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kandi has reviewed auto-sklearn and discovered the below as its top functions. This is intended to give you an instant insight into auto-sklearn implemented functionality, and help decide if they suit your requirements.
Automated Machine Learning with scikit-learn
auto-sklearn in four lines of code
import autosklearn.classification
cls = autosklearn.classification.AutoSklearnClassifier()
cls.fit(X_train, y_train)
predictions = cls.predict(X_test)
Relevant publications
@inproceedings{feurer-neurips15a,
title = {Efficient and Robust Automated Machine Learning},
author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina Springenberg, Jost and Blum, Manuel and Hutter, Frank},
booktitle = {Advances in Neural Information Processing Systems 28 (2015)},
pages = {2962--2970},
year = {2015}
}
How can update trained IsolationForest model with new datasets/datafarmes in python?
# Model
from sklearn.ensemble import IsolationForest
# Saving file
import joblib
# Data
import numpy as np
# Create a new model
model = IsolationForest()
# Generate some old data
df1 = np.random.randint(1,100,(100,10))
# Train the model
model.fit(df1)
# Save it off
joblib.dump(model, 'isf_model.joblib')
# Load the model
model = joblib.load('isf_model.joblib')
# Generate new data
df2 = np.random.randint(1,500,(1000,10))
# If the original data is now not important, I can just call .fit() again.
# If you are using time-series based data, this is preferred, as older data may not be representative of the current state
model.fit(df2)
# If the original data is important, I can simply join the old data to new data. There are multiple options for this:
# Pandas: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
# Numpy: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
combined_data = np.concatenate((df1, df2))
model.fit(combined_data)
How to specify Search Space in Auto-Sklearn
cs = mdl.get_configuration_space(X, y)
config = cs.sample_configuration()
config._values['classifier:random_forest:n_estimators'] = 1000
pipeline, run_info, run_value = mdl.fit_pipeline(X=X_train, y=y_train,
config=config,
X_test=X_test, y_test=y_test)
QUESTION
How can update trained IsolationForest model with new datasets/datafarmes in python?
Asked 2022-Mar-02 at 20:42Let's say I fit IsolationForest()
algorithm from scikit-learn on time-series based Dataset1 or dataframe1 df1
and save the model using the methods mentioned here & here. Now I want to update my model for new dataset2 or df2
.
My findings:
...learn incrementally from a mini-batch of instances (sometimes called “online learning”) is key to out-of-core learning as it guarantees that at any given time, there will be only a small amount of instances in the main memory. Choosing a good size for the mini-batch that balances relevancy and memory footprint could involve tuning.
but Sadly IF algorithm doesn't support estimator.partial_fit(newdf)
How I can update the trained on Dataset1 and saved IF model with a new Dataset2?
ANSWER
Answered 2022-Mar-02 at 17:41You can simply reuse the .fit()
call available to the estimator on the new data.
This would be preferred, especially in a time series, as the signal changes and you do not want older, non-representative data to be understood as potentially normal (or anomalous).
If old data is important, you can simply join the older training data and newer input signal data together, and then call .fit()
again.
Also sidenote, according to sklearn documentation, it is better to use joblib
than pickle
An MRE with resources below:
# Model
from sklearn.ensemble import IsolationForest
# Saving file
import joblib
# Data
import numpy as np
# Create a new model
model = IsolationForest()
# Generate some old data
df1 = np.random.randint(1,100,(100,10))
# Train the model
model.fit(df1)
# Save it off
joblib.dump(model, 'isf_model.joblib')
# Load the model
model = joblib.load('isf_model.joblib')
# Generate new data
df2 = np.random.randint(1,500,(1000,10))
# If the original data is now not important, I can just call .fit() again.
# If you are using time-series based data, this is preferred, as older data may not be representative of the current state
model.fit(df2)
# If the original data is important, I can simply join the old data to new data. There are multiple options for this:
# Pandas: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
# Numpy: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
combined_data = np.concatenate((df1, df2))
model.fit(combined_data)
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