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    "info": {
        "author": "Daoud Clarke",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5"
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        "description": "PyPastry - the opinionated machine learning experimentation framework\n=====================================================================\n\nPyPastry is a framework for developers and data scientists to run\nmachine learning experiments. We enable you to:\n\n - Iterate quickly. The more experiments you do, the more likely you\n   are to find something that works well.\n - Experiment correctly and consistently. Anything else is not really\n   an experiment, is it?\n - Make experiments reproducible. That means keeping track of your\n   code state and results.\n - Experiment locally. None of that Spark rubbish.\n - Use standard tools. Everything is based on Scikit-learn, Pandas and Git.\n\nQuick start\n-----------\n\nPyPastry requires python 3.5 or greater.\n\n    > git clone https://github.com/datapastry/pypastry.git\n    > pip install -e pypastry\n\t> pastry init pastry-test\n    > cd pastry-test\n    > pastry run -m \"First experiment\"\n    Got dataset with 10 rows\n       Git hash Dataset hash            Run start                   Model          Score Duration (s)\n    0  aa87ce62     71e8f4fd  2019-08-28 06:39:07  DecisionTreeClassifier  0.933 \u00b1 0.067         0.03\n\nThe command `pastry init` creates a file called `pie.py` in the `pastry-test` directory. If you open\nthat up, you should see some code. The important bit is:\n\n    def get_experiment():\n        dataset = pd.DataFrame({\n            'feature': [1, 0, 1, 1, 0, 0, 1, 1, 0, 1],\n            'class': [True, False, True, True, False, False, True, True, False, False],\n        })\n        predictor = DecisionTreeClassifier()\n        cross_validator = StratifiedKFold(n_splits=5)\n        scorer = make_scorer(f1_score)\n        label_column = 'class'\n        return Experiment(dataset, label_column, predictor, cross_validator, scorer)\n\nThis returns an `Experiment` instance that specifies how the experiment should be run. An experiment\nconsists of:\n - `dataset`: a Pandas `DataFrame` where each row is an instance to be used in the experiment.\n - `label_column`: the name of the column in `dataset` that contains the label we wish to predict.\n - `predictor`: a Scikit-learn predictor, e.g. a classifier, regressor or `Pipeline` object.\n - `cross_validator`: a Scikit-learn cross validator that specifies how the data should be split\n   up when running the experiment.\n - `scorer` a Scikit-learn scorer that will be used as an indication of how well the classifier has\n   learnt to generate predictions.\n\nWhen you type `pastry run`, PyPastry does this:\n - Splits `dataset` into one or more train and test sets.\n - For each train and test set, it trains the `predictor` on the train set and generate predictions\n   on the test set, and computes the score on the test set using the `scorer`.\n - Generates a results file in JSON format and stores it in a folder called `results`\n - Adds the new file and any modified files to git staging and runs a git commit.\n - Outputs the results of the experiment.\n\nThe results includes:\n - Git hash: the commit identifier from git that allows you to return to this version of the code\n   at any later point in time.\n - Dataset hash: a hash generated from the dataset that will change if the dataset changes.\n - Run start: the time that the experiment run started\n - Model: the name of the `predictor` class used\n - Score: the mean \u00b1 the standard error in the mean, computed over the different folds generated\n   by the `cross_validator`.\n - Duration: how long the experiment took to run, in seconds.\n\nContributing\n------------\n\nPyPastry is at an early stage so there's plenty to do and we'd love to have your contribution.\n\nCheck out the issues for a list of things that need doing and post a comment if you'd like to take\nsomething on.\n\nIf you have an idea for something you'd like to do, create an issue.\n\nThanks for using PyPastry!\n\n\n",
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        "name": "pypastry",
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