Adding auto readme progress

README.md

@@ -1,24 +1,114 @@
 # keras-pandas
 
-TODO Project description
+keras-pandas allows us to rapidly get a baseline model an iterate, by (automatically) providing:
+
+ - A cleaned, transformed and correctly formatted `X` and `y`
+ - A smart baseline 'input nub', without the hassle of worrying about input shapes or data types
+ - A smart baseline output layer
 
 ## Quick Start
 
-TODO
+Let's say we're looking at the [titanic data set](https://www.kaggle.com/c/titanic/data), and wanted to train a model. 
+This data set is particularly fun because this data set contains a mix of variables types, and features a lot of null 
+values. 
+
+We could install `keras-pandas`
+
+```bash
+pip install -U keras-pandas
+```
+
+And then run the following snippet to create and train a model:
+
+```python
+# Import a few things
+from keras import Model
+from keras.layers import Dense
+
+from keras_pandas.Automater import Automater
+from keras_pandas.lib import load_titanic
+
+# Load the data set
+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
 
-TODO
+You can install `keras-pandas` with `pip`:
 
-### Creating a mapper
+```bash
+pip install -U keras-pandas
+```
 
-TODO
+### Creating an Automater
 
-### Fitting the mapper
+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).
 
-TODO
+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!
+```python
+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!
+
+```python
+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
+
+```python
+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 upon initialization.
+
+```python
+auto.fit(observations)
+```
 
 ### Transforming data
 

examples/titanic.py

@@ -4,7 +4,7 @@
 from keras.layers import Dense
 
 from keras_pandas.Automater import Automater
-from keras_pandas.lib import load_mushrooms, load_titanic
+from keras_pandas.lib import load_titanic
 
 
 def main():