Add release files generated with make release
This commit is contained in:
Rafa de la Torre
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[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]␍
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[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]
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@@ -0,0 +1,52 @@
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[[0.9319096128346788, "HH"],
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[-1.135787401862846, "HL"],
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[0.11732030672508517, "LL"],
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[0.6152779669180425, "LL"],
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[-0.14657336660125297, "LH"],
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[0.6967858120189607, "LL"],
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[0.07949310115714454, "HH"],
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[0.4703198759258987, "HH"],
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[0.4421125200498064, "HH"],
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[0.5724288737143592, "LL"],
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[0.8970743435692062, "LL"],
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[0.18327334401918674, "LL"],
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[-0.01466729201304962, "HL"],
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[0.3481559372544409, "LL"],
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[0.06547094736902978, "LL"],
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[0.15482141569329988, "HH"],
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[0.4373841193538136, "HH"],
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[0.15971286468915544, "LL"],
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[1.0543588860308968, "HH"],
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[1.7372866900020818, "HH"],
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[1.091998586053999, "LL"],
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[0.1171572584252222, "HH"],
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[0.08438455015300014, "LL"],
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[0.06547094736902978, "LL"],
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[0.15482141569329985, "HH"],
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[1.1627044812890683, "HH"],
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[0.06547094736902978, "LL"],
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[0.795275137550483, "HH"],
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[0.18562939195219, "LL"],
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[0.3010757406693439, "LL"],
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[2.8205795942839376, "HH"],
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[0.11259190602909264, "LL"],
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[-0.07116352791516614, "HL"],
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[-0.09945240794119009, "LH"],
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[0.18562939195219, "LL"],
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[0.1832733440191868, "LL"],
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[-0.39054253768447705, "HL"],
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[-0.1672071289487642, "HL"],
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[0.3337669247916343, "HH"],
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[0.2584386102554792, "HH"],
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[-0.19733845476322634, "HL"],
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[-0.9379282899805409, "LH"],
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[-0.028770969951095866, "LH"],
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[0.051367269430983485, "LL"],
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[-0.2172548045913472, "LH"],
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[0.05136726943098351, "LL"],
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[0.04191046803899837, "LL"],
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[0.7482357030403517, "HH"],
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[-0.014585767863118111, "LH"],
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[0.5410013139159929, "HH"],
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[1.0223932668429925, "LL"],
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[1.4179402898927476, "LL"]]
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@@ -0,0 +1,54 @@
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[
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{"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
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{"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
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{"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
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{"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
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{"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
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{"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
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{"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
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{"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
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{"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
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{"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
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{"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
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{"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
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{"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
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{"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
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{"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
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{"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
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{"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
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{"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
|
||||
{"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
|
||||
{"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
|
||||
{"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
|
||||
{"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
|
||||
{"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
|
||||
{"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
|
||||
{"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
|
||||
{"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
|
||||
{"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
|
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{"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
|
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{"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
|
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{"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
|
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{"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
|
||||
{"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
|
||||
{"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
|
||||
{"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
|
||||
{"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
|
||||
{"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
|
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{"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
|
||||
{"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
|
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{"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
|
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{"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
|
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{"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
|
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{"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
|
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{"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
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{"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
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{"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
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{"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
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{"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
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{"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
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{"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
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{"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
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{"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
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{"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
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]
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File diff suppressed because one or more lines are too long
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import unittest
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from mock_plpy import MockPlPy
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plpy = MockPlPy()
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import sys
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sys.modules['plpy'] = plpy
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import os
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def fixture_file(name):
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dir = os.path.dirname(os.path.realpath(__file__))
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return os.path.join(dir, 'fixtures', name)
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import re
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class MockCursor:
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def __init__(self, data):
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self.cursor_pos = 0
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self.data = data
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def fetch(self, batch_size):
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batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
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self.cursor_pos += batch_size
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return batch
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class MockPlPy:
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def __init__(self):
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self._reset()
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def _reset(self):
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self.infos = []
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self.notices = []
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self.debugs = []
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self.logs = []
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self.warnings = []
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self.errors = []
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self.fatals = []
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self.executes = []
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self.results = []
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self.prepares = []
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self.results = []
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def _define_result(self, query, result):
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pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
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self.results.append([pattern, result])
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def notice(self, msg):
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self.notices.append(msg)
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def debug(self, msg):
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self.notices.append(msg)
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def info(self, msg):
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self.infos.append(msg)
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def cursor(self, query):
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data = self.execute(query)
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return MockCursor(data)
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def execute(self, query): # TODO: additional arguments
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for result in self.results:
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if result[0].match(query):
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return result[1]
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return []
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import unittest
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import numpy as np
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# from mock_plpy import MockPlPy
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# plpy = MockPlPy()
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#
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# import sys
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# sys.modules['plpy'] = plpy
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from helper import plpy, fixture_file
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import numpy as np
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import crankshaft.clustering as cc
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import crankshaft.pysal_utils as pu
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from crankshaft import random_seeds
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import json
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class KMeansTest(unittest.TestCase):
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"""Testing class for Moran's I functions"""
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def setUp(self):
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plpy._reset()
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self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
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self.params = {"subquery": "select * from table",
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"no_clusters": "10"
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}
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def test_kmeans(self):
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data = self.cluster_data
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plpy._define_result('select' ,data)
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clusters = cc.kmeans('subquery', 2)
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labels = [a[1] for a in clusters]
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c1 = [a for a in clusters if a[1]==0]
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c2 = [a for a in clusters if a[1]==1]
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self.assertEqual(len(np.unique(labels)),2)
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self.assertEqual(len(c1),20)
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self.assertEqual(len(c2),20)
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import unittest
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import numpy as np
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# from mock_plpy import MockPlPy
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# plpy = MockPlPy()
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#
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# import sys
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# sys.modules['plpy'] = plpy
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from helper import plpy, fixture_file
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import crankshaft.clustering as cc
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import crankshaft.pysal_utils as pu
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from crankshaft import random_seeds
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import json
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class MoranTest(unittest.TestCase):
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"""Testing class for Moran's I functions"""
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def setUp(self):
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plpy._reset()
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self.params = {"id_col": "cartodb_id",
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"attr1": "andy",
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"attr2": "jay_z",
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"subquery": "SELECT * FROM a_list",
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"geom_col": "the_geom",
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"num_ngbrs": 321}
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self.params_markov = {"id_col": "cartodb_id",
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"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
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"subquery": "SELECT * FROM a_list",
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"geom_col": "the_geom",
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"num_ngbrs": 321}
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self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
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self.moran_data = json.loads(open(fixture_file('moran.json')).read())
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def test_map_quads(self):
|
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"""Test map_quads"""
|
||||
self.assertEqual(cc.map_quads(1), 'HH')
|
||||
self.assertEqual(cc.map_quads(2), 'LH')
|
||||
self.assertEqual(cc.map_quads(3), 'LL')
|
||||
self.assertEqual(cc.map_quads(4), 'HL')
|
||||
self.assertEqual(cc.map_quads(33), None)
|
||||
self.assertEqual(cc.map_quads('andy'), None)
|
||||
|
||||
def test_quad_position(self):
|
||||
"""Test lisa_sig_vals"""
|
||||
|
||||
quads = np.array([1, 2, 3, 4], np.int)
|
||||
|
||||
ans = np.array(['HH', 'LH', 'LL', 'HL'])
|
||||
test_ans = cc.quad_position(quads)
|
||||
|
||||
self.assertTrue((test_ans == ans).all())
|
||||
|
||||
def test_moran_local(self):
|
||||
"""Test Moran's I local"""
|
||||
data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = self.moran_data
|
||||
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_val, exp_val)
|
||||
self.assertEqual(res_quad, exp_quad)
|
||||
|
||||
def test_moran_local_rate(self):
|
||||
"""Test Moran's I rate"""
|
||||
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
print 'result == None? ', result == None
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = self.moran_data
|
||||
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_val, exp_val)
|
||||
|
||||
def test_moran(self):
|
||||
"""Test Moran's I global"""
|
||||
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1235)
|
||||
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
print 'result == None?', result == None
|
||||
result_moran = result[0][0]
|
||||
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
|
||||
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
|
||||
@@ -0,0 +1,142 @@
|
||||
import unittest
|
||||
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
|
||||
|
||||
class PysalUtilsTest(unittest.TestCase):
|
||||
"""Testing class for utility functions related to PySAL integrations"""
|
||||
|
||||
def setUp(self):
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"attr1": "andy",
|
||||
"attr2": "jay_z",
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
|
||||
self.params_array = {"id_col": "cartodb_id",
|
||||
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
|
||||
def test_query_attr_select(self):
|
||||
"""Test query_attr_select"""
|
||||
|
||||
ans = "i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, "
|
||||
|
||||
ans_array = "i.\"_2013_dec\"::numeric As attr1, " \
|
||||
"i.\"_2014_jan\"::numeric As attr2, " \
|
||||
"i.\"_2014_feb\"::numeric As attr3, "
|
||||
|
||||
self.assertEqual(pu.query_attr_select(self.params), ans)
|
||||
self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
|
||||
|
||||
def test_query_attr_where(self):
|
||||
"""Test pu.query_attr_where"""
|
||||
|
||||
ans = "idx_replace.\"andy\" IS NOT NULL AND " \
|
||||
"idx_replace.\"jay_z\" IS NOT NULL AND " \
|
||||
"idx_replace.\"jay_z\" <> 0"
|
||||
|
||||
ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"idx_replace.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"idx_replace.\"_2014_feb\" IS NOT NULL"
|
||||
|
||||
self.assertEqual(pu.query_attr_where(self.params), ans)
|
||||
self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
|
||||
|
||||
def test_knn(self):
|
||||
"""Test knn neighbors constructor"""
|
||||
|
||||
ans = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
"FROM (SELECT * FROM a_list) As j " \
|
||||
"WHERE " \
|
||||
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"j.\"andy\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" <> 0 " \
|
||||
"ORDER BY " \
|
||||
"j.\"the_geom\" <-> i.\"the_geom\" ASC " \
|
||||
"LIMIT 321)) As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"andy\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" <> 0 " \
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
ans_array = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"_2013_dec\"::numeric As attr1, " \
|
||||
"i.\"_2014_jan\"::numeric As attr2, " \
|
||||
"i.\"_2014_feb\"::numeric As attr3, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
"FROM (SELECT * FROM a_list) As j " \
|
||||
"WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"j.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"j.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"j.\"_2014_feb\" IS NOT NULL " \
|
||||
"ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \
|
||||
"LIMIT 321)) As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"i.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"i.\"_2014_feb\" IS NOT NULL "\
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(pu.knn(self.params), ans)
|
||||
self.assertEqual(pu.knn(self.params_array), ans_array)
|
||||
|
||||
def test_queen(self):
|
||||
"""Test queen neighbors constructor"""
|
||||
|
||||
ans = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
"FROM (SELECT * FROM a_list) As j " \
|
||||
"WHERE " \
|
||||
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"ST_Touches(i.\"the_geom\", " \
|
||||
"j.\"the_geom\") AND " \
|
||||
"j.\"andy\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" <> 0)" \
|
||||
") As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"andy\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" <> 0 " \
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(pu.queen(self.params), ans)
|
||||
|
||||
def test_construct_neighbor_query(self):
|
||||
"""Test construct_neighbor_query"""
|
||||
|
||||
# Compare to raw knn query
|
||||
self.assertEqual(pu.construct_neighbor_query('knn', self.params),
|
||||
pu.knn(self.params))
|
||||
|
||||
def test_get_attributes(self):
|
||||
"""Test get_attributes"""
|
||||
|
||||
## need to add tests
|
||||
|
||||
self.assertEqual(True, True)
|
||||
|
||||
def test_get_weight(self):
|
||||
"""Test get_weight"""
|
||||
|
||||
self.assertEqual(True, True)
|
||||
|
||||
def test_empty_zipped_array(self):
|
||||
"""Test empty_zipped_array"""
|
||||
ans2 = [(None, None)]
|
||||
ans4 = [(None, None, None, None)]
|
||||
self.assertEqual(pu.empty_zipped_array(2), ans2)
|
||||
self.assertEqual(pu.empty_zipped_array(4), ans4)
|
||||
@@ -0,0 +1,64 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
from helper import plpy, fixture_file
|
||||
import crankshaft.segmentation as segmentation
|
||||
import json
|
||||
|
||||
class SegmentationTest(unittest.TestCase):
|
||||
"""Testing class for Moran's I functions"""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
|
||||
def generate_random_data(self,n_samples,random_state, row_type=False):
|
||||
x1 = random_state.uniform(size=n_samples)
|
||||
x2 = random_state.uniform(size=n_samples)
|
||||
x3 = random_state.randint(0, 4, size=n_samples)
|
||||
|
||||
y = x1+x2*x2+x3
|
||||
cartodb_id = range(len(x1))
|
||||
|
||||
if row_type:
|
||||
return [ {'features': vals} for vals in zip(x1,x2,x3)], y
|
||||
else:
|
||||
return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
|
||||
|
||||
def test_replace_nan_with_mean(self):
|
||||
test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
|
||||
|
||||
def test_create_and_predict_segment(self):
|
||||
n_samples = 1000
|
||||
|
||||
random_state_train = np.random.RandomState(13)
|
||||
random_state_test = np.random.RandomState(134)
|
||||
training_data = self.generate_random_data(n_samples, random_state_train)
|
||||
test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
|
||||
|
||||
|
||||
ids = [{'cartodb_ids': range(len(test_data))}]
|
||||
rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
|
||||
|
||||
plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
|
||||
plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
|
||||
plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
|
||||
plpy._define_result('.*select \* from test.*' ,test_data)
|
||||
|
||||
model_parameters = {'n_estimators': 1200,
|
||||
'max_depth': 3,
|
||||
'subsample' : 0.5,
|
||||
'learning_rate': 0.01,
|
||||
'min_samples_leaf': 1}
|
||||
|
||||
result = segmentation.create_and_predict_segment(
|
||||
'select * from training',
|
||||
'target',
|
||||
'select * from test',
|
||||
model_parameters)
|
||||
|
||||
prediction = [r[1] for r in result]
|
||||
|
||||
accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
|
||||
|
||||
self.assertEqual(len(result),len(test_data))
|
||||
self.assertTrue( result[0][2] < 0.01)
|
||||
self.assertTrue( accuracy < 0.5*np.mean(test_y) )
|
||||
@@ -0,0 +1,324 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
import unittest
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
|
||||
import crankshaft.space_time_dynamics as std
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
|
||||
class SpaceTimeTests(unittest.TestCase):
|
||||
"""Testing class for Markov Functions."""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
|
||||
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
|
||||
|
||||
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
|
||||
|
||||
self.transition_matrix = np.array([
|
||||
[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
|
||||
[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
|
||||
[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
|
||||
[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
|
||||
[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
|
||||
[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
|
||||
[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
|
||||
[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
|
||||
[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
|
||||
[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
|
||||
[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
|
||||
[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
|
||||
[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
|
||||
[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
|
||||
[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
|
||||
[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
|
||||
[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
|
||||
[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
|
||||
)
|
||||
|
||||
def test_spatial_markov(self):
|
||||
"""Test Spatial Markov."""
|
||||
data = [ { 'id': d['id'],
|
||||
'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'],
|
||||
'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
print(str(data[0]))
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
|
||||
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
|
||||
|
||||
self.assertTrue(result != None)
|
||||
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
|
||||
print result[0]
|
||||
expected = self.markov_data
|
||||
for ([res_trend, res_up, res_down, res_vol, res_id],
|
||||
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
|
||||
) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_trend, exp_trend)
|
||||
|
||||
def test_get_time_data(self):
|
||||
"""Test get_time_data"""
|
||||
data = [ { 'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'] } for d in self.neighbors_data]
|
||||
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
|
||||
|
||||
## expected was prepared from PySAL example:
|
||||
### f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
|
||||
### rpci = pci / (pci.mean(axis = 0))
|
||||
|
||||
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
|
||||
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
|
||||
, 0.86148267, 0.86252252, 0.86746356],
|
||||
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
|
||||
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
|
||||
, 0.90139505, 0.87897455, 0.86216858],
|
||||
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
|
||||
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
|
||||
, 0.83675961, 0.83574199, 0.84647177],
|
||||
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
|
||||
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
|
||||
, 1.12958508, 1.11371818, 1.09936775],
|
||||
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
|
||||
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
|
||||
, 1.10863242, 1.09642516, 1.07762337],
|
||||
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
|
||||
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
|
||||
, 1.45241216, 1.43306098, 1.4174431 ],
|
||||
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
|
||||
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
|
||||
, 1.08401709, 1.06959206, 1.07875225],
|
||||
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
|
||||
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
|
||||
, 1.01749645, 0.9936394, 0.98279746],
|
||||
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
|
||||
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
|
||||
, 0.91284091, 0.89689833, 0.88928858],
|
||||
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
|
||||
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
|
||||
, 0.86633032, 0.84946077, 0.83287146],
|
||||
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
|
||||
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
|
||||
, 1.11059842, 1.10509795, 1.10020097],
|
||||
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
|
||||
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
|
||||
, 0.89280392, 0.89298243, 0.89165385],
|
||||
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
|
||||
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
|
||||
, 0.94281559, 0.96520315, 0.96715441],
|
||||
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
|
||||
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
|
||||
, 0.98423613, 0.99823861, 0.99545704],
|
||||
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
|
||||
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
|
||||
, 0.8198736, 0.82265395, 0.8399404 ],
|
||||
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
|
||||
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
|
||||
, 0.9422231, 0.93902708, 0.94479184],
|
||||
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
|
||||
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
|
||||
, 0.90543476, 0.91737138, 0.94793657],
|
||||
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
|
||||
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
|
||||
, 1.23418676, 1.23626739, 1.26754398],
|
||||
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
|
||||
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
|
||||
, 1.32936244, 1.33001241, 1.33288462],
|
||||
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
|
||||
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
|
||||
, 0.92754547, 0.92549227, 0.92138102],
|
||||
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
|
||||
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
|
||||
, 1.10577769, 1.11166825, 1.09985155],
|
||||
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
|
||||
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
|
||||
, 0.77487118, 0.77768683, 0.78801192],
|
||||
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
|
||||
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
|
||||
, 0.93069645, 0.94020973, 0.94358232],
|
||||
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
|
||||
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
|
||||
, 0.87863799, 0.87497981, 0.87888675],
|
||||
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
|
||||
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
|
||||
, 0.99323123, 1.00204939, 0.99602148],
|
||||
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
|
||||
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
|
||||
, 1.08603695, 1.05997314, 1.02160943],
|
||||
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
|
||||
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
|
||||
, 1.12357937, 1.1159258, 1.12570828],
|
||||
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
|
||||
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
|
||||
, 1.29734097, 1.29390205, 1.29345446],
|
||||
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
|
||||
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
|
||||
, 0.84349247, 0.85637272, 0.86539395],
|
||||
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
|
||||
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
|
||||
, 1.27962009, 1.27968392, 1.25907738],
|
||||
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
|
||||
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
|
||||
, 0.91475304, 0.90249622, 0.9021363 ],
|
||||
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
|
||||
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
|
||||
, 0.96635366, 1.02520312, 1.0394296 ],
|
||||
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
|
||||
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
|
||||
, 0.93759089, 0.93353431, 0.94121705],
|
||||
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
|
||||
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
|
||||
, 0.88283929, 0.90349491, 0.90600675],
|
||||
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
|
||||
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
|
||||
, 0.95011401, 0.9434686, 0.94637765],
|
||||
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
|
||||
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
|
||||
, 1.02347523, 1.02517684, 1.04359571],
|
||||
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
|
||||
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
|
||||
, 1.08859544, 1.09177338, 1.1096083 ],
|
||||
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
|
||||
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
|
||||
, 0.83374329, 0.82812044, 0.82878599],
|
||||
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
|
||||
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
|
||||
, 0.96947771, 1.00362626, 0.98102351],
|
||||
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
|
||||
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
|
||||
, 0.90430364, 0.89994694, 0.90073864],
|
||||
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
|
||||
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
|
||||
, 0.9700702, 0.97158948, 0.95884908],
|
||||
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
|
||||
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
|
||||
, 0.83511679, 0.82136617, 0.80921874],
|
||||
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
|
||||
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
|
||||
, 0.97001634, 0.97082733, 0.98782366],
|
||||
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
|
||||
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
|
||||
, 1.14474747, 1.14084037, 1.16142926],
|
||||
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
|
||||
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
|
||||
, 1.09831768, 1.09298232, 1.09176125],
|
||||
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
|
||||
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
|
||||
, 0.80172182, 0.82168155, 0.85587911],
|
||||
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
|
||||
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
|
||||
, 0.96519561, 0.96173403, 0.95890284],
|
||||
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
|
||||
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
|
||||
, 1.17025154, 1.18730553, 1.14242645]])
|
||||
|
||||
self.assertTrue(np.allclose(result, expected))
|
||||
self.assertTrue(type(result) == type(expected))
|
||||
self.assertTrue(result.shape == expected.shape)
|
||||
|
||||
def test_rebin_data(self):
|
||||
"""Test rebin_data"""
|
||||
## sample in double the time (even case since 10 % 2 = 0):
|
||||
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
## = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
|
||||
for i in range(0, 10, 2)]).T
|
||||
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
|
||||
## sample in triple the time (uneven since 10 % 3 = 1):
|
||||
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
## = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
|
||||
def test_get_prob_dist(self):
|
||||
"""Test get_prob_dist"""
|
||||
lag_indices = np.array([1, 2, 3, 4])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
])
|
||||
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
|
||||
|
||||
self.assertTrue(np.array_equal(result, answer))
|
||||
|
||||
def test_get_prob_stats(self):
|
||||
"""Test get_prob_stats"""
|
||||
|
||||
probs = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
|
||||
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
|
||||
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
|
||||
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
|
||||
|
||||
result = std.get_prob_stats(probs, unit_indices)
|
||||
result_up = result[0]
|
||||
result_down = result[1]
|
||||
result_trend = result[2]
|
||||
result_volatility = result[3]
|
||||
|
||||
self.assertTrue(np.allclose(result_up, answer_up))
|
||||
self.assertTrue(np.allclose(result_down, answer_down))
|
||||
self.assertTrue(np.allclose(result_trend, answer_trend))
|
||||
self.assertTrue(np.allclose(result_volatility, answer_volatility))
|
||||
Reference in New Issue
Block a user