Welcome to pjautoml ‘s documentation!¶

Install¶

Requirements¶

The pjautoml package requires the following dependencies:

numpy
scipy
pjml

Install¶

The pjautoml is available on the PyPi . You can install it via pip as follow:

pip install -U pjautoml

It is possible to use the development version installing from GitHub:

pip install -U git@github.com:end-to-end-data-science/pjautoml.git

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from Github and install all dependencies:

git clone git@github.com:end-to-end-data-science/pjautoml.git
cd pjautoml
pip install .

Test and coverage¶

If you want to test/test-coverage the code before to install:

$ make install-dev
$ make test-cov

Or:

$ make install-dev
$ pytest --cov=pjautoml/ tests/

Using pjautoml¶

TODO.

For more examples see The pjautoml example gallery.

API Documentation¶

This is the full API documentation of the pjautoml package.

`pjautoml.cs`: Configuration Space¶

Operand¶

`graph.graph.Graph`([name, path, nodes])	TODO.
`graph.node.Node`([params, children])	Partial settings for a component.
`list.flist.ListCS`(*css)	Finite Config Space (FCS) is a representation of a discrete CS.
`list.flist.CList`(*css)
`list.flist.FList`(*css)

Operator¶

Data-driven configuration space operator¶

`optimization.modelfree.best.Best`(listcs[, …])
`optimization.modelfree.random.RandomSearch`(cs)

Configuration space operators¶

`container.Container`(*args, seed, name, path, …)	TODO.
`map.Map`(*args[, seed])	TODO.
`multi.Multi`(*args[, seed])	TODO.
`sample.Sample`(cs[, n])	TODO.
`chain.Chain`(css, *kwargs)	TODO.
`select.Select`(css, *kwargs)	TODO.
`shuffle.Shuffle`(css, *kwargs)	A permutation is sampled.

`pjautoml.util`: Util Classes and Functions¶

`parameter.Param`(function, **kwargs)	Base class for all kinds of algorithm (hyper)parameters.
`parameter.CatP`(function, **kwargs)
`parameter.IntP`(function, **kwargs)
`parameter.FixedP`(value)
`parameter.OrdP`(function, **kwargs)
`parameter.RealP`(function, **kwargs)

`pjautoml.abs`: Abstract Classes and Mixin¶

The pjautoml.abs submodule contains abstract classes and mixin.

mixin.asoperand.AsOperandCS

The pjautoml example gallery¶

The pjautoml library aims to provide easy tools to create AutoML systems from scratch. It adds new and elegant ways to deal and operates with configuration space. Moreover, it provides some AutoML system based on the literature.

Below we present a gallery with examples of use:

Introductory Examples¶

Introductory examples of pjautoml package.

Operating configuration spaces (basic)

Note

Click here to download the full example code

Operating configuration spaces (basic)¶

A configuration space can represent the hyperparameters of a single component (an algorithm) or the hyperparameters of all components contained in the pipeline.

We represent as a workflow the union of configuration spaces of different algorithms that together can create a multitude of machine learning pipeline types.

You can create workflows using the following configuration space operators:

Chain –> It creates a sequential chain of configuration spaces

Shuffle –> It shuffles the configuration spaces order

Select –> It selects one of the given configuration spaces

Importing the required packages

import numpy as np

from pjautoml.cs.operator.free.chain import Chain
from pjautoml.cs.operator.free.select import Select
from pjautoml.cs.operator.free.shuffle import Shuffle
from pjpy.modeling.supervised.classifier.dt import DT
from pjpy.modeling.supervised.classifier.svmc import SVMC
from pjpy.processing.feature.reductor.pca import PCA
from pjpy.processing.feature.scaler.minmax import MinMax

np.random.seed(0)

Using Chain¶

The Chain is a configuration space operator that concatenates other spaces in a sequence. Intuitively you can see it as a Cartesian product between two or more search spaces.

exp = Chain(SVMC, DT)
print(exp.sample())

# You can also use the python operator ``*``

exp = SVMC * DT
print(exp.sample())

Out:

{
    "info": {
        "_id": "SVMC@pjpy.modeling.supervised.classifier.svmc",
        "config": {
            "C": 59.28450253804001,
            "kernel": "linear",
            "degree": 3,
            "gamma": "scale",
            "coef0": 0.0,
            "shrinking": true,
            "probability": false,
            "tol": 0.001,
            "cache_size": 200,
            "class_weight": "balanced",
            "verbose": false,
            "max_iter": 1000000,
            "decision_function_shape": "ovo",
            "break_ties": false,
            "random_state": null,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "DT@pjpy.modeling.supervised.classifier.dt",
        "config": {
            "criterion": "entropy",
            "splitter": "best",
            "class_weight": "balanced",
            "max_features": null,
            "max_depth": 647,
            "min_samples_split": 0.1312767257915965,
            "min_samples_leaf": 0.2675320084616231,
            "min_weight_fraction_leaf": 0.2890988281503088,
            "min_impurity_decrease": 0.07668830376515555,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "SVMC@pjpy.modeling.supervised.classifier.svmc",
        "config": {
            "C": 81.21689166067644,
            "kernel": "linear",
            "degree": 3,
            "gamma": "scale",
            "coef0": 0.0,
            "shrinking": true,
            "probability": false,
            "tol": 100,
            "cache_size": 200,
            "class_weight": "balanced",
            "verbose": false,
            "max_iter": 1000000,
            "decision_function_shape": "ovo",
            "break_ties": false,
            "random_state": null,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "DT@pjpy.modeling.supervised.classifier.dt",
        "config": {
            "criterion": "entropy",
            "splitter": "best",
            "class_weight": "balanced",
            "max_features": "auto",
            "max_depth": 89,
            "min_samples_split": 0.006066499013700276,
            "min_samples_leaf": 0.24978612104453587,
            "min_weight_fraction_leaf": 0.23344702528495515,
            "min_impurity_decrease": 0.17400242964936385,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}

Using Shuffle¶

The Select is a configuration space operator that works like a bifurcation, where only one of the spaces will be selected. Intuitively you can see it as a branch created in your search space in which a random factor can enable one or other configuration space.

exp = Chain(PCA, MinMax)
print(exp.sample())

Out:

{
    "info": {
        "_id": "PCA@pjpy.processing.feature.reductor.pca",
        "config": {
            "n": 0.978618342232764
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "MinMax@pjpy.processing.feature.scaler.minmax",
        "config": {
            "feature_range": [
                -1,
                1
            ]
        }
    },
    "enhance": true,
    "model": true
}

You can also use the python operator @

exp = PCA @ MinMax
print(exp.sample())

Out:

{
    "info": {
        "_id": "PCA@pjpy.processing.feature.reductor.pca",
        "config": {
            "n": 0.46147936225293185
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "MinMax@pjpy.processing.feature.scaler.minmax",
        "config": {
            "feature_range": [
                0,
                1
            ]
        }
    },
    "enhance": true,
    "model": true
}

Using Select¶

The Shuffle is a configuration space operator that concatenate configurations spaces in a sequence, but the order is not maintained. Intuitively, you can see it as the union of the Cartesian product of all configuration space combinations.

exp = Chain(SVMC, DT)
print(exp.sample())

Out:

{
    "info": {
        "_id": "SVMC@pjpy.modeling.supervised.classifier.svmc",
        "config": {
            "C": 53.737369207578126,
            "kernel": "sigmoid",
            "degree": 3,
            "gamma": 58.201983387312794,
            "coef0": 72.06326547259168,
            "shrinking": true,
            "probability": false,
            "tol": 10000,
            "cache_size": 200,
            "class_weight": null,
            "verbose": false,
            "max_iter": 1000000,
            "decision_function_shape": "ovo",
            "break_ties": false,
            "random_state": null,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "DT@pjpy.modeling.supervised.classifier.dt",
        "config": {
            "criterion": "entropy",
            "splitter": "best",
            "class_weight": null,
            "max_features": null,
            "max_depth": 737,
            "min_samples_split": 0.06496588977695714,
            "min_samples_leaf": 0.04056631680346222,
            "min_weight_fraction_leaf": 0.09724230233796423,
            "min_impurity_decrease": 0.029934973436736637,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}

You can also use the python operator +

exp = SVMC + DT
print(exp.sample())

Out:

{
    "info": {
        "_id": "SVMC@pjpy.modeling.supervised.classifier.svmc",
        "config": {
            "C": 38.64895946368809,
            "kernel": "linear",
            "degree": 3,
            "gamma": "scale",
            "coef0": 0.0,
            "shrinking": true,
            "probability": false,
            "tol": 1000,
            "cache_size": 200,
            "class_weight": null,
            "verbose": false,
            "max_iter": 1000000,
            "decision_function_shape": "ovr",
            "break_ties": false,
            "random_state": null
        }
    },
    "enhance": true,
    "model": true
}

Using them all:¶

Using these simple operations, you can create diverse kind of configuration spaces to represent an end-to-end AutoML problem.

exp = Chain(Shuffle(PCA, MinMax), Select(SVMC + DT))
print(exp.sample())

Out:

{
    "info": {
        "_id": "PCA@pjpy.processing.feature.reductor.pca",
        "config": {
            "n": 0.43703195379934145
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "MinMax@pjpy.processing.feature.scaler.minmax",
        "config": {
            "feature_range": [
                0,
                1
            ]
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "DT@pjpy.modeling.supervised.classifier.dt",
        "config": {
            "criterion": "entropy",
            "splitter": "best",
            "class_weight": "balanced",
            "max_features": null,
            "max_depth": 654,
            "min_samples_split": 0.05127370463122841,
            "min_samples_leaf": 0.10744629193869054,
            "min_weight_fraction_leaf": 0.22520584236553687,
            "min_impurity_decrease": 0.12156613374309355,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}

You can also use python operators

exp = PCA @ MinMax * (SVMC + DT)
print(exp.sample())

Out:

{
    "info": {
        "_id": "MinMax@pjpy.processing.feature.scaler.minmax",
        "config": {
            "feature_range": [
                0,
                1
            ]
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "PCA@pjpy.processing.feature.reductor.pca",
        "config": {
            "n": 0.038425426472734725
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "DT@pjpy.modeling.supervised.classifier.dt",
        "config": {
            "criterion": "gini",
            "splitter": "best",
            "class_weight": null,
            "max_features": "auto",
            "max_depth": 653,
            "min_samples_split": 0.19051802702219556,
            "min_samples_leaf": 0.2985898750037986,
            "min_weight_fraction_leaf": 0.17455509883156028,
            "min_impurity_decrease": 0.08287371764527376,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}

Total running time of the script: ( 0 minutes 0.324 seconds)

Gallery generated by Sphinx-Gallery

Searching for good pipelines from a workflow

Note

Click here to download the full example code

Searching for good pipelines from a workflow¶

Let’s run a random search to find a good machine learning pipeline to a given problem.

Importing the required packages

import numpy as np

from pjautoml.cs.operator.datadriven.optimization.modelfree.random import RandomSearch
from pjautoml.cs.operator.free.map import Map
from pjautoml.cs.operator.free.select import Select
from pjautoml.cs.operator.free.shuffle import Shuffle
from pjautoml.cs.workflow import Workflow
from pjml.data.communication.report import Report
from pjml.data.evaluation.metric import Metric
from pjml.data.flow.file import File
from pjml.stream.expand.partition import Partition
from pjml.stream.reduce.reduce import Reduce
from pjml.stream.reduce.summ import Summ
from pjpy.modeling.supervised.classifier.dt import DT
from pjpy.modeling.supervised.classifier.svmc import SVMC
from pjpy.processing.feature.reductor.pca import PCA
from pjpy.processing.feature.scaler.minmax import MinMax

np.random.seed(0)

First, we must create a workflow.

workflow = Workflow(
    File("../data/iris.arff"),
    Partition(),
    Map(Shuffle(PCA, MinMax), Select(SVMC + DT), Metric()),
    Summ(function="mean"),
    Reduce(),
    Report("Mean S: $S"),
)

Now, we run the random search over the workflow created. We will get the best pipeline found after 30 samples. The testing performance by pipeline will be printed.

rs1 = RandomSearch(workflow, sample=30)
print(len(rs1.datas))
print(len(rs1.components))

Out:

   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94]])
   [model]  Mean S: array([[0.93333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.97333333]])
   [model]  Mean S: array([[0.38]])
   [model]  Mean S: array([[0.00666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.95333333]])
   [model]  Mean S: array([[0.94]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.96]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.93333333]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.96]])
   [model]  Mean S: array([[0.95333333]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.96]])
   [model]  Mean S: array([[0.94]])
1
1

The best pipeline found is:

res_train, res_test = rs1.datas[0]
print("Train result: ", res_train)
print("test result: ", res_test)

Out:

Train result:  <pjdata.content.data.Data object at 0x7f6f3fcdae80>
test result:  <pjdata.content.data.Data object at 0x7f6f3fc846d0>

Total running time of the script: ( 0 minutes 3.518 seconds)

Gallery generated by Sphinx-Gallery

Creating an end-to-end workflow

Note

Click here to download the full example code

Creating an end-to-end workflow¶

Let create an end-to-end machine learning workflow.

Importing the required packages

import numpy as np

from pjautoml.cs.operator.free.chain import Chain
from pjautoml.cs.operator.free.map import Map
from pjautoml.cs.operator.free.select import Select
from pjautoml.cs.operator.free.shuffle import Shuffle
from pjautoml.cs.workflow import Workflow
from pjml.data.communication.report import Report
from pjml.data.evaluation.metric import Metric
from pjml.data.flow.file import File
from pjml.stream.expand.partition import Partition
from pjml.stream.reduce.reduce import Reduce
from pjml.stream.reduce.summ import Summ
from pjpy.modeling.supervised.classifier.dt import DT
from pjpy.modeling.supervised.classifier.svmc import SVMC
from pjpy.processing.feature.reductor.pca import PCA
from pjpy.processing.feature.scaler.minmax import MinMax

np.random.seed(0)

First, we create a machine learning expression.

exp = Chain(Shuffle(PCA, MinMax), Select(SVMC + DT))

It represents a configuration space. Let’s get a sample from it.

print(exp.sample())

Out:

{
    "info": {
        "_id": "MinMax@pjpy.processing.feature.scaler.minmax",
        "config": {
            "feature_range": [
                0,
                1
            ]
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "PCA@pjpy.processing.feature.reductor.pca",
        "config": {
            "n": 0.7151893663724195
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "DT@pjpy.modeling.supervised.classifier.dt",
        "config": {
            "criterion": "entropy",
            "splitter": "best",
            "class_weight": "balanced",
            "max_features": null,
            "max_depth": 425,
            "min_samples_split": 0.1937685880258838,
            "min_samples_leaf": 0.1312767257915965,
            "min_weight_fraction_leaf": 0.2675319002346239,
            "min_impurity_decrease": 0.19273255210020587,
            "seed": 0
        }
    },
    "enhance": true,
    "model": true
}

Defined our machine learning expression, we will create an end-to-end workflow.

workflow = Workflow(
    File("../data/iris.arff"),
    Partition(),
    Map(exp, Metric()),
    Summ(function="mean"),
    Reduce(),
    Report("Mean S: $S"),
)

or using only python operators

workflow = (
    File("../data/iris.arff")
    * Partition()
    * Map(exp * Metric())
    * Summ(function="mean")
    * Reduce()
    * Report("Mean S: $S")
)

This workflow represents the union of all configuration spaces. Let get a sample of it:

spl = workflow.sample()
print(spl)

Out:

{
    "info": {
        "_id": "File@pjml.data.flow.file",
        "config": {
            "name": "../data/iris.arff",
            "path": "./",
            "description": "No description.",
            "hashes": {
                "X": "0ǏǍɽĊũÊүȏŵҖSîҕ",
                "Y": "0ЄϒɐĵǏȂϗƽўýÎʃȆ",
                "Xd": "5ɫңɖŇǓήŉÝʑΏƀЀǔ",
                "Yd": "5mϛǖͶƅĞOȁЎžʛѲƨ",
                "Xt": "5ȥΔĨӑËҭȨƬδſΧȰɩ",
                "Yt": "5έēPaӹЄźգǩȱɟǟǹ"
            }
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "Partition@pjml.stream.expand.partition",
        "config": {
            "split_type": "cv",
            "partitions": 10,
            "seed": 0,
            "fields": "X,Y"
        }
    },
    "enhance": true,
    "model": true
}
Map>>
    {"info": {"_id": "Pipeline@pjml.operator.pipeline","config": {"components": [{"info": {"_id": "MinMax@pjpy.processing.feature.scaler.minmax","config": {"feature_range": [0,1],"model": true,"enhance": true}},"enhance": true,"model": true},{"info": {"_id": "PCA@pjpy.processing.feature.reductor.pca","config": {"n": 0.7917250380826646,"model": true,"enhance": true}},"enhance": true,"model": true}],"model": true,"enhance": true}},"enhance": true,"model": true}
    {"info": {"_id": "DT@pjpy.modeling.supervised.classifier.dt","config": {"criterion": "gini","splitter": "best","class_weight": null,"max_features": "sqrt","max_depth": 926,"min_samples_split": 0.021311746423307888,"min_samples_leaf": 0.026139702781162514,"min_weight_fraction_leaf": 0.006065519232097715,"min_impurity_decrease": 0.1665239691095876,"seed": 0,"model": true,"enhance": true}},"enhance": true,"model": true}
    {"info": {"_id": "Metric@pjml.data.evaluation.metric","config": {"functions": ["accuracy"],"target": "Y","prediction": "Z","model": true,"enhance": true}},"enhance": true,"model": true}
<<Map
{
    "info": {
        "_id": "Summ@pjml.stream.reduce.summ",
        "config": {
            "field": "R",
            "function": "mean"
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "Reduce@pjml.stream.reduce.reduce",
        "config": {}
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "Report@pjml.data.communication.report",
        "config": {
            "text": "Mean S: $S"
        }
    },
    "enhance": true,
    "model": true
}

Total running time of the script: ( 0 minutes 0.041 seconds)

Gallery generated by Sphinx-Gallery

Advanced Examples¶

Advanced examples of pjautoml package.

Creating random search with configuration spaces’ operations

Note

Click here to download the full example code

Creating random search with configuration spaces’ operations¶

Let’s create a random search from scratch using the other two essential configuration space operation: Best and Sample.

Importing the required packages

import numpy as np

from pjautoml.cs.operator.datadriven.optimization.modelfree.best import Best
from pjautoml.cs.operator.free.map import Map
from pjautoml.cs.operator.free.sample import Sample
from pjautoml.cs.operator.free.select import Select
from pjautoml.cs.operator.free.shuffle import Shuffle
from pjautoml.cs.workflow import Workflow
from pjml.data.communication.report import Report
from pjml.data.evaluation.metric import Metric
from pjml.data.flow.file import File
from pjml.stream.expand.partition import Partition
from pjml.stream.reduce.reduce import Reduce
from pjml.stream.reduce.summ import Summ
from pjpy.modeling.supervised.classifier.dt import DT
from pjpy.modeling.supervised.classifier.svmc import SVMC
from pjpy.processing.feature.reductor.pca import PCA
from pjpy.processing.feature.scaler.minmax import MinMax

np.random.seed(0)

This is the workflow we will work on. The workflow is also a representation of our configuration space, i.e., it represents all machine learning pipelines that can be achieved.

workflow = Workflow(
    File("../data/iris.arff"),
    Partition(),
    Map(Shuffle(PCA, MinMax), Select(SVMC + DT), Metric()),
    Summ(function="mean"),
    Reduce(),
    Report("Mean S: $S"),
)

Using Sample: The operation Sample will sample n different pipelines. It will transform infinity configuration space in finite.

spl = Sample(workflow, n=10)
print(len(spl))

Out:

Using Best: The operation Best will return the pipeline with the best performance (highest value in data.S). By default, we define the best as the highest value, but you can also set the best as the lowest value.

best_2 = Best(spl, n=2)
print(len(best_2.datas))
print(len(best_2.components))

Out:

   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94]])
   [model]  Mean S: array([[0.93333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.97333333]])
   [model]  Mean S: array([[0.38]])
2
2

Total running time of the script: ( 0 minutes 1.186 seconds)

Gallery generated by Sphinx-Gallery

Creating an end-to-end AutoML from scratch

Note

Click here to download the full example code

Creating an end-to-end AutoML from scratch¶

Let’s create an AutoML from scratch.

Importing the required packages

import numpy as np

from pjautoml.cs.operator.datadriven.optimization.modelfree.random import RandomSearch
from pjautoml.cs.operator.free.map import Map
from pjautoml.cs.operator.free.select import Select
from pjautoml.cs.operator.free.shuffle import Shuffle
from pjautoml.cs.workflow import Workflow
from pjml.data.communication.report import Report
from pjml.data.evaluation.metric import Metric
from pjml.data.flow.file import File
from pjml.stream.expand.partition import Partition
from pjml.stream.reduce.reduce import Reduce
from pjml.stream.reduce.summ import Summ
from pjpy.modeling.supervised.classifier.dt import DT
from pjpy.modeling.supervised.classifier.svmc import SVMC
from pjpy.processing.feature.reductor.pca import PCA
from pjpy.processing.feature.scaler.minmax import MinMax

np.random.seed(0)

First, we can define a workflow. Notice we not add a File. Then, we use random search as the optimization process to select the best pipeline. Finally, we should also give it a name. The name of my AutoML, of course, is my_automl :)

def my_automl(data):
    workflow = Workflow(
        Partition(),
        Map(Shuffle(PCA, MinMax), Select(SVMC + DT), Metric()),
        Summ(function="mean"),
        Reduce(),
        Report("Mean S: $S"),
    )

    rs = RandomSearch(workflow, sample=30, train=data, test=data)
    return rs.components[0]

Now, let’s find a good pipeline for the iris dataset:

data = File("../data/iris.arff").data
best_pipeline = my_automl(data)
print(best_pipeline)

Out:

   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94]])
   [model]  Mean S: array([[0.93333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.97333333]])
   [model]  Mean S: array([[0.38]])
   [model]  Mean S: array([[0.00666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.95333333]])
   [model]  Mean S: array([[0.94]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.96]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.93333333]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.96666667]])
   [model]  Mean S: array([[0.94666667]])
   [model]  Mean S: array([[0.96]])
   [model]  Mean S: array([[0.95333333]])
   [model]  Mean S: array([[0.33333333]])
   [model]  Mean S: array([[0.96]])
   [model]  Mean S: array([[0.94]])
{
    "info": {
        "_id": "Partition@pjml.stream.expand.partition",
        "config": {
            "split_type": "cv",
            "partitions": 10,
            "seed": 0,
            "fields": "X,Y"
        }
    },
    "enhance": true,
    "model": true
}
Map>>
    {"info": {"_id": "Pipeline@pjml.operator.pipeline","config": {"components": [{"info": {"_id": "MinMax@pjpy.processing.feature.scaler.minmax","config": {"feature_range": [-1,1],"model": true,"enhance": true}},"enhance": true,"model": true},{"info": {"_id": "PCA@pjpy.processing.feature.reductor.pca","config": {"n": 0.9764594650133958,"model": true,"enhance": true}},"enhance": true,"model": true}],"model": true,"enhance": true}},"enhance": true,"model": true}
    {"info": {"_id": "SVMC@pjpy.modeling.supervised.classifier.svmc","config": {"C": 97.6761111429249,"kernel": "linear","degree": 3,"gamma": "scale","coef0": 0.0,"shrinking": false,"probability": false,"tol": 0.1,"cache_size": 200,"class_weight": "balanced","verbose": false,"max_iter": 1000000,"decision_function_shape": "ovo","break_ties": false,"random_state": null,"seed": 0,"model": true,"enhance": true}},"enhance": true,"model": true}
    {"info": {"_id": "Metric@pjml.data.evaluation.metric","config": {"functions": ["accuracy"],"target": "Y","prediction": "Z","model": true,"enhance": true}},"enhance": true,"model": true}
<<Map
{
    "info": {
        "_id": "Summ@pjml.stream.reduce.summ",
        "config": {
            "field": "R",
            "function": "mean"
        }
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "Reduce@pjml.stream.reduce.reduce",
        "config": {}
    },
    "enhance": true,
    "model": true
}
{
    "info": {
        "_id": "Report@pjml.data.communication.report",
        "config": {
            "text": "Mean S: $S"
        }
    },
    "enhance": true,
    "model": true
}

Total running time of the script: ( 0 minutes 3.250 seconds)

Gallery generated by Sphinx-Gallery

What is new on pjautoml package?¶

The pjautoml releases are available in PyPI and GitHub.

Version 0.X¶

Todo

About us¶

Contributors¶

You can find the contributors of this package here.

Citing pjautoml¶

If you use the pjautoml in scientific publication, we would appreciate citations to the following paper:

TODO

Welcome to pjautoml ‘s documentation!¶

Install¶

Requirements¶

Install¶

Test and coverage¶

Using pjautoml¶

API Documentation¶

`pjautoml.cs`: Configuration Space¶

Operand¶

Operator¶

Data-driven configuration space operator¶

Configuration space operators¶

`pjautoml.util`: Util Classes and Functions¶

`pjautoml.abs`: Abstract Classes and Mixin¶

The pjautoml example gallery¶

Introductory Examples¶

Operating configuration spaces (basic)¶

Using Chain¶

Using Shuffle¶

Using Select¶

Using them all:¶

Searching for good pipelines from a workflow¶

Creating an end-to-end workflow¶

Advanced Examples¶

Creating random search with configuration spaces’ operations¶

Creating an end-to-end AutoML from scratch¶

What is new on pjautoml package?¶

Version 0.X¶

About us¶

Contributors¶

Citing pjautoml¶

Getting started ¶

API Documentation ¶

Examples ¶

What’s new ?¶

About us ¶

Welcome to pjautoml ‘s documentation!¶

Install¶

Requirements¶

Install¶

Test and coverage¶

Using pjautoml¶

API Documentation¶

pjautoml.cs: Configuration Space¶

Operand¶

Operator¶

Data-driven configuration space operator¶

Configuration space operators¶

pjautoml.util: Util Classes and Functions¶

pjautoml.abs: Abstract Classes and Mixin¶

The pjautoml example gallery¶

Introductory Examples¶

Operating configuration spaces (basic)¶

Using Chain¶

Using Shuffle¶

Using Select¶

Using them all:¶

Searching for good pipelines from a workflow¶

Creating an end-to-end workflow¶

Advanced Examples¶

Creating random search with configuration spaces’ operations¶

Creating an end-to-end AutoML from scratch¶

What is new on pjautoml package?¶

Version 0.X¶

About us¶

Contributors¶

Citing pjautoml¶

Getting started¶

API Documentation¶

Examples¶

What’s new ?¶

About us¶

`pjautoml.cs`: Configuration Space¶

`pjautoml.util`: Util Classes and Functions¶

`pjautoml.abs`: Abstract Classes and Mixin¶

Getting started ¶

API Documentation ¶

Examples ¶

About us ¶