ipython: machine learning snippets

keras_drawModel.py

from IPython.display import SVG
from keras.utils.visualize_util import model_to_dot

SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))

# and to save it directly to a file
from keras.utils.visualize_util import plot
plot(model, show_shapes=True, to_file='/tmp/model.png')

pandas_crosstab.py

import pandas as pd
# simple crosstab with totals
pd.crosstab(y, yHat, rownames=["actual"], colnames=["predicted"], margins=True)
# normalized cross tab, rounded to two decimals
(pd.crosstab(test_labels_1d, pred, rownames=["actual"], colnames=["predicted"], margins=False, normalize="index")*100).round(2)

sklearn_crosstab.py

# source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import itertools

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          decimals=2,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, np.round(cm[i, j], decimals=decimals),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    
    
# EXAMPLE: Plot normalized confusion matrix
cnf_matrix = confusion_matrix(test_labels_1d, pred)
class_names = [str(i) for i in range(0, 10)]

plt.figure(figsize=(6,6))
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

plt.show()