genelens.fselector.fsplot

class documentation

class fsplot(object):

Constructor: fsplot(fs_model, color)

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Implementation of graphics methods for FeatureSelector module logs

Method __init__ Undocumented
Method C_searching plot C searching dynamic and C-optimal value. Available after get_optimal_C() procedure
Method feature_selection plot feature selection dynamic for to assess convergence. Available after fit FeatureSelector
Method feature_selection_exted plot feature selection weight dynamic for all- and best-feature comparison. Available after fit FeatureSelector
Method feature_weights plot feature weight distribution histogram and cut_off_w_feature level. Available after fit FeatureSelector
Method plot_all plot all graphs on one figure
Method ROCdistr plot ROC-AUC distribution histogram. Available after fit FeatureSelector
Method weights_cut_of_level_searching plot cut_off_w_feature searching dynamic and C-optimal value. Available after _get_optimal_cut_off_level() procedure
Instance Variable fs_model fitted FeatureSelector model
Instance Variable one_color first color
Instance Variable third_color third color
Instance Variable two_color second color
def __init__(self, fs_model, color=[(18 / 255, 163 / 255, 173 / 255, 1), (200 / 255, 13 / 255, 63 / 255, 1), (248 / 255, 155 / 255, 54 / 255, 1), (242 / 255, 104 / 255, 73 / 255, 1), (65 / 255, 100 / 255, 175 / 255, 1)]):

Undocumented

def C_searching(self, ax=None, fontsize=20, path=None):

plot C searching dynamic and C-optimal value. Available after get_optimal_C() procedure

def feature_selection(self, ax=None, fontsize=20, path=None):

plot feature selection dynamic for to assess convergence. Available after fit FeatureSelector

def feature_selection_exted(self, ax=None, fontsize=20, path=None):

plot feature selection weight dynamic for all- and best-feature comparison. Available after fit FeatureSelector

def feature_weights(self, ax=None, fontsize=20, path=None):

plot feature weight distribution histogram and cut_off_w_feature level. Available after fit FeatureSelector

def plot_all(self, fontsize=20, labels=['a.', 'b.', 'c.', 'd.', 'e.', 'f.'], left=0.1, right=0.9, top=0.9, bottom=0.1, hspace=0.3, wspace=0.4):

plot all graphs on one figure

def ROCdistr(self, ax=None, fontsize=20, path=None):

plot ROC-AUC distribution histogram. Available after fit FeatureSelector

def weights_cut_of_level_searching(self, ax=None, fontsize=20, path=None):

plot cut_off_w_feature searching dynamic and C-optimal value. Available after _get_optimal_cut_off_level() procedure

fs_model =

fitted FeatureSelector model

one_color =

first color

third_color =

third color

two_color =

second color