I have developed a text model for multilabel classification. The OneVsRestClassifier LinearSVC model uses sklearns Pipeline and FeatureUnion for model preparation.
The primary input features consist of a text column called response but also 5 topic probabilities (generated from a previous LDA Topic Model) called t1_prob - t5_prob to predict the 5 possible labels. There are other feature creation steps in the pipeline for the the generation of the TfidfVectorizer.
I ended up calling each column with ItemSelector and performing the ArrayCaster (see the code below for function definition) 5 times on these topic probability columns individually. Is there a better way to use FeatureUnion to select multiple columns in a pipeline? (so I don't have to do it 5 times)
I am wondering if it is necessary to duplicate the topic1_feature -topic5_feature code or if multiple columns can be selected in a more concise way?
The data I am feeding in is a Pandas dataFrame:
id response label_1 label_2 label3 label_4 label_5 t1_prob t2_prob t3_prob t4_prob t5_prob
1 Text from response... 0.0 0.0 0.0 0.0 0.0 0.0 0.0625 0.0625 0.1875 0.0625 0.1250
2 Text to model with... 0.0 0.0 0.0 0.0 0.0 0.0 0.1333 0.1333 0.0667 0.0667 0.0667
3 Text to work with ... 0.0 0.0 0.0 0.0 0.0 0.0 0.1111 0.0938 0.0393 0.0198 0.2759
4 Free text comments ... 0.0 0.0 1.0 1.0 0.0 0.0 0.2162 0.1104 0.0341 0.0847 0.0559
The x_train is response and the 5 topic probability columns (t1_prob, t2_prob, t3_prob, t4_prob, t5_prob).
The y_train is the 5 label columns which I have called .values on to return a numpy representation of the DataFrame. (label_1, label_2, label3, label_4, label_5)
Sample DataFrame:
import pandas as pd
column_headers = ["id", "response",
"label_1", "label_2", "label3", "label_4", "label_5",
"t1_prob", "t2_prob", "t3_prob", "t4_prob", "t5_prob"]
input_data = [
[1, "Text from response",0.0,0.0,1.0,0.0,0.0,0.0625,0.0625,0.1875,0.0625,0.1250],
[2, "Text to model with",0.0,0.0,0.0,0.0,0.0,0.1333,0.1333,0.0667,0.0667,0.0667],
[3, "Text to work with",0.0,0.0,0.0,0.0,0.0,0.1111,0.0938,0.0393,0.0198,0.2759],
[4, "Free text comments",0.0,0.0,1.0,1.0,1.0,0.2162,0.1104,0.0341,0.0847,0.0559]
]
df = pd.DataFrame(input_data, columns = column_headers)
df = df.set_index('id')
df
I think my implementation is a little bit round about because FeatureUnion will only handle 2-D arrays when combining them, so any other type like DataFrame have been problematic for me. However, this example works--I am just looking for ways to improve it and make it more DRY.
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.base import BaseEstimator, TransformerMixin
class ItemSelector(BaseEstimator, TransformerMixin):
def __init__(self, column):
self.column = column
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
return X[self.column]
class ArrayCaster(BaseEstimator, TransformerMixin):
def fit(self, x, y=None):
return self
def transform(self, data):
return np.transpose(np.matrix(data))
def basic_text_model(trainX, testX, trainY, testY, classLabels, plotPath):
'''OneVsRestClassifier for multi-label prediction'''
pipeline = Pipeline([
('features', FeatureUnion([
('topic1_feature', Pipeline([
('selector', ItemSelector(column='t1_prob')),
('caster', ArrayCaster())
])),
('topic2_feature', Pipeline([
('selector', ItemSelector(column='t2_prob')),
('caster', ArrayCaster())
])),
('topic3_feature', Pipeline([
('selector', ItemSelector(column='t3_prob')),
('caster', ArrayCaster())
])),
('topic4_feature', Pipeline([
('selector', ItemSelector(column='t4_prob')),
('caster', ArrayCaster())
])),
('topic5_feature', Pipeline([
('selector', ItemSelector(column='t5_prob')),
('caster', ArrayCaster())
])),
('word_features', Pipeline([
('vect', CountVectorizer(analyzer="word", stop_words='english')),
('tfidf', TfidfTransformer(use_idf = True)),
])),
])),
('clf', OneVsRestClassifier(svm.LinearSVC(random_state=random_state)))
])
# Fit the model
pipeline.fit(trainX, trainY)
predicted = pipeline.predict(testX)
My incorporation of ArrayCaster into the process arose from this answer.
