── data
    │       ├── __init__.py
    │       ├── _dataset.py
    │       ├── adaptest_dataset.py
    │       └── train_dataset.py
└── scripts
    ├── test.py
    └── train.py


/datasets/__init__.py:
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https://raw.githubusercontent.com/bigdata-ustc/MAAT/55226e29a5948dda766137935f532c687a05c231/datasets/__init__.py


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/datasets/assistment/concept_map.json:
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/datasets/assistment/metadata.json:
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1 | {"num_students": 1473, "num_questions": 903, "num_concepts": 22, "num_records": 58427, "num_train_students": 1426, "num_test_students": 47}


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/datasets/assistment/test_triplets.csv:
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   1 | student_id,question_id,correct
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1500 | 8,838,0
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1800 | 10,692,0
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1859 | 10,886,0
1860 | 10,892,1
1861 | 10,893,1
1862 | 10,894,1
1863 | 10,897,1
1864 | 10,898,1
1865 | 10,899,0
1866 | 10,900,1
1867 | 11,1,1
1868 | 11,6,1
1869 | 11,17,1
1870 | 11,22,1
1871 | 11,59,1
1872 | 11,61,0
1873 | 11,66,1
1874 | 11,79,1
1875 | 11,80,1
1876 | 11,81,0
1877 | 11,91,1
1878 | 11,95,1
1879 | 11,96,1
1880 | 11,98,1
1881 | 11,115,0
1882 | 11,118,0
1883 | 11,119,1
1884 | 11,121,1
1885 | 11,122,1
1886 | 11,123,1
1887 | 11,125,0
1888 | 11,126,0
1889 | 11,149,0
1890 | 11,150,1
1891 | 11,163,1
1892 | 11,169,1
1893 | 11,172,1
1894 | 11,175,1
1895 | 11,177,1
1896 | 11,178,1
1897 | 11,179,0
1898 | 11,197,1
1899 | 11,202,1
1900 | 11,204,0
1901 | 11,206,0
1902 | 11,207,0
1903 | 11,211,1
1904 | 11,212,1
1905 | 11,215,1
1906 | 11,216,0
1907 | 11,217,0
1908 | 11,223,1
1909 | 11,224,1
1910 | 11,226,1
1911 | 11,228,0
1912 | 11,229,1
1913 | 11,230,0
1914 | 11,245,0
1915 | 11,262,1
1916 | 11,263,1
1917 | 11,268,0
1918 | 11,284,1
1919 | 11,296,1
1920 | 11,308,1
1921 | 11,309,1
1922 | 11,327,0
1923 | 11,328,1
1924 | 11,342,1
1925 | 11,343,1
1926 | 11,359,1
1927 | 11,363,0
1928 | 11,366,1
1929 | 11,367,1
1930 | 11,370,1
1931 | 11,378,0
1932 | 11,379,0
1933 | 11,411,1
1934 | 11,414,1
1935 | 11,432,1
1936 | 11,433,1
1937 | 11,443,0
1938 | 11,445,0
1939 | 11,450,0
1940 | 11,451,1
1941 | 11,458,1
1942 | 11,459,1
1943 | 11,462,1
1944 | 11,463,1
1945 | 11,464,0
1946 | 11,465,0
1947 | 11,472,1
1948 | 11,478,1
1949 | 11,487,1
1950 | 11,488,1
1951 | 11,490,1
1952 | 11,495,0
1953 | 11,496,0
1954 | 11,497,1
1955 | 11,498,0
1956 | 11,499,1
1957 | 11,502,0
1958 | 11,516,1
1959 | 11,520,0
1960 | 11,522,1
1961 | 11,542,0
1962 | 11,543,0
1963 | 11,546,1
1964 | 11,549,1
1965 | 11,550,0
1966 | 11,553,0
1967 | 11,554,0
1968 | 11,555,0
1969 | 11,556,0
1970 | 11,561,1
1971 | 11,562,1
1972 | 11,564,0
1973 | 11,578,1
1974 | 11,586,1
1975 | 11,590,0
1976 | 11,594,1
1977 | 11,595,1
1978 | 11,597,0
1979 | 11,598,1
1980 | 11,612,1
1981 | 11,618,0
1982 | 11,622,1
1983 | 11,625,1
1984 | 11,636,1
1985 | 11,645,1
1986 | 11,646,1
1987 | 11,648,1
1988 | 11,658,0
1989 | 11,666,1
1990 | 11,669,1
1991 | 11,675,0
1992 | 11,687,0
1993 | 11,689,1
1994 | 11,691,0
1995 | 11,692,0
1996 | 11,693,0
1997 | 11,694,1
1998 | 11,695,1
1999 | 11,696,1
2000 | 11,726,1
2001 | 11,729,1
2002 | 11,730,0
2003 | 11,747,1
2004 | 11,751,1
2005 | 11,756,1
2006 | 11,763,0
2007 | 11,776,1
2008 | 11,777,1
2009 | 11,788,1
2010 | 11,789,0
2011 | 11,792,1
2012 | 11,800,0
2013 | 11,805,1
2014 | 11,809,1
2015 | 11,825,1
2016 | 11,830,0
2017 | 11,832,0
2018 | 11,834,0
2019 | 11,839,0
2020 | 11,840,0
2021 | 11,847,1
2022 | 11,859,1
2023 | 11,860,0
2024 | 11,882,0
2025 | 11,884,0
2026 | 11,886,0
2027 | 11,890,0
2028 | 12,15,1
2029 | 12,17,1
2030 | 12,31,0
2031 | 12,43,1
2032 | 12,46,1
2033 | 12,58,0
2034 | 12,61,0
2035 | 12,64,0
2036 | 12,65,1
2037 | 12,74,1
2038 | 12,75,0
2039 | 12,80,1
2040 | 12,91,1
2041 | 12,92,0
2042 | 12,93,1
2043 | 12,95,0
2044 | 12,96,1
2045 | 12,97,1
2046 | 12,98,1
2047 | 12,101,1
2048 | 12,110,1
2049 | 12,113,1
2050 | 12,134,0
2051 | 12,136,0
2052 | 12,138,0
2053 | 12,139,1
2054 | 12,140,0
2055 | 12,142,0
2056 | 12,143,1
2057 | 12,159,1
2058 | 12,168,1
2059 | 12,169,1
2060 | 12,170,0
2061 | 12,171,1
2062 | 12,174,0
2063 | 12,177,1
2064 | 12,179,0
2065 | 12,180,1
2066 | 12,217,0
2067 | 12,223,0
2068 | 12,225,0
2069 | 12,226,1
2070 | 12,229,0
2071 | 12,230,1
2072 | 12,233,1
2073 | 12,237,1
2074 | 12,240,1
2075 | 12,252,1
2076 | 12,255,1
2077 | 12,259,0
2078 | 12,261,0
2079 | 12,263,1
2080 | 12,264,1
2081 | 12,279,1
2082 | 12,281,1
2083 | 12,285,1
2084 | 12,286,1
2085 | 12,298,0
2086 | 12,306,1
2087 | 12,307,1
2088 | 12,313,0
2089 | 12,316,1
2090 | 12,334,0
2091 | 12,350,0
2092 | 12,362,1
2093 | 12,363,0
2094 | 12,382,1
2095 | 12,388,1
2096 | 12,390,0
2097 | 12,391,0
2098 | 12,392,1
2099 | 12,409,0
2100 | 12,424,1
2101 | 12,427,0
2102 | 12,431,0
2103 | 12,449,1
2104 | 12,451,0
2105 | 12,453,1
2106 | 12,472,0
2107 | 12,479,1
2108 | 12,488,1
2109 | 12,502,1
2110 | 12,513,1
2111 | 12,518,1
2112 | 12,523,0
2113 | 12,530,1
2114 | 12,536,1
2115 | 12,538,1
2116 | 12,552,0
2117 | 12,557,1
2118 | 12,561,1
2119 | 12,563,0
2120 | 12,564,1
2121 | 12,565,0
2122 | 12,582,0
2123 | 12,583,1
2124 | 12,586,1
2125 | 12,587,1
2126 | 12,603,0
2127 | 12,611,1
2128 | 12,613,0
2129 | 12,620,0
2130 | 12,632,1
2131 | 12,644,1
2132 | 12,645,1
2133 | 12,646,1
2134 | 12,647,1
2135 | 12,650,0
2136 | 12,651,0
2137 | 12,652,0
2138 | 12,658,0
2139 | 12,669,1
2140 | 12,676,1
2141 | 12,677,0
2142 | 12,678,0
2143 | 12,679,0
2144 | 12,683,1
2145 | 12,686,1
2146 | 12,688,1
2147 | 12,689,0
2148 | 12,696,0
2149 | 12,706,1
2150 | 12,725,1
2151 | 12,727,1
2152 | 12,733,1
2153 | 12,737,1
2154 | 12,742,1
2155 | 12,751,1
2156 | 12,759,1
2157 | 12,762,1
2158 | 12,763,1
2159 | 12,765,0
2160 | 12,766,0
2161 | 12,774,0
2162 | 12,775,1
2163 | 12,776,1
2164 | 12,778,1
2165 | 12,781,1
2166 | 12,788,1
2167 | 12,790,1
2168 | 12,791,0
2169 | 12,792,1
2170 | 12,793,1
2171 | 12,794,1
2172 | 12,795,0
2173 | 12,796,0
2174 | 12,797,0
2175 | 12,800,0
2176 | 12,805,1
2177 | 12,806,1
2178 | 12,826,0
2179 | 12,830,1
2180 | 12,831,0
2181 | 12,832,0
2182 | 12,833,1
2183 | 12,834,0
2184 | 12,835,0
2185 | 12,836,1
2186 | 12,838,0
2187 | 12,839,0
2188 | 12,840,0
2189 | 12,841,0
2190 | 12,842,1
2191 | 12,847,0
2192 | 12,851,1
2193 | 12,852,0
2194 | 12,855,1
2195 | 12,859,0
2196 | 12,861,0
2197 | 12,865,1
2198 | 12,867,1
2199 | 12,868,1
2200 | 12,869,1
2201 | 12,872,1
2202 | 12,877,0
2203 | 12,884,0
2204 | 12,885,0
2205 | 12,886,0
2206 | 12,887,1
2207 | 12,888,0
2208 | 12,889,0
2209 | 12,890,0
2210 | 12,892,0
2211 | 12,897,1
2212 | 12,898,1
2213 | 12,899,1
2214 | 12,902,1
2215 | 13,20,0
2216 | 13,38,1
2217 | 13,43,1
2218 | 13,45,1
2219 | 13,49,0
2220 | 13,52,0
2221 | 13,53,1
2222 | 13,61,0
2223 | 13,69,1
2224 | 13,80,0
2225 | 13,81,1
2226 | 13,84,1
2227 | 13,91,1
2228 | 13,92,1
2229 | 13,94,1
2230 | 13,95,1
2231 | 13,96,1
2232 | 13,97,1
2233 | 13,98,1
2234 | 13,99,0
2235 | 13,100,1
2236 | 13,106,1
2237 | 13,108,0
2238 | 13,111,0
2239 | 13,122,0
2240 | 13,150,0
2241 | 13,159,1
2242 | 13,171,0
2243 | 13,172,0
2244 | 13,174,1
2245 | 13,175,0
2246 | 13,176,0
2247 | 13,177,0
2248 | 13,180,1
2249 | 13,182,0
2250 | 13,197,1
2251 | 13,198,0
2252 | 13,201,1
2253 | 13,208,0
2254 | 13,210,1
2255 | 13,215,1
2256 | 13,216,0
2257 | 13,217,0
2258 | 13,218,0
2259 | 13,219,1
2260 | 13,220,0
2261 | 13,224,0
2262 | 13,226,0
2263 | 13,227,1
2264 | 13,228,0
2265 | 13,229,1
2266 | 13,230,0
2267 | 13,247,0
2268 | 13,250,1
2269 | 13,251,1
2270 | 13,259,1
2271 | 13,261,0
2272 | 13,285,1
2273 | 13,298,0
2274 | 13,299,0
2275 | 13,305,0
2276 | 13,321,1
2277 | 13,326,0
2278 | 13,327,0
2279 | 13,328,0
2280 | 13,343,0
2281 | 13,347,1
2282 | 13,364,0
2283 | 13,365,1
2284 | 13,368,1
2285 | 13,378,0
2286 | 13,381,0
2287 | 13,410,0
2288 | 13,412,0
2289 | 13,413,1
2290 | 13,430,0
2291 | 13,431,0
2292 | 13,432,0
2293 | 13,433,0
2294 | 13,439,0
2295 | 13,440,1
2296 | 13,457,0
2297 | 13,459,1
2298 | 13,472,0
2299 | 13,473,0
2300 | 13,487,1
2301 | 13,488,0
2302 | 13,514,0
2303 | 13,515,1
2304 | 13,517,1
2305 | 13,518,0
2306 | 13,534,1
2307 | 13,536,0
2308 | 13,537,1
2309 | 13,538,0
2310 | 13,545,1
2311 | 13,552,0
2312 | 13,556,1
2313 | 13,576,1
2314 | 13,579,0
2315 | 13,580,1
2316 | 13,581,1
2317 | 13,584,0
2318 | 13,590,0
2319 | 13,611,0
2320 | 13,613,0
2321 | 13,622,0
2322 | 13,624,1
2323 | 13,628,1
2324 | 13,636,1
2325 | 13,643,1
2326 | 13,644,1
2327 | 13,645,1
2328 | 13,656,0
2329 | 13,658,0
2330 | 13,662,0
2331 | 13,670,1
2332 | 13,686,0
2333 | 13,688,1
2334 | 13,689,1
2335 | 13,691,0
2336 | 13,706,0
2337 | 13,707,0
2338 | 13,708,0
2339 | 13,712,0
2340 | 13,725,1
2341 | 13,729,1
2342 | 13,737,0
2343 | 13,744,1
2344 | 13,745,0
2345 | 13,746,1
2346 | 13,747,1
2347 | 13,748,0
2348 | 13,750,1
2349 | 13,751,1
2350 | 13,756,1
2351 | 13,758,0
2352 | 13,759,0
2353 | 13,760,0
2354 | 13,763,1
2355 | 13,776,1
2356 | 13,777,0
2357 | 13,784,1
2358 | 13,800,0
2359 | 13,802,1
2360 | 13,804,0
2361 | 13,805,1
2362 | 13,806,0
2363 | 13,812,1
2364 | 13,813,0
2365 | 13,818,1
2366 | 13,822,0
2367 | 13,824,1
2368 | 13,825,0
2369 | 13,828,1
2370 | 13,847,1
2371 | 13,848,1
2372 | 13,849,1
2373 | 13,853,0
2374 | 13,862,0
2375 | 13,876,0
2376 | 13,877,1
2377 | 14,13,1
2378 | 14,15,1
2379 | 14,17,0
2380 | 14,21,1
2381 | 14,22,1
2382 | 14,23,1
2383 | 14,27,1
2384 | 14,46,0
2385 | 14,47,1
2386 | 14,48,1
2387 | 14,49,1
2388 | 14,50,1
2389 | 14,51,0
2390 | 14,52,1
2391 | 14,58,1
2392 | 14,59,1
2393 | 14,61,1
2394 | 14,63,1
2395 | 14,73,0
2396 | 14,74,0
2397 | 14,75,1
2398 | 14,85,1
2399 | 14,92,1
2400 | 14,94,1
2401 | 14,96,1
2402 | 14,98,0
2403 | 14,107,1
2404 | 14,109,1
2405 | 14,111,1
2406 | 14,112,1
2407 | 14,117,0
2408 | 14,118,1
2409 | 14,119,0
2410 | 14,121,0
2411 | 14,123,1
2412 | 14,125,1
2413 | 14,126,0
2414 | 14,127,0
2415 | 14,136,1
2416 | 14,137,1
2417 | 14,138,1
2418 | 14,139,1
2419 | 14,140,0
2420 | 14,148,1
2421 | 14,164,0
2422 | 14,171,1
2423 | 14,173,1
2424 | 14,183,0
2425 | 14,184,1
2426 | 14,197,1
2427 | 14,200,1
2428 | 14,201,1
2429 | 14,204,1
2430 | 14,207,0
2431 | 14,215,1
2432 | 14,217,1
2433 | 14,219,1
2434 | 14,226,1
2435 | 14,233,0
2436 | 14,243,1
2437 | 14,245,1
2438 | 14,246,0
2439 | 14,255,1
2440 | 14,260,1
2441 | 14,263,1
2442 | 14,278,1
2443 | 14,308,1
2444 | 14,309,1
2445 | 14,333,1
2446 | 14,355,1
2447 | 14,367,0
2448 | 14,368,0
2449 | 14,378,0
2450 | 14,390,1
2451 | 14,430,1
2452 | 14,442,0
2453 | 14,443,0
2454 | 14,445,0
2455 | 14,448,0
2456 | 14,449,1
2457 | 14,451,1
2458 | 14,463,0
2459 | 14,464,1
2460 | 14,465,0
2461 | 14,476,0
2462 | 14,477,0
2463 | 14,487,1
2464 | 14,491,0
2465 | 14,495,0
2466 | 14,496,0
2467 | 14,497,1
2468 | 14,498,0
2469 | 14,502,1
2470 | 14,513,0
2471 | 14,522,1
2472 | 14,536,1
2473 | 14,542,1
2474 | 14,547,0
2475 | 14,550,0
2476 | 14,551,0
2477 | 14,552,0
2478 | 14,553,0
2479 | 14,554,0
2480 | 14,556,0
2481 | 14,561,0
2482 | 14,562,1
2483 | 14,563,1
2484 | 14,564,1
2485 | 14,576,1
2486 | 14,578,1
2487 | 14,595,0
2488 | 14,596,0
2489 | 14,597,1
2490 | 14,602,1
2491 | 14,624,1
2492 | 14,626,1
2493 | 14,635,1
2494 | 14,643,1
2495 | 14,644,1
2496 | 14,673,1
2497 | 14,674,0
2498 | 14,675,1
2499 | 14,682,1
2500 | 14,688,1
2501 | 14,690,0
2502 | 14,705,0
2503 | 14,707,1
2504 | 14,719,1
2505 | 14,726,1
2506 | 14,737,1
2507 | 14,740,1
2508 | 14,741,1
2509 | 14,751,1
2510 | 14,752,0
2511 | 14,753,0
2512 | 14,754,1
2513 | 14,755,1
2514 | 14,758,0
2515 | 14,763,1
2516 | 14,775,1
2517 | 14,776,1
2518 | 14,783,0
2519 | 14,790,0
2520 | 14,791,0
2521 | 14,792,0
2522 | 14,794,0
2523 | 14,804,0
2524 | 14,805,1
2525 | 14,808,1
2526 | 14,816,1
2527 | 14,822,0
2528 | 14,823,1
2529 | 14,831,0
2530 | 14,834,1
2531 | 14,835,0
2532 | 14,836,1
2533 | 14,838,0
2534 | 14,840,1
2535 | 14,842,0
2536 | 14,853,1
2537 | 14,860,0
2538 | 14,870,1
2539 | 14,871,1
2540 | 14,882,1
2541 | 14,890,0
2542 | 14,895,1
2543 | 14,897,1
2544 | 14,898,1
2545 | 14,900,1
2546 | 14,901,1
2547 | 15,2,1
2548 | 15,18,1
2549 | 15,44,0
2550 | 15,51,0
2551 | 15,60,0
2552 | 15,61,0
2553 | 15,62,0
2554 | 15,63,1
2555 | 15,64,1
2556 | 15,65,0
2557 | 15,66,0
2558 | 15,67,1
2559 | 15,69,0
2560 | 15,70,1
2561 | 15,74,1
2562 | 15,75,0
2563 | 15,76,1
2564 | 15,77,1
2565 | 15,78,1
2566 | 15,91,0
2567 | 15,96,1
2568 | 15,101,0
2569 | 15,122,0
2570 | 15,123,1
2571 | 15,125,0
2572 | 15,137,0
2573 | 15,139,0
2574 | 15,159,0
2575 | 15,160,0
2576 | 15,161,1
2577 | 15,162,0
2578 | 15,163,0
2579 | 15,164,1
2580 | 15,165,0
2581 | 15,166,0
2582 | 15,173,1
2583 | 15,175,0
2584 | 15,176,1
2585 | 15,180,1
2586 | 15,197,0
2587 | 15,208,0
2588 | 15,211,1
2589 | 15,217,0
2590 | 15,218,1
2591 | 15,220,0
2592 | 15,221,1
2593 | 15,222,0
2594 | 15,223,0
2595 | 15,224,0
2596 | 15,225,1
2597 | 15,226,1
2598 | 15,228,0
2599 | 15,240,1
2600 | 15,254,1
2601 | 15,260,1
2602 | 15,263,1
2603 | 15,272,0
2604 | 15,278,1
2605 | 15,309,1
2606 | 15,327,1
2607 | 15,328,0
2608 | 15,334,1
2609 | 15,335,0
2610 | 15,363,1
2611 | 15,364,1
2612 | 15,373,0
2613 | 15,411,1
2614 | 15,414,1
2615 | 15,431,0
2616 | 15,432,1
2617 | 15,459,1
2618 | 15,473,1
2619 | 15,487,1
2620 | 15,488,0
2621 | 15,495,1
2622 | 15,508,1
2623 | 15,538,1
2624 | 15,542,1
2625 | 15,553,1
2626 | 15,556,0
2627 | 15,564,1
2628 | 15,574,1
2629 | 15,575,1
2630 | 15,576,0
2631 | 15,577,0
2632 | 15,578,1
2633 | 15,579,1
2634 | 15,580,0
2635 | 15,581,0
2636 | 15,587,1
2637 | 15,590,0
2638 | 15,602,1
2639 | 15,610,0
2640 | 15,612,1
2641 | 15,613,1
2642 | 15,622,1
2643 | 15,623,1
2644 | 15,633,1
2645 | 15,643,1
2646 | 15,644,1
2647 | 15,645,1
2648 | 15,646,1
2649 | 15,668,1
2650 | 15,669,1
2651 | 15,670,1
2652 | 15,674,1
2653 | 15,684,1
2654 | 15,685,1
2655 | 15,689,1
2656 | 15,691,0
2657 | 15,703,1
2658 | 15,705,0
2659 | 15,706,1
2660 | 15,707,0
2661 | 15,708,0
2662 | 15,723,0
2663 | 15,724,0
2664 | 15,726,0
2665 | 15,737,0
2666 | 15,738,0
2667 | 15,744,1
2668 | 15,745,0
2669 | 15,746,0
2670 | 15,747,0
2671 | 15,748,0
2672 | 15,750,0
2673 | 15,751,1
2674 | 15,752,0
2675 | 15,753,1
2676 | 15,754,1
2677 | 15,755,0
2678 | 15,758,1
2679 | 15,759,0
2680 | 15,760,0
2681 | 15,761,0
2682 | 15,763,0
2683 | 15,775,1
2684 | 15,778,0
2685 | 15,805,1
2686 | 15,806,1
2687 | 15,812,1
2688 | 15,825,0
2689 | 15,829,0
2690 | 15,844,1
2691 | 15,847,0
2692 | 15,848,1
2693 | 15,856,1
2694 | 15,861,0
2695 | 15,862,0
2696 | 15,878,1
2697 | 15,879,1
2698 | 15,880,0
2699 | 16,1,1
2700 | 16,3,0
2701 | 16,12,1
2702 | 16,17,0
2703 | 16,30,0
2704 | 16,45,1
2705 | 16,52,1
2706 | 16,58,0
2707 | 16,61,1
2708 | 16,68,0
2709 | 16,69,0
2710 | 16,73,0
2711 | 16,74,0
2712 | 16,75,0
2713 | 16,78,1
2714 | 16,83,1
2715 | 16,84,1
2716 | 16,85,0
2717 | 16,90,1
2718 | 16,91,1
2719 | 16,95,1
2720 | 16,97,1
2721 | 16,99,1
2722 | 16,104,0
2723 | 16,105,1
2724 | 16,108,0
2725 | 16,110,1
2726 | 16,111,1
2727 | 16,113,0
2728 | 16,115,1
2729 | 16,117,1
2730 | 16,118,1
2731 | 16,127,0
2732 | 16,133,1
2733 | 16,135,0
2734 | 16,140,1
2735 | 16,143,1
2736 | 16,161,1
2737 | 16,162,1
2738 | 16,165,0
2739 | 16,167,0
2740 | 16,170,1
2741 | 16,172,0
2742 | 16,173,0
2743 | 16,174,0
2744 | 16,175,1
2745 | 16,176,1
2746 | 16,178,1
2747 | 16,179,0
2748 | 16,181,0
2749 | 16,182,0
2750 | 16,183,1
2751 | 16,198,1
2752 | 16,205,1
2753 | 16,208,0
2754 | 16,216,0
2755 | 16,220,0
2756 | 16,223,1
2757 | 16,228,0
2758 | 16,229,0
2759 | 16,240,0
2760 | 16,242,0
2761 | 16,246,0
2762 | 16,249,1
2763 | 16,254,1
2764 | 16,256,1
2765 | 16,259,1
2766 | 16,266,1
2767 | 16,274,1
2768 | 16,283,1
2769 | 16,284,1
2770 | 16,298,0
2771 | 16,300,0
2772 | 16,308,1
2773 | 16,310,0
2774 | 16,315,1
2775 | 16,320,1
2776 | 16,323,1
2777 | 16,326,0
2778 | 16,327,1
2779 | 16,329,0
2780 | 16,339,1
2781 | 16,349,1
2782 | 16,363,1
2783 | 16,367,0
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2785 | 16,382,1
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2787 | 16,398,1
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2789 | 16,433,0
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2791 | 16,453,1
2792 | 16,457,0
2793 | 16,458,0
2794 | 16,459,1
2795 | 16,463,1
2796 | 16,473,1
2797 | 16,487,1
2798 | 16,488,1
2799 | 16,499,0
2800 | 16,502,1
2801 | 16,505,1
2802 | 16,523,0
2803 | 16,536,1
2804 | 16,537,1
2805 | 16,561,0
2806 | 16,562,1
2807 | 16,564,0
2808 | 16,565,0
2809 | 16,574,1
2810 | 16,576,0
2811 | 16,580,1
2812 | 16,584,0
2813 | 16,590,1
2814 | 16,594,1
2815 | 16,598,1
2816 | 16,611,0
2817 | 16,612,1
2818 | 16,622,1
2819 | 16,640,0
2820 | 16,643,1
2821 | 16,646,1
2822 | 16,649,0
2823 | 16,651,1
2824 | 16,658,0
2825 | 16,669,0
2826 | 16,675,0
2827 | 16,676,1
2828 | 16,678,1
2829 | 16,679,0
2830 | 16,683,0
2831 | 16,696,1
2832 | 16,708,1
2833 | 16,709,1
2834 | 16,711,1
2835 | 16,725,0
2836 | 16,726,1
2837 | 16,728,0
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2839 | 16,739,1
2840 | 16,747,1
2841 | 16,748,0
2842 | 16,761,0
2843 | 16,764,1
2844 | 16,765,0
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2846 | 16,774,0
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2848 | 16,776,1
2849 | 16,777,1
2850 | 16,778,1
2851 | 16,788,1
2852 | 16,790,1
2853 | 16,791,0
2854 | 16,792,1
2855 | 16,793,1
2856 | 16,794,0
2857 | 16,795,1
2858 | 16,797,0
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2860 | 16,806,1
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2862 | 16,813,0
2863 | 16,816,1
2864 | 16,824,0
2865 | 16,825,0
2866 | 16,830,0
2867 | 16,834,0
2868 | 16,835,1
2869 | 16,836,1
2870 | 16,838,0
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2875 | 16,851,0
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2880 | 16,881,1
2881 | 16,885,0
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2884 | 16,889,0
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2886 | 17,12,1
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2888 | 17,19,1
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2894 | 17,64,0
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2900 | 17,76,0
2901 | 17,80,1
2902 | 17,91,1
2903 | 17,93,1
2904 | 17,95,1
2905 | 17,96,0
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2911 | 17,113,0
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2913 | 17,121,0
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2918 | 17,139,0
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2920 | 17,142,1
2921 | 17,150,1
2922 | 17,160,1
2923 | 17,162,0
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2925 | 17,165,1
2926 | 17,170,1
2927 | 17,173,1
2928 | 17,203,1
2929 | 17,204,0
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2933 | 17,225,0
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2942 | 17,280,1
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2944 | 17,328,1
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2946 | 17,343,0
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2948 | 17,354,0
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2950 | 17,366,0
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2960 | 17,536,1
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2962 | 17,538,1
2963 | 17,544,1
2964 | 17,552,0
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2966 | 17,556,0
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2968 | 17,564,0
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2973 | 17,599,0
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2978 | 17,623,1
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2990 | 17,688,1
2991 | 17,690,1
2992 | 17,691,1
2993 | 17,692,0
2994 | 17,693,0
2995 | 17,694,1
2996 | 17,695,1
2997 | 17,696,1
2998 | 17,705,1
2999 | 17,706,1
3000 | 17,709,1
3001 | 17,726,1
3002 | 17,736,1
3003 | 17,737,1
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3007 | 17,762,1
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3024 | 17,832,0
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3041 | 17,900,0
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3043 | 18,27,0
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3045 | 18,35,0
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3047 | 18,56,1
3048 | 18,59,0
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3050 | 18,74,0
3051 | 18,76,0
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3062 | 18,122,0
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3068 | 18,133,0
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3081 | 18,176,0
3082 | 18,180,0
3083 | 18,187,0
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3086 | 18,216,1
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3090 | 18,222,0
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3101 | 18,255,1
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3111 | 18,327,0
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3119 | 18,392,1
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3141 | 18,553,0
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3200 | 18,747,0
3201 | 18,748,0
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3210 | 18,760,0
3211 | 18,762,0
3212 | 18,769,0
3213 | 18,776,0
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3218 | 18,791,0
3219 | 18,792,0
3220 | 18,794,0
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3242 | 18,882,0
3243 | 18,884,0
3244 | 18,885,0
3245 | 18,886,0
3246 | 18,887,1
3247 | 18,892,1
3248 | 18,900,1
3249 | 19,17,0
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3251 | 19,30,0
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3255 | 19,63,1
3256 | 19,67,1
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3260 | 19,77,0
3261 | 19,80,0
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3300 | 19,252,1
3301 | 19,253,1
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3392 | 19,884,1
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3397 | 19,896,1
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3399 | 19,900,0
3400 | 19,902,1
3401 | 20,23,1
3402 | 20,31,1
3403 | 20,54,1
3404 | 20,56,0
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3406 | 20,60,0
3407 | 20,62,1
3408 | 20,63,0
3409 | 20,64,0
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3434 | 20,162,0
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3437 | 20,179,0
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3456 | 20,363,0
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3458 | 20,378,1
3459 | 20,389,0
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3483 | 20,517,0
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3486 | 20,536,1
3487 | 20,538,1
3488 | 20,542,0
3489 | 20,543,0
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7538 | 43,851,0
7539 | 43,852,1
7540 | 43,859,0
7541 | 43,860,0
7542 | 43,877,1
7543 | 43,882,0
7544 | 43,884,1
7545 | 43,885,0
7546 | 43,886,0
7547 | 43,887,1
7548 | 43,888,0
7549 | 43,889,0
7550 | 44,2,1
7551 | 44,21,0
7552 | 44,44,1
7553 | 44,59,0
7554 | 44,62,1
7555 | 44,66,0
7556 | 44,67,1
7557 | 44,69,0
7558 | 44,71,1
7559 | 44,73,0
7560 | 44,74,1
7561 | 44,76,0
7562 | 44,80,1
7563 | 44,81,0
7564 | 44,93,1
7565 | 44,94,1
7566 | 44,95,1
7567 | 44,96,1
7568 | 44,97,1
7569 | 44,99,1
7570 | 44,100,1
7571 | 44,108,1
7572 | 44,113,1
7573 | 44,133,0
7574 | 44,134,0
7575 | 44,135,1
7576 | 44,139,0
7577 | 44,140,0
7578 | 44,159,0
7579 | 44,160,1
7580 | 44,161,0
7581 | 44,162,1
7582 | 44,163,0
7583 | 44,164,0
7584 | 44,166,1
7585 | 44,167,0
7586 | 44,173,1
7587 | 44,178,1
7588 | 44,197,1
7589 | 44,200,0
7590 | 44,202,1
7591 | 44,216,1
7592 | 44,218,1
7593 | 44,219,0
7594 | 44,220,0
7595 | 44,221,1
7596 | 44,223,0
7597 | 44,225,0
7598 | 44,227,1
7599 | 44,246,0
7600 | 44,247,1
7601 | 44,256,1
7602 | 44,262,0
7603 | 44,263,1
7604 | 44,264,0
7605 | 44,265,0
7606 | 44,280,1
7607 | 44,306,1
7608 | 44,347,1
7609 | 44,365,0
7610 | 44,366,1
7611 | 44,367,1
7612 | 44,390,1
7613 | 44,440,0
7614 | 44,453,1
7615 | 44,478,1
7616 | 44,483,1
7617 | 44,485,1
7618 | 44,486,1
7619 | 44,487,1
7620 | 44,516,1
7621 | 44,517,1
7622 | 44,521,1
7623 | 44,536,0
7624 | 44,537,1
7625 | 44,545,1
7626 | 44,550,0
7627 | 44,559,0
7628 | 44,561,1
7629 | 44,563,1
7630 | 44,565,1
7631 | 44,574,1
7632 | 44,577,0
7633 | 44,578,0
7634 | 44,579,0
7635 | 44,580,1
7636 | 44,581,0
7637 | 44,610,0
7638 | 44,611,1
7639 | 44,612,1
7640 | 44,613,1
7641 | 44,644,1
7642 | 44,649,1
7643 | 44,650,1
7644 | 44,651,0
7645 | 44,652,1
7646 | 44,661,1
7647 | 44,669,0
7648 | 44,678,0
7649 | 44,679,0
7650 | 44,681,1
7651 | 44,683,1
7652 | 44,685,1
7653 | 44,686,0
7654 | 44,691,1
7655 | 44,692,0
7656 | 44,693,0
7657 | 44,694,1
7658 | 44,695,1
7659 | 44,697,1
7660 | 44,706,0
7661 | 44,712,1
7662 | 44,725,0
7663 | 44,726,0
7664 | 44,727,1
7665 | 44,745,0
7666 | 44,746,1
7667 | 44,750,0
7668 | 44,751,1
7669 | 44,759,1
7670 | 44,760,0
7671 | 44,761,1
7672 | 44,762,1
7673 | 44,764,1
7674 | 44,765,0
7675 | 44,774,0
7676 | 44,775,1
7677 | 44,776,1
7678 | 44,777,1
7679 | 44,779,0
7680 | 44,789,0
7681 | 44,790,0
7682 | 44,791,0
7683 | 44,792,1
7684 | 44,794,1
7685 | 44,796,1
7686 | 44,812,0
7687 | 44,813,1
7688 | 44,825,1
7689 | 44,826,1
7690 | 44,831,0
7691 | 44,833,1
7692 | 44,834,0
7693 | 44,835,0
7694 | 44,852,0
7695 | 44,854,0
7696 | 44,861,1
7697 | 44,862,1
7698 | 44,863,1
7699 | 44,880,1
7700 | 44,884,0
7701 | 44,886,0
7702 | 44,888,0
7703 | 44,890,0
7704 | 45,4,0
7705 | 45,26,1
7706 | 45,32,1
7707 | 45,34,1
7708 | 45,45,1
7709 | 45,48,1
7710 | 45,49,0
7711 | 45,59,0
7712 | 45,60,1
7713 | 45,61,0
7714 | 45,62,0
7715 | 45,64,0
7716 | 45,65,1
7717 | 45,67,0
7718 | 45,68,0
7719 | 45,69,1
7720 | 45,70,1
7721 | 45,72,0
7722 | 45,73,1
7723 | 45,74,1
7724 | 45,77,0
7725 | 45,79,1
7726 | 45,82,0
7727 | 45,84,1
7728 | 45,85,1
7729 | 45,87,1
7730 | 45,91,0
7731 | 45,92,1
7732 | 45,93,1
7733 | 45,94,1
7734 | 45,95,1
7735 | 45,96,1
7736 | 45,97,0
7737 | 45,99,0
7738 | 45,101,1
7739 | 45,104,1
7740 | 45,112,1
7741 | 45,116,0
7742 | 45,118,1
7743 | 45,122,0
7744 | 45,125,1
7745 | 45,126,1
7746 | 45,127,0
7747 | 45,131,1
7748 | 45,138,0
7749 | 45,139,0
7750 | 45,140,0
7751 | 45,161,0
7752 | 45,162,0
7753 | 45,163,1
7754 | 45,164,0
7755 | 45,165,1
7756 | 45,166,0
7757 | 45,167,1
7758 | 45,169,0
7759 | 45,210,1
7760 | 45,215,1
7761 | 45,219,1
7762 | 45,220,1
7763 | 45,222,0
7764 | 45,225,1
7765 | 45,227,0
7766 | 45,228,1
7767 | 45,230,1
7768 | 45,231,0
7769 | 45,234,0
7770 | 45,280,1
7771 | 45,284,0
7772 | 45,313,0
7773 | 45,333,1
7774 | 45,356,1
7775 | 45,368,0
7776 | 45,373,1
7777 | 45,389,1
7778 | 45,391,1
7779 | 45,411,1
7780 | 45,414,1
7781 | 45,442,0
7782 | 45,445,1
7783 | 45,449,0
7784 | 45,458,1
7785 | 45,463,0
7786 | 45,464,0
7787 | 45,474,1
7788 | 45,487,1
7789 | 45,488,1
7790 | 45,495,0
7791 | 45,496,0
7792 | 45,497,1
7793 | 45,498,1
7794 | 45,502,1
7795 | 45,513,1
7796 | 45,516,0
7797 | 45,517,1
7798 | 45,523,0
7799 | 45,536,1
7800 | 45,538,1
7801 | 45,543,0
7802 | 45,549,1
7803 | 45,550,1
7804 | 45,556,1
7805 | 45,561,0
7806 | 45,562,1
7807 | 45,563,0
7808 | 45,564,1
7809 | 45,565,0
7810 | 45,574,0
7811 | 45,576,1
7812 | 45,577,1
7813 | 45,579,0
7814 | 45,581,0
7815 | 45,582,0
7816 | 45,583,1
7817 | 45,601,0
7818 | 45,610,1
7819 | 45,612,0
7820 | 45,613,1
7821 | 45,643,1
7822 | 45,645,1
7823 | 45,646,1
7824 | 45,649,0
7825 | 45,650,1
7826 | 45,651,1
7827 | 45,652,1
7828 | 45,669,1
7829 | 45,670,1
7830 | 45,673,0
7831 | 45,674,0
7832 | 45,675,0
7833 | 45,676,1
7834 | 45,677,0
7835 | 45,678,0
7836 | 45,679,0
7837 | 45,690,0
7838 | 45,691,1
7839 | 45,692,0
7840 | 45,693,1
7841 | 45,694,0
7842 | 45,695,1
7843 | 45,705,1
7844 | 45,706,1
7845 | 45,707,0
7846 | 45,731,1
7847 | 45,733,0
7848 | 45,736,1
7849 | 45,737,0
7850 | 45,738,1
7851 | 45,744,0
7852 | 45,748,0
7853 | 45,750,1
7854 | 45,751,1
7855 | 45,760,1
7856 | 45,761,0
7857 | 45,763,0
7858 | 45,765,0
7859 | 45,766,1
7860 | 45,774,1
7861 | 45,775,1
7862 | 45,778,1
7863 | 45,779,0
7864 | 45,780,0
7865 | 45,782,1
7866 | 45,789,0
7867 | 45,791,1
7868 | 45,792,0
7869 | 45,793,0
7870 | 45,794,0
7871 | 45,795,0
7872 | 45,797,1
7873 | 45,802,0
7874 | 45,805,0
7875 | 45,813,0
7876 | 45,825,1
7877 | 45,827,0
7878 | 45,828,0
7879 | 45,831,0
7880 | 45,833,1
7881 | 45,834,0
7882 | 45,835,0
7883 | 45,836,1
7884 | 45,837,0
7885 | 45,838,0
7886 | 45,839,1
7887 | 45,840,0
7888 | 45,841,0
7889 | 45,842,0
7890 | 45,844,1
7891 | 45,845,0
7892 | 45,846,0
7893 | 45,847,1
7894 | 45,848,0
7895 | 45,849,0
7896 | 45,851,0
7897 | 45,852,0
7898 | 45,859,0
7899 | 45,860,0
7900 | 45,862,0
7901 | 45,877,0
7902 | 45,878,1
7903 | 45,881,0
7904 | 45,882,1
7905 | 45,884,0
7906 | 45,885,0
7907 | 45,886,1
7908 | 45,887,1
7909 | 45,888,0
7910 | 45,889,0
7911 | 46,15,1
7912 | 46,16,1
7913 | 46,18,0
7914 | 46,19,1
7915 | 46,20,0
7916 | 46,23,1
7917 | 46,44,1
7918 | 46,46,1
7919 | 46,50,1
7920 | 46,51,0
7921 | 46,52,0
7922 | 46,58,1
7923 | 46,59,0
7924 | 46,61,1
7925 | 46,65,0
7926 | 46,66,0
7927 | 46,73,0
7928 | 46,74,0
7929 | 46,75,1
7930 | 46,94,0
7931 | 46,95,1
7932 | 46,96,1
7933 | 46,98,1
7934 | 46,99,1
7935 | 46,101,1
7936 | 46,107,1
7937 | 46,109,1
7938 | 46,116,1
7939 | 46,118,0
7940 | 46,128,1
7941 | 46,133,1
7942 | 46,138,1
7943 | 46,139,1
7944 | 46,141,0
7945 | 46,159,0
7946 | 46,160,1
7947 | 46,161,0
7948 | 46,163,1
7949 | 46,164,1
7950 | 46,165,0
7951 | 46,166,1
7952 | 46,167,0
7953 | 46,180,1
7954 | 46,184,1
7955 | 46,204,0
7956 | 46,209,0
7957 | 46,214,1
7958 | 46,216,0
7959 | 46,217,0
7960 | 46,221,1
7961 | 46,222,1
7962 | 46,224,0
7963 | 46,225,0
7964 | 46,227,0
7965 | 46,228,0
7966 | 46,240,1
7967 | 46,256,1
7968 | 46,276,1
7969 | 46,278,1
7970 | 46,279,1
7971 | 46,280,1
7972 | 46,299,1
7973 | 46,323,0
7974 | 46,326,1
7975 | 46,328,1
7976 | 46,346,1
7977 | 46,350,1
7978 | 46,364,1
7979 | 46,403,1
7980 | 46,414,1
7981 | 46,445,1
7982 | 46,446,1
7983 | 46,450,1
7984 | 46,451,0
7985 | 46,453,1
7986 | 46,486,1
7987 | 46,487,1
7988 | 46,495,1
7989 | 46,499,0
7990 | 46,502,1
7991 | 46,508,1
7992 | 46,516,1
7993 | 46,532,0
7994 | 46,536,1
7995 | 46,538,1
7996 | 46,546,1
7997 | 46,550,0
7998 | 46,551,1
7999 | 46,554,1
8000 | 46,555,0
8001 | 46,561,0
8002 | 46,573,1
8003 | 46,574,1
8004 | 46,575,0
8005 | 46,576,1
8006 | 46,577,0
8007 | 46,580,0
8008 | 46,581,0
8009 | 46,582,1
8010 | 46,583,0
8011 | 46,586,1
8012 | 46,593,1
8013 | 46,610,1
8014 | 46,611,1
8015 | 46,612,1
8016 | 46,623,1
8017 | 46,632,0
8018 | 46,635,1
8019 | 46,637,1
8020 | 46,645,1
8021 | 46,646,0
8022 | 46,649,1
8023 | 46,653,1
8024 | 46,658,1
8025 | 46,668,1
8026 | 46,669,1
8027 | 46,675,1
8028 | 46,683,1
8029 | 46,684,0
8030 | 46,685,1
8031 | 46,688,1
8032 | 46,696,1
8033 | 46,705,0
8034 | 46,706,1
8035 | 46,707,1
8036 | 46,721,1
8037 | 46,725,0
8038 | 46,727,1
8039 | 46,728,1
8040 | 46,729,1
8041 | 46,733,1
8042 | 46,736,1
8043 | 46,740,1
8044 | 46,744,1
8045 | 46,745,1
8046 | 46,746,1
8047 | 46,747,1
8048 | 46,750,0
8049 | 46,751,0
8050 | 46,759,0
8051 | 46,761,1
8052 | 46,763,1
8053 | 46,775,1
8054 | 46,777,0
8055 | 46,791,0
8056 | 46,802,0
8057 | 46,813,1
8058 | 46,815,1
8059 | 46,820,1
8060 | 46,824,1
8061 | 46,825,0
8062 | 46,827,0
8063 | 46,834,0
8064 | 46,839,1
8065 | 46,841,0
8066 | 46,844,1
8067 | 46,849,0
8068 | 46,851,0
8069 | 46,852,0
8070 | 46,857,1
8071 | 46,859,1
8072 | 46,860,1
8073 | 46,861,0
8074 | 46,863,1
8075 | 46,865,1
8076 | 46,870,1
8077 | 46,871,1
8078 | 46,872,1
8079 | 46,873,1
8080 | 46,878,1
8081 | 46,879,0
8082 | 46,881,1
8083 | 46,882,1
8084 | 46,890,0
8085 | 46,892,0
8086 | 46,898,1
8087 | 


--------------------------------------------------------------------------------
/datasets/data_prep.py:
--------------------------------------------------------------------------------
  1 | import random
  2 | import pandas as pd
  3 | from collections import defaultdict
  4 | 
  5 | 
  6 | class DataPrep(object):
  7 |     """ Data Preprocessor for adaptive testing
  8 |         A collection of static util methods to process datasets
  9 |     """
 10 | 
 11 |     @staticmethod
 12 |     def deduplicate_data(data, policy):
 13 |         """
 14 | 
 15 |         Args:
 16 |             data: pandas DataFrame containing at least 'student_id', 'question_id', 'correct'
 17 |             policy: str in ('keep_first', 'keep_last', 'average')
 18 |         
 19 |         Returns:
 20 |             deduplicated data: pandas DataFrame
 21 |         """
 22 |         # TODO
 23 |         raise NotImplementedError
 24 |         
 25 |     @staticmethod
 26 |     def parse_data(data):
 27 |         """ 
 28 | 
 29 |         Args:
 30 |             data: list of triplets (sid, qid, score)
 31 |         
 32 |         Returns:
 33 |             student based datasets: defaultdict {sid: {qid: score}}
 34 |             question based datasets: defaultdict {qid: {sid: score}}
 35 |         """
 36 |         stu_data = defaultdict(lambda: defaultdict(dict))
 37 |         ques_data = defaultdict(lambda: defaultdict(dict))
 38 |         for sid, qid, correct in data:
 39 |             stu_data[sid][qid] = correct
 40 |             ques_data[qid][sid] = correct
 41 |         return stu_data, ques_data
 42 |     
 43 |     @staticmethod
 44 |     def prep_data(data, **kwargs):
 45 |         """
 46 | 
 47 |         Args:
 48 |             data: list of triplets (sid, qid, score)
 49 |         
 50 |         Returns:
 51 |             processed datasets: list of triplets (sid, qid, score)
 52 |         """
 53 |         # TODO
 54 |         raise NotImplementedError
 55 |     
 56 |     @staticmethod
 57 |     def split_data_by_student(data, test_size=0.2, least_test_length=None):
 58 |         """
 59 | 
 60 |         Args:
 61 |             data: list of triplets (sid, qid, score)
 62 |             test_size: float or int, indicating the size of the test datasets
 63 |             least_test_length: int > 0, the least number of questions required
 64 | 
 65 |         Returns:
 66 |             train_data: list of triplets (sid, qid, score)
 67 |             test_data: list of triplets (sid, qid, score)
 68 |         """
 69 |         stu_data, ques_data = DataPrep.parse_data(data)
 70 |         n_students = len(stu_data)
 71 |         if isinstance(test_size, float):
 72 |             test_size = int(n_students * test_size)
 73 |         train_size = n_students - test_size
 74 |         assert(train_size > 0 and test_size > 0)
 75 |         students = list(range(n_students))
 76 |         random.shuffle(students)
 77 |         if least_test_length is not None:
 78 |             student_lens = defaultdict(int)
 79 |             for t in data:
 80 |                 student_lens[t[0]] += 1
 81 |             students = [student for student in students
 82 |                         if student_lens[student] >= least_test_length]
 83 |         test_students = set(students[:test_size])
 84 |         train_data = [record for record in data if record[0] not in test_students]
 85 |         test_data = [record for record in data if record[0] in test_students]
 86 |         train_data = DataPrep.renumber_student_id(train_data)
 87 |         test_data = DataPrep.renumber_student_id(test_data)
 88 |         return train_data, test_data
 89 | 
 90 |     @staticmethod
 91 |     def save_to_csv(data, path):
 92 |         """
 93 | 
 94 |         Args:
 95 |             data: list of triplets (sid, qid, correct)
 96 |             path: str representing saving path
 97 |         """
 98 |         pd.DataFrame.from_records(sorted(data), columns=['student_id', 'question_id', 'correct']).to_csv(path, index=False)
 99 | 
100 |     @staticmethod
101 |     def renumber_student_id(data):
102 |         """
103 | 
104 |         Args:
105 |             data: list of triplets (sid, qid, score)
106 |         
107 |         Returns:
108 |             renumbered datasets: list of triplets (sid, qid, score)
109 |         """
110 |         student_ids = sorted(set(t[0] for t in data))
111 |         renumber_map = {sid: i for i, sid in enumerate(student_ids)}
112 |         data = [(renumber_map[t[0]], t[1], t[2]) for t in data]
113 |         return data
114 | 


--------------------------------------------------------------------------------
/models/irt/checkpoint.pt:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/bigdata-ustc/MAAT/55226e29a5948dda766137935f532c687a05c231/models/irt/checkpoint.pt


--------------------------------------------------------------------------------
/models/ncd/checkpoint.pt:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/bigdata-ustc/MAAT/55226e29a5948dda766137935f532c687a05c231/models/ncd/checkpoint.pt


--------------------------------------------------------------------------------
/pyat/__init__.py:
--------------------------------------------------------------------------------
1 | from .model import NCDModel, IRTModel
2 | from .strategy import RandomStrategy, ExpectedModelChangeStrategy, MAATStrategy
3 | from .driver import AdapTestDriver
4 | from .utils.data import AdapTestDataset, TrainDataset
5 | 


--------------------------------------------------------------------------------
/pyat/driver.py:
--------------------------------------------------------------------------------
 1 | import logging
 2 | from .utils.data import AdapTestDataset
 3 | from tensorboardX import SummaryWriter
 4 | 
 5 | 
 6 | class AdapTestDriver(object):
 7 | 
 8 |     @staticmethod
 9 |     def run(model, strategy, adaptest_data,
10 |             test_length, log_dir):
11 |         writer = SummaryWriter(log_dir)
12 | 
13 |         logging.info(f'start adaptive testing with {strategy.name} strategy')
14 | 
15 |         logging.info(f'Iteration 0')
16 |         # evaluate models
17 |         results = model.adaptest_evaluate(adaptest_data)
18 |         # log results
19 |         for name, value in results.items():
20 |             logging.info(f'{name}:{value}')
21 |             writer.add_scalars(name, {strategy.name: value}, 0)
22 | 
23 |         for it in range(1, test_length + 1):
24 |             logging.info(f'Iteration {it}')
25 |             # select question
26 |             selected_questions = strategy.adaptest_select(model, adaptest_data)
27 |             for student, question in selected_questions.items():
28 |                 adaptest_data.apply_selection(student, question)
29 |             # update models
30 |             model.adaptest_update(adaptest_data)
31 |             # evaluate models
32 |             results = model.adaptest_evaluate(adaptest_data)
33 |             # log results
34 |             for name, value in results.items():
35 |                 logging.info(f'{name}:{value}')
36 |                 writer.add_scalars(name, {strategy.name: value}, it)
37 | 


--------------------------------------------------------------------------------
/pyat/model/__init__.py:
--------------------------------------------------------------------------------
1 | from .abstract_model import AbstractModel
2 | from .ncd_model import NCDModel
3 | from .irt_model import IRTModel
4 | # from .dkt_model import DKTModel
5 | 


--------------------------------------------------------------------------------
/pyat/model/abstract_model.py:
--------------------------------------------------------------------------------
 1 | from abc import ABC, abstractmethod
 2 | from ..utils.data import TrainDataset, AdapTestDataset, _Dataset
 3 | 
 4 | 
 5 | class AbstractModel(ABC):
 6 | 
 7 |     @property
 8 |     @abstractmethod
 9 |     def name(self):
10 |         raise NotImplementedError
11 | 
12 |     @abstractmethod
13 |     def adaptest_update(self, adaptest_data: AdapTestDataset):
14 |         raise NotImplementedError
15 | 
16 |     @abstractmethod
17 |     def adaptest_evaluate(self, adaptest_data: AdapTestDataset):
18 |         raise NotImplementedError
19 | 
20 |     @abstractmethod
21 |     def adaptest_init(self, data: _Dataset):
22 |         raise NotImplementedError
23 | 
24 |     @abstractmethod
25 |     def adaptest_train(self, train_data: TrainDataset):
26 |         raise NotImplementedError
27 | 
28 |     @abstractmethod
29 |     def adaptest_save(self, path):
30 |         raise NotImplementedError
31 | 
32 |     @abstractmethod
33 |     def adaptest_preload(self, path):
34 |         raise NotImplementedError


--------------------------------------------------------------------------------
/pyat/model/irt_model.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import time
  3 | import copy
  4 | import logging
  5 | import torch
  6 | import torch.nn as nn
  7 | import numpy as np
  8 | import torch.utils.data as data
  9 | from sklearn.metrics import mean_squared_error, r2_score, roc_auc_score
 10 | 
 11 | from .abstract_model import AbstractModel
 12 | from ..utils import make_hot_vector
 13 | from ..utils.data import AdapTestDataset, TrainDataset, _Dataset
 14 | 
 15 | 
 16 | class IRT(nn.Module):
 17 |     def __init__(self, num_students, num_questions, num_dim):
 18 |         super().__init__()
 19 |         self.num_dim = num_dim
 20 |         self.num_students = num_students
 21 |         self.num_questions = num_questions
 22 |         self.theta = nn.Embedding(self.num_students, self.num_dim)
 23 |         self.alpha = nn.Embedding(self.num_questions, self.num_dim)
 24 |         self.beta = nn.Embedding(self.num_questions, 1)
 25 | 
 26 |         for name, param in self.named_parameters():
 27 |             if 'weight' in name:
 28 |                 nn.init.xavier_normal_(param)
 29 | 
 30 |     def forward(self, student_ids, question_ids):
 31 |         theta = self.theta(student_ids)
 32 |         alpha = self.alpha(question_ids)
 33 |         beta = self.beta(question_ids)
 34 |         pred = (alpha * theta).sum(dim=1, keepdim=True) + beta
 35 |         pred = torch.sigmoid(pred)
 36 |         return pred
 37 | 
 38 |     def get_knowledge_status(self, stu_ids):
 39 |         stu_emb = self.theta(stu_ids)
 40 |         return stu_emb.data
 41 | 
 42 | 
 43 | class IRTModel(AbstractModel):
 44 | 
 45 |     def __init__(self, **config):
 46 |         super().__init__()
 47 |         self.config = config
 48 |         self.model = None  # type: IRT
 49 | 
 50 |     @property
 51 |     def name(self):
 52 |         return 'Item Response Theory'
 53 | 
 54 |     def adaptest_init(self, data: _Dataset):
 55 |         self.model = IRT(data.num_students, data.num_questions, self.config['num_dim'])
 56 |     
 57 |     def adaptest_train(self, train_data: TrainDataset):
 58 |         lr = self.config['learning_rate']
 59 |         bsz = self.config['batch_size']
 60 |         epochs = self.config['num_epochs']
 61 |         device = self.config['device']
 62 |         logging.info('train on {}'.format(device))
 63 | 
 64 |         self.model.to(device)
 65 |         train_loader = data.DataLoader(train_data, batch_size=bsz, shuffle=True)
 66 |         optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
 67 | 
 68 |         for ep in range(1, epochs + 1):
 69 |             running_loss = 0.0
 70 |             batch_count = 0
 71 |             log_batch = 1
 72 |             for student_ids, question_ids, correctness, _ in train_loader:
 73 |                 optimizer.zero_grad()
 74 |                 student_ids = student_ids.to(device)
 75 |                 question_ids = question_ids.to(device)
 76 |                 correctness = correctness.to(device).float()
 77 |                 pred = self.model(student_ids, question_ids).view(-1)
 78 |                 loss = self._loss_function(pred, correctness)
 79 |                 loss.backward()
 80 |                 optimizer.step()
 81 |                 batch_count += 1
 82 |                 running_loss += loss.item()
 83 |                 if batch_count % log_batch == 0:
 84 |                     print('Epoch [{}] Batch [{}]: loss={:.3f}'.format(ep, batch_count, running_loss / log_batch))
 85 |                     running_loss = 0.0
 86 | 
 87 |     def adaptest_save(self, path):
 88 |         model_dict = self.model.state_dict()
 89 |         model_dict = {k:v for k,v in model_dict.items() if 'alpha' in k or 'beta' in k}
 90 |         torch.save(model_dict, path)
 91 | 
 92 |     def adaptest_preload(self, path):
 93 |         self.model.load_state_dict(torch.load(path), strict=False)
 94 |         self.model.to(self.config['device'])
 95 | 
 96 |     def adaptest_update(self, adaptest_data: AdapTestDataset):
 97 | 
 98 |         lr = self.config['learning_rate']
 99 |         bsz = self.config['batch_size']
100 |         epochs = self.config['num_epochs']
101 |         device = self.config['device']
102 |         optimizer = torch.optim.Adam(self.model.theta.parameters(), lr=lr)
103 | 
104 |         # for name, param in self.model.named_parameters():
105 |         #     if 'theta' not in name:
106 |         #         param.requires_grad = False
107 | 
108 |         tested_dataset = adaptest_data.get_tested_dataset(last=True)
109 |         dataloader = torch.utils.data.DataLoader(tested_dataset, batch_size=bsz, shuffle=True)
110 | 
111 |         for ep in range(1, epochs + 1):
112 |             running_loss = 0.0
113 |             batch_count = 0
114 |             log_batch = 100
115 |             for student_ids, question_ids, correctness, _ in dataloader:
116 |                 optimizer.zero_grad()
117 |                 student_ids = student_ids.to(device)
118 |                 question_ids = question_ids.to(device)
119 |                 correctness = correctness.to(device).float()
120 |                 pred = self.model(student_ids, question_ids).view(-1)
121 |                 loss = self._loss_function(pred, correctness)
122 |                 loss.backward()
123 |                 optimizer.step()
124 |                 batch_count += 1
125 |                 running_loss += loss.item()
126 |                 if batch_count % log_batch == 0:
127 |                     print('Epoch [{}] Batch [{}]: loss={:.3f}'.format(ep, batch_count, running_loss / log_batch))
128 |                     running_loss = 0.0
129 | 
130 |         # for name, param in self.model.named_parameters():
131 |         #     param.requires_grad = True
132 |     
133 |     def adaptest_evaluate(self, adaptest_data: AdapTestDataset):
134 |         data = adaptest_data.data
135 |         concept_map = adaptest_data.concept_map
136 |         device = self.config['device']
137 | 
138 |         real = []
139 |         pred = []
140 |         with torch.no_grad():
141 |             self.model.eval()
142 |             for sid in data:
143 |                 student_ids = [sid] * len(data[sid])
144 |                 question_ids = list(data[sid].keys())
145 |                 student_ids = torch.LongTensor(student_ids).to(device)
146 |                 question_ids = torch.LongTensor(question_ids).to(device)
147 |                 output = self.model(student_ids, question_ids)
148 |                 output = output.view(-1)
149 |                 pred += output.tolist()
150 |                 real += [data[sid][qid] for qid in question_ids.cpu().numpy()]
151 |             self.model.train()
152 | 
153 |         coverages = []
154 |         for sid in data:
155 |             all_concepts = set()
156 |             tested_concepts = set()
157 |             for qid in data[sid]:
158 |                 all_concepts |= set(concept_map[qid])
159 |             for qid in adaptest_data.tested[sid]:
160 |                 tested_concepts |= set(concept_map[qid])
161 |             coverage = len(tested_concepts) / len(all_concepts)
162 |             coverages.append(coverage)
163 |         cov = sum(coverages) / len(coverages)
164 | 
165 |         real = np.array(real)
166 |         pred = np.array(pred)
167 |         auc = roc_auc_score(real, pred)
168 | 
169 |         return {
170 |             'auc': auc,
171 |             'cov': cov,
172 |         }
173 | 
174 |     def expected_model_change(self, sid: int, qid: int, adaptest_data: AdapTestDataset):
175 | 
176 |         epochs = self.config['num_epochs']
177 |         lr = self.config['learning_rate']
178 |         device = self.config['device']
179 |         optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
180 | 
181 |         for name, param in self.model.named_parameters():
182 |             if 'theta' not in name:
183 |                 param.requires_grad = False
184 | 
185 |         original_weights = self.model.theta.weight.data.clone()
186 | 
187 |         student_id = torch.LongTensor([sid]).to(device)
188 |         question_id = torch.LongTensor([qid]).to(device)
189 |         correct = torch.LongTensor([1]).to(device).float()
190 |         wrong = torch.LongTensor([0]).to(device).float()
191 | 
192 |         for ep in range(epochs):
193 |             optimizer.zero_grad()
194 |             pred = self.model(student_id, question_id)
195 |             loss = self._loss_function(pred, correct)
196 |             loss.backward()
197 |             optimizer.step()
198 | 
199 |         pos_weights = self.model.theta.weight.data.clone()
200 |         self.model.theta.weight.data.copy_(original_weights)
201 | 
202 |         for ep in range(epochs):
203 |             optimizer.zero_grad()
204 |             pred = self.model(student_id, question_id)
205 |             loss = self._loss_function(pred, wrong)
206 |             loss.backward()
207 |             optimizer.step()
208 | 
209 |         neg_weights = self.model.theta.weight.data.clone()
210 |         self.model.theta.weight.data.copy_(original_weights)
211 | 
212 |         for param in self.model.parameters():
213 |             param.requires_grad = True
214 | 
215 |         pred = self.model(student_id, question_id).item()
216 |         return pred * torch.norm(pos_weights - original_weights).item() + \
217 |                (1 - pred) * torch.norm(neg_weights - original_weights).item()
218 | 
219 |     def _loss_function(self, pred, real):
220 |         return -(real * torch.log(0.0001 + pred) + (1 - real) * torch.log(1.0001 - pred)).mean()
221 |     
222 |     def get_alpha(self, question_id):
223 |         return self.model.alpha.weight.data.numpy()[question_id]
224 |     
225 |     def get_beta(self, question_id):
226 |         return self.model.beta.weight.data.numpy()[question_id]
227 |     
228 |     def get_theta(self, student_id):
229 |         return self.model.theta.weight.data.numpy()[student_id]
230 | 


--------------------------------------------------------------------------------
/pyat/strategy/__init__.py:
--------------------------------------------------------------------------------
1 | from .abstract_strategy import AbstractStrategy
2 | from .random_strategy import RandomStrategy
3 | from .expected_model_change_strategy import ExpectedModelChangeStrategy
4 | from .maat_strategy import MAATStrategy


--------------------------------------------------------------------------------
/pyat/strategy/abstract_strategy.py:
--------------------------------------------------------------------------------
 1 | from abc import ABC, abstractmethod
 2 | 
 3 | 
 4 | class AbstractStrategy(ABC):
 5 | 
 6 |     @property
 7 |     @abstractmethod
 8 |     def name(self):
 9 |         """ the name of the strategy
10 | 
11 |         Returns:
12 |             name: str
13 |         """
14 |         raise NotImplementedError
15 | 
16 |     @abstractmethod
17 |     def adaptest_select(self, model, adaptest_data):
18 |         """
19 | 
20 |         Args:
21 |             model: AbstractModel
22 |             adaptest_data: AdapTestDataset
23 | 
24 |         Returns:
25 |             selected_questions: dict, {student_idx: question_idx}
26 |         """
27 |         raise NotImplementedError
28 | 


--------------------------------------------------------------------------------
/pyat/strategy/maat_strategy.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from ..strategy.abstract_strategy import AbstractStrategy
 3 | from ..model import AbstractModel
 4 | from ..utils.data import AdapTestDataset
 5 | 
 6 | 
 7 | 
 8 | class MAATStrategy(AbstractStrategy):
 9 | 
10 |     def __init__(self, n_candidates=10):
11 |         super().__init__()
12 |         self.n_candidates = n_candidates
13 | 
14 |     @property
15 |     def name(self):
16 |         return 'Model Agnostic Adaptive Testing'
17 | 
18 |     def _compute_coverage_gain(self, sid, qid, adaptest_data: AdapTestDataset):
19 |         concept_cnt = {}
20 |         for q in adaptest_data.data[sid]:
21 |             for c in adaptest_data.concept_map[q]:
22 |                 concept_cnt[c] = 0
23 |         for q in list(adaptest_data.tested[sid]) + [qid]:
24 |             for c in adaptest_data.concept_map[q]:
25 |                 concept_cnt[c] += 1
26 |         return (sum(cnt / (cnt + 1) for c, cnt in concept_cnt.items())
27 |                 / sum(1 for c in concept_cnt))
28 | 
29 |     def adaptest_select(self, model: AbstractModel, adaptest_data: AdapTestDataset):
30 |         assert hasattr(model, 'expected_model_change'), \
31 |             'the models must implement expected_model_change method'
32 |         selection = {}
33 |         for sid in range(adaptest_data.num_students):
34 |             untested_questions = np.array(list(adaptest_data.untested[sid]))
35 |             emc_arr = [getattr(model, 'expected_model_change')(sid, qid, adaptest_data)
36 |                        for qid in untested_questions]
37 |             candidates = untested_questions[np.argsort(emc_arr)[::-1][:self.n_candidates]]
38 |             selection[sid] = max(candidates, key=lambda qid: self._compute_coverage_gain(sid, qid, adaptest_data))
39 |         return selection
40 | 


--------------------------------------------------------------------------------
/pyat/utils/__init__.py:
--------------------------------------------------------------------------------
1 | from ._utils import *


--------------------------------------------------------------------------------
/pyat/utils/_utils.py:
--------------------------------------------------------------------------------
 1 | def make_hot_vector(indices, num_dim):
 2 |     """
 3 | 
 4 |     Args:
 5 |         indices: list of indices indicating 1s
 6 |         num_dim: total length of the vector
 7 | 
 8 |     Returns:
 9 |         v: list representing hot vector
10 |     """
11 | 
12 |     v = [0] * num_dim
13 |     for i in indices:
14 |         v[i] = 1
15 |     return v
16 | 


--------------------------------------------------------------------------------
/pyat/utils/data/__init__.py:
--------------------------------------------------------------------------------
1 | from .adaptest_dataset import AdapTestDataset
2 | from .train_dataset import TrainDataset
3 | from ._dataset import _Dataset
4 | 


--------------------------------------------------------------------------------
/pyat/utils/data/_dataset.py:
--------------------------------------------------------------------------------
 1 | from collections import defaultdict, deque
 2 | 
 3 | 
 4 | class _Dataset(object):
 5 | 
 6 |     def __init__(self, data, concept_map,
 7 |                  num_students, num_questions, num_concepts):
 8 |         """
 9 | 
10 |         Args:
11 |             data: list, [(sid, qid, score)]
12 |             concept_map: dict, concept map {qid: cid}
13 |             num_students: int, total student number
14 |             num_questions: int, total question number
15 |             num_concepts: int, total concept number
16 | 
17 |         Requirements:
18 |             ids of students, questions, concepts all renumbered
19 |         """
20 |         self._raw_data = data
21 |         self._concept_map = concept_map
22 |         # reorganize datasets
23 |         self._data = {}
24 |         for sid, qid, correct in data:
25 |             self._data.setdefault(sid, {})
26 |             self._data[sid].setdefault(qid, {})
27 |             self._data[sid][qid] = correct
28 | 
29 |         self.n_students = num_students
30 |         self.n_questions = num_questions
31 |         self.n_concepts = num_concepts
32 |         student_ids = set(x[0] for x in data)
33 |         question_ids = set(x[1] for x in data)
34 |         concept_ids = set(sum(concept_map.values(), []))
35 |         assert max(student_ids) < num_students, \
36 |             'Require student ids renumbered'
37 |         assert max(question_ids) < num_questions, \
38 |             'Require student ids renumbered'
39 |         assert max(concept_ids) < num_concepts, \
40 |             'Require student ids renumbered'
41 | 
42 |     @property
43 |     def num_students(self):
44 |         return self.n_students
45 | 
46 |     @property
47 |     def num_questions(self):
48 |         return self.n_questions
49 | 
50 |     @property
51 |     def num_concepts(self):
52 |         return self.n_concepts
53 | 
54 |     @property
55 |     def raw_data(self):
56 |         return self._raw_data
57 | 
58 |     @property
59 |     def data(self):
60 |         return self._data
61 | 
62 |     @property
63 |     def concept_map(self):
64 |         return self._concept_map
65 | 


--------------------------------------------------------------------------------
/pyat/utils/data/adaptest_dataset.py:
--------------------------------------------------------------------------------
 1 | from collections import defaultdict, deque
 2 | 
 3 | import torch
 4 | 
 5 | from ._dataset import _Dataset
 6 | from .train_dataset import TrainDataset
 7 | 
 8 | 
 9 | class AdapTestDataset(_Dataset):
10 | 
11 |     def __init__(self, data, concept_map,
12 |                  num_students, num_questions, num_concepts):
13 |         """
14 | 
15 |         Args:
16 |             data: list, [(sid, qid, score)]
17 |             concept_map: dict, concept map {qid: cid}
18 |             num_students: int, total student number
19 |             num_questions: int, total question number
20 |             num_concepts: int, total concept number
21 | 
22 |         Requirements:
23 |             ids of students, questions, concepts all renumbered
24 |         """
25 |         super().__init__(data, concept_map,
26 |                          num_students, num_questions, num_concepts)
27 | 
28 |         # initialize tested and untested set
29 |         self._tested = None
30 |         self._untested = None
31 |         self.reset()
32 | 
33 |     def apply_selection(self, student_idx, question_idx):
34 |         """ Add one untested question to the tested set
35 | 
36 |         Args:
37 |             student_idx: int
38 |             question_idx: int
39 | 
40 |         Returns:
41 | 
42 |         """
43 |         assert question_idx in self._untested[student_idx], \
44 |             'Selected question not allowed'
45 |         self._untested[student_idx].remove(question_idx)
46 |         self._tested[student_idx].append(question_idx)
47 | 
48 |     def reset(self):
49 |         self._tested = defaultdict(deque)
50 |         self._untested = defaultdict(set)
51 |         for sid in self._data:
52 |             self._untested[sid] = set(self._data[sid].keys())
53 | 
54 |     @property
55 |     def tested(self):
56 |         return self._tested
57 | 
58 |     @property
59 |     def untested(self):
60 |         return self._untested
61 | 
62 |     def get_tested_dataset(self, last=False):
63 |         triplets = []
64 |         for sid, qids in self._tested.items():
65 |             if last:
66 |                 qid = qids[-1]
67 |                 triplets.append((sid, qid, self.data[sid][qid]))
68 |             else:
69 |                 for qid in qids:
70 |                     triplets.append((sid, qid, self.data[sid][qid]))
71 |         return TrainDataset(triplets, self.concept_map,
72 |                             self.num_students, self.num_questions, self.num_concepts)
73 | 


--------------------------------------------------------------------------------
/pyat/utils/data/train_dataset.py:
--------------------------------------------------------------------------------
 1 | from torch.utils import data
 2 | from ._dataset import _Dataset
 3 | 
 4 | 
 5 | class TrainDataset(_Dataset, data.dataset.Dataset):
 6 | 
 7 |     def __init__(self, data, concept_map,
 8 |                  num_students, num_questions, num_concepts):
 9 |         """
10 | 
11 |         Args:
12 |             data: list, [(sid, qid, score)]
13 |             concept_map: dict, concept map {qid: cid}
14 |             num_students: int, total student number
15 |             num_questions: int, total question number
16 |             num_concepts: int, total concept number
17 | 
18 |         Requirements:
19 |             ids of students, questions, concepts all renumbered
20 |         """
21 |         super().__init__(data, concept_map,
22 |                          num_students, num_questions, num_concepts)
23 | 
24 |     def __getitem__(self, item):
25 |         sid, qid, score = self._raw_data[item]
26 |         concepts = self.concept_map[qid]
27 |         return sid, qid, score, concepts
28 | 
29 |     def __len__(self):
30 |         return len(self._raw_data)
31 | 


--------------------------------------------------------------------------------
/scripts/test.py:
--------------------------------------------------------------------------------
 1 | import sys
 2 | import json
 3 | import datetime
 4 | import logging
 5 | 
 6 | import torch
 7 | import numpy as np
 8 | import pandas as pd
 9 | 
10 | sys.path.append('..')
11 | import pyat
12 | 
13 | seed = 0
14 | np.random.seed(seed)
15 | torch.manual_seed(seed)
16 | 
17 | dataset = 'assistment'
18 | # read datasets
19 | test_triplets = pd.read_csv(f'../datasets/{dataset}/test_triplets.csv', encoding='utf-8').to_records(index=False)
20 | concept_map = json.load(open(f'../datasets/{dataset}/concept_map.json', 'r'))
21 | concept_map = {int(k):v for k,v in concept_map.items()}
22 | metadata = json.load(open(f'../datasets/{dataset}/metadata.json', 'r'))
23 | 
24 | test_data = pyat.AdapTestDataset(test_triplets, concept_map,
25 |                                  metadata['num_test_students'], metadata['num_questions'], metadata['num_concepts'])
26 | 
27 | config = {
28 |     'learning_rate': 0.0025,
29 |     'batch_size': 2048,
30 |     'num_epochs': 8,
31 |     'num_dim': 1,
32 |     'device': 'cpu',
33 | }
34 | 
35 | logging.basicConfig(
36 |     level=logging.INFO,
37 |     datefmt='%Y-%m-%d %H:%M:%S',
38 |     format='[%(asctime)s %(levelname)s] %(message)s',
39 | )
40 | 
41 | test_length = 50
42 | now = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
43 | 
44 | strategies = (pyat.RandomStrategy(), pyat.MAATStrategy(n_candidates=10), )
45 | 
46 | for strategy in strategies:
47 |     model = pyat.IRTModel(**config)
48 |     model.adaptest_init(test_data)
49 |     model.adaptest_preload('../models/irt/checkpoint.pt')
50 |     test_data.reset()
51 |     pyat.AdapTestDriver.run(model, strategy, test_data, test_length, f'../results/{now}')
52 | 


--------------------------------------------------------------------------------
/scripts/train.py:
--------------------------------------------------------------------------------
 1 | import sys
 2 | import json
 3 | import logging
 4 | import numpy as np
 5 | import pandas as pd
 6 | 
 7 | sys.path.append('..')
 8 | import pyat
 9 | 
10 | dataset = 'assistment'
11 | 
12 | # read datasets
13 | train_triplets = pd.read_csv(f'../datasets/{dataset}/train_triplets.csv', encoding='utf-8').to_records(index=False)
14 | concept_map = json.load(open(f'../datasets/{dataset}/concept_map.json', 'r'))
15 | concept_map = {int(k):v for k,v in concept_map.items()}
16 | metadata = json.load(open(f'../datasets/{dataset}/metadata.json', 'r'))
17 | 
18 | # construct
19 | train_data = pyat.TrainDataset(train_triplets, concept_map,
20 |                                metadata['num_train_students'], metadata['num_questions'], metadata['num_concepts'])
21 | 
22 | config = {
23 |     'learning_rate': 0.002,
24 |     'batch_size': 2048,
25 |     'num_epochs': 100,
26 |     'num_dim': 1,
27 |     'device': 'cpu',
28 | }
29 | 
30 | logging.basicConfig(
31 |     level=logging.INFO,
32 |     datefmt='%Y-%m-%d %H:%M:%S',
33 |     format='[%(asctime)s %(levelname)s] %(message)s',
34 | )
35 | 
36 | model = pyat.IRTModel(**config)
37 | model.adaptest_init(train_data)
38 | model.adaptest_train(train_data)
39 | model.adaptest_save('../models/irt/checkpoint.pt')
40 | 


--------------------------------------------------------------------------------