tensorflow

New in version 1.2.

General Idea

Ibex wraps tensorflow.contrib.keras.wrappers.scikit_learn, which has only two scikit-learn type estimators: tensorflow.contrib.keras.wrappers.scikit_learn.KerasClassifier and tensorflow.contrib.keras.wrappers.scikit_learn.KerasRegressor), with a number of changes to make them more scikit-learn compliant (see Differences From tensorflow.contrib.keras.wrappers.scikit_learn).

Note

Ibex does not require the installation of tensorflow. If tensorflow is not installed on the system, though, then this module will not be available.

Tip

Ibex does not modify the code of tensorflow in any way. It is absolutely possibly to import and use both tensorflow and ibex.tensorflow simultaneously.

See also

Differences From tensorflow.contrib.keras.wrappers.scikit_learn

ibex.tensorflow.contrib.keras.wrappers.scikit_learn differs from the estimators of tensorflow.contrib.keras.wrappers.scikit_learn, which it wraps, in three ways:

  1. In ibex.tensorflow.contrib.keras.wrappers.scikit_learn, KerasClassifier subclasses sklearn.base.ClassifierMixin, and KerasClassifier subclasses sklearn.base.ClassifierMixin.

  2. In tensorflow.contrib.keras.wrappers.scikit_learn, KerasClassifier and KerasRegressor each have a fit method which returns a tensorflow.keras.History object. E.g., starting with

    >>> import pandas as pd
>>> import numpy as np
>>> from ibex.sklearn import datasets
>>> from ibex.sklearn.linear_model import LinearRegression as PdLinearRegression
>>> import tensorflow
...
...
>>> iris = datasets.load_iris()
>>> features = iris['feature_names']
>>> iris = pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=features+['class'])
...
>>> def build_regressor_nn():
...     model = tensorflow.contrib.keras.models.Sequential()
...     model.add(
...         tensorflow.contrib.keras.layers.Dense(20, input_dim=4, activation='relu'))
...     model.add(
...         tensorflow.contrib.keras.layers.Dense(1))
...     model.compile(loss='mean_squared_error', optimizer='adagrad')
...     return model
...
>>> prd = tensorflow.contrib.keras.wrappers.scikit_learn.KerasRegressor(
...     build_fn=build_regressor_nn,
...     verbose=0)

    Then

    >>> prd.fit(iris[features].values, iris['class'].values)
<tensorflow.contrib.keras.python.keras.callbacks.History object at ...>

    This differs from the usual convention in sklearn:

    >>> prd.fit(iris[features].values, iris['class'].values).predict(iris[features].values)
Traceback (most recent call last):
...
AttributeError: 'History' object has no attribute 'predict'

    This differs from the usual convention in sklearn of allowing chained methods:

    Conversely, in ibex.tensorflow.contrib.keras.wrappers.scikit_learn, history_ is an attribute of an estimator following fit:

    >>> import ibex
>>> prd = ibex.tensorflow.contrib.keras.wrappers.scikit_learn.KerasRegressor(
...     build_fn=build_regressor_nn,
...     verbose=0)
>>> prd.fit(iris[features], iris['class'])
Adapter[KerasRegressor](...build_fn=<function build_regressor_nn at ...)
>>> prd.history_
<tensorflow.contrib.keras.python.keras.callbacks.History object at ...>
>>> prd.fit(iris[features], iris['class']).predict(iris[features])
0      ...
1      ...
2      ...
3      ...
4      ...

  3. In ibex.tensorflow.contrib.keras.wrappers.scikit_learn, KerasClassifier takes a one-hot encoding of the dependent variable. For example, using the above dataset, if we have

    >>> iris['class'].head()
        0    0.0
        1    0.0
        2    0.0
        3    0.0
        4    0.0
        Name: class, dtype: float64

    then fit needs to be used on something like

    >>> pd.get_dummies(iris['class']).head()
                    0.0  1.0  2.0
        0    1...   0...    0...
        1    1...   0...    0...
        2    1...   0...    0...
        3    1...   0...    0...
        4    1...   0...    0...

    which is nonstandard.

    Conversely, in ibex.tensorflow.contrib.keras.wrappers.scikit_learn, the dependent variable is a :clas:`pandas.Series` (see ee Tensorflow/Keras Classification In The Iris Dataset in Examples).