# Machine Learning: the Titanic dataset¶

If you want to try out this notebook with a live Python kernel, use mybinder:

In the following is a more involved machine learning example, in which we will use a larger variety of methods in veax to do data cleaning, feature engineering, pre-processing and finally to train a couple of models. To do this, we will use the well known Titanic dataset. Our task is to predict which passengers are more likely to have survived the disaster.

Before we begin, there are two important notes to consider: - The following example is not to provide a competitive score for any competitions that might use the Titanic dataset. It’s primary goal is to show how various methods provided by vaex and vaex.ml can be used to clean data, create new features, and do general data manipulations in a machine learning context. - While the Titanic dataset is rather small in side, all the methods and operations presented in the solution below will work on a dataset of arbitrary size, as long as the data fits on the hard-drive of your machine.

Now, with that out of the way, let’s get started!

[1]:

import vaex
import vaex.ml

import numpy as np
import matplotlib.pyplot as plt


## Adjusting matplotlib parmeters¶

Intermezzo: we modify some of the matplotlib default settings, just to make the plots a bit more legible.

[2]:

SMALL_SIZE = 12
MEDIUM_SIZE = 14
BIGGER_SIZE = 16

plt.rc('font', size=SMALL_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title


## Get the data¶

First of all we need to read in the data. Since the Titanic dataset is quite well known for trying out different classification algorithms, as well as commonly used as a teaching tool for aspiring data scientists, it ships (no pun intended) together with vaex.ml. So let’s read it in, see the description of its contents, and get a preview of the data.

[3]:

# Load the titanic dataset
df = vaex.datasets.titanic()

# See the description
df.info()


rows: 1,309

## Columns:

columntypeunitdescriptionexpression
pclassint64
survivedbool
namestr
sexstr
agefloat64
sibspint64
parchint64
ticketstr
farefloat64
cabinstr
embarkedstr
boatstr
bodyfloat64
home_deststr

## Data:

# pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home_dest
0 1 True Allen, Miss. Elisabeth Walton female29.0 0 0 24160 211.3375B5 S 2 nan St Louis, MO
1 1 True Allison, Master. Hudson Trevor male 0.91671 2 113781 151.55 C22 C26S 11 nan Montreal, PQ / Chesterville, ON
2 1 False Allison, Miss. Helen Loraine female2.0 1 2 113781 151.55 C22 C26S -- nan Montreal, PQ / Chesterville, ON
3 1 False Allison, Mr. Hudson Joshua Creighton male 30.0 1 2 113781 151.55 C22 C26S -- 135.0 Montreal, PQ / Chesterville, ON
4 1 False Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0 1 2 113781 151.55 C22 C26S -- nan Montreal, PQ / Chesterville, ON
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1,3043 False Zabour, Miss. Hileni female14.5 1 0 2665 14.4542 -- C -- 328.0 --
1,3053 False Zabour, Miss. Thamine femalenan 1 0 2665 14.4542 -- C -- nan --
1,3063 False Zakarian, Mr. Mapriededer male 26.5 0 0 2656 7.225 -- C -- 304.0 --
1,3073 False Zakarian, Mr. Ortin male 27.0 0 0 2670 7.225 -- C -- nan --
1,3083 False Zimmerman, Mr. Leo male 29.0 0 0 315082 7.875 -- S -- nan --

### Shuffling¶

From the preview of the DataFrame we notice that the data is sorted alphabetically by name and by passenger class. Thus we need to shuffle it before we split it into train and test sets.

[4]:

# The dataset is ordered, so let's shuffle it
df = df.shuffle(random_state=31)


### Shuffling for large datasets¶

As mentioned in The Iris tutorial, you are likely to get a better performance if you export to disk your shuffled dataset, especially when the dataset is larger in size:

df.shuffle().export("shuffled.hdf5")
df = vaex.open("shuffled.hdf5")
df_train, df_test = df.ml.train_test_split(test_size=0.2)


### Split into train and test¶

Once the data is shuffled, let’s split it into train and test sets. The test set will comprise 20% of the data. Note that we do not shuffle the data for you, since vaex cannot assume your data fits into memory, you are responsible for either writing it in shuffled order on disk, or shuffle it in memory (the previous step).

[5]:

# Train and test split, no shuffling occurs
df_train, df_test = df.ml.train_test_split(test_size=0.2, verbose=False)


### Sanity checks¶

Before we move on to process the data, let’s verify that our train and test sets are “similar” enough. We will not be very rigorous here, but just look at basic statistics of some of the key features.

For starters, let’s check that the fraction of survivals is similar between the train and test sets.

[6]:

# Inspect the target variable
train_survived_value_counts = df_train.survived.value_counts()
test_survived_value_counts = df_test.survived.value_counts()

plt.figure(figsize=(12, 4))

plt.subplot(121)
train_survived_value_counts.plot.bar()
train_sex_ratio = train_survived_value_counts[True]/train_survived_value_counts[False]
plt.title(f'Train set: survivied ratio: {train_sex_ratio:.2f}')
plt.ylabel('Number of passengers')

plt.subplot(122)
test_survived_value_counts.plot.bar()
test_sex_ratio = test_survived_value_counts[True]/test_survived_value_counts[False]
plt.title(f'Test set: surived ratio: {test_sex_ratio:.2f}')

plt.tight_layout()
plt.show()


Next up, let’s check whether the ratio of male to female passengers is not too dissimilar between the two sets.

[7]:

# Check the sex balance
train_sex_value_counts = df_train.sex.value_counts()
test_sex_value_counts = df_test.sex.value_counts()

plt.figure(figsize=(12, 4))

plt.subplot(121)
train_sex_value_counts.plot.bar()
train_sex_ratio = train_sex_value_counts['male']/train_sex_value_counts['female']
plt.title(f'Train set: male vs female ratio: {train_sex_ratio:.2f}')
plt.ylabel('Number of passengers')

plt.subplot(122)
test_sex_value_counts.plot.bar()
test_sex_ratio = test_sex_value_counts['male']/test_sex_value_counts['female']
plt.title(f'Test set: male vs female ratio: {test_sex_ratio:.2f}')

plt.tight_layout()
plt.show()


Finally, lets check that the relative number of passenger per class is similar between the train and test sets.

[8]:

# Check the class balance
train_pclass_value_counts = df_train.pclass.value_counts() / len(df_train)
test_pclass_value_counts = df_test.pclass.value_counts() / len(df_test)

plt.figure(figsize=(12, 4))

plt.subplot(121)
plt.title('Train set: passenger class')
plt.ylabel('Fraction of passengers')
train_pclass_value_counts.plot.bar()

plt.subplot(122)
plt.title('Test set: passenger class')
test_pclass_value_counts.plot.bar()

plt.tight_layout()
plt.show()


From the above diagnostics, we are satisfied that, at least in these few categories, the train and test are similar enough, and we can move forward.

## Feature engineering¶

In this section we will use vaex to create meaningful features that will be used to train a classification model. To start with, let’s get a high level overview of the training data.

[9]:

df_train.describe()

[9]:

pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home_dest
data_type int64 bool string string float64 int64 int64 string float64 string string string float64 string
count 1047 1047 1047 1047 841 1047 1047 1047 1046 233 1046 380 102 592
NA 0 0 0 0 206 0 0 0 1 814 1 667 945 455
mean 2.3075453677172875 0.3744030563514804 -- -- 29.565299286563608 0.5100286532951289 0.3982808022922636 -- 32.92609101338429 -- -- -- 159.6764705882353 --
std 0.833269 0.483968 -- -- 14.161953 1.071309 0.890852 -- 50.678261 -- -- -- 96.220759 --
min 1 False -- -- 0.1667 0 0 -- 0.0 -- -- -- 1.0 --
max 3 True -- -- 80.0 8 9 -- 512.3292 -- -- -- 327.0 --

### Imputing¶

We notice that there are 3 columns that have missing data, so our first task will be to impute the missing values with suitable substitutes. This is our strategy:

• age: impute with the median age value

• fare: impute with the mean fare of the 5 most common values.

• cabin: impute with “M” for “Missing”

• Embarked: Impute with with the most common value.

[10]:

# Handle missing values

# Age - just do the median of the training set for now
fill_age = df_train.percentile_approx(expression='age', percentage=50.0)
# For some numpy versions the np.percentile method is broken and returns nan.
# As a failsafe, in those cases fill with the mean.
if np.isnan(fill_age):
fill_age = df_train.mean(expression='age')
df_train['age'] = df_train.age.fillna(value=fill_age)

# Fare: the mean of the 5 most common ticket prices.
fill_fares = df_train.fare.value_counts(dropna=True)
fill_fare = fill_fares.iloc[:5].index.values.mean()
df_train['fare'] = df_train.fare.fillna(value=fill_fare)

# Cabing: this is a string column so let's mark it as "M" for "Missing"
df_train['cabin'] = df_train.cabin.fillna(value='M')

# Embarked: Similar as for Cabin, let's mark the missing values with "U" for unknown
fill_embarked = df_train.embarked.value_counts(dropna=True).index[0]
df_train['embarked'] = df_train.embarked.fillna(value=fill_embarked)


### String processing¶

Next up, let’s engineer some new, more meaningful features out of the “raw” data that is present in the dataset. Starting with the name of the passengers, we are going to extract the titles, as well as we are going to count the number of words a name contains. These features can be a loose proxy to the age and status of the passengers.

[11]:

# Engineer features from the names

# Titles
df_train['name_title'] = df_train['name'].str.replace('.* ([A-Z][a-z]+)\..*', "\\1", regex=True)
display(df_train['name_title'])

# Number of words in the name
df_train['name_num_words'] = df_train['name'].str.count("[ ]+", regex=True) + 1
display(df_train['name_num_words'])

Expression = name_title
Length: 1,047 dtype: large_string (column)
------------------------------------------
0      Mr
1      Mr
2     Mrs
3    Miss
4      Mr
...
1042  Master
1043     Mrs
1044  Master
1045      Mr
1046      Mr

Expression = name_num_words
Length: 1,047 dtype: int64 (column)
-----------------------------------
0  3
1  4
2  5
3  4
4  4
...
1042  4
1043  6
1044  4
1045  4
1046  3


From the cabin colum, we will engineer 3 features: - “deck”: extacting the deck on which the cabin is located, which is encoded in each cabin value; - “multi_cabin: a boolean feature indicating whether a passenger is allocated more than one cabin - “has_cabin”: since there were plenty of values in the original cabin column that had missing values, we are just going to build a feature which tells us whether a passenger had an assigned cabin or not.

[12]:

#  Extract the deck
df_train['deck'] = df_train.cabin.str.slice(start=0, stop=1)
display(df_train['deck'])

# Passengers under which name have several rooms booked, these are all for 1st class passengers
df_train['multi_cabin'] = ((df_train.cabin.str.count(pat='[A-Z]', regex=True) > 1) &\
~(df_train.deck == 'F')).astype('int')
display(df_train['multi_cabin'])

# Out of these, cabin has the most missing values, so let's create a feature tracking if a passenger had a cabin
df_train['has_cabin'] = df_train.cabin.notna().astype('int')
display(df_train['has_cabin'])

Expression = deck
Length: 1,047 dtype: string (column)
------------------------------------
0  M
1  B
2  M
3  M
4  M
...
1042  M
1043  M
1044  M
1045  B
1046  M

Expression = multi_cabin
Length: 1,047 dtype: int64 (column)
-----------------------------------
0  0
1  0
2  0
3  0
4  0
...
1042  0
1043  0
1044  0
1045  1
1046  0

Expression = has_cabin
Length: 1,047 dtype: int64 (column)
-----------------------------------
0  1
1  1
2  1
3  1
4  1
...
1042  1
1043  1
1044  1
1045  1
1046  1


### More features¶

There are two features that give an indication whether a passenger is travelling alone, or with a famly. These are the “sibsp” and “parch” columns that tell us the number of siblinds or spouses and the number of parents or children each passenger has on-board respectively. We are going to use this information to build two columns: - “family_size” the size of the family of each passenger; - “is_alone” an additional boolean feature which indicates whether a passenger is traveling without their family.

[13]:

# Size of family that are on board: passenger + number of siblings, spouses, parents, children.
df_train['family_size'] = (df_train.sibsp + df_train.parch + 1)
display(df_train['family_size'])

# Whether or not a passenger is alone
df_train['is_alone'] = (df_train.family_size == 0).astype('int')
display(df_train['is_alone'])

Expression = family_size
Length: 1,047 dtype: int64 (column)
-----------------------------------
0  1
1  1
2  3
3  4
4  1
...
1042  8
1043  2
1044  3
1045  2
1046  1

Expression = is_alone
Length: 1,047 dtype: int64 (column)
-----------------------------------
0  0
1  0
2  0
3  0
4  0
...
1042  0
1043  0
1044  0
1045  0
1046  0


Finally, let’s create two new features: - age $$\times$$ class - fare per family member, i.e. fare $$/$$ family_size

[14]:

# Create new features
df_train['age_times_class'] = df_train.age * df_train.pclass

# fare per person in the family
df_train['fare_per_family_member'] = df_train.fare / df_train.family_size


## Modeling (part 1): gradient boosted trees¶

Since this dataset contains a lot of categorical features, we will start with a tree based model. This we will gear the following feature pre-processing towards the use of tree-based models.

### Feature pre-processing for boosted tree models¶

The features “sex”, “embarked”, and “deck” can be simply label encoded. The feature “name_tite” contains certain a larger degree of cardinality, relative to the size of the training set, and in this case we will use the Frequency Encoder.

[15]:

label_encoder = vaex.ml.LabelEncoder(features=['sex', 'embarked', 'deck'], allow_unseen=True)
df_train = label_encoder.fit_transform(df_train)

# While doing a transform, previously unseen values will be encoded as "zero".
frequency_encoder = vaex.ml.FrequencyEncoder(features=['name_title'], unseen='zero')
df_train = frequency_encoder.fit_transform(df_train)
df_train

[15]:

# pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home_dest name_title name_num_words deck multi_cabin has_cabin family_size is_alone age_times_class fare_per_family_member label_encoded_sex label_encoded_embarked label_encoded_deck frequency_encoded_name_title
0 3 False Stoytcheff, Mr. Ilia male 19.0 0 0 349205 7.8958 M S -- nan -- Mr 3 M 0 1 1 0 57.0 7.8958 1 1 0 0.5787965616045845
1 1 False Payne, Mr. Vivian Ponsonby male 23.0 0 0 12749 93.5 B24 S -- nan Montreal, PQ Mr 4 B 0 1 1 0 23.0 93.5 1 1 1 0.5787965616045845
2 3 True Abbott, Mrs. Stanton (Rosa Hunt) female35.0 1 1 C.A. 267320.25 M S A nan East Providence, RI Mrs 5 M 0 1 3 0 105.0 6.75 0 1 0 0.1451766953199618
3 2 True Hocking, Miss. Ellen "Nellie" female20.0 2 1 29105 23.0 M S 4 nan Cornwall / Akron, OH Miss 4 M 0 1 4 0 40.0 5.75 0 1 0 0.20152817574021012
4 3 False Nilsson, Mr. August Ferdinand male 21.0 0 0 350410 7.8542 M S -- nan -- Mr 4 M 0 1 1 0 63.0 7.8542 1 1 0 0.5787965616045845
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1,0423 False Goodwin, Master. Sidney Leonard male 1.0 5 2 CA 2144 46.9 M S -- nan Wiltshire, England Niagara Falls, NYMaster 4 M 0 1 8 0 3.0 5.8625 1 1 0 0.045845272206303724
1,0433 False Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female40.0 1 0 7546 9.475 M S -- nan Sweden Akeley, MN Mrs 6 M 0 1 2 0 120.0 4.7375 0 1 0 0.1451766953199618
1,0443 True Johnson, Master. Harold Theodor male 4.0 1 1 347742 11.1333 M S 15 nan -- Master 4 M 0 1 3 0 12.0 3.7111 1 1 0 0.045845272206303724
1,0451 False Baxter, Mr. Quigg Edmond male 24.0 0 1 PC 17558 247.5208B58 B60C -- nan Montreal, PQ Mr 4 B 1 1 2 0 24.0 123.7604 1 0 1 0.5787965616045845
1,0463 False Coleff, Mr. Satio male 24.0 0 0 349209 7.4958 M S -- nan -- Mr 3 M 0 1 1 0 72.0 7.4958 1 1 0 0.5787965616045845

Once all the categorical data is encoded, we can select the features we are going to use for training the model.

[16]:

# features to use for the trainin of the boosting model
encoded_features = df_train.get_column_names(regex='^freque|^label')
features = encoded_features + ['multi_cabin', 'name_num_words',
'has_cabin', 'is_alone',
'family_size', 'age_times_class',
'fare_per_family_member',
'age', 'fare']

# Preview the feature matrix

[16]:

# label_encoded_sex label_encoded_embarked label_encoded_deck frequency_encoded_name_title multi_cabin name_num_words has_cabin is_alone family_size age_times_class fare_per_family_member age fare
0 1 1 0 0.578797 0 3 1 0 1 57 7.8958 19 7.8958
1 1 1 1 0.578797 0 4 1 0 1 23 93.5 2393.5
2 0 1 0 0.145177 0 5 1 0 3 105 6.75 3520.25
3 0 1 0 0.201528 0 4 1 0 4 40 5.75 2023
4 1 1 0 0.578797 0 4 1 0 1 63 7.8542 21 7.8542

### Estimator: xgboost¶

Now let’s feed this data into an a tree based estimator. In this example we will use xgboost. In principle, any algorithm that follows the scikit-learn API convention, i.e. it contains the .fit, .predict methods is compatable with vaex. However, the data will be materialized, i.e. will be read into memory before it is passed on to the estimators. We are hard at work trying to make at least some of the estimators from scikit-learn run out-of-core!

[17]:

import xgboost
import vaex.ml.sklearn

# Instantiate the xgboost model normally, using the scikit-learn API
xgb_model = xgboost.sklearn.XGBClassifier(max_depth=11,
learning_rate=0.1,
n_estimators=500,
subsample=0.75,
colsample_bylevel=1,
colsample_bytree=1,
scale_pos_weight=1.5,
reg_lambda=1.5,
reg_alpha=5,
n_jobs=8,
random_state=42,
use_label_encoder=False,
verbosity=0)

# Make it work with vaex (for the automagic pipeline and lazy predictions)
vaex_xgb_model = vaex.ml.sklearn.Predictor(features=features,
target='survived',
model=xgb_model,
prediction_name='prediction_xgb')
# Train the model
vaex_xgb_model.fit(df_train)
# Get the prediction of the model on the training data
df_train = vaex_xgb_model.transform(df_train)

# Preview the resulting train dataframe that contans the predictions
df_train

[17]:

# pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home_dest name_title name_num_words deck multi_cabin has_cabin family_size is_alone age_times_class fare_per_family_member label_encoded_sex label_encoded_embarked label_encoded_deck frequency_encoded_name_title prediction_xgb
0 3 False Stoytcheff, Mr. Ilia male 19.0 0 0 349205 7.8958 M S -- nan -- Mr 3 M 0 1 1 0 57.0 7.8958 1 1 0 0.5787965616045845 0
1 1 False Payne, Mr. Vivian Ponsonby male 23.0 0 0 12749 93.5 B24 S -- nan Montreal, PQ Mr 4 B 0 1 1 0 23.0 93.5 1 1 1 0.5787965616045845 0
2 3 True Abbott, Mrs. Stanton (Rosa Hunt) female35.0 1 1 C.A. 267320.25 M S A nan East Providence, RI Mrs 5 M 0 1 3 0 105.0 6.75 0 1 0 0.1451766953199618 1
3 2 True Hocking, Miss. Ellen "Nellie" female20.0 2 1 29105 23.0 M S 4 nan Cornwall / Akron, OH Miss 4 M 0 1 4 0 40.0 5.75 0 1 0 0.20152817574021012 1
4 3 False Nilsson, Mr. August Ferdinand male 21.0 0 0 350410 7.8542 M S -- nan -- Mr 4 M 0 1 1 0 63.0 7.8542 1 1 0 0.5787965616045845 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1,0423 False Goodwin, Master. Sidney Leonard male 1.0 5 2 CA 2144 46.9 M S -- nan Wiltshire, England Niagara Falls, NYMaster 4 M 0 1 8 0 3.0 5.8625 1 1 0 0.045845272206303724 0
1,0433 False Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female40.0 1 0 7546 9.475 M S -- nan Sweden Akeley, MN Mrs 6 M 0 1 2 0 120.0 4.7375 0 1 0 0.1451766953199618 0
1,0443 True Johnson, Master. Harold Theodor male 4.0 1 1 347742 11.1333 M S 15 nan -- Master 4 M 0 1 3 0 12.0 3.7111 1 1 0 0.045845272206303724 1
1,0451 False Baxter, Mr. Quigg Edmond male 24.0 0 1 PC 17558 247.5208B58 B60C -- nan Montreal, PQ Mr 4 B 1 1 2 0 24.0 123.7604 1 0 1 0.5787965616045845 0
1,0463 False Coleff, Mr. Satio male 24.0 0 0 349209 7.4958 M S -- nan -- Mr 3 M 0 1 1 0 72.0 7.4958 1 1 0 0.5787965616045845 0

Notice that in the above cell block, we call .transform on the vaex_xgb_model object. This adds the “prediction_xgb” column as virtual column in the output dataframe. This can be quite convenient when calculating various metrics and making diagnosic plots. Of course, one can call a .predict on the vaex_xgb_model object, which returns an in-memory numpy array object housing the predictions.

### Performance on training set¶

Anyway, let’s see what the performance is of the model on the training set. First let’s create a convenience function that will help us get multiple metrics at once.

[18]:

from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
def binary_metrics(y_true, y_pred):
acc = accuracy_score(y_true=y_true, y_pred=y_pred)
f1 = f1_score(y_true=y_true, y_pred=y_pred)
roc = roc_auc_score(y_true=y_true, y_score=y_pred)
print(f'Accuracy: {acc:.3f}')
print(f'f1 score: {f1:.3f}')
print(f'roc-auc: {roc:.3f}')


Now let’s check the performance of the model on the training set.

[19]:

print('Metrics for the training set:')
binary_metrics(y_true=df_train.survived.values, y_pred=df_train.prediction_xgb.values)

Metrics for the training set:
Accuracy: 0.924
f1 score: 0.896
roc-auc: 0.914


### Automatic pipelines¶

Now, let’s inspect the performance of the model on the test set. You probably noticed that, unlike when using other libraries, we did not bother to create a pipeline while doing all the cleaning, inputing, feature engineering and categorial encoding. Well, we did not explicitly create a pipeline. In fact veax keeps track of all the changes one applies to a DataFrame in something called a state. A state is the place which contains all the informations regarding, for instance, the virtual columns we’ve created, which includes the newly engineered features, the categorically encoded columns, and even the model prediction! So all we need to do, is to extract the state from the training DataFrame, and apply it to the test DataFrame.

[20]:

# state transfer to the test set
state = df_train.state_get()
df_test.state_set(state)

# Preview of the "transformed" test set

[20]:

# pclasssurvived name sex age sibsp parchticket farecabin embarked boat bodyhome_dest name_title name_num_wordsdeck multi_cabin has_cabin family_size is_alone age_times_class fare_per_family_member label_encoded_sex label_encoded_embarked label_encoded_deck frequency_encoded_name_title prediction_xgb
0 3False O'Connor, Mr. Patrick male 28.032 0 0366713 7.75 M Q -- nan-- Mr 3M 0 1 1 0 84.096 7.75 1 2 0 0.578797 0
1 3False Canavan, Mr. Patrick male 21 0 0364858 7.75 M Q -- nanIreland Philadelphia, PAMr 3M 0 1 1 0 63 7.75 1 2 0 0.578797 0
2 1False Ovies y Rodriguez, Mr. Servando male 28.5 0 0PC 17562 27.7208D43 C -- 189?Havana, Cuba Mr 5D 0 1 1 0 28.5 27.7208 1 0 4 0.578797 1
3 3False Windelov, Mr. Einar male 21 0 0SOTON/OQ 3101317 7.25 M S -- nan-- Mr 3M 0 1 1 0 63 7.25 1 1 0 0.578797 0
4 2True Shelley, Mrs. William (Imanita Parrish Hall)female25 0 1230433 26 M S 12 nanDeer Lodge, MT Mrs 6M 0 1 2 0 50 13 0 1 0 0.145177 1

Notice that once we apply the state from the train to the test set, the test DataFrame contains all the features we created or modified in the training data, and even the predictions of the xgboost model!

The state is a simple Python dictionary, which can be easily stored as JSON to disk, which makes it very easy to deploy.

### Performance on test set¶

Now it is trivial to check the model performance on the test set:

[21]:

print('Metrics for the test set:')
binary_metrics(y_true=df_test.survived.values, y_pred=df_test.prediction_xgb.values)

Metrics for the test set:
Accuracy: 0.786
f1 score: 0.728
roc-auc: 0.773


### Feature importance¶

Let’s now look at the feature importance of the xgboost model.

[22]:

plt.figure(figsize=(6, 9))

ind = np.argsort(xgb_model.feature_importances_)[::-1]
features_sorted = np.array(features)[ind]
importances_sorted = xgb_model.feature_importances_[ind]

plt.barh(y=range(len(features)), width=importances_sorted, height=0.2)
plt.title('Gain')
plt.yticks(ticks=range(len(features)), labels=features_sorted)
plt.gca().invert_yaxis()
plt.show()


## Modeling (part 2): Linear models & Ensembles¶

Given the randomness of the Titanic dataset , we can be satisfied with the performance of xgboost model above. Still, it is always usefull to try a variety of models and approaches, especially since vaex makes makes this process rather simple.

In the following part we will use a couple of linear models as our predictors, this time straight from scikit-learn. This requires us to pre-process the data in a slightly different way.

### Feature pre-processing for linear models¶

When using linear models, the safest option is to encode categorical variables with the one-hot encoding scheme, especially if they have low cardinality. We will do this for the “family_size” and “deck” features. Note that the “sex” feature is already encoded since it has only unique values options.

The “name_title” feature is a bit more tricky. Since in its original form it has some values that only appear a couple of times, we will do a trick: we will one-hot encode the frequency encoded values. This will reduce cardinality of the feature, while also preserving the most important, i.e. most common values.

Regarding the “age” and “fare”, to add some variance in the model, we will not convert them to categorical as before, but simply remove their mean and standard-deviations (standard-scaling). We will do the same to the “fare_per_family_member” feature.

Finally, we will drop out any other features.

[23]:

# One-hot encode categorical features
one_hot = vaex.ml.OneHotEncoder(features=['deck', 'family_size', 'name_title'])
df_train = one_hot.fit_transform(df_train)

[24]:

# Standard scale numerical features
standard_scaler = vaex.ml.StandardScaler(features=['age', 'fare', 'fare_per_family_member'])
df_train = standard_scaler.fit_transform(df_train)

[25]:

# Get the features for training a linear model
features_linear = df_train.get_column_names(regex='^deck_|^family_size_|^frequency_encoded_name_title_')
features_linear += df_train.get_column_names(regex='^standard_scaled_')
features_linear += ['label_encoded_sex']
features_linear

[25]:

['deck_A',
'deck_B',
'deck_C',
'deck_D',
'deck_E',
'deck_F',
'deck_G',
'deck_M',
'family_size_1',
'family_size_2',
'family_size_3',
'family_size_4',
'family_size_5',
'family_size_6',
'family_size_7',
'family_size_8',
'family_size_11',
'standard_scaled_age',
'standard_scaled_fare',
'standard_scaled_fare_per_family_member',
'label_encoded_sex']


### Estimators: SVC and LogisticRegression¶

[26]:

from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression

[27]:

# The Support Vector Classifier
vaex_svc = vaex.ml.sklearn.Predictor(features=features_linear,
target='survived',
model=SVC(max_iter=1000, random_state=42),
prediction_name='prediction_svc')

# Logistic Regression
vaex_logistic = vaex.ml.sklearn.Predictor(features=features_linear,
target='survived',
model=LogisticRegression(max_iter=1000, random_state=42),
prediction_name='prediction_lr')

# Train the new models and apply the transformation to the train dataframe
for model in [vaex_svc, vaex_logistic]:
model.fit(df_train)
df_train = model.transform(df_train)

# Preview of the train DataFrame

/home/jovan/miniconda3/lib/python3.7/site-packages/sklearn/svm/_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)

[27]:

# pclasssurvived name sex age sibsp parchticket farecabin embarked boat bodyhome_dest name_title name_num_wordsdeck multi_cabin has_cabin family_size is_alone age_times_class fare_per_family_member label_encoded_sex label_encoded_embarked label_encoded_deck frequency_encoded_name_title prediction_xgb deck_A deck_B deck_C deck_D deck_E deck_F deck_G deck_M family_size_1 family_size_2 family_size_3 family_size_4 family_size_5 family_size_6 family_size_7 family_size_8 family_size_11 name_title_Capt name_title_Col name_title_Countess name_title_Don name_title_Dona name_title_Dr name_title_Jonkheer name_title_Lady name_title_Major name_title_Master name_title_Miss name_title_Mlle name_title_Mme name_title_Mr name_title_Mrs name_title_Ms name_title_Rev standard_scaled_age standard_scaled_fare standard_scaled_fare_per_family_memberprediction_svc prediction_lr
0 3False Stoytcheff, Mr. Ilia male 19 0 0349205 7.8958M S -- nan-- Mr 3M 0 1 1 0 57 7.8958 1 1 0 0.578797 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.807704 -0.493719 -0.342804False False
1 1False Payne, Mr. Vivian Ponsonby male 23 0 012749 93.5 B24 S -- nanMontreal, PQ Mr 4B 0 1 1 0 23 93.5 1 1 1 0.578797 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.492921 1.19613 1.99718 False True
2 3True Abbott, Mrs. Stanton (Rosa Hunt)female 35 1 1C.A. 267320.25 M S A nanEast Providence, RI Mrs 5M 0 1 3 0 105 6.75 0 1 0 0.145177 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.45143 -0.249845 -0.374124True True
3 2True Hocking, Miss. Ellen "Nellie" female 20 2 129105 23 M S 4 nanCornwall / Akron, OHMiss 4M 0 1 4 0 40 5.75 0 1 0 0.201528 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 -0.729008 -0.195559 -0.401459True True
4 3False Nilsson, Mr. August Ferdinand male 21 0 0350410 7.8542M S -- nan-- Mr 4M 0 1 1 0 63 7.8542 1 1 0 0.578797 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.650312 -0.494541 -0.343941False False

### Ensemble¶

Just as before, the predictions from the SVC and the LogisticRegression classifiers are added as virtual columns in the training dataset. This is quite powerful, since now we can easily use them to create an ensemble! For example, let’s do a weighted mean.

[28]:

# Weighed mean of the classes
prediction_final = (df_train.prediction_xgb.astype('int') * 0.3 +
df_train.prediction_svc.astype('int') * 0.5 +
df_train.prediction_xgb.astype('int') * 0.2)
# Get the predicted class
prediction_final = (prediction_final >= 0.5)
# Add the expression to the train DataFrame
df_train['prediction_final'] = prediction_final

# Preview
df_train[df_train.get_column_names(regex='^predict')]

[28]:

# prediction_xgb prediction_svc prediction_lr prediction_final
0 0 False False False
1 0 False True False
2 1 True True True
3 1 True True True
4 0 False False False
... ... ... ... ...
1,0420 False False False
1,0430 True True True
1,0441 True False True
1,0450 True True True
1,0460 False False False

### Performance (part 2)¶

Applying the ensembler to the test set is just as easy as before. We just need to get the new state of the training DataFrame, and transfer it to the test DataFrame.

[29]:

# State transfer
state_new = df_train.state_get()
df_test.state_set(state_new)

# Preview

[29]:

# pclasssurvived name sex age sibsp parchticket farecabin embarked boat bodyhome_dest name_title name_num_wordsdeck multi_cabin has_cabin family_size is_alone age_times_class fare_per_family_member label_encoded_sex label_encoded_embarked label_encoded_deck frequency_encoded_name_title prediction_xgb deck_A deck_B deck_C deck_D deck_E deck_F deck_G deck_M family_size_1 family_size_2 family_size_3 family_size_4 family_size_5 family_size_6 family_size_7 family_size_8 family_size_11 name_title_Capt name_title_Col name_title_Countess name_title_Don name_title_Dona name_title_Dr name_title_Jonkheer name_title_Lady name_title_Major name_title_Master name_title_Miss name_title_Mlle name_title_Mme name_title_Mr name_title_Mrs name_title_Ms name_title_Rev standard_scaled_age standard_scaled_fare standard_scaled_fare_per_family_memberprediction_svc prediction_lr prediction_final
0 3False O'Connor, Mr. Patrick male 28.032 0 0366713 7.75 M Q -- nan-- Mr 3M 0 1 1 0 84.096 7.75 1 2 0 0.578797 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.096924 -0.496597 -0.346789False False False
1 3False Canavan, Mr. Patrick male 21 0 0364858 7.75 M Q -- nanIreland Philadelphia, PAMr 3M 0 1 1 0 63 7.75 1 2 0 0.578797 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.650312 -0.496597 -0.346789False False False
2 1False Ovies y Rodriguez, Mr. Servando male 28.5 0 0PC 17562 27.7208D43 C -- 189?Havana, Cuba Mr 5D 0 1 1 0 28.5 27.7208 1 0 4 0.578797 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.0600935 -0.102369 0.19911 False False True
3 3False Windelov, Mr. Einar male 21 0 0SOTON/OQ 3101317 7.25 M S -- nan-- Mr 3M 0 1 1 0 63 7.25 1 1 0 0.578797 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 -0.650312 -0.506468 -0.360456False False False
4 2True Shelley, Mrs. William (Imanita Parrish Hall)female25 0 1230433 26 M S 12 nanDeer Lodge, MT Mrs 6M 0 1 2 0 50 13 0 1 0 0.145177 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 -0.335529 -0.136338 -0.203281True True True

Finally, let’s check the performance of all the individual models as well as on the ensembler, on the test set.

[30]:

pred_columns = df_train.get_column_names(regex='^prediction_')
for i in pred_columns:
print(i)
binary_metrics(y_true=df_test.survived.values, y_pred=df_test[i].values)
print(' ')

prediction_xgb
Accuracy: 0.786
f1 score: 0.728
roc-auc: 0.773

prediction_svc
Accuracy: 0.802
f1 score: 0.743
roc-auc: 0.786

prediction_lr
Accuracy: 0.779
f1 score: 0.713
roc-auc: 0.762

prediction_final
Accuracy: 0.809
f1 score: 0.771
roc-auc: 0.804



We see that our ensembler is doing a better job than any idividual model, as expected.

Thanks you for going over this example. Feel free to copy, modify, and in general play around with this notebook.