ValueError: Unknown label type: 'unknown'
Question:
I try to run following code. Btw, I am new to both python and sklearn.
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
# data import and preparation
trainData = pd.read_csv('train.csv')
train = trainData.values
testData = pd.read_csv('test.csv')
test = testData.values
X = np.c_[train[:, 0], train[:, 2], train[:, 6:7], train[:, 9]]
X = np.nan_to_num(X)
y = train[:, 1]
Xtest = np.c_[test[:, 0:1], test[:, 5:6], test[:, 8]]
Xtest = np.nan_to_num(Xtest)
# model
lr = LogisticRegression()
lr.fit(X, y)
where y is a np.ndarray of 0’s and 1’s
I receive the following:
File “C:Anaconda3libsite-packagessklearnlinear_modellogistic.py”, line >1174, in fit
check_classification_targets(y)
File “C:Anaconda3libsite-packagessklearnutilsmulticlass.py”, line 172, >in check_classification_targets
raise ValueError(“Unknown label type: %r” % y_type)
ValueError: Unknown label type: ‘unknown’
from sklearn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.fit
y : array-like, shape (n_samples,)
Target values (class labels in classification, real numbers in regression)
What is my error?
upd:
y is array([0.0, 1.0, 1.0, …, 0.0, 1.0, 0.0], dtype=object) size is (891,)
Answers:
Your y is of type object, so sklearn cannot recognize its type. Add the line y=y.astype('int') right after the line y = train[:, 1].
Adding to Miriam ,I also got the similar error but in my case individual elements of y_pred was of type 'np.int32' and individual elements of y was of type 'int'.
I solved it by doing:
for i,x in enumerate(y_pred):
y_pred[i]=x.astype('int')
I also got a similar type of error. I found out that my target is a non-integer type. After casting my Target variable to the integer type. Boom, error is solved
y = train_data['Y'].astype('int')
I try to run following code. Btw, I am new to both python and sklearn.
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
# data import and preparation
trainData = pd.read_csv('train.csv')
train = trainData.values
testData = pd.read_csv('test.csv')
test = testData.values
X = np.c_[train[:, 0], train[:, 2], train[:, 6:7], train[:, 9]]
X = np.nan_to_num(X)
y = train[:, 1]
Xtest = np.c_[test[:, 0:1], test[:, 5:6], test[:, 8]]
Xtest = np.nan_to_num(Xtest)
# model
lr = LogisticRegression()
lr.fit(X, y)
where y is a np.ndarray of 0’s and 1’s
I receive the following:
File “C:Anaconda3libsite-packagessklearnlinear_modellogistic.py”, line >1174, in fit
check_classification_targets(y)File “C:Anaconda3libsite-packagessklearnutilsmulticlass.py”, line 172, >in check_classification_targets
raise ValueError(“Unknown label type: %r” % y_type)ValueError: Unknown label type: ‘unknown’
from sklearn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.fit
y : array-like, shape (n_samples,)
Target values (class labels in classification, real numbers in regression)
What is my error?
upd:
y is array([0.0, 1.0, 1.0, …, 0.0, 1.0, 0.0], dtype=object) size is (891,)
Your y is of type object, so sklearn cannot recognize its type. Add the line y=y.astype('int') right after the line y = train[:, 1].
Adding to Miriam ,I also got the similar error but in my case individual elements of y_pred was of type 'np.int32' and individual elements of y was of type 'int'.
I solved it by doing:
for i,x in enumerate(y_pred):
y_pred[i]=x.astype('int')
I also got a similar type of error. I found out that my target is a non-integer type. After casting my Target variable to the integer type. Boom, error is solved
y = train_data['Y'].astype('int')