On the one hand, people say pandas goes along great with scikit-learn. For example, pandas series objects fit well with sklearn models in this video. On the other hand, there is sklearn-pandas providing a bridge between Scikit-Learn’s machine learning methods and pandas-style Data Frames which means there is a need for such libraries. Moreover, some people, for example, convert pandas data frames to numpy array for fitting a model.
I wonder whether it's possible to combine pandas and scikit-learn without any additional methods and libraries. My problem is that whenever I fit my data set to sklearn models in the following way:
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.svm import SVC
d = {'x': np.linspace(1., 100., 20), 'y': np.linspace(1., 10., 20)}
df = pd.DataFrame(d)
train, test = train_test_split(df, test_size = 0.2)
trainX = train['x']
trainY = train['y']
lin_svm = SVC(kernel='linear').fit(trainX, trainY)
I receive an error:
ValueError: Unknown label type: 19 10.000000
0 1.000000
17 9.052632
18 9.526316
12 6.684211
11 6.210526
16 8.578947
14 7.631579
10 5.736842
7 4.315789
8 4.789474
2 1.947368
13 7.157895
1 1.473684
6 3.842105
3 2.421053
Name: y, dtype: float64
As far as I understand that's because of the data structure. However, there are few examples on the internet using similar code without any problems.
What you might want to do is a regression and not a classification.
Think about it, to do a classification, you need either a binary output or a multiclass one. In your case you give continuous data to your classifier.
If you trace back your error and dig a little bit deeper in sklearn's implementation of the method .fit() you will find the following function:
def check_classification_targets(y):
"""Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
y : array-like
"""
y_type = type_of_target(y)
if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences']:
raise ValueError("Unknown label type: %r" % y)
And the doc string of the function type_of_target is :
def type_of_target(y):
"""Determine the type of data indicated by target `y`
Parameters
----------
y : array-like
Returns
-------
target_type : string
One of:
* 'continuous': `y` is an array-like of floats that are not all
integers, and is 1d or a column vector.
* 'continuous-multioutput': `y` is a 2d array of floats that are
not all integers, and both dimensions are of size > 1.
* 'binary': `y` contains <= 2 discrete values and is 1d or a column
vector.
* 'multiclass': `y` contains more than two discrete values, is not a
sequence of sequences, and is 1d or a column vector.
* 'multiclass-multioutput': `y` is a 2d array that contains more
than two discrete values, is not a sequence of sequences, and both
dimensions are of size > 1.
* 'multilabel-indicator': `y` is a label indicator matrix, an array
of two dimensions with at least two columns, and at most 2 unique
values.
* 'unknown': `y` is array-like but none of the above, such as a 3d
array, sequence of sequences, or an array of non-sequence objects.
In your case type_of_target(trainY)=='continuous' and then it raises aValueErrorin the functioncheck_classification_targets()`.
Conclusion :
y. (eg. use a binary vector)svm.SVR.