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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 23 mins ago

Ethan

600224

asked 6 hours ago

RahulRahul

1

New contributor

Rahul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

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 23 mins ago

Ethan

600224

asked 6 hours ago

RahulRahul

1

New contributor

Rahul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

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 23 mins ago

Ethan

600224

asked 6 hours ago

RahulRahul

1

New contributor

Rahul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

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 23 mins ago

Ethan

600224

asked 6 hours ago

RahulRahul

1

New contributor

Rahul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

edited 23 mins ago

Ethan

600224

asked 6 hours ago

RahulRahul

1

New contributor

Rahul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

edited 23 mins ago

Ethan

600224

edited 23 mins ago

Ethan

600224

asked 6 hours ago

RahulRahul

1

New contributor

Rahul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 6 hours ago

RahulRahul

1