eagles 0.1.0

Data science utility package to help practitioners do ML and EDA work.

eagles

Creator: Jon-Frederick Landrigan

Description:

This repository contains utilities to perform tasks relating to data science including supervised and unsupervised machine learning, data exploration and statistical testing. The functions primarily act as utility wrappers.

For examples of how to use the functions contained within the package see the following jupyter notebooks:

• Supervised Tuning.ipynb
• Unsupervised Tuning.ipynb
• Exploratory.ipynb

Install

To install you can use either pip3 install eagles to install from pypi or pip3 install git+https://github.com/JFLandrigan/eagles.git#"egg=eagles" to install direct from the github repo in order to get the latest merges. Note when installing direct from github, while it may contain the latest updates, it may not be as stable as compared to releases installed from pypi.

Once installed it can be imported like any other python package. For example:

from eagles.Supervised import supervised_tuner as st
from eagles.Unsupervised import unsupervised_tuner as ut
from eagles.Exploratory import explore, missing, distributions, categories , outcomes

Supported Model Abbreviations

Note that the functions primarily support sklearn model objects however if a model follows the standard fit, predict and predict_proba methods it can be passed in directly as well.

Supervised

Classification	Regression
"rf_clf" : RandomForestClassifier	"rf_regress" : RandomForestRegressor
"gb_clf" : GradientBoostingClassifier	"gb_regress" : GradientBoostingRegressor
"dt_clf" : DecisionTreeClassifier	"dt_regress" : DecisionTreeRegressor
"logistic" : LogisticRegression	"linear" : LinearRegression
"svc" : SVC	"lasso" : Lasso
"knn_clf" : KNeighborsClassifier	"elastic" : ElasticNet
"nn" : MLPClassifier	"svr" : SVR
"ada_clf" : AdaBoostClassifier	"knn_regress" : KNeighborsRegressor
"et_clf": ExtraTreesClassifier	"ada_regress" : AdaBoostRegressor
	"et_regress": ExtraTreesRegressor

Unsupervised

Currently the functions primarily support the following the sklearn algorithms however other model objects can be passed in assuming they support the fit_predict() methodology like other sklearn clustering algorithms.

• "kmeans"
• "agglomerativeclustering"
• "dbscan"

Metric Options

Supervised

When using supervised_tuner.tune_test_model() the tune_metric argument is used for the parameter search and the eval_metric argument is used for the final model evaluation (eval metrics should be passed in as a list). For supervised_tuner.model_eval() the metrics argument is used to tell the function what metrics to use (these should be passed in a list). If no metrics are passed in (for tuning and/or eval) classification problems will default to 'f1' and regression problems will default to 'mse'. Note that for multi-class classification problems the metrics default to "macro" averages.

Classification	Regression
'accuracy'	'mse' - mean square error
'f1'	'rmse' - root mean square error
'precision'	'mae' - mean absolute error
'recall'	'mape' - mean absolute percent error (note zeros are excluded)
'roc_auc' - Area Under the Receiver Operating Characteristic Curve	'r2' - r squared
'precision_recall_auc' - Area Under the Precision Recall Curve

Unsupervised

When unsupervised_tuner.find_optimal_clusters() with K-Means or Agglomerative Clustering is used the following metrics can be used to find the "optimal" or "suggested "number of clusters however thorough analysis should be performed.

• "max_sil" : After generating models based on the range of cluster numbers desired the algorithm will pick the optimal number of clusters as the number of clusters which resulted in the highest max silhouette score.
• "knee_wss" : After generating models based on the range of cluster numbers desired the KneeLocator() method used (provided by the kneed package) to find the "elbow" or point at which increasing the number of clusters does not significantly decrease the amount of within cluster variability.

Note the DBSCAN algorithm uses internal methods to find the optimal number of clusters.

supervised_tuner.tune_test_model() parameter search options

• 'random_cv' : sklearn implementation of random parameter search. Can pass in n_jobs to parallelize fits and n_iterations to determine total number of fits.
• 'bayes_cv' : scikit-optimize implementation of bayes parameter search. Can pass in n_jobs to parallelize fits and n_iterations to determine total number of fits.
• 'grid_cv' : sklearn implementation of paramter grid search. Can pass in n_jobs to parallelize fits.

How to pass in parameters for supervised_tuner.tune_test_model()

• Single model: pass in relative parameters as a dictionary with key (parameter) value (listed parameter setting) pairs

• Model embedded within a pipeline: pass in relative parameters as dictionary with key (parameter) value (listed parameter setting) pairs. Note that the parameter keys should follow the format clf__< parameter >

• Models embedded within a VotingClassifier or Voting Regressor: pass in relative parameters as dictionary with key (parameter) value (listed parameter setting) pairs. Note that the parameter keys should follow the format (note the follwing example assumes a random forest and a logistic regression):

  pars = {'rf__clf__n_estimators':[x for x in range(100,300,50)]
          ,'rf__clf__max_depth':[5, 10, 20]
          ,'rf__clf__class_weight': [None, 'balanced_subsample']
  
          ,'lr__clf__penalty': ['l1','l2']
          ,'lr__clf__class_weight':[None, 'balanced']
          ,'lr__clf__max_iter':[100, 1000]
          ,'lr__clf__C': [.25,.5,1]
  
          ,'weights':[[1,1],[1,2],[2,1]] 
         }
  

Passing in Pipelines

Sklearn pipeline objects can be passed directly into supervised_tuner.model_eval() and supervised_tuner.tune_test_model() via the model argument (i.e. when a model is embedded within a pipeline) or the pipe argument. When the pipeline is passed into the pipe argument the model will be appended and/or embedded within the passed in pipeline. Note the following conventions for pipeline prefixes should be followed:

• 'impute' : for imputation steps
• 'feature_selection' : for feature selection steps (using sklearn feature selection select from model)
• 'scale': for scaling steps
• 'clf' : for the classifiers

See the following pipeline construction examples

from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler

lr_pipe = Pipeline([('impute', SimpleImputer(strategy='median'))
                    ,('scale', MinMaxScaler())
                    ,('clf', LogisticRegression())])

Logging

The supervised_tuner model_eval() allows the used to log out a text file of the models performance and tune_test_model() allows the user to log out a text file containing relevant information for the call (e.g. tuning parameters, and model performance), the final model object and the data used for training and testing. model_eval() does not currently allow for data and model logging. See the following examples for argument definitions:

# Model evaluation
st.model_eval(
    X=iris[fts],
    y=iris['dummy'],
    model='logistic',
    params={'solver':'liblinear'},
    metrics=["accuracy", "f1", "roc_auc"],
    bins=None,
    pipe=None,
    scale=None,
    num_top_fts=None,
    num_cv=5,
    get_ft_imp=True,
    random_seed=4,
    binary=True,
    log="log",
    log_name="model_eval_test.txt",
    log_path=None,
    log_note="This is a test of the model eval function"
)

# Tuning and testing a model (Note if only a log is wanted the argument can be set to 'log')
clf, params, clf_fts = st.tune_test_model(X=iris[fts]
                                            ,y=iris['dummy']
                                            ,model=vc_clf
                                            ,params=pars
                                            ,tune_metric="f1"
                                            ,eval_metrics=["accuracy", "f1", "precision"]
                                            ,num_cv=5
                                            ,pipe=None
                                            ,scale=None
                                            ,select_features=select_features_dict
                                            ,bins=None
                                            ,num_top_fts=None
                                            ,tuner="random_cv"
                                            ,n_iterations=15
                                            ,get_ft_imp=True
                                            ,n_jobs=2
                                            ,random_seed=None
                                            ,binary=True
                                            ,log=["log","mod","data"]
                                            ,log_name="model_tunetest_test.txt"
                                            ,log_path=None
                                            ,log_note=note)

Note that if no log path is passed in a data subdirectory will be created in eagles/eagles/Supervised/utils/

Exploratory Module

The Exploratory module contains functions and tools for performing exploratory data analysis on pandas data frames. Currently the module includes the following:

• explore: Includes run_battery(), get_base_descriptives() and get_correlations()
  • run_battery() options include info, missing, descriptive, distributions, correlations and category_stats
• missing: Includes get_proportion_missing()
• distributions: Includes find_caps()
• categories: Includes get_sample_stats() and get_multi_group_stats()
• outcomes: Includes stats_by_outcome()
  • stats_by_outcome() analysis options include descriptives, proportions, regress. Note when the outcome type is continuous the descriptives option also includes a correlations analysis.

Notes

Currently the functions primarily rely on the use of pandas data frames. Numpy matrices can be passed in however this may result in unexpected behavior.

Packages Required (see requirements.txt for versions)

• kneed
• matplotlib
• numpy
• pandas
• scikit-learn
• scikit-optimize
• scipy
• seaborn
• statsmodels