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Showing content from https://github.com/marella/evaluate below:

marella/evaluate: A tool to evaluate the performance of various machine learning algorithms and preprocessing steps to find a good baseline for a given task.

A tool to evaluate the performance of various machine learning algorithms and preprocessing steps to find a good baseline for a given task.

import evaluate
from sklearn import datasets

data = datasets.load_iris()
x, y = data.data, data.target

results = evaluate(task='classification', data=(x, y))
results['test_score'].plot.bar()

This tool performs common preprocessing steps such as feature scaling, one-hot encoding etc., and runs various ML algorithms such as Random Forests, SVM etc. It then evaluates the performance of each preprocessing step and ML algorithm and provides scores for each. These results can be used to quickly identify preprocessing steps and ML algorithms that perform well to form a good baseline which can be used to develop better models.

evaluate(task,
         data,
         test_data=.2,
         columns=None,
         preprocessors=None,
         estimators=None)

task: 'classification' or 'regression'
data: Tuple of x, y used for training the model
test_data: Tuple of x, y or a number representing the proportion of data to be used for scoring the model
columns: Dictionary of lists mapping column types to column names. If not specified numeric and categorical columns are automatically identified
preprocessors: List of names of available preprocessors or a custom Preprocessors object
estimators: List of names of available estimators or a custom Estimators object

Dictionary of pandas DataFrames with estimator names as index and preprocessor names as column names with the following keys:

{
    'test_score': ...,
    'train_score': ...,
    'fit_time': ...,
    'score_time': ...,
}

results = evaluate(...)
assert isinstance(results, dict)
scores = results['test_score']
assert isinstance(scores, pandas.DataFrame)
scores.plot.bar()

Name Column Type Description n numeric Handle missing data n:s numeric Standardize features c categorical Handle missing data and perform one-hot encoding o ordinal Handle missing data and perform ordinal encoding t:c text Convert to a matrix of token counts t:c=2 text Convert to a matrix of token counts including bigrams t:t text Convert to a matrix of TF-IDF features t:t=2 text Convert to a matrix of TF-IDF features including bigrams

Multiple preprocessors can be combined into one by separating them with ,:

results = evaluate(..., preprocessors=['n,c,o', 'n:s,c,o'])

Custom preprocessors can be added as:

from evaluate import evaluate, Preprocessors

preprocessors = Preprocessors()
preprocessors.add('custom_preprocessor', CustomPreprocessor())
results = evaluate(..., preprocessors=preprocessors)

Name of the custom preprocessor must be unique.

Classification Regression XGBClassifier XGBRegressor LGBMClassifier LGBMRegressor RandomForestClassifier RandomForestRegressor SVC SVR LogisticRegression LinearRegression KNeighborsClassifier KNeighborsRegressor AdaBoostClassifier AdaBoostRegressor ExtraTreesClassifier ExtraTreesRegressor GradientBoostingClassifier GradientBoostingRegressor DecisionTreeClassifier DecisionTreeRegressor DummyClassifier DummyRegressor

Custom estimators can be added as:

from evaluate import evaluate, Estimators

estimators = Estimators(task='classification')
estimators.add('custom_estimator', CustomEstimator())
results = evaluate(..., estimators=estimators)

Name of the custom estimator must be unique.

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