Mixing
value error while implementing the stochastic regression using sklearn library in python
-1
There is need to update training set automatically when new observations are filled.
I have used Stochastic Gradient Descent Algorithm using sklearn library in Python. I converted dataframe to array but still I am having problem in conversion. I do this following:
import pandas as pd
from pandas import DataFrame
from sklearn.linear_model import SGDClassifier
#Collecting Numeric data
data = pd.read_csv('/home/system/Documents/Heena/Regression/Data.csv')
df = pd.DataFrame(data, columns = ['years_of_exp', 'company', 'location', 'education','score'])
xSGD = df[['years_of_exp', 'company', 'location', 'education']]
ySGD = df['score']
#Conversion of dataframe to numpy array
X = np.asarray(xSGD)
Y = np.asarray(ySGD)
clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=5)
clf.fit(xSGD,ySGD)
Error:
I expect the output of this must fit the model. But the actual output is
ValueError.
File "/home/system/anaconda3/lib/python3.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([1. , 2. , 3. , 3.8 , 4. , 4.25, 5. ]),)
python machine-learning scikit-learn
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
asked Jan 1 at 9:57
HeenaHeena
1116
add a comment |
-1
There is need to update training set automatically when new observations are filled.
I have used Stochastic Gradient Descent Algorithm using sklearn library in Python. I converted dataframe to array but still I am having problem in conversion. I do this following:
import pandas as pd
from pandas import DataFrame
from sklearn.linear_model import SGDClassifier
#Collecting Numeric data
data = pd.read_csv('/home/system/Documents/Heena/Regression/Data.csv')
df = pd.DataFrame(data, columns = ['years_of_exp', 'company', 'location', 'education','score'])
xSGD = df[['years_of_exp', 'company', 'location', 'education']]
ySGD = df['score']
#Conversion of dataframe to numpy array
X = np.asarray(xSGD)
Y = np.asarray(ySGD)
clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=5)
clf.fit(xSGD,ySGD)
Error:
I expect the output of this must fit the model. But the actual output is
ValueError.
File "/home/system/anaconda3/lib/python3.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([1. , 2. , 3. , 3.8 , 4. , 4.25, 5. ]),)
python machine-learning scikit-learn
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
asked Jan 1 at 9:57
HeenaHeena
1116
add a comment |
-1
-1
-1
There is need to update training set automatically when new observations are filled.
I have used Stochastic Gradient Descent Algorithm using sklearn library in Python. I converted dataframe to array but still I am having problem in conversion. I do this following:
import pandas as pd
from pandas import DataFrame
from sklearn.linear_model import SGDClassifier
#Collecting Numeric data
data = pd.read_csv('/home/system/Documents/Heena/Regression/Data.csv')
df = pd.DataFrame(data, columns = ['years_of_exp', 'company', 'location', 'education','score'])
xSGD = df[['years_of_exp', 'company', 'location', 'education']]
ySGD = df['score']
#Conversion of dataframe to numpy array
X = np.asarray(xSGD)
Y = np.asarray(ySGD)
clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=5)
clf.fit(xSGD,ySGD)
Error:
I expect the output of this must fit the model. But the actual output is
ValueError.
File "/home/system/anaconda3/lib/python3.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([1. , 2. , 3. , 3.8 , 4. , 4.25, 5. ]),)
python machine-learning scikit-learn
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
asked Jan 1 at 9:57
HeenaHeena
1116
There is need to update training set automatically when new observations are filled.
I have used Stochastic Gradient Descent Algorithm using sklearn library in Python. I converted dataframe to array but still I am having problem in conversion. I do this following:
import pandas as pd
from pandas import DataFrame
from sklearn.linear_model import SGDClassifier
#Collecting Numeric data
data = pd.read_csv('/home/system/Documents/Heena/Regression/Data.csv')
df = pd.DataFrame(data, columns = ['years_of_exp', 'company', 'location', 'education','score'])
xSGD = df[['years_of_exp', 'company', 'location', 'education']]
ySGD = df['score']
#Conversion of dataframe to numpy array
X = np.asarray(xSGD)
Y = np.asarray(ySGD)
clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=5)
clf.fit(xSGD,ySGD)
Error:
I expect the output of this must fit the model. But the actual output is
ValueError.
File "/home/system/anaconda3/lib/python3.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([1. , 2. , 3. , 3.8 , 4. , 4.25, 5. ]),)
python machine-learning scikit-learn
python machine-learning scikit-learn
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
asked Jan 1 at 9:57
HeenaHeena
1116
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
asked Jan 1 at 9:57
HeenaHeena
1116
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
edited Jan 4 at 8:13
Vivek Kumar
16.4k42155
16.4k42155
asked Jan 1 at 9:57
HeenaHeena
1116
asked Jan 1 at 9:57
HeenaHeena
1116
asked Jan 1 at 9:57
HeenaHeena
1116
1116
add a comment |
add a comment |
1 Answer
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From the stack trace, I see two problems:
ySGDis a tuple, but it should be a numpy array (probably the first and only element of that tuple)- You are using
SGDClassifier, but your labels are real numbers (and are called "score" in the dataframe). This suggests your task is actually a regression problem, so you should useSGDRegressorinstead
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
1
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
From the stack trace, I see two problems:
ySGDis a tuple, but it should be a numpy array (probably the first and only element of that tuple)- You are using
SGDClassifier, but your labels are real numbers (and are called "score" in the dataframe). This suggests your task is actually a regression problem, so you should useSGDRegressorinstead
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
1
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
add a comment |
0
From the stack trace, I see two problems:
ySGDis a tuple, but it should be a numpy array (probably the first and only element of that tuple)- You are using
SGDClassifier, but your labels are real numbers (and are called "score" in the dataframe). This suggests your task is actually a regression problem, so you should useSGDRegressorinstead
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
1
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
add a comment |
0
0
0
From the stack trace, I see two problems:
ySGDis a tuple, but it should be a numpy array (probably the first and only element of that tuple)- You are using
SGDClassifier, but your labels are real numbers (and are called "score" in the dataframe). This suggests your task is actually a regression problem, so you should useSGDRegressorinstead
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
From the stack trace, I see two problems:
ySGDis a tuple, but it should be a numpy array (probably the first and only element of that tuple)- You are using
SGDClassifier, but your labels are real numbers (and are called "score" in the dataframe). This suggests your task is actually a regression problem, so you should useSGDRegressorinstead
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
answered Jan 1 at 10:29
BlackBearBlackBear
15.4k83368
15.4k83368
1
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
add a comment |
1
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
1
1
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
Perfect answer. Thank you so much @BlackBear :)
– Heena
Jan 1 at 10:35
add a comment |
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