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A hyperopt wrapper - simplifying hyperparameter tuning with Scikit-learn style estimators.

Project description

Skperopt

A hyperopt wrapper - simplifying hyperparameter tuning with Scikit-learn style estimators.

Works with either classification evaluation metrics "f1", "auc" or "accuracy" AND regression "rmse" and "mse".

Installation:

pip install skperopt

Usage:

Just pass in an estimator, a parameter grid and Hyperopt will do the rest. No need to define objectives or write hyoperopt specific parameter grids.

Recipe (vanilla flavour):

  • [x] Import skperopt
  • [x] Initalize skperopt
  • [x] Run skperopt.HyperSearch.search
  • [x] Collect the results

Code example below.

import skperopt as sk

import pandas as pd

from sklearn.datasets import make_classification
from sklearn.neighbors import KNeighborsClassifier

#generate classification data
data = make_classification(n_samples=1000, n_features=10, n_classes=2)
X = pd.DataFrame(data[0])
y = pd.DataFrame(data[1])

#init the classifier
kn = KNeighborsClassifier()
param = {"n_neighbors": [int(x) for x in np.linspace(1, 60, 30)],
         "leaf_size": [int(x) for x in np.linspace(1, 60, 30)],
         "p": [1, 2, 3, 4, 5, 10, 20],
         "algorithm": ['auto', 'ball_tree', 'kd_tree', 'brute'],
         "weights": ["uniform", "distance"]}


#search parameters
search = sk.HyperSearch(kn, X, y, params=param)
search.search()

#gather and apply the best parameters
kn.set_params(**search.best_params)

#view run results
print(search.stats)

HyperSearch parameters

  • est ([sklearn estimator] required)

any sklearn style estimator

  • X ([pandas Dataframe] required)

your training data

  • y ([pandas Dataframe] required)

your training data

  • params ([dictionary] required)

a parameter search grid

  • iters (default 500 [int])

number of iterations to try before early stopping

  • time_to_search (default None [int])

time in seconds to run for before early stopping (None = no time limit)

  • cv (default 5 [int])

number of folds to use in cross_vaidation tests

  • cv_times (default 1 [int])

number of times to perfrom cross validation on a new random sample of the data -higher values decrease variance but increase run time

  • randomState (default 10 [int])

random state for the data shuffling

  • scorer (default "f1" [str])

type of evaluation metric to use - accepts classification "f1","auc","accuracy" or regression "rmse" and "mse"

  • verbose (default 1 [int])

amount of verbosity

     0 = none 

     1 = some 

     2 = debug
  • random (default - False)

should the data be randomized during the cross validation

  • foldtype (default "Kfold" [str])

type of folds to use - accepts "KFold", "Stratified"

HyperSearch methods

  • HyperSearch.search() (None)

Used to search the parameter grid using hyperopt. No parameters need to be passed to the function. All parameters are set during initialization.

Testing

With 100 tests of 150 search iterations for both RandomSearch and Skperopt Searches.

Skperopt (hyperopt) performs better than a RandomSearch, producing higher average f1 score with a smaller standard deviation.

alt chart

Skperopt Search Results

f1 score over 100 test runs:

Mean 0.9340930

Standard deviation 0.0062275

Random Search Results

f1 score over 100 test runs

Mean 0.927461652

Standard deviation 0.0063314


Updates

V0.0.73

  • Added cv_times attr - runs the cross validation n times (ie cv (5x5) ) each iteration on a new randomly sampled data set this should reduce overfitting

V0.0.7

  • Added FIXED RMSE eval metric

  • Added MSE eval metric

Project details


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