Readme File¶
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
andy
(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](https://www.kaggle.com/c/titanic/data. 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¶
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.
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