diff options
Diffstat (limited to 'sklearn_pandas')
| -rw-r--r-- | sklearn_pandas/__init__.py | 161 | ||||
| -rw-r--r-- | sklearn_pandas/cross_validation.py | 37 | ||||
| -rw-r--r-- | sklearn_pandas/dataframe_mapper.py | 132 | ||||
| -rw-r--r-- | sklearn_pandas/pipeline.py | 64 |
4 files changed, 236 insertions, 158 deletions
diff --git a/sklearn_pandas/__init__.py b/sklearn_pandas/__init__.py index 0f5d94c..537ab56 100644 --- a/sklearn_pandas/__init__.py +++ b/sklearn_pandas/__init__.py @@ -1,159 +1,4 @@ -__version__ = '0.0.12' +__version__ = '1.1.0' -import numpy as np -import pandas as pd -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn import cross_validation -from sklearn import grid_search -import sys - -# load in the correct stringtype: str for py3, basestring for py2 -string_types = str if sys.version_info >= (3, 0) else basestring - - -def cross_val_score(model, X, *args, **kwargs): - X = DataWrapper(X) - return cross_validation.cross_val_score(model, X, *args, **kwargs) - - -class GridSearchCV(grid_search.GridSearchCV): - def fit(self, X, *params, **kwparams): - super(GridSearchCV, self).fit(DataWrapper(X), *params, **kwparams) - - def predict(self, X, *params, **kwparams): - super(GridSearchCV, self).fit(DataWrapper(X), *params, **kwparams) - - -try: - class RandomizedSearchCV(grid_search.RandomizedSearchCV): - def fit(self, X, *params, **kwparams): - super(RandomizedSearchCV, self).fit(DataWrapper(X), *params, **kwparams) - - def predict(self, X, *params, **kwparams): - super(RandomizedSearchCV, self).fit(DataWrapper(X), *params, **kwparams) -except AttributeError: - pass - - -class DataWrapper(object): - def __init__(self, df): - self.df = df - - def __len__(self): - return len(self.df) - - def __getitem__(self, key): - return self.df.iloc[key] - - -class PassthroughTransformer(TransformerMixin): - def fit(self, X, y=None, **fit_params): - return self - - def transform(self, X): - return np.array(X).astype(np.float) - - -def _handle_feature(fea): - if hasattr(fea, 'toarray'): - # sparse arrays should be converted to regular arrays - # for hstack. - fea = fea.toarray() - - if len(fea.shape) == 1: - fea = np.array([fea]).T - - return fea - - -class DataFrameMapper(BaseEstimator, TransformerMixin): - """ - Map Pandas data frame column subsets to their own - sklearn transformation. - """ - - def __init__(self, features): - """ - Params: - - features a list of pairs. The first element is the pandas column - selector. This can be a string (for one column) or a list - of strings. The second element is an object that supports - sklearn's transform interface. - """ - self.features = features - - def _get_col_subset(self, X, cols): - """ - Get a subset of columns from the given table X. - - X a Pandas dataframe; the table to select columns from - cols a string or list of strings representing the columns - to select - - Returns a numpy array with the data from the selected columns - """ - return_vector = False - if isinstance(cols, string_types): - return_vector = True - cols = [cols] - - if isinstance(X, list): - X = [x[cols] for x in X] - X = pd.DataFrame(X) - - elif isinstance(X, DataWrapper): - # if it's a datawrapper, unwrap it - X = X.df - - if return_vector: - t = X[cols[0]].values - else: - t = X.as_matrix(cols) - - return t - - def fit(self, X, y=None): - """ - Fit a transformation from the pipeline - - X the data to fit - """ - for columns, transformers in self.features: - if transformers is not None: - if isinstance(transformers, list): - # first fit_transform all transformers except the last one - Xt = self._get_col_subset(X, columns) - for transformer in transformers[:-1]: - Xt = transformer.fit_transform(Xt) - # then fit the last one without transformation - transformers[-1].fit(Xt) - else: - transformers.fit(self._get_col_subset(X, columns)) - return self - - def transform(self, X): - """ - Transform the given data. Assumes that fit has already been called. - - X the data to transform - """ - extracted = [] - for columns, transformers in self.features: - # columns could be a string or list of - # strings; we don't care because pandas - # will handle either. - Xt = self._get_col_subset(X, columns) - if transformers is not None: - if isinstance(transformers, list): - for transformer in transformers: - Xt = transformer.transform(Xt) - else: - Xt = transformers.transform(Xt) - extracted.append(_handle_feature(Xt)) - - # combine the feature outputs into one array. - # at this point we lose track of which features - # were created from which input columns, so it's - # assumed that that doesn't matter to the model. - return np.hstack(extracted) +from .dataframe_mapper import DataFrameMapper # NOQA +from .cross_validation import cross_val_score, GridSearchCV, RandomizedSearchCV # NOQA diff --git a/sklearn_pandas/cross_validation.py b/sklearn_pandas/cross_validation.py new file mode 100644 index 0000000..9cd8cbe --- /dev/null +++ b/sklearn_pandas/cross_validation.py @@ -0,0 +1,37 @@ +from sklearn import cross_validation +from sklearn import grid_search + + +def cross_val_score(model, X, *args, **kwargs): + X = DataWrapper(X) + return cross_validation.cross_val_score(model, X, *args, **kwargs) + + +class GridSearchCV(grid_search.GridSearchCV): + def fit(self, X, *params, **kwparams): + return super(GridSearchCV, self).fit(DataWrapper(X), *params, **kwparams) + + def predict(self, X, *params, **kwparams): + return super(GridSearchCV, self).predict(DataWrapper(X), *params, **kwparams) + + +try: + class RandomizedSearchCV(grid_search.RandomizedSearchCV): + def fit(self, X, *params, **kwparams): + return super(RandomizedSearchCV, self).fit(DataWrapper(X), *params, **kwparams) + + def predict(self, X, *params, **kwparams): + return super(RandomizedSearchCV, self).predict(DataWrapper(X), *params, **kwparams) +except AttributeError: + pass + + +class DataWrapper(object): + def __init__(self, df): + self.df = df + + def __len__(self): + return len(self.df) + + def __getitem__(self, key): + return self.df.iloc[key] diff --git a/sklearn_pandas/dataframe_mapper.py b/sklearn_pandas/dataframe_mapper.py new file mode 100644 index 0000000..9a59f6d --- /dev/null +++ b/sklearn_pandas/dataframe_mapper.py @@ -0,0 +1,132 @@ +import sys +import pandas as pd +import numpy as np +from scipy import sparse +from sklearn.base import BaseEstimator, TransformerMixin + +from .cross_validation import DataWrapper +from .pipeline import make_transformer_pipeline + +# load in the correct stringtype: str for py3, basestring for py2 +string_types = str if sys.version_info >= (3, 0) else basestring + + +def _handle_feature(fea): + """ + Convert 1-dimensional arrays to 2-dimensional column vectors. + """ + if len(fea.shape) == 1: + fea = np.array([fea]).T + + return fea + + +def _build_transformer(transformers): + if isinstance(transformers, list): + transformers = make_transformer_pipeline(*transformers) + return transformers + + +class DataFrameMapper(BaseEstimator, TransformerMixin): + """ + Map Pandas data frame column subsets to their own + sklearn transformation. + """ + + def __init__(self, features, sparse=False): + """ + Params: + + features a list of pairs. The first element is the pandas column + selector. This can be a string (for one column) or a list + of strings. The second element is an object that supports + sklearn's transform interface, or a list of such objects. + sparse will return sparse matrix if set True and any of the + extracted features is sparse. Defaults to False. + """ + if isinstance(features, list): + features = [(columns, _build_transformer(transformers)) + for (columns, transformers) in features] + self.features = features + self.sparse = sparse + + def __setstate__(self, state): + # compatibility shim for pickles created with sklearn-pandas<1.0.0 + self.features = [(columns, _build_transformer(transformers)) + for (columns, transformers) in state['features']] + self.sparse = state.get('sparse', False) + + def _get_col_subset(self, X, cols): + """ + Get a subset of columns from the given table X. + + X a Pandas dataframe; the table to select columns from + cols a string or list of strings representing the columns + to select + + Returns a numpy array with the data from the selected columns + """ + return_vector = False + if isinstance(cols, string_types): + return_vector = True + cols = [cols] + + if isinstance(X, list): + X = [x[cols] for x in X] + X = pd.DataFrame(X) + + elif isinstance(X, DataWrapper): + # if it's a datawrapper, unwrap it + X = X.df + + if return_vector: + t = X[cols[0]].values + else: + t = X[cols].values + + return t + + def fit(self, X, y=None): + """ + Fit a transformation from the pipeline + + X the data to fit + """ + for columns, transformers in self.features: + if transformers is not None: + transformers.fit(self._get_col_subset(X, columns)) + return self + + def transform(self, X): + """ + Transform the given data. Assumes that fit has already been called. + + X the data to transform + """ + extracted = [] + for columns, transformers in self.features: + # columns could be a string or list of + # strings; we don't care because pandas + # will handle either. + Xt = self._get_col_subset(X, columns) + if transformers is not None: + Xt = transformers.transform(Xt) + extracted.append(_handle_feature(Xt)) + + # combine the feature outputs into one array. + # at this point we lose track of which features + # were created from which input columns, so it's + # assumed that that doesn't matter to the model. + + # If any of the extracted features is sparse, combine sparsely. + # Otherwise, combine as normal arrays. + if any(sparse.issparse(fea) for fea in extracted): + stacked = sparse.hstack(extracted).tocsr() + # return a sparse matrix only if the mapper was initialized + # with sparse=True + if not self.sparse: + stacked = stacked.toarray() + else: + stacked = np.hstack(extracted) + + return stacked diff --git a/sklearn_pandas/pipeline.py b/sklearn_pandas/pipeline.py new file mode 100644 index 0000000..04cb053 --- /dev/null +++ b/sklearn_pandas/pipeline.py @@ -0,0 +1,64 @@ +import six +from sklearn.pipeline import _name_estimators, Pipeline +from sklearn.utils import tosequence + + +class TransformerPipeline(Pipeline): + """ + Pipeline that expects all steps to be transformers taking a single argument + and having fit and transform methods. + + Code is copied from sklearn's Pipeline, leaving out the `y=None` argument. + """ + def __init__(self, steps): + names, estimators = zip(*steps) + if len(dict(steps)) != len(steps): + raise ValueError("Provided step names are not unique: %s" % (names,)) + + # shallow copy of steps + self.steps = tosequence(steps) + estimator = estimators[-1] + + for e in estimators: + if (not (hasattr(e, "fit") or hasattr(e, "fit_transform")) or not + hasattr(e, "transform")): + raise TypeError("All steps of the chain should " + "be transforms and implement fit and transform" + " '%s' (type %s) doesn't)" % (e, type(e))) + + if not hasattr(estimator, "fit"): + raise TypeError("Last step of chain should implement fit " + "'%s' (type %s) doesn't)" + % (estimator, type(estimator))) + + def _pre_transform(self, X, **fit_params): + fit_params_steps = dict((step, {}) for step, _ in self.steps) + for pname, pval in six.iteritems(fit_params): + step, param = pname.split('__', 1) + fit_params_steps[step][param] = pval + Xt = X + for name, transform in self.steps[:-1]: + if hasattr(transform, "fit_transform"): + Xt = transform.fit_transform(Xt, **fit_params_steps[name]) + else: + Xt = transform.fit(Xt, **fit_params_steps[name]) \ + .transform(Xt) + return Xt, fit_params_steps[self.steps[-1][0]] + + def fit(self, X, **fit_params): + Xt, fit_params = self._pre_transform(X, **fit_params) + self.steps[-1][-1].fit(Xt, **fit_params) + return self + + def fit_transform(self, X, **fit_params): + Xt, fit_params = self._pre_transform(X, **fit_params) + if hasattr(self.steps[-1][-1], 'fit_transform'): + return self.steps[-1][-1].fit_transform(Xt, **fit_params) + else: + return self.steps[-1][-1].fit(Xt, **fit_params).transform(Xt) + + +def make_transformer_pipeline(*steps): + """Construct a TransformerPipeline from the given estimators. + """ + return TransformerPipeline(_name_estimators(steps)) |