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AutoML package for model comparison tasks

Project description

ATOM

Automated Tool for Optimized Modelling

Author: tvdboom
Email: m.524687@gmail.com

Python 3.6|3.7 License: MIT PyPI version

Description

Automated Tool for Optimized Modelling (ATOM) is a python package designed for fast exploration of ML solutions. With just a few lines of code, you can perform basic data cleaning steps, feature selection and compare the performance of multiple machine learning models on a given dataset. ATOM should be able to provide quick insights on which algorithms perform best for the task at hand and provide an indication of the feasibility of the ML solution.

NOTE: A data scientist with knowledge of the data will quickly outperform ATOM if he applies usecase-specific feature engineering or data cleaning methods. Use ATOM only for a fast exploration of the problem!

Possible steps taken by the ATOM pipeline:

  1. Data Cleaning
    • Handle missing values
    • Encode categorical features
    • Balance the dataset
    • Remove outliers
  2. Perform feature selection
    • Remove features with too high collinearity
    • Remove features with too low variance
    • Select best features according to a chosen strategy
  3. Fit all selected models (either direct or via successive halving)
    • Select hyperparameters using a Bayesian Optimization approach
    • Perform bagging to assess the robustness of the model
  4. Analyze the results using the provided plotting functions!



diagram

Installation

Intall ATOM easily using pip

pip install atom-ml

Usage

Call the ATOMClassifier or ATOMRegressor class and provide the data you want to use:

from atom import ATOMClassifier  
from sklearn.metrics import f1_score

atom = ATOMClassifier(X, Y, log='atom_log', n_jobs=2, verbose=1)

ATOM has multiple data cleaning methods to help you prepare the data for modelling:

atom.impute(strat_num='mean', strat_cat='most_frequent',  max_frac=0.1)  
atom.encode(max_onehot=10)  
atom.outliers(max_sigma=4)  
atom.balance(oversample=0.8, neighbors=15)  
atom.feature_selection(strategy='univariate', max_features=0.9)

Fit the data to different models:

atom.fit(models=['logreg', 'LDA', 'XGB', 'lSVM'],
         metric=f1_score,
         successive_halving=True,
         max_iter=10,
         max_time=1000,
         init_points=3,
         cv=4,
         bagging=5)  

Make plots and analyze results:

atom.boxplot(filename='boxplot.png')  
atom.lSVM.plot_probabilities()  
atom.lda.plot_confusion_matrix()  

API

  • ATOMClassifier(X, Y=None, target=None, percentage=100, test_size=0.3, log=None, n_jobs=1, warnings=False, verbose=0, random_state=None)
    ATOM class for classification tasks. When initializing the class, ATOM will automatically proceed to apply some standard data cleaning steps unto the data. These steps include transforming the input data into a pd.DataFrame (if it wasn't one already) that can be accessed through the class' attributes, removing columns with prohibited data types, removing categorical columns with maximal cardinality (the number of unique values is equal to the number of instances, usually the case for IDs, names, etc...), and removing duplicate rows and rows with missing values in the target column.
    • X: np.array or pd.DataFrame
      Data features with shape = [n_samples, n_features]. If Y and target are None, the last column of X is selected as target column.
    • Y: np.array or pd.Series, optional (default=None)
      Data target column with shape = [n_samples].
    • target: string, optional (default=None)
      Name of the target column in X (X needs to be a pd.DataFrame). If Y is provided, target will be ignored.
    • percentage: int, optional (default=100)
      Percentage of data to use.
    • test_size: float, optional (default=0.3)
      Split ratio of the train and test set.
    • log: string, optional (default=None)
      Name of the log file, None to not save any log.
    • n_jobs: int, optional (default=1)
      Number of cores to use for parallel processing.
      • If -1, use all available cores
      • If <-1, use available_cores - 1 + n_jobs
    • warnings: bool, optional (default=False)
      Wether to show warnings when running the pipeline.
    • verbose: int, optional (default=0)
      Verbosity level of the class. Possible values are:
      • 0 to not print anything
      • 1 to print minimum information
      • 2 to print average information
      • 3 to print maximum information
    • random_state: int, optional (default=None)
      Seed used by the random number generator. If None, the random number generator is the RandomState instance used by np.random.

  • ATOMRegressor(X, Y=None, target=None, percentage=100, test_size=0.3, log=None, n_jobs=1, warnings=False, verbose=0, random_state=None)
    ATOM class for regression tasks. See ATOMClassifier for an explanation of the class' parameters.

Class methods

ATOM contains multiple methods for standard data cleaning and feature selection processes. Calling on one of them will automatically apply the method on the dataset in the class and update the class' attributes accordingly.

TIP: Use the report method to examine the data and help you determine suitable parameters for the methods
  • impute(strat_num='remove', strat_cat='remove', max_frac=0.5, missing=[np.nan, None, '', '?', 'NA', 'nan', 'NaN', np.inf, -np.inf])
    Handle missing values according to the selected strategy. Also removes columns with too many missing values.
    • strat_num: int, float or string, optional (default='remove')
      Imputing strategy for numerical columns. Possible values are:
      • 'remove': remove row if any missing value
      • 'mean': impute with mean of column
      • 'median': impute with median of column
      • 'most_frequent': impute with most frequent value
      • int or float: impute with provided numerical value
    • strat_cat: string, optional (default='remove')
      Imputing strategy for categorical columns. Possible values are:
      • 'remove': remove row if any missing value
      • 'most_frequent': impute with most frequent value
      • string: impute with provided string
    • max_frac: float, optional (default=0.5)
      Maximum allowed fraction of rows with any missing values. If more, the column is removed.
    • missing: value or list of values, optional (default=[np.nan, None, '', '?', 'NA', 'nan', 'NaN', np.inf, -np.inf])
      List of values to consider as missing. None, np.nan, '', np.inf and -np.inf are always added to the list since they are incompatible with sklearn models.

  • encode(max_onehot=10, fraction_to_other=0)
    Perform encoding of categorical features. The encoding type depends on the number of unique values in the column: label-encoding for n_unique=2, one-hot-encoding for 2 < n_unique <= max_onehot and target-encoding for n_unique > max_onehot. It also can replace classes with low occurences with the value 'other' in order to prevent too high cardinality.
    • max_onehot: int, optional (default=10)
      Maximum number of unique values in a feature to perform one-hot-encoding.
    • fraction_to_other: float, optional (default=0)
      Classes with less instances than n_rows * fraction_to_other are replaced with 'other'.

  • outliers(max_sigma=3, include_target=False)
    Remove outliers from the training set.
    • max_sigma: int or float, optional (default=3)
      Remove rows containing any value with a maximum standard deviation (on the respective column) above max_sigma.
    • include_target: bool, optional (default=False)
      Wether to include the target column when searching for outliers.

  • balance(oversample=None, undersample=None, neighbors=5)
    Balance the number of instances per target class. Only for classification tasks.
    • oversample: float or string, optional (default=None)
      Oversampling strategy using ADASYN. Choose from:
      • None: do not perform oversampling
      • float: fraction minority/majority (only for binary classification)
      • 'minority': resample only the minority class
      • 'not minority': resample all but minority class
      • 'not majority': resample all but majority class
      • 'all': resample all classes
    • undersample: float or string, optional (default=None)
      Undersampling strategy using NearMiss methods. Choose from:
      • None: do not perform undersampling
      • float: fraction majority/minority (only for binary classification)
      • 'majority': resample only the majority class
      • 'not minority': resample all but minority class
      • 'not majority': resample all but majority class
      • 'all': resample all classes
    • n_neighbors: int, optional (default=5)
      Number of nearest neighbors used for any of the algorithms.

  • feature_insertion(n_features=2, generations=20, population=500)
    Use a genetic algorithm to create new combinations of existing features and add them to the original dataset in order to capture the non-linear relations between the original features. A dataframe containing the description of the newly generated features and their scores can be accessed through the genetic_features attribute. This method is implemented using the gplearn package. It is adviced to only use this method when fitting linear models.
    • n_features: int, optional (default=2)
      Maximum number of newly generated features (no more than 1% of the population).
    • generations: int, optional (default=20)
      Number of generations to evolve.
    • population: int, optional (default=500)
      Number of entities in each generation.

  • feature_selection(strategy=None, solver=None, max_features=None, threshold=-np.inf, frac_variance=1., max_correlation=0.98)
    Select best features according to the selected strategy. Ties between features with equal scores will be broken in an unspecified way. Also removes features with too low variance and too high collinearity.
    • strategy: string, optional (default='univariate')
      Feature selection strategy to use. Choose from:
      • None: do not perform any feature selection algorithm (it does still look for multicollinearity and variance)
      • 'univariate': perform a univariate statistical test
      • 'PCA': perform a principal component analysis
      • 'SFM': select best features from an existing model
      • 'RFE': recursive feature eliminator
    • solver: string or callable (default=depend on strategy)
      Solver or model to use for the feature selection strategy. See the sklearn documentation for an extended descrition of the choices. Select None for the default option per strategy (not applicable for SFM).
      • for 'univariate', choose from:
        • 'f_classif' (default for classification tasks)
        • 'f_regression' (default for regression tasks)
        • 'mutual_info_classif'
        • 'mutual_info_regression'
        • 'chi2'
        • Any function taking two arrays (X, y), and returning arrays (scores, pvalues). See the documentation.
      • for 'PCA', choose from:
        • 'auto' (default)
        • 'full'
        • 'arpack'
        • 'randomized'
      • for 'SFM': choose a base estimator from which the transformer is built. The estimator must have either a feature_importances_ or coef_ attribute after fitting. This parameter has no default option.
      • for 'RFE': choose a supervised learning estimator. The estimator must have either a feature_importances_ or coef_ attribute after fitting. This parameter has no default option.
    • max_features: int or float, optional (default=None)
      Number of features to select.
      • None: select all features
      • if >= 1: number of features to select
      • if < 1: fraction of features to select
    • threshold: string or float, optional (default=-np.inf)
      Threshold value to attain when selecting the best features (works only when strategy='SFM'). Features whose importance is greater or equal are kept while the others are discarded.
      • if 'mean': set the mean of feature_importances as threshold
      • if 'median': set the median of feature_importances as threshold
    • frac_variance: float, optional (default=1)
      Remove features with the same value in at least this fraction of the total. None to skip this step.
    • max_correlation: float, optional (default=0.98)
      Minimum value of the Pearson correlation cofficient to identify correlated features. A dataframe of the removed features and their correlation values can be accessed through the collinear attribute. None to skip this step.

  • fit(models, metric, greater_is_better=True, successive_halving=False, skip_steps=0, max_iter=15, max_time=np.inf, eps=1e-08, batch_size=1, init_points=5, plot_bo=False, cv=3, bagging=None)
    Fit class to the selected models. The optimal hyperparameters per model are selectred using a Bayesian Optimization (BO) algorithm with gaussian process as kernel. The resulting score of each step of the BO is either computed by cross-validation on the complete training set or by creating a validation set from the training set. This process will create some minimal leakage but ensures a maximal use of the provided data. The test set, however, does not contain any leakage and will be used to determine the final score of every model. Note that the best score on the BO can be consistently lower than the final score on the test set (despite the leakage) due to the considerable fewer instances on which it is trained. At the end of te pipeline, you can choose to test the robustness of the model applying a bagging algorithm, providing a distribution of the models' performance.
    • models: string or list of strings
      List of models to fit on the data. If 'all', all available models are used. Use the predefined acronyms to select the models. Possible values are (case insensitive):
      • 'GNB' for Gaussian Naïve Bayes (no hyperparameter tuning)
      • 'MNB' for Multinomial Naïve Bayes
      • 'BNB' for Bernoulli Naïve Bayes
      • 'GP' for Gaussian Process (no hyperparameter tuning)
      • 'LinReg' for Linear Regression (OLS, ridge, lasso and elasticnet)
      • 'BayReg' for Bayesian Linear Regression (with ridge regularization)
      • 'LogReg' for Logistic Regression
      • 'LDA' for Linear Discriminant Analysis
      • 'QDA' for Quadratic Discriminant Analysis
      • 'KNN' for K-Nearest Neighbors
      • 'Tree' for a single Decision Tree
      • 'Bag' for Bagging (with decision tree as base estimator)
      • 'ET' for Extra-Trees
      • 'RF' for Random Forest
      • 'AdaB' for AdaBoost
      • 'GBM' for Gradient Boosting Machine
      • 'XGB' for XGBoost (if package is available)
      • 'LGB' for LightGBM (if package is available)
      • 'CatB' for CatBoost (if package is available)
      • 'lSVM' for Linear Support Vector Machine
      • 'kSVM' for Non-linear Support Vector Machine
      • 'PA' for Passive Aggressive
      • 'SGD' for Stochastic Gradient Descent
      • 'MLP' for Multilayer Perceptron
    • metric: function callable
      Metric on which the pipeline fits the models. Score function (or loss function) with signature metric(y, y_pred, **kwargs).
    • greater_is_better: bool, otional (default=True)
      Wether the metric is a score function or a loss function, i.e. if True, a higher score is better and if False, lower is better.
    • successive_halving: bool, optional (default=False)
      Fit the pipeline using a successive halving approach, that is, fitting the model on 1/N of the data, where N stands for the number of models still in the pipeline. After this, the best half of the models are selected for the next iteration. This process is repeated until only one model is left. Since models perform quite differently depending on the size of the training set, we recommend to use this feature when fitting similar models (e.g: only using tree-based models).
    • skip_iter: int, optional (default=0)
      Skip n last iterations of the successive halving.
    • max_iter: int, optional (default=15)
      Maximum number of iterations of the BO. 0 to not use the BO and fit the model directly on its default parameters.
    • max_time: int, optional (default=np.inf)
      Maximum time allowed for the BO (in seconds).
    • eps: float, optional (default=1e-08)
      Minimum hyperparameter distance between two consecutive steps in the BO.
    • batch_size: int, optional (default=1)
      Size of the batch in which the objective is evaluated.
    • init_points: int, optional (default=5)
      Initial number of tests the BO runs before fitting the surrogate function.
    • plot_bo: bool, optional (default=False)
      Wether to plot the BO's progress as it runs. Creates a canvas with two plots: the first plot shows the score of every trial and the second shows the distance between the last consecutive steps. Don't forget to call %matplotlib at the start of the cell if you are using jupyter notebook!
    • cv: bool, optional (default=3)
      Strategy to fit and score the model selected after every step of the BO.
      • if 1, randomly split the training data into a train and validation set
      • if >1, perform a k-fold cross validation on the training set
    • bagging: int, optional (default=None)
      Number of bootstrapped samples used for bagging. If None, no bagging is performed. The algorithm is trained on the complete training set and validated on the test set.

Class methods (utilities)

  • stats()
    Print out a list of basic statistics on the dataset.

  • report(df='dataset', filename=None)
    Get an extensive report of the data using Pandas Profiling. The profile analysis is written in HTML5 and CSS3 and can be accessed through the report attribute. Note that this method can be very slow for large datasets.
    • df: string, optional (default='dataset')
      Name of the data class attribute to get the report from.
    • rows: int, optional (default=None)
      Number of rows selected randomly from the dataset to perform the analysis on. None to select all rows.
    • filename: string, optional (default=None)
      Name of the file when saved (as .html). None to not save anything.

  • reset_attributes(truth='all')
    If you change any of the class' data attributes (dataset, X, Y, train, test, X_train, X_test, Y_train, Y_test) in between the pipeline, you should call this method to change all other data attributes to their correct values. Independent attributes are updated in unison, that is, setting truth='X_train' will also update X_test, Y_train and Y_test, or truth='train' will also update test, etc...
    • truth: string, optional (default='all')
      Data attribute that has been changed (as string)

  • boxplot(iteration=-1, figsize=None, filename=None)
    Make a boxplot of the bagging's results after fitting the class.
    • iteration: int, optional (default=-1)
      Iteration of the successive_halving to plot. If -1, use the last iteration.
    • figsize: 2d-tuple, optional (default=None)
      Figure size: format as (x, y). If None, adjust to number of models.
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_correlation(figsize=(10, 6), filename=None)
    Make a correlation maxtrix plot of the dataset. Ignores non-numeric columns.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_successive_halving(figsize=(10, 6), filename=None)
    Make a plot of the models' scores per iteration of the successive halving.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • Additionnaly, you can call different metrics as methods of the main class to get the results of the fit method on this specific metric, e.g: atom.precision(). For a list of the available metrics see the subclass attributes (metrics).

Class attributes

  • dataset: Dataframe of the complete dataset.
  • X, Y: Data features and target.
  • train, test: Train and test set.
  • X_train, Y_train: Training set features and target.
  • X_test, Y_test: Test set features and target.
  • target_mapping: Dictionary of the target values mapped to their encoded integer (only for classification tasks).
  • report: Pandas profiling report of the selected dataset (if the report method was used).
  • genetic_algorithm: Genetic algorithm instance (if feature_insertion was used), from gplearn Symbolic Transformer.
  • genetic_features: Dataframe containing the description of the newly generated genetic features and their scores.
  • collinear: Dataframe of the collinear features and their correlation values (only if feature_selection was used).
  • univariate: Univariate feature selection class (if used), from sklearn SelectKBest.
  • PCA: Principal component analysis class (if used), from sklearn PCA.
  • SFM: Select from model class (if used), from sklearn SelectFromModel.
  • RFE: Recursive feature eliminator (if used), from sklearn RFE.
  • errors: Dictionary of the encountered exceptions (if any) while fitting the models.
  • results: Dataframe (or array of dataframes if successive_halving=True) of the results.

After fitting, the models become subclasses of the main class. They can be called upon for handy plot functions and attributes. If successive_halving=True, the model subclass corresponds to the last fitted model.

Subclass methods

  • plot_threshold(metric=None, steps=100, figsize=(10, 6), filename=None)
    Plot performance metrics against multiple threshold values. If None, the metric used to fit the model will be selected. Only for binary classification tasks.
    • metric: function callable or list of callables, optional (default=None)
      Metric(s) to plot. If None, the selected metric will be the one chosen to fit the model.
    • steps: int, optional (default=100)
      Number of thresholds to try between 0 and 1.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_probabilities(target_class=1, figsize=(10, 6), filename=None)
    Plots the probability of every class in the target variable against the class selected by target_class. Only for classification tasks.
    • target_class: int, optional (default=1) Target class to plot the probabilities against. A value of 0 corresponds to the first class, 1 to the second class, etc...
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_feature_importance(show=20, figsize=(10, 6), filename=None)
    Plots the feature importance scores. Only works with tree based algorithms (Tree, Bag, ET, RF, AdaBoost, GBM, XGB, LGB and CatB).
    • show: int, optional (default=20)
      Number of best features to show in the plot. None for all features.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_ROC(figsize=(10, 6), filename=None)
    Plots the ROC curve. Only for binary classification tasks.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_confusion_matrix(normalize=True, figsize=(10, 6), filename=None)
    Plot the confusion matrix for the model. Only for binary classification tasks.
    • normalize: bool, optional (default=True) Wether to normalize the confusion matrix.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • plot_tree(num_trees=0, max_depth=None, rotate=False, figsize=(10, 6), filename=None)
    Plot a single decision tree of the model. Only for tree-based algorithms. Dependency: graphviz.
    • num_trees: int, otional (default=0 --> first tree)
      Number of the tree to plot (if ensemble).
    • max_depth: int, optional (default=None)
      Maximum depth of the plotted tree. None for no limit.
    • rotate: bool, optional (default=False)
      When True, orientate the tree left-right instead of top-bottom.
    • figsize: 2d-tuple, optional (default=(10, 6))
      Figure size: format as (x, y).
    • filename: string, optional (default=None)
      Name of the file when saved. None to not save anything.

  • save(filename=None)
    Save the best found model as a pickle file.
    • filename: string, optional (default=None)
      Name of the file when saved. If None, it will be saved as ATOM_[model_type].

Subclass attributes

  • atom.MNB.error: If the model encountered an exception, this shows it.
  • atom.MLP.best_params: Get parameters of the model with highest score.
  • atom.SVM.best_model: Get the model with highest score (not fitted).
  • atom.SVM.best_model_fit: Get the model with highest score fitted on the training set.
  • atom.Tree.predict_train: Get the predictions on the training set.
  • atom.Bag.predict_test: Get the predictions on the test set.
  • atom.RF.predict_proba: Get the predicted probabilities on the test set.
  • atom.LGB.score_train: Metric score of the BO's selected model on the training set.
  • atom.XGB.score_test: Metric score of the BO's selected model on the test set.
  • atom.PA.bagging_scores: Array of the bagging's results.
  • atom.KNN.BO: Dictionary containing the information of every step taken by the BO.
    • 'params': Parameters used for the model
    • 'score': Score of the chosen metric

Subclass attributes (metrics)

Some of the most common metrics are saved as attributes of the model subclass, e.g. atom.rf.recall. They are calculated on the test set. For multiclass tasks, the type of averaging performed on the data is 'weighted'. Note that for classification tasks, the regression metrics are computed directly from the prediction, not the predicted probability! The available metrics are:

Dependencies

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