Newer
Older
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is machine learning ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Discipline in the overlap of computer science and statistics\n",
"- Term \"Machine Learning\" was first used in 1959 by AI pioneer Arthur Samuel\n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the field is not as new as one might think, but due to more available data, processing power and development of better algorithms more applications of machine learning appeared during the last 15 years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 2. Where on the night sky will I see the moon tonight ?\n",
" 2. Is the email I received spam ? \n",
" 4. What article X should I recommend to my customers Y ?\n",
"The first two questions can be answered based on existing mathematically explicit models (formulas). \n",
"\n",
"For the questions 3 and 4 it is difficult to develop explicitly formulated models. \n",
"\n",
"These problems 3 and 4 have the following in common:\n",
"- Vague understanding of the problem domain\n",
"- Enough data with sufficient (implicit) information available\n",
"\n",
"E.g. for the spamming example:\n",
"\n",
"- We have no explicit formula for such a task\n",
"- We know that specific words are specific for spam emails, other words are specific for my personal and job emails.\n",
"- My mailbox is full with examples for spam vs non-spam.\n",
"\n",
"\n",
"**In such cases machine learning offers approaches to build models based on example data.**\n"
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some history\n",
"\n",
"\n",
" \n",
" 1812: Bayes Theorem\n",
" 1913: Markov Chains\n",
" 1951: First neural network\n",
" 1969: Book \"Perceptrons\": Limitations of Neural Networks\n",
" 1986: Backpropagation to learn neural networks\n",
" 1995: Randomized Forests and Support Vector Machines\n",
" 1998: Application of naive Bayes Classifier for Spam detection\n",
" 2000+: Deep learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In most cases we can arange data used for machine learning as a matrix:"
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" <th>is_yummy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>0.215791</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>0.380420</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>0.145231</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>0.191321</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>0.260218</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness is_yummy\n",
"0 3.739295 0.422503 0.989463 0.215791 0\n",
"1 4.207849 0.841668 0.928626 0.380420 0\n",
"2 4.709494 0.322037 5.374682 0.145231 1\n",
"3 4.684743 0.434315 4.072805 0.191321 1\n",
"4 4.148710 0.570586 1.461568 0.260218 0"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"features = pd.read_csv(\"beers.csv\")\n",
"features.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"- every row of such a matrix is called a **sample** or **feature vector**. \n",
"- every column name is called a **feature name** or **attribute**.\n",
"- the cells are **feature values**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This table holds five samples.\n",
"\n",
"The feature names are `alcohol_content`, `bitterness`, `darkness`, `fruitiness` and `is_yummy`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Almost) all machine learning algorithms require that your data is numerical and/or categorial. In some applications it is not obvious how to transform data to a numerical presentation.\n",
"\n",
"Definition:\n",
"\n",
"*Categorical data*: data which has only a limited set of allowed values. A `taste` feature could only allow values `sour`, `bitter`, `sweet`, `salty`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How to represent images as feature vectors\n",
"\n",
"Computers represent images as matrices. Every cell in the matrix represents one pixel, and the value in the matrix cell its color.\n",
"\n",
"`scikit-learn` includes some example data sets which we load now:"
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"dd = load_digits()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we plot the first nine digits from this data set:"
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1296x360 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = 9\n",
"plt.figure(figsize=(2 * N, 5))\n",
"for i, image in enumerate(dd.images[:N], 1):\n",
" plt.subplot(1, N, i)\n",
" plt.imshow(image, cmap=\"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And this is the first image from the data set, it is a 8 x 8 matrix with values 0 to 15:"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8, 8)\n",
"[[ 0. 0. 5. 13. 9. 1. 0. 0.]\n",
" [ 0. 0. 13. 15. 10. 15. 5. 0.]\n",
" [ 0. 3. 15. 2. 0. 11. 8. 0.]\n",
" [ 0. 4. 12. 0. 0. 8. 8. 0.]\n",
" [ 0. 5. 8. 0. 0. 9. 8. 0.]\n",
" [ 0. 4. 11. 0. 1. 12. 7. 0.]\n",
" [ 0. 2. 14. 5. 10. 12. 0. 0.]\n",
" [ 0. 0. 6. 13. 10. 0. 0. 0.]]\n"
]
}
],
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To transform such an image to a feature vectore we just have to concatenate the rows to one single vector of size 64:"
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(64,)\n",
"[ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5. 0. 0. 3.\n",
" 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8. 8. 0. 0. 5. 8. 0.\n",
" 0. 9. 8. 0. 0. 4. 11. 0. 1. 12. 7. 0. 0. 2. 14. 5. 10. 12.\n",
" 0. 0. 0. 0. 6. 13. 10. 0. 0. 0.]\n"
]
}
],
"source": [
"vector = dd.images[0].flatten()\n",
"print(vector.shape)\n",
"print(vector)"
]
},
{
"cell_type": "markdown",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To transform some text into a feature vector, we first need a enumerated dictionary. Such a dictionary can be very large, but for the sake of simplicity we use a very small dictionary to explain the overall procedure:\n",
"\n",
"\n",
"| Word | Index |\n",
"|----------|-------|\n",
"| like | 0 |\n",
"| dislike | 1 |\n",
"| american | 2 |\n",
"| italian | 3 |\n",
"| beer | 4 |\n",
"| pizza | 5 |\n",
"\n",
"To \"vectorize\" a given text we count the words in the text which also exist in the vocabulary and put the counts at the given position `Index`.\n",
"\n",
"E.g. `\"I dislike american pizza, but american beer is nice\"`:\n",
"\n",
"|----------|-------|-------|\n",
"| like | 0 | 1 |\n",
"| dislike | 1 | 1 |\n",
"| american | 2 | 2 |\n",
"| italian | 3 | 0 |\n",
"| beer | 4 | 1 |\n",
"| pizza | 5 | 1 |\n",
"\n",
"The according feature vector is the `Count` column, which is:\n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And this is how we can compute such a word vector using Python:"
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 0 1 1]\n"
]
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from itertools import count\n",
"\n",
"# map words to index in created vector:\n",
"vocabulary = [\"like\", \"dislike\", \"american\", \"italian\", \"beer\", \"pizza\"]\n",
"\n",
"vectorizer = CountVectorizer(vocabulary=dict(zip(vocabulary, count())))\n",
"\n",
"# crate count vector for a pice of text:\n",
"vector = vectorizer.fit_transform([\"I dislike american pizza. But american beer is nice\"]).toarray()[0]\n",
"print(vector)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taxonomy of machine learning\n",
"\n",
"We can separate learning problems in a few large categories: **supervised** and **unsupervised** learning.\n",
"\n",
"In **supervised learning** the the data comes with additional attributes that we want to predict. Such a problem can be either \n",
"\n",
"- **classification**: samples belong to two or more discrete classes and we want to learn from already labeled data how to predict the class of unlabeled data. This is the same as saying, that the output is categorical.\n",
" \n",
"- **regression**: if the desired output consists of one or more continuous variables, then the task is called regression.\n",
" \n",
" \n",
"\n",
"Examples for supervised learning:\n",
"\n",
"- Classification: Predict the class `is_yummy` based on the attributes `alcohol_content`,\t`bitterness`, \t`darkness` and `fruitiness`. (two class problem).\n",
"\n",
"- Classification: predict the digit-shown based on a 8 x 8 pixel image (this is a multi-class problem).\n",
"\n",
"- Regression: Predict the length of a salmon based on its age and weight.\n",
"\n",
"\n",
"In **unsupervised learning**, in which the training data consists of samples without any corresponding target values, one tries to find structure in data. Common applications are\n",
"\n",
"- Clustering \n",
"- Density estimation\n",
"- Dimension reduction (PCA, ...)\n",
"\n",
"This course will only introduce concepts and methods from **supervised learning**."
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to apply machine learning in practice ?\n",
"\n",
"Application of machine learning in practice consists of several phases:\n",
"\n",
"1. Learn / train a model from example data\n",
"2. Analyze model for its quality / performance\n",
"2. Apply this model to new incoming data\n",
"\n",
"In practice steps 1. and 2. are iterated for different machine learning algorithms until performance is optimal or sufficient. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise section"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our example beer data set reflects the very personal opinion of one of the tutors which beer he likes and which not. To learn a predictive model and to understand influential factors all beers went through some lab analysis to measure alcohol content, bitterness, darkness and fruitiness."
]
},
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" <th>is_yummy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>0.215791</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>0.380420</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>0.145231</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>0.191321</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>0.260218</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness is_yummy\n",
"0 3.739295 0.422503 0.989463 0.215791 0\n",
"1 4.207849 0.841668 0.928626 0.380420 0\n",
"2 4.709494 0.322037 5.374682 0.145231 1\n",
"3 4.684743 0.434315 4.072805 0.191321 1\n",
"4 4.148710 0.570586 1.461568 0.260218 0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"\n",
"# read some data\n",
"\n",
"beer_data = pd.read_csv(\"beers.csv\")\n",
"beer_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" alcohol_content bitterness darkness fruitiness\n",
"0 3.739295 0.422503 0.989463 0.215791\n",
"1 4.207849 0.841668 0.928626 0.380420\n",
"2 4.709494 0.322037 5.374682 0.145231\n",
"3 4.684743 0.434315 4.072805 0.191321\n",
"4 4.148710 0.570586 1.461568 0.260218\n",
"\n",
"0 0\n",
"1 0\n",
"2 1\n",
"3 1\n",
"4 0\n",
"Name: is_yummy, dtype: int64\n"
]
}
],
"source": [
"# split matrix into features and labels\n",
"features = beer_data.iloc[:, :-1]\n",
"labels = beer_data.iloc[:, -1]\n",
"\n",
"print(features.head())\n",
"print()\n",
"print(labels.head())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticRegression(C=2, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)\n"
]
}
],
"source": [
"classifier = LogisticRegression(C=1)\n",
"classifier.fit(features, labels)\n",
"print(classifier)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"225 examples\n",
"199 labeled correctly\n"
]
}
],
"source": [
"predicted_labels = classifier.predict(features)\n",
"\n",
"\n",
"print(len(labels), \"examples\")\n",
"print(sum(predicted_labels == labels), \"labeled correctly\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comment\n",
"Are you surprised that not all labels where predicted correctly ?\n",
"\n",
"Reasons for this can be:\n",
"- missing information: maybe other features of beer which contribute to the rating where not measured or can not be measured.\n",
"- noisy information: features can be noisy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(225,)\n",
"(225,)\n",
"205\n"
]
}
],
"source": [
"classifier = SVC()\n",
"classifier.fit(features, labels)\n",
"\n",
"predicted_labels = classifier.predict(features)\n",
"\n",
"print(predicted_labels.shape)\n",
"print(labels.shape)\n",
"print(sum(predicted_labels == labels))"
]
},
{
"cell_type": "code",
"execution_count": 1,
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/uweschmitt/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/html": [
"<style>\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
" @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lato');\n",
" @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
"\n",
" \n",
" body {\n",
" font-family: 'Lora', Consolas, sans-serif;\n",
" \n",
" -webkit-print-color-adjust: exact important !;\n",
" \n",
" }\n",
" .rendered_html code\n",
" {\n",
" color: black;\n",
" background: #eaf0ff;\n",
" \n",
" padding: 1pt;\n",
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .CodeMirror pre {\n",
" font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .cm-s-ipython span.cm-keyword {\n",
" font-weight: normal;\n",
" }\n",
" \n",
" strong {\n",
" background: #ffe7e7;\n",
" padding: 1pt;\n",
" }\n",
" \n",
" \n",
" div #notebook {\n",
" # font-size: 10pt; \n",
" line-height: 145%;\n",
" }\n",
" \n",
" li {\n",
" line-heigt: 145%;\n",
" }\n",
"\n",
" div.output_area pre {\n",
" background: #fff9d8 !important;\n",
" padding: 5pt;\n",
" \n",
" -webkit-print-color-adjust: exact; \n",
" \n",
" }\n",
" \n",
" \n",
" \n",
" h1, h2, h3, h4 {\n",
" font-family: Kameron, arial;\n",
" }\n",
" \n",
" div#maintoolbar {display: none !important;}\n",
" </style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#REMOVEBEGIN\n",
"# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
"\n",
"from IPython import utils\n",
"from IPython.core.display import HTML\n",
"import os\n",
"def css_styling():\n",
" \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
" base = utils.path.get_ipython_dir()\n",
" styles = \"\"\"<style>\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
" @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lato');\n",
" @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
"\n",
" \n",
" body {\n",
" font-family: 'Lora', Consolas, sans-serif;\n",
" \n",
" -webkit-print-color-adjust: exact important !;\n",
" \n",
" }\n",
" .rendered_html code\n",
" {\n",
" color: black;\n",
" background: #eaf0ff;\n",
" \n",
" padding: 1pt;\n",
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .CodeMirror pre {\n",
" font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .cm-s-ipython span.cm-keyword {\n",
" font-weight: normal;\n",
" }\n",
" \n",
" strong {\n",
" background: #ffe7e7;\n",
" padding: 1pt;\n",
" }\n",
" \n",
" \n",
" div #notebook {\n",
" # font-size: 10pt; \n",
" line-height: 145%;\n",
" }\n",
" \n",
" li {\n",
" line-heigt: 145%;\n",
" }\n",
"\n",
" div.output_area pre {\n",
" background: #fff9d8 !important;\n",
" padding: 5pt;\n",
" \n",
" -webkit-print-color-adjust: exact; \n",
" \n",
" }\n",
" \n",
" \n",
" \n",
" h1, h2, h3, h4 {\n",
" font-family: Kameron, arial;\n",
" }\n",
" \n",
" div#maintoolbar {display: none !important;}\n",
" </style>\"\"\"\n",
" return HTML(styles)\n",
"css_styling()\n",
"#REMOVEEND"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}