def preprocess(words_file="../tools/word_data.pkl",
authors_file="../tools/email_authors.pkl"):
"""
this function takes a pre-made list of email texts (by default word_data.pkl)
and the corresponding authors (by default email_authors.pkl) and performs
a number of preprocessing steps:
-- splits into training/testing sets (10% testing)
-- vectorizes into tfidf matrix
-- selects/keeps most helpful features
after this, the feaures and labels are put into numpy arrays, which play nice with sklearn functions
4 objects are returned:
-- training/testing features
-- training/testing labels
"""
### the words (features) and authors (labels), already largely preprocessed
### this preprocessing will be repeated in the text learning mini-project
authors_file_handler = open(authors_file, "rb")
authors = pickle.load(authors_file_handler)
authors_file_handler.close()
words_file_handler = open(words_file, "rb")
#word_data = cPickle.load(words_file_handler)
word_data = pickle.load(words_file_handler)
words_file_handler.close()
### test_size is the percentage of events assigned to the test set
### (remainder go into training)
features_train, features_test, labels_train, labels_test = model_selection.train_test_split(
word_data, authors, test_size=0.1, random_state=42)
### text vectorization--go from strings to lists of numbers
vectorizer = TfidfVectorizer(sublinear_tf=True,
max_df=0.5,
stop_words='english')
features_train_transformed = vectorizer.fit_transform(features_train)
features_test_transformed = vectorizer.transform(features_test)
### feature selection, because text is super high dimensional and
### can be really computationally chewy as a result
selector = SelectPercentile(f_classif, percentile=10)
# selector = SelectPercentile(f_classif, percentile=1)
selector.fit(features_train_transformed, labels_train)
features_train_transformed = selector.transform(
features_train_transformed).toarray()
features_test_transformed = selector.transform(
features_test_transformed).toarray()
### info on the data
print("no. of Chris training emails:", sum(labels_train))
print("no. of Sara training emails:",
len(labels_train) - sum(labels_train))
print("selector percentile (percent of features used):",
selector.get_params()['percentile'])
return features_train_transformed, features_test_transformed, labels_train, labels_test
class SelectPercentile(FeatureSelectionAlgorithm):
r"""Implementation of feature selection using percentile selection of best features according to used score function.
Date:
2020
Author:
Luka Pečnik
License:
MIT
Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectPercentile.html
See Also:
* :class:`niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm`
"""
Name = 'Select Percentile'
def __init__(self, **kwargs):
r"""Initialize SelectPercentile feature selection algorithm.
"""
self._params = dict(score_func=ParameterDefinition(
[chi2, f_classif, mutual_info_classif]),
percentile=ParameterDefinition(
MinMax(10, 100), np.uint))
self.__select_percentile = SelectPerc()
def set_parameters(self, **kwargs):
r"""Set the parameters/arguments of the algorithm.
"""
self.__select_percentile.set_params(**kwargs)
def select_features(self, x, y, **kwargs):
r"""Perform the feature selection process.
Arguments:
x (pandas.core.frame.DataFrame): Array of original features.
y (pandas.core.series.Series) Expected classifier results.
Returns:
numpy.ndarray[bool]: Mask of selected features.
"""
self.__select_percentile.fit(x, y)
return self.__select_percentile.get_support()
def to_string(self):
r"""User friendly representation of the object.
Returns:
str: User friendly representation of the object.
"""
return FeatureSelectionAlgorithm.to_string(self).format(
name=self.Name,
args=self._parameters_to_string(
self.__select_percentile.get_params()))