def test__subset_and_standardize(self, expression_data_no_na,
standardize, feature_ids,
sample_ids):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data_no_na)
base_data.subset, base_data.means = \
base_data._subset_and_standardize(base_data.data,
sample_ids=sample_ids,
feature_ids=feature_ids,
return_means=True,
standardize=standardize)
subset = base_data._subset(base_data.data, sample_ids=sample_ids,
feature_ids=feature_ids)
means = subset.mean().rename_axis(base_data.feature_renamer)
subset = subset.fillna(means).fillna(0)
subset = subset.rename_axis(base_data.feature_renamer, 1)
if standardize:
data = StandardScaler().fit_transform(subset)
else:
data = subset
subset_standardized = pd.DataFrame(data, index=subset.index,
columns=subset.columns)
pdt.assert_frame_equal(subset_standardized, base_data.subset)
pdt.assert_series_equal(means, base_data.means)
def test_feature_subset_to_feature_ids(self, expression_data_no_na,
expression_feature_data,
feature_subset):
from flotilla.data_model.base import BaseData
expression = BaseData(expression_data_no_na,
feature_data=expression_feature_data)
test_feature_ids = expression.feature_subset_to_feature_ids(
feature_subset, rename=False)
true_feature_ids = pd.Index([])
if feature_subset is not None:
try:
if feature_subset in expression.feature_subsets:
true_feature_ids = expression.feature_subsets[
feature_subset]
elif feature_subset.startswith('all'):
true_feature_ids = expression.data.columns
except TypeError:
if not isinstance(feature_subset, str):
feature_ids = feature_subset
n_custom = expression.feature_data.columns.map(
lambda x: x.startswith('custom')).sum()
ind = 'custom_{}'.format(n_custom + 1)
expression.feature_data[ind] = \
expression.feature_data.index.isin(feature_ids)
else:
raise ValueError(
"There are no {} features in this data: "
"{}".format(feature_subset, self))
else:
true_feature_ids = expression.data.columns
pdt.assert_numpy_array_equal(test_feature_ids, true_feature_ids)
def test__subset_and_standardize(self, expression_data_no_na, standardize,
feature_ids, sample_ids):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data_no_na)
base_data.subset, base_data.means = \
base_data._subset_and_standardize(base_data.data,
sample_ids=sample_ids,
feature_ids=feature_ids,
return_means=True,
standardize=standardize)
subset = base_data._subset(base_data.data,
sample_ids=sample_ids,
feature_ids=feature_ids)
means = subset.mean().rename_axis(base_data.feature_renamer)
subset = subset.fillna(means).fillna(0)
subset = subset.rename_axis(base_data.feature_renamer, 1)
if standardize:
data = StandardScaler().fit_transform(subset)
else:
data = subset
subset_standardized = pd.DataFrame(data,
index=subset.index,
columns=subset.columns)
pdt.assert_frame_equal(subset_standardized, base_data.subset)
pdt.assert_series_equal(means, base_data.means)
def test_feature_subset_to_feature_ids(self, expression_data_no_na,
expression_feature_data,
feature_subset):
from flotilla.data_model.base import BaseData
expression = BaseData(expression_data_no_na,
feature_data=expression_feature_data)
test_feature_ids = expression.feature_subset_to_feature_ids(
feature_subset, rename=False)
true_feature_ids = pd.Index([])
if feature_subset is not None:
try:
if feature_subset in expression.feature_subsets:
true_feature_ids = expression.feature_subsets[
feature_subset]
elif feature_subset.startswith('all'):
true_feature_ids = expression.data.columns
except TypeError:
if not isinstance(feature_subset, str):
feature_ids = feature_subset
n_custom = expression.feature_data.columns.map(
lambda x: x.startswith('custom')).sum()
ind = 'custom_{}'.format(n_custom + 1)
expression.feature_data[ind] = \
expression.feature_data.index.isin(feature_ids)
else:
raise ValueError("There are no {} features in this data: "
"{}".format(feature_subset, self))
else:
true_feature_ids = expression.data.columns
pdt.assert_array_equal(test_feature_ids, true_feature_ids)
def test__init_sample_thresholds(self,
expression_data,
expression_thresh,
metadata_minimum_samples,
pooled):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data,
thresh=expression_thresh,
minimum_samples=metadata_minimum_samples,
pooled=pooled)
data = expression_data.copy()
pooled_samples = pooled.copy() if pooled is not None else []
single_samples = data.index[~data.index.isin(pooled_samples)]
singles_df = data.ix[single_samples]
if expression_thresh > -np.inf or metadata_minimum_samples > 0:
if not singles_df.empty:
data = base_data._threshold(data, singles_df)
else:
data = base_data._threshold(data)
singles_df = data.ix[single_samples]
pooled_df = data.ix[pooled_samples]
pdt.assert_frame_equal(base_data.data_original, expression_data)
pdt.assert_frame_equal(base_data.data, data)
pdt.assert_equal(base_data.thresh, expression_thresh)
pdt.assert_equal(base_data.minimum_samples, metadata_minimum_samples)
pdt.assert_frame_equal(base_data.pooled, pooled_df)
pdt.assert_frame_equal(base_data.singles, singles_df)
def test__init_sample_thresholds(self, expression_data, expression_thresh,
metadata_minimum_samples, pooled):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data,
thresh=expression_thresh,
minimum_samples=metadata_minimum_samples,
pooled=pooled)
data = expression_data.copy()
pooled_samples = pooled.copy() if pooled is not None else []
single_samples = data.index[~data.index.isin(pooled_samples)]
singles_df = data.ix[single_samples]
if expression_thresh > -np.inf or metadata_minimum_samples > 0:
if not singles_df.empty:
data = base_data._threshold(data, singles_df)
else:
data = base_data._threshold(data)
singles_df = data.ix[single_samples]
pooled_df = data.ix[pooled_samples]
pdt.assert_frame_equal(base_data.data_original, expression_data)
pdt.assert_frame_equal(base_data.data, data)
pdt.assert_equal(base_data.thresh, expression_thresh)
pdt.assert_equal(base_data.minimum_samples, metadata_minimum_samples)
pdt.assert_frame_equal(base_data.pooled, pooled_df)
pdt.assert_frame_equal(base_data.singles, singles_df)
def test__init_multiindex(self, df_norm):
from flotilla.data_model.base import BaseData
data = df_norm.copy()
level1 = data.columns.map(lambda x: 'level1_{}'.format(x))
data.columns = pd.MultiIndex.from_arrays([data.columns, level1])
BaseData(data)
def test__init__featuredata(self, expression_data_no_na,
expression_feature_data,
expression_feature_rename_col):
from flotilla.data_model.base import BaseData, \
subsets_from_metadata, MINIMUM_FEATURE_SUBSET
base_data = BaseData(expression_data_no_na,
feature_data=expression_feature_data,
feature_rename_col=expression_feature_rename_col)
if expression_feature_rename_col is not None:
feature_renamer_series = expression_feature_data[
expression_feature_rename_col]
else:
feature_renamer_series = pd.Series(
expression_feature_data.index,
index=expression_feature_data.index)
feature_subsets = subsets_from_metadata(expression_feature_data,
MINIMUM_FEATURE_SUBSET,
'features')
feature_subsets['variant'] = base_data.variant
pdt.assert_frame_equal(base_data.data_original, expression_data_no_na)
pdt.assert_frame_equal(base_data.feature_data, expression_feature_data)
pdt.assert_frame_equal(base_data.data, expression_data_no_na)
pdt.assert_series_equal(base_data.feature_renamer_series,
feature_renamer_series)
pdt.assert_dict_equal(base_data.feature_subsets, feature_subsets)
def test__subset(self, expression_data_no_na, sample_ids, feature_ids):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data_no_na)
subset = base_data._subset(base_data.data, sample_ids=sample_ids,
feature_ids=feature_ids)
data = base_data.data
if feature_ids is None:
feature_ids = data.columns
else:
feature_ids = pd.Index(set(feature_ids).intersection(data.columns))
if sample_ids is None:
sample_ids = data.index
else:
sample_ids = pd.Index(set(sample_ids).intersection(data.index))
true_subset = data.ix[sample_ids, feature_ids]
pdt.assert_frame_equal(subset, true_subset)
def test_feature_renamer_series_change_col(self, expression_data_no_na,
expression_feature_data,
expression_feature_rename_col,
n_genes):
from flotilla.data_model.base import BaseData
expression_feature_data = expression_feature_data.copy()
gene_numbers = np.arange(n_genes)
new_renamer = 'new_renamer'
expression_feature_data[new_renamer] = \
expression_feature_data.index.map(
lambda x: 'new_renamed{}'.format(
np.random.choice(gene_numbers)))
base_data = BaseData(expression_data_no_na,
feature_data=expression_feature_data,
feature_rename_col=expression_feature_rename_col)
base_data.feature_rename_col = new_renamer
pdt.assert_series_equal(base_data.feature_renamer_series,
expression_feature_data[new_renamer])
def test__subset(self, expression_data_no_na, sample_ids, feature_ids):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data_no_na)
subset = base_data._subset(base_data.data,
sample_ids=sample_ids,
feature_ids=feature_ids)
data = base_data.data
if feature_ids is None:
feature_ids = data.columns
else:
feature_ids = pd.Index(set(feature_ids).intersection(data.columns))
if sample_ids is None:
sample_ids = data.index
else:
sample_ids = pd.Index(set(sample_ids).intersection(data.index))
true_subset = data.ix[sample_ids, feature_ids]
pdt.assert_frame_equal(subset, true_subset)
def test__variant(self, expression_data):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data)
var = expression_data.var()
var_cut = var.mean() + 2 * var.std()
variant = expression_data.columns[var > var_cut]
pdt.assert_equal(base_data._var_cut, var_cut)
pdt.assert_array_equal(base_data.variant, variant)
def test_feature_renamer_series_change_col(self, expression_data_no_na,
expression_feature_data,
expression_feature_rename_col,
n_genes):
from flotilla.data_model.base import BaseData
expression_feature_data = expression_feature_data.copy()
gene_numbers = np.arange(n_genes)
new_renamer = 'new_renamer'
expression_feature_data[new_renamer] = \
expression_feature_data.index.map(
lambda x: 'new_renamed{}'.format(
np.random.choice(gene_numbers)))
base_data = BaseData(expression_data_no_na,
feature_data=expression_feature_data,
feature_rename_col=expression_feature_rename_col)
base_data.feature_rename_col = new_renamer
pdt.assert_series_equal(base_data.feature_renamer_series,
expression_feature_data[new_renamer])
def test_reduce(self, shalek2013_data, featurewise):
# TODO: parameterize and test with featurewise and subsets
from flotilla.compute.decomposition import DataFramePCA
from flotilla.data_model.base import BaseData
expression = BaseData(shalek2013_data.expression)
test_reduced = expression.reduce(featurewise=featurewise)
subset, means = expression._subset_and_standardize(
expression.data, return_means=True, standardize=True)
if featurewise:
subset = subset.T
true_reduced = DataFramePCA(subset)
true_reduced.means = means
pdt.assert_frame_equal(test_reduced.X, subset)
npt.assert_array_equal(test_reduced.reduced_space,
true_reduced.reduced_space)
pdt.assert_series_equal(test_reduced.means,
true_reduced.means)
def test__init_technical_outliers(self, expression_data_no_na,
technical_outliers):
from flotilla.data_model.base import BaseData
base_data = BaseData(expression_data_no_na,
technical_outliers=technical_outliers)
data = expression_data_no_na.copy()
if technical_outliers is not None:
good_samples = ~data.index.isin(technical_outliers)
data = data.ix[good_samples]
pdt.assert_frame_equal(base_data.data, data)
pdt.assert_frame_equal(base_data.data_original, expression_data_no_na)
def test_reduce(self, expression_data_no_na, featurewise):
# TODO: parameterize and test with featurewise and subsets
from flotilla.compute.decomposition import DataFramePCA
from flotilla.data_model.base import BaseData
expression = BaseData(expression_data_no_na)
test_reduced = expression.reduce(featurewise=featurewise)
subset, means = expression._subset_and_standardize(expression.data,
return_means=True,
standardize=True)
if featurewise:
subset = subset.T
true_reduced = DataFramePCA(subset)
true_reduced.means = means
pdt.assert_frame_equal(test_reduced.X, subset)
npt.assert_array_equal(test_reduced.reduced_space,
true_reduced.reduced_space)
pdt.assert_series_equal(test_reduced.means, true_reduced.means)
def test__threshold(self, expression_data_no_na, pooled):
from flotilla.data_model.base import BaseData
thresh = 0.5
minimum_samples = 5
base_data = BaseData(expression_data_no_na,
thresh=thresh,
minimum_samples=minimum_samples,
pooled=pooled)
data = expression_data_no_na.copy()
if pooled is not None:
other = base_data.singles
else:
other = data
filtered = data.ix[:, other[other > thresh].count() >= minimum_samples]
pdt.assert_frame_equal(base_data.data, filtered)
def test__init(self, expression_data_no_na, outliers):
from flotilla.data_model.base import BaseData
from flotilla.compute.predict import PredictorConfigManager, \
PredictorDataSetManager
base_data = BaseData(expression_data_no_na, outliers=outliers)
outlier_samples = outliers.copy() if outliers is not None else []
outliers_df = expression_data_no_na.ix[outlier_samples]
feature_renamer_series = pd.Series(expression_data_no_na.columns,
index=expression_data_no_na.columns)
pdt.assert_frame_equal(base_data.data_original, expression_data_no_na)
pdt.assert_equal(base_data.feature_data, None)
pdt.assert_frame_equal(base_data.data, expression_data_no_na)
pdt.assert_series_equal(base_data.feature_renamer_series,
feature_renamer_series)
pdt.assert_frame_equal(base_data.outliers, outliers_df)
pdt.assert_array_equal(base_data.outlier_samples, outlier_samples)
assert isinstance(base_data.predictor_config_manager,
PredictorConfigManager)
assert isinstance(base_data.predictor_dataset_manager,
PredictorDataSetManager)
def test_basedata_init(example_data):
base_data = BaseData(example_data.expression)
pdt.assert_frame_equal(base_data.data, example_data.expression)
def base_data(example_data):
return BaseData(example_data.expression)
def base_data(expression_data):
from flotilla.data_model.base import BaseData
return BaseData(expression_data)