Machine Learning with vaex.ml

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The vaex.ml package brings some machine learning algorithms to vaex. If you installed the individual subpackages (vaex-core, vaex-hdf5, …) instead of the vaex metapackage, you may need to install it by running pip install vaex-ml, or conda install -c conda-forge vaex-ml.

The API of vaex.ml stays close to that of scikit-learn, while providing better performance and the ability to efficiently perform operations on data that is larger than the available RAM. This page is an overview and a brief introduction to the capabilities offered by vaex.ml.

[1]:
import vaex
vaex.multithreading.thread_count_default = 8
import vaex.ml

import numpy as np
import pylab as plt

We will use the well known Iris flower and Titanic passenger list datasets, two classical datasets for machine learning demonstrations.

[2]:
df = vaex.ml.datasets.load_iris()
df
[2]:
# sepal_length sepal_width petal_length petal_width class_
0 5.9 3.0 4.2 1.5 1
1 6.1 3.0 4.6 1.4 1
2 6.6 2.9 4.6 1.3 1
3 6.7 3.3 5.7 2.1 2
4 5.5 4.2 1.4 0.2 0
... ... ... ... ... ...
1455.2 3.4 1.4 0.2 0
1465.1 3.8 1.6 0.2 0
1475.8 2.6 4.0 1.2 1
1485.7 3.8 1.7 0.3 0
1496.2 2.9 4.3 1.3 1
[3]:
df.scatter(df.petal_length, df.petal_width, c_expr=df.class_);
/home/jovan/vaex/packages/vaex-core/vaex/viz/mpl.py:205: UserWarning: `scatter` is deprecated and it will be removed in version 5.x. Please use `df.viz.scatter` instead.
  warnings.warn('`scatter` is deprecated and it will be removed in version 5.x. Please use `df.viz.scatter` instead.')
_images/tutorial_ml_5_1.png

Preprocessing

Scaling of numerical features

vaex.ml packs the common numerical scalers:

  • vaex.ml.StandardScaler - Scale features by removing their mean and dividing by their variance;

  • vaex.ml.MinMaxScaler - Scale features to a given range;

  • vaex.ml.RobustScaler - Scale features by removing their median and scaling them according to a given percentile range;

  • vaex.ml.MaxAbsScaler - Scale features by their maximum absolute value.

The usage is quite similar to that of scikit-learn, in the sense that each transformer implements the .fit and .transform methods.

[4]:
features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
scaler = vaex.ml.StandardScaler(features=features, prefix='scaled_')
scaler.fit(df)
df_trans = scaler.transform(df)
df_trans
[4]:
# sepal_length sepal_width petal_length petal_width class_ scaled_petal_length scaled_petal_width scaled_sepal_length scaled_sepal_width
0 5.9 3.0 4.2 1.5 1 0.25096730693923325 0.39617188299171285 0.06866179325140277 -0.12495760117130607
1 6.1 3.0 4.6 1.4 1 0.4784301228962429 0.26469891297233916 0.3109975341387059 -0.12495760117130607
2 6.6 2.9 4.6 1.3 1 0.4784301228962429 0.13322594295296575 0.9168368863569659 -0.3563605663033572
3 6.7 3.3 5.7 2.1 2 1.1039528667780207 1.1850097031079545 1.0380047568006185 0.5692512942248463
4 5.5 4.2 1.4 0.2 0 -1.341272404759837 -1.3129767272601438 -0.4160096885232057 2.6518779804133055
... ... ... ... ... ... ... ... ... ...
1455.2 3.4 1.4 0.2 0 -1.341272404759837 -1.3129767272601438 -0.7795132998541615 0.8006542593568975
1465.1 3.8 1.6 0.2 0 -1.2275409967813318 -1.3129767272601438 -0.9006811702978141 1.726266119885101
1475.8 2.6 4.0 1.2 1 0.13723589896072813 0.0017529729335920385-0.052506077192249874-1.0505694616995096
1485.7 3.8 1.7 0.3 0 -1.1706752927920796 -1.18150375724077 -0.17367394763590144 1.726266119885101
1496.2 2.9 4.3 1.3 1 0.30783301092848553 0.13322594295296575 0.4321654045823586 -0.3563605663033572

The output of the .transform method of any vaex.ml transformer is a shallow copy of a DataFrame that contains the resulting features of the transformations in addition to the original columns. A shallow copy means that this new DataFrame just references the original one, and no extra memory is used. In addition, the resulting features, in this case the scaled numerical features are virtual columns, which do not take any memory but are computed on the fly when needed. This approach is ideal for working with very large datasets.

Encoding of categorical features

vaex.ml contains several categorical encoders:

  • vaex.ml.LabelEncoder - Encoding features with as many integers as categories, startinfg from 0;

  • vaex.ml.OneHotEncoder - Encoding features according to the one-hot scheme;

  • vaex.ml.FrequencyEncoder - Encode features by the frequency of their respective categories;

  • vaex.ml.BayesianTargetEncoder - Encode categories with the mean of their target value;

  • vaex.ml.WeightOfEvidenceEncoder - Encode categories their weight of evidence value.

The following is a quick example using the Titanic dataset.

[5]:
df =  vaex.ml.datasets.load_titanic()
df.head(5)
[5]:
# pclasssurvived name sex age sibsp parch ticket farecabin embarked boat bodyhome_dest
0 1True Allen, Miss. Elisabeth Walton female29 0 0 24160211.338B5 S 2 nanSt Louis, MO
1 1True Allison, Master. Hudson Trevor male 0.9167 1 2 113781151.55 C22 C26S 11 nanMontreal, PQ / Chesterville, ON
2 1False Allison, Miss. Helen Loraine female 2 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON
3 1False Allison, Mr. Hudson Joshua Creighton male 30 1 2 113781151.55 C22 C26S -- 135Montreal, PQ / Chesterville, ON
4 1False Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON
[6]:
label_encoder = vaex.ml.LabelEncoder(features=['embarked'])
one_hot_encoder = vaex.ml.OneHotEncoder(features=['embarked'])
freq_encoder = vaex.ml.FrequencyEncoder(features=['embarked'])
bayes_encoder = vaex.ml.BayesianTargetEncoder(features=['embarked'], target='survived')
woe_encoder = vaex.ml.WeightOfEvidenceEncoder(features=['embarked'], target='survived')

df = label_encoder.fit_transform(df)
df = one_hot_encoder.fit_transform(df)
df = freq_encoder.fit_transform(df)
df = bayes_encoder.fit_transform(df)
df = woe_encoder.fit_transform(df)

df.head(5)
[6]:
# pclasssurvived name sex age sibsp parch ticket farecabin embarked boat bodyhome_dest label_encoded_embarked embarked_missing embarked_C embarked_Q embarked_S frequency_encoded_embarked mean_encoded_embarked woe_encoded_embarked
0 1True Allen, Miss. Elisabeth Walton female29 0 0 24160211.338B5 S 2 nanSt Louis, MO 1 0 0 0 1 0.698243 0.337472 -0.696431
1 1True Allison, Master. Hudson Trevor male 0.9167 1 2 113781151.55 C22 C26S 11 nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431
2 1False Allison, Miss. Helen Loraine female 2 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431
3 1False Allison, Mr. Hudson Joshua Creighton male 30 1 2 113781151.55 C22 C26S -- 135Montreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431
4 1False Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431

Notice that the transformed features are all included in the resulting DataFrame and are appropriately named. This is excellent for the construction of various diagnostic plots, and engineering of more complex features. The fact that the resulting (encoded) features take no memory, allows one to try out or combine a variety of preprocessing steps without spending any extra memory.

Feature Engineering

KBinsDiscretizer

With the KBinsDiscretizer you can convert a continous into a discrete feature by binning the data into specified intervals. You can specify the number of bins, the strategy on how to determine their size:

  • “uniform” - all bins have equal sizes;

  • “quantile” - all bins have (approximately) the same number of samples in them;

  • “kmeans” - values in each bin belong to the same 1D cluster as determined by the KMeans algorithm.

[7]:
kbdisc = vaex.ml.KBinsDiscretizer(features=['age'], n_bins=5, strategy='quantile')
df = kbdisc.fit_transform(df)
df.head(5)
/home/jovan/vaex/packages/vaex-core/vaex/ml/transformations.py:1089: UserWarning: Bins whose width are too small (i.e., <= 1e-8) in   age are removed.Consider decreasing the number of bins.
  warnings.warn(f'Bins whose width are too small (i.e., <= 1e-8) in   {feat} are removed.'
[7]:
# pclasssurvived name sex age sibsp parch ticket farecabin embarked boat bodyhome_dest label_encoded_embarked embarked_missing embarked_C embarked_Q embarked_S frequency_encoded_embarked mean_encoded_embarked woe_encoded_embarked binned_age
0 1True Allen, Miss. Elisabeth Walton female29 0 0 24160211.338B5 S 2 nanSt Louis, MO 1 0 0 0 1 0.698243 0.337472 -0.696431 0
1 1True Allison, Master. Hudson Trevor male 0.9167 1 2 113781151.55 C22 C26S 11 nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0
2 1False Allison, Miss. Helen Loraine female 2 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0
3 1False Allison, Mr. Hudson Joshua Creighton male 30 1 2 113781151.55 C22 C26S -- 135Montreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0
4 1False Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0

GroupBy Transformer

The GroupByTransformer is a handy feature in vaex-ml that lets you perform a groupby aggregations on the training data, and then use those aggregations as features in the training and test sets.

[8]:
gbt = vaex.ml.GroupByTransformer(by='pclass', agg={'age': ['mean', 'std'],
                                                   'fare': ['mean', 'std'],
                                                  })
df = gbt.fit_transform(df)
df.head(5)
[8]:
# pclasssurvived name sex age sibsp parch ticket farecabin embarked boat bodyhome_dest label_encoded_embarked embarked_missing embarked_C embarked_Q embarked_S frequency_encoded_embarked mean_encoded_embarked woe_encoded_embarked binned_age age_mean age_std fare_mean fare_std
0 1True Allen, Miss. Elisabeth Walton female29 0 0 24160211.338B5 S 2 nanSt Louis, MO 1 0 0 0 1 0.698243 0.337472 -0.696431 0 39.1599 14.5224 87.509 80.3226
1 1True Allison, Master. Hudson Trevor male 0.9167 1 2 113781151.55 C22 C26S 11 nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0 39.1599 14.5224 87.509 80.3226
2 1False Allison, Miss. Helen Loraine female 2 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0 39.1599 14.5224 87.509 80.3226
3 1False Allison, Mr. Hudson Joshua Creighton male 30 1 2 113781151.55 C22 C26S -- 135Montreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0 39.1599 14.5224 87.509 80.3226
4 1False Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25 1 2 113781151.55 C22 C26S -- nanMontreal, PQ / Chesterville, ON 1 0 0 0 1 0.698243 0.337472 -0.696431 0 39.1599 14.5224 87.509 80.3226

CycleTransformer

The CycleTransformer provides a strategy for transforming cyclical features, such as angles or time. This is done by considering each feature to be describing a polar coordinate system, and converting it to Cartesian coorindate system. This is shown to help certain ML models to achieve better performance.

[9]:
df = vaex.from_arrays(days=[0, 1, 2, 3, 4, 5, 6])
cyctrans = vaex.ml.CycleTransformer(n=7, features=['days'])
cyctrans.fit_transform(df)
[9]:
# days days_x days_y
0 0 1 0
1 1 0.62349 0.781831
2 2-0.222521 0.974928
3 3-0.900969 0.433884
4 4-0.900969-0.433884
5 5-0.222521-0.974928
6 6 0.62349 -0.781831

Dimensionality reduction

Principal Component Analysis

The PCA implemented in vaex.ml can scale to a very large number of samples, even if that data we want to transform does not fit into RAM. To demonstrate this, let us do a PCA transformation on the Iris dataset. For this example, we have replicated this dataset thousands of times, such that it contains over 1 billion samples.

[10]:
df = vaex.ml.datasets.load_iris_1e9()
n_samples = len(df)
print(f'Number of samples in DataFrame: {n_samples:,}')
Number of samples in DataFrame: 1,005,000,000
[11]:
features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
pca = vaex.ml.PCA(features=features, n_components=4)
pca.fit(df, progress='widget')

The PCA transformer implemented in vaex.ml can be fit in well under a minute, even when the data comprises 4 columns and 1 billion rows.

[12]:
df_trans = pca.transform(df)
df_trans
[12]:
# sepal_length sepal_width petal_length petal_width class_ PCA_0 PCA_1 PCA_2 PCA_3
0 5.9 3.0 4.2 1.5 1 -0.51109806050657190.10228410590320294 0.13232789125239366 -0.05010053260756789
1 6.1 3.0 4.6 1.4 1 -0.89016044564845710.03381244269907491 -0.0097680289049917950.1534482059864868
2 6.6 2.9 4.6 1.3 1 -1.0432977809309918-0.2289569106597803 -0.41481456509035997 0.03752354509774891
3 6.7 3.3 5.7 2.1 2 -2.275853649246034 -0.3333865237191275 0.28467815436304544 0.062230281630705805
4 5.5 4.2 1.4 0.2 0 2.5971594768136956 -1.1000219282272325 0.16358191524058419 0.09895807321522321
... ... ... ... ... ... ... ... ... ...
1,004,999,9955.2 3.4 1.4 0.2 0 2.6398212682948925 -0.3192900674870881 -0.1392533720548284 -0.06514104909063131
1,004,999,9965.1 3.8 1.6 0.2 0 2.537573370908207 -0.5103675457748862 0.17191840236558648 0.19216594960009262
1,004,999,9975.8 2.6 4.0 1.2 1 -0.22887904987726520.4022576190683287 -0.22736270650701024 -0.01862045442675292
1,004,999,9985.7 3.8 1.7 0.3 0 2.199077961161723 -0.8792440894091085 -0.11452146077196179 -0.025326942106218664
1,004,999,9996.2 2.9 4.3 1.3 1 -0.6416902782168139-0.019071177408365406-0.20417287674016232 0.02050967222367117

Recall that the transformed DataFrame, which includes the PCA components, takes no extra memory.

Incremental PCA

The PCA implementation in vaex is very fast, but more so for “tall” DataFrames, i.e. DataFrames that have many rows, but not many columns. For DataFrames that have hundreds of columns, it is more efficient to use an Incremental PCA method. vaex.ml provides a convenient method that essentialy wraps sklearn.decomposition.IncrementalPCA, the fitting of which is more efficient for “wide” DataFrames.

The usage is practically identical to the regular PCA method. Consider the following example:

[13]:
n_samples = 100_000
n_columns = 50
data_dict = {f'feat_{i}': np.random.normal(0, i+1, size=n_samples) for i in range(n_columns)}
df = vaex.from_dict(data_dict)


features = df.get_column_names()
pca = vaex.ml.PCAIncremental(n_components=10, features=features, batch_size=42_000)
pca.fit(df, progress='widget')
pca.transform(df)
[13]:
# feat_0 feat_1 feat_2 feat_3 feat_4 feat_5 feat_6 feat_7 feat_8 feat_9 feat_10 feat_11 feat_12 feat_13 feat_14 feat_15 feat_16 feat_17 feat_18 feat_19 feat_20 feat_21 feat_22 feat_23 feat_24 feat_25 feat_26 feat_27 feat_28 feat_29 feat_30 feat_31 feat_32 feat_33 feat_34 feat_35 feat_36 feat_37 feat_38 feat_39 feat_40 feat_41 feat_42 feat_43 feat_44 feat_45 feat_46 feat_47 feat_48 feat_49 PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5 PCA_6 PCA_7 PCA_8 PCA_9
0 0.21916619701436382-1.1435438188965208-2.236473242690611 -8.81728920352771 1.9931414225984159 0.8289809515418928 -7.847441537857684 -5.990636964340006 0.43889103534482576-6.4855757436955965-14.48532696768287113.825392548457543 -5.5661773929038185-3.1816868599382633 27.66565101972783650.541940500115366 16.001390451665785 32.510983357481614 8.342038455860216 -1.7293759207235855-6.451472523437187 22.55340570655327 -2.543125122041264528.75425936065127 -39.487762558467345 -6.871003398404642 11.198673922236354 -86.63832306461876 -7.32368079105989237.35407351193795 23.653897939827836 39.52047029873747 42.79143756690254 -33.3810495394693 33.05317072490505 14.818285601642208 -67.03187283353228 -19.01476952180615 22.4905763733386 35.33833686808974 11.79457050704157 -86.70070654092856 25.185781359852896 20.521240128349977 19.814114866123216 78.05531698592385 10.029892443326418 -97.39820288821723 -0.9603735180566161-64.45083314406774 -67.59977551168708 9.37969253153906 -96.6057651764448 11.206098841188833 74.90790318762694 17.531645576460654 21.26591694292548 27.215113714718253-85.31326664717933 10.507088586039371
1 -0.42076958781498162.3850692704428043 -1.3661921493141755-0.57464980721204832.2588675039630703 -5.100101894797036 -0.0005433423021984177-3.0055202143012365 5.749693220009271 11.379708067727588 10.119772822286162 0.15698369211085733-10.937595546203902-31.110839874678003 -5.593388174686233-17.48851742053923519.942127063793418 -0.6804349583522779-19.03708392463745428.74230527011865 12.40206875918237 -9.990549218761593 -5.733244330514869 3.171827795840886 -43.944372783025386 -25.8820588524763123.517534442545183 -25.10463172872150417.068162563601867-26.188188765123446-17.51765346352225 -5.803234686368941 23.37461204071744 85.58386322836444 -24.84250900935848 42.2583557612343 -34.83625774127584447.25447854289113 -5.903960946365425 47.891908734840925 -9.673715993876817 -17.5774774820285274.066254744412671 -51.377913297883865-11.51987006746566810.497653831847085 16.358701536495925 -18.3914825056028029.858101501060483 -39.819369217021595-38.74298336407881 12.412960580526423 -16.79176108824452714.714058887306741 8.607153125744537 -6.384705477156807 -52.8779915958480663.667728062420572 -19.219755720289232 -16.20164176309122
2 -0.50247974091959910.9897062935454243 -1.152229281759237 -1.682033038083704 -4.091345910790923 -4.52742403771885552.129578282936375 10.936320913755608 -1.5695520680947808-6.034199421988269 -28.46431144964817 -15.32129294377632 -8.194011820344523 -16.218630438043398 12.021916867709596-4.908477966578501 -29.56619559878632 7.772108300044394 7.680046493196698 13.815505542053483 3.9208120473170016 47.34661694033482 1.544881077052938 9.440027347582042 18.56198304730558 22.3336072648248 -21.578332510459486-48.93092663572265616.5701671385727 16.656088505245513 19.8406469884787 5.384567961213235 -16.73392428744861614.376438801233908-35.323974854495155-7.411178531711759 -12.19133679331107557.91740496088699 34.873491696833774 88.28464395597479 87.65337555912684 -2.4096431528212445-7.8171455961597385-4.016403896979926 -22.96261029782406 -75.8940296403038 -38.8951677113029 -89.75675908427556 -79.5994302281645 -44.45310265105787 -42.34987503786076 -74.13417710288375 -94.54423466637282 -40.877591489278196-73.38521818144409 -14.487330945685514-6.8530939766408885-10.84894017617582-0.0388656483260952478.63468911909872
3 0.12617606561304665-0.91728226378698231.8277090696240983 -1.8883963021695365-3.26085343817413436.94314682034098 -1.964291832580844 5.476441728997025 5.985807394356193 -4.152754646002149 15.497819324027216 1.9473222994398216 -11.1546653716116812.1502221820849754 7.402217623202724 -20.974198348221123-18.49611969411084 -11.197532751079477-4.167571500828548 -16.7492676033496866.873971547452746 -22.28958212850625421.69520422160094 10.732001896726413 -24.901621899667955 13.663451847361172 40.92498717076184 62.02571061444625 97.46935359691241 1.3197202988059933 -13.355307678605655-59.98623606960067 -15.3460319107594843.85479178918432068.451030763844253 -37.3610034378942059.316605927851759 -15.936791503025487-14.200047091850191-96.04376311885646 6.793212237372706 -89.28406931570937 -6.342536181747704 9.84276729692308 -44.15480258178421 -19.716315609075178-8.963766643638541 13.328160220454095 -81.91979053839731 -58.49057458242536 -63.82740201878286 -78.04284003367316 6.898497938656784 -9.975022259994258 -24.581867540712196-43.13228076360685 5.384602201485904 -5.104240140134616-88.56822933573116 18.63888133757838
4 -1.5391949931048126-0.84243862338608713.808044749153777 -1.15040861016063344.975092670034785 -4.03814322037485956.475255733889277 -8.492789285986634 -0.71070840841147211.9868439665217876 -6.335098977847596 18.156422121050845 -3.9319838484429286-0.303888675665301 -18.038103704497153.6137256391127717 12.72102405166281 6.1797872895139765 -17.965746423694828-6.457595529218324 -11.1195782584740362.124546751440085 2.074247115486158 48.526431477044895 -47.7501423866134 -13.2189838629703170.7076755883915242 21.272708498626173 20.218314701800175-4.052289437744317 -28.29098298558251744.10471192261346 27.505033879695844 28.4585973718932739.564898635025768 -6.2001475733889375-33.28464087248315 13.562356933449957 72.47202649403566 -17.63088820680735222.257347577113283 19.793786901529828 -0.888840951088124115.45297619768772 80.01687713977846 -33.02953241445338 47.36388577265113 47.96488983389095 30.47783230830538 52.702201767487 56.4647664098084 27.388702583308334 47.716980722531005 48.86243093017444 -29.47766470897874 76.66863902366097 23.114022602360667 -3.03590434662457820.751371509793366 25.70018487608435
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
99,995-1.160081518789358 -1.5967802399231468-2.15232040817518 -5.152880656063202 -2.81607683456671464.528707893808043 -9.219048918475725 -4.1152783877843895 15.434762333635224 -8.352240079142867 3.2341379115026694 7.679896402408659 19.99465474797146 -15.987822176846745 17.610005841221454-2.9940634500799996-36.9849615488119246.455731448290355 0.8700910607593357 -4.458798902046075 -8.573291238859795 1.7866347197434056 -5.748202862095839 -78.73536930217278 0.8664468950376607 -31.185290130437014-33.40360643789874548.79496517134476 4.273021608667145 -14.76645480929473223.034033698309216 47.916505903411704 22.82356373157275 58.17074570864146 13.075446180847607 5.357406097709567 19.301741918502767 30.91481630395726 -18.99658045583839429.068050048521297 -11.50032407194181 -94.16793562743486 10.247859328520715 -23.33364253340996864.88951899816107 -5.970342533069689 22.724974186922207 -46.358784230253264-76.06357310802707 36.34299568143191 34.5263251515797 -74.93722963856585 -51.83676476605647 28.086594105181963 -1.148488347990102264.59414944331482 -19.3363913041026487.146369194433403 -94.50249266159257 -11.416642775370095
99,9960.133221661855605742.0608209742055763 2.1641428725239287 -2.450274442812819 0.5729664553821341 11.655164926233269 -9.864613671442203 -4.600216494861485 10.08600220223909 5.916293624542951 14.812935982731668 -6.453293834403917 -11.90549514770099 -3.26727352515574 1.8764801411441934-20.02012175801679 20.579289884690567 -7.95774658159159 -8.387038826710807 -18.0222209635527342.692329970764943 -14.30398788132729721.66822494391352 -15.938191880312708-35.29052532512791 -8.631818482611655 9.787860087044647 -53.67539155301477 -6.29070859522252334.35010506794386 6.565193250636609 -15.486170359730892-3.031599295669413 -1.80098865175289345.55563650252154 -37.38886935392985 68.02203785140463 69.71021558546443 67.33004345391464 38.09747878907309 -15.32336767996999276.84362563371494 -35.79579407415943 -32.88316495646942 -23.620694143487448-90.01728440515039 -24.77449621235016567.92281355721133 30.03415640434173 -29.32574935340052 -21.82606452589530525.41085028514592 70.39416642353444 -29.213531794756513-90.47462518115402 -14.585892147549302-36.17160238891088 -33.2209566185244976.76852716941656 -18.539072237418367
99,9971.011157114782744 -0.80040986269630711.2571486498281934 3.8492594702419245 0.7592605926849842 -4.098302780814329 -1.9485099180060705 16.684513355922583 10.087604365608211 3.7452922672933973 -16.33173839915188 19.92199866574765 6.5771681345498845 -0.3230579773623871714.72654802079624613.583443459677845 -4.952279711617992 17.030998980346084 4.201801219449127 -3.910793205671661441.77733885408281 7.96614686571076 -39.10848664323428 -33.69630280939279 -7.463352385087283 7.458696462843669 -5.883303405785125 6.6310954865277845 -6.552748916196248-9.325031603876797 -11.7337490011325093.627520914240156 18.155090307885395 33.4073875839576 45.52621736035822 -22.938060053594263-27.364572553649534-58.35071648799318 -62.86375816449011 19.272818436422003 47.61050132614527 -11.301762317420524-82.24660966605563 16.961463120018315 13.762199024990316 9.330554417908111 -96.02479832620445 -24.711048464719337-2.078012378653908 -10.604821752483073-11.558372427683931-3.6825332773046875-23.548620629546026-95.72823548883444 15.77594599796893 14.557196623771969 15.812183077424558 -82.30672442508799-8.68501822662248 44.23079310012721
99,9980.9852518578365336 0.8203281912686264 -3.884122502896842 -0.95908400432742780.16746213933285223-0.8886763063332375-16.842052417441188 0.0198139466128886246.1752951086966466 -18.13326524831207 -0.33033598775980267.829297546305325 -10.4252625074002822.7819145440653568 1.158097590630274 30.6780239575918 -23.9448164051634155.6018938249159245 -35.65399756657973 2.673171211427327 -2.90883222148649 -3.59167991497657157.002401397456594 14.353272681106485 -20.458739593063836 -47.09280369705129 25.90478920629466 1.8398979773599367 20.39037292398545 6.635600259567852 21.290136759712006 -30.6802383525156 -32.70023383447721 -28.294300515770139.030591834969087 41.28614556628407 -3.340280013558715 -6.387187312457969 -6.795058954505738 -29.239868647721906-84.84487823247701 21.53413969040578 -9.656174756794805 85.86389211836673 -54.80830511204367 -30.709179188326925-20.51621281362256680.1393974655775 -15.86831043391858 69.46209659371226 66.36652900849339 -25.10453716959171579.18237523289388 -25.577375106247562-30.87284219351464 -56.81179164164408 83.71581743144066 -9.27379265343866519.727630954137673 -85.96069547051928
99,9990.280172477999310550.8792488188373339 -2.611294241397942 -1.271843401381004 -5.583106681289557 2.0063535490559556 8.803561240522425 5.065652252075632 8.014785992140089 2.726435130640515 12.46703945978122 -0.87624409106155750.313008136552742734.259569516217728 -8.76361980315363527.42697941843017 -18.4957182932119153.2235230804059354 19.09973219172654 -21.25726264511826 -10.180990877752983-1.519950417648088522.71070295724785 29.616379288189506 -0.1316424396912179417.225907298944403 5.9791658138855075 11.74845639489894 -4.90066391424355351.065677623825266 -3.7948783924044243-32.70626521313637 -49.77902739808171 -38.9673863548757 4.223577391775786 -26.91850352108989666.81964173436637 76.24293014754961 -31.65153708363635622.893190015052674 -36.482595175686725-25.30090587669703 -10.0417262668186585.274361409552595 -34.88489743571424498.35907785706063 23.57152847224355 26.457155702616525 -86.30659590503936 12.050979659904716 3.057710144296827 -86.50100893855216 23.845662599505307 27.79510549576583 97.55955420927998 -40.44816836188145 2.789198094433643 -4.188993886405869-29.329836024823493 -40.232345894787784

Note that you need scikit-learn installed to only fit the PCAIncremental transformer. The the transform method does not rely on scikit-learn being installed.

Random projections

Random projections is another popular way of doing dimensionality reduction, especially when the dimensionality of the data is very high. vaex.ml conveniently wraps both scikit-learn.random_projection.GaussianRandomProjection and scikit-learn.random_projection.SparseRandomProjection in a single vaex.ml transformer.

[14]:
rand_proj = vaex.ml.RandomProjections(features=features, n_components=10)
rand_proj.fit(df)
rand_proj.transform(df)
[14]:
# feat_0 feat_1 feat_2 feat_3 feat_4 feat_5 feat_6 feat_7 feat_8 feat_9 feat_10 feat_11 feat_12 feat_13 feat_14 feat_15 feat_16 feat_17 feat_18 feat_19 feat_20 feat_21 feat_22 feat_23 feat_24 feat_25 feat_26 feat_27 feat_28 feat_29 feat_30 feat_31 feat_32 feat_33 feat_34 feat_35 feat_36 feat_37 feat_38 feat_39 feat_40 feat_41 feat_42 feat_43 feat_44 feat_45 feat_46 feat_47 feat_48 feat_49 random_projection_0 random_projection_1 random_projection_2 random_projection_3 random_projection_4 random_projection_5 random_projection_6 random_projection_7 random_projection_8 random_projection_9
0 0.21916619701436382-1.1435438188965208-2.236473242690611 -8.81728920352771 1.9931414225984159 0.8289809515418928 -7.847441537857684 -5.990636964340006 0.43889103534482576-6.4855757436955965-14.48532696768287113.825392548457543 -5.5661773929038185-3.1816868599382633 27.66565101972783650.541940500115366 16.001390451665785 32.510983357481614 8.342038455860216 -1.7293759207235855-6.451472523437187 22.55340570655327 -2.543125122041264528.75425936065127 -39.487762558467345 -6.871003398404642 11.198673922236354 -86.63832306461876 -7.32368079105989237.35407351193795 23.653897939827836 39.52047029873747 42.79143756690254 -33.3810495394693 33.05317072490505 14.818285601642208 -67.03187283353228 -19.01476952180615 22.4905763733386 35.33833686808974 11.79457050704157 -86.70070654092856 25.185781359852896 20.521240128349977 19.814114866123216 78.05531698592385 10.029892443326418 -97.39820288821723 -0.9603735180566161-64.45083314406774 -50.62485790513975 -8.969974902164104 -75.59787959901278 -32.23015488522056 -8.839635748773595 25.52280920491688 -67.81125847807398 20.625813141370337 -8.9492512335752 -38.397093148408445
1 -0.42076958781498162.3850692704428043 -1.3661921493141755-0.57464980721204832.2588675039630703 -5.100101894797036 -0.0005433423021984177-3.0055202143012365 5.749693220009271 11.379708067727588 10.119772822286162 0.15698369211085733-10.937595546203902-31.110839874678003 -5.593388174686233-17.48851742053923519.942127063793418 -0.6804349583522779-19.03708392463745428.74230527011865 12.40206875918237 -9.990549218761593 -5.733244330514869 3.171827795840886 -43.944372783025386 -25.8820588524763123.517534442545183 -25.10463172872150417.068162563601867-26.188188765123446-17.51765346352225 -5.803234686368941 23.37461204071744 85.58386322836444 -24.84250900935848 42.2583557612343 -34.83625774127584447.25447854289113 -5.903960946365425 47.891908734840925 -9.673715993876817 -17.5774774820285274.066254744412671 -51.377913297883865-11.51987006746566810.497653831847085 16.358701536495925 -18.3914825056028029.858101501060483 -39.819369217021595-24.167592671736728 -83.6194525409906 -31.474566122257382 -53.51874280599636 -9.295953556730474 12.065310248051029 21.935134361477004 -72.0479982398111 -66.96195351258001 76.22398276816658
2 -0.50247974091959910.9897062935454243 -1.152229281759237 -1.682033038083704 -4.091345910790923 -4.52742403771885552.129578282936375 10.936320913755608 -1.5695520680947808-6.034199421988269 -28.46431144964817 -15.32129294377632 -8.194011820344523 -16.218630438043398 12.021916867709596-4.908477966578501 -29.56619559878632 7.772108300044394 7.680046493196698 13.815505542053483 3.9208120473170016 47.34661694033482 1.544881077052938 9.440027347582042 18.56198304730558 22.3336072648248 -21.578332510459486-48.93092663572265616.5701671385727 16.656088505245513 19.8406469884787 5.384567961213235 -16.73392428744861614.376438801233908-35.323974854495155-7.411178531711759 -12.19133679331107557.91740496088699 34.873491696833774 88.28464395597479 87.65337555912684 -2.4096431528212445-7.8171455961597385-4.016403896979926 -22.96261029782406 -75.8940296403038 -38.8951677113029 -89.75675908427556 -79.5994302281645 -44.45310265105787 -30.370561351797924 -69.21024877654797 -131.21336032017504 -23.81397986098913 90.48694640695885 27.981469036784446 -71.13131857248655 -165.47320481693575 30.36401943353085 -37.55586272094929
3 0.12617606561304665-0.91728226378698231.8277090696240983 -1.8883963021695365-3.26085343817413436.94314682034098 -1.964291832580844 5.476441728997025 5.985807394356193 -4.152754646002149 15.497819324027216 1.9473222994398216 -11.1546653716116812.1502221820849754 7.402217623202724 -20.974198348221123-18.49611969411084 -11.197532751079477-4.167571500828548 -16.7492676033496866.873971547452746 -22.28958212850625421.69520422160094 10.732001896726413 -24.901621899667955 13.663451847361172 40.92498717076184 62.02571061444625 97.46935359691241 1.3197202988059933 -13.355307678605655-59.98623606960067 -15.3460319107594843.85479178918432068.451030763844253 -37.3610034378942059.316605927851759 -15.936791503025487-14.200047091850191-96.04376311885646 6.793212237372706 -89.28406931570937 -6.342536181747704 9.84276729692308 -44.15480258178421 -19.716315609075178-8.963766643638541 13.328160220454095 -81.91979053839731 -58.49057458242536 125.12748803342656 -25.206573635553035 61.805492059522535 15.847357808911099 -76.71575173832926 86.50353271166043 86.55719953897724 64.19018426217575 -109.12935339038033 -76.8186950536783
4 -1.5391949931048126-0.84243862338608713.808044749153777 -1.15040861016063344.975092670034785 -4.03814322037485956.475255733889277 -8.492789285986634 -0.71070840841147211.9868439665217876 -6.335098977847596 18.156422121050845 -3.9319838484429286-0.303888675665301 -18.038103704497153.6137256391127717 12.72102405166281 6.1797872895139765 -17.965746423694828-6.457595529218324 -11.1195782584740362.124546751440085 2.074247115486158 48.526431477044895 -47.7501423866134 -13.2189838629703170.7076755883915242 21.272708498626173 20.218314701800175-4.052289437744317 -28.29098298558251744.10471192261346 27.505033879695844 28.4585973718932739.564898635025768 -6.2001475733889375-33.28464087248315 13.562356933449957 72.47202649403566 -17.63088820680735222.257347577113283 19.793786901529828 -0.888840951088124115.45297619768772 80.01687713977846 -33.02953241445338 47.36388577265113 47.96488983389095 30.47783230830538 52.702201767487 9.100443729937155 -98.2487363365348 -86.04861549617408 -10.27966060169664 57.67907962932948 -74.56592607052885 -16.669282052441403 -26.583518157157688 47.49051485779235 178.45202653205695
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
99,995-1.160081518789358 -1.5967802399231468-2.15232040817518 -5.152880656063202 -2.81607683456671464.528707893808043 -9.219048918475725 -4.1152783877843895 15.434762333635224 -8.352240079142867 3.2341379115026694 7.679896402408659 19.99465474797146 -15.987822176846745 17.610005841221454-2.9940634500799996-36.9849615488119246.455731448290355 0.8700910607593357 -4.458798902046075 -8.573291238859795 1.7866347197434056 -5.748202862095839 -78.73536930217278 0.8664468950376607 -31.185290130437014-33.40360643789874548.79496517134476 4.273021608667145 -14.76645480929473223.034033698309216 47.916505903411704 22.82356373157275 58.17074570864146 13.075446180847607 5.357406097709567 19.301741918502767 30.91481630395726 -18.99658045583839429.068050048521297 -11.50032407194181 -94.16793562743486 10.247859328520715 -23.33364253340996864.88951899816107 -5.970342533069689 22.724974186922207 -46.358784230253264-76.06357310802707 36.34299568143191 79.74173570372625 -120.99425995411295 -158.6863110682003 51.08724948440816 45.49604758883528 -92.51884988772696 -33.86586167918684 -110.19228327900962 10.471099356215348 95.03245666604596
99,9960.133221661855605742.0608209742055763 2.1641428725239287 -2.450274442812819 0.5729664553821341 11.655164926233269 -9.864613671442203 -4.600216494861485 10.08600220223909 5.916293624542951 14.812935982731668 -6.453293834403917 -11.90549514770099 -3.26727352515574 1.8764801411441934-20.02012175801679 20.579289884690567 -7.95774658159159 -8.387038826710807 -18.0222209635527342.692329970764943 -14.30398788132729721.66822494391352 -15.938191880312708-35.29052532512791 -8.631818482611655 9.787860087044647 -53.67539155301477 -6.29070859522252334.35010506794386 6.565193250636609 -15.486170359730892-3.031599295669413 -1.80098865175289345.55563650252154 -37.38886935392985 68.02203785140463 69.71021558546443 67.33004345391464 38.09747878907309 -15.32336767996999276.84362563371494 -35.79579407415943 -32.88316495646942 -23.620694143487448-90.01728440515039 -24.77449621235016567.92281355721133 30.03415640434173 -29.32574935340052 12.801266126889404 17.612236115044166 -31.111396519869256 -160.72849754950767 6.480988179687637 4.231265515946373 -52.555790176785194 -65.21246117529064 35.89601203569984 127.45678271483702
99,9971.011157114782744 -0.80040986269630711.2571486498281934 3.8492594702419245 0.7592605926849842 -4.098302780814329 -1.9485099180060705 16.684513355922583 10.087604365608211 3.7452922672933973 -16.33173839915188 19.92199866574765 6.5771681345498845 -0.3230579773623871714.72654802079624613.583443459677845 -4.952279711617992 17.030998980346084 4.201801219449127 -3.910793205671661441.77733885408281 7.96614686571076 -39.10848664323428 -33.69630280939279 -7.463352385087283 7.458696462843669 -5.883303405785125 6.6310954865277845 -6.552748916196248-9.325031603876797 -11.7337490011325093.627520914240156 18.155090307885395 33.4073875839576 45.52621736035822 -22.938060053594263-27.364572553649534-58.35071648799318 -62.86375816449011 19.272818436422003 47.61050132614527 -11.301762317420524-82.24660966605563 16.961463120018315 13.762199024990316 9.330554417908111 -96.02479832620445 -24.711048464719337-2.078012378653908 -10.604821752483073-2.4863267734391865 -10.434958342024952 -37.55392055999496 6.171867513827003 -29.256283776632728 -72.71591584878013 40.24611847925469 -102.31580552627864 -14.905953231227388 -11.740055851590997
99,9980.9852518578365336 0.8203281912686264 -3.884122502896842 -0.95908400432742780.16746213933285223-0.8886763063332375-16.842052417441188 0.0198139466128886246.1752951086966466 -18.13326524831207 -0.33033598775980267.829297546305325 -10.4252625074002822.7819145440653568 1.158097590630274 30.6780239575918 -23.9448164051634155.6018938249159245 -35.65399756657973 2.673171211427327 -2.90883222148649 -3.59167991497657157.002401397456594 14.353272681106485 -20.458739593063836 -47.09280369705129 25.90478920629466 1.8398979773599367 20.39037292398545 6.635600259567852 21.290136759712006 -30.6802383525156 -32.70023383447721 -28.294300515770139.030591834969087 41.28614556628407 -3.340280013558715 -6.387187312457969 -6.795058954505738 -29.239868647721906-84.84487823247701 21.53413969040578 -9.656174756794805 85.86389211836673 -54.80830511204367 -30.709179188326925-20.51621281362256680.1393974655775 -15.86831043391858 69.46209659371226 -70.00012029923253 198.0368255008663 129.3714720510582 30.652606384505287 -65.3920698996377 49.51640293990293 11.882703005485045 93.26651618256129 35.206089617027985 -61.77494520916369
99,9990.280172477999310550.8792488188373339 -2.611294241397942 -1.271843401381004 -5.583106681289557 2.0063535490559556 8.803561240522425 5.065652252075632 8.014785992140089 2.726435130640515 12.46703945978122 -0.87624409106155750.313008136552742734.259569516217728 -8.76361980315363527.42697941843017 -18.4957182932119153.2235230804059354 19.09973219172654 -21.25726264511826 -10.180990877752983-1.519950417648088522.71070295724785 29.616379288189506 -0.1316424396912179417.225907298944403 5.9791658138855075 11.74845639489894 -4.90066391424355351.065677623825266 -3.7948783924044243-32.70626521313637 -49.77902739808171 -38.9673863548757 4.223577391775786 -26.91850352108989666.81964173436637 76.24293014754961 -31.65153708363635622.893190015052674 -36.482595175686725-25.30090587669703 -10.0417262668186585.274361409552595 -34.88489743571424498.35907785706063 23.57152847224355 26.457155702616525 -86.30659590503936 12.050979659904716 45.50866581430373 33.59123204918983 66.48747993035953 93.58220327847411 -113.34727146050997 34.20894130389669 94.5050429333418 98.6447663145478 -42.700555543235716 -3.632586769281134

Clustering

K-Means

vaex.ml implements a fast and scalable K-Means clustering algorithm. The usage is similar to that of scikit-learn.

[15]:
import vaex.ml.cluster

df = vaex.ml.datasets.load_iris()

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
kmeans = vaex.ml.cluster.KMeans(features=features, n_clusters=3, max_iter=100, verbose=True, random_state=42)
kmeans.fit(df)

df_trans = kmeans.transform(df)
df_trans
Iteration    0, inertia  519.0500000000001
Iteration    1, inertia  156.70447116074328
Iteration    2, inertia  88.70688235734133
Iteration    3, inertia  80.23054939305554
Iteration    4, inertia  79.28654263977778
Iteration    5, inertia  78.94084142614601
Iteration    6, inertia  78.94084142614601
[15]:
# sepal_length sepal_width petal_length petal_width class_ prediction_kmeans
0 5.9 3.0 4.2 1.5 1 0
1 6.1 3.0 4.6 1.4 1 0
2 6.6 2.9 4.6 1.3 1 0
3 6.7 3.3 5.7 2.1 2 1
4 5.5 4.2 1.4 0.2 0 2
... ... ... ... ... ... ...
1455.2 3.4 1.4 0.2 0 2
1465.1 3.8 1.6 0.2 0 2
1475.8 2.6 4.0 1.2 1 0
1485.7 3.8 1.7 0.3 0 2
1496.2 2.9 4.3 1.3 1 0

K-Means is an unsupervised algorithm, meaning that the predicted cluster labels in the transformed dataset do not necessarily correspond to the class label. We can map the predicted cluster identifiers to match the class labels, making it easier to construct diagnostic plots.

[16]:
df_trans['predicted_kmean_map'] = df_trans.prediction_kmeans.map(mapper={0: 1, 1: 2, 2: 0})
df_trans
[16]:
# sepal_length sepal_width petal_length petal_width class_ prediction_kmeans predicted_kmean_map
0 5.9 3.0 4.2 1.5 1 0 1
1 6.1 3.0 4.6 1.4 1 0 1
2 6.6 2.9 4.6 1.3 1 0 1
3 6.7 3.3 5.7 2.1 2 1 2
4 5.5 4.2 1.4 0.2 0 2 0
... ... ... ... ... ... ... ...
1455.2 3.4 1.4 0.2 0 2 0
1465.1 3.8 1.6 0.2 0 2 0
1475.8 2.6 4.0 1.2 1 0 1
1485.7 3.8 1.7 0.3 0 2 0
1496.2 2.9 4.3 1.3 1 0 1

Now we can construct simple scatter plots, and see that in the case of the Iris dataset, K-Means does a pretty good job splitting the data into 3 classes.

[17]:
fig = plt.figure(figsize=(12, 5))

plt.subplot(121)
df_trans.scatter(df_trans.petal_length, df_trans.petal_width, c_expr=df_trans.class_)
plt.title('Original classes')

plt.subplot(122)
df_trans.scatter(df_trans.petal_length, df_trans.petal_width, c_expr=df_trans.predicted_kmean_map)
plt.title('Predicted classes')

plt.tight_layout()
plt.show()
/home/jovan/vaex/packages/vaex-core/vaex/viz/mpl.py:205: UserWarning: `scatter` is deprecated and it will be removed in version 5.x. Please use `df.viz.scatter` instead.
  warnings.warn('`scatter` is deprecated and it will be removed in version 5.x. Please use `df.viz.scatter` instead.')
_images/tutorial_ml_35_1.png

As with any algorithm implemented in vaex.ml, K-Means can be used on billions of samples. Fitting takes under 2 minutes when applied on the oversampled Iris dataset, numbering over 1 billion samples.

[18]:
df = vaex.ml.datasets.load_iris_1e9()
n_samples = len(df)
print(f'Number of samples in DataFrame: {n_samples:,}')
Number of samples in DataFrame: 1,005,000,000
[19]:
%%time

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
kmeans = vaex.ml.cluster.KMeans(features=features, n_clusters=3, max_iter=100, verbose=True, random_state=31)
kmeans.fit(df)
Iteration    0, inertia  838974000.0037192
Iteration    1, inertia  535903134.000306
Iteration    2, inertia  530190921.4848897
Iteration    3, inertia  528931941.03372437
Iteration    4, inertia  528931941.0337243
CPU times: user 2min 37s, sys: 1.26 s, total: 2min 39s
Wall time: 19.9 s

Supervised learning

While vaex.ml does not yet implement any supervised machine learning models, it does provide wrappers to several popular libraries such as scikit-learn, XGBoost, LightGBM and CatBoost.

The main benefit of these wrappers is that they turn the models into vaex.ml transformers. This means the models become part of the DataFrame state and thus can be serialized, and their predictions can be returned as virtual columns. This is especially useful for creating various diagnostic plots and evaluating performance metrics at no memory cost, as well as building ensembles.

Scikit-Learn example

The vaex.ml.sklearn module provides convenient wrappers to the scikit-learn estimators. In fact, these wrappers can be used with any library that follows the API convention established by scikit-learn, i.e. implements the .fit and .transform methods.

Here is an example:

[20]:
from vaex.ml.sklearn import Predictor
from sklearn.ensemble import GradientBoostingClassifier

df = vaex.ml.datasets.load_iris()

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
target = 'class_'

model = GradientBoostingClassifier(random_state=42)
vaex_model = Predictor(features=features, target=target, model=model, prediction_name='prediction')

vaex_model.fit(df=df)

df = vaex_model.transform(df)
df
[20]:
# sepal_length sepal_width petal_length petal_width class_ prediction
0 5.9 3.0 4.2 1.5 1 1
1 6.1 3.0 4.6 1.4 1 1
2 6.6 2.9 4.6 1.3 1 1
3 6.7 3.3 5.7 2.1 2 2
4 5.5 4.2 1.4 0.2 0 0
... ... ... ... ... ... ...
1455.2 3.4 1.4 0.2 0 0
1465.1 3.8 1.6 0.2 0 0
1475.8 2.6 4.0 1.2 1 1
1485.7 3.8 1.7 0.3 0 0
1496.2 2.9 4.3 1.3 1 1

One can still train a predictive model on datasets that are too big to fit into memory by leveraging the on-line learners provided by scikit-learn. The vaex.ml.sklearn.IncrementalPredictor conveniently wraps these learners and provides control on how the data is passed to them from a vaex DataFrame.

Let us train a model on the oversampled Iris dataset which comprises over 1 billion samples.

[21]:
from vaex.ml.sklearn import IncrementalPredictor
from sklearn.linear_model import SGDClassifier

df = vaex.ml.datasets.load_iris_1e9()

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
target = 'class_'

model = SGDClassifier(learning_rate='constant', eta0=0.0001, random_state=42)
vaex_model = IncrementalPredictor(features=features, target=target, model=model,
                                  batch_size=500_000, partial_fit_kwargs={'classes':[0, 1, 2]})

vaex_model.fit(df=df, progress='widget')

df = vaex_model.transform(df)
df
[21]:
# sepal_length sepal_width petal_length petal_width class_ prediction
0 5.9 3.0 4.2 1.5 1 1
1 6.1 3.0 4.6 1.4 1 1
2 6.6 2.9 4.6 1.3 1 1
3 6.7 3.3 5.7 2.1 2 2
4 5.5 4.2 1.4 0.2 0 0
... ... ... ... ... ... ...
1,004,999,9955.2 3.4 1.4 0.2 0 0
1,004,999,9965.1 3.8 1.6 0.2 0 0
1,004,999,9975.8 2.6 4.0 1.2 1 1
1,004,999,9985.7 3.8 1.7 0.3 0 0
1,004,999,9996.2 2.9 4.3 1.3 1 1

XGBoost example

Libraries such as XGBoost provide more options such as validation during training and early stopping for example. We provide wrappers that keeps close to the native API of these libraries, in addition to the scikit-learn API.

While the following example showcases the XGBoost wrapper, vaex.ml implements similar wrappers for LightGBM and CatBoost.

[22]:
from vaex.ml.xgboost import XGBoostModel

df = vaex.ml.datasets.load_iris_1e5()
df_train, df_test = df.ml.train_test_split(test_size=0.2, verbose=False)

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
target = 'class_'

params = {'learning_rate': 0.1,
          'max_depth': 3,
          'num_class': 3,
          'objective': 'multi:softmax',
          'subsample': 1,
          'random_state': 42,
          'n_jobs': -1}


booster = XGBoostModel(features=features, target=target, num_boost_round=500, params=params)
booster.fit(df=df_train, evals=[(df_train, 'train'), (df_test, 'test')], early_stopping_rounds=5)

df_test = booster.transform(df_train)
df_test
[13:41:31] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1607604574104/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softmax' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[22]:
# sepal_length sepal_width petal_length petal_width class_ xgboost_prediction
0 5.9 3.0 4.2 1.5 1 1.0
1 6.1 3.0 4.6 1.4 1 1.0
2 6.6 2.9 4.6 1.3 1 1.0
3 6.7 3.3 5.7 2.1 2 2.0
4 5.5 4.2 1.4 0.2 0 0.0
... ... ... ... ... ... ...
80,3955.2 3.4 1.4 0.2 0 0.0
80,3965.1 3.8 1.6 0.2 0 0.0
80,3975.8 2.6 4.0 1.2 1 1.0
80,3985.7 3.8 1.7 0.3 0 0.0
80,3996.2 2.9 4.3 1.3 1 1.0

CatBoost example

The CatBoost library supports summing up models. With this feature, we can use CatBoost to train a model using data that is otherwise too large to fit in memory. The idea is to train a single CatBoost model per chunk of data, and than sum up the invidiual models to create a master model. To use this feature via vaex.ml just specify the batch_size argument in the CatBoostModel wrapper. One can also specify additional options such as the strategy on how to sum up the individual models, or how they should be weighted.

[23]:
from vaex.ml.catboost import CatBoostModel

df = vaex.ml.datasets.load_iris_1e8()
df_train, df_test = df.ml.train_test_split(test_size=0.2, verbose=False)

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
target = 'class_'

params = {
    'leaf_estimation_method': 'Gradient',
    'learning_rate': 0.1,
    'max_depth': 3,
    'bootstrap_type': 'Bernoulli',
    'subsample': 0.8,
    'sampling_frequency': 'PerTree',
    'colsample_bylevel': 0.8,
    'reg_lambda': 1,
    'objective': 'MultiClass',
    'eval_metric': 'MultiClass',
    'random_state': 42,
    'verbose': 0,
}

booster = CatBoostModel(features=features, target=target, num_boost_round=23,
                        params=params, prediction_type='Class', batch_size=11_000_000)
booster.fit(df=df_train, progress='widget')

df_test = booster.transform(df_train)
df_test
[23]:
# sepal_length sepal_width petal_length petal_width class_ catboost_prediction
0 5.9 3.0 4.2 1.5 1 array([1])
1 6.1 3.0 4.6 1.4 1 array([1])
2 6.6 2.9 4.6 1.3 1 array([1])
3 6.7 3.3 5.7 2.1 2 array([2])
4 5.5 4.2 1.4 0.2 0 array([0])
... ... ... ... ... ... ...
80,399,9955.2 3.4 1.4 0.2 0 array([0])
80,399,9965.1 3.8 1.6 0.2 0 array([0])
80,399,9975.8 2.6 4.0 1.2 1 array([1])
80,399,9985.7 3.8 1.7 0.3 0 array([0])
80,399,9996.2 2.9 4.3 1.3 1 array([1])

River example

River is an up-and-coming library for online learning, and provides a variety of models that can learn incrementally. While most of the river models currently support per-sample training, few do support mini-batch training which is extremely fast - a great synergy to do machine learning with vaex.

[24]:
from vaex.ml.incubator.river import RiverModel
from river.linear_model import LinearRegression
from river import optim


df = vaex.ml.datasets.load_iris_1e9()
df_train, df_test = df.ml.train_test_split(test_size=0.2, verbose=False)

features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']
target = 'class_'

river_model = RiverModel(features=features,
                         target=target,
                         model=LinearRegression(optimizer=optim.SGD(0.001), intercept_lr=0.001),
                         prediction_name='prediction_raw',
                         batch_size=500_000)
river_model.fit(df_train, progress='widget')
river_model.transform(df_test)
[24]:
# sepal_length sepal_width petal_length petal_width class_ prediction_raw
0 5.9 3.0 4.2 1.5 1 1.2262451850482554
1 6.1 3.0 4.6 1.4 1 1.3372106202149072
2 6.6 2.9 4.6 1.3 1 1.3080263625894342
3 6.7 3.3 5.7 2.1 2 1.8246442870772779
4 5.5 4.2 1.4 0.2 0 -0.1719159051653813
... ... ... ... ... ... ...
200,999,9955.2 3.4 1.4 0.2 0 -0.06961837848289065
200,999,9965.1 3.8 1.6 0.2 0 -0.04133966888449841
200,999,9975.8 2.6 4.0 1.2 1 1.1380612859534056
200,999,9985.7 3.8 1.7 0.3 0 -0.005633275295105093
200,999,9996.2 2.9 4.3 1.3 1 1.2171097577656713

State transfer - pipelines made easy

Each vaex DataFrame consists of two parts: data and state. The data is immutable, and any operation such as filtering, adding new columns, or applying transformers or predictive models just modifies the state. This is extremely powerful concept and can completely redefine how we imagine machine learning pipelines.

As an example, let us once again create a model based on the Iris dataset. Here, we will create a couple of new features, do a PCA transformation, and finally train a predictive model.

[25]:
# Load data and split it in train and test sets
df = vaex.ml.datasets.load_iris()
df_train, df_test = df.ml.train_test_split(test_size=0.2, verbose=False)

# Create new features
df_train['petal_ratio'] = df_train.petal_length / df_train.petal_width
df_train['sepal_ratio'] = df_train.sepal_length / df_train.sepal_width

# Do a PCA transformation
features = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width', 'petal_ratio', 'sepal_ratio']
pca = vaex.ml.PCA(features=features, n_components=6)
df_train = pca.fit_transform(df_train)

# Display the training DataFrame at this stage
df_train
[25]:
# sepal_length sepal_width petal_length petal_width class_ petal_ratio sepal_ratio PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5
0 5.4 3.0 4.5 1.5 1 3.0 1.8 -1.510547480171215 0.3611524321126822 -0.4005106138591812 0.5491844107628985 0.21135370342329635 -0.009542243224854377
1 4.8 3.4 1.6 0.2 0 8.0 1.411764705882353 4.447550641536847 0.2799644730487585 -0.04904458661276928 0.18719360579644695 0.10928493945448532 0.005228919010020094
2 6.9 3.1 4.9 1.5 1 3.266666666666667 2.2258064516129035-1.777649528149752 -0.60828897708458910.48007833550651513 -0.377620118668313350.05174472701894024 -0.04673816474220924
3 4.4 3.2 1.3 0.2 0 6.5 1.375 3.400548263702555 1.437036928591846 -0.3662652846960042 0.23420836198441913 0.05750021481634099 -0.023055011653267066
4 5.6 2.8 4.9 2.0 2 2.45 2.0 -2.32450987662220940.14710673877401348-0.5150809942258257 0.5471824391426298 -0.12154714382375817 0.0044686197532133876
... ... ... ... ... ... ... ... ... ... ... ... ... ...
1155.2 3.4 1.4 0.2 0 6.999999999999999 1.52941176470588253.623794583238953 0.8255759252729563 0.23453320686724874 -0.17599408825208826-0.04687036865354327 -0.02424621891240747
1165.1 3.8 1.6 0.2 0 8.0 1.34210526315789474.42115266246093 0.222875055336637040.4450642830179705 0.2184424557783562 0.14504752606375293 0.07229123907677276
1175.8 2.6 4.0 1.2 1 3.33333333333333352.230769230769231 -1.069062832993727 0.3874258314654399 -0.4471767749236783 -0.2956609879568117 -0.0010695982441835394-0.0065225306610744715
1185.7 3.8 1.7 0.3 0 5.666666666666667 1.50000000000000022.2846521048417037 1.1920826609681359 0.8273738848637026 -0.210489464627257370.03381892388998425 0.018792165273013528
1196.2 2.9 4.3 1.3 1 3.30769230769230752.137931034482759 -1.29882299587484520.06960434514054464-0.0012167985718341268-0.240722552191808830.05282732890885841 -0.032459999314411514

At this point, we are ready to train a predictive model. In this example, let’s use LightGBM with its scikit-learn API.

[26]:
import lightgbm

features = df_train.get_column_names(regex='^PCA')

booster = lightgbm.LGBMClassifier()

vaex_model = Predictor(model=booster, features=features, target='class_')

vaex_model.fit(df=df_train)
df_train = vaex_model.transform(df_train)

df_train
[26]:
# sepal_length sepal_width petal_length petal_width class_ petal_ratio sepal_ratio PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5 prediction
0 5.4 3.0 4.5 1.5 1 3.0 1.8 -1.510547480171215 0.3611524321126822 -0.4005106138591812 0.5491844107628985 0.21135370342329635 -0.009542243224854377 1
1 4.8 3.4 1.6 0.2 0 8.0 1.411764705882353 4.447550641536847 0.2799644730487585 -0.04904458661276928 0.18719360579644695 0.10928493945448532 0.005228919010020094 0
2 6.9 3.1 4.9 1.5 1 3.266666666666667 2.2258064516129035-1.777649528149752 -0.60828897708458910.48007833550651513 -0.377620118668313350.05174472701894024 -0.04673816474220924 1
3 4.4 3.2 1.3 0.2 0 6.5 1.375 3.400548263702555 1.437036928591846 -0.3662652846960042 0.23420836198441913 0.05750021481634099 -0.023055011653267066 0
4 5.6 2.8 4.9 2.0 2 2.45 2.0 -2.32450987662220940.14710673877401348-0.5150809942258257 0.5471824391426298 -0.12154714382375817 0.0044686197532133876 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1155.2 3.4 1.4 0.2 0 6.999999999999999 1.52941176470588253.623794583238953 0.8255759252729563 0.23453320686724874 -0.17599408825208826-0.04687036865354327 -0.02424621891240747 0
1165.1 3.8 1.6 0.2 0 8.0 1.34210526315789474.42115266246093 0.222875055336637040.4450642830179705 0.2184424557783562 0.14504752606375293 0.07229123907677276 0
1175.8 2.6 4.0 1.2 1 3.33333333333333352.230769230769231 -1.069062832993727 0.3874258314654399 -0.4471767749236783 -0.2956609879568117 -0.0010695982441835394-0.00652253066107447151
1185.7 3.8 1.7 0.3 0 5.666666666666667 1.50000000000000022.2846521048417037 1.1920826609681359 0.8273738848637026 -0.210489464627257370.03381892388998425 0.018792165273013528 0
1196.2 2.9 4.3 1.3 1 3.30769230769230752.137931034482759 -1.29882299587484520.06960434514054464-0.0012167985718341268-0.240722552191808830.05282732890885841 -0.032459999314411514 1

The final df_train DataFrame contains all the features we created, including the predictions right at the end. Now, we would like to apply the same transformations to the test set. All we need to do, is to simply extract the state from df_train and apply it to df_test. This will propagate all the changes that were made to the training set on the test set.

[27]:
state = df_train.state_get()

df_test.state_set(state)
df_test
[27]:
# sepal_length sepal_width petal_length petal_width class_ petal_ratio sepal_ratio PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5 prediction
0 5.9 3.0 4.2 1.5 1 2.80000000000000031.9666666666666668-1.642627940409072 0.49931302910747727 -0.063088008066644660.10842057110641677 -0.03924298664189224-0.0273944397002728221
1 6.1 3.0 4.6 1.4 1 3.28571428571428562.033333333333333 -1.445047446393471 -0.1019091578746504 -0.018990122394938010.0209807676460904080.1614215276667148 -0.02716639637934938 1
2 6.6 2.9 4.6 1.3 1 3.538461538461538 2.2758620689655173-1.330564613235537 -0.419784747491312670.1759590589290671 -0.4631301992308477 0.08304243689815374 -0.0333517336774292741
3 6.7 3.3 5.7 2.1 2 2.71428571428571442.0303030303030303-2.6719170661531013-0.9149428897499291 0.4156162725009377 0.34633692661436644 0.03742964707590906 -0.0132542861962457742
4 5.5 4.2 1.4 0.2 0 6.999999999999999 1.30952380952380953.6322930267831404 0.8198526437905096 1.046277579362938 0.09738737839850209 0.09412658096734221 0.1329137026697501 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
255.5 2.5 4.0 1.3 1 3.07692307692307662.2 -1.25231200886008960.5975071562677784 -0.7019801415469216 -0.11489031841855571-0.036159457820878690.005496321827264977 1
265.8 2.7 3.9 1.2 1 3.25 2.148148148148148 -1.07923521659046570.5236883751378523 -0.34037717939532286-0.23743695029955128-0.00936891422024664-0.02184110533380834 1
274.4 2.9 1.4 0.2 0 6.999999999999999 1.517241379310345 3.7422969192506095 1.048460304741977 -0.636475521315278 0.07623157913054074 0.004215355833312173-0.06354157393133958 0
284.5 2.3 1.3 0.3 0 4.333333333333334 1.956521739130435 1.4537380535696471 2.4197864889383505 -1.0301500321688102 -0.5150263062576134 -0.2631218962099228 -0.06608059456656257 0
296.9 3.2 5.7 2.3 2 2.47826086956521772.15625 -2.963110301521378 -0.924626055589704 0.44833006106219797 0.20994670504662372 -0.2012725506779131 -0.0189004142877193532

And just like that df_test contains all the columns, transformations and the prediction we modelled on the training set. The state can be easily serialized to disk in a form of a JSON file. This makes deployment of a machine learning model as trivial as simply copying a JSON file from one environment to another.

[28]:
df_train.state_write('./iris_model.json')

df_test.state_load('./iris_model.json')
df_test
[28]:
# sepal_length sepal_width petal_length petal_width class_ petal_ratio sepal_ratio PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5 prediction
0 5.9 3.0 4.2 1.5 1 2.80000000000000031.9666666666666668-1.642627940409072 0.49931302910747727 -0.063088008066644660.10842057110641677 -0.03924298664189224-0.0273944397002728221
1 6.1 3.0 4.6 1.4 1 3.28571428571428562.033333333333333 -1.445047446393471 -0.1019091578746504 -0.018990122394938010.0209807676460904080.1614215276667148 -0.02716639637934938 1
2 6.6 2.9 4.6 1.3 1 3.538461538461538 2.2758620689655173-1.330564613235537 -0.419784747491312670.1759590589290671 -0.4631301992308477 0.08304243689815374 -0.0333517336774292741
3 6.7 3.3 5.7 2.1 2 2.71428571428571442.0303030303030303-2.6719170661531013-0.9149428897499291 0.4156162725009377 0.34633692661436644 0.03742964707590906 -0.0132542861962457742
4 5.5 4.2 1.4 0.2 0 6.999999999999999 1.30952380952380953.6322930267831404 0.8198526437905096 1.046277579362938 0.09738737839850209 0.09412658096734221 0.1329137026697501 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
255.5 2.5 4.0 1.3 1 3.07692307692307662.2 -1.25231200886008960.5975071562677784 -0.7019801415469216 -0.11489031841855571-0.036159457820878690.005496321827264977 1
265.8 2.7 3.9 1.2 1 3.25 2.148148148148148 -1.07923521659046570.5236883751378523 -0.34037717939532286-0.23743695029955128-0.00936891422024664-0.02184110533380834 1
274.4 2.9 1.4 0.2 0 6.999999999999999 1.517241379310345 3.7422969192506095 1.048460304741977 -0.636475521315278 0.07623157913054074 0.004215355833312173-0.06354157393133958 0
284.5 2.3 1.3 0.3 0 4.333333333333334 1.956521739130435 1.4537380535696471 2.4197864889383505 -1.0301500321688102 -0.5150263062576134 -0.2631218962099228 -0.06608059456656257 0
296.9 3.2 5.7 2.3 2 2.47826086956521772.15625 -2.963110301521378 -0.924626055589704 0.44833006106219797 0.20994670504662372 -0.2012725506779131 -0.0189004142877193532