Is there a way to create a dynamic regression model (ARIMAX) with multiple explanatory variables? - dynamic
Is there a way to create a dynamic regression model (ARIMAX) with multiple explanatory variables?
The explanatory variables are introduced into the model with the regression part (xreg()).
(Fit <- auto.arima(Oct_Mar[, "ctr"],
xreg = Oct_Mar[, "price_purchase"]))`
As long as I use a variable it works to create the model.
As soon as I want to use several variables:
(Fit <- auto.arima(Oct_Mar[, "ctr"],
xreg = Oct_Mar[, "price_purchase"],
xreg = Oct_Mar[, "price_view"],
xreg = Oct_Mar[, "price_cart"]))`
Error:
Error in auto.arima(Oct_Mar[, "ctr"], xreg = Oct_Mar[, "price_purchase"], :
formal argument "xreg" matched by multiple actual arguments
When I try it like this:
(Fit <- auto.arima(Oct_Mar[, "ctr"],
xreg = Oct_Mar[, 2:6])) `
Error:
Error in auto.arima(Oct_Mar[, "ctr"], xreg = Oct_Mar[, 2:6]) :
xreg should be a numeric matrix or a numeric vector
All chosen variables should be numeric.
Data Subset:
structure(list(date = c("2019.10.01", "2019.10.02", "2019.10.03",
"2019.10.04", "2019.10.05", "2019.10.06", "2019.10.07", "2019.10.08",
"2019.10.09", "2019.10.10", "2019.10.11", "2019.10.12", "2019.10.13",
"2019.10.14", "2019.10.15", "2019.10.16", "2019.10.17", "2019.10.18",
"2019.10.19", "2019.10.20", "2019.10.21", "2019.10.22", "2019.10.23",
"2019.10.24", "2019.10.25", "2019.10.26", "2019.10.27", "2019.10.28",
"2019.10.29", "2019.10.30", "2019.10.31", "2019.11.01", "2019.11.02",
"2019.11.03", "2019.11.04", "2019.11.05", "2019.11.06", "2019.11.07",
"2019.11.08", "2019.11.09", "2019.11.10", "2019.11.11", "2019.11.12",
"2019.11.13", "2019.11.14", "2019.11.15", "2019.11.16", "2019.11.17",
"2019.11.18", "2019.11.19", "2019.11.20", "2019.11.21", "2019.11.22",
"2019.11.23", "2019.11.24", "2019.11.25", "2019.11.26", "2019.11.27",
"2019.11.28", "2019.11.29", "2019.11.30", "2019.12.01", "2019.12.02",
"2019.12.03", "2019.12.04", "2019.12.05", "2019.12.06", "2019.12.07",
"2019.12.08", "2019.12.09", "2019.12.10", "2019.12.11", "2019.12.12",
"2019.12.13", "2019.12.14", "2019.12.15", "2019.12.16", "2019.12.17",
"2019.12.18", "2019.12.19", "2019.12.20", "2019.12.21", "2019.12.22",
"2019.12.23", "2019.12.24", "2019.12.25", "2019.12.26", "2019.12.27",
"2019.12.28", "2019.12.29", "2019.12.30", "2019.12.31", "2020.01.01",
"2020.01.02", "2020.01.03", "2020.01.04", "2020.01.05", "2020.01.06",
"2020.01.07", "2020.01.08", "2020.01.09", "2020.01.10", "2020.01.11",
"2020.01.12", "2020.01.13", "2020.01.14", "2020.01.15", "2020.01.16",
"2020.01.17", "2020.01.18", "2020.01.19", "2020.01.20", "2020.01.21",
"2020.01.22", "2020.01.23", "2020.01.24", "2020.01.25", "2020.01.26",
"2020.01.27", "2020.01.28", "2020.01.29", "2020.01.30", "2020.01.31",
"2020.02.01", "2020.02.02", "2020.02.03", "2020.02.04", "2020.02.05",
"2020.02.06", "2020.02.07", "2020.02.08", "2020.02.09", "2020.02.10",
"2020.02.11", "2020.02.12", "2020.02.13", "2020.02.14", "2020.02.15",
"2020.02.16", "2020.02.17", "2020.02.18", "2020.02.19", "2020.02.20",
"2020.02.21", "2020.02.22", "2020.02.23", "2020.02.24", "2020.02.25",
"2020.02.26", "2020.02.27", "2020.02.28", "2020.02.29", "2020.03.01",
"2020.03.02", "2020.03.03", "2020.03.04", "2020.03.05", "2020.03.06",
"2020.03.07", "2020.03.08", "2020.03.09", "2020.03.10", "2020.03.11",
"2020.03.12", "2020.03.13", "2020.03.14", "2020.03.15", "2020.03.16",
"2020.03.17", "2020.03.18", "2020.03.19", "2020.03.20", "2020.03.21",
"2020.03.22", "2020.03.23", "2020.03.24", "2020.03.25", "2020.03.26",
"2020.03.27", "2020.03.28", "2020.03.29", "2020.03.30", "2020.03.31"
), price_view = c(35.79, 180.16, 437.57, 10.3, 74.26, 79.8, 89.84,
121.24, 461.95, 142.06, 241.71, 52, 43.24, 41.16, 167.05, 764.06,
91.64, 189.82, 38.59, 152.64, 86.23, 321.33, 411.83, 256.88,
352.39, 76.32, 360.11, 123.53, 43.41, 149.38, 14.16, 489.07,
1661.74, 1253.07, 25.71, 154.42, 990.89, 1645.93, 144.12, 84.43,
240.25, 148.18, 41.13, 262.56, 168.78, 860.85, 239.31, 372.98,
165.64, 134.32, 20.7, 43.73, 765.76, 51.48, 599.49, 893.79, 155.29,
334.37, 46.82, 1814.72, 196.27, 1302.48, 40.16, 1161.68, 381.48,
184.48, 48.91, 221.11, 434.73, 149.27, 77.22, 882.49, 106.05,
669.23, 282.86, 179.67, 12.97, 460.24, 38.59, 278.26, 243.76,
1904.79, 84.93, 32.18, 25.71, 496.54, 29.6, 1466.83, 164.33,
234.76, 19.95, 308.37, 1130.02, 7.47, 79.8, 65.9, 746.45, 1347.78,
1270.82, 69.42, 231.41, 195.6, 715.33, 208.47, 720.46, 414.68,
24.45, 217.82, 434.45, 483.92, 1500.42, 318.15, 339.29, 267.45,
133.85, 9.03, 11.81, 280.57, 916.74, 58.51, 339.78, 33.98, 263.58,
19.31, 239.88, 489.07, 84.92, 344.9, 95.24, 99.1, 142.58, 480.58,
104.74, 14.83, 252, 1039.41, 28.3, 328.97, 341.55, 278.26, 43.73,
91.35, 102.32, 131.25, 155.15, 77.74, 14.67, 132.63, 1185.36,
291.13, 1106.59, 849.42, 117.63, 171.32, 167.31, 252.23, 248.14,
111.15, 257.15, 27.62, 169.86, 101.89, 282.89, 298.57, 86.49,
196.32, 1415.45, 898.35, 334.6, 17.99, 13.62, 566.27, 60.41,
36.34, 62.04, 308.81, 32.95, 127.44, 836.57, 221.34, 360.34,
159.31, 20.57), view = c(1206151, 1152770, 1087372, 1344804,
1270060, 1262993, 1159265, 1323522, 1301376, 1240347, 1445162,
1432321, 1583572, 1376274, 1462409, 1443323, 1337174, 1413405,
1382403, 1443838, 1342668, 1353053, 1318395, 1252747, 1369922,
1288939, 1330209, 1220710, 1187883, 1169955, 1207854, 1402754,
1513400, 1524803, 1743304, 1670637, 1644359, 1748812, 1789808,
1783142, 1845552, 1907417, 1892753, 1920411, 2864410, 5691766,
5986292, 5759703, 1905351, 1627672, 1598554, 1573101, 1471242,
1474138, 1500022, 1496128, 1557252, 1547199, 1560191, 1727852,
1644405, 1706901, 1629904, 1547658, 1468085, 1540157, 1652208,
1725106, 1724452, 1627222, 1651328, 1605421, 1650612, 1634861,
1760750, 2167056, 2875847, 2780816, 2665285, 2528244, 2387520,
2340327, 2471739, 2372930, 2326654, 2322753, 2240514, 2058141,
2089081, 2474226, 2294820, 1603749, 1427733, 1700904, 1765457,
1754424, 1738774, 1696188, 1701769, 1585870, 1556542, 1557542,
1618230, 1645866, 1627433, 1612956, 1555416, 1773179, 1826768,
2021676, 2104199, 1801073, 1733142, 1593991, 1645225, 1557626,
1637470, 1721003, 1545472, 1594688, 1565742, 1651606, 1999670,
2217825, 1985751, 1680034, 1608904, 1620473, 1628906, 1726835,
1589058, 1714745, 1751044, 1896265, 2429526, 2268487, 1935249,
1916034, 2239698, 1916650, 1981570, 1948648, 1987134, 1749514,
1822349, 1830307, 1748590, 1734610, 1798308, 162557, 1000204,
1257475, 1770064, 2416707, 2477258, 2487470, 2457500, 2210539,
2377633, 2026050, 2301337, 2218894, 2012789, 1700619, 1481115,
1562027, 1560348, 1338829, 1244973, 1142989, 1260747, 1316975,
1387394, 1319559, 1440470, 1451015, 1439649, 1390411, 1336076,
1369834, 1255626, 1244163, 1283731), price_cart = c(29.51, 1415.48,
99.86, 358.57, 617.51, 1052.79, 1747.79, 190.56, 128.28, 252.38,
250.91, 720.48, 33.42, 643, 191.77, 460.11, 408.5, 789.9, 577.94,
49.36, 380.7, 19.56, 994.86, 756.71, 223.66, 437.33, 1684.28,
366.16, 968.34, 1683.07, 550.77, 503.09, 29.09, 179.67, 210.62,
22.66, 131.66, 68.96, 360.06, 494.22, 1023.62, 1569.92, 28.29,
694.97, 127.05, 37.85, 282.89, 178.9, 913.28, 1022.42, 424.7,
573.7, 1029.34, 30.12, 20.82, 17.99, 107.53, 41.19, 85.82, 1002.55,
140.98, 167.03, 231.67, 25.71, 205.64, 30.81, 51.22, 65.9, 7.08,
308.63, 227.79, 16.22, 7.89, 62.52, 48.88, 586.63, 602.07, 1312.26,
128.32, 179.9, 849.42, 100.9, 1284.2, 12.84, 128.42, 59.18, 176.99,
38.02, 48.88, 694.54, 262.3, 1402.84, 1453.18, 3.84, 453.01,
76.93, 7.04, 865.93, 865.4, 40.75, 1423.07, 1534.66, 679.27,
11.25, 102.63, 436.3, 853.93, 694.97, 850.47, 477.49, 1234.97,
10.27, 23.94, 643.23, 89.84, 290.34, 320.99, 6.44, 140.28, 188.89,
56.88, 1326.31, 194.34, 140.28, 771.96, 140.03, 20.21, 1464.39,
59.18, 57.92, 1156.81, 50.43, 300.12, 38.1, 832.71, 57.91, 174.5,
100.36, 248.14, 109.34, 100.7, 242.7, 266.67, 592.01, 242.18,
22.66, 566.04, 38.61, 812.06, 123.92, 168.6, 172.03, 49.91, 16.73,
108.04, 347.47, 97.79, 111.15, 514.79, 126.1, 178.87, 870.03,
529.31, 43.5, 2110.48, 771.94, 15.32, 105.25, 7.14, 312.67, 61.75,
165.51, 48.37, 643.49, 303.48, 35.78, 154.42, 209.71, 76.69,
25.46, 1415.45, 123.53, 602.31), cart = c(16658, 17268, 19323,
43826, 35493, 32145, 18052, 18442, 18432, 18997, 21450, 20691,
24833, 44821, 49513, 45272, 40368, 40127, 39455, 40533, 36675,
36945, 36407, 35721, 36800, 34776, 34256, 17838, 17455, 16996,
16798, 18911, 19350, 20211, 21960, 19231, 19670, 19446, 77319,
70093, 71585, 75135, 69669, 71613, 170183, 481862, 405584, 426261,
83117, 72450, 72311, 75530, 70171, 64801, 68099, 71405, 71622,
71324, 71504, 92345, 81760, 84473, 80869, 70192, 66718, 71048,
83618, 84231, 80773, 80675, 81420, 78947, 80162, 82360, 86689,
109721, 183764, 155406, 146906, 137487, 127900, 124577, 127381,
126700, 124797, 127554, 123966, 120940, 127769, 148663, 148608,
119062, 57614, 71342, 95608, 80629, 78782, 79099, 77396, 74671,
72772, 74827, 72221, 73406, 72999, 71182, 70235, 79414, 104791,
103481, 102597, 94354, 90666, 83642, 83223, 73075, 73582, 73849,
70067, 71600, 72179, 130757, 208231, 169156, 137970, 116560,
104701, 102836, 101145, 101605, 90864, 92635, 95114, 100283,
158447, 131720, 118661, 126405, 132399, 98277, 96270, 95284,
96886, 89046, 91384, 89585, 83771, 83241, 84151, 6104, 40574,
48944, 81869, 146953, 135144, 132819, 134255, 131648, 141696,
122204, 122752, 120927, 112159, 102239, 95998, 97600, 99032,
79662, 76622, 69585, 73822, 74488, 75621, 69098, 73761, 76429,
75664, 77671, 77090, 77835, 68888, 69091, 73986), price_purchase = c(130.76,
419.6, 251.74, 252.88, 64.02, 272.59, 172.72, 88.81, 28.73, 1003.86,
346.47, 130.48, 29.86, 280.11, 358.57, 385.83, 287.61, 22.95,
58.08, 854.08, 28.28, 62.91, 994.86, 51.22, 9.01, 77.21, 244.15,
366.16, 366.8, 213.25, 35.52, 566.3, 35.78, 1106.82, 64.35, 722.18,
131.66, 166.1, 823.9, 138.23, 334.6, 328.19, 243.51, 488.8, 159.57,
106.8, 54.03, 27, 308.63, 1022.42, 463.31, 144.66, 44.53, 25.48,
126.18, 365.52, 133.92, 97.27, 12.84, 1002.55, 107.41, 132.31,
131.2, 789.57, 230.2, 12.36, 229.86, 1386.91, 154.19, 18.19,
76.96, 882.49, 191.55, 46.08, 24.17, 102.65, 326.62, 924.06,
923.73, 88.29, 41.16, 128.42, 326.88, 137.96, 30.68, 108.88,
181.19, 241.34, 128.32, 137.46, 1279.81, 643.23, 1275.16, 717.245,
159.33, 745.37, 288.27, 177.26, 168.58, 66.85, 331.51, 437.31,
643.23, 9.3, 0.85, 436.3, 105.51, 7.7, 79.44, 1321.37, 160.89,
107.21, 172.25, 514.79, 141.06, 900.64, 153.22, 924.4, 176.34,
94.98, 162.6, 1326.25, 39.9, 38.15, 2162.22, 180.95, 153.41,
720.48, 720.48, 15.42, 140.28, 514.02, 720.47, 174.7, 197.69,
411.08, 741.07, 230.12, 501.89, 109.34, 643.26, 23.17, 242.48,
1317.36, 69.76, 178.11, 153.55, 32.18, 812.06, 302.2, 153.34,
172.03, 128.68, 939.54, 108.04, 165.89, 56.63, 43.76, 171.17,
98.59, 21.95, 280.28, 181.47, 730.01, 159.31, 60.75, 31.15, 1412.39,
7.14, 942.84, 321.06, 165.51, 284.95, 169.42, 303.48, 224.3,
416.43, 385.85, 492.08, 334.6, 1415.45, 123.53, 308.55), purchase = c(19307,
19469, 19255, 27041, 23494, 22171, 21378, 23072, 22748, 21993,
26224, 25373, 29561, 28405, 26372, 31394, 28318, 25850, 24657,
25098, 25167, 25385, 24731, 23999, 23929, 22653, 23403, 21112,
20374, 20817, 20099, 22458, 21864, 22145, 26889, 24875, 25319,
24863, 25714, 22768, 22878, 24931, 22725, 22548, 22124, 45185.5,
68247, 185195, 28537, 24967, 24947, 25266, 24187, 22243, 23163,
24827, 24226, 24443, 24305, 32107, 28178, 28345, 28548, 24358,
24473, 25469, 27505, 27012, 25766, 26802, 27059, 25906, 26044,
26712, 26559, 35077, 63796, 51899, 49578, 48212, 46405, 44255,
44719, 46602, 44917, 44949, 44154, 43081, 45287, 49597, 50729,
38233, 3574, 13975.5, 24377, 28938, 28427, 28875, 27722, 27510,
26492, 27481, 26059, 25869, 27525, 26322, 27121, 29614, 35086,
32884, 32548, 32619, 31698, 30089, 30398, 27579, 26662, 26880,
27052, 26841, 27403, 27612, 33750, 32536, 32308, 28645, 27652,
28276, 28533, 28426, 25379, 26027, 46480, 60013, 102117, 83216,
76048, 72365, 87586, 63260, 36377, 31438, 31258, 29324, 28946,
29017, 27884, 28063, 27809, 27499, 27490, 26316, 31358, 55087,
46356, 45228, 44406, 43521, 45501, 40281, 41091, 41681, 39266,
36268, 34754, 35341, 35943, 28852, 27810, 25186, 25501, 26232,
25775, 23698, 25314, 25960, 26259, 27487, 26966, 25817, 21294,
22704, 23997), ctr = c(0.0157890561813006, 0.0166396305077271,
0.0173986509381537, 0.0194731497951218, 0.0179954394804347, 0.01711863909483,
0.0181582360570687, 0.0171927115779559, 0.0172358403646591, 0.0174638541971058,
0.0178806664612045, 0.017462347179514, 0.0183790774089859, 0.0199881077619723,
0.0174426987635606, 0.021089685240109, 0.0205569049800296, 0.0177842661874661,
0.0173413941476575, 0.0169081718788632, 0.0182456430344012, 0.0182626162052032,
0.0182543279386951, 0.0186259961442581, 0.0170104683085926, 0.0171132003490177,
0.0171517774365777, 0.0170457664943143, 0.016903142521019, 0.0175382134561578,
0.0164120092891695, 0.0157969704536582, 0.014264557168488, 0.0143332034531726,
0.0152322825367764, 0.0147200846456646, 0.0152154800186776, 0.0140607309566817,
0.0137719608789332, 0.0122855439272407, 0.0119334194687182, 0.0125752060979989,
0.01158007808718, 0.0113191407332442, 0.00729059877222415, 0.00731911608538772,
0.0106771470535411, 0.029937936916542, 0.0143512493034839, 0.0146854166936255,
0.0149305898441825, 0.015325442746133, 0.015691446743994, 0.0144534643673336,
0.0147711815606066, 0.0158382630541111, 0.0148728508159624, 0.015102040564144,
0.0148955533969277, 0.0176392994824187, 0.0163240478169816, 0.0158230497930639,
0.0166871934499785, 0.0150557839107457, 0.0159453688844757, 0.0158074236363467,
0.0158454822084702, 0.0149292254566175, 0.0142730130593139, 0.0156929838274791,
0.0156162350209032, 0.0153802494466767, 0.0150476029799385, 0.015555365325721,
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0.0176296702464377, 0.01808584587117, 0.0184482114318881, 0.0179540460804964,
0.0172054387638893, 0.0186435592467685, 0.0183226179107802, 0.0183442319676677,
0.0186738733252132, 0.0197702609494553, 0.0204285359857455, 0.0189093019186096,
0.0207614056972417, 0.0221922195760301, 0.00240617175649865,
0.00788575626634226, 0.01309841408011, 0.0157695717780358, 0.0156402333683254,
0.0162649757475833, 0.0155814665868539, 0.0165668899473124, 0.0162596037350689,
0.0168350415867981, 0.0154154128099543, 0.0150464847912372, 0.0161870630522126,
0.0156293605393382, 0.0166831626222356, 0.0159851624182969, 0.0181646017543342,
0.0154736802975027, 0.0147489845006063, 0.0172093148404027, 0.0173801189598905,
0.0179353887292393, 0.017586875624838, 0.0169123585500959, 0.0155822266067893,
0.0149761651657073, 0.0167448758587691, 0.0161082597966258, 0.0167303551270177,
0.0154917937591837, 0.015286011465188, 0.0136306070303869, 0.0152129210946259,
0.0159440585908669, 0.0161367409642245, 0.0164079686231546, 0.016492577386447,
0.0155465861608803, 0.0151072490270382, 0.0144004027929932, 0.0251766100192941,
0.0300583807652007, 0.0394582941939502, 0.0346703430162482, 0.0370259651104479,
0.0354306787130485, 0.0369234479028471, 0.0313956783546004, 0.0175071227813499,
0.0153811379243536, 0.0149988963637585, 0.0159494386911496, 0.0151254119566314,
0.0151138709885764, 0.0152175253675449, 0.015437458845637, 0.0147726989007463,
0.163043027137275, 0.0264129334017437, 0.0201436139554002, 0.0169325780144314,
0.0214876387664511, 0.0177445890793224, 0.0172606914733451, 0.017133563936406,
0.0185813515317095, 0.0180607614170281, 0.0187505760492009, 0.0169511102933927,
0.0178137558385877, 0.0184785698285323, 0.0201169476464591, 0.0220364679005246,
0.0212945438945016, 0.021660499704709, 0.0203399246100257, 0.021042755155702,
0.0207706911083365, 0.0191080416224264, 0.0188521002714409, 0.0176177277744931,
0.0170654092407268, 0.0167173964870618, 0.0169957130997929, 0.0173290930652611,
0.018723068602435, 0.0190819762151085, 0.0178334964691514, 0.0160768402598991,
0.0172883539665594, 0.0176745227466401)), row.names = c(NA, -183L
), class = "data.frame")
As the error explains, xreg should be a numeric matrix, not a data frame. The following works.
Fit <- auto.arima(Oct_Mar[, "ctr"], xreg = as.matrix(Oct_Mar[, 2:6]))
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Update records on one2many fields in wizard for odoo16
Geting Issue 'TypeError: unhashable type: 'dict' for insert values in one2many field from onchange method in odoo16
My code is below:
class EmployeeAddWizard(models.TransientModel):
_name = 'employee.add.wizard'
line_ids = fields.One2many('employee.goal.add.line', 'wizard_id', string="Lines")
#api.onchange('challenge_id', 'employee_id')
def _onchange_action_goal_add(self):
r = []
value = {}
self.line_ids = {}
if self.challenge_id and self.employee_id:
goal_records = self.env['gamification.challenge.line'].search([('challenge_id', '=', self.challenge_id.id)])
for emp in self.employee_id:
for line in goal_records:
data = {'wizard_id': self.id, # Other table m2o
'goal_definition_id': line.definition_id.id,
'goal_rating': 0.0,
'goal_target': line.target_goal,
'employee_id': emp.id,
}
r.append(data)
value.update(records=r)
self.line_ids = value['records']
class GoalLine(models.Model):
_name = 'employee.goal.add.line'
wizard_id = fields.Integer()
goal_definition_id = fields.Many2one('gamification.goal.definition', string='Goal Definition', required=True, ondelete="cascade")
goal_rating = fields.Float('Rating', required=True)
goal_target = fields.Float('Target Value ', required=True)
employee_id = fields.Many2one('res.users', string="Employee", required=True, ondelete="cascade")
Thanks in advance
You passed a list of dicts which is not valid, you need to use special commands
Example:
r.append(Command.create(data))
or:
r.append((0, 0, data))
You can use Command.clear(), to remove previous lines if needed ( self.line_ids = {} should raise an error: ValueError: Wrong value).
Check this answer
How to create a Dynamic Regression Model (ARIMAX) in R?
I created an ARIMAX model. Unfortunately, the forecasting that is created is not as it should be.
I want to predict the clickthrough rate "ctr" and use 6 explanatory variables.
enter image description here
#Step 4: Fit the forecasting model (ARIMAX) to the data
Fit <- auto.arima(Oct_Mar[, "ctr"], xreg = as.matrix(Oct_Mar[, 2:7]))
#Step 5: Predict values for Apr
Forecast_ctr <- Fit %>% forecast(xreg = as.matrix(Oct_Mar[, 2:7], h=30)
#Step 6: Forecast values
Forecast_ctr
#Step 7: Plot the forecast
autoplot(Forecast_ctr)+ xlab("Anzahl an Tagen")+ ylab("CTR")
Dataset:
structure(list(date = c("2019.10.01", "2019.10.02", "2019.10.03",
"2019.10.04", "2019.10.05", "2019.10.06", "2019.10.07", "2019.10.08",
"2019.10.09", "2019.10.10", "2019.10.11", "2019.10.12", "2019.10.13",
"2019.10.14", "2019.10.15", "2019.10.16", "2019.10.17", "2019.10.18",
"2019.10.19", "2019.10.20", "2019.10.21", "2019.10.22", "2019.10.23",
"2019.10.24", "2019.10.25", "2019.10.26", "2019.10.27", "2019.10.28",
"2019.10.29", "2019.10.30", "2019.10.31", "2019.11.01", "2019.11.02",
"2019.11.03", "2019.11.04", "2019.11.05", "2019.11.06", "2019.11.07",
"2019.11.08", "2019.11.09", "2019.11.10", "2019.11.11", "2019.11.12",
"2019.11.13", "2019.11.14", "2019.11.15", "2019.11.16", "2019.11.17",
"2019.11.18", "2019.11.19", "2019.11.20", "2019.11.21", "2019.11.22",
"2019.11.23", "2019.11.24", "2019.11.25", "2019.11.26", "2019.11.27",
"2019.11.28", "2019.11.29", "2019.11.30", "2019.12.01", "2019.12.02",
"2019.12.03", "2019.12.04", "2019.12.05", "2019.12.06", "2019.12.07",
"2019.12.08", "2019.12.09", "2019.12.10", "2019.12.11", "2019.12.12",
"2019.12.13", "2019.12.14", "2019.12.15", "2019.12.16", "2019.12.17",
"2019.12.18", "2019.12.19", "2019.12.20", "2019.12.21", "2019.12.22",
"2019.12.23", "2019.12.24", "2019.12.25", "2019.12.26", "2019.12.27",
"2019.12.28", "2019.12.29", "2019.12.30", "2019.12.31", "2020.01.01",
"2020.01.02", "2020.01.03", "2020.01.04", "2020.01.05", "2020.01.06",
"2020.01.07", "2020.01.08", "2020.01.09", "2020.01.10", "2020.01.11",
"2020.01.12", "2020.01.13", "2020.01.14", "2020.01.15", "2020.01.16",
"2020.01.17", "2020.01.18", "2020.01.19", "2020.01.20", "2020.01.21",
"2020.01.22", "2020.01.23", "2020.01.24", "2020.01.25", "2020.01.26",
"2020.01.27", "2020.01.28", "2020.01.29", "2020.01.30", "2020.01.31",
"2020.02.01", "2020.02.02", "2020.02.03", "2020.02.04", "2020.02.05",
"2020.02.06", "2020.02.07", "2020.02.08", "2020.02.09", "2020.02.10",
"2020.02.11", "2020.02.12", "2020.02.13", "2020.02.14", "2020.02.15",
"2020.02.16", "2020.02.17", "2020.02.18", "2020.02.19", "2020.02.20",
"2020.02.21", "2020.02.22", "2020.02.23", "2020.02.24", "2020.02.25",
"2020.02.26", "2020.02.27", "2020.02.28", "2020.02.29", "2020.03.01",
"2020.03.02", "2020.03.03", "2020.03.04", "2020.03.05", "2020.03.06",
"2020.03.07", "2020.03.08", "2020.03.09", "2020.03.10", "2020.03.11",
"2020.03.12", "2020.03.13", "2020.03.14", "2020.03.15", "2020.03.16",
"2020.03.17", "2020.03.18", "2020.03.19", "2020.03.20", "2020.03.21",
"2020.03.22", "2020.03.23", "2020.03.24", "2020.03.25", "2020.03.26",
"2020.03.27", "2020.03.28", "2020.03.29", "2020.03.30", "2020.03.31"
), price_view = c(35.79, 180.16, 437.57, 10.3, 74.26, 79.8, 89.84,
121.24, 461.95, 142.06, 241.71, 52, 43.24, 41.16, 167.05, 764.06,
91.64, 189.82, 38.59, 152.64, 86.23, 321.33, 411.83, 256.88,
352.39, 76.32, 360.11, 123.53, 43.41, 149.38, 14.16, 489.07,
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240.25, 148.18, 41.13, 262.56, 168.78, 860.85, 239.31, 372.98,
165.64, 134.32, 20.7, 43.73, 765.76, 51.48, 599.49, 893.79, 155.29,
334.37, 46.82, 1814.72, 196.27, 1302.48, 40.16, 1161.68, 381.48,
184.48, 48.91, 221.11, 434.73, 149.27, 77.22, 882.49, 106.05,
669.23, 282.86, 179.67, 12.97, 460.24, 38.59, 278.26, 243.76,
1904.79, 84.93, 32.18, 25.71, 496.54, 29.6, 1466.83, 164.33,
234.76, 19.95, 308.37, 1130.02, 7.47, 79.8, 65.9, 746.45, 1347.78,
1270.82, 69.42, 231.41, 195.6, 715.33, 208.47, 720.46, 414.68,
24.45, 217.82, 434.45, 483.92, 1500.42, 318.15, 339.29, 267.45,
133.85, 9.03, 11.81, 280.57, 916.74, 58.51, 339.78, 33.98, 263.58,
19.31, 239.88, 489.07, 84.92, 344.9, 95.24, 99.1, 142.58, 480.58,
104.74, 14.83, 252, 1039.41, 28.3, 328.97, 341.55, 278.26, 43.73,
91.35, 102.32, 131.25, 155.15, 77.74, 14.67, 132.63, 1185.36,
291.13, 1106.59, 849.42, 117.63, 171.32, 167.31, 252.23, 248.14,
111.15, 257.15, 27.62, 169.86, 101.89, 282.89, 298.57, 86.49,
196.32, 1415.45, 898.35, 334.6, 17.99, 13.62, 566.27, 60.41,
36.34, 62.04, 308.81, 32.95, 127.44, 836.57, 221.34, 360.34,
159.31, 20.57), view = c(1206151L, 1152770L, 1087372L, 1344804L,
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2021676L, 2104199L, 1801073L, 1733142L, 1593991L, 1645225L, 1557626L,
1637470L, 1721003L, 1545472L, 1594688L, 1565742L, 1651606L, 1999670L,
2217825L, 1985751L, 1680034L, 1608904L, 1620473L, 1628906L, 1726835L,
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1257475L, 1770064L, 2416707L, 2477258L, 2487470L, 2457500L, 2210539L,
2377633L, 2026050L, 2301337L, 2218894L, 2012789L, 1700619L, 1481115L,
1562027L, 1560348L, 1338829L, 1244973L, 1142989L, 1260747L, 1316975L,
1387394L, 1319559L, 1440470L, 1451015L, 1439649L, 1390411L, 1336076L,
1369834L, 1255626L, 1244163L, 1283731L), price_cart = c(29.51,
1415.48, 99.86, 358.57, 617.51, 1052.79, 1747.79, 190.56, 128.28,
252.38, 250.91, 720.48, 33.42, 643, 191.77, 460.11, 408.5, 789.9,
577.94, 49.36, 380.7, 19.56, 994.86, 756.71, 223.66, 437.33,
1684.28, 366.16, 968.34, 1683.07, 550.77, 503.09, 29.09, 179.67,
210.62, 22.66, 131.66, 68.96, 360.06, 494.22, 1023.62, 1569.92,
28.29, 694.97, 127.05, 37.85, 282.89, 178.9, 913.28, 1022.42,
424.7, 573.7, 1029.34, 30.12, 20.82, 17.99, 107.53, 41.19, 85.82,
1002.55, 140.98, 167.03, 231.67, 25.71, 205.64, 30.81, 51.22,
65.9, 7.08, 308.63, 227.79, 16.22, 7.89, 62.52, 48.88, 586.63,
602.07, 1312.26, 128.32, 179.9, 849.42, 100.9, 1284.2, 12.84,
128.42, 59.18, 176.99, 38.02, 48.88, 694.54, 262.3, 1402.84,
1453.18, 3.84, 453.01, 76.93, 7.04, 865.93, 865.4, 40.75, 1423.07,
1534.66, 679.27, 11.25, 102.63, 436.3, 853.93, 694.97, 850.47,
477.49, 1234.97, 10.27, 23.94, 643.23, 89.84, 290.34, 320.99,
6.44, 140.28, 188.89, 56.88, 1326.31, 194.34, 140.28, 771.96,
140.03, 20.21, 1464.39, 59.18, 57.92, 1156.81, 50.43, 300.12,
38.1, 832.71, 57.91, 174.5, 100.36, 248.14, 109.34, 100.7, 242.7,
266.67, 592.01, 242.18, 22.66, 566.04, 38.61, 812.06, 123.92,
168.6, 172.03, 49.91, 16.73, 108.04, 347.47, 97.79, 111.15, 514.79,
126.1, 178.87, 870.03, 529.31, 43.5, 2110.48, 771.94, 15.32,
105.25, 7.14, 312.67, 61.75, 165.51, 48.37, 643.49, 303.48, 35.78,
154.42, 209.71, 76.69, 25.46, 1415.45, 123.53, 602.31), cart = c(16658L,
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Add values on One2many field onchange
I'm trying to add values in my one2many field onchange.
I tried using the [(0,0, {values})] but nothing happened. Any idea on how to implement it?
custom_line_ids = fields.One2many('mrp.production', 'product_id', 'Custom Line')
#api.onchange('product_id')
def add_custom_line_ids(self):
mrp = self.env['mrp.productions'].search([])
result = []
vals = {
'sequence': self.sequence,
'name': self.name,
'product_id': self.product_id,
'date_planned_start': self.date_planned_start,
'state': self.state,
}
self.update({'custom_line_ids':[(0, 0, vals)]})
Actually you are using update method, which only update the model's value but not yet stored on database. You should use write method instead.
You need to return the value in onchange. This would work:
custom_line_ids = fields.One2many('mrp.production', 'product_id', 'Custom Line')
#api.onchange('product_id')
def add_custom_line_ids(self):
vals = {}
mrp_ids = self.env['mrp.productions'].search([])
if mrp_ids:
for mrp in mrp_ids:
vals['custom_line_ids']=[(0,0,{
'date': mrp.date,
})]
return {'value': vals}
Add labels to estimator.export_saved_model when exporting a keras model for google cloud
I am trying to export a hdf5 model created by Keras training to Google cloud ML Engine. I have everything except the labels after making an online prediction and I would like to have the labels with probability after making a prediction.
Here is my code after the training and the creation of an hdf5 model with Keras.
First, I create an estimator from a keras model.
estimator = keras.estimator.model_to_estimator(
keras_model_path="model.hdf5",
model_dir="output/")
Now, I export the model like this:
estimator.export_saved_model(
"output/model"
serving_input_receiver_fn=serving_input_receiver_fn)
with the function serving_input_receiver_fn that will allow me to accept a base 64 json file as input for an online prediction with google cloud.
def serving_input_receiver_fn():
def prepare_image(image_str_tensor):
image = tf.image.decode_jpeg(image_str_tensor, channels=3)
return image_preprocessing(image)
input_ph = tf.placeholder(tf.string, shape=[None])
images_tensor = tf.map_fn(
prepare_image, input_ph, back_prop=False, dtype=tf.uint8)
images_tensor = tf.image.convert_image_dtype(images_tensor,
dtype=tf.float32)
return tf.estimator.export.ServingInputReceiver(
{'input_1': images_tensor},
{'image_bytes': input_ph})
However, I want to have a classification result with the labels ( I have around 10 classes in my results ). Now my only result is like this :
{"input_1": [0.001,0.9,...]}
I would like to have the result with the labels. Is it possible to do it with a small change and not by doing an other training but by keeping my hdf5 model file ?
Thank you in advance.
We had similar requirement for a demo:
I have a dictionary of classes:
https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus', ...}
and so on.
I did the conversion at client level:
def get_classes():
url = 'https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw' \
'/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5' \
'/imagenet1000_clsidx_to_labels.txt'
response = requests.get(url)
classes = literal_eval(response.text)
return classes
...
classes = get_classes()
response = model_predict(predict_request)
if response:
prediction_class = response.get('predictions')[0].get('classes') - 1
prediction_probabilities = response.get('predictions')[0].get('probabilities')
print(
'Prediction: [%d] %s Probability [%.2f] ' % (
prediction_class, classes[prediction_class], max(prediction_probabilities)))
Code here:
https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/dlvm/nvidia/inference.py
Response looks like this:
{'predictions': [{'probabilities': [1.55508e-05, 8.52272e-05, 0.000124575, 0.000202289, 7.25561e-05, 0.00125153, 0.000195685, 0.000298364, 6.305e-05, 0.000101759, 0.000189796, 9.83266e-06, 6.09115e-06, 2.93628e-05, 3.79306e-05, 1.80906e-05, 3.27449e-05, 1.44569e-05, 2.08072e-05, 0.000211307, 2.92737e-05, 2.62217e-05, 5.72919e-05, 0.000113042, 2.7489e-05, 4.75314e-05, 3.24912e-05, 9.47271e-06, 0.000175823, 1.07195e-05, 2.23769e-05, 2.77867e-05, 9.41769e-06, 2.75326e-06, 5.0539e-05, 0.000196899, 1.57362e-05, 9.59799e-05, 3.38195e-05, 7.26347e-06, 6.13557e-05, 5.6595e-05, 2.1883e-05, 3.92613e-05, 2.65449e-05, 5.75036e-05, 0.000152569, 8.00665e-05, 2.52358e-05, 7.63134e-05, 1.58771e-05, 0.00046693, 8.97672e-05, 2.64159e-05, 0.000107967, 0.000105322, 2.51052e-05, 0.000134213, 2.02501e-05, 8.42264e-05, 5.74879e-05, 0.000147237, 8.60201e-05, 0.000159229, 2.82999e-05, 7.0453e-05, 9.804e-05, 1.53984e-05, 0.000442353, 4.83388e-05, 0.000111974, 1.64856e-05, 3.9036e-05, 8.38488e-06, 8.2569e-05, 4.60937e-05, 1.9807e-05, 0.000101196, 0.00014236, 0.000169874, 0.000836153, 9.40354e-05, 4.6951e-05, 0.000131597, 2.86648e-05, 0.000158368, 5.29119e-05, 3.52403e-05, 7.17581e-05, 0.000116447, 0.000253711, 5.35324e-05, 8.56567e-06, 4.87063e-05, 0.000110679, 2.18005e-05, 8.59478e-06, 7.40535e-05, 2.38494e-05, 3.12719e-05, 0.000714874, 0.000145422, 0.000137946, 9.94839e-05, 0.000283478, 0.000357132, 2.73016e-05, 0.0002482, 8.15625e-05, 7.40048e-05, 3.81499e-05, 9.95147e-06, 2.86458e-05, 6.22204e-05, 0.000123885, 8.62779e-05, 3.16152e-05, 2.91354e-05, 5.67827e-05, 0.000652813, 0.000101906, 1.61919e-05, 2.92731e-05, 4.40727e-05, 8.18691e-06, 2.21699e-05, 5.32086e-05, 3.21545e-05, 3.22796e-05, 2.6318e-05, 1.88785e-05, 2.11514e-05, 1.48076e-05, 7.21377e-05, 7.36493e-06, 0.000353744, 0.000141821, 8.97949e-06, 1.61471e-05, 0.000122686, 4.4602e-05, 2.3205e-05, 4.94825e-05, 1.67007e-05, 6.61634e-05, 8.84246e-05, 0.000172353, 7.35944e-05, 0.000391683, 0.000185004, 0.00039224, 0.000324578, 0.000331324, 1.30768e-05, 0.000374572, 9.66308e-05, 0.000116723, 9.00387e-05, 9.85038e-05, 6.74917e-05, 0.000493128, 7.29576e-05, 0.00450054, 0.00298045, 0.00203722, 0.00164224, 0.000846203, 0.00017548, 0.000317891, 0.000788288, 0.000143928, 0.000854663, 0.000351869, 0.000692566, 0.000377429, 0.000629245, 6.21199e-05, 0.000200465, 0.00307031, 0.000723996, 0.000117597, 0.000115785, 5.71359e-05, 0.000496389, 8.51815e-05, 0.000474041, 0.000364161, 2.79947e-05, 6.08602e-05, 0.000209289, 0.00255232, 8.92871e-05, 0.00296907, 0.000557994, 3.34299e-05, 2.18733e-05, 0.00575903, 0.000440953, 0.00127525, 6.44013e-05, 0.0101199, 0.000215603, 4.69506e-05, 0.000803412, 0.0136009, 0.000157733, 0.000238714, 0.000401412, 0.000817349, 0.00053235, 0.00576978, 0.0001202, 0.00019474, 0.00292783, 0.000253787, 0.000986474, 0.000137718, 3.35719e-05, 0.00109508, 3.28824e-05, 0.00050906, 8.51038e-05, 0.000519757, 0.000235538, 0.00788667, 0.00105736, 0.000530407, 0.000325091, 0.00107374, 0.000230244, 0.00076778, 0.000659183, 0.000224508, 0.000202612, 0.000678197, 0.00448022, 0.00289446, 9.32325e-05, 0.00977335, 0.000435942, 0.004958, 0.000386235, 0.00736235, 0.00115626, 0.000177735, 2.56265e-05, 0.0015713, 0.000136755, 0.000602412, 0.0012884, 0.00719902, 0.0119457, 0.000945569, 0.000219249, 0.000347739, 0.00519515, 0.000225742, 0.00246375, 0.000453139, 0.0029661, 0.000804986, 0.000914697, 0.000287989, 0.000264725, 0.000630987, 0.000351136, 0.000733556, 2.37422e-05, 0.000517571, 4.77645e-05, 0.0003319, 0.000405211, 0.000283034, 0.00159746, 0.00177935, 5.88713e-05, 7.11461e-05, 5.91759e-05, 0.000503971, 8.6978e-06, 0.00120087, 0.000813549, 0.000343971, 0.000687556, 0.000340041, 0.000355475, 0.000275208, 0.000696902, 8.2474e-05, 7.0166e-05, 0.000210933, 3.90627e-05, 1.89524e-05, 0.000368501, 0.000351007, 0.00060404, 0.000106084, 0.000331506, 8.32554e-05, 2.15891e-05, 0.000193192, 6.84955e-06, 0.000331523, 3.66652e-05, 1.12194e-05, 1.92205e-05, 2.50185e-05, 5.33112e-05, 1.32986e-05, 8.2467e-05, 5.92258e-05, 0.00283379, 2.84016e-05, 5.96508e-05, 0.000182353, 0.000241587, 4.9405e-05, 0.000256186, 6.64698e-06, 4.18161e-05, 4.28466e-05, 1.34901e-05, 1.77119e-05, 1.13135e-05, 1.55582e-05, 1.01246e-05, 2.15158e-06, 4.84249e-06, 0.000493645, 5.86358e-05, 3.79742e-05, 0.000325346, 0.000130454, 5.81232e-05, 6.95239e-05, 0.000220773, 5.40073e-05, 0.000293129, 9.38502e-05, 3.10892e-05, 0.000135327, 0.000216272, 0.000535513, 0.000271741, 0.000101224, 7.6724e-05, 0.000640805, 0.000273377, 0.00658155, 0.000621818, 0.00113372, 0.000305679, 3.32551e-05, 2.73262e-05, 0.000193103, 0.000359639, 0.00254883, 0.000114939, 5.88418e-05, 8.44255e-05, 8.56286e-05, 0.00014276, 9.35917e-05, 0.000153505, 6.59843e-05, 0.000114629, 0.000208262, 4.78108e-05, 3.78621e-05, 2.35319e-05, 1.77599e-05, 1.24966e-05, 6.25085e-05, 0.000493294, 9.21813e-05, 5.45179e-05, 4.59296e-05, 4.71062e-06, 7.51103e-05, 0.000342672, 0.000133267, 3.98604e-05, 0.000152585, 7.34054e-05, 0.000268039, 3.68195e-05, 0.000300117, 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Odoo: Two selection fields, second selection depends on the first selected value
I have a model with two selection fields. In the first selection field the user chooses a project, and in second the user chooses a tranche, depends on the selected first field. (for one project, we have many tranche)
I use this declaration to my function:
#api.multi
#api.onchange('project')
def _get_tranche(self):
print res
return res`
If I'll print in console the result of returned value is correct, and I have exactly what I search, but in the view, the second field value does not displays computed values to choose after setting the second field value.
Is there any method to make it work properly?
This is my code:
#api.model
def _get_project(self):
values = {'$top' : 100,
'$filter' : "'name==:1&&parent==null'",
'$params' : '\'["*"]\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((record['__KEY'], record['name']))
return res
#property
def ret_value(self):
return self.project
#api.multi
#api.onchange('project')
def _get_tranche(self):
if self.ret_value == False:
return []
values = {'$top' : 100,
'$filter' : "'parent.ID==:1'",
'$params' : '\'[' + self.ret_value + ']\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((record['__KEY'], record['name']))
print res
return res
_columns = {
'name': fields.char("Name", required=True),
'description' : fields.text(),
'project' :fields.selection(selection=_get_project, string="Project"),
'tranche' : fields.selection(selection=_get_tranche, string="Tranche"),
}
NB: a tranche is a project with parent_id (not null) = id of a project, I need do the following: when I select a project X from the first field selection "project", I can select the tranches of this project (project with parent_id=X) from the second field selection "tranche" what I can't understand is the result of print in the console is correct, but in the view the field tranche is empty??!!
Now I am trying this:
_columns = {
'name': fields.char("Name", required=True),
'description' : fields.text(),
'project' :fields.selection(selection=_get_project, string="Project"),
'tranche' : fields.selection(selection=_get_tranche,
string="Tranche", compute='_return_tranche'),
}
#api.depends('project')
def _return_tranche(self):
if self.ret_value == False:
self.tranche = []
return
values = {'$top' : 100,
'$filter' : "'parent.ID==:1'",
'$params' : '\'[' + self.ret_value + ']\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((str(record['__KEY']),str(record['name'])))
print res
self.tranche = res
return
#api.model
def _get_tranche(self):
values = {'$top' : 100,
'$filter' : "'name==:1&&parent!=null'",
'$params' : '\'["*"]\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((record['__KEY'], record['name']))
return res
But I get this error
File "/opt/odoo/odoo/openerp/fields.py", line 1529, in convert_to_cache
raise ValueError("Wrong value for %s: %r" % (self, value))
ValueError: Wrong value for crm.lead.tranche: [('6', 'Tranche I'), ('7', 'Tranche II'), ('8', 'Tranche III')]
Help me please...