How to use cross_val_score() Sklearn?
Question
I am trying to do a k-fold cross-validation using Sklearn in Python and have followed two tutorials now, but my code will not run for the validation.
Every time I try doing
cross_val_score(dt, x, y, cv=5)
I get the error:
Traceback (most recent call last):
File "C:/Users/djsg38/Documents/CS6001-SpatialTemporal/HW2/main.py", line 573, in <module>
scores = cross_val_score(dt, x, y, cv=5)
File "C:\Python27\lib\site-packages\sklearn\model_selection\_validation.py", line 128, in cross_val_score
X, y, groups = indexable(X, y, groups)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 206, in indexable
check_consistent_length(*result)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 177, in check_consistent_length
lengths = [_num_samples(X) for X in arrays if X is not None]
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 116, in _num_samples
'estimator %s' % x)
TypeError: Expected sequence or array-like, got estimator Its official US President Barack Obama wants lawmakers weigh \
0 1 4 12 3 2 12 4 4 2
1 0 0 1 0 0 0 0 0 0
2 1 0 4 0 0 0 0 0 0
3 0 0 0 0 0 0 4 0 0
4 0 3 10 0 0 1 0 0 0
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8 1 0 4 0 0 0 0 0 0
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11 0 0 0 1 0 0 0 0 0
12 0 2 1 0 0 0 0 0 0
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25 0 0 0 1 0 0 0 0 0
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.. ... ... .. ... ... ... ... ... ...
70 0 0 0 0 0 0 0 0 0
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84 0 0 2 4 1 3 1 0 0
85 0 0 0 1 0 0 0 0 0
86 0 0 1 0 0 0 0 0 0
87 0 0 0 0 0 0 0 0 0
88 0 0 0 0 0 0 0 0 0
89 0 0 0 0 0 0 0 0 0
90 0 0 0 0 0 0 0 0 0
91 0 0 2 1 0 0 0 0 0
92 0 0 0 0 0 0 0 0 0
93 0 0 0 0 0 0 0 0 0
94 1 0 0 0 0 0 0 0 0
95 0 2 1 0 0 0 0 0 0
96 0 0 0 0 0 0 0 0 0
97 0 0 4 1 0 0 0 0 0
98 0 0 11 1 0 0 0 0 0
99 0 0 0 0 0 0 0 0 0
whether ... Heh heh funny disassociate personWere \
0 4 ... 0 0 0 0 0
1 0 ... 0 0 0 0 0
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13 1 ... 0 0 0 0 0
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17 1 ... 0 0 0 0 0
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20 0 ... 0 0 0 0 0
21 8 ... 0 0 0 0 0
22 0 ... 0 0 0 0 0
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24 0 ... 0 0 0 0 0
25 0 ... 0 0 0 0 0
26 1 ... 0 0 0 0 0
27 0 ... 0 0 0 0 0
28 0 ... 0 0 0 0 0
29 0 ... 0 0 0 0 0
.. ... ... ... ... ... ... ...
70 0 ... 0 0 0 0 0
71 1 ... 0 0 0 0 0
72 0 ... 0 0 0 0 0
73 0 ... 0 0 0 0 0
74 0 ... 0 0 0 0 0
75 0 ... 0 0 0 0 0
77 0 ... 0 0 0 0 0
78 0 ... 0 0 0 0 0
79 1 ... 0 0 0 0 0
80 0 ... 0 0 0 0 0
81 3 ... 0 0 0 0 0
82 0 ... 0 0 0 0 0
83 0 ... 0 0 0 0 0
84 0 ... 0 0 0 0 0
85 0 ... 0 0 0 0 0
86 0 ... 0 0 0 0 0
87 0 ... 0 0 0 0 0
88 0 ... 0 0 0 0 0
89 1 ... 0 0 0 0 0
90 0 ... 0 0 0 0 0
91 0 ... 0 0 0 0 0
92 0 ... 0 0 0 0 0
93 0 ... 0 0 0 0 0
94 1 ... 0 0 0 0 0
95 0 ... 0 0 0 0 0
96 0 ... 0 0 0 0 0
97 0 ... 0 0 0 0 0
98 1 ... 0 0 0 0 0
99 0 ... 1 1 1 1 1
therehighlightAs indepth umpireshighlightThe headhighlightTwo \
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
7 0 0 0 0
8 0 0 0 0
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10 0 0 0 0
11 0 0 0 0
12 0 0 0 0
13 0 0 0 0
14 0 0 0 0
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18 0 0 0 0
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.. ... ... ... ...
70 0 0 0 0
71 0 0 0 0
72 0 0 0 0
73 0 0 0 0
74 0 0 0 0
75 0 0 0 0
76 0 0 0 0
77 0 0 0 0
78 0 0 0 0
79 0 0 0 0
80 0 0 0 0
81 0 0 0 0
82 0 0 0 0
83 0 0 0 0
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85 0 0 0 0
86 0 0 0 0
87 0 0 0 0
88 0 0 0 0
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91 0 0 0 0
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96 0 0 0 0
97 0 0 0 0
98 0 0 0 0
99 1 1 1 1
disrespect
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
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28 0
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.. ...
70 0
71 0
72 0
73 0
74 0
75 0
76 0
77 0
78 0
79 0
80 0
81 0
82 0
83 0
84 0
85 0
86 0
87 0
88 0
89 0
90 0
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92 0
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94 0
95 0
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99 1
[100 rows x 12993 columns]
Here is my code:
def encode_target(df, target_column):
df_mod = df.copy()
targets = df_mod[target_column].unique()
map_to_int = {name: n for n, name in enumerate(targets)}
df_mod["Target"] = df_mod[target_column].replace(map_to_int)
return (df_mod, targets)
df = pd.read_csv("C:/Users/djsg38/Documents/CS6001- SpatialTemporal/HW2/finalCounts.csv")
df2, targets = encode_target(df, "MYLABEL")
features = list(df2.columns[:12338])
y = df2["TARGET"]
x = df2[features]
dt = DecisionTreeClassifier()
dt.fit(x, y)
scores = cross_val_score(dt, x, y, cv=5)
My DecisionTreeClassifier seems to work fine, and when I output it as an image it looks good, but the problem herein lies with the last line.
PS I am not sure if maybe there is a column limit? The classic example I followed used the Iris dataset, so there was on four columns to look at data over. For me, though, I have 12,338 columns of data (word count of each unique word from 100 articles).
1 answers
solution1
0 ACCPTED 2017-02-26 05:47:23
Contrary to what the tutorial I was following did, I could not through my X value as it received errors. Reasons could be due to having string headers in it, not positive.
The solution I did was just manually 5-fold split my data up and perform 5 different decision trees on the data, with 1 test set each time.
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