old_scripts/learnme_linear.py
def main():
    """
    Takes the pickle file of the training set, splits the dataframe into the 
    appropriate X and Ys for prediction of reactor type, cooling time, fuel 
    enrichment, and burnup. Scales the training data set for the algorithms, then 
    calls for the training and prediction. Saves all results as .csv files. 
            # Generally, you want to peek at validation curve results to set 
            # the best params for the learning curves, perhaps even
            # with one more iteration (i.e. if trainset is too big, give a 
            # fraction of it to the validation curve). Thus, only run this script 
            # one at time
    """
    pkl_base = './pkl_trainsets/2jul2018/2jul2018_trainset'
    for trainset in ('1', '2'):
        for subset in ('fiss', 'act', 'fissact', 'all'):
            pkl = pkl_base + trainset + '_nucs_' + subset + '_not-scaled.pkl'
            trainXY = pd.read_pickle(pkl, compression=None)
            
            trainX, rY, cY, eY, bY = splitXY(trainXY)
            if subset == 'all':
                top_n = 100
                nuc_set = top_nucs(trainX, top_n)
                trainX = filter_nucs(trainX, nuc_set, top_n)

            # Scale the trainset below. This treatment is for nucs only.
            # For gammas: trainX = scale(trainX, with_mean=False)
            trainX = scale(trainX) 
            
            # Run one diagnostic curve at a time
            validation_curves(trainX, rY, cY, eY, bY, subset)
            #learning_curves(trainX, rY, cY, eY, bY, subset)
            
            print("The {} predictions in trainset {} are complete\n".format(subset, trainset), flush=True)

    Takes the pickle file of the training set, splits the dataframe into the 
    appropriate X and Ys for prediction of reactor type, cooling time, fuel 
    enrichment, and burnup. Scales the training data set for the algorithms, then 
    calls for the training and prediction. Saves all results as .csv files. 
            # Generally, you want to peek at validation curve results to set 
            # the best params for the learning curves, perhaps even
            # with one more iteration (i.e. if trainset is too big, give a 
            # fraction of it to the validation curve). Thus, only run this script 
            # one at time
    
Takes the pickle file of the training set, splits the dataframe into the
appropriate X and Ys for prediction of reactor type, cooling time, fuel
enrichment, and burnup. Scales the training data set for the algorithms, then
calls for the training and prediction. Saves all results as .csv files.
Generally, you want to peek at validation curve results to set
the best params for the learning curves, perhaps even
with one more iteration . Thus, only run this script
one at time
Takes the pickle file of the training set splits the dataframe into the appropriate X and Ys for prediction of reactor type cooling time fuel enrichment and burnup . Scales the training data set for the algorithms then calls for the training and prediction . Saves all results as . csv files . Generally you want to peek at validation curve results to set the best params for the learning curves perhaps even with one more iteration . Thus only run this script one at time
def main():
    pkl_base = './pkl_trainsets/2jul2018/2jul2018_trainset'
    for trainset in ('1', '2'):
        for subset in ('fiss', 'act', 'fissact', 'all'):
            pkl = pkl_base + trainset + '_nucs_' + subset + '_not-scaled.pkl'
            trainXY = pd.read_pickle(pkl, compression=None)
            trainX, rY, cY, eY, bY = splitXY(trainXY)
            if subset == 'all':
                top_n = 100
                nuc_set = top_nucs(trainX, top_n)
                trainX = filter_nucs(trainX, nuc_set, top_n)
            trainX = scale(trainX) 
            validation_curves(trainX, rY, cY, eY, bY, subset)
            print("The {} predictions in trainset {} are complete\n".format(subset, trainset), flush=True)
def main ( ) : pkl_base = './pkl_trainsets/2jul2018/2jul2018_trainset' for trainset in ( '1' , '2' ) : for subset in ( 'fiss' , 'act' , 'fissact' , 'all' ) : pkl = pkl_base + trainset + '_nucs_' + subset + '_not-scaled.pkl' trainXY = pd . read_pickle ( pkl , compression = None ) trainX , rY , cY , eY , bY = splitXY ( trainXY ) if subset == 'all' : top_n = 100 nuc_set = top_nucs ( trainX , top_n ) trainX = filter_nucs ( trainX , nuc_set , top_n ) trainX = scale ( trainX ) validation_curves ( trainX , rY , cY , eY , bY , subset ) print ( "The {} predictions in trainset {} are complete\n" . format ( subset , trainset ) , flush = True )
Takes the pickle file of the training set, splits the dataframe into the
appropriate X and Ys for prediction of reactor type, cooling time, fuel
enrichment, and burnup.
Takes the pickle file of the training set splits the dataframe into the appropriate X and Ys for prediction of reactor type cooling time fuel enrichment and burnup .
"""
    Takes the pickle file of the training set, splits the dataframe into the 
    appropriate X and Ys for prediction of reactor type, cooling time, fuel 
    enrichment, and burnup. Scales the training data set for the algorithms, then 
    calls for the training and prediction. Saves all results as .csv files. 
            # Generally, you want to peek at validation curve results to set 
            # the best params for the learning curves, perhaps even
            # with one more iteration (i.e. if trainset is too big, give a 
            # fraction of it to the validation curve). Thus, only run this script 
            # one at time
    """ # Scale the trainset below. This treatment is for nucs only. # For gammas: trainX = scale(trainX, with_mean=False) # Run one diagnostic curve at a time #learning_curves(trainX, rY, cY, eY, bY, subset)