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Make the McrAR API more sklearn-like #32

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Make the McrAR API more sklearn-like #32

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96 changes: 54 additions & 42 deletions pymcr/mcr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,6 +118,22 @@ class McrAR:
exit_tol_n_above_min : bool
Exited iterations due to maximum number of half-iterations for which
the error metric increased above the minimum error
C : ndarray
Initial C matrix estimate. Only provide initial C OR S^T.

ST : ndarray
Initial S^T matrix estimate. Only provide initial C OR S^T.

st_fix : list
The spectral component numbers to keep fixed.

c_fix : list
The concentration component numbers to keep fixed.

c_first : bool
Calculate C first when both C and ST are provided. c_fix and st_fix
must also be provided in this circumstance.


Notes
-----
Expand All @@ -136,11 +152,31 @@ def __init__(self, c_regr=OLS(), st_regr=OLS(), c_fit_kwargs={},
st_constraints=[ConstraintNonneg()],
max_iter=50, err_fcn=mse,
tol_increase=0.0, tol_n_increase=10, tol_err_change=None,
tol_n_above_min=10
tol_n_above_min=10, C=None, ST=None, c_fix=None, st_fix=None, c_first=True,
):
"""
Multivariate Curve Resolution - Alternating Regression
"""
# Ensure only C or ST provided
if (C is None) & (ST is None):
raise TypeError('C or ST estimate must be provided')
elif (C is not None) & (ST is not None) & ((c_fix is None) |
(st_fix is None)):
err_str1 = 'Only C or ST estimate must be provided, '
raise TypeError(
err_str1 + 'unless c_fix and st_fix are both provided')
else:
if C is not None:
C = _np.asanyarray(C)
C = C.reshape([_np.prod([C.shape[:-1]]), -1])
if ST is not None:
ST = _np.asanyarray(ST)
ST = ST.reshape([-1, _np.prod([ST.shape[1:]])])
self.C_ = C
self.ST_ = ST
self.c_fix = c_fix
self.st_fix = st_fix
self.c_first = c_first

self.max_iter = max_iter

Expand All @@ -160,9 +196,6 @@ def __init__(self, c_regr=OLS(), st_regr=OLS(), c_fit_kwargs={},
self.c_fit_kwargs = c_fit_kwargs
self.st_fit_kwargs = st_fit_kwargs

self.C_ = None
self.ST_ = None

self.C_opt_ = None
self.ST_opt_ = None
self.n_iter_opt = None
Expand Down Expand Up @@ -203,6 +236,13 @@ def _check_regr(self, mth):
raise ValueError('Input class '
'{} does not have a \'fit\' method'.format(mth))

def transform(self, data, **kwargs):
return self.C_opt_

@property
def components_(self):
return self.ST_opt_

@property
def D_(self):
""" D matrix with current C and S^T matrices """
Expand Down Expand Up @@ -244,8 +284,7 @@ def _ismin_err(self, val):
else:
return ([val > x for x in self.err].count(True) == 0)

def fit(self, D, C=None, ST=None, st_fix=None, c_fix=None, c_first=True,
verbose=False, post_iter_fcn=None, post_half_fcn=None):
def fit(self, D, verbose=False, post_iter_fcn=None, post_half_fcn=None):
"""
Perform MCR-AR. D = CS^T. Solve for C and S^T iteratively.

Expand All @@ -254,22 +293,6 @@ def fit(self, D, C=None, ST=None, st_fix=None, c_fix=None, c_first=True,
D : ndarray
D matrix

C : ndarray
Initial C matrix estimate. Only provide initial C OR S^T.

ST : ndarray
Initial S^T matrix estimate. Only provide initial C OR S^T.

st_fix : list
The spectral component numbers to keep fixed.

c_fix : list
The concentration component numbers to keep fixed.

c_first : bool
Calculate C first when both C and ST are provided. c_fix and st_fix
must also be provided in this circumstance.

verbose : bool
Log iteration and per-least squares err results. See Notes.

Expand All @@ -294,17 +317,6 @@ def fit(self, D, C=None, ST=None, st_fix=None, c_fix=None, c_first=True,
else:
_logger.setLevel(_logging.INFO)

# Ensure only C or ST provided
if (C is None) & (ST is None):
raise TypeError('C or ST estimate must be provided')
elif (C is not None) & (ST is not None) & ((c_fix is None) |
(st_fix is None)):
err_str1 = 'Only C or ST estimate must be provided, '
raise TypeError(
err_str1 + 'unless c_fix and st_fix are both provided')
else:
self.C_ = C
self.ST_ = ST

self.n_increase = 0
self.n_above_min = 0
Expand All @@ -317,7 +329,7 @@ def fit(self, D, C=None, ST=None, st_fix=None, c_fix=None, c_first=True,
self.n_iter = num + 1

# Both st and c provided, but c_first is False
if both_condition & (num == 0) & (not c_first):
if both_condition & (num == 0) & (not self.c_first):
special_skip_c = True
else:
special_skip_c = False
Expand All @@ -333,16 +345,16 @@ def fit(self, D, C=None, ST=None, st_fix=None, c_fix=None, c_first=True,
C_temp = self.c_regressor.coef_

# Apply fixed C's
if c_fix:
C_temp[:, c_fix] = self.C_[:, c_fix]
if self.c_fix:
C_temp[:, self.c_fix] = self.C_[:, self.c_fix]

# Apply c-constraints
for constr in self.c_constraints:
C_temp = constr.transform(C_temp)

# Apply fixed C's
if c_fix:
C_temp[:, c_fix] = self.C_[:, c_fix]
if self.c_fix:
C_temp[:, self.c_fix] = self.C_[:, self.c_fix]

D_calc = _np.dot(C_temp, self.ST_)

Expand Down Expand Up @@ -419,16 +431,16 @@ def fit(self, D, C=None, ST=None, st_fix=None, c_fix=None, c_first=True,
ST_temp = self.st_regressor.coef_.T

# Apply fixed ST's
if st_fix:
ST_temp[st_fix] = self.ST_[st_fix]
if self.st_fix:
ST_temp[self.st_fix] = self.ST_[self.st_fix]

# Apply ST-constraints
for constr in self.st_constraints:
ST_temp = constr.transform(ST_temp)

# Apply fixed ST's
if st_fix:
ST_temp[st_fix] = self.ST_[st_fix]
if self.st_fix:
ST_temp[self.st_fix] = self.ST_[self.st_fix]

D_calc = _np.dot(self.C_, ST_temp)

Expand Down