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code/bonus/README.md
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Sebastian Raschka, 2015
Python Machine Learning - Code Examples
## Bonus Material
A collection of additional notebooks and code examples to clarify and explain concepts based on reader feedback.
- A Basic Pipeline and Grid Search Setup [[GitHub ipynb](./svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
- An Extended Nested Cross-Validation Example [[GitHub ipynb](./nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
- A Simple(r) Barebones Flask Webapp Template [[view directory](./flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
- Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
- Multinomial logistic regression / softmax regression [[GitHub ipynb](./softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
code/bonus/flask_webapp_ex01/README.md
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Sebastian Raschka, 2015
Python Machine Learning - Code Examples (Bonus Material)
# A Simple(r) Barebones Flask Webapp Template
A simple Flask app that calculates the sum of two numbers entered in the respective input fields.
You can run the app locally by executing `python app.py` within this directory.
<hr>
![](./img/img_1.png)
Click [here](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip) to download this example as zip-file.
code/bonus/flask_webapp_ex01/app.py
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from flask import Flask, render_template, request
from wtforms import Form, DecimalField, validators
app = Flask(__name__)
class EntryForm(Form):
x_entry = DecimalField('x:',
places=10,
validators=[validators.NumberRange(-1e10, 1e10)])
y_entry = DecimalField('y:',
places=10,
validators=[validators.NumberRange(-1e10, 1e10)])
@app.route('/')
def index():
form = EntryForm(request.form)
return render_template('entry.html', form=form, z='')
@app.route('/results', methods=['POST'])
def results():
form = EntryForm(request.form)
z = ''
if request.method == 'POST' and form.validate():
x = request.form['x_entry']
y = request.form['y_entry']
z = float(x) + float(y)
return render_template('entry.html', form=form, z=z)
if __name__ == '__main__':
app.run(debug=True)
code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip
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code/bonus/flask_webapp_ex01/static/style.css
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body{
width:600px;
}
#button{
padding-top: 20px;
}
code/bonus/flask_webapp_ex01/templates/_formhelpers.html
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{% macro render_field(field) %}
<dt>{{ field.label }}
<dd>{{ field(**kwargs)|safe }}
{% if field.errors %}
<ul class=errors>
{% for error in field.errors %}
<li>{{ error }}</li>
{% endfor %}
</ul>
{% endif %}
</dd>
{% endmacro %}
code/bonus/flask_webapp_ex01/templates/entry.html
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<!doctype html>
<html>
<head>
<title>Webapp Ex 1</title>
<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
</head>
<body>
{% from "_formhelpers.html" import render_field %}
<form method=post action="/results">
<dl>
{{ render_field(form.x_entry, cols='1', rows='1') }}
{{ render_field(form.y_entry, cols='1', rows='1') }}
</dl>
<div>
<input type=submit value='Submit' name='submit_btn'>
</div>
<dl>
x + y = {{ z }}
</dl>
</form>
</body>
</html>
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code/bonus/logistic_regression.ipynb
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code/bonus/nested_cross_validation.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Sebastian Raschka](http://sebastianraschka.com), 2015\n",
"\n",
"https://github.com/rasbt/python-machine-learning-book"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python Machine Learning - Code Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bonus Material - An Extended Nested Cross-Validation Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For an explanation of nested cross-validation, please see:\n",
" \n",
"- Chapter 6, section \"Algorithm-selection-with-nested-cross-validation\" (open the code example via [nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb#Algorithm-selection-with-nested-cross-validation))\n",
"- FAQ, section: [How do I evaluate a model?](https://github.com/rasbt/python-machine-learning-book/blob/master/faq/evaluate-a-model.md)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"Last updated: 11/30/2015 \n",
"\n",
"CPython 3.5.0\n",
"IPython 4.0.0\n",
"\n",
"numpy 1.10.1\n",
"pandas 0.17.1\n",
"matplotlib 1.5.0\n",
"scikit-learn 0.17\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset and Estimator Setup"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.svm import SVC\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"\n",
"# load and split data\n",
"iris = load_iris()\n",
"X, y = iris.data, iris.target\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
"\n",
"# pipeline setup\n",
"cls = SVC(C=10.0, kernel='rbf', gamma=0.1, decision_function_shape='ovr')\n",
"kernel_svm = Pipeline([('std', StandardScaler()), \n",
" ('svc', cls)])\n",
"\n",
"# gridsearch setup\n",
"param_grid = [\n",
" {'svc__C': [1, 10, 100, 1000], \n",
" 'svc__gamma': [0.001, 0.0001], \n",
" 'svc__kernel': ['rbf']},\n",
" ]\n",
"\n",
"\n",
"# setup multiple GridSearchCV objects, 1 for each algorithm\n",
"\n",
"gs_svm = GridSearchCV(estimator=kernel_svm, \n",
" param_grid=param_grid, \n",
" scoring='accuracy', \n",
" n_jobs=-1, \n",
" cv=5, \n",
" verbose=0, \n",
" refit=True,\n",
" pre_dispatch='2*n_jobs')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A. Nested Crossvalidation - Quick Version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, the `cross_val_function` runs the 5 outer loops, and the the `GridSearch` object (`gs`) peforms the hyperparameter optimization during the 5 inner loops."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Average Accuracy 0.95 +/- 0.06\n"
]
}
],
"source": [
"import numpy as np \n",
"\n",
"from sklearn.cross_validation import cross_val_score\n",
"scores = cross_val_score(gs_svm, X_train, y_train, scoring='accuracy', cv=5)\n",
"print('\\nAverage Accuracy %.2f +/- %.2f' % (np.mean(scores), np.std(scores)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## B. Nested Crossvalidation - Manual Approach Printing the Model Parameters"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.cross_validation import StratifiedKFold\n",
"from sklearn.metrics import accuracy_score\n",
"import numpy as np\n",
"\n",
"params = []\n",
"scores = []\n",
"\n",
"skfold = StratifiedKFold(y=y_train, n_folds=5, shuffle=False, random_state=1)\n",
"for train_idx, test_idx in skfold:\n",
" gs_svm.fit(X_train[train_idx], y_train[train_idx])\n",
" y_pred = gs_svm.predict(X_train[test_idx])\n",
" acc = accuracy_score(y_true=y_train[test_idx], y_pred=y_pred)\n",
" params.append(gs_svm.best_params_)\n",
" scores.append(acc)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVM models:\n",
"1. Acc: 0.96 Params: {'svc__C': 100, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n",
"2. Acc: 1.00 Params: {'svc__C': 100, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n",
"3. Acc: 0.83 Params: {'svc__C': 1000, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n",
"4. Acc: 1.00 Params: {'svc__C': 100, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n",
"5. Acc: 0.96 Params: {'svc__C': 100, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n",
"\n",
"Average Accuracy 0.95 +/- 0.06\n"
]
}
],
"source": [
"print('SVM models:')\n",
"for idx, m in enumerate(zip(params, scores)):\n",
" print('%s. Acc: %.2f Params: %s' % (idx+1, m[1], m[0]))\n",
"print('\\nAverage Accuracy %.2f +/- %.2f' % (np.mean(scores), np.std(scores)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regular K-fold CV to Optimize the Model on the Complete Training Set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Repeat the nested cross-validation for different algorithms. Then, pick the \"best\" algorithm (not the best model!). Next, use the complete training set to tune the best algorithm via grid search:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters {'svc__C': 100, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n"
]
}
],
"source": [
"gs_svm.fit(X_train, y_train)\n",
"print('Best parameters %s' % gs_svm.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training accuracy: 0.97\n",
"Test accuracy: 0.97\n",
"Parameters: {'svc__C': 100, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}\n"
]
}
],
"source": [
"train_acc = accuracy_score(y_true=y_train, y_pred=gs_svm.predict(X_train))\n",
"test_acc = accuracy_score(y_true=y_test, y_pred=gs_svm.predict(X_test))\n",
"print('Training accuracy: %.2f' % train_acc)\n",
"print('Test accuracy: %.2f' % test_acc)\n",
"print('Parameters: %s' % gs_svm.best_params_)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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{
"classes_":[
0,
1,
2
],
"coef_":[
[
0.42625236403173844,
-8.557501546363858
],
[
1.5644231337040186,
-1.6783659020502222
],
[
-1.990675497337773,
10.235867448186507
]
],
"intercept_":[
27.533384852155145,
4.18509910962595,
-31.71848396177913
],
"n_iter_":[
27
]
}
code/bonus/sckit-model-to-json/params.json
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{"dual": false, "max_iter": 100, "warm_start": false, "verbose": 0, "C": 100.0, "class_weight": null, "random_state": 1, "fit_intercept": true, "multi_class": "multinomial", "intercept_scaling": 1, "penalty": "l2", "solver": "newton-cg", "n_jobs": 1, "tol": 0.0001}
code/bonus/softmax-regression.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Sebastian Raschka](http://sebastianraschka.com), 2015\n",
"\n",
"https://github.com/rasbt/python-machine-learning-book"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python Machine Learning - Code Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bonus Material - A Basic Pipeline and Grid Search Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"Last updated: 01/20/2016 \n",
"\n",
"CPython 3.5.1\n",
"IPython 4.0.1\n",
"\n",
"numpy 1.10.1\n",
"pandas 0.17.1\n",
"matplotlib 1.5.0\n",
"scikit-learn 0.17\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 40 out of 40 | elapsed: 0.2s finished\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise',\n",
" estimator=Pipeline(steps=[('std', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=10.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False))]),\n",
" fit_params={}, iid=True, n_jobs=-1,\n",
" param_grid=[{'svc__kernel': ['rbf'], 'svc__C': [1, 10, 100, 1000], 'svc__gamma': [0.001, 0.0001]}],\n",
" pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=1)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.svm import SVC\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"\n",
"# load and split data\n",
"iris = load_iris()\n",
"X, y = iris.data, iris.target\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
"\n",
"# pipeline setup\n",
"cls = SVC(C=10.0, \n",
" kernel='rbf', \n",
" gamma=0.1, \n",
" decision_function_shape='ovr')\n",
"\n",
"kernel_svm = Pipeline([('std', StandardScaler()), \n",
" ('svc', cls)])\n",
"\n",
"# gridsearch setup\n",
"param_grid = [\n",
" {'svc__C': [1, 10, 100, 1000], \n",
" 'svc__gamma': [0.001, 0.0001], \n",
" 'svc__kernel': ['rbf']},\n",
" ]\n",
"\n",
"gs = GridSearchCV(estimator=kernel_svm, \n",
" param_grid=param_grid, \n",
" scoring='accuracy', \n",
" n_jobs=-1, \n",
" cv=5, \n",
" verbose=1, \n",
" refit=True,\n",
" pre_dispatch='2*n_jobs')\n",
"\n",
"# run gridearch\n",
"gs.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best GS Score 0.96\n",
"best GS Params {'svc__kernel': 'rbf', 'svc__C': 100, 'svc__gamma': 0.001}\n",
"\n",
"Train Accuracy: 0.97\n",
"\n",
"Test Accuracy: 0.97\n"
]
}
],
"source": [
"print('Best GS Score %.2f' % gs.best_score_)\n",
"print('best GS Params %s' % gs.best_params_)\n",
"\n",
"\n",
"# prediction on the training set\n",
"y_pred = gs.predict(X_train)\n",
"train_acc = (y_train == y_pred).sum()/len(y_train)\n",
"print('\\nTrain Accuracy: %.2f' % (train_acc))\n",
"\n",
"# evaluation on the test set\n",
"y_pred = gs.predict(X_test)\n",
"test_acc = (y_test == y_pred).sum()/len(y_test)\n",
"print('\\nTest Accuracy: %.2f' % (test_acc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### A Note about `GridSearchCV`'s `best_score_` attribute"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please note that `gs.best_score_` is the average k-fold cross-validation score. I.e., if we have a `GridSearchCV` object with 5-fold cross-validation (like the one above), the `best_score_` attribute returns the average score over the 5-folds of the best model. To illustrate this with an example:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.6, 0.4, 0.6, 0.2, 0.6])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.cross_validation import StratifiedKFold, cross_val_score\n",
"from sklearn.linear_model import LogisticRegression\n",
"import numpy as np\n",
"\n",
"np.random.seed(0)\n",
"np.set_printoptions(precision=6)\n",
"y = [np.random.randint(3) for i in range(25)]\n",
"X = (y + np.random.randn(25)).reshape(-1, 1)\n",
"\n",
"cv5_idx = list(StratifiedKFold(y, n_folds=5, shuffle=False, random_state=0))\n",
"cross_val_score(LogisticRegression(random_state=123), X, y, cv=cv5_idx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By executing the code above, we created a simple data set of random integers that shall represent our class labels. Next, we fed the indices of 5 cross-validation folds (`cv3_idx`) to the `cross_val_score` scorer, which returned 5 accuracy scores -- these are the 5 accuracy values for the 5 test folds. \n",
"\n",
"Next, let us use the `GridSearchCV` object and feed it the same 5 cross-validation sets (via the pre-generated `cv3_idx` indices):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
"[CV] ................................................................\n",
"[CV] ....................................... , score=0.600000 - 0.0s\n",
"[CV] ................................................................\n",
"[CV] ....................................... , score=0.400000 - 0.0s\n",
"[CV] ................................................................\n",
"[CV] ....................................... , score=0.600000 - 0.0s\n",
"[CV] ................................................................\n",
"[CV] ....................................... , score=0.200000 - 0.0s\n",
"[CV] ................................................................\n",
"[CV] ....................................... , score=0.600000 - 0.0s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.0s finished\n"
]
}
],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"gs = GridSearchCV(LogisticRegression(), {}, cv=cv5_idx, verbose=3).fit(X, y) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, the scores for the 5 folds are exactly the same as the ones from `cross_val_score` earlier. \n",
"Now, the best_score_ attribute of the `GridSearchCV` object, which becomes available after `fit`ting, returns the average accuracy score of the best model:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.47999999999999998"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, the result above is consistent with the average score computed the `cross_val_score`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.47999999999999998"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_val_score(LogisticRegression(), X, y, cv=cv5_idx).mean()"
]
}
],
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"display_name": "Python 3",
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