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Error: ValueError('%r cannot be used to seed a numpy.random.RandomState')

0

$begingroup$

I am getting this error message while trying to fit a model for the isolationForest algorithm.

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

Below is my code:

 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.ensemble import IsolationForest
 import pandas as pd

np.random.RandomState(1234)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)
for column in df.columns:
if df[column].dtype == type(object):
 le = LabelEncoder()
 df[column] = le.fit_transform(df[column])
 np.random.get_state()

X_train, X_test = train_test_split(df, test_size=0.3)
print(X_test)
print(X_train)
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
clf = IsolationForest(behaviour='new', max_samples=100,
 random_state=df, contamination='auto')
clf.fit(X_train)

Can anyone give any insight as to why I might be getting this error?

machine-learning scikit-learn machine-learning-model

share|improve this question

edited Mar 30 at 2:47

Ethan

701625

asked Mar 29 at 21:07

RahulRahul

1

$endgroup$

add a comment | 

0

$begingroup$

I am getting this error message while trying to fit a model for the isolationForest algorithm.

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

Below is my code:

 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.ensemble import IsolationForest
 import pandas as pd

np.random.RandomState(1234)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)
for column in df.columns:
if df[column].dtype == type(object):
 le = LabelEncoder()
 df[column] = le.fit_transform(df[column])
 np.random.get_state()

X_train, X_test = train_test_split(df, test_size=0.3)
print(X_test)
print(X_train)
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
clf = IsolationForest(behaviour='new', max_samples=100,
 random_state=df, contamination='auto')
clf.fit(X_train)

Can anyone give any insight as to why I might be getting this error?

machine-learning scikit-learn machine-learning-model

share|improve this question

edited Mar 30 at 2:47

Ethan

701625

asked Mar 29 at 21:07

RahulRahul

1

$endgroup$

add a comment | 

$begingroup$

I am getting this error message while trying to fit a model for the isolationForest algorithm.

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

Below is my code:

 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.ensemble import IsolationForest
 import pandas as pd

np.random.RandomState(1234)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)
for column in df.columns:
if df[column].dtype == type(object):
 le = LabelEncoder()
 df[column] = le.fit_transform(df[column])
 np.random.get_state()

X_train, X_test = train_test_split(df, test_size=0.3)
print(X_test)
print(X_train)
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
clf = IsolationForest(behaviour='new', max_samples=100,
 random_state=df, contamination='auto')
clf.fit(X_train)

Can anyone give any insight as to why I might be getting this error?

machine-learning scikit-learn machine-learning-model

share|improve this question

edited Mar 30 at 2:47

Ethan

701625

asked Mar 29 at 21:07

RahulRahul

1

$endgroup$

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.ensemble import IsolationForest
 import pandas as pd

np.random.RandomState(1234)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)
for column in df.columns:
if df[column].dtype == type(object):
 le = LabelEncoder()
 df[column] = le.fit_transform(df[column])
 np.random.get_state()

X_train, X_test = train_test_split(df, test_size=0.3)
print(X_test)
print(X_train)
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
clf = IsolationForest(behaviour='new', max_samples=100,
 random_state=df, contamination='auto')
clf.fit(X_train)

share|improve this question

edited Mar 30 at 2:47

Ethan

701625

asked Mar 29 at 21:07

RahulRahul

1

share|improve this question

edited Mar 30 at 2:47

Ethan

701625

asked Mar 29 at 21:07

RahulRahul

1

edited Mar 30 at 2:47

Ethan

701625

edited Mar 30 at 2:47

Ethan

701625

asked Mar 29 at 21:07

RahulRahul

1

asked Mar 29 at 21:07

RahulRahul

1

1 Answer
1

active

oldest

votes

0

$begingroup$

Because you are creating a IsolationForest instance with random_state getting initialised to pandas.DataFrame. The code docs for random_state explain it as

random_state : int, RandomState instance or None, optional (default=None). If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

share|improve this answer

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  so how can i correct it
  $endgroup$
  – Rahul
  Apr 1 at 14:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Pass an integer value
  $endgroup$
  – Kiritee Gak
  Apr 1 at 14:02
  

  
  
  
  

add a comment | 

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0

$begingroup$

Because you are creating a IsolationForest instance with random_state getting initialised to pandas.DataFrame. The code docs for random_state explain it as

random_state : int, RandomState instance or None, optional (default=None). If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

share|improve this answer

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  so how can i correct it
  $endgroup$
  – Rahul
  Apr 1 at 14:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Pass an integer value
  $endgroup$
  – Kiritee Gak
  Apr 1 at 14:02
  

  
  
  
  

add a comment | 

0

$begingroup$

Because you are creating a IsolationForest instance with random_state getting initialised to pandas.DataFrame. The code docs for random_state explain it as

random_state : int, RandomState instance or None, optional (default=None). If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

share|improve this answer

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  so how can i correct it
  $endgroup$
  – Rahul
  Apr 1 at 14:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Pass an integer value
  $endgroup$
  – Kiritee Gak
  Apr 1 at 14:02
  

  
  
  
  

add a comment | 

$begingroup$

Because you are creating a IsolationForest instance with random_state getting initialised to pandas.DataFrame. The code docs for random_state explain it as

random_state : int, RandomState instance or None, optional (default=None). If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

share|improve this answer

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521

$endgroup$

share|improve this answer

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521

answered Mar 30 at 6:47

Kiritee GakKiritee Gak

1,3591521