ease/util_functions.py
def gen_cv_preds(clf, arr, sel_score, num_chunks=3):
    """
    Generates cross validated predictions using an input classifier and data.
    clf is a classifier that implements that implements the fit and predict methods.
    arr is the input data array (X)
    sel_score is the target list (y).  y[n] corresponds to X[n,:]
    num_chunks is the number of cross validation folds to use
    Returns an array of the predictions where prediction[n] corresponds to X[n,:]
    """
    cv_len = int(math.floor(len(sel_score) / num_chunks))
    chunks = []
    for i in range(0, num_chunks):
        range_min = i * cv_len
        range_max = ((i + 1) * cv_len)
        if i == num_chunks - 1:
            range_max = len(sel_score)
        chunks.append(range(range_min, range_max))
    preds = []
    set_score = numpy.asarray(sel_score, dtype=numpy.int)
    chunk_vec = numpy.asarray(range(0, len(chunks)))
    for i in xrange(0, len(chunks)):
        loop_inds = list(
            chain.from_iterable([chunks[int(z)] for z, m in enumerate(range(0, len(chunks))) if int(z) != i]))
        sim_fit = clf.fit(arr[loop_inds], set_score[loop_inds])
        preds.append(list(sim_fit.predict(arr[chunks[i]])))
    all_preds = list(chain(*preds))
    return(all_preds)
def gen_cv_preds(clf, arr, sel_score, num_chunks=3):
    """
    Generates cross validated predictions using an input classifier and data.
    clf is a classifier that implements that implements the fit and predict methods.
    arr is the input data array (X)
    sel_score is the target list (y).  y[n] corresponds to X[n,:]
    num_chunks is the number of cross validation folds to use
    Returns an array of the predictions where prediction[n] corresponds to X[n,:]
    """
    cv_len = int(math.floor(len(sel_score) / num_chunks))
    chunks = []
    for i in range(0, num_chunks):
        range_min = i * cv_len
        range_max = ((i + 1) * cv_len)
        if i == num_chunks - 1:
            range_max = len(sel_score)
        chunks.append(range(range_min, range_max))
    preds = []
    set_score = numpy.asarray(sel_score, dtype=numpy.int)
    chunk_vec = numpy.asarray(range(0, len(chunks)))
    for i in xrange(0, len(chunks)):
        loop_inds = list(
            chain.from_iterable([chunks[int(z)] for z, m in enumerate(range(0, len(chunks))) if int(z) != i]))
        sim_fit = clf.fit(arr[loop_inds], set_score[loop_inds])
        preds.append(list(sim_fit.predict(arr[chunks[i]])))
    all_preds = list(chain(*preds))
    return(all_preds)
def gen_cv_preds ( clf , arr , sel_score , num_chunks = 3 ) : cv_len = int ( math . floor ( len ( sel_score ) / num_chunks ) ) chunks = [ ] for i in range ( 0 , num_chunks ) : range_min = i * cv_len range_max = ( ( i + 1 ) * cv_len ) if i == num_chunks - 1 : range_max = len ( sel_score ) chunks . append ( range ( range_min , range_max ) ) preds = [ ] set_score = numpy . asarray ( sel_score , dtype = numpy . int ) chunk_vec = numpy . asarray ( range ( 0 , len ( chunks ) ) ) for i in xrange ( 0 , len ( chunks ) ) : loop_inds = list ( chain . from_iterable ( [ chunks [ int ( z ) ] for z , m in enumerate ( range ( 0 , len ( chunks ) ) ) if int ( z ) != i ] ) ) sim_fit = clf . fit ( arr [ loop_inds ] , set_score [ loop_inds ] ) preds . append ( list ( sim_fit . predict ( arr [ chunks [ i ] ] ) ) ) all_preds = list ( chain ( * preds ) ) return ( all_preds )
Generates cross validated predictions using an input classifier and data.
    clf is a classifier that implements that implements the fit and predict methods.
    arr is the input data array (X)
    sel_score is the target list (y).  y[n] corresponds to X[n,:]
    num_chunks is the number of cross validation folds to use
    Returns an array of the predictions where prediction[n] corresponds to X[n,:]
Generates cross validated predictions using an input classifier and data . clf is a classifier that implements that implements the fit and predict methods . arr is the input data array ( X ) sel_score is the target list ( y ) . y [ n ] corresponds to X [ n : ] num_chunks is the number of cross validation folds to use Returns an array of the predictions where prediction [ n ] corresponds to X [ n : ]