pydra-ml 0.4.0

Pydra dataflow engine

pydra-ml

Pydra-ML is a demo application that leverages Pydra together with scikit-learn to perform model comparison across a set of classifiers. The intent is to use this as an application to make Pydra more robust while allowing users to generate classification reports more easily. This application leverages Pydra's powerful splitters and combiners to scale across a set of classifiers and metrics. It will also use Pydra's caching to not redo model training and evaluation when new metrics are added, or when number of iterations (n_splits) is increased.

1. Output report contains SHAP feature analysis.
2. Allows for comparing some scikit-learn pipelines in addition to base classifiers.

Installation

pydraml requires Python 3.7+.

pip install pydra-ml

CLI usage

This repo installs pydraml a CLI to allow usage without any programming.

To test the CLI for a classification example, copy the pydra_ml/tests/data/breast_cancer.csv and short-spec.json.sample to a folder and run.

$ pydraml -s short-spec.json.sample

To check a regression example, copy pydra_ml/tests/data/diabetes_table.csv and diabetes_spec.json to a folder and run.

$ pydraml -s diabetes_spec.json

For each case pydra-ml will generate a result folder with the spec file name that includes test-{metric}-{timestamp}.png file for each metric together with a pickled results file containing all the scores from the model evaluations.

$ pydraml --help
Usage: pydraml [OPTIONS]

Options:
  -s, --specfile PATH   Specification file to use  [required]
  -p, --plugin TEXT...  Pydra plugin to use  [default: cf, n_procs=1]
  -c, --cache TEXT      Cache dir  [default:
                        /Users/satra/software/sensein/pydra-ml/cache-wf]

  --help                Show this message and exit.

With the plugin option you can use local multiprocessing

$ pydraml -s ../short-spec.json.sample -p cf "n_procs=8"

or execution via dask.

$ pydraml -s ../short-spec.json.sample -p dask "address=tcp://192.168.1.154:8786"

Current specification

The current specification is a JSON file as shown in the example below. It needs to contain all the fields described here. For datasets with many features, you will want to generate x_indices programmatically.

• filename: Absolute path to the CSV file containing data. Can contain a column, named group to support GroupShuffleSplit, else each sample is treated as a group.
• x_indices: Numeric (0-based) or string list of columns to use as input features
• target_vars: String list of target variable (at present only one is supported)
• group_var: String to indicate column to use for grouping
• n_splits: Number of shuffle split iterations to use
• test_size: Fraction of data to use for test set in each iteration
• clf_info: List of scikit-learn classifiers to use.
• permute: List of booleans to indicate whether to generate a null model or not
• gen_shap: Boolean indicating whether shap values are generated
• nsamples: Number of samples to use for shap estimation
• l1_reg: Type of regularizer to use for shap estimation
• plot_top_n_shap: Number or proportion of top SHAP values to plot (e.g., 16 or 0.1 for top 10%). Set to 1.0 (float) to plot all features or 1 (int) to plot top first feature.
• metrics: scikit-learn metric to use

clf_info specification

This is a list of classifiers from scikit learn and uses an array to encode:

- module
- classifier
- (optional) classifier parameters
- (optional) gridsearch param grid

when param grid is provided and default classifier parameters are not changed, then an empty dictionary MUST be provided as parameter 3.

This can also be embedded as a list indicating a scikit-learn Pipeline. For example:

 [ ["sklearn.impute", "SimpleImputer"],
   ["sklearn.preprocessing", "StandardScaler"],
   ["sklearn.tree", "DecisionTreeClassifier", {"max_depth": 5}]
  ]

Example specification:

{"filename": "breast_cancer.csv",
 "x_indices": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
 "target_vars": ["target"],
 "group_var": null,
 "n_splits": 100,
 "test_size": 0.2,
 "clf_info": [
 ["sklearn.ensemble", "AdaBoostClassifier"],
 ["sklearn.tree", "DecisionTreeClassifier", {"max_depth": 5}],
 ["sklearn.neural_network", "MLPClassifier", {"alpha": 1, "max_iter": 1000}],
 ["sklearn.svm", "SVC", {"probability": true},
  [{"kernel": ["rbf", "linear"], "C": [1, 10, 100, 1000]}]],
 ["sklearn.neighbors", "KNeighborsClassifier", {},
  [{"n_neighbors": [3, 5, 7, 9, 11, 13, 15, 17, 19],
    "weights": ["uniform", "distance"]}]],
 [ ["sklearn.impute", "SimpleImputer"],
   ["sklearn.preprocessing", "StandardScaler"],
   ["sklearn.tree", "DecisionTreeClassifier", {"max_depth": 5}]
  ]
 ],
 "permute": [true, false],
 "gen_shap": true,
 "nsamples": 100,
 "l1_reg": "aic",
 "plot_top_n_shap": 16,
 "metrics": ["roc_auc_score"]
 }

Output:

The workflow will output:

• results-{timestamp}.pkl containing 1 list per model used. For example, if assigned to variable results, it is accessed through results[0] to results[N] (e.g., if permute: [true,false] then it will output the model trained on permuted labels first results[0] and the model trained on the labels second results[1]. If there is an additional model, these will be accesed through results[2] and results[3]). Each model contains:
  • dict accesed through results[0][0] with model information: {'ml_wf.clf_info': ['sklearn.neural_network', 'MLPClassifier', {'alpha': 1, 'max_iter': 1000}], 'ml_wf.permute': False}
  • pydra Result obj accesed through results[0][1] with attribute output which itself has attributes:
    • feature_names: from the columns of the data csv. And the following attributes organized in N lists for N bootstrapping samples:
    • output: N lists, each one with two lists for true and predicted labels.
    • score: N lists each one containing M different metric scores.
    • shaps: N lists each one with a list of shape (P,F) where P is the amount of predictions and F the different SHAP values for each feature. shaps is empty if gen_shap is set to false or if permute is set to true.
    • model: A pickled version of the model trained on all the input data. One can use this model to test on new data that has the exact same input shape and features as the trained model. For example:

    import pickle as pk
    import numpy as np
    with open("results-20201208T010313.229190.pkl", "rb") as fp:
        data = pk.load(fp)
    trained_model = data[0][1].output.model
    trained_model.predict(np.random.rand(1, 30))
    

    Please check the value of data[N][0] to ensure that you are not using a permuted model.
• One figure per metric with performance distribution across splits (with or without null distribution trained on permuted labels)
• One figure per any metric with the word score in it reporting the results of a Wilcoxon signed rank test. The figure reports one-sided stats values as the color of each cell and the corresponding -log10(pvalue) as the annotation. Higher numbers indicate stronger effect (color) and lower p-values (annotation). The actual numeric values are stored in a correspondingly named pkl file.
• shap-{timestamp} dir
  • SHAP values are computed for each prediction in each split's test set (e.g., 30 bootstrapping splits with 100 prediction will create (30,100) array). The mean is taken across predictions for each split (e.g., resulting in a (64,30) array for 64 features and 30 bootstrapping samples).
  • For binary classification, a more accurate display of feature importance obtained by splitting predictions into TP, TN, FP, and FN, which in turn can allow for error auditing (i.e., what a model pays attention to when making incorrect/false predictions)
    • quadrant_indexes.pkl: The TP, TN, FP, FN indexes are saved in as a dict with one key per model (permuted models without SHAP values will be skipped automatically), and each key values being a bootstrapping split.
    • summary_values_shap_{model_name}_{prediction_type}.csv contains all SHAP values and summary statistics ranked by the mean SHAP value across bootstrapping splits. A sample_n column can be empty or NaN if this split did not have the type of prediction in the filename (e.g., you may not have FNs or FPs in a given split with high performance).
    • summary_shap_{model_name}_{plot_top_n_shap}.png contains SHAP value summary statistics for all features (set to 1.0) or only the top N most important features for better visualization.

Debugging

You will need to understand a bit of pydra to know how to debug this application for now. If the process crashes, the easiest way to restart is to remove the cache-wf folder first. However, if you are rerunning, you could also remove any .lock file in the cache-wfdirectory.

Developer installation

Install repo in developer mode:

git clone https://github.com/nipype/pydra-ml.git
cd pydra-ml
pip install -e .[dev]

It is also useful to install pre-commit, which takes care of styling when committing code. When pre-commit is used you may have to run git commit twice, since pre-commit may make additional changes to your code for styling and will not commit these changes by default:

pip install pre-commit
pre-commit install

Project structure

• tasks.py contain the Python functions.
• classifier.py contains the Pydra workflow and the annotated tasks.
• report.py contains report generation code.