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' )