def getBestLsumOfFunctionsPredictorTEST1(plot: False):
(HEADERS, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT) = utilsTest.testFromFile1()
myLsumOfFunctionsPredictor = modStats.mktPredictor.predictorFactory(
modStats.TYPE_LSUM_OF_FUNCTIONS,
DF,
HEADER_X,
HEADER_Y,
HEADER_WEIGHT,
utils.HEADER_MARKET,
utils.HEADER_STOCK,
displayCharts=plot)
estimate = myLsumOfFunctionsPredictor.estimate(utilsTest.forPrediction1(),
HEADER_X, HEADER_Y)
return (
np.array_equal(np.around(estimate, 6), [0.023071, 0.022401])
and (round(myLsumOfFunctionsPredictor.getAvgCoeffDetermin(), 6)
== 0.943534)
and np.array_equal(
[f.__name__ for f in myLsumOfFunctionsPredictor.getXFunctions()], [
'log', 'digit90Percent', 'log', 'digit80Percent',
'digit90Percent', 'log', 'log', 'identity', 'identity',
'digit90Percent', 'log'
]),
'LsumOfFunctionsPredictor returns unexpected Value for prediction 1 from file 1'
)
def getBestLsumOfFunctionsPredictorTEST3(plot: False):
(HEADERS, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT) = utilsTest.testFromFile1()
utils.Constants().incrementalFunctionFit = True
myLsumOfFunctionsPredictor = modStats.mktPredictor.predictorFactory(
modStats.TYPE_LSUM_OF_FUNCTIONS,
DF,
HEADER_X,
HEADER_Y,
HEADER_WEIGHT,
utils.HEADER_MARKET,
utils.HEADER_STOCK,
displayCharts=plot)
estimate = myLsumOfFunctionsPredictor.estimate(utilsTest.forPrediction1(),
HEADER_X, HEADER_Y)
return (
np.array_equal(np.around(estimate, 6), [-0.085291, -0.900866])
and (round(myLsumOfFunctionsPredictor.getAvgCoeffDetermin(), 6)
== 0.945467)
and np.array_equal(
[f.__name__ for f in myLsumOfFunctionsPredictor.getXFunctions()], [
'log', 'identity', 'identity', 'digit80PercentOfAddRatio',
'digit90Percent', 'logOfMultRatio', 'identity', 'identity',
'identity', 'digit90Percent', 'log'
]),
'LsumOfFunctionsPredictor returns unexpected Value for prediction 1 from file 1 when functions are searched incrementally'
)
def getLsumOfFunctionsPerStockPredictorTEST0(plot: False):
(HEADERS, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT) = utilsTest.testFromFile726()
myLsumOfFunctionsPerStockPredictor = modStats.mktPredictor.predictorFactory(
modStats.TYPE_LSUM_OF_FUNCTIONS_PER_STCK, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT, utils.HEADER_MARKET, utils.HEADER_STOCK)
estimate = myLsumOfFunctionsPerStockPredictor.estimate(
utilsTest.forPrediction1(), HEADER_X, HEADER_Y)
return (np.array_equal(np.around(estimate, 6), [2.530599, 3.0229]) and (
round(myLsumOfFunctionsPerStockPredictor.getAvgCoeffDetermin(),
6) == 0.932669
), 'LinearPerStockPredictor returns unexpected Value for prediction 1 from file 726'
)
def getLsumOfFunctionsPerStockPredictorTEST1(plot: False):
(HEADERS, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT) = utilsTest.testFromFile726()
utils.Constants().incrementalFunctionFit = True
myLsumOfFunctionsPerStockPredictor = modStats.mktPredictor.predictorFactory(
modStats.TYPE_LSUM_OF_FUNCTIONS_PER_STCK, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT, utils.HEADER_MARKET, utils.HEADER_STOCK)
estimate = myLsumOfFunctionsPerStockPredictor.estimate(
utilsTest.forPrediction1(), HEADER_X, HEADER_Y)
return (
np.array_equal(np.around(estimate, 6), [-0.100358, -0.008756])
and (round(myLsumOfFunctionsPerStockPredictor.getAvgCoeffDetermin(), 6)
== 0.937514),
'LinearPerStockPredictor returns unexpected Value for prediction 1 from file 726'
)
def getLinearPerStockPredictorTEST0(plot: False):
utils.Constants().fractionFullSampleForTest = 0.2
(HEADERS, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT) = utilsTest.testFromFile726()
myLinearPerStockPredictor = modStats.mktPredictor.predictorFactory(
modStats.TYPE_LINEAR_PER_STCK, DF, HEADER_X, HEADER_Y, HEADER_WEIGHT,
utils.HEADER_MARKET, utils.HEADER_STOCK)
estimate = myLinearPerStockPredictor.estimate(utilsTest.forPrediction1(),
HEADER_X, HEADER_Y)
return (np.array_equal(np.around(estimate, 6), [
-0.850607, -1.915488
]) and (
round(myLinearPerStockPredictor.getAvgCoeffDetermin(), 6) == 0.847778
), 'LinearPerStockPredictor returns unexpected Value for prediction 1 from file 726'
)
def getAccurateLinearPredictorTEST1(plot: False):
(HEADERS, DF, HEADER_X, HEADER_Y,
HEADER_WEIGHT) = utilsTest.testFromFile1()
myLinearPredictor = modStats.mktPredictor.predictorFactory(
modStats.TYPE_LINEAR,
DF,
HEADER_X,
HEADER_Y,
HEADER_WEIGHT,
utils.HEADER_MARKET,
utils.HEADER_STOCK,
displayCharts=plot)
estimate = myLinearPredictor.estimate(utilsTest.forPrediction1(), HEADER_X,
HEADER_Y)
return (
np.array_equal(np.around(estimate, 6), [-0.823778, -1.519227])
and (round(myLinearPredictor.getAvgCoeffDetermin(), 6) == 0.941377)
and (myLinearPredictor.getk() == 6),
'LinearPredictor returns unexpected Value for prediction 1 from file 1'
)