Initial commit
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commit
71a065e52c
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### IntelliJ files
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.idea/
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### VirtualEnv template
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# Virtualenv
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||||
# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
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||||
.Python
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||||
[Bb]in
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||||
[Ii]nclude
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||||
[Ll]ib
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||||
[Ll]ib64
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||||
[Ll]ocal
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||||
[Ss]cripts
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||||
pyvenv.cfg
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||||
.venv
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||||
pip-selfcheck.json
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||||
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||||
### Python template
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||||
# Byte-compiled / optimized / DLL files
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||||
__pycache__/
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*.py[cod]
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*$py.class
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||||
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||||
# C extensions
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||||
*.so
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||||
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||||
# Distribution / packaging
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||||
.Python
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||||
build/
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||||
develop-eggs/
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dist/
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||||
downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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||||
sdist/
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var/
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||||
wheels/
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||||
share/python-wheels/
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||||
*.egg-info/
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||||
.installed.cfg
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||||
*.egg
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||||
MANIFEST
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||||
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||||
# PyInstaller
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||||
# Usually these files are written by a python script from a template
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||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
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||||
*.manifest
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||||
*.spec
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||||
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||||
# Installer logs
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||||
pip-log.txt
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pip-delete-this-directory.txt
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||||
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||||
# Unit test / coverage reports
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||||
htmlcov/
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.tox/
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.nox/
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.coverage
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||||
.coverage.*
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.cache
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||||
nosetests.xml
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||||
coverage.xml
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||||
*.cover
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*.py,cover
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||||
.hypothesis/
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||||
.pytest_cache/
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||||
cover/
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||||
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||||
# Translations
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||||
*.mo
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||||
*.pot
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||||
|
||||
# Django stuff:
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||||
*.log
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||||
local_settings.py
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||||
db.sqlite3
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||||
db.sqlite3-journal
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||||
|
||||
# Flask stuff:
|
||||
instance/
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||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
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||||
|
||||
# Sphinx documentation
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||||
docs/_build/
|
||||
|
||||
# PyBuilder
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||||
.pybuilder/
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||||
target/
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||||
|
||||
# Jupyter Notebook
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||||
.ipynb_checkpoints
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||||
|
||||
# IPython
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||||
profile_default/
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||||
ipython_config.py
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||||
|
||||
# pyenv
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||||
# For a library or package, you might want to ignore these files since the code is
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||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
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||||
|
||||
# pipenv
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||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
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||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
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||||
#Pipfile.lock
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||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
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||||
.venv
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env/
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||||
venv/
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||||
ENV/
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||||
env.bak/
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||||
venv.bak/
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||||
|
||||
# Spyder project settings
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||||
.spyderproject
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||||
.spyproject
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||||
|
||||
# Rope project settings
|
||||
.ropeproject
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||||
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||||
# mkdocs documentation
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||||
/site
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||||
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||||
# mypy
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||||
.mypy_cache/
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||||
.dmypy.json
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||||
dmypy.json
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||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# pytype static type analyzer
|
||||
.pytype/
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||||
|
||||
# Cython debug symbols
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||||
cython_debug/
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||||
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||||
|
|
@ -0,0 +1,16 @@
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|||
# This is a sample Python script.
|
||||
|
||||
# Press Shift+F10 to execute it or replace it with your code.
|
||||
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
|
||||
|
||||
|
||||
def print_hi(name):
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||||
# Use a breakpoint in the code line below to debug your script.
|
||||
print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
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||||
|
||||
|
||||
# Press the green button in the gutter to run the script.
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||||
if __name__ == '__main__':
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||||
print_hi('PyCharm')
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||||
|
||||
# See PyCharm help at https://www.jetbrains.com/help/pycharm/
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||||
|
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@ -0,0 +1,282 @@
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"""
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Linear Regression with Tensorflow following this tutorial: https://www.tensorflow.org/tutorials/keras/regression
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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# Makes numpy stuff easier readable
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np.set_printoptions(precision=3, suppress=True)
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def load_data():
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url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
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column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
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'Acceleration', 'Model Year', 'Origin']
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raw_dataset = pd.read_csv(url, names=column_names,
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na_values='?', comment='\t',
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sep=' ', skipinitialspace=True)
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return raw_dataset
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def plot_loss(history):
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plt.plot(history.history['loss'], label='loss')
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plt.plot(history.history['val_loss'], label='val_loss')
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plt.ylim([0, 10])
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plt.xlabel('Epoch')
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plt.ylabel('Error [MPG]')
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plt.legend()
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plt.grid(True)
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plt.show()
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def plot_horsepower(x, y, train_features, train_labels):
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plt.scatter(train_features['Horsepower'], train_labels, label='Data')
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plt.plot(x, y, color='k', label='Predictions')
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plt.xlabel('Horsepower')
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plt.ylabel('MPG')
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plt.legend()
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plt.show()
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def regression_single_input():
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raw_dataset = load_data()
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dataset = raw_dataset.copy()
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#################################
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### Prepare the training data ###
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#################################
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# Print the end of the dataset
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print(dataset.tail())
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# Check for any undefined values
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print(dataset.isna().sum())
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# Drops unknown values
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dataset = dataset.dropna()
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# Replaces the alphanumerical values with numerical ones. Can be done via keras model, but is overkill for 3 values.
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dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})
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dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')
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# Split into training and test data
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train_dataset = dataset.sample(frac=0.8, random_state=0)
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test_dataset = dataset.drop(train_dataset.index)
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# Print pair plots of the data to see if there are any probable correlations
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sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')
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plt.show()
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# Print statistics about the features
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print(train_dataset.describe().transpose())
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# Separate the label from the features
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train_features = train_dataset.copy()
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test_features = test_dataset.copy()
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train_labels = train_features.pop('MPG')
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test_labels = test_features.pop('MPG')
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###########################
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### Normalize the model ###
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###########################
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# Create the normalization layer
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normalizer = tf.keras.layers.Normalization(axis=-1)
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# Fit the preprocessing layer to the data
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normalizer.adapt(np.array(train_features))
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# See what effect the normalization has
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first = np.array(train_features[:1])
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with np.printoptions(precision=2, suppress=True):
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print('First example:', first)
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print()
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print('Normalized:', normalizer(first).numpy())
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#######################################
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### Start with the regression stuff ###
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#######################################
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# Create Horsepower NP array and normalize it
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horsepower = np.array(train_features['Horsepower'])
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horsepower_normalizer = layers.Normalization(input_shape=[1, ], axis=None)
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horsepower_normalizer.adapt(horsepower)
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# Build the Keras Sequential Model
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# Apply a linear transformation to produce 1 output using a linear layer (layers.Dense)
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horsepower_model = tf.keras.Sequential([
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horsepower_normalizer,
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layers.Dense(units=1)
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])
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print(horsepower_model.summary())
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# Run the model without training
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print(horsepower_model.predict(horsepower[:10]))
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# Configure the training process
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horsepower_model.compile(
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optimizer=tf.optimizers.Adam(learning_rate=0.1),
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loss='mean_absolute_error'
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)
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# Train for 100 epochs
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history = horsepower_model.fit(
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train_features['Horsepower'],
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train_labels,
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epochs=100,
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# Suppress logging.
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verbose=0,
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# Calculate validation results on 20% of the training data.
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validation_split=0.2
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)
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# Show training progress
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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print(hist.tail())
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# Show loss plot
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plot_loss(history)
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# Collect results on test set
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test_results = {}
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test_results['horsepower_model'] = horsepower_model.evaluate(
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test_features['Horsepower'],
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test_labels, verbose=0
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)
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# Plot the regression line
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x = tf.linspace(0.0, 250, 251)
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y = horsepower_model.predict(x)
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plot_horsepower(x, y, train_features, train_labels)
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def regression_multiple_inputs():
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raw_dataset = load_data()
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dataset = raw_dataset.copy()
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#################################
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### Prepare the training data ###
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#################################
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# Print the end of the dataset
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print(dataset.tail())
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# Check for any undefined values
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print(dataset.isna().sum())
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# Drops unknown values
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dataset = dataset.dropna()
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# Replaces the alphanumerical values with numerical ones. Can be done via keras model, but is overkill for 3 values.
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dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})
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dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')
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# Split into training and test data
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train_dataset = dataset.sample(frac=0.8, random_state=0)
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test_dataset = dataset.drop(train_dataset.index)
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# Print pair plots of the data to see if there are any probable correlations
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sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')
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plt.show()
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# Print statistics about the features
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print(train_dataset.describe().transpose())
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# Separate the label from the features
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train_features = train_dataset.copy()
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test_features = test_dataset.copy()
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train_labels = train_features.pop('MPG')
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test_labels = test_features.pop('MPG')
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###########################
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### Normalize the model ###
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###########################
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# Create the normalization layer
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normalizer = tf.keras.layers.Normalization(axis=-1)
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# Fit the preprocessing layer to the data
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normalizer.adapt(np.array(train_features))
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# See what effect the normalization has
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first = np.array(train_features[:1])
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with np.printoptions(precision=2, suppress=True):
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print('First example:', first)
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print()
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print('Normalized:', normalizer(first).numpy())
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#######################################
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### Start with the regression stuff ###
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#######################################
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# Create Horsepower NP array and normalize it
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horsepower = np.array(train_features['Horsepower'])
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horsepower_normalizer = layers.Normalization(input_shape=[1, ], axis=None)
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horsepower_normalizer.adapt(horsepower)
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# Build the Keras Sequential Model
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# Apply a linear transformation to produce 1 output using a linear layer (layers.Dense)
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linear_model = tf.keras.Sequential([
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normalizer,
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layers.Dense(units=1)
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])
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# Run the model without training
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print(linear_model.predict(train_features[:10]))
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# Configure the training process
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linear_model.compile(
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optimizer=tf.optimizers.Adam(learning_rate=0.1),
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loss='mean_absolute_error'
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)
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||||
# Train for 100 epochs
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||||
history = linear_model.fit(
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||||
train_features,
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||||
train_labels,
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||||
epochs=100,
|
||||
# Suppress logging.
|
||||
verbose=0,
|
||||
# Calculate validation results on 20% of the training data.
|
||||
validation_split=0.2
|
||||
)
|
||||
|
||||
# Show training progress
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||||
hist = pd.DataFrame(history.history)
|
||||
hist['epoch'] = history.epoch
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||||
print(hist.tail())
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||||
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||||
# Show loss plot
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||||
plot_loss(history)
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||||
|
||||
# Collect results on test set
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||||
test_results = {}
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||||
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||||
test_results['linear_model'] = linear_model.evaluate(
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test_features, test_labels, verbose=0
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||||
)
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||||
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||||
# Plot the regression line
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||||
# Apparently, this doesnt work for multi inputs because i guess 3D plotting is kinda hart
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#x = tf.linspace(0.0, 250, 251)
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#y = linear_model.predict(x)
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#plot_horsepower(x, y, train_features, train_labels)
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||||
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||||
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||||
if __name__ == '__main__':
|
||||
regression_single_input()
|
||||
regression_multiple_inputs()
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
scikit-learn
|
||||
tensorflow
|
||||
seaborn
|
||||
matplotlib
|
||||
numpy
|
||||
pandas
|
||||
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