def _fit(self, X, y):
self.X, y = self._check_params(X, y)
n, p = X.shape
self.y = y.reshape((n, 1))
# list of selected features
S = []
# list of all features
F = range(p)
if self.n_features != 'auto':
feature_mi_matrix = np.zeros((self.n_features, p))
else:
feature_mi_matrix = np.zeros((n, p))
feature_mi_matrix[:] = np.nan
S_mi = []
# ----------------------------------------------------------------------
# FIND FIRST FEATURE
# ----------------------------------------------------------------------
# check a range of ks (3-10), and choose the one with the max median MI
k_min = 3
k_max = 11
xy_MI = np.zeros((k_max-k_min, p))
xy_MI[:] = np.nan
for i, k in enumerate(range(k_min, k_max)):
xy_MI [i, :] = mi.get_first_mi_vector(self, k)
xy_MI = bn.nanmedian(xy_MI, axis=0)
# choose the best, add it to S, remove it from F
S, F = self._add_remove(S, F, bn.nanargmax(xy_MI))
S_mi.append(bn.nanmax(xy_MI))
# notify user
if self.verbose > 0:
self._print_results(S, S_mi)
# ----------------------------------------------------------------------
# FIND SUBSEQUENT FEATURES
# ---------------------------------------------------------------------
while self.n_features=='auto' or len(S) < self.n_features:
# loop through the remaining unselected features and calculate MI
s = len(S) - 1
feature_mi_matrix[s, F] = mi.get_mi_vector(self, F, s)
# make decision based on the chosen FS algorithm
fmm = feature_mi_matrix[:len(S),F]
if self.method == 'JMI':
selected = F[bn.nanargmax(bn.nansum(fmm, axis=0))]
elif self.method == 'JMIM':
selected = F[bn.nanargmax(bn.nanmin(fmm, axis=0))]
elif self.method == 'MRMR':
MRMR = xy_MI[F] - bn.nanmean(fmm, axis=0)
selected = F[bn.nanargmax(MRMR)]
# record the JMIM of the newly selected feature and add it to S
S_mi.append(bn.nanmax(bn.nanmin(fmm, axis=0)))
S, F = self._add_remove(S, F, selected)
# notify user
if self.verbose > 0:
self._print_results(S, S_mi)
# if n_features == 'auto', let's check the S_mi to stop
if self.n_features == 'auto' and len(S) > 10:
# smooth the 1st derivative of the MI values of previously sel
MI_dd = signal.savgol_filter(S_mi[1:],9,2,1)
# does the mean of the last 5 converge to 0?
if np.abs(np.mean(MI_dd[-5:])) < 1e-3:
break
# ----------------------------------------------------------------------
# SAVE RESULTS
# ----------------------------------------------------------------------
self.n_features_ = len(S)
self.support_ = np.zeros(p, dtype=np.bool)
self.support_[S] = 1
self.ranking_ = S
self.mi_ = S_mi
return self
def _fit(self, X, y):
self.X, y = self._check_params(X, y)
n, p = X.shape
self.y = y.reshape((n, 1))
# list of selected features
S = []
# list of all features
F = range(p)
if self.n_features != 'auto':
feature_mi_matrix = np.zeros((self.n_features, p))
else:
feature_mi_matrix = np.zeros((n, p))
feature_mi_matrix[:] = np.nan
S_mi = []
# ----------------------------------------------------------------------
# FIND FIRST FEATURE
# ----------------------------------------------------------------------
# check a range of ks (3-10), and choose the one with the max median MI
k_min = 3
k_max = 11
xy_MI = np.zeros((k_max-k_min, p))
xy_MI[:] = np.nan
for i, k in enumerate(range(k_min, k_max)):
xy_MI [i, :] = mi.get_first_mi_vector(self, k)
xy_MI = bn.nanmedian(xy_MI, axis=0)
# choose the best, add it to S, remove it from F
S, F = self._add_remove(S, F, bn.nanargmax(xy_MI))
S_mi.append(bn.nanmax(xy_MI))
# notify user
if self.verbose > 0:
self._print_results(S, S_mi)
# ----------------------------------------------------------------------
# FIND SUBSEQUENT FEATURES
# ----------------------------------------------------------------------
while len(S) < self.n_features:
# loop through the remaining unselected features and calculate MI
s = len(S) - 1
feature_mi_matrix[s, F] = mi.get_mi_vector(self, F, s)
# make decision based on the chosen FS algorithm
fmm = feature_mi_matrix[:len(S),F]
if self.method == 'JMI':
selected = F[bn.nanargmax(bn.nansum(fmm, axis=0))]
elif self.method == 'JMIM':
selected = F[bn.nanargmax(bn.nanmin(fmm, axis=0))]
elif self.method == 'MRMR':
MRMR = xy_MI[F] - bn.nanmean(fmm, axis=0)
selected = F[bn.nanargmax(MRMR)]
# record the JMIM of the newly selected feature and add it to S
S_mi.append(bn.nanmax(bn.nanmin(fmm, axis=0)))
S, F = self._add_remove(S, F, selected)
# notify user
if self.verbose > 0:
self._print_results(S, S_mi)
# if n_features == 'auto', let's check the S_mi to stop
if self.n_features == 'auto' and len(S) > 10:
# smooth the 1st derivative of the MI values of previously sel
MI_dd = signal.savgol_filter(S_mi[1:],9,2,1)
# does the mean of the last 5 converge to 0?
if np.abs(np.mean(MI_dd[-5:])) < 1e-3:
break
# ----------------------------------------------------------------------
# SAVE RESULTS
# ----------------------------------------------------------------------
self.n_features_ = len(S)
self.support_ = np.zeros(p, dtype=np.bool)
self.support_[S] = 1
self.ranking_ = S
self.mi_ = S_mi
return self