Text classification for logistic regression with pipelines
I am trying to use LogisticRegression for text classification. I am using FeatureUnion for the features of the DataFrame and then cross_val_score to test the accuracy of the classifier. However, I don't know how to include the feature with the free text, called tweets, within the pipeline. I am using the TfidfVectorizer for the bag of words model.
nominal_features = ["tweeter", "job", "country"]
numeric_features = ["age"]
numeric_pipeline = Pipeline([
("selector", DataFrameSelector(numeric_features))
])
nominal_pipeline = Pipeline([
("selector", DataFrameSelector(nominal_features)),
"onehot", OneHotEncoder()])
text_pipeline = Pipeline([
("selector", DataFrameSelector("tweets")),
("vectorizer", TfidfVectorizer(stop_words='english'))])
pipeline = Pipeline([("union", FeatureUnion([("numeric_pipeline", numeric_pipeline),
("nominal_pipeline", nominal_pipeline)])),
("estimator", LogisticRegression())])
np.mean(cross_val_score(pipeline, df, y, scoring="accuracy", cv=5))
Is this the right way to include the tweets free text data in the pipeline?
python machine-learning sklearn-pandas
asked Nov 25 '18 at 13:40
Paul KPaul K
111
add a comment |
I am trying to use LogisticRegression for text classification. I am using FeatureUnion for the features of the DataFrame and then cross_val_score to test the accuracy of the classifier. However, I don't know how to include the feature with the free text, called tweets, within the pipeline. I am using the TfidfVectorizer for the bag of words model.
nominal_features = ["tweeter", "job", "country"]
numeric_features = ["age"]
numeric_pipeline = Pipeline([
("selector", DataFrameSelector(numeric_features))
])
nominal_pipeline = Pipeline([
("selector", DataFrameSelector(nominal_features)),
"onehot", OneHotEncoder()])
text_pipeline = Pipeline([
("selector", DataFrameSelector("tweets")),
("vectorizer", TfidfVectorizer(stop_words='english'))])
pipeline = Pipeline([("union", FeatureUnion([("numeric_pipeline", numeric_pipeline),
("nominal_pipeline", nominal_pipeline)])),
("estimator", LogisticRegression())])
np.mean(cross_val_score(pipeline, df, y, scoring="accuracy", cv=5))
Is this the right way to include the tweets free text data in the pipeline?
python machine-learning sklearn-pandas
asked Nov 25 '18 at 13:40
Paul KPaul K
111
You have not included yourtext_pipelineinto the mainpipeline. So how will it work?
– Vivek Kumar
Nov 28 '18 at 10:38
add a comment |
I am trying to use LogisticRegression for text classification. I am using FeatureUnion for the features of the DataFrame and then cross_val_score to test the accuracy of the classifier. However, I don't know how to include the feature with the free text, called tweets, within the pipeline. I am using the TfidfVectorizer for the bag of words model.
nominal_features = ["tweeter", "job", "country"]
numeric_features = ["age"]
numeric_pipeline = Pipeline([
("selector", DataFrameSelector(numeric_features))
])
nominal_pipeline = Pipeline([
("selector", DataFrameSelector(nominal_features)),
"onehot", OneHotEncoder()])
text_pipeline = Pipeline([
("selector", DataFrameSelector("tweets")),
("vectorizer", TfidfVectorizer(stop_words='english'))])
pipeline = Pipeline([("union", FeatureUnion([("numeric_pipeline", numeric_pipeline),
("nominal_pipeline", nominal_pipeline)])),
("estimator", LogisticRegression())])
np.mean(cross_val_score(pipeline, df, y, scoring="accuracy", cv=5))
Is this the right way to include the tweets free text data in the pipeline?
python machine-learning sklearn-pandas
asked Nov 25 '18 at 13:40
Paul KPaul K
111
I am trying to use LogisticRegression for text classification. I am using FeatureUnion for the features of the DataFrame and then cross_val_score to test the accuracy of the classifier. However, I don't know how to include the feature with the free text, called tweets, within the pipeline. I am using the TfidfVectorizer for the bag of words model.
nominal_features = ["tweeter", "job", "country"]
numeric_features = ["age"]
numeric_pipeline = Pipeline([
("selector", DataFrameSelector(numeric_features))
])
nominal_pipeline = Pipeline([
("selector", DataFrameSelector(nominal_features)),
"onehot", OneHotEncoder()])
text_pipeline = Pipeline([
("selector", DataFrameSelector("tweets")),
("vectorizer", TfidfVectorizer(stop_words='english'))])
pipeline = Pipeline([("union", FeatureUnion([("numeric_pipeline", numeric_pipeline),
("nominal_pipeline", nominal_pipeline)])),
("estimator", LogisticRegression())])
np.mean(cross_val_score(pipeline, df, y, scoring="accuracy", cv=5))
Is this the right way to include the tweets free text data in the pipeline?
python machine-learning sklearn-pandas
python machine-learning sklearn-pandas
asked Nov 25 '18 at 13:40
Paul KPaul K
111
asked Nov 25 '18 at 13:40
Paul KPaul K
111
asked Nov 25 '18 at 13:40
Paul KPaul K
111
asked Nov 25 '18 at 13:40
Paul KPaul K
111
asked Nov 25 '18 at 13:40
Paul KPaul K
111
111
You have not included yourtext_pipelineinto the mainpipeline. So how will it work?
– Vivek Kumar
Nov 28 '18 at 10:38
add a comment |
You have not included yourtext_pipelineinto the mainpipeline. So how will it work?
– Vivek Kumar
Nov 28 '18 at 10:38
You have not included your
text_pipeline into the main pipeline. So how will it work?– Vivek Kumar
Nov 28 '18 at 10:38
You have not included your
text_pipeline into the main pipeline. So how will it work?– Vivek Kumar
Nov 28 '18 at 10:38
add a comment |
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You have not included your
text_pipelineinto the mainpipeline. So how will it work?– Vivek Kumar
Nov 28 '18 at 10:38