import numpy as np
import sklearn.decomposition as skl_decomposition

try:
    from orangecontrib.remote import aborted, save_state
except ImportError:
    def aborted():
        return False

    def save_state(_):
        pass

import Orange.data
from Orange.data.util import SharedComputeValue
from Orange.data import Variable
from Orange.misc.wrapper_meta import WrapperMeta
from Orange.preprocess import Continuize
from Orange.projection import SklProjector, Projection, LinearCombinationSql
from Orange.preprocess.score import LearnerScorer

__all__ = ["PCA", "SparsePCA", "IncrementalPCA", "TruncatedSVD"]


class _FeatureScorerMixin(LearnerScorer):
    feature_type = Variable
    component = 0

    def score(self, data):
        model = self(data)
        return np.abs(model.components_[:self.component]) \
            if self.component else np.abs(model.components_)


class PCA(SklProjector, _FeatureScorerMixin):
    __wraps__ = skl_decomposition.PCA
    name = 'PCA'
    supports_sparse = False

    def __init__(self, n_components=None, copy=True, whiten=False,
                 svd_solver='auto', tol=0.0, iterated_power='auto',
                 random_state=None, preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        params = self.params.copy()
        if params["n_components"] is not None:
            params["n_components"] = min(min(X.shape), params["n_components"])
        proj = self.__wraps__(**params)
        proj = proj.fit(X, Y)
        return PCAModel(proj, self.domain)


class SparsePCA(SklProjector):
    __wraps__ = skl_decomposition.SparsePCA
    name = 'Sparse PCA'
    supports_sparse = False

    def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01,
                 max_iter=1000, tol=1e-8, method='lars', n_jobs=1, U_init=None,
                 V_init=None, verbose=False, random_state=None, preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        proj = self.__wraps__(**self.params)
        proj = proj.fit(X, Y)
        return PCAModel(proj, self.domain)


class _PCATransformDomain:
    """Computation common for all PCA variables."""

    def __init__(self, pca):
        self.pca = pca

    def __call__(self, data):
        if data.domain != self.pca.pre_domain:
            data = data.transform(self.pca.pre_domain)
        return self.pca.transform(data.X)


class PCAModel(Projection, metaclass=WrapperMeta):

    def __init__(self, proj, domain):
        pca_transform = _PCATransformDomain(self)

        def pca_variable(i):
            v = Orange.data.ContinuousVariable(
                'PC%d' % (i + 1),
                compute_value=Projector(self, i, pca_transform))
            v.to_sql = LinearCombinationSql(
                domain.attributes, self.components_[i, :],
                getattr(self, 'mean_', None))
            return v

        super().__init__(proj=proj)
        self.orig_domain = domain
        self.n_components = self.components_.shape[0]
        self.domain = Orange.data.Domain(
            [pca_variable(i) for i in range(self.n_components)],
            domain.class_vars, domain.metas)


class IncrementalPCA(SklProjector):
    __wraps__ = skl_decomposition.IncrementalPCA
    name = 'Incremental PCA'
    supports_sparse = False

    def __init__(self, n_components=None, whiten=False, copy=True,
                 batch_size=None, preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        proj = self.__wraps__(**self.params)
        proj = proj.fit(X, Y)
        return IncrementalPCAModel(proj, self.domain)

    def partial_fit(self, data):
        return self(data)


class IncrementalPCAModel(PCAModel):
    def partial_fit(self, data):
        if isinstance(data, Orange.data.Storage):
            if data.domain != self.pre_domain:
                data = data.from_table(self.pre_domain, data)
            self.proj.partial_fit(data.X)
        else:
            self.proj.partial_fit(data)
        self.__dict__.update(self.proj.__dict__)
        return self


class TruncatedSVD(SklProjector, _FeatureScorerMixin):
    __wraps__ = skl_decomposition.TruncatedSVD
    name = 'Truncated SVD'
    supports_sparse = True

    def __init__(self, n_components=2, algorithm='randomized', n_iter=5,
                 random_state=None, tol=0.0, preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        params = self.params.copy()
        # strict requirement in scikit fit_transform:
        # n_components must be < n_features
        params["n_components"] = min(min(X.shape)-1, params["n_components"])

        proj = self.__wraps__(**params)
        proj = proj.fit(X, Y)
        return PCAModel(proj, self.domain)


class Projector(SharedComputeValue):
    """Transform into a given PCA component."""

    def __init__(self, projection, feature, pca_transform):
        super().__init__(pca_transform)
        self.projection = projection
        self.feature = feature
        self.transformed = None

    def compute(self, data, pca_space):
        return pca_space[:, self.feature]


class RemotePCA:
    def __new__(cls, data, batch=100, max_iter=100):
        cont = Continuize(multinomial_treatment=Continuize.Remove)
        data = cont(data)
        model = Orange.projection.IncrementalPCA()
        n = data.approx_len()
        percent = batch / n * 100 if n else 100
        for i in range(max_iter):
            data_sample = data.sample_percentage(percent, no_cache=True)
            if not data_sample:
                continue
            data_sample.download_data(1000000)
            data_sample = Orange.data.Table.from_numpy(
                Orange.data.Domain(data_sample.domain.attributes),
                data_sample.X)
            model = model.partial_fit(data_sample)
            model.iteration = i
            save_state(model)
            if aborted() or data_sample is data:
                break
        return model
