Sklearn OneVsRestClassifier - get probabilities for all possibilities of target class
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2
I have a pipeline that performs feature engineering and model selection.
Feature engineering and model selection
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
Pipeline of feature engineering and model
model = Pipeline([('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Paramater selection
from sklearn.model_selection import GridSearchCV
parameters = {'vectorizer__ngram_range': [(1, 1), (1, 2),(2,2)],
'tfidf__use_idf': (True, False)}
gs_clf_svm = GridSearchCV(model, parameters, n_jobs=-1)
gs_clf_svm = gs_clf_svm.fit(X, y)
print(gs_clf_svm.best_score_)
print(gs_clf_svm.best_params_)
Preparing the final pipeline using the selected parameters
model = Pipeline([('vectorizer', CountVectorizer(ngram_range=(1,2))),
('tfidf', TfidfTransformer(use_idf=True)),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Fit model with training data
model.fit(X_train, y_train)
Save the model
from sklearn.externals import joblib
joblib.dump(model, 'model_question_topic.pkl', compress=1)
NOW in another file, I am loading model and predicting
from sklearn.externals import joblib
model = joblib.load('model_question_topic.pkl')
Now it is predicting the classes properly as class 1
question = "apply leave"
model.predict([question])[0]
BUT the problem is I need the confidence rate or percentage like
Class1 = 0.8 -- Class2 = 0.05 -- Class3 = 0.05 -- Class4 = 0.1
model.predict_proba([question])[0]
How do I do this in python3?
python machine-learning scikit-learn nlp svm
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
|
show 2 more comments
2
I have a pipeline that performs feature engineering and model selection.
Feature engineering and model selection
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
Pipeline of feature engineering and model
model = Pipeline([('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Paramater selection
from sklearn.model_selection import GridSearchCV
parameters = {'vectorizer__ngram_range': [(1, 1), (1, 2),(2,2)],
'tfidf__use_idf': (True, False)}
gs_clf_svm = GridSearchCV(model, parameters, n_jobs=-1)
gs_clf_svm = gs_clf_svm.fit(X, y)
print(gs_clf_svm.best_score_)
print(gs_clf_svm.best_params_)
Preparing the final pipeline using the selected parameters
model = Pipeline([('vectorizer', CountVectorizer(ngram_range=(1,2))),
('tfidf', TfidfTransformer(use_idf=True)),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Fit model with training data
model.fit(X_train, y_train)
Save the model
from sklearn.externals import joblib
joblib.dump(model, 'model_question_topic.pkl', compress=1)
NOW in another file, I am loading model and predicting
from sklearn.externals import joblib
model = joblib.load('model_question_topic.pkl')
Now it is predicting the classes properly as class 1
question = "apply leave"
model.predict([question])[0]
BUT the problem is I need the confidence rate or percentage like
Class1 = 0.8 -- Class2 = 0.05 -- Class3 = 0.05 -- Class4 = 0.1
model.predict_proba([question])[0]
How do I do this in python3?
python machine-learning scikit-learn nlp svm
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
You are aware that Class2 and Class3 have the same probability in your description? If you really, really want it that way, you calculate which of these thresholds is "closest" to your actual probability. If the result is not unique (like with Class2 and Class3), then use random choice.
– Thomas Lang
Nov 29 '18 at 5:29
1
What do you get when you runmodel.predict_proba([question])[0]?
– Clock Slave
Nov 29 '18 at 6:31
model.predict_proba()will do the same. Have you tried it?
– Vivek Kumar
Nov 29 '18 at 6:55
model.predict_proba([question])[0] gives as class1
– Chethan Kumar GN
Nov 29 '18 at 12:22
But i need the confidence rate as this Class1 = 0.8 -- Class2 = 0.04 -- Class3 = 0.06 -- Class4 = 0.1 But when i use model.predict_proba() i am getting this error I tried AttributeError: 'LinearSVC' object has no attribute 'predict_proba'
– Chethan Kumar GN
Nov 29 '18 at 12:53
|
show 2 more comments
2
2
2
I have a pipeline that performs feature engineering and model selection.
Feature engineering and model selection
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
Pipeline of feature engineering and model
model = Pipeline([('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Paramater selection
from sklearn.model_selection import GridSearchCV
parameters = {'vectorizer__ngram_range': [(1, 1), (1, 2),(2,2)],
'tfidf__use_idf': (True, False)}
gs_clf_svm = GridSearchCV(model, parameters, n_jobs=-1)
gs_clf_svm = gs_clf_svm.fit(X, y)
print(gs_clf_svm.best_score_)
print(gs_clf_svm.best_params_)
Preparing the final pipeline using the selected parameters
model = Pipeline([('vectorizer', CountVectorizer(ngram_range=(1,2))),
('tfidf', TfidfTransformer(use_idf=True)),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Fit model with training data
model.fit(X_train, y_train)
Save the model
from sklearn.externals import joblib
joblib.dump(model, 'model_question_topic.pkl', compress=1)
NOW in another file, I am loading model and predicting
from sklearn.externals import joblib
model = joblib.load('model_question_topic.pkl')
Now it is predicting the classes properly as class 1
question = "apply leave"
model.predict([question])[0]
BUT the problem is I need the confidence rate or percentage like
Class1 = 0.8 -- Class2 = 0.05 -- Class3 = 0.05 -- Class4 = 0.1
model.predict_proba([question])[0]
How do I do this in python3?
python machine-learning scikit-learn nlp svm
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
I have a pipeline that performs feature engineering and model selection.
Feature engineering and model selection
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
Pipeline of feature engineering and model
model = Pipeline([('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Paramater selection
from sklearn.model_selection import GridSearchCV
parameters = {'vectorizer__ngram_range': [(1, 1), (1, 2),(2,2)],
'tfidf__use_idf': (True, False)}
gs_clf_svm = GridSearchCV(model, parameters, n_jobs=-1)
gs_clf_svm = gs_clf_svm.fit(X, y)
print(gs_clf_svm.best_score_)
print(gs_clf_svm.best_params_)
Preparing the final pipeline using the selected parameters
model = Pipeline([('vectorizer', CountVectorizer(ngram_range=(1,2))),
('tfidf', TfidfTransformer(use_idf=True)),
('clf', OneVsRestClassifier(LinearSVC(class_weight="balanced")))])
Fit model with training data
model.fit(X_train, y_train)
Save the model
from sklearn.externals import joblib
joblib.dump(model, 'model_question_topic.pkl', compress=1)
NOW in another file, I am loading model and predicting
from sklearn.externals import joblib
model = joblib.load('model_question_topic.pkl')
Now it is predicting the classes properly as class 1
question = "apply leave"
model.predict([question])[0]
BUT the problem is I need the confidence rate or percentage like
Class1 = 0.8 -- Class2 = 0.05 -- Class3 = 0.05 -- Class4 = 0.1
model.predict_proba([question])[0]
How do I do this in python3?
python machine-learning scikit-learn nlp svm
python machine-learning scikit-learn nlp svm
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
edited Nov 29 '18 at 6:27
Clock Slave
2,39062664
2,39062664
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
asked Nov 29 '18 at 5:21
Chethan Kumar GNChethan Kumar GN
316
316
You are aware that Class2 and Class3 have the same probability in your description? If you really, really want it that way, you calculate which of these thresholds is "closest" to your actual probability. If the result is not unique (like with Class2 and Class3), then use random choice.
– Thomas Lang
Nov 29 '18 at 5:29
1
What do you get when you runmodel.predict_proba([question])[0]?
– Clock Slave
Nov 29 '18 at 6:31
model.predict_proba()will do the same. Have you tried it?
– Vivek Kumar
Nov 29 '18 at 6:55
model.predict_proba([question])[0] gives as class1
– Chethan Kumar GN
Nov 29 '18 at 12:22
But i need the confidence rate as this Class1 = 0.8 -- Class2 = 0.04 -- Class3 = 0.06 -- Class4 = 0.1 But when i use model.predict_proba() i am getting this error I tried AttributeError: 'LinearSVC' object has no attribute 'predict_proba'
– Chethan Kumar GN
Nov 29 '18 at 12:53
|
show 2 more comments
You are aware that Class2 and Class3 have the same probability in your description? If you really, really want it that way, you calculate which of these thresholds is "closest" to your actual probability. If the result is not unique (like with Class2 and Class3), then use random choice.
– Thomas Lang
Nov 29 '18 at 5:29
1
What do you get when you runmodel.predict_proba([question])[0]?
– Clock Slave
Nov 29 '18 at 6:31
model.predict_proba()will do the same. Have you tried it?
– Vivek Kumar
Nov 29 '18 at 6:55
model.predict_proba([question])[0] gives as class1
– Chethan Kumar GN
Nov 29 '18 at 12:22
But i need the confidence rate as this Class1 = 0.8 -- Class2 = 0.04 -- Class3 = 0.06 -- Class4 = 0.1 But when i use model.predict_proba() i am getting this error I tried AttributeError: 'LinearSVC' object has no attribute 'predict_proba'
– Chethan Kumar GN
Nov 29 '18 at 12:53
You are aware that Class2 and Class3 have the same probability in your description? If you really, really want it that way, you calculate which of these thresholds is "closest" to your actual probability. If the result is not unique (like with Class2 and Class3), then use random choice.
– Thomas Lang
Nov 29 '18 at 5:29
You are aware that Class2 and Class3 have the same probability in your description? If you really, really want it that way, you calculate which of these thresholds is "closest" to your actual probability. If the result is not unique (like with Class2 and Class3), then use random choice.
– Thomas Lang
Nov 29 '18 at 5:29
1
1
What do you get when you run
model.predict_proba([question])[0] ?– Clock Slave
Nov 29 '18 at 6:31
What do you get when you run
model.predict_proba([question])[0] ?– Clock Slave
Nov 29 '18 at 6:31
model.predict_proba() will do the same. Have you tried it?– Vivek Kumar
Nov 29 '18 at 6:55
model.predict_proba() will do the same. Have you tried it?– Vivek Kumar
Nov 29 '18 at 6:55
model.predict_proba([question])[0] gives as class1
– Chethan Kumar GN
Nov 29 '18 at 12:22
model.predict_proba([question])[0] gives as class1
– Chethan Kumar GN
Nov 29 '18 at 12:22
But i need the confidence rate as this Class1 = 0.8 -- Class2 = 0.04 -- Class3 = 0.06 -- Class4 = 0.1 But when i use model.predict_proba() i am getting this error I tried AttributeError: 'LinearSVC' object has no attribute 'predict_proba'
– Chethan Kumar GN
Nov 29 '18 at 12:53
But i need the confidence rate as this Class1 = 0.8 -- Class2 = 0.04 -- Class3 = 0.06 -- Class4 = 0.1 But when i use model.predict_proba() i am getting this error I tried AttributeError: 'LinearSVC' object has no attribute 'predict_proba'
– Chethan Kumar GN
Nov 29 '18 at 12:53
|
show 2 more comments
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You are aware that Class2 and Class3 have the same probability in your description? If you really, really want it that way, you calculate which of these thresholds is "closest" to your actual probability. If the result is not unique (like with Class2 and Class3), then use random choice.
– Thomas Lang
Nov 29 '18 at 5:29
1
What do you get when you run
model.predict_proba([question])[0]?– Clock Slave
Nov 29 '18 at 6:31
model.predict_proba()will do the same. Have you tried it?– Vivek Kumar
Nov 29 '18 at 6:55
model.predict_proba([question])[0] gives as class1
– Chethan Kumar GN
Nov 29 '18 at 12:22
But i need the confidence rate as this Class1 = 0.8 -- Class2 = 0.04 -- Class3 = 0.06 -- Class4 = 0.1 But when i use model.predict_proba() i am getting this error I tried AttributeError: 'LinearSVC' object has no attribute 'predict_proba'
– Chethan Kumar GN
Nov 29 '18 at 12:53