Python Machine Learning by Example Yuxi (Hayden) Liu

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Python Machine Learning by Example Yuxi (Hayden) Liu
Digital Instant Download
Author(s): Yuxi (Hayden) Liu
ISBN(s): 9781783553129, 178355312X
Edition: ebook
File Details: PDF, 6.48 MB
Year: 2017
Language: english

1: Getting Started with Python and Machine Learning
Chapter 1: Getting Started with Python and Machine Learning
What is machine learning and why do we need it?
A very high level overview of machine learning
A brief history of the development of machine learning algorithms
Generalizing with data
Overfitting, underfitting and the bias-variance tradeoff
Avoid overfitting with feature selection and dimensionality reduction
Preprocessing, exploration, and feature engineering
Combining models
Installing software and setting up
Troubleshooting and asking for help
Summary
2: Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
Chapter 2: Exploring the 20 Newsgroups Dataset with Text Analysis
Algorithms
What is NLP?
Touring powerful NLP libraries in Python
The newsgroups data
Getting the data
Thinking about features
Visualization
Data preprocessing
Clustering
Topic modeling
Summary
3: Spam Email Detection with Naive Bayes
Chapter 3: Spam Email Detection with Naive Bayes
Getting started with classification
Types of classification
Applications of text classification
Exploring naive Bayes
Bayes' theorem by examples
The mechanics of naive Bayes
The naive Bayes implementations
Classifier performance evaluation
Model tuning and cross-validation

Summary
4: News Topic Classification with Support Vector Machine
Chapter 4: News Topic Classification with Support Vector Machine
Recap and inverse document frequency
Support vector machine
News topic classification with support vector machine
More examples - fetal state classification on cardiotocography with
SVM
Summary
5: Click-Through Prediction with Tree-Based Algorithms
Chapter 5: Click-Through Prediction with Tree-Based Algorithms
Brief overview of advertising click-through prediction
Getting started with two types of data, numerical and categorical
Decision tree classifier
Click-through prediction with decision tree
Random forest - feature bagging of decision tree
Summary
6: Click-Through Prediction with Logistic Regression
Chapter 6: Click-Through Prediction with Logistic Regression
One-hot encoding - converting categorical features to numerical
Logistic regression classifier
Click-through prediction with logistic regression by gradient descent
Feature selection via random forest
Summary
7: Stock Price Prediction with Regression Algorithms
Chapter 7: Stock Price Prediction with Regression Algorithms
Brief overview of the stock market and stock price
What is regression?
Predicting stock price with regression algorithms
Summary
8: Best Practices
Chapter 8: Best Practices
Machine learning workflow
Best practices in the data preparation stage
Best practices in the training sets generation stage
Best practices in the model training, evaluation, and selection stage
Best practices in the deployment and monitoring stage

Chapter 1. Getting Started with Python and
Machine Learning
We kick off our Python and machine learning journey with the basic, yet
important concepts of machine learning. We will start with what machine
learning is about, why we need it, and its evolution over the last few decades.
We will then discuss typical machine learning tasks and explore several
essential techniques of working with data and working with models. It is a
great starting point of the subject and we will learn it in a fun way. Trust me.
At the end, we will also set up the software and tools needed in this book.
We will get into details for the topics mentioned:
What is machine learning and why do we need it?
A very high level overview of machine learning
Overfitting and the bias variance trade off
Cross validation
Regularization
Dimensions and features
Preprocessing, exploration, and feature engineering
Missing Values
Label encoding
One hot encoding
Scaling
Polynomial features
Power transformations
Binning
Combining models
Bagging
Boosting
Stacking
Blending
V
oting and averaging
Installing software and setting up

Troubleshooting and asking for help

What is machine learning and why do we
need it?
Machine learning is a term coined around 1960 composed of two words—
machine corresponding to a computer, robot, or other device, and learning an
activity, or event patterns, which humans are good at.
So why do we need machine learning, why do we want a machine to learn as a
human? There are many problems involving huge datasets, or complex
calculations for instance, where it makes sense to let computers do all the
work. In general, of course, computers and robots don't get tired, don't have to
sleep, and may be cheaper. There is also an emerging school of thought called
active learning or human-in-the-loop, which advocates combining the efforts of
machine learners and humans. The idea is that there are routine boring tasks
more suitable for computers, and creative tasks more suitable for humans.
According to this philosophy, machines are able to learn, by following rules
(or algorithms) designed by humans and to do repetitive and logic tasks
desired by a human.
Machine learning does not involve the traditional type of programming that
uses business rules. A popular myth says that the majority of the code in the
world has to do with simple rules possibly programmed in Cobol, which
covers the bulk of all the possible scenarios of client interactions. So why can't
we just hire many software programmers and continue programming new
rules?
One reason is that defining, maintaining, and updating rules becomes more and
more expensive over time. The number of possible patterns for an activity or
event could be enormous and therefore exhausting all enumeration is not
practically feasible. It gets even more challenging to do so when it comes to
events that are dynamic, ever-changing, or evolve in real-time. It is much
easier and more efficient to develop learning rules or algorithms which
command computers to learn and extract patterns, and to figure things out
themselves from abundant data.

Another reason is that the volume of data is exponentially growing. Nowadays,
the floods of textual, audio, image, and video data are hard to fathom. The
Internet of Things (IoT) is a recent development of a new kind of Internet,
which interconnects everyday devices. The Internet of Things will bring data
from household appliances and autonomous cars to the forefront. The average
company these days has mostly human clients, but, for instance, social media
companies tend to have many bot accounts. This trend is likely to continue and
we will have more machines talking to each other. Besides the quantity, the
quality of data available has kept increasing over the past few years due to
cheaper storage. These have empowered the evolution of machine learning
algorithms and data-driven solutions.
Jack Ma from Alibaba explained in a speech that Information Technology
(IT) was the focus over the past 20 years and now, for the next 30 years, we
will be at the age of Data Technology (DT). During the age of IT, companies
have grown larger and stronger thanks to computer software and infrastructure.
Now that businesses in most industries have already gathered enormous
amounts of data, it is presently the right time for exploiting DT to unlock
insights, derive patterns, and to boost new business growth. Broadly speaking,
machine learning technologies enable businesses to better understand customer
behavior and engage with customers, also to optimize operations management.
As for us individuals, machine learning technologies are already making our
life better every day.
An application of machine learning that we all are familiar with is spam email
filtering. Another is online advertising where ads are served automatically
based on information advertisers have collected about us. Stay tuned for the
next chapters where we will learn how to develop algorithms in solving these
two problems. An application of machine learning we basically can not live
without is search engines. Search engines involve information retrieval which
parses what we look for and queries related records, and contextual ranking
and personalized ranking which sorts pages by topical relevance and to the
user's liking. E-commerce and media companies have been at the forefront of
employing recommendation systems, which help customers find products,
services, articles faster. The application of machine learning is boundless and
we just keep hearing new examples everyday, credit card fraud detection,
disease diagnosis, presidential election prediction, instant speech translation,

robo-advisor, you name it!
In the 1983 War Games movie, a computer made life and death decisions,
which could have resulted in Word War III. As far as we know, technology
wasn't able to pull off such feats at the time. However, in 1997 the Deep Blue
supercomputer did manage to beat a world chess champion. In 2005, a
Stanford self-driving car drove by itself for more than 130 kilometers in a
desert. In 2007, the car of another team drove through regular traffic for more
than 50 kilometers. In 2011, the Watson computer won a quiz against human
opponents. In 2016, the AlphaGo program beat one of the best Go players in
the world. If we assume that computer hardware is the limiting factor, then we
can try to extrapolate into the future. Ray Kurzweil did just that and according
to him, we can expect human level intelligence around 2029. What's next?

A very high level overview of machine
learning
Machine learning mimicking human intelligence is a subfield of artificial
intelligence—a field of computer science concerned with creating systems.
Software engineering is another field in computer science. Generally, we can
label Python programming as a type of software engineering. Machine learning
is also closely related to linear algebra, probability theory, statistics, and
mathematical optimization. We usually build machine learning models based
on statistics, probability theory, and linear algebra, then optimize the models
using mathematical optimization. The majority of us reading this book should
have at least sufficient knowledge of Python programming. Those who are not
feeling confident about mathematical knowledge, might be wondering, how
much time should be spent learning or brushing up the knowledge of the
aforementioned subjects. Don't panic. We will get machine learning to work
for us without going into any mathematical details in this book. It just requires
some basic, 101 knowledge of probability theory and linear algebra, which
helps us understand the mechanics of machine learning techniques and
algorithms. And it gets easier as we will be building models both from scratch
and with popular packages in Python, a language we like and are familiar with.
Note
Those who want to study machine learning systematically can
enroll into computer science, artificial intelligence, and more
recently, data science master's programs. There are also various
data science bootcamps. However the selection for bootcamps is
usually stricter as they are more job oriented, and the program
duration is often short ranging from 4 to 10 weeks. Another option
is the free massive open online courses (MOOC), for example, the
popular one is Andrew Ng's Machine Learning. Last but not least,
industry blogs and websites are great resources for us to keep up
with the latest development.

Note
Machine learning is not only a skill, but also a bit of sport. We can
compete in several machine learning competitions; sometimes for
decent cash prizes, sometimes for joy, most of the time for playing
to strengths. However, to win these competitions, we may need to
utilize certain techniques, which are only useful in the context of
competitions and not in the context of trying to solve a business
problem. That's right, the "no free lunch" theorem applies here.
A machine learning system is fed with input data—this can be numerical,
textual, visual, or audiovisual. The system usually has outputs—this can be a
floating-point number, for instance, the acceleration of a self-driving car, can
be an integer representing a category (also called a class), for example, a cat
or tiger from image recognition.
The main task of machine learning is to explore and construct algorithms that
can learn from historical data and make predictions on new input data. For a
data-driven solution, we need to define (or have it defined for us by an
algorithm) an evaluation function called loss or cost function, which measures
how well the models are learning. In this setup, we create an optimization
problem with the goal of learning in the most efficient and effective way.
Depending on the nature of the learning data, machine learning tasks can be
broadly classified into three categories as follows:
Unsupervised learning: when learning data contains only indicative
signals without any description attached, it is up to us to find structure of
the data underneath, to discover hidden information, or to determine how
to describe the data. This kind of learning data is called unlabeled data.
Unsupervised learning can be used to detect anomalies, such as fraud or
defective equipment, or to group customers with similar online behaviors
for a marketing campaign.
Supervised learning: when learning data comes with description, targets
or desired outputs besides indicative signals, the learning goal becomes
to find a general rule that maps inputs to outputs. This kind of learning
data is called labeled data. The learned rule is then used to label new

data with unknown outputs. The labels are usually provided by event
logging systems and human experts. Besides, if it is feasible, they may
also be produced by members of the public through crowdsourcing for
instance. Supervised learning is commonly used in daily applications,
such as face and speech recognition, products or movie recommendations,
and sales forecasting.
We can further subdivide supervised learning into regression and
classification. Regression trains on and predicts a continuous-valued
response, for example predicting house prices, while classification
attempts to find the appropriate class label, such as analyzing
positive/negative sentiment and prediction loan defaults.
If not all learning samples are labeled, but some are, we will have semi-
supervised learning. It makes use of unlabeled data (typically a large
amount) for training, besides a small amount of labeled. Semi-supervised
learning is applied in cases where it is expensive to acquire a fully
labeled dataset while more practical to label a small subset. For example,
it often requires skilled experts to label hyperspectral remote sensing
images, and lots of field experiments to locate oil at a particular location,
while acquiring unlabeled data is relatively easy.
Reinforcement learning: learning data provides feedback so that the
system adapts to dynamic conditions in order to achieve a certain goal.
The system evaluates its performance based on the feedback responses
and reacts accordingly. The best known instances include self-driving
cars and chess master AlphaGo.
Feeling a little bit confused by the abstract concepts? Don't worry. We will
encounter many concrete examples of these types of machine learning tasks
later in the book. In Chapter 3, Spam Email Detection with Naive Bayes, to
Chapter 6, Click-Through Prediction with Logistic Regression, we will see
some supervised learning tasks and several classification algorithms; in
Chapter 7, Stock Price Prediction with Regression Algorithms, we will
continue with another supervised learning task, regression, and assorted
regression algorithms; while in Chapter 2, Exploring the 20 Newsgroups
Dataset with Text Analysis Algorithms, we will be given an unsupervised task
and explore various unsupervised techniques and algorithms.

A brief history of the development of
machine learning algorithms
In fact, we have a whole zoo of machine learning algorithms with popularity
varying over time. We can roughly categorize them into four main approaches:
logic-based learning, statistical learning, artificial neural networks, and
genetic algorithms.
The logic-based systems were the first to be dominant. They used basic rules
specified by human experts, and with these rules, systems tried to reason using
formal logic, background knowledge, and hypotheses. In the mid-1980s,
artificial neural networks (ANN) came to the foreground, to be then pushed
aside by statistical learning systems in the 1990s. Artificial neural networks
imitate animal brains, and consist of interconnected neurons that are also an
imitation of biological neurons. They try to model complex relationships
between inputs and outputs and to capture patterns in data. Genetic algorithms
(GA) were popular in the 1990s. They mimic the biological process of
evolution and try to find the optimal solutions using methods such as mutation
and crossover.
We are currently (2017) seeing a revolution in deep learning, which we may
consider to be a rebranding of neural networks. The term deep learning was
coined around 2006, and refers to deep neural networks with many layers. The
breakthrough in deep learning is amongst others caused by the integration and
utilization of graphical processing units (GPU), which massively speed up
computation. GPUs were originally developed to render video games, and are
very good in parallel matrix and vector algebra. It is believed that deep
learning resembles the way humans learn, therefore may be able to deliver on
the promise of sentient machines.
Some of us may have heard of Moore's law-an empirical observation claiming
that computer hardware improves exponentially with time. The law was first
formulated by Gordon Moore, the co-founder of Intel, in 1965. According to

the law, the number of transistors on a chip should double every two years. In
the following graph, you can see that the law holds up nicely (the size of the
bubbles corresponds to the average transistor count in GPUs):
The consensus seems to be that Moore's law should continue to be valid for a
couple of decades. This gives some credibility to Ray Kurzweil's predictions
of achieving true machine intelligence in 2029.

The good thing about data is that we have a lot of data in the world. The bad
thing is that it is hard to process this data. The challenges stem from the
diversity and noisiness of the data. We as humans, usually process data coming
in our ears and eyes. These inputs are transformed into electrical or chemical
signals. On a very basic level, computers and robots also work with electrical
signals. These electrical signals are then translated into ones and zeroes.
However, we program in Python in this book, and on that level normally we
represent the data either as numbers, images, or text. Actually images and text
are not very convenient, so we need to transform images and text into
numerical values.
Especially in the context of supervised learning we have a scenario similar to
studying for an exam. We have a set of practice questions and the actual exams.
We should be able to answer exam questions without knowing the answers for
them. This is called generalization—we learn something from our practice
questions and hopefully are able to apply this knowledge to other similar
questions. In machine learning, these practice questions are called training
sets or training samples. They are where the models derive patterns from.
And the actual exams are testing sets or testing samples. They are where the
models are eventually applied and how compatible they are is what it's all
about. Sometimes between practice questions and actual exams, we have mock
exams to assess how well we will do in actual ones and to aid revision. These
mock exams are called validation sets or validation samples in machine
learning. They help us verify how well the models will perform in a simulated
setting then we fine-tune the models accordingly in order to achieve greater
hits.
An old-fashioned programmer would talk to a business analyst or other expert,
then implement a rule that adds a certain value multiplied by another value
corresponding, for instance, to tax rules. In a machine learning setting we give
the computer example input values and example output values. Or if we are
more ambitious, we can feed the program the actual tax texts and let the

machine process the data further just like an autonomous car doesn't need a lot
of human input.
This means implicitly that there is some function, for instance, a tax formula
we are trying to figure out. In physics we have almost the same situation. We
want to know how the universe works and formulate laws in a mathematical
language. Since we don't know the actual function, all we can do is measure
what error is produced, and try to minimize it. In supervised learning tasks we
compare our results against the expected values. In unsupervised learning we
measure our success with related metrics. For instance, we want clusters of
data to be well defined, the metrics could be how similar the data points
within one cluster are and how different the data points from two clusters are.
In reinforcement learning, a program evaluates its moves, for example, in a
chess game using some predefined function.

Overfitting, underfitting and the bias-
variance tradeoff
Overfitting (one word) is such an important concept that I decided to start
discussing it very early in the book.
If we go through many practice questions for an exam, we may start to find
ways to answer questions which have nothing to do with the subject material.
For instance, given only five practice questions, we find that if there are two
potato and one tomato in a question, the answer is always A, if there are one
potato and three tomato in a question, the answer is always B, then we
conclude this is always true and apply such theory later on even though the
subject or answer may not be relevant to potatoes or tomatoes. Or even worse,
you may memorize the answers for each question verbatim. We can then score
high on the practice questions; we do so with the hope that the questions in the
actual exams will be the same as practice questions. However, in reality, we
will score very low on the exam questions as it is rare that the exact same
questions will occur in the actual exams.
The phenomenon of memorization can cause overfitting. We are over extracting
too much information from the training sets and making our model just work
well with them, which is called low bias in machine learning. However, at the
same time, it will not help us generalize with data and derive patterns from
them. The model as a result will perform poorly on datasets that were not seen
before. We call this situation high variance in machine learning.
Overfitting occurs when we try to describe the learning rules based on a
relatively small number of observations, instead of the underlying relationship,
such the preceding potato and tomato example. Overfitting also takes place
when we make the model excessively complex so that it fits every training
sample, such as memorizing the answers for all questions as mentioned
previously.
The opposite scenario is called underfitting. When a model is underfit, it does

not perform well on the training sets, and will not so on the testing sets, which
means it fails to capture the underlying trend of the data. Underfitting may
occur if we are not using enough data to train the model, just like we will fail
the exam if we did not review enough material; it may also happen if we are
trying to fit a wrong model to the data, just like we will score low in any
exercises or exams if we take the wrong approach and learn it the wrong way.
We call any of these situations high bias in machine learning, although its
variance is low as performance in training and test sets are pretty consistent, in
a bad way.
We want to avoid both overfitting and underfitting. Recall bias is the error
stemming from incorrect assumptions in the learning algorithm; high bias
results in underfitting, and variance measures how sensitive the model
prediction is to variations in the datasets. Hence, we need to avoid cases
where any of bias or variance is getting high. So, does it mean we should
always make both bias and variance as low as possible? The answer is yes, if
we can. But in practice, there is an explicit trade-off between themselves,
where decreasing one increases the other. This is the so-called bias–variance
tradeoff. Does it sound abstract? Let’s take a look at the following example.
We were asked to build a model to predict the probability of a candidate being
the next president based on the phone poll data. The poll was conducted by zip
codes. We randomly choose samples from one zip code, and from these, we
estimate that there's a 61% chance the candidate will win. However, it turns
out he loses the election. Where did our model go wrong? The first thing we
think of is the small size of samples from only one zip code. It is the source of
high bias, also because people in a geographic area tend to share similar
demographics. However, it results in a low variance of estimates. So, can we
fix it simply by using samples from a large number zip codes? Yes, but don’t
get happy so early. This might cause an increased variance of estimates at the
same time. We need to find the optimal sample size, the best number of zip
codes to achieve the lowest overall bias and variance. Minimizing the total
error of a model requires a careful balancing of bias and variance. Given a set
of training samples x_1, x_2, …, x_n and their targets y_1, y_2, …, y_n, we
want to find a regression function, ŷ(x), which estimates the true relation y(x)
as correctly as possible. We measure the error of estimation, how good (or
bad) the regression model is by mean squared error (MSE):

The E denotes the expectation. This error can be decomposed into bias and
variance components following the analytical derivation as follows (although
it requires a bit of basic probability theory to understand):
The bias term measures the error of estimations, and the variance term
describes how much the estimation ŷ moves around its mean. The more
complex the learning model ŷ(x) and the larger the size of training samples, the
lower the bias will be. However, these will also create more shift on the
model in order to fit better the increased data points. As a result, the variance
will be lifted. We usually employ the cross-validation technique to find the
optimal model balancing bias and variance and to diminish overfitting.
The last term is the irreducible error.
Avoid overfitting with cross-validation
Recall that between practice questions and actual exams, there are mock exams
where we can assess how well we will perform in the actual ones and conduct
necessary revision. In machine learning, the validation procedure helps
evaluate how the models will generalize to independent or unseen datasets in a
simulated setting. In a conventional validation setting, the original data is
partitioned into three subsets, usually 60% for the training set, 20% for the
validation set, and the rest 20% for the testing set. This setting suffices if we
have enough training samples after partition and we only need a rough estimate
of simulated performance. Otherwise, cross-validation is preferable.
In one round of cross-validation, the original data is divided into two subsets,
for training and testing (or validation) respectively. The testing performance is
recorded. Similarly, multiple rounds of cross-validation are performed under

different partitions. Testing results from all rounds are finally averaged to
generate a more accurate estimate of model prediction performance. Cross-
validation helps reduce variability and therefore limit problems like
overfitting.
There are mainly two cross-validation schemes in use, exhaustive and non-
exhaustive. In the exhaustive scheme, we leave out a fixed number of
observations in each round as testing (or validation) samples, the remaining
observations as training samples. This process is repeated until all possible
different subsets of samples are used for testing once. For instance, we can
apply leave-one-out-cross-validation (LOOCV) and let each datum be in the
testing set once. For a dataset of size n, LOOCV requires n rounds of cross-
validation. This can be slow when n gets large.On the other hand, the non-
exhaustive scheme, as the name implies, does not try out all possible
partitions. The most widely used type of this scheme is k-foldcross-validation.
The original data first randomly splits the data into k equal-sized folds. In each
trail, one of these folds becomes the testing set, and the rest of the data
becomes the training set. We repeat this process k times with each fold being
the designated testing set once. Finally, we average the k sets of test results for
the purpose of evaluation. Common values for k are 3, 5, and 10. The
following table illustrates the setup for five folds:
IterationFold 1 Fold 2 Fold 3 Fold 4 Fold 5
1 Testing TrainingTrainingTrainingTraining
2 TrainingTesting TrainingTrainingTraining
3 TrainingTrainingTesting TrainingTraining
4 TrainingTrainingTrainingTesting Training

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could miss none of those little items, no matter how she tried to ignore
them.
On the first of December after looking over a horde of frightening bills,
Cecily went to see her stepfather at his office. She chose the office as
affording a chance for a less intimate meeting than one at her house. The
array of clerks and stenographers and glazed doors made her feel very
impersonal and keyed her up until she found herself confronting her
stepfather over a glass-topped table and looked straight into his grave keen
eyes.
“What is it, Cecily? Do you need some money?”
“I can’t get along on what I have. That house won’t run on two hundred
and fifty dollars a month.”
“Of course you can’t. I could have told you that. Do you know how
much it costs to run mine? Well, I won’t tell you. I’ll place a check to your
credit to-day, my dear. And it will make me very happy.”
“That wasn’t what I came about, but it’s what I knew you’d suggest,”
she said, smiling at him. “I want to mortgage my house.’
“Why? Mortgage? You’ll do nothing of the sort”
“Well, I could sell it, but I’d like to live there during the winter, you see.
Then I could sell it later. I thought of a mortgage in bed last night. I don’t
quite know how to do it, but I’m sure that would be a way out. You’ll help
me, won’t you?”
The old man got up and walked about the office angrily.
“You’re making me very angry, Cecily. Very angry.”
“I don’t mean to. I don’t want to. But I’ve got to do this the fair way. I
can’t take money under these circumstances.”
“You’re obsessed,” said Mr. Warner. “Now, look here, Cecily, I’m your
father, you know.”
“I know you are—and you’re more than most fathers ever could be, and
so you must see that——”
“I’ve a right to support my own grandchildren.”
“If they were in need,” said Cecily, slowly, “it would be different. But
they aren’t. They could live on my money easily if we had a flat or a little
house. I’d sooner have a little house than a flat. But to keep that big house
running on your money—I can’t do it. Don’t you see that the children are

my job—all I have left! I want them to be mine, and I can’t feel that they
are unless I do it.”
Mr. Warner looked at her with his brows knit.
“How much is the house worth? Fifty thousand?”
“Forty-five, wasn’t it?”
“I’ll see about your mortgage, Cecily, but you must promise not to sell it
or do anything further without consulting me.”
She thanked him and promised and left the office, conscious as she was
nowadays that a buzz of comment and gossip followed her. Mr. Warner,
watching from the window, saw that she was not driving a car, saw her
cross the street on foot.
“Steel underneath,” he said aloud.
At noon he met Dick at the club and called him. “Do you know Cecily is
mortgaging her house?”
“Hell! And I deposited money to her credit last month.”
“She won’t touch it, Dick.”
“One of those ridiculous notions. The money isn’t important. I argued
about that, Mr. Warner. I did my level best. It makes me feel a fool, you
know.”
“You haven’t argued any more than I have. But I guess she’s got an idea
back of it all. She wants to feel that it’s she who looks after the kids.”
A dull flush crept up into Dick’s cheeks.
“She looks after them, anyway, no matter whether I pay a bill here and
there or not, doesn’t she? And it puts me in a rotten position.”
Mr. Warner laid a comradely arm over Dick’s shoulders.
“You are in a rotten position,” he agreed, “and you got yourself into it—
wanting to have everything in the world. Cecily is worth sacrificing some
things for, my boy.”
“It wasn’t that I wasn’t willing to give up anything,” Dick shrugged
away. “What’s the use of going over it? The thing’s threadbare.”
“How are things with you now, Dick?”
“Hell to pay on the ranges. Matthew wants me to spend part of the
winter there and I think I may. It was well enough to keep your hand on

from here in the old days, but things are different now. It’s going to take
brains to handle that yapping crowd of Slovaks up there.”
“How can you handle it on the ground?”
“Watch things. Let them know you’re there. Be a bit more friendly. They
like the idea of the city men being on the ground, you know. And I could
keep an eye on some of these raw superintendents. There’s a good deal of
mishandling, you know.”
“Well, I’m telling you, Dick, that if things get any worse we’ll have a
time handling every one.”
Dick nodded and went away. He was in a nasty temper. He was
becoming increasingly resourceless out of actual working hours. While he
was at work he was all right. It was after six o’clock that things wore out.
He plowed through diversions too quickly, try as he might to make them
last. The crowd he had been “traveling with,” he told himself, were
becoming very tiresome. He knew exactly when they would do everything
they did—at what stage of the evening each one of them would change his
temper or his way of talking, and it was all boring. He drifted into cafés, but
the acquaintances whom he met there all knew about him and Cecily, and
though plenty of groups tried to attach him, and would gladly have done so,
he had an irritated sense of the comments about him that kept him from
wanting to draw down the fire of more of them.
It irritated him, too, to know that his gayety was becoming more and
more perfunctory and that he was not getting fun out of things which should
have been fun. Several deliberate efforts to amuse himself had failed
completely, ending with reaction and disgust. He had tried, in company
with some men who were used to that sort of thing, to pick up some girls at
a public dance hall—and long before the expected end of the evening,
found himself so let down and bored that he left the crowd, with the
flimsiest excuse. He had tried a bit of joyriding, but the seductions of that
experience with the almost professional joyriding girl who knew just how to
curve into the arm of the man at the wheel of the car had left him not only
cold, but amused in a kind of sarcastic, unsmiling way. There were times
when he felt that he had lost his youth and his power to be amused—his
ability to enjoy things. Cecily had taken the verve out of the very things
which had been contested points and for the right to enjoy which he had so
stubbornly contended.

Also Christmas was approaching and the nervous effort to banish all
thought of the holiday from his mind was wearing. With problems all day
and little relaxation anywhere, Dick was beginning to show baggy pouches
under his eyes and little lines trailed down from the corners of his mouth. In
his office that afternoon he was so exacting that his exasperated
stenographer, escaping finally from his criticism, told her friend in the next
office that she “didn’t wonder Mr. Harrison’s wife wouldn’t live with him.
He’s got a terrible temper—fierce.”
Poor Dick, when the office was empty and he had to leave it, strolling
back to the club to a choice of chicken casserole or English muttonchop,
without enthusiasm, trying not to notice that the shop windows were hung
with tinsel and gaudy with that gaudiness which is so beautiful at Christmas
time, swinging past the toyshops with carelessness, and then turning back to
look into the window of one of them.
Then came the day before the holiday with one of those fine pictorial
snowstorms to make it all the more festive, with the unavoidable truce with
work and struggle which pervaded even the world of business, with the
greeting on every one’s lips. It seemed to Dick that he could not turn around
without stirring up some memory of his home. He could see it so clearly,
decorated for Christmas with a green English holly wreath below the brass
knocker on the white door, poinsettias in the hall, the tree in its corner of the
long living-room and the fire blazing; Dorothea’s little mind aflame like her
cheeks with excitement; Leslie walking stumblingly among his presents; the
baby walking now, too, probably. He would catch himself wondering what
new words Leslie had learned. He wondered if Cecily would have a
Christmas celebration this year. Yes, she would. It was like her to uphold
things for the children even if her own fun were shattered. But she must be
short of money. At that thought the glimmer of tenderness in his heart went
out. It was bad enough not to see the children, but not to be allowed to
support them! To be treated as a criminal outcast! Well, he’d left of his own
free will, he reminded himself, and went on to the embittering thought that
he’d tried to solve things without a break. They couldn’t have gone on—
silent evenings, the constant sense that he wasn’t pleasing his wife, that she
felt he was deteriorating, the slight that her rigorous standards, single track
virtues put upon him. He wasn’t happy; she wasn’t happy. It couldn’t go on.
Matthew had said it couldn’t go on as it was. Probably not. Probably end in

a divorce. That would be a nasty business; no worse than lots of other
people went through, but Cecily would hate it. Poor Cecily! And in its
endless track his mind pondered again whose was the fault and where the
remedy.
He had a stupid supper at the almost deserted club, fancying sensitively
that the few men sitting at nearby tables were pitying him. Then, following
instinct rather than intention as he started out for his usual walk, he found
himself headed in the direction of his former home. It was several miles, but
he walked them quickly. In the familiar street which he had avoided since
he had left Cecily, he felt a desire to escape notice. But he would not yield
to that and kept his head up as he went along the side of the street opposite
his own house.
There were lights in the house. That made him feel a little queer. He’d
known of course that the place was inhabited, that life was going on there as
usual, but the sight of the lights made it so much more real. The wrought
iron porch lanterns brought out the holly on the door. It was not a wreath
this year. A sprig of green tied to the knocker.
With the strangest clandestine feeling, Dick crossed the street and stood
beside the gate. Three months and more since he had opened that high iron
gate whose beautiful workmanship had made him so proud. He opened it
now softly and went up the side of the brick walk, in the shadow cast by the
great fir trees. There, under the living-room windows, he saw them all—
Ellen holding the baby, Dorothea and Leslie on the floor beside the tree and
Cecily in the corner of the big couch. As he saw, he knew what he had been
hoping. There was no one else there.
The children had grown so unbelievably in three months!
Cecily heard the door open. She and Ellen turned their heads
simultaneously, and then at a swift movement of Cecily’s, Ellen sat still and
let Cecily go through the hall.
“Dropped in to see the tree,” said Dick, trying to be jocular.
Then he held out his arms and Cecily crept into them, both of them too
glad for the blessed relief which had come to their starved emotions to
question the right or safety of such an end to their separation. It was such
sheer joy to be together again, to see each other, to deliberately forget all
issues in the hour of delight. Dick had not let himself think of his hunger for

the children. But to hold them now, play the old games with them, hear
them laugh, try their toys——
“I wasn’t sure you’d let me in,” he said apologetically, “or I’d have
brought some presents for all of you.”
The tears in her eyes and the joy of her smile were consolation for that.
Because the children were so small and ready to accept all events and
because Ellen’s eyes never pried, it was very easy for them. After a while
the children had to go to bed and Dick and Cecily took them up together in
a riot of fun. It was all as it had been, thought Dick. He drew the familiar
blue blanket up over Leslie, who was too tired to struggle and resent being
put to bed. And Dorothea put her arms around his neck in that same baby
way.
When they went down again they found that Ellen had spread a cloth on
a little table before the fire and left supper there for them. She went out.
They heard the back door close and her firm heavy footsteps crunch
through the snow. And embarrassment settled over the man and the woman
sitting in such apparent comfort there. They were paying already for that
leap to emotional conclusions. Each of them asking fearfully what all this
meant and what was passing in the mind of the other, each trying to avoid
subjects which might hurt the other and conscious of the multitude of
dangerous subjects. The supper gave them something more to do, but that,
too, came to an end and Dick carried the little table out through the dining-
room to the kitchen. Cecily tried to busy herself with something, unsettling
things that she might settle them again. It was horrible, she told herself, that
she should be so afraid of Dick’s presence. Why did he have that air of half-
apology, of intrusion?
He did not help her when he came back, but stood, looking curiously
about the room.
“Same place,” said Cecily, trying pathetically to be light.
“Did you mind my coming?”
“It was a dreadful evening until you came,” she answered.
They were conscious that several hours lay before them. It was only
half-past eight o’clock. And on both of them, afraid to begin to talk, hung
those two hours for which they were so hungry and yet which were so
oppressive. They sat down beside each other on the long davenport and
talked of the children—anecdote after anecdote—but stories of them led to

plans, and as plans began to be suggested in their minds they became wary
again. Dick drew his wife’s head down against him finally, and for a long
time they rested so. Odd, that the mere contact could give them such
security. Farther and farther drifted their thoughts from discussion and
analysis; back to memories of each other.
In the morning, while they were at breakfast with the children, the
telephone rang.
“It’s father,” said Cecily, returning from answering it, “wanting to wish
me a Merry Christmas. He was so upset because he couldn’t get here for the
tree last night. He’s coming to get us for dinner. We’re dining with him.
Della and Walter are going out. Will you come too, Dick?” The last a little
hesitantly for, after all, Dick had not declared his plans.
“Did you tell him I was here?” countered Dick.
She flushed. “I didn’t like to tell over the telephone.”
Dick looked at her gravely. She realized as his eyes met hers that he
looked older—and harder.
“I’ll tell grandfather for a surprise,” said Dorothea complacently.
They both winced. “We must talk things over this morning.”
“But it’s Christmas day,” protested Cecily.
He frowned that old familiar frown, the frown that always came when
she had sought to lay down a rule as to when he should or should not do a
thing.
“Better right away.”
In answer she rang for Ellen and asked her to take the children out.
“Take them for a walk before their naps, will you, since we’ll be out for
dinner.”
Ellen nodded and hurried them out, all smiles. It was a true holiday for
Ellen, appealing to all her romantic sense. The quarrel was over; the
husband had come home.
They did not seek to leave the room. Dick wandered to the window and
looked out at the children stumbling down the snowy path. Then he turned
to Cecily, sitting so slim and erect in her breakfast place.
“I suppose it was silly to come back,” he blundered.

“Silly?”
There she was, tripping him over a triviality again.
“Well, perhaps not silly, but unwise.”
“Why? Didn’t you want to?”
“You know I wanted to. But we’ve settled nothing, dear.”
“Why did you come?” asked Cecily.
“I suppose I was called by the holiday,” answered Dick with simple
truthfulness. “I came instinctively. I—couldn’t help it.”
“But you feel just the same?”
“The same as what?”
“As you did when you went.”
Dick seemed to search his memory. “Well, it never was very clear to me
just what the issue was. If you mean that I feel terribly in the wrong now,
terribly culpable for all those misunderstandings which broke us up, I don’t
believe I do feel that. I still don’t know quite where I was wrong. I was
stupid about things, little things, of course.”
“But, Dick, don’t you feel that—that this life is the best? With me, with
the children? Don’t you see that the things I wanted to avoid had to be
avoided?”
He scrutinized the dogmatism of her face carefully, painfully. It was such
an exalted face. It seemed such a pity that he couldn’t put it at rest. All she
wants, he thought, is a whale of an apology for sins I may not be conscious
of, but which I may have committed.
“Can’t we be happy together—as we were last night—after this?”
He felt now that she was trying to pledge him. The fury of her ideals was
pursuing him again.
“Do you mean spend every night sitting on the davenport together?” He
meant to lighten it, but as usual he failed. Her face showed her shrinking.
“Don’t mock,” she said.
Dick took a restless turn or two around the room and came back to stand
over his wife.
“Cecily, I was a fool to disturb you last night. I was worse. I was a
robber. I robbed you of the peace that was beginning to come to you. I

shan’t ask you to forgive me. It was because I couldn’t help it. But I won’t
repeat it. I’ll not bother you again.”
“Are you going away again?”
“It’s better, I think. We aren’t closer—aren’t easy together—aren’t really
happy. Isn’t it better that I go?”
There was pleading in his voice, but she was too hurt to hear it; pain, but
she was deaf to that, too.
She could only see that he could go; that his going was an insult to her
desire that he should stay.
She got up, clutching the edge of the table.
“Then, go—go quickly!”
Dick put his hands to his head. They’d done it again. Again. Well, this
time she meant it. But to go without a caress—— He went towards her.
“Can’t I kiss you once before I go?”
And because she yearned for it, she taunted him.
“Your kisses aren’t love,” she said bitterly. “They’re desire!”
Ellen, returning with the children from the walk, found that Cecily was
shut in with the baby. When she finally came from her room she was
dressed to go out.
“Mr. Warner will be here any moment,” she said. “I’ll dress Dorothea.
Put that white dress with the yellow embroidery on Leslie. Don’t forget to
lock up the house when you go out, will you? And Ellen, don’t say anything
to any one about Mr. Harrison’s having been here. He won’t be here again.”
“Oh, I’m sorry,” said Ellen irrepressibly.
Dorothea, hearing her mother’s sentence, had set up a howl, and Leslie
joined her for company.
“My father is gone again,” wailed Dorothea with spirit, making comedy
out of tragedy, until Ellen, contrary to all discipline, presented her with an
irrelevant chocolate cream which distracted her temporarily.
Ellen did not say anything more, but she kept her eyes on Cecily’s
drooping figure and Cecily smiled at her rather pathetically.
“I suppose it’s all my fault, Ellen. Something’s gone wrong with the
world and I’m tangled up in the machinery.”
Ellen never spoke in metaphors.

“It’s not what should be with a loving couple like you and Mr. Harrison.”
There came to Mr. Warner’s house a few hours later an A. D. T.
messenger, weighted with an immense box of flowers. In the depths of the
layers of roses, so fragrant and cool, was Dick’s note. “Cecily dear, I had no
right to come, no right surely when I had come, to hurt you so. Please try to
forget it and me; and if you can’t quite do that think of me as having a heart
full of gratitude and respect and affection which expresses itself badly, but
as best it can. To-morrow I want to send the children some Christmas
things. Please let me do that, and if there’s any way I can help you—
anything you will let me do for you—it will make me happy. Merry
Christmas! Dick.”
He wrote that, after many drafts of its wording, sitting in his room at the
club, and he hunted all over the city for flowers, finding some at length in a
hotel flower-shop. If Cecily could have seen him as he wrote, his big hand
thoughtfully penning the words, his face drawn and set, she would have
known that it was not she alone who was humiliated and outraged by the
turn of things. When he had sent the flowers he wrote other letters, one of
them to Matthew, telling him that he contemplated going at once into the
mining towns.
“It’s necessary, gentlemen,” he told a certain group of men a few days
later as he outlined his plans. “We’ve put roughnecks up there in charge;
we’ve let the social workers in and paid their salaries; we’ve put young
fellows out of college up there, and none of it has worked. There’s more
trouble all the time. The only thing we haven’t tried is for one of us who
really is responsible to go up himself. At the present moment I can see my
way to spending some time there. I won’t bring about a reformation, you
know. I don’t promise to stop all the strikes, or to clean out the I. W. W. But
I can promise you accurate information after a few months as to what all the
trouble is about, and if anything can be done about the situation other than
let it go to pot and save what we can of our money. How about it?”
They listened to him with interest—all these men who had money and
all the things dependent on money at stake; seeming not too serious, as is
the usual way of men of big affairs, showing no great enthusiasm, no
excitement.

They liked Dick because he was, compared to most of them, so young
and yet so sane and so successful, and they knew he’d “had trouble” with
his wife, L. A. Warner’s stepdaughter, and they were sorry for that because,
though the age of romance was past for them and the word love had lost its
magnetism, they knew that a domestic upset was a serious thing for a young
man. “Might send him to the dogs,” they would have said.
“The point is that with the ‘Senator’ away (that was their jovial term for
Matthew), we can’t really spare our other young man, can we? When
Allenby went he assured us he was turning everything over to you to
handle, and that you’d look out for us,” said old Mr. Cox, tilting his chair
back and looking at Dick astutely.
“The more I look at the work there is to be done, the more I see that the
machinery of distribution, which is here, is in such good shape that any one
of three fellows in the office, whom I could mention, could look out for it.
The big problem is the problem of production, and that needs to be
investigated from the ground up—not on the basis of what things were ten
years ago, or five years ago, or one year ago—but as they are now.”
“Let him do it,” said A. C. Miller, who spoke briefly.
“Some I. W. W.’ll put a bomb under him, and we can’t spare him.”
“Gentlemen,” said the chairman, with the ponderosity which had given
him his position on so many boards, “may we have a motion?”
They were given a motion and Dick had his way. He heard the chairs
scrape back, saw the discussion break up into fragments, saw the men go
out, with their friendly, terse nods to each other and a wave of energy swept
over him. Mare’s nest, chimera, windmills—whatever it was that he was
going to find or to tilt with, at least he had a job that was fresh and that
would take him out of town.
He scribbled on a telegraph blank, “Put it over all right,” and addressed
it to Senator Matthew Allenby.
In his delight he wanted to do something for somebody, and his thoughts
turned to Cecily.
He sought a florist’s again.
“Every morning,” he said, to the dapper young clerk who waited on him,
“every morning I want you to make a selection of the flowers which you

have which are suitable—suitable for the middle of a table, centerpiece, you
know. Ever have an order like that?”
“Yes, sir,” said the omniscient clerk. “Several ladies have left standing
orders for their tables.”
“Good line, that last. Well, you do as you do to them. This address for
the flowers; that for the bills.”
“Any length of time, sir?”
“Oh, yes,” said Dick. “Always. As long as we both (you and I, my
friend) shall live. Always. Why not? Sure.”
“Kind of a nut,” said the clerk, gratefully regarding the departing figure
of the man who had given him the biggest order he’d ever booked. “Sweet
on her now, you know. But he’ll get sick of that. Wait till he gets a bill or
two.”
Dick went out and jumped into the roadster which stood by the curb, and
started for the garage. He wouldn’t sell the car, but he would put it up. He
couldn’t take it up to the range and play the game he wanted to play.
He skirted the main streets, for the traffic was heavy during this late
afternoon hour. Just off one of the big commercial streets he turned down an
avenue always of doubtful repute, even in these days of supposedly high
city morals—a street of ramshackle buildings which were supposed to be
apartments, but behind whose dingy lace curtains quack doctors and
dentists had their lucrative offices, spiritualists held cheap séances and other
money-making transients had their headquarters. Just in front of his car,
held up by a confusion of trucks in front of it, Dick saw a singularly well-
dressed woman whom he looked at with interest and then amazement as he
recognized her. It was Della. No doubt of it. They had all criticized that
short white fur coat when she bought it. She was going along slowly, close
in to the walls which bordered the sidewalk, looking for something in some
window. It struck Dick that she wanted to avoid detection. Then he turned
to put on his brakes and when he looked up again she had gone—through
one of those dingy wood framed doorways. Dick pulled up his car and
waited farther down the street. It was growing darker now and he was
worried. No place for a girl to be alone. What could she be up to? After half
an hour he saw her again, the white fur coat starting out of the dusky street.
He followed her with the car and hailed her when she was four or five
blocks away from the place she had stopped.

I
“Hey, Della. Can I bring you home?”
She got in with an attempt at her usual hilarity, which struck him as very
forced. He maneuvered until they stopped under an arc light and then shot a
swift glance at her face. She had been crying, all right. Crying her eyes out.
“Where have you been all afternoon?” he asked her casually.
“Just shopping. I was late for dinner. Awfully nice of you to pick me up,
Dick.”
So she didn’t mean to be communicative. Dick dropped her at her door
and went on, pondering. Well, it might have been some obscure relative,
some crazy notion to see a fortune-teller who told her she was going to die
young. It wasn’t any love affair, anyhow. Not from the look of those eyes.
She looked as if she’d been beaten. Dick decided to dismiss it from his
mind. It wasn’t his business, after all.
CHAPTER XXVI
T was two days after the New Year had come in. Only nine o’clock, but
Cecily had almost decided to go to bed. The timidity which she had felt
often at first in being in a house alone at night had almost gone now, and
she was locking the doors and windows and turning off lights with
mechanical routine. The house was full of the fragrance of hothouse
flowers. Dick’s generosity had not taken into account the fact that flowers
last more than a day and his “middle of the table” bouquet had overflowed
into the other rooms. Cecily looked at them, wondering how long he
planned to keep this up. She knew from the accounts in the paper that he
had left town, for the papers had got wind of the new plan, and, to Dick’s
rage, played it up in one or two issues before they were suppressed.
The flowers pleased Cecily. For once she had hardly thought of whether
they were consistent with the separation between her and her husband or
not. It had been sheer delight to have flowers. As she went down to put
some little yellow pink rose deeper in water she heard an unfamiliar sound
and straightened up suddenly. There was surely some one at the side door
fumbling with the door-knob. She looked at the clock; quarter past nine
only. It couldn’t be a burglar. Somebody drunk, perhaps. Pushing open the
door to the pantry, she whistled softly and the big Airedale came rushing in.
With him jumping beside her, she went towards the door.

This time the person, whoever it was, decided to ring. Cecily turned on
all the lights and opened the door, prepared to threaten or command. But at
sight of her visitor she stepped back in amazement, a restraining hand on
the leaping dog.
“Why, Della! where did you drop from?”
Della, swathed in white fur, slipped inside the storm door and closed the
inner one before she answered.
“I just came over,” she said, a little breathlessly. “I wanted to see you—
you know, to talk to you.”
“I thought you were a burglar,” laughed Cecily. “I was going to let Bill
fly at your throat. Why did you try the side door and where is Walter?”
“Walter——” Della was standing in the light now and Cecily could see
that her little pink face so made for powder and smiles was streaked with
tears and still distorted with some violent emotion. “Walter’s home, I guess.
He doesn’t know I came here. I thought he wouldn’t know, so that’s why I
came to you. He thinks I’m afraid of you and wouldn’t dare to come. But I
showed him I did come here and he doesn’t know where I am and——”
“Hush,” said Cecily, “you’re hysterical, Della. Have you been walking in
the snow in those slippers?” She looked down at the slim black satin
slippers from which caked snow was already melting. “Come upstairs to my
room and get dry. Then,” she rode over an immense impulse to refuse
sanctuary to Della, “then you can tell me all about it.”
Della followed her. But she could not be silent.
“I came to you, Cecily, and I know you don’t like me very well, and I
don’t know as I blame you, because you and I aren’t like each other at all,
and I’ve always been scared to death of you, but what could I do, for I
couldn’t go to any of the girls because they don’t know anything about
things like this—at least, I hope they don’t, although perhaps the married
ones have troubles of their own. It isn’t as if I were older and I thought, of
course, Walter would be nice and sympathetic and he wasn’t at all, but he
was so stern and cross and scolding—and I can’t be scolded and I won’t
have it.”
She ran on, plunging through incoherent phrases and sentences, with the
tears running down her foolish little cheeks. Cecily looked at her in
amazement.

“Hush,” she said again, “you’ll make yourself ill. What is it, Della?
Have you had a quarrel with Walter? Is that it?”
“That’s only a little bit of it,” sobbed Della, as the cause of her trouble
rose again to bring her horror, “only a little of it. Although I thought he’d be
nice to me! I thought he’d be nice to me; a girl gives up a lot when she
marries a man, and it’s pretty hard to have him turn against her.”
Cecily had her upstairs now, sitting on her chaise longue, and she was
forcing quilted slippers upon her.
“If you really want to tell me, Della, you must stop being hysterical and
begin at the beginning. And you mustn’t be quite so noisy or you’ll wake
the babies in the nursery.”
Della shuddered from head to foot.
“That’s it!” she cried, her eyes distended. “Babies! Cecily, Cecily, they
say I’m going to have a baby and I won’t, I won’t! And Walter is so cruel
about it.”
Cecily was no longer naïve about such things. She had heard many
conversations which had told her unwilling ear much about involuntary
motherhood. But never had she seen such horror as was Della’s. Mixed with
her recoil from the violence and the ugly mood of the girl was a queer
feeling of responsibility that was almost pride. Della had come to her.
“You’re going to have a baby, Della? Why, you silly girl, that’s the nicest
thing that could happen to you. I’m awfully glad.”
“Don’t! I thought you’d help me! I know it’s all right for you to have
children, Cecily—you’re the domestic type—but I’m not. I won’t, I won’t, I
tell you! I won’t lose my figure and have to give up dancing and get old and
ugly and repulsive and listen to babies all the night and die!”
Cecily sat down beside her and took hold of one clenched little hand.
“Tell me all about it, Della,” she said quietly. “When did you find out all
this?”
“Last week.”
“Did you see Dr. Norton?”
“Not him. He was horrid to me once. I wouldn’t go to see him.”
“But how do you know?”
“I saw another doctor.” She had stopped crying. “I hunted one out on
Eighth Street where no one would know me or begin to talk about it or tell

anybody.”
“Eighth Street! Why, Della, that’s no street for decent doctors!”
“Lots of doctors advertise in the windows along there,” said Della
sullenly. “Anyway, I wasn’t going to Dr. Norton.”
“And this other doctor told you what?”
Della shuddered convulsively. “He was horrible—horrible. Such an
awful place with dreadful looking women around in the waiting room. And
a man tried to flirt with me and the doctor patted me on the arm and told me
that he could tell me more about it and that he’d have to see a hundred
dollars first. And I didn’t know what to do, Cecily, so I hurried out of there
and it’s lucky I wasn’t murdered or didn’t have my gold purse stolen.”
“What happened then?”
“I’ve been worrying till I was almost dead. Walter knew I wasn’t myself
—my skin was all dead looking! Look at me, Cecily. I’m a perfect fright!
So to-night he found me crying and he asked me what the matter was, and I
told him, and he was glad, he said. Glad! Glad that I was going to die!”
“Nonsense, Della. Why shouldn’t he be glad?”
“He said he thought it would mature me and that we’d both be happier
and that he’d try to make it easy—as if it could be easy! I’d die; I’m the
type that always dies, leaving a baby, and the man marries again. And I told
Walter he’d marry again; no, I told him I wouldn’t have it, I wouldn’t, and I
won’t!”
“But there’s nothing to do about it.”
“There must be things. There must be. Walter said that same thing—
nothing to do. But I’ll kill myself! I’ll show you all! I’ll show him!”
For a few minutes Cecily let her storm. She was collecting her thoughts.
The mad ignorance, the uncontrolled violence of this girl in the face of one
responsibility horrified her. And astonished her, too. She had not dreamed
Della was so ignorant.
“But, Della, dear,” she found herself pleading, “you must have known
that was one of the things that happen to people when they marry—babies.”
Della shrugged her shoulders in angry impatience.
“Don’t be silly, Cecily. You know lots of girls don’t have them and don’t
ever intend to have them. Once in a while a girl makes a mistake.”

At her full height Cecily looked down on Della in disgust. The things
she was instinctively fighting against, for which she had endured so much,
all seemed epitomized in this hysteric figure of inconsequence which was
so helpless up against a fact of life—a fact demanding personal sacrifice.
“Incapable,” she thought, feeling strong as never before. And then there
came through her scorn that pride again that Della had come to her, and a
real anxiety for Della and the child which might be hers.
“If this is true,” she said, “you know you ought not to be letting yourself
get wrought up. It’s bad for you and bad for the baby,” she finished
tactlessly.
A shiver went over the crouching girl.
“Don’t! Don’t!” she moaned. “I won’t have it, I tell you. I wish I’d never
seen Walter. He’s let me in for this now, just as he let me in for marrying
him after that mess at college. I wish I never had married him in spite of all
their silly talk. Anybody could have known I wasn’t a tough girl. I didn’t
stay out all night because I wanted to. I couldn’t help it if the silly car froze
up and we had to go to the nearest place to keep from freezing ourselves.
But he was so fussy that he got me all worked up, too, and I married him.
And now, after all I’ve done for him, he was so cruel to-night. He didn’t
care about my feelings. He just wanted the horrible——”
“Hush, Della.”
“I thought you’d help me. You’ve been married a long while. You——”
“Della, if you mean what I suppose you mean by ‘helping’ you, I
wouldn’t have the faintest idea of how to help you in that way. And of
course I wouldn’t anyway. It’s not only wicked to consider such a thing; it’s
the worst kind of wickedness. It’s dreadful, awful, criminal.”
Her words seemed to dry Della’s tears. She got up, wrapping her coat
around her.
“Well, I can kill myself,” she said, her blurred blue eyes full of childish
drama. There was something in this pure childishness that went home to
Cecily’s heart. There were only three or four years between her age and
Della’s, but she felt decades older and wiser. It came to her suddenly that
this was no figure of evil before her. It was just a frightened little girl,
uttering angry threats in the face of her fear—ignorant of all things that
might stand her in stead in a crisis, equipped only to meet gayety and

enjoyment. Cecily had the heavier equipment and she longed to lend it, to
bolster up this frail little soul. She took her by both shoulders.
“You won’t do any such ridiculous thing. Sit down and let me tell you a
few things. Let me tell you,” she went on to Della, reluctant beside her,
“what a joy it is, what wonderful happiness it really is, in spite of all the
pain and trouble, to have a baby of your own, something living that you
really created and made strong. Why, it’s most beautiful.”
But Della’s face was hard and drawn and sullen.
“That’s if you like that sort of thing. I don’t, that’s all. I’m not the type,”
she repeated.
The motion picture phrase struck Cecily as true. Della wasn’t the “type”
to understand what she was talking about. Della had been told that she was
the type to wear electric blue, to carry off a Marcel, to dance the toddle.
And it was true. She hadn’t got a word of what Cecily said, and she
wouldn’t. To her little mind there was no entrance for the abstract or the
philosophical. It must all be pictorial.
Cecily felt failure, and as she felt she wasn’t gaining her point, she cast
about for new weapons. She was on the offensive with her philosophy this
time, and she realized that the old weapons of defense were useless. She
could not make Della see these things by talking to her. But there must be a
way; she must find it.
“You and Walter can talk about how low I am when you see him. He
feels like you do,” said Della bitterly.
“Walter thinks you’re wonderful,” answered Cecily. “He’s crazy about
you.”
Della began to cry again. “Then why is he so cruel? Why does he want
me to go through such an awful thing?”
“He doesn’t mean to be cruel. Of course he doesn’t realize what it means
to a woman.”
That was the right note. A hint of martyrdom.
“I should say not.”
“And men are always pleased at news like that. I tell you, Della” (Cecily
was striking her stride now), “men are never as crazy about a woman as
when she is expecting her first baby. You see, they feel so grateful and so
miserable.”

“I should think they would,” moaned Della.
“Of course you can’t see it as I do, but really the nicest time of my
married life was before Dorothea came.” She faltered a little at that, for it
was hard to use those memories for Della’s sake, but she realized that Della
was listening finally.
“Dick couldn’t do enough for me.”
“Didn’t you die—staying in for months?”
“But you don’t, my dear—that’s awfully old-fashioned. That belonged to
the days (here was an inspiration) when they didn’t have the modern point
of view. And you can get the loveliest dresses. You’d be like a picture,
Della.”
It was clear that at last Della was getting a Madonna vision of herself.
She had stopped crying except for an occasional dab at her eyes and with
her eyes fixed on the other side of the room looked very young and pathetic.
“There’s one thing,” said Cecily. “It isn’t all misery by any means.
Everybody’s so awfully interested in you and so awfully nice to you; and
you can do everything except maybe dive and dance—along towards the
end—and then of course all the——”
“But the end when you die, maybe!”
“You don’t die. You have so much anesthetic you don’t know anything
about it. Then you have a cunning little baby and you can have the loveliest
baby things. Of course Walter wouldn’t want you to have any of the hard
care of it.” Skillful Cecily, sliding over all the things that had made her
children real to her; nights of watching and caring when a baby had a cough
or a touch of croup, the routine of nursing, the fatigue and ill health which
might be eased, but which could never be destroyed. She was fighting for
Della’s baby now, and if Della saw it as a pink and white doll in a dotted
Swiss cloud, with herself as an invalid in interesting negligees, still she was
gaining her end. That it was a great step for Cecily—this relinquishing of
her own fastidiousness in discussion, this generosity of method—she did
not realize, yet.
Della was growing calmer. Her hysteria had half spent itself and Cecily
had turned her mind away from horrors for the moment, anyway. It was
probably the first time for many days that she had been able to see anything
except blackness, and the relief showed in the relaxation of her body. Cecily
ventured a little further. She was feeling a sudden warmth of affection for

Della. The sense of her usefulness to some one outside her own group of
children and the feeling that some one had turned to her for confidence and
compassion was expanding all her starved emotions. She put her arm
around Della.
“Poor little Della.”
“Isn’t life terrible?” asked Delia mournfully.
“You want to stop worrying about everything,” answered Cecily. “You
want to just lie back and let us take care of you. We’ll all see you through it
and make it just as easy. Just let me talk to Walter and explain things and he
won’t upset you again.”
“He was so cruel.” Della relapsed again. “I didn’t tell you everything. He
said I was a coward and——”
“He was excited. You’d better spend the night here now, Della. I’m
going to tuck you up and telephone to Walter where you are.”
“Do you think he’ll come over?”
“Of course—to make sure you are all right.”
She got Della into bed finally, diverting her momentarily by pressing
upon her her most elaborate nightgown and negligee. Della cast a fleeting,
discouraged glance at herself in the glass and slid between the covers, too
worn out to resist longer.
“Of course I shan’t be able to sleep,” she said, “so if Walter comes over,
he might come up for just a moment.”
Cecily nodded and turned the light low. Then, from the safe distance of
the kitchen extension telephone, she telephoned Walter and half an hour
later let him in.
“You say she’s gone to bed?”
“Quite worn out. But she wanted to see you.”
“Is she still hysterical?”
“No; she’s calmer.”
Walter’s boyish face had grave lines of anxiety traced on it. He paced up
and down the room for a minute, then turned to Cecily.
“Tell you what was the matter with her?”
“Yes.”
He took a few more turns.

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