TypeError while using predict_proba on a previously built model
I trained and saved a model as below
filename = 'finalized_model.sav'
pickle.dump(rf, open(filename, 'wb'))
Now I am writing a webservice where I am getting a test dataset, so I load the model and predict probability
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
# load the model
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
#predict
predicted_labels = loaded_model.predict_proba(X_test)
But I am getting error
TypeError: '<' not supported between instances of 'NoneType' and 'int'
in line predicted_labels = loaded_model.predict_proba(X_test)
What am I missing?
EDIT:
Based on the comments, I tried below
content=request.json()
test = pd.io.json.json_normalize(content)
But then I get error TypeError: 'list' object is not callable . See the sample input below.
Nonetheless don't think the issue is with reading the request. I tried panda's read with a local file as below but still getting the original error TypeError: '<' not supported between instances of 'NoneType' and 'int'
test = pd.read_json('./test.json')
Sample input
[
{
"id": 25693,
"cuisine": "southern_us",
"ingredients": [
"plain flour",
"ground pepper",
"salt",
"tomatoes",
"ground black pepper",
"thyme",
"eggs",
"green tomatoes",
"yellow corn meal",
"milk",
"vegetable oil"
]
}
]
python machine-learning scikit-learn
asked Nov 25 '18 at 18:48
nadnad
61231428
add a comment |
I trained and saved a model as below
filename = 'finalized_model.sav'
pickle.dump(rf, open(filename, 'wb'))
Now I am writing a webservice where I am getting a test dataset, so I load the model and predict probability
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
# load the model
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
#predict
predicted_labels = loaded_model.predict_proba(X_test)
But I am getting error
TypeError: '<' not supported between instances of 'NoneType' and 'int'
in line predicted_labels = loaded_model.predict_proba(X_test)
What am I missing?
EDIT:
Based on the comments, I tried below
content=request.json()
test = pd.io.json.json_normalize(content)
But then I get error TypeError: 'list' object is not callable . See the sample input below.
Nonetheless don't think the issue is with reading the request. I tried panda's read with a local file as below but still getting the original error TypeError: '<' not supported between instances of 'NoneType' and 'int'
test = pd.read_json('./test.json')
Sample input
[
{
"id": 25693,
"cuisine": "southern_us",
"ingredients": [
"plain flour",
"ground pepper",
"salt",
"tomatoes",
"ground black pepper",
"thyme",
"eggs",
"green tomatoes",
"yellow corn meal",
"milk",
"vegetable oil"
]
}
]
python machine-learning scikit-learn
asked Nov 25 '18 at 18:48
nadnad
61231428
It should berequest.json()
– coldspeed
Nov 25 '18 at 18:54
@coldspeed not sure what you meant?
– nad
Nov 25 '18 at 18:59
Note the brackets in the comment, and then compare it with your code.
– Vivek Kumar
Nov 26 '18 at 8:02
@VivekKumar see the edits
– nad
Nov 26 '18 at 16:48
add a comment |
I trained and saved a model as below
filename = 'finalized_model.sav'
pickle.dump(rf, open(filename, 'wb'))
Now I am writing a webservice where I am getting a test dataset, so I load the model and predict probability
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
# load the model
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
#predict
predicted_labels = loaded_model.predict_proba(X_test)
But I am getting error
TypeError: '<' not supported between instances of 'NoneType' and 'int'
in line predicted_labels = loaded_model.predict_proba(X_test)
What am I missing?
EDIT:
Based on the comments, I tried below
content=request.json()
test = pd.io.json.json_normalize(content)
But then I get error TypeError: 'list' object is not callable . See the sample input below.
Nonetheless don't think the issue is with reading the request. I tried panda's read with a local file as below but still getting the original error TypeError: '<' not supported between instances of 'NoneType' and 'int'
test = pd.read_json('./test.json')
Sample input
[
{
"id": 25693,
"cuisine": "southern_us",
"ingredients": [
"plain flour",
"ground pepper",
"salt",
"tomatoes",
"ground black pepper",
"thyme",
"eggs",
"green tomatoes",
"yellow corn meal",
"milk",
"vegetable oil"
]
}
]
python machine-learning scikit-learn
asked Nov 25 '18 at 18:48
nadnad
61231428
I trained and saved a model as below
filename = 'finalized_model.sav'
pickle.dump(rf, open(filename, 'wb'))
Now I am writing a webservice where I am getting a test dataset, so I load the model and predict probability
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
# load the model
filename = 'finalized_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
#predict
predicted_labels = loaded_model.predict_proba(X_test)
But I am getting error
TypeError: '<' not supported between instances of 'NoneType' and 'int'
in line predicted_labels = loaded_model.predict_proba(X_test)
What am I missing?
EDIT:
Based on the comments, I tried below
content=request.json()
test = pd.io.json.json_normalize(content)
But then I get error TypeError: 'list' object is not callable . See the sample input below.
Nonetheless don't think the issue is with reading the request. I tried panda's read with a local file as below but still getting the original error TypeError: '<' not supported between instances of 'NoneType' and 'int'
test = pd.read_json('./test.json')
Sample input
[
{
"id": 25693,
"cuisine": "southern_us",
"ingredients": [
"plain flour",
"ground pepper",
"salt",
"tomatoes",
"ground black pepper",
"thyme",
"eggs",
"green tomatoes",
"yellow corn meal",
"milk",
"vegetable oil"
]
}
]
python machine-learning scikit-learn
python machine-learning scikit-learn
asked Nov 25 '18 at 18:48
nadnad
61231428
asked Nov 25 '18 at 18:48
nadnad
61231428
edited Nov 26 '18 at 16:48
nad
asked Nov 25 '18 at 18:48
nadnad
61231428
asked Nov 25 '18 at 18:48
nadnad
61231428
asked Nov 25 '18 at 18:48
nadnad
61231428
61231428
It should berequest.json()
– coldspeed
Nov 25 '18 at 18:54
@coldspeed not sure what you meant?
– nad
Nov 25 '18 at 18:59
Note the brackets in the comment, and then compare it with your code.
– Vivek Kumar
Nov 26 '18 at 8:02
@VivekKumar see the edits
– nad
Nov 26 '18 at 16:48
add a comment |
It should berequest.json()
– coldspeed
Nov 25 '18 at 18:54
@coldspeed not sure what you meant?
– nad
Nov 25 '18 at 18:59
Note the brackets in the comment, and then compare it with your code.
– Vivek Kumar
Nov 26 '18 at 8:02
@VivekKumar see the edits
– nad
Nov 26 '18 at 16:48
It should be
request.json()– coldspeed
Nov 25 '18 at 18:54
It should be
request.json()– coldspeed
Nov 25 '18 at 18:54
@coldspeed not sure what you meant?
– nad
Nov 25 '18 at 18:59
@coldspeed not sure what you meant?
– nad
Nov 25 '18 at 18:59
Note the brackets in the comment, and then compare it with your code.
– Vivek Kumar
Nov 26 '18 at 8:02
Note the brackets in the comment, and then compare it with your code.
– Vivek Kumar
Nov 26 '18 at 8:02
@VivekKumar see the edits
– nad
Nov 26 '18 at 16:48
@VivekKumar see the edits
– nad
Nov 26 '18 at 16:48
add a comment |
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It should be
request.json()– coldspeed
Nov 25 '18 at 18:54
@coldspeed not sure what you meant?
– nad
Nov 25 '18 at 18:59
Note the brackets in the comment, and then compare it with your code.
– Vivek Kumar
Nov 26 '18 at 8:02
@VivekKumar see the edits
– nad
Nov 26 '18 at 16:48