tpot-master-source

Master status: [](https://travis-ci.com/EpistasisLab/tpot) [](https://ci.appveyor.com/project/weixuanfu/tpot?branch=master) [](https://coveralls.io/github/EpistasisLab/tpot?branch=master) Development status: [](https://travis-ci.com/EpistasisLab/tpot/branches) [](https://ci.appveyor.com/project/weixuanfu/tpot?branch=development) [](https://coveralls.io/github/EpistasisLab/tpot?branch=development) Package information: [](https://www.python.org/downloads/release/python-370/) [](http://www.gnu.org/licenses/lgpl-3.0) [](https://badge.fury.io/py/TPOT) <p align="center"> <img src="https://raw.githubusercontent.com/EpistasisLab/tpot/master/images/tpot-logo.jpg" width=300 /> </p> **TPOT** stands for **T**ree-based **P**ipeline **O**ptimization **T**ool. Consider TPOT your **Data Science Assistant**. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.  TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.  <p align="center"><strong>An example Machine Learning pipeline</strong></p> Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.  TPOT is built on top of scikit-learn, so all of the code it generates should look familiar... if you're familiar with scikit-learn, anyway. **TPOT is still under active development** and we encourage you to check back on this repository regularly for updates. For further information about TPOT, please see the [project documentation](http://epistasislab.github.io/tpot/). ## License Please see the [repository license](https://github.com/EpistasisLab/tpot/blob/master/LICENSE) for the licensing and usage information for TPOT. Generally, we have licensed TPOT to make it as widely usable as possible. ## Installation We maintain the [TPOT installation instructions](http://epistasislab.github.io/tpot/installing/) in the documentation. TPOT requires a working installation of Python. ## Usage TPOT can be used [on the command line](http://epistasislab.github.io/tpot/using/#tpot-on-the-command-line) or [with Python code](http://epistasislab.github.io/tpot/using/#tpot-with-code). Click on the corresponding links to find more information on TPOT usage in the documentation. ## Examples ### Classification Below is a minimal working example with the optical recognition of handwritten digits dataset. ```python from tpot import TPOTClassifier from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split digits = load_digits() X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, train_size=0.75, test_size=0.25, random_state=42) tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2, random_state=42) tpot.fit(X_train, y_train) print(tpot.score(X_test, y_test)) tpot.export('tpot_digits_pipeline.py') ``` Running this code should discover a pipeline that achieves about 98% testing accuracy, and the corresponding Python code should be exported to the `tpot_digits_pipeline.py` file and look similar to the following: ```python import numpy as np import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline, make_union from sklearn.preprocessing import PolynomialFeatures from tpot.builtins import StackingEstimator from tpot.export_utils import set_param_recursive # NOTE: Make sure that the outcome column is labeled 'target' in the data file tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64) features = tpot_data.drop('target', axis=1) training_features, testing_features, training_target, testing_target = \ train_test_split(features, tpot_data['target'], random_state=42) # Average CV score on the training set was: 0.9799428471757372 exported_pipeline = make_pipeline( PolynomialFeatures(degree=2, include_bias=False, interaction_only=False), StackingEstimator(estimator=LogisticRegression(C=0.1, dual=False, penalty="l1")), RandomForestClassifier(bootstrap=True, criterion="entropy", max_features=0.35000000000000003, min_samples_leaf=20, min_samples_split=19, n_estimators=100) ) # Fix random state for all the steps in exported pipeline set_param_recursive(exported_pipeline.steps, 'random_state', 42) exported_pipeline.fit(training_features, training_target) results = exported_pipeline.predict(testing_features) ``` ### Regression Similarly, TPOT can optimize pipelines for regression problems. Below is a minimal working example with the practice Boston housing prices data set. ```python from tpot import TPOTRegressor from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split housing = load_boston() X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, train_size=0.75, test_size=0.25, random_state=42) tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2, random_state=42) tpot.fit(X_train, y_train) print(tpot.score(X_test, y_test)) tpot.export('tpot_boston_pipeline.py') ``` which should result in a pipeline that achieves about 12.77 mean squared error (MSE), and the Python code in `tpot_boston_pipeline.py` should look similar to: ```python import numpy as np import pandas as pd from sklearn.ensemble import ExtraTreesRegressor from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline from sklearn.preprocessing import PolynomialFeatures from tpot.export_utils import set_param_recursive # NOTE: Make sure that the outcome column is labeled 'target' in the data file tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64) features = tpot_data.drop('target', axis=1) training_features, testing_features, training_target, testing_target = \ train_test_split(features, tpot_data['target'], random_state=42) # Average CV score on the training set was: -10.812040755234403 exported_pipeline = make_pipeline( PolynomialFeatures(degree=2, include_bias=False, interaction_only=False), ExtraTreesRegressor(bootstrap=False, max_features=0.5, min_samples_leaf=2, min_samples_split=3, n_estimators=100) ) # Fix random state for all the steps in exported pipeline set_param_recursive(exported_pipeline.steps, 'random_state', 42) exported_pipeline.fit(training_features, training_target) results = exported_pipeli