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Easy and rapid deep learning

Project description

keras-pandas

tl;dr: keras-pandas allows users to rapidly build and iterate on deep learning models.

Getting data formatted and into keras can be tedious, time consuming, and difficult, whether your a veteran or new to Keras. keras-pandas overcomes these issues by (automatically) providing:

  • A cleaned, transformed and correctly formatted X and y (good for keras, sklearn or any other ML platform)
  • An 'input nub', without the hassle of worrying about input shapes or data types
  • An output layer, correctly formatted for the kind of response variable provided

With these resources, it's possible to rapidly build and iterate on deep learning models, and focus on the parts of modeling that you enjoy!

Quick Start

Let's build a model with the titanic data set. This data set is particularly fun because this data set contains a mix of categorical and numerical data types, and features a lot of null values.

We'll keras-pandas

pip install -U keras-pandas

And then run the following snippet to create and train a model:

from keras import Model
from keras.layers import Dense

from keras_pandas.Automater import Automater
from keras_pandas.lib import load_titanic

# Load the titanic data set, as a pandas dataframe
observations = load_titanic()

# Transform the data set, using keras_pandas
categorical_vars = ['pclass', 'sex', 'survived']
numerical_vars = ['age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare']

auto = Automater(categorical_vars=categorical_vars, numerical_vars=numerical_vars, response_var='survived')
X, y = auto.fit_transform(observations)

# Create model, using the auto-generated input and output layers
x = auto.input_nub
x = Dense(30)(x)
x = auto.output_nub(x)

model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train model
model.fit(X, y, epochs=10, validation_split=.5)

Usage

Installation

You can install keras-pandas with pip:

pip install -U keras-pandas

Creating an Automater

The core feature of keras-pandas is the Automater, which accepts lists of variable types (all optional), and a response variable (optional, for supervised problems). Together, all of these variables are the user_input_variables, which may be different than the variables fed into Keras.

As a side note, the response variable must be in one of the variable type lists (e.g. survived is in categorical_vars)

One variable type

If you only have one variable type, only use that variable type!

categorical_vars = ['pclass', 'sex', 'survived']
auto = Automater(categorical_vars=categorical_vars, response_var='survived')

Multiple variable types

If you have multiple variable types, throw them all in!

categorical_vars = ['pclass', 'sex', 'survived']
numerical_vars = ['age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare']

auto = Automater(categorical_vars=categorical_vars, numerical_vars=numerical_vars, response_var='survived')

No response_var

If all variables are always available, and / or your problems space doesn't have a single response variable, you can omit the response variable.

categorical_vars = ['pclass', 'sex', 'survived']
numerical_vars = ['age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare']

auto = Automater(categorical_vars=categorical_vars, numerical_vars=numerical_vars)

In this case, an output nub will not be auto-generated

Fitting the Automater

Before use, the Automator must be fit. The fit() method accepts a pandas DataFrame, which must contain all of the columns listed during initialization.

auto.fit(observations)

Transforming data

Now, we can use our Automater to transform the dataset, from a pandas DataFrame to numpy objects properly formatted for Keras's input and output layers.

X, y = auto.transform(observations, df_out=False)

This will return two objects:

  • X: An array, containing numpy object for each Keras input. This is generally one Keras input for each user input variable.
  • y: A numpy object, containing the response variable (if one was provided)

Using input / output nubs

Setting up correctly formatted, heuristically 'good' input and output layers is often

  • Tedious
  • Time consuming
  • Difficult for those new to Keras

With this in mind, keras-pandas provides correctly formatted input and output 'nubs'.

The input nub is correctly formatted to accept the output from auto.transform(). It contains one Keras Input layer for each generated input, may contain addition layers, and has all input piplines joined with a Concatenate layer.

The output layer is correctly formatted to accept the response variable numpy object.

Contributing

If you're interested in helping out, all open tasks are listed the GitHub Issues tab. The issues tagged with first issue are a good place to start if your new to the project or new to open source projects.

If you're interested in a new major feature, please feel free to reach out to me

Bug reports

The best bug reports are Pull Requests. The second best bug reports are new issues on this repo.

Test

This framework uses unittest for unit testing. Tests can be run by calling:

cd tests/

python -m unittest discover -s . -t .

Style guide

This codebase should follow Google's Python Style Guide.

Generating documentation

This codebase uses sphinx's autodoc feature. To generate new documentation, to reflect updated documentation, run:

cd docs

make html

Contact

Hey, I'm Brendan Herger, avaiable at https://www.hergertarian.com/. Please feel free to reach out to me at 13herger <at> gmail <dot> com

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