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        "author": "Brendan Herger",
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        "description": "# keras-pandas\n\n[![CircleCI](https://circleci.com/gh/bjherger/keras-pandas.svg?style=svg)](https://circleci.com/gh/bjherger/keras-pandas)\n[![Documentation Status](https://readthedocs.org/projects/keras-pandas/badge/?version=latest)](https://keras-pandas.readthedocs.io/en/latest/?badge=latest)\n\n**tl;dr:** keras-pandas allows users to rapidly build and iterate on deep learning models. \n\nGetting data formatted and into keras can be tedious, time consuming, and difficult, whether your a veteran or new to \nKeras. `keras-pandas` overcomes these issues by (automatically) providing:\n\n - A cleaned, transformed and correctly formatted `X` and `y` (good for keras, sklearn or any other ML platform)\n - An 'input nub', without the hassle of worrying about input shapes or data types\n - An output layer, correctly formatted for the kind of response variable provided\n\nWith these resources, it's possible to rapidly build and iterate on deep learning models, and focus on the parts of \nmodeling that you enjoy!\n\nFor more info, check out the:\n\n - [Code](https://github.com/bjherger/keras-pandas)\n - [Documentation](http://keras-pandas.readthedocs.io/en/latest/intro.html)\n - [Issue tracker](https://github.com/bjherger/keras-pandas/issues)\n - [Author's website](https://www.hergertarian.com/)\n - [PyPi](https://pypi.org/project/keras-pandas/)\n - [CI/CD](https://travis-ci.org/bjherger/keras-pandas/builds)\n\n## Quick Start\n\nLet's build a model with the [lending club data set](https://www.lendingclub.com/info/download-data.action). This data set is \nparticularly fun because this data set contains a mix of text, categorical and numerical data types, and features a \nlot of null values. \n\n```python\nfrom keras import Model\nfrom keras_pandas import lib\nfrom keras_pandas.Automater import Automater\nfrom sklearn.model_selection import train_test_split\n\n# Load data\nobservations = lib.load_lending_club()\n\n# Train /test split\ntrain_observations, test_observations = train_test_split(observations)\ntrain_observations = train_observations.copy()\ntest_observations = test_observations.copy()\n\n# List out variable types\n\ndata_type_dict = {'numerical': ['loan_amnt', 'annual_inc', 'open_acc', 'dti', 'delinq_2yrs',\n                                'inq_last_6mths', 'mths_since_last_delinq', 'pub_rec', 'revol_bal',\n                                'revol_util',\n                                'total_acc', 'pub_rec_bankruptcies'],\n                  'categorical': ['term', 'grade', 'emp_length', 'home_ownership', 'loan_status', 'addr_state',\n                                  'application_type', 'disbursement_method'],\n                  'text': ['desc', 'purpose', 'title']}\noutput_var = 'loan_status'\n\n# Create and fit Automater\nauto = Automater(data_type_dict=data_type_dict, output_var=output_var)\nauto.fit(train_observations)\n\n# Transform data\ntrain_X, train_y = auto.fit_transform(train_observations)\ntest_X, test_y = auto.transform(test_observations)\n\n# Create and fit keras (deep learning) model.\n\nx = auto.input_nub\nx = auto.output_nub(x)\n\nmodel = Model(inputs=auto.input_layers, outputs=x)\nmodel.compile(optimizer='adam', loss=auto.suggest_loss())\n```\n\nAnd that's it! In a couple of lines, we've created a model that accepts a few dozen variables, and can create a world\n class deep learning model\n\n## Usage\n\n### Installation\n\nYou can install `keras-pandas` with `pip`:\n\n```bash\npip install -U keras-pandas\n```\n\n### Creating an Automater\n\nThe `Automater` object is the central object in `keras-pandas`. It accepts a dictionary of the format `{'datatype': \n['var1', var2']}`\n\nFor example we could create an automater using the built in `numerical`, `categorical`, and `text` datatypes, by \ncalling: \n\n```python\n# List out variable types\ndata_type_dict = {'numerical': ['loan_amnt', 'annual_inc', 'open_acc', 'dti', 'delinq_2yrs',\n                                'inq_last_6mths', 'mths_since_last_delinq', 'pub_rec', 'revol_bal',\n                                'revol_util',\n                                'total_acc', 'pub_rec_bankruptcies'],\n                  'categorical': ['term', 'grade', 'emp_length', 'home_ownership', 'loan_status', 'addr_state',\n                                  'application_type', 'disbursement_method'],\n                  'text': ['desc', 'purpose', 'title']}\noutput_var = 'loan_status'\n\n# Create and fit Automater\nauto = Automater(data_type_dict=data_type_dict, output_var=output_var)\n```\n\nAs a side note, the response variable must be in one of the variable type lists (e.g. `loan_status` is in `categorical_vars`)\n\n#### One variable type\n\nIf you only have one variable type, only use one variable type!\n\n```python\n# List out variable types\ndata_type_dict = {'categorical': ['term', 'grade', 'emp_length', 'home_ownership', 'loan_status', 'addr_state',\n                                  'application_type', 'disbursement_method']}\noutput_var = 'loan_status'\n\n# Create and fit Automater\nauto = Automater(data_type_dict=data_type_dict, output_var=output_var)\n```\n\n#### Multiple variable types\n\nIf you have multiple variable types, feel free to use all of them! Built in datatypes are listed in `Automater.datatype_handlers`\n\n```python\n# List out variable types\ndata_type_dict = {'numerical': ['loan_amnt', 'annual_inc', 'open_acc', 'dti', 'delinq_2yrs',\n                                'inq_last_6mths', 'mths_since_last_delinq', 'pub_rec', 'revol_bal',\n                                'revol_util',\n                                'total_acc', 'pub_rec_bankruptcies'],\n                  'categorical': ['term', 'grade', 'emp_length', 'home_ownership', 'loan_status', 'addr_state',\n                                  'application_type', 'disbursement_method'],\n                  'text': ['desc', 'purpose', 'title']}\noutput_var = 'loan_status'\n\n# Create and fit Automater\nauto = Automater(data_type_dict=data_type_dict, output_var=output_var)\n```\n\n#### Custom datatypes\n\nIf there's a specific datatype you'd like to use that's not built in (such as images, videos, or geospatial), you can \ninclude it by using `Automater`'s `datatype_handlers` parameter. \n\nA template datatype can be found in `keras_pandas/data_types/Abstract.py`. Filling out this template will yield a new\n datatype handler. If you're happy with your work and want to share your new datatype handler, create a PR (and check\n  out `contributing.md`)\n\n#### No `output_var`\n\nIf your model doesn't need a response var, or your use case doesn't use `keras-pandas`'s output functionality, you \ncan skip the `output_var` by setting it to None\n\n```python\n# List out variable types\ndata_type_dict = {'categorical': ['term', 'grade', 'emp_length', 'home_ownership', 'loan_status', 'addr_state',\n                                  'application_type', 'disbursement_method']}\noutput_var = None\n\n# Create and fit Automater\nauto = Automater(data_type_dict=data_type_dict, output_var=output_var)\n```\n\n### Fitting the Automater\n\nBefore use, the `Automator` must be fit. The `fit()` method accepts a pandas DataFrame, which must contain all of the \ncolumns listed during initialization.\n\n```python\nauto.fit(observations)\n```\n\n### Transforming data\n\nNow, we can use our `Automater` to transform the dataset, from a pandas DataFrame to numpy objects properly formatted\nfor Keras's input and output layers. \n\n```python\nX, y = auto.transform(observations, df_out=False)\n```\n\nThis will return two objects:\n\n  - `X`: An array, containing numpy object for each Keras input. This is generally one Keras input for each user \n  input variable. \n  - `y`: A numpy object, containing the response variable (if one was provided) \n\n### Using input / output nubs\n\nSetting up correctly formatted, heuristically 'good' input and output layers is often\n\n - Tedious\n - Time consuming\n - Difficult for those new to Keras\n\nWith this in mind, `keras-pandas` provides correctly formatted input and output 'nubs'. \n\nThe input nub is correctly formatted to accept the output from `auto.transform()`. It contains one Keras Input layer \nfor each generated input, may contain addition layers, and has all input piplines joined with a `Concatenate` layer. \n\nThe output layer is correctly formatted to accept the response variable numpy object.  \n\n\n## Contact\n\nHey, I'm Brendan Herger, avaiable at [https://www.hergertarian.com/](https://www.hergertarian.com/). Please feel free \nto reach out to me at `13herger <at> gmail <dot> com`\n\nI enjoy bridging the gap between data science and engineering, to build and \ndeploy data products. I'm not currently pursuing contract work. \n\nI've enjoyed building a unique combination of machine learning, deep learning, and software engineering skills. In my \nprevious work at Capital One and startups, I've has built authorization fraud, insider threat, and legal discovery \nautomation platforms. In each of these cases I've lead a team of data scientists and data engineers to enable and \nelevate our client's business workflow (and capture some amazing data).\n\nWhen I'm not knee deep in a code base, I can be found traveling, sharing my collection of Japanese teas, and playing \nboard games with my partner in Seattle. \n\n## Changelog\n\n - PR title (#PR number, or #Issue if no PR)\n - There's nothing here! (yet)\n\n### Development\n\n - There's nothing here! (yet)\n\n### 3.1.0\n\n - Add boolean datatype (#104)\n - Added Contributing.md section for new datatypes (#101)\n - Added datatypes to docs in index.rst (#101)\n - Modified documentation to automatically generate API docs (#101)\n\n\n### 3.0.1\n\n - Changing CI to Circleci (#100)\n - Adding datatypes to CONTRIBUTING.md, adding CONTRIBUTING.md to docs (#96)\n - Adding docs badge (#95)\n - Adding support for unusual variable names / format keras names to be valid in name scope (#92)\n - Adding examples (#93)\n - Upgraded `requests` library to `requests==2.20.1`, based on security concern (#94)\n\n\n### 3.0.0\n\nBrand new release, with\n\nAdded\n\n - New `Datatype` interface, with easier to understand pipelines for each datatype\n   - All existing datatypes (`Numerical`, `Categorical`, `Text` & `TimeSeries`) re-implmented in this new format\n   - Support for custom data types generated by users\n   - Duck-typing helper method (`keras_pandas/lib.check_valid_datatype()`) to confirm that a datatype has valid \n   signature\n - New testing, streamlined and standardized\n - Support for transforming unseen categorical levels, via the `UNK` token (experimental)\n\nModified\n\n - Updated `Automater` interface, which accepts a dictionary of data types\n - Heavily updated README\n - More consistent logging and data formatting for sample data sets\n\nRemoved\n\n - Removed examples, will be re-implemented in future release\n - All existing unittests\n - Bulk of new datatypes in `contributing.md`, will be re-added in future release\n\n### 2.2.0\n\n - Add timeseries support (#78)\n - Add timeseries examples (#79)\n\n### 2.1.0\n\n - Boolean support deprecated. Boolean (bool) data type can be treated as a special case of categorical data types\n\n### 2.0.2\n\n - Remove a lot of the unnecessary dependencies (#75)\n - Update dependencies to contemporary versions (#74)\n\n### 2.0.1\n\n - Fix issue w/ PyPi conflict\n\n### 2.0.0\n\n - Adding CI/CD and PyPi links, and updating contact section w/ about the author (#70)\n - Major rewrite / update of examples (#72)\n   - Fixes bug in embedding transformer. Embeddings will now be at least length 1. \n   - Add functionality to check if `resp_var` is in the list of user provided variables\n   - Added better null filling w/ `CategoricalImputer`\n   - Added filling unseen values w/ `CategoricalImputer`\n   - Converted default transformer pipeline to use `copy.deepcopy` instead of `copy.copy`. This was a hotfix for a \n   previously unknown issue. \n   - Standardizing setting logging level, only in test base class and examples (when `__main__`)\n\n\n### 1.3.5\n\n - Adding regression example w/ inverse_transformation (#64)\n - Fixing issue where web socket connections were being opened needlessly (#65)\n\n### 1.3.4\n\n - Adding `Manifest.in`, with including files references in `setup.py` (#54) \n\n### 1.3.2\n\n - Fixed poorly written text embedding index unit test (#52)\n - Added license (#49)\n\n### Earlier\n\n - Lots of things happened. Break things and move fast \n\n\n",
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