Isolation forest results every value -1
$begingroup$
I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the isolation forest gives out every single value -1. Is that a bug or should I take it as that the values are fine? Here is a piece of the code. (I know I need to stop using ix).
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
df1 = pd.read_csv('C:/Users/smotapar/Desktop/ase/source/data.csv')
clf = IsolationForest(n_estimators=200, random_state=10, bootstrap=False)
clf.fit(df1.ix[:,"target"].values.reshape(-1, 1))
clf.predict(df1.ix[:,"target"].values.reshape(-1, 1))
Which gives out an output:
array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])
So, what did I do wrong? If I didn't, why is the output like this?
python scikit-learn pandas outlier
edited Mar 19 '18 at 7:53
tuomastik
745418
asked Mar 19 '18 at 2:53
RamRam
313
$endgroup$
add a comment |
$begingroup$
I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the isolation forest gives out every single value -1. Is that a bug or should I take it as that the values are fine? Here is a piece of the code. (I know I need to stop using ix).
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
df1 = pd.read_csv('C:/Users/smotapar/Desktop/ase/source/data.csv')
clf = IsolationForest(n_estimators=200, random_state=10, bootstrap=False)
clf.fit(df1.ix[:,"target"].values.reshape(-1, 1))
clf.predict(df1.ix[:,"target"].values.reshape(-1, 1))
Which gives out an output:
array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])
So, what did I do wrong? If I didn't, why is the output like this?
python scikit-learn pandas outlier
edited Mar 19 '18 at 7:53
tuomastik
745418
asked Mar 19 '18 at 2:53
RamRam
313
$endgroup$
$begingroup$
What's your dataset?
$endgroup$
– Aditya
Mar 19 '18 at 3:29
$begingroup$
Just send your dataset here. I will try.
$endgroup$
– Shivanya
2 days ago
add a comment |
$begingroup$
I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the isolation forest gives out every single value -1. Is that a bug or should I take it as that the values are fine? Here is a piece of the code. (I know I need to stop using ix).
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
df1 = pd.read_csv('C:/Users/smotapar/Desktop/ase/source/data.csv')
clf = IsolationForest(n_estimators=200, random_state=10, bootstrap=False)
clf.fit(df1.ix[:,"target"].values.reshape(-1, 1))
clf.predict(df1.ix[:,"target"].values.reshape(-1, 1))
Which gives out an output:
array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])
So, what did I do wrong? If I didn't, why is the output like this?
python scikit-learn pandas outlier
edited Mar 19 '18 at 7:53
tuomastik
745418
asked Mar 19 '18 at 2:53
RamRam
313
$endgroup$
I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the isolation forest gives out every single value -1. Is that a bug or should I take it as that the values are fine? Here is a piece of the code. (I know I need to stop using ix).
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
df1 = pd.read_csv('C:/Users/smotapar/Desktop/ase/source/data.csv')
clf = IsolationForest(n_estimators=200, random_state=10, bootstrap=False)
clf.fit(df1.ix[:,"target"].values.reshape(-1, 1))
clf.predict(df1.ix[:,"target"].values.reshape(-1, 1))
Which gives out an output:
array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])
So, what did I do wrong? If I didn't, why is the output like this?
python scikit-learn pandas outlier
python scikit-learn pandas outlier
edited Mar 19 '18 at 7:53
tuomastik
745418
asked Mar 19 '18 at 2:53
RamRam
313
edited Mar 19 '18 at 7:53
tuomastik
745418
asked Mar 19 '18 at 2:53
RamRam
313
edited Mar 19 '18 at 7:53
tuomastik
745418
edited Mar 19 '18 at 7:53
tuomastik
745418
edited Mar 19 '18 at 7:53
tuomastik
745418
745418
asked Mar 19 '18 at 2:53
RamRam
313
asked Mar 19 '18 at 2:53
RamRam
313
asked Mar 19 '18 at 2:53
RamRam
313
313
$begingroup$
What's your dataset?
$endgroup$
– Aditya
Mar 19 '18 at 3:29
$begingroup$
Just send your dataset here. I will try.
$endgroup$
– Shivanya
2 days ago
add a comment |
$begingroup$
What's your dataset?
$endgroup$
– Aditya
Mar 19 '18 at 3:29
$begingroup$
Just send your dataset here. I will try.
$endgroup$
– Shivanya
2 days ago
$begingroup$
What's your dataset?
$endgroup$
– Aditya
Mar 19 '18 at 3:29
$begingroup$
What's your dataset?
$endgroup$
– Aditya
Mar 19 '18 at 3:29
$begingroup$
Just send your dataset here. I will try.
$endgroup$
– Shivanya
2 days ago
$begingroup$
Just send your dataset here. I will try.
$endgroup$
– Shivanya
2 days ago
add a comment |
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$begingroup$
What's your dataset?
$endgroup$
– Aditya
Mar 19 '18 at 3:29
$begingroup$
Just send your dataset here. I will try.
$endgroup$
– Shivanya
2 days ago