def fit(self, df, fnames, by): self.cprobs, self.fnames = [], [] for f in fnames: if not utility.is_categorical(df, f): raise ValueError(f+" must be categorical, use encoding or discretizing") cprob = pd.crosstab(df[by], df[f]) cprob = cprob / cprob.sum(axis = 0) cprob = cprob.iloc[1, :] cprob.name = "%sIs%s_on_%s" % (by, cprob.name, f) self.cprobs.append(cprob) self.fnames.append(f) return self
def _extract_cprobs_by_biclass(df, fnames, by, copy = True): """ Depreciated - use BiClassProbabilityFeatureExtractor to better handle train and validate data Extract conditional probability features based on binary class labels, see Ref 1 chapter 6 for details. df: DataFrame fnames: features to be extracted from - the features must be categorical or discretized numerical (call transformation.discretize_numerical for that) by: binary class labels (for multiple labels, use one-hot-encoding to get the cprobs-features separately) copy: whether copy dataframe or modify in place """ result = df.copy() if copy else df for f in fnames: if not utility.is_categorical(df, f): raise ValueError(f+" must be categorical, use encoding or discretizing") cprobs = pd.crosstab(df[by], df[f]) cprobs = cprobs / cprobs.sum(axis = 0) cprobs = cprobs.iloc[1, :] cprobs.name = "%sIs%s_on_%s" % (by, cprobs.name, f) result = result.join(cprobs, on = f) return result
def find_categorical_features(df): return np.asarray([f for f in df.columns if is_categorical(df, f)])
