Welcome to the ChemML’s documentation!¶

ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data.

source repository on github: https://github.com/hachmannlab/chemml

Code Design:¶

ChemML is developed in the Python 3 programming language and makes use of a host of data analysis and ML libraries(accessible through the Anaconda distribution), as well as domain-specific libraries. The development follows a strictly modular and object-oriented design to make the overall code as flexible and versatile as possible.

The format of library is similar to the well known libraries like Scikit-learn. ChemML will be soon available via graphical user interface provided by [ChemEco](https://github.com/hachmannlab/chemeco). ChemEco is a general-purpose framework for data mining without coding. It also interfaces with many of the libraries that supply methods for the representation, preprocessing, analysis, mining, and modeling of large-scale chemical data sets.

Latest Version:¶

to find the latest version and release history, click here: https://pypi.org/project/chemml/#history

Installation and Dependencies:¶

You can download ChemML from Python Package Index (PyPI) via pip.

pip install chemml --user -U

Here is a list of external libraries that will be installed with chemml:

numpy
pandas
tensorflow
keras
scikit-learn
matplotlib
seaborn
lxml

Since conda installation is not available for ChemML yet, we recommend installing rdkit and openbabel in a conda virtual environment prior to installing ChemML. For doing so, you need to follow the conda installer:

conda create --name my_chemml_env python=3.6
source activate my_chemml_env
conda install -c openbabel openbabel
conda install -c rdkit rdkit
pip install chemml

Python 2 Fans:¶

The library was initially compatible with Python 2 and 3, and we still develop compatible codes. However, since the Python community and some of the dependencies are phasing out support for Python 2, we also removed it from the classifier languages. However, you should still be able to clone the ChemML repository from github and install the older versions of some of the dependencies that support Python 2, prior to using ChemML locally.

Citation:¶

Please cite the use of ChemML as:

Main citation:

@article{chemml2019,
author = {Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava Urala and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes},
journal = {ChemRxiv},
pages = {8323271},
title = {ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data},
doi = {10.26434/chemrxiv.8323271.v1},
year = {2019}
}


Other references:

@article{chemml_review2019,
author = {Haghighatlari, Mojtaba and Hachmann, Johannes},
doi = {https://doi.org/10.1016/j.coche.2019.02.009},
issn = {2211-3398},
journal = {Current Opinion in Chemical Engineering},
month = {jan},
pages = {51--57},
title = {Advances of machine learning in molecular modeling and simulation},
volume = {23},
year = {2019}
}

@article{Hachmann2018,
author = {Hachmann, Johannes and Afzal, Mohammad Atif Faiz and Haghighatlari, Mojtaba and Pal, Yudhajit},
doi = {10.1080/08927022.2018.1471692},
issn = {10290435},
journal = {Molecular Simulation},
number = {11},
pages = {921--929},
title = {Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space},
volume = {44},
year = {2018}
}

ChemML Wrapper Tutorial¶

…::: ChemML Wrapper is currently only available in the version 0.4.* (Python 2.7) :::…

ChemML Wrapper carry out a workflow of operations hosted by different open-source or commercial libraries/software. The workflow is similar to a directed graph and it can be designed in a text file. An input file (configuration file) is required to run the ChemML Wrapper.

In this section we walk you through all the required steps to run an input file.

Step #1: prepare an input file¶

ChemML Wrapper requires an input file to configure and run an arbitrary workflow. This file must be a text file in any arbitrary format. If you want to create or modify an input file manually, see Input File Manual for more information. You can also create, modify, and even visualize an input file using the graphical interface, Input File GUI.

The input file consists of methods that are connected to each other and make a data mining workflow. Here you can see a simple input script:

## (Enter,datasets)
    << host = chemml
    << function = load_xyz_polarizability
    >> coordinates 0
    >> polarizability 4
    >> polarizability 5

## (Represent,molecular descriptors)
    << host = chemml
    << function = CoulombMatrix
    >> 0 molecules
    >> df 1
    >> df 6

## (Store,file)
    << host = chemml
    << function = SaveFile
    << filename = CM_features
    >> 1 df

## (Store,figure)
    << host = chemml
    << function = SavePlot
    << kwargs = {'normed':True, 'bbox_inches':'tight'}
    << output_directory = plots
    << filename = performance
    >> 2 fig

## (Visualize,artist)
    << host = chemml
    << function = decorator
    << title = training performance
    << grid_color = g
    << xlabel = predicted polarizability (Bohr^3)
    << ylabel = calculated polarizability (Bohr^3)
    << grid = True
    << size = 12
    >> fig 2
    >> 3 fig

## (Visualize,plot)
    << host = chemml
    << function = scatter2D
    << y = 0
    << marker = o
    << x = 0
    >> fig 3
    >> 4 dfy
    >> 7 dfx

## (Model,regression)
    << host = sklearn
    << function = KernelRidge
    << func_method = fit
    << kernel = rbf
    >> 5 dfy
    >> 6 dfx
    >> dfy_predict 7

Let’s create a text file with name ‘tutorial.config’ and copy the above input script into that file. If we copy this script into ‘existing script’ tab of the Input File GUI, a graphical visualization of the workflow will be presented as follow:

Step #2: run ChemML Wrapper¶

Now that we have prepared an input script/file, we can run it using any of the following ways.

method one: Running in the Terminal¶

Let’s say you saved your script with the name ‘tutorial.config’ in your Desktop folder (name and format are both arbitrary). In the Terminal, navigate to the Desktop folder and enter the following command:

chemmlwrapper -i tutorial.config -o output_directory

method two: Running in any Python Interpreter¶

You can also run your input script with python codes as a python script or interactively, using:

# we assume you saved your script with the name 'tutorial.config' in your Desktop folder
from cheml import wrapperRUN
wrapperRUN(INPUT_FILE = '/Desktop/tutorial.config', OUTPUT_DIRECTORY = 'output_directory')

# or

script = """
            ## (Enter,datasets)
                << host = chemml
                << function = load_xyz_polarizability
                >> coordinates 0
                >> polarizability 4
                >> polarizability 5

            ## (Represent,molecular descriptors)
                << host = chemml
                << function = CoulombMatrix
                >> 0 molecules
                >> df 1
                >> df 6

            ## (Store,file)
                << host = chemml
                << function = SaveFile
                << filename = CM_features
                >> 1 df

            ## (Store,figure)
                << host = chemml
                << function = SavePlot
                << kwargs = {'normed':True, 'bbox_inches':'tight'}
                << output_directory = plots
                << filename = performance
                >> 2 fig

            ## (Visualize,artist)
                << host = chemml
                << function = decorator
                << title = training performance
                << grid_color = g
                << xlabel = predicted polarizability (Bohr^3)
                << ylabel = calculated polarizability (Bohr^3)
                << grid = True
                << size = 12
                >> fig 2
                >> 3 fig

            ## (Visualize,plot)
                << host = chemml
                << function = scatter2D
                << y = 0
                << marker = o
                << x = 0
                >> fig 3
                >> 4 dfy
                >> 7 dfx

            ## (Model,regression)
                << host = sklearn
                << function = KernelRidge
                << func_method = fit
                << kernel = rbf
                >> 5 dfy
                >> 6 dfx
                >> dfy_predict 7

         """
wrapperRUN(INPUT_FILE = script, OUTPUT_DIRECTORY = 'output_directory')

Although you can run your script interactively, we recommend using the first method along with ‘nohup’ command to prevent Terminal disconnection from killing your job.

Note

in all these methods if your arbitrary name of ouput_directory already exists, chemML Wrapper automatically creates a folder with sequential number.

Step #3: check the output directory¶

After running ChemML Wrapper you can collect all your saved files by navigating to the output directory. These saved files can be any type of results and fugures from your workflow, or default files, e.g. error file, log file, a copy of input file, and citation file.

Input File Manual¶

An instruction to the ChemML Wrapper input file is provided here. The input file configures parameters and settings for running a data mining workflow using ChemML Wrapper.

Although you can generate an input file manually in a text editor, a graphical user interface is also available to facilitate this process (see Input File GUI).

Input File Overview¶

The input file structure corresponds to a directed graph, where nodes are computation blocks and represent any available method in the package (Table of Contents), and edges represent the flow of data between nodes.

To determine edges of the workflow graph, fixed input and output tokens are defined for each node and you should connect them using unique random integers.

Here you can see a pseudo computation block in the input file:

## Task
    << host = name        << function = name
    << parameter = value    << ...
    << ...                  << ...
    >> token id
    >> id token

All the methods in the ChemML Wrapper are divided into the following 8 tasks:

Enter
Represent
Prepare
Model
Search
Mix
Visualize
Store

Each task in turn is divided into a number of subtasks to allow easy retrieval of the methods/functions.

Note

you always need an Enter method in your workflow to initialize the computation graph with some data.

Specific Characters¶

Only five specific characters (#, <, =, >, @) are defined and can be used in the input files.

Pound sign (#)
- Pound sign (#) determines a computation block.
- It must be used to separate different blocks from each other.
- A double pound sign (##) is for an active block and a single pound sign (#) keeps the block out of the graph.
- No other specific signs can be in the same line as pound sign. Having a task name or any other text after the pound sign is arbitrary.
Less-than sign (<)
- Less-than sign (<) are used to pass the parameters’ value.
- The parameter’s name comes right after this sign.
- A Double less-than sign (<<) keeps a parameter in the block and single less-than sign (<) ignore the assigned value to that parameter.
- If you are going to ignore a parameter value make sure it’s not a required parameter.
Note

two parameters are mandatory in all the blocks:
- host: to specify the main library/dependency that you want to get the method from
- function: to specify the ChemML Wrapper method
You can find a comprehensive list of the available methods in the Table of Contents
Equals sign (=)
- Equals sign (=) is for setting the value of parameters.
- It comes right after the parameter name and is followed by its value.
- The parameter value should be in its python format.
Greater-than sign (>)
- Greater-than sign (>) is all you need to connect blocks to each other (to send outputs or to receive inputs).
- To keep track of inputs and outputs you have to assign a unique identification (ID) number to input/output tokens.
- The ID number can be any integer. The ChemML Wrapper will extract, and assign the sent output of one block to the received input of another block through these unique IDs.
- Note that the tokens are predefined for each block and can be found in the Table of Contents.
- To distinguish the send and receive actions you just need to switch the position of token and ID as described below:
  to send an output token:
  
  >> token ID
  
  e.g. >> molfile 7
  
  to receive an input token:
  
  >> ID token
  
  e.g. >> 7 molfile
At sign (@)
- At sign (@) can be used to get a parameter value from the input/output values.
- It comes right after equals sign (=) and should be followed by one of the input tokens (e.g. parameter = @df).

Note

please note that the first three characters (#, <, >) are reserved and you should avoid using them in the parameter values.

General Rules¶

A few general restrictions:

You are not allowed to have two different specific charecters in one line of input file (except ‘=’ and ‘@’ signs).

The input tokens and output tokens of each block may be similar but they might not have similar values.

Only one input per an input token can be received.

You are allowed to send output tokens to as many input tokens of different block as you want.

Avoid any type of short loop. A short loop will be made when inputs of any block_i are received from one or a set of blocks that they require an output of block_i.

If you make a short loop any place inside your workflow your run will be aborted immediately.

Input File GUI¶

…::: ChemML Wrapper is currently only available in the version 0.4.* (Python 2.7) :::…

The ChemML wrapper’s graphical user interface (GUI) facilitate the generation of the input files. You can run GUI locally in the Jupyter notebook with two lines of python code:

from cheml.notebooks import wrapperGUI
ui = wrapperGUI()

Requirements:

Jupyter notebook
- installation: http://jupyter.org/install.html
ipywidgets and widgetsnbextension
- installation: https://github.com/jupyter-widgets/ipywidgets/blob/master/docs/source/user_install.md
- ipywidgets and widgetsnbextension will be installed accompanied by chemml via pip.
graphviz
- installation: https://graphviz.readthedocs.io/en/stable/manual.html#installation
- Using graphviz library, you will see a graphical visualization of your project’s workflow simultaneously.
- graphviz will be installed accompanied by chemml via pip.

We recommend downloading and installing Anaconda for python 2. This way Jupyter will be installed automatically. If you are using anaconda and you plan to use a virtual environment, please run the following commands to install ChemML and wrapperGUI (the first and third lines are unnecessary if you have already installed chemml):

conda create --name my_chemml_env python=2.7
source activate my_chemml_env
pip install chemml --user -U

jupyter nbextension install --py widgetsnbextension --user
jupyter nbextension enable --sys-prefix --py widgetsnbextension
conda install -c conda-forge nb_conda_kernels

The last command installs nb_conda_kernels, which provides a seprate Jupyter kernel for each conda environment. You need it to run a Jupyter notebook with a kernel pointing to ‘my_chemml_env’ environment.

To run a notebook, you just need to run the following command in the Terminal:

jupyter notebook

This will consequently open a notebook dashboard in your browser. Now if you click on the ‘New’ button in the top right corner and select the ‘python: my_chemml_env’, an empty notebook will be opened in a new tab. Please type the two above-mentioned lines of python code and press Ctrl+Enter to run the wrapperGUI.

A link to the web application of this GUI will be posted here soon.

Table of Contents¶

This is a complete list of all the methods that are available through ChemML Wrapper interface. You can click on each function for further information.

Table’s columns describe:

task and subtask: for an easier classification of methods
host: the main library/dependency required for running a method
function: the method name that determines a block/node of computation graph
input and output tokens: available tokens in each block that collect specific information and send/receive it to/from other blocks

Wrapper Reference¶

Enter¶

ConvertFile¶

task: Enter
subtask: Convert
host: cheml
function: ConvertFile
input tokens (receivers): file_path : the path to the file that needs to be converted

types: (“<type ‘str’>”, “<type ‘dict’>”)
output tokens (senders): converted_file_paths : list of paths to the converted files

types: <type ‘list’>
required packages: ChemML, 0.4.1

Babel, 2.3.4
config file view: ##

<< host = cheml << function = ConvertFile

<< to_format = required_required

<< file_path = required_required

<< from_format = required_required

>> id file_path

>> id converted_file_paths

Note

The documentation page for function parameters: https://openbabel.org/wiki/Babel

PyScript¶

task: Enter
subtask: python script
host: cheml
function: PyScript
input tokens (receivers): iv4 : input variable, of any format

types: ()

iv5 : input variable, of any format

types: ()

iv6 : input variable, of any format

types: ()

iv1 : input variable, of any format

types: ()

iv2 : input variable, of any format

types: ()

iv3 : input variable, of any format

types: ()
output tokens (senders): ov2 : output variable, of any format

types: ()

ov3 : output variable, of any format

types: ()

ov1 : output variable, of any format

types: ()

ov6 : output variable, of any format

types: ()

ov4 : output variable, of any format

types: ()

ov5 : output variable, of any format

types: ()
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = PyScript

<< line08 = type python code

<< line09 = type python code

<< line01 = type python code

<< line02 = input tokens are available as ...

<< line03 = ... python variables

<< line04 = type python code

<< line05 = type python code

<< line20 = type python code

<< line07 = type python code

<< line06 = type python code

<< line17 = type python code

<< line16 = type python code

<< line15 = type python code

<< line14 = type python code

<< line13 = type python code

<< line12 = type python code

<< line11 = type python code

<< line10 = type python code

<< line19 = type python code

<< line18 = type python code

>> id iv4

>> id iv5

>> id iv6

>> id iv1

>> id iv2

>> id iv3

>> id ov2

>> id ov3

>> id ov1

>> id ov6

>> id ov4

>> id ov5

Note

The documentation page for function parameters:

XYZreader¶

task: Enter
subtask: xyz
host: cheml
function: XYZreader
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): molecules : dictionary of molecules with [‘mol’, ‘file’] keys

types: (“<type ‘dict’>”,)
required packages: ChemML, 0.4.1
config file view: ##

<< host = cheml << function = XYZreader

<< path_root = None

<< skip_lines = [2, 0]

<< reader = auto

<< path_pattern = required_required

<< Z = {'Ru': 44.0, 'Re': 75.0, 'Rf': 104.0, 'Rg': 111.0, 'Ra': 88.0, 'Rb': 37.0, 'Rn': 86.0, 'Rh': 45.0, 'Be': 4.0, 'Ba': 56.0, 'Bh': 107.0, 'Bi': 83.0, 'Bk': 97.0, 'Br': 35.0, 'H': 1.0, 'P': 15.0, 'Os': 76.0, 'Ge': 32.0, 'Gd': 64.0, 'Ga': 31.0, 'Pr': 59.0, 'Pt': 78.0, 'Pu': 94.0, 'C': 6.0, 'Pb': 82.0, 'Pa': 91.0, 'Pd': 46.0, 'Cd': 48.0, 'Po': 84.0, 'Pm': 61.0, 'Hs': 108.0, 'Uup': 115.0, 'Uus': 117.0, 'Uuo': 118.0, 'Ho': 67.0, 'Hf': 72.0, 'Hg': 80.0, 'He': 2.0, 'Md': 101.0, 'Mg': 12.0, 'K': 19.0, 'Mn': 25.0, 'O': 8.0, 'Mt': 109.0, 'S': 16.0, 'W': 74.0, 'Zn': 30.0, 'Eu': 63.0, 'Zr': 40.0, 'Er': 68.0, 'Ni': 28.0, 'No': 102.0, 'Na': 11.0, 'Nb': 41.0, 'Nd': 60.0, 'Ne': 10.0, 'Np': 93.0, 'Fr': 87.0, 'Fe': 26.0, 'Fl': 114.0, 'Fm': 100.0, 'B': 5.0, 'F': 9.0, 'Sr': 38.0, 'N': 7.0, 'Kr': 36.0, 'Si': 14.0, 'Sn': 50.0, 'Sm': 62.0, 'V': 23.0, 'Sc': 21.0, 'Sb': 51.0, 'Sg': 106.0, 'Se': 34.0, 'Co': 27.0, 'Cn': 112.0, 'Cm': 96.0, 'Cl': 17.0, 'Ca': 20.0, 'Cf': 98.0, 'Ce': 58.0, 'Xe': 54.0, 'Tm': 69.0, 'Cs': 55.0, 'Cr': 24.0, 'Cu': 29.0, 'La': 57.0, 'Li': 3.0, 'Lv': 116.0, 'Tl': 81.0, 'Lu': 71.0, 'Lr': 103.0, 'Th': 90.0, 'Ti': 22.0, 'Te': 52.0, 'Tb': 65.0, 'Tc': 43.0, 'Ta': 73.0, 'Yb': 70.0, 'Db': 105.0, 'Dy': 66.0, 'Ds': 110.0, 'At': 85.0, 'I': 53.0, 'In': 49.0, 'U': 92.0, 'Y': 39.0, 'Ac': 89.0, 'Ag': 47.0, 'Ir': 77.0, 'Am': 95.0, 'Al': 13.0, 'As': 33.0, 'Ar': 18.0, 'Au': 79.0, 'Es': 99.0, 'Uut': 113.0, 'Mo': 42.0}

>> id molecules

Note

The documentation page for function parameters:

load_cep_homo¶

task: Enter
subtask: datasets
host: cheml
function: load_cep_homo
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): smiles : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

homo : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = load_cep_homo

>> id smiles

>> id homo

Note

The documentation page for function parameters:

load_comp_energy¶

task: Enter
subtask: datasets
host: cheml
function: load_comp_energy
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): formation_energy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = load_comp_energy

>> id formation_energy

>> id entries

Note

The documentation page for function parameters:

load_crystal_structures¶

task: Enter
subtask: datasets
host: cheml
function: load_crystal_structures
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = load_crystal_structures

>> id entries

Note

The documentation page for function parameters:

load_organic_density¶

task: Enter
subtask: datasets
host: cheml
function: load_organic_density
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): smiles : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

features : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

density : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = load_organic_density

>> id smiles

>> id features

>> id density

Note

The documentation page for function parameters:

load_xyz_polarizability¶

task: Enter
subtask: datasets
host: cheml
function: load_xyz_polarizability
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): coordinates : dictionary of molecules represented by their xyz coordinates and atomic numbers

types: (“<type ‘dict’>”,)

polarizability : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = load_xyz_polarizability

>> id coordinates

>> id polarizability

Note

The documentation page for function parameters:

read_excel¶

task: Enter
subtask: table
host: pandas
function: read_excel
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: pandas, 0.20.3
config file view: ##

<< host = pandas << function = read_excel

<< engine = None

<< squeeze = False

<< index_col = None

<< date_parser = None

<< na_values = None

<< parse_dates = False

<< dtype = None

<< skiprows = None

<< sheet_name = 0

<< header = 0

<< skip_footer = 0

<< convert_float = True

<< names = None

<< io = required_required

<< usecols = None

<< true_values = None

<< false_values = None

<< thousands = None

<< converters = None

>> id df

Note

The documentation page for function parameters: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html

read_table¶

task: Enter
subtask: table
host: pandas
function: read_table
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: pandas, 0.20.3
config file view: ##

<< host = pandas << function = read_table

<< comment = None

<< escapechar = None

<< float_precision = None

<< na_filter = True

<< iterator = False

<< sep = required_required

<< mangle_dupe_cols = True

<< skip_blank_lines = True

<< keep_default_na = True

<< false_values = None

<< header = infer

<< prefix = None

<< memory_map = False

<< names = None

<< skipfooter = 0

<< verbose = False

<< compact_ints = None

<< lineterminator = None

<< compression = infer

<< dayfirst = False

<< low_memory = True

<< encoding = None

<< parse_dates = False

<< skip_footer = 0

<< dtype = None

<< quotechar = "

<< thousands = None

<< converters = None

<< warn_bad_lines = True

<< as_recarray = None

<< engine = None

<< dialect = None

<< chunksize = None

<< tupleize_cols = None

<< na_values = None

<< infer_datetime_format = False

<< keep_date_col = False

<< use_unsigned = None

<< nrows = None

<< true_values = None

<< delim_whitespace = False

<< usecols = None

<< squeeze = False

<< buffer_lines = None

<< index_col = None

<< skipinitialspace = False

<< decimal = .

<< skiprows = None

<< filepath_or_buffer = required_required

<< date_parser = None

<< delimiter = None

<< error_bad_lines = True

<< doublequote = True

<< quoting = 0

>> id df

Note

The documentation page for function parameters: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html

Represent¶

APEAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: APEAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = APEAttributeGenerator

<< packing_threshold = None

<< n_nearest_to_eval = None

<< radius_property = None

>> id entries

>> id df

Note

The documentation page for function parameters:

APRDFAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: APRDFAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = APRDFAttributeGenerator

<< cut_off_distance = 10.0

<< num_points = 6

<< elemental_properties = required_required

<< smooth_parameter = 4.0

>> id entries

>> id df

Note

The documentation page for function parameters:

BagofBonds¶

task: Represent
subtask: molecular descriptors
host: cheml
function: BagofBonds
input tokens (receivers): molecules : the molecule numpy array or data frame

types: (“<class ‘pandas.core.frame.DataFrame’>”, “<type ‘numpy.ndarray’>”, “<type ‘dict’>”)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = BagofBonds

<< const = 1

>> id molecules

>> id df

Note

The documentation page for function parameters:

ChargeDependentAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: ChargeDependentAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ChargeDependentAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

ChemicalOrderingAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: ChemicalOrderingAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ChemicalOrderingAttributeGenerator

<< weighted = True

<< shells = [1, 2, 3]

>> id entries

>> id df

Note

The documentation page for function parameters:

CompositionEntry¶

task: Represent
subtask: inorganic input
host: cheml
function: CompositionEntry
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = CompositionEntry

<< filepath = required_required

>> id entries

Note

The documentation page for function parameters:

CoordinationNumberAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: CoordinationNumberAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = CoordinationNumberAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

CoulombMatrix¶

task: Represent
subtask: molecular descriptors
host: cheml
function: CoulombMatrix
input tokens (receivers): molecules : the molecule numpy array or data frame

types: (“<class ‘pandas.core.frame.DataFrame’>”, “<type ‘numpy.ndarray’>”, “<type ‘dict’>”)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = CoulombMatrix

<< const = 1

<< CMtype = SC

<< nPerm = 3

<< max_n_atoms = auto

>> id molecules

>> id df

Note

The documentation page for function parameters:

CoulombMatrixAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: CoulombMatrixAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = CoulombMatrixAttributeGenerator

<< n_eigenvalues = 30

>> id entries

>> id df

Note

The documentation page for function parameters:

CrystalStructureEntry¶

task: Represent
subtask: inorganic input
host: cheml
function: CrystalStructureEntry
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = CrystalStructureEntry

<< directory_path = required_required

>> id entries

Note

The documentation page for function parameters:

DistanceMatrix¶

task: Represent
subtask: distance matrix
host: cheml
function: DistanceMatrix
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = DistanceMatrix

<< norm_type = fro

<< nCores = 1

>> id df

>> id df

Note

The documentation page for function parameters:

Dragon¶

task: Represent
subtask: molecular descriptors
host: cheml
function: Dragon
input tokens (receivers): molfile : the molecule file path

types: (“<type ‘str’>”, “<type ‘dict’>”, “<type ‘list’>”)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: script : , (default:new)

choose one of: []
required packages: ChemML, 0.4.1

pandas, 0.20.3

Dragon, 7 or 6

lxml, 3.4.0
config file view: ##

<< host = cheml << function = Dragon

<< script = new

<< SaveExcludeConst = False

<< MaxSR = '35'

<< SaveFilePath = Dragon_descriptors.txt

<< output_directory = ./

<< DisconnectedCalculationOption = '0'

<< SaveExcludeNearConst = False

<< Add2DHydrogens = False

<< SaveProject = False

<< Decimal_Separator = .

<< SaveOnlyData = False

<< script = new

<< RejectDisconnectedStrucuture = False

<< SaveExclusionOptionsToVariables = False

<< LogEdge = True

<< LogPathWalk = True

<< SaveLabelsOnSeparateFile = False

<< version = 6

<< DefaultMolFormat = '1'

<< MaxSRDetour = '30'

<< HelpBrowser = /usr/bin/xdg-open

<< SaveExcludeRejectedMolecules = False

<< knimemode = False

<< RejectUnusualValence = False

<< SaveProjectFile = Dragon_project.drp

<< SaveStdOut = False

<< SaveFormatSubBlock = %b-%s-%n-%m.txt

<< blocks = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]

<< SaveExcludeCorrelated = False

<< molFile = required_required

<< consecutiveDelimiter = False

<< molInputFormat = SMILES

<< MaxAtomWalkPath = '2000'

<< SaveExcludeAllMisVal = False

<< SaveExcludeStdDev = False

<< Weights = ['Mass', 'VdWVolume', 'Electronegativity', 'Polarizability', 'Ionization', 'I-State']

<< external = False

<< RoundWeights = True

<< MaxSRforAllCircuit = '19'

<< fileName = None

<< RoundCoordinates = True

<< Missing_String = NaN

<< SaveExcludeMisVal = False

<< logFile = Dragon_log.txt

<< PreserveTemporaryProjects = True

<< SaveLayout = True

<< molInput = file

<< SaveFormatBlock = %b-%n.txt

<< MissingValue = NaN

<< SaveCorrThreshold = '0.95'

<< SaveType = singlefile

<< ShowWorksheet = False

<< delimiter = ,

<< RetainBiggestFragment = False

<< CheckUpdates = True

<< RoundDescriptorValues = True

<< SaveExcludeMisMolecules = False

<< SaveStdDevThreshold = '0.0001'

<< SaveFile = True

<< logMode = file

>> id molfile

>> id df

Note

The documentation page for function parameters:

EffectiveCoordinationNumberAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: EffectiveCoordinationNumberAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = EffectiveCoordinationNumberAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

ElementFractionAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: ElementFractionAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ElementFractionAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

ElementPairPropertyAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: ElementPairPropertyAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ElementPairPropertyAttributeGenerator

<< elemental_pair_properties = None

>> id entries

>> id df

Note

The documentation page for function parameters:

ElementalPropertyAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: ElementalPropertyAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: elemental_properties : , (default:None)

choose one of: []
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ElementalPropertyAttributeGenerator

<< elemental_properties = None

<< use_default_properties = True

>> id entries

>> id df

Note

The documentation page for function parameters:

GCLPAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: GCLPAttributeGenerator
input tokens (receivers): energies : to be passed to the parameter energies

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

phases : to be passed to the parameter phases

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = GCLPAttributeGenerator

<< count_phases = None

<< energies = []

<< phases = []

>> id energies

>> id phases

>> id entries

>> id df

Note

The documentation page for function parameters:

IonicCompoundProximityAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: IonicCompoundProximityAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = IonicCompoundProximityAttributeGenerator

<< max_formula_unit = 14

>> id entries

>> id df

Note

The documentation page for function parameters:

IonicityAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: IonicityAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = IonicityAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

LatticeSimilarityAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: LatticeSimilarityAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = LatticeSimilarityAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

LocalPropertyDifferenceAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: LocalPropertyDifferenceAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = LocalPropertyDifferenceAttributeGenerator

<< elemental_properties = required_required

<< shells = [1]

>> id entries

>> id df

Note

The documentation page for function parameters:

LocalPropertyVarianceAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: LocalPropertyVarianceAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = LocalPropertyVarianceAttributeGenerator

<< elemental_properties = required_required

<< shells = [1]

>> id entries

>> id df

Note

The documentation page for function parameters:

MeredigAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: MeredigAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = MeredigAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

PRDFAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: PRDFAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = PRDFAttributeGenerator

<< cut_off_distance = 10.0

<< n_points = 20

>> id entries

>> id df

Note

The documentation page for function parameters:

PackingEfficiencyAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: PackingEfficiencyAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = PackingEfficiencyAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

RDKitFingerprint¶

task: Represent
subtask: molecular descriptors
host: cheml
function: RDKitFingerprint
input tokens (receivers): molfile : the molecule file path

types: (“<type ‘str’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

removed_rows : output variable, of any format

types: ()
required packages: ChemML, 0.4.1

pandas, 0.20.3

RDKit, 2016.03.1
config file view: ##

<< host = cheml << function = RDKitFingerprint

<< nBits = 1024

<< molfile = required_required

<< removeHs = True

<< vector = bit

<< radius = 2

<< arguments = []

<< path = None

<< FPtype = Morgan

>> id molfile

>> id df

>> id removed_rows

Note

The documentation page for function parameters:

StoichiometricAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: StoichiometricAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: use_default_norms : , (default:None)

choose one of: []
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = StoichiometricAttributeGenerator

<< use_default_norms = None

<< p_norms = None

>> id entries

>> id df

Note

The documentation page for function parameters:

StructuralHeterogeneityAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: StructuralHeterogeneityAttributeGenerator
input tokens (receivers): entries : list of entries from CrystalStructureEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = StructuralHeterogeneityAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

ValenceShellAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: ValenceShellAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ValenceShellAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

YangOmegaAttributeGenerator¶

task: Represent
subtask: inorganic descriptors
host: cheml
function: YangOmegaAttributeGenerator
input tokens (receivers): entries : list of entries from CompositionEntry class.

types: (“<type ‘list’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = YangOmegaAttributeGenerator

>> id entries

>> id df

Note

The documentation page for function parameters:

Prepare¶

ConstantColumns¶

task: Prepare
subtask: data cleaning
host: cheml
function: ConstantColumns
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘cheml.preprocessing.purge.ConstantColumns’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘cheml.preprocessing.purge.ConstantColumns’>”,)

removed_columns_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = ConstantColumns

<< func_method = None

>> id df

>> id api

>> id df

>> id api

>> id removed_columns_

Note

The documentation page for function parameters:

MissingValues¶

task: Prepare
subtask: data cleaning
host: cheml
function: MissingValues
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s MissingValues class

types: (“<class ‘cheml.preprocessing.handle_missing.missing_values’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s MissingValues class

types: (“<class ‘cheml.preprocessing.handle_missing.missing_values’>”,)
wrapper parameters: func_method : String, (default:None)

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = MissingValues

<< func_method = None

<< strategy = ignore_row

<< inf_as_null = True

<< string_as_null = True

<< missing_values = False

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters:

Outliers¶

task: Prepare
subtask: data cleaning
host: cheml
function: Outliers
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘cheml.preprocessing.purge.Outliers’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘cheml.preprocessing.purge.Outliers’>”,)

removed_columns_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = Outliers

<< func_method = None

<< m = 2.0

<< strategy = median

>> id df

>> id api

>> id df

>> id api

>> id removed_columns_

Note

The documentation page for function parameters:

Split¶

task: Prepare
subtask: data manipulation
host: cheml
function: Split
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): df1 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

df2 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = Split

<< selection = 1

>> id df

>> id df1

>> id df2

Note

The documentation page for function parameters:

Binarizer¶

task: Prepare
subtask: feature representation
host: sklearn
function: Binarizer
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Binarizer class

types: (“<class ‘sklearn.preprocessing.data.Binarizer’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Binarizer class

types: (“<class ‘sklearn.preprocessing.data.Binarizer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Binarizer

<< track_header = True

<< func_method = None

<< threshold = 0.0

<< copy = True

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html#sklearn.preprocessing.Binarizer

Imputer¶

task: Prepare
subtask: data cleaning
host: sklearn
function: Imputer
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Imputer class

types: (“<class ‘sklearn.preprocessing.imputation.Imputer’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Imputer class

types: (“<class ‘sklearn.preprocessing.imputation.Imputer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Imputer

<< track_header = True

<< func_method = None

<< verbose = 0

<< missing_values = NaN

<< strategy = mean

<< copy = True

<< axis = 0

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html#sklearn.preprocessing.Imputer

KFold¶

task: Prepare
subtask: split
host: sklearn
function: KFold
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s KFold class

types: (“<class ‘sklearn.model_selection._split.KFold’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = KFold

<< func_method = None

<< random_state = None

<< shuffle = False

<< n_splits = 3

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

KernelPCA¶

task: Prepare
subtask: feature transformation
host: sklearn
function: KernelPCA
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s KernelPCA class

types: (“<class ‘sklearn.decomposition.kernel_pca.KernelPCA’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s KernelPCA class

types: (“<class ‘sklearn.decomposition.kernel_pca.KernelPCA’>”,)
wrapper parameters: track_header : Boolean, (default:False)

Always False, the header of input dataframe is not equivalent with the transformed dataframe

choose one of: False

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = KernelPCA

<< track_header = False

<< func_method = None

<< fit_inverse_transform = False

<< kernel = linear

<< n_jobs = 1

<< eigen_solver = auto

<< degree = 3

<< max_iter = None

<< copy_X = True

<< kernel_params = None

<< random_state = None

<< n_components = None

<< remove_zero_eig = False

<< tol = 0

<< alpha = 1.0

<< coef0 = 1

<< gamma = None

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA

LeaveOneOut¶

task: Prepare
subtask: split
host: sklearn
function: LeaveOneOut
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s LeaveOneOut class

types: (“<class ‘sklearn.model_selection._split.LeaveOneOut’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = LeaveOneOut

<< func_method = None

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html

MaxAbsScaler¶

task: Prepare
subtask: scaling
host: sklearn
function: MaxAbsScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MaxAbsScaler class

types: (“<class ‘sklearn.preprocessing.data.MaxAbsScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MaxAbsScaler class

types: (“<class ‘sklearn.preprocessing.data.MaxAbsScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MaxAbsScaler

<< track_header = True

<< func_method = None

<< copy = True

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler

MinMaxScaler¶

task: Prepare
subtask: scaling
host: sklearn
function: MinMaxScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MinMaxScaler class

types: (“<class ‘sklearn.preprocessing.data.MinMaxScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MinMaxScaler class

types: (“<class ‘sklearn.preprocessing.data.MinMaxScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MinMaxScaler

<< track_header = True

<< func_method = None

<< copy = True

<< feature_range = (0, 1)

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler

Normalizer¶

task: Prepare
subtask: scaling
host: sklearn
function: Normalizer
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Normalizer class

types: (“<class ‘sklearn.preprocessing.data.Normalizer’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Normalizer class

types: (“<class ‘sklearn.preprocessing.data.Normalizer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Normalizer

<< track_header = True

<< func_method = None

<< copy = True

<< norm = l2

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer

OneHotEncoder¶

task: Prepare
subtask: feature representation
host: sklearn
function: OneHotEncoder
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s OneHotEncoder class

types: (“<class ‘sklearn.preprocessing.data.OneHotEncoder’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s OneHotEncoder class

types: (“<class ‘sklearn.preprocessing.data.OneHotEncoder’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = OneHotEncoder

<< track_header = True

<< func_method = None

<< dtype = <type'numpy.float64'>

<< categorical_features = all

<< n_values = auto

<< sparse = True

<< handle_unknown = error

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder

PCA¶

task: Prepare
subtask: feature transformation
host: sklearn
function: PCA
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PCA class

types: (“<class ‘sklearn.decomposition.pca.PCA’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PCA class

types: (“<class ‘sklearn.decomposition.pca.PCA’>”,)
wrapper parameters: track_header : Boolean, (default:False)

Always False, the header of input dataframe is not equivalent with the transformed dataframe

choose one of: False

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = PCA

<< track_header = False

<< func_method = None

<< svd_solver = auto

<< iterated_power = auto

<< random_state = None

<< whiten = False

<< tol = 0.0

<< copy = True

<< n_components = None

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA

PolynomialFeatures¶

task: Prepare
subtask: feature representation
host: sklearn
function: PolynomialFeatures
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PolynomialFeatures class

types: (“<class ‘sklearn.preprocessing.data.PolynomialFeatures’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PolynomialFeatures class

types: (“<class ‘sklearn.preprocessing.data.PolynomialFeatures’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = PolynomialFeatures

<< track_header = True

<< func_method = None

<< include_bias = True

<< interaction_only = False

<< degree = 2

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures

RobustScaler¶

task: Prepare
subtask: scaling
host: sklearn
function: RobustScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s RobustScaler class

types: (“<class ‘sklearn.preprocessing.data.RobustScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s RobustScaler class

types: (“<class ‘sklearn.preprocessing.data.RobustScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = RobustScaler

<< track_header = True

<< func_method = None

<< copy = True

<< with_scaling = True

<< with_centering = True

<< quantile_range = (25.0, 75.0)

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler

ShuffleSplit¶

task: Prepare
subtask: split
host: sklearn
function: ShuffleSplit
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s ShuffleSplit class

types: (“<class ‘sklearn.model_selection._split.ShuffleSplit’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = ShuffleSplit

<< func_method = None

<< n_splits = 10

<< train_size = None

<< random_state = None

<< test_size = default

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit

StandardScaler¶

task: Prepare
subtask: scaling
host: sklearn
function: StandardScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s StandardScaler class

types: (“<class ‘sklearn.preprocessing.data.StandardScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s StandardScaler class

types: (“<class ‘sklearn.preprocessing.data.StandardScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = StandardScaler

<< track_header = True

<< func_method = None

<< copy = True

<< with_mean = True

<< with_std = True

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler

StratifiedShuffleSplit¶

task: Prepare
subtask: split
host: sklearn
function: StratifiedShuffleSplit
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s StratifiedShuffleSplit class

types: (“<class ‘sklearn.model_selection._split.StratifiedShuffleSplit’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = StratifiedShuffleSplit

<< func_method = None

<< n_splits = 10

<< train_size = None

<< random_state = None

<< test_size = default

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit

train_test_split¶

task: Prepare
subtask: split
host: sklearn
function: train_test_split
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): dfx_test : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfy_train : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfy_test : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx_train : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = train_test_split

<< track_header = True

<< shuffle = True

<< train_size = None

<< random_state = None

<< test_size = 0.25

<< stratify = None

>> id dfy

>> id dfx

>> id dfx_test

>> id dfy_train

>> id dfy_test

>> id dfx_train

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

concat¶

task: Prepare
subtask: data manipulation
host: pandas
function: concat
input tokens (receivers): df1 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

df3 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

df2 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: pandas, 0.20.3
config file view: ##

<< host = pandas << function = concat

<< join = outer

<< verify_integrity = False

<< keys = None

<< levels = None

<< ignore_index = False

<< names = None

<< join_axes = None

<< copy = True

<< axis = 0

>> id df1

>> id df3

>> id df2

>> id df

Note

The documentation page for function parameters: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html

Model¶

MLP¶

task: Model
subtask: regression
host: cheml
function: MLP
input tokens (receivers): api : instance of cheml.nn.keras.MLP class

types: (“<class ‘cheml.nn.keras.mlp.MLP’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of cheml.nn.keras.MLP class

types: (“<class ‘cheml.nn.keras.mlp.MLP’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: ChemML, 0.4.1

keras, 2.1.2

tensorflow, 1.4.1
config file view: ##

<< host = cheml << function = MLP

<< func_method = None

<< nhidden = 1

<< loss = mean_squared_error

<< learning_rate = 0.01

<< layer_config_file = None

<< batch_size = 100

<< lr_decay = 0.0

<< regression = True

<< nclasses = None

<< activations = None

<< opt_config_file = None

<< nepochs = 100

<< nneurons = 100

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters:

MLP_sklearn¶

task: Model
subtask: regression
host: cheml
function: MLP_sklearn
input tokens (receivers): api : instance of cheml.nn.keras.MLP_sklearn class

types: (“<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of cheml.nn.keras.MLP_sklearn class

types: (“<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: ChemML, 0.4.1

scikit-learn, 0.19.0

keras, 2.1.2

tensorflow, 1.4.1
config file view: ##

<< host = cheml << function = MLP_sklearn

<< func_method = None

<< nhidden = 1

<< loss = mean_squared_error

<< learning_rate = 0.01

<< layer_config_file = None

<< batch_size = 100

<< lr_decay = 0.0

<< regression = True

<< nclasses = None

<< activations = None

<< opt_config_file = None

<< nepochs = 100

<< nneurons = 100

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters:

ARDRegression¶

task: Model
subtask: regression
host: sklearn
function: ARDRegression
input tokens (receivers): api : instance of scikit-learn’s ARDRegression class

types: (“<class ‘sklearn.linear_model.bayes.ARDRegression’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s ARDRegression class

types: (“<class ‘sklearn.linear_model.bayes.ARDRegression’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = ARDRegression

<< track_header = True

<< func_method = None

<< normalize = False

<< n_iter = 300

<< verbose = False

<< lambda_2 = 1e-06

<< fit_intercept = True

<< threshold_lambda = 10000.0

<< compute_score = False

<< alpha_2 = 1e-06

<< tol = 0.001

<< alpha_1 = 1e-06

<< copy_X = True

<< lambda_1 = 1e-06

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ARDRegression.html

BayesianRidge¶

task: Model
subtask: regression
host: sklearn
function: BayesianRidge
input tokens (receivers): api : instance of scikit-learn’s BayesianRidge class

types: (“<class ‘sklearn.linear_model.bayes.BayesianRidge’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s BayesianRidge class

types: (“<class ‘sklearn.linear_model.bayes.BayesianRidge’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = BayesianRidge

<< track_header = True

<< func_method = None

<< normalize = False

<< n_iter = 300

<< verbose = False

<< lambda_2 = 1e-06

<< fit_intercept = True

<< compute_score = False

<< alpha_2 = 1e-06

<< tol = 0.001

<< alpha_1 = 1e-06

<< copy_X = True

<< lambda_1 = 1e-06

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html

ElasticNet¶

task: Model
subtask: regression
host: sklearn
function: ElasticNet
input tokens (receivers): api : instance of scikit-learn’s ElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s ElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = ElasticNet

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< l1_ratio = 0.5

<< max_iter = 1000

<< precompute = False

<< random_state = None

<< tol = 0.0001

<< positive = False

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html

KernelRidge¶

task: Model
subtask: regression
host: sklearn
function: KernelRidge
input tokens (receivers): api : instance of scikit-learn’s KernelRidge class

types: (“<class ‘sklearn.kernel_ridge.KernelRidge’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s KernelRidge class

types: (“<class ‘sklearn.kernel_ridge.KernelRidge’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = KernelRidge

<< track_header = True

<< func_method = None

<< kernel = linear

<< degree = 3

<< kernel_params = None

<< alpha = 1

<< coef0 = 1

<< gamma = None

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html

Lars¶

task: Model
subtask: regression
host: sklearn
function: Lars
input tokens (receivers): api : instance of scikit-learn’s Lars class

types: (“<class ‘sklearn.linear_model.least_angle.Lars’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s Lars class

types: (“<class ‘sklearn.linear_model.least_angle.Lars’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Lars

<< track_header = True

<< func_method = None

<< n_nonzero_coefs = 500

<< normalize = True

<< fit_path = True

<< fit_intercept = True

<< positive = False

<< eps = 2.22044604925e-16

<< precompute = auto

<< copy_X = True

<< verbose = False

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html

Lasso¶

task: Model
subtask: regression
host: sklearn
function: Lasso
input tokens (receivers): api : instance of scikit-learn’s Lasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s Lasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Lasso

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< positive = False

<< max_iter = 1000

<< precompute = False

<< random_state = None

<< tol = 0.0001

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

LassoLars¶

task: Model
subtask: regression
host: sklearn
function: LassoLars
input tokens (receivers): api : instance of scikit-learn’s LassoLars class

types: (“<class ‘sklearn.linear_model.least_angle.LassoLars’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s LassoLars class

types: (“<class ‘sklearn.linear_model.least_angle.LassoLars’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = LassoLars

<< track_header = True

<< func_method = None

<< normalize = True

<< fit_path = True

<< fit_intercept = True

<< positive = False

<< max_iter = 500

<< eps = 2.22044604925e-16

<< precompute = auto

<< copy_X = True

<< alpha = 1.0

<< verbose = False

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html

LinearRegression¶

task: Model
subtask: regression
host: sklearn
function: LinearRegression
input tokens (receivers): api : instance of scikit-learn’s LinearRegression class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s LinearRegression class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = LinearRegression

<< track_header = True

<< func_method = None

<< normalize = False

<< n_jobs = 1

<< fit_intercept = True

<< copy_X = True

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

LinearSVR¶

task: Model
subtask: regression
host: sklearn
function: LinearSVR
input tokens (receivers): api : instance of scikit-learn’s LinearSVR class

types: (“<class ‘sklearn.svm.classes.LinearSVR’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s LinearSVR class

types: (“<class ‘sklearn.svm.classes.LinearSVR’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = LinearSVR

<< track_header = True

<< func_method = None

<< loss = epsilon_insensitive

<< intercept_scaling = 1.0

<< fit_intercept = True

<< epsilon = 0.0

<< max_iter = 1000

<< C = 1.0

<< random_state = None

<< dual = True

<< tol = 0.0001

<< verbose = 0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html

LogisticRegression¶

task: Model
subtask: regression
host: sklearn
function: LogisticRegression
input tokens (receivers): api : instance of scikit-learn’s LogisticRegression class

types: (“<class ‘sklearn.linear_model.logistic.LogisticRegression’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s LogisticRegression class

types: (“<class ‘sklearn.linear_model.logistic.LogisticRegression’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = LogisticRegression

<< track_header = True

<< func_method = None

<< warm_start = False

<< n_jobs = 1

<< intercept_scaling = 1

<< fit_intercept = True

<< max_iter = 100

<< class_weight = None

<< C = 1.0

<< penalty = l2

<< multi_class = ovr

<< random_state = None

<< dual = False

<< tol = 0.0001

<< solver = liblinear

<< verbose = 0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

MLPRegressor¶

task: Model
subtask: regression
host: sklearn
function: MLPRegressor
input tokens (receivers): api : instance of scikit-learn’s MLPRegressor class

types: (“<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s MLPRegressor class

types: (“<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MLPRegressor

<< track_header = True

<< func_method = None

<< shuffle = True

<< verbose = False

<< random_state = None

<< tol = 0.0001

<< validation_fraction = 0.1

<< learning_rate = constant

<< momentum = 0.9

<< warm_start = False

<< epsilon = 1e-08

<< activation = relu

<< max_iter = 200

<< batch_size = auto

<< alpha = 0.0001

<< early_stopping = False

<< beta_1 = 0.9

<< beta_2 = 0.999

<< nesterovs_momentum = True

<< hidden_layer_sizes = (100,)

<< solver = adam

<< power_t = 0.5

<< learning_rate_init = 0.001

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor

MultiTaskElasticNet¶

task: Model
subtask: regression
host: sklearn
function: MultiTaskElasticNet
input tokens (receivers): api : instance of scikit-learn’s MultiTaskElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s MultiTaskElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MultiTaskElasticNet

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< l1_ratio = 0.5

<< max_iter = 1000

<< random_state = None

<< tol = 0.0001

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html

MultiTaskLasso¶

task: Model
subtask: regression
host: sklearn
function: MultiTaskLasso
input tokens (receivers): api : instance of scikit-learn’s MultiTaskLasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s MultiTaskLasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MultiTaskLasso

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< max_iter = 1000

<< random_state = None

<< tol = 0.0001

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html

NuSVR¶

task: Model
subtask: regression
host: sklearn
function: NuSVR
input tokens (receivers): api : instance of scikit-learn’s NuSVR class

types: (“<class ‘sklearn.svm.classes.NuSVR’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s NuSVR class

types: (“<class ‘sklearn.svm.classes.NuSVR’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = NuSVR

<< track_header = True

<< func_method = None

<< kernel = rbf

<< verbose = False

<< degree = 3

<< coef0 = 0.0

<< max_iter = -1

<< C = 1.0

<< tol = 0.001

<< cache_size = 200

<< shrinking = True

<< nu = 0.5

<< gamma = auto

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html

Ridge¶

task: Model
subtask: regression
host: sklearn
function: Ridge
input tokens (receivers): api : instance of scikit-learn’s Ridge class

types: (“<class ‘sklearn.linear_model.ridge.Ridge’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s Ridge class

types: (“<class ‘sklearn.linear_model.ridge.Ridge’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Ridge

<< track_header = True

<< func_method = None

<< normalize = False

<< fit_intercept = True

<< max_iter = None

<< random_state = None

<< tol = 0.001

<< copy_X = True

<< alpha = 1.0

<< solver = auto

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html

SGDRegressor¶

task: Model
subtask: regression
host: sklearn
function: SGDRegressor
input tokens (receivers): api : instance of scikit-learn’s SGDRegressor class

types: (“<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s SGDRegressor class

types: (“<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = SGDRegressor

<< track_header = True

<< func_method = None

<< warm_start = False

<< loss = squared_loss

<< eta0 = 0.01

<< verbose = 0

<< fit_intercept = True

<< l1_ratio = 0.15

<< average = False

<< n_iter = 5

<< penalty = l2

<< power_t = 0.25

<< alpha = 0.0001

<< random_state = None

<< epsilon = 0.1

<< shuffle = True

<< learning_rate = invscaling

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html

SVR¶

task: Model
subtask: regression
host: sklearn
function: SVR
input tokens (receivers): api : instance of scikit-learn’s SVR class

types: (“<class ‘sklearn.svm.classes.SVR’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s SVR class

types: (“<class ‘sklearn.svm.classes.SVR’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = SVR

<< track_header = True

<< func_method = None

<< kernel = rbf

<< verbose = False

<< degree = 3

<< coef0 = 0.0

<< epsilon = 0.1

<< max_iter = -1

<< C = 1.0

<< tol = 0.001

<< cache_size = 200

<< shrinking = True

<< gamma = auto

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html

Search¶

GA_DEAP¶

task: Search
subtask: genetic algorithm
host: cheml
function: GA_DEAP
input tokens (receivers): evaluate : a function that receives a list of individuals and returns the score

types: (“<type ‘function’>”,)
output tokens (senders): best_individual : pandas dataframe of the best individual

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

best_ind_df : pandas dataframe of best individuals after each iteration

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:algorithm_1)

a method of the GA_DEAP class that should be applied

choose one of: (‘algorithm_1’, ‘algorithm_2’, ‘algorithm_3’, ‘algorithm_4’)
required packages: ChemML, 0.4.1

pandas, 0.20.3

deap, 1.2.2
config file view: ##

<< host = cheml << function = GA_DEAP

<< func_method = algorithm_1

<< mut_float_dev = 1

<< init_pop_frac = 0.35

<< crossover_type = Blend

<< chromosome_type = (1,)

<< n_generations = 20

<< Evaluate = @evaluate

<< chromosome_length = 1

<< crossover_pop_frac = 0.35

<< crossover_prob = 0.4

<< Weights = (-1.0,)

<< mut_float_mean = 0

<< bit_limits = ((0, 10),)

<< mut_int_lower = (1,)

<< mut_int_upper = (10,)

<< pop_size = 50

<< mutation_prob = 0.4

>> id evaluate

>> id best_individual

>> id best_ind_df

Note

The documentation page for function parameters:

GridSearchCV¶

task: Search
subtask: grid
host: sklearn
function: GridSearchCV
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

estimator : instance of a machine learning class

types: (“<type ‘str’>”, “<class ‘sklearn.linear_model.base.LinearRegression’>”, “<class ‘sklearn.linear_model.ridge.Ridge’>”, “<class ‘sklearn.kernel_ridge.KernelRidge’>”, “<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”, “<class ‘sklearn.linear_model.least_angle.Lars’>”, “<class ‘sklearn.linear_model.least_angle.LassoLars’>”, “<class ‘sklearn.linear_model.bayes.BayesianRidge’>”, “<class ‘sklearn.linear_model.bayes.ARDRegression’>”, “<class ‘sklearn.linear_model.logistic.LogisticRegression’>”, “<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”, “<class ‘sklearn.svm.classes.SVR’>”, “<class ‘sklearn.svm.classes.NuSVR’>”, “<class ‘sklearn.svm.classes.LinearSVR’>”, “<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”, “<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”)

scorer : instance of scikit-learn’s make_scorer class

types: (“<class ‘sklearn.metrics.scorer._PredictScorer’>”,)

cv : instance of scikit-learn’s cross validation generator or instance object

types: (“<type ‘generator’>”, “<class ‘sklearn.model_selection._split.KFold’>”, “<class ‘sklearn.model_selection._split.ShuffleSplit’>”, “<class ‘sklearn.model_selection._split.StratifiedShuffleSplit’>”, “<class ‘sklearn.model_selection._split.LeaveOneOut’>”)
output tokens (senders): cv_results_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s GridSearchCV class

types: (“<class ‘sklearn.grid_search.GridSearchCV’>”,)

best_estimator_ : instance of a machine learning class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”, “<class ‘sklearn.linear_model.ridge.Ridge’>”, “<class ‘sklearn.kernel_ridge.KernelRidge’>”, “<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”, “<class ‘sklearn.linear_model.least_angle.Lars’>”, “<class ‘sklearn.linear_model.least_angle.LassoLars’>”, “<class ‘sklearn.linear_model.bayes.BayesianRidge’>”, “<class ‘sklearn.linear_model.bayes.ARDRegression’>”, “<class ‘sklearn.linear_model.logistic.LogisticRegression’>”, “<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”, “<class ‘sklearn.svm.classes.SVR’>”, “<class ‘sklearn.svm.classes.NuSVR’>”, “<class ‘sklearn.svm.classes.LinearSVR’>”, “<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”, “<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = GridSearchCV

<< track_header = True

<< scoring = None

<< n_jobs = 1

<< verbose = 0

<< fit_params = None

<< refit = True

<< return_train_score = True

<< iid = True

<< estimator = @estimator

<< error_score = raise

<< pre_dispatch = 2 * n_jobs

<< param_grid = {}

<< cv = None

>> id dfy

>> id dfx

>> id estimator

>> id scorer

>> id cv

>> id cv_results_

>> id api

>> id best_estimator_

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html

cross_val_predict¶

task: Search
subtask: validate
host: sklearn
function: cross_val_predict
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

estimator : instance of a machine learning class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”, “<class ‘sklearn.linear_model.ridge.Ridge’>”, “<class ‘sklearn.kernel_ridge.KernelRidge’>”, “<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”, “<class ‘sklearn.linear_model.least_angle.Lars’>”, “<class ‘sklearn.linear_model.least_angle.LassoLars’>”, “<class ‘sklearn.linear_model.bayes.BayesianRidge’>”, “<class ‘sklearn.linear_model.bayes.ARDRegression’>”, “<class ‘sklearn.linear_model.logistic.LogisticRegression’>”, “<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”, “<class ‘sklearn.svm.classes.SVR’>”, “<class ‘sklearn.svm.classes.NuSVR’>”, “<class ‘sklearn.svm.classes.LinearSVR’>”, “<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”, “<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”)

scorer : instance of scikit-learn’s make_scorer class

types: (“<class ‘sklearn.metrics.scorer._PredictScorer’>”,)

cv : cross-validation generator or instance object

types: (“<type ‘generator’>”, “<class ‘sklearn.model_selection._split.KFold’>”, “<class ‘sklearn.model_selection._split.ShuffleSplit’>”, “<class ‘sklearn.model_selection._split.StratifiedShuffleSplit’>”, “<class ‘sklearn.model_selection._split.LeaveOneOut’>”)
output tokens (senders): dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = cross_val_predict

<< track_header = True

<< n_jobs = 1

<< verbose = 0

<< fit_params = None

<< method = predict

<< pre_dispatch = 2 * n_jobs

<< estimator = @estimator

<< groups = None

<< y = None

<< X = @dfx

<< cv = None

>> id dfy

>> id dfx

>> id estimator

>> id scorer

>> id cv

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html#sklearn.model_selection.cross_val_predict

cross_val_score¶

task: Search
subtask: validate
host: sklearn
function: cross_val_score
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

estimator : instance of a machine learning class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”, “<class ‘sklearn.linear_model.ridge.Ridge’>”, “<class ‘sklearn.kernel_ridge.KernelRidge’>”, “<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”, “<class ‘sklearn.linear_model.least_angle.Lars’>”, “<class ‘sklearn.linear_model.least_angle.LassoLars’>”, “<class ‘sklearn.linear_model.bayes.BayesianRidge’>”, “<class ‘sklearn.linear_model.bayes.ARDRegression’>”, “<class ‘sklearn.linear_model.logistic.LogisticRegression’>”, “<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”, “<class ‘sklearn.svm.classes.SVR’>”, “<class ‘sklearn.svm.classes.NuSVR’>”, “<class ‘sklearn.svm.classes.LinearSVR’>”, “<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”, “<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”)

scorer : instance of scikit-learn’s make_scorer class

types: (“<class ‘sklearn.metrics.scorer._PredictScorer’>”,)

cv : cross-validation generator or instance object

types: (“<type ‘generator’>”, “<class ‘sklearn.model_selection._split.KFold’>”, “<class ‘sklearn.model_selection._split.ShuffleSplit’>”, “<class ‘sklearn.model_selection._split.StratifiedShuffleSplit’>”, “<class ‘sklearn.model_selection._split.LeaveOneOut’>”)
output tokens (senders): scores : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = cross_val_score

<< track_header = True

<< scoring = None

<< n_jobs = 1

<< verbose = 0

<< fit_params = None

<< pre_dispatch = 2 * n_jobs

<< estimator = @estimator

<< groups = None

<< y = None

<< X = @dfx

<< cv = None

>> id dfy

>> id dfx

>> id estimator

>> id scorer

>> id cv

>> id scores

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score

evaluate_regression¶

task: Search
subtask: evaluate
host: sklearn
function: evaluate_regression
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): evaluation_results_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

evaluator : dictionary of metrics and their score function

types: (“<type ‘dict’>”,)
wrapper parameters: mae_multioutput : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error, (default:uniform_average)

choose one of: (‘raw_values’, ‘uniform_average’)

r2_score : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score, (default:False)

choose one of: (True, False)

mean_absolute_error : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error, (default:False)

choose one of: (True, False)

multioutput : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score, (default:uniform_average)

choose one of: (‘raw_values’, ‘uniform_average’, ‘variance_weighted’)

mse_sample_weight : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error, (default:None)

choose one of: []

rmse_multioutput : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error, (default:uniform_average)

choose one of: (‘raw_values’, ‘uniform_average’)

median_absolute_error : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.median_absolute_error.html#sklearn.metrics.median_absolute_error, (default:False)

choose one of: (True, False)

mae_sample_weight : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error, (default:None)

choose one of: []

rmse_sample_weight : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error, (default:None)

choose one of: []

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

mean_squared_error : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error, (default:False)

choose one of: (True, False)

root_mean_squared_error : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error, (default:False)

choose one of: (True, False)

explained_variance_score : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score, (default:False)

choose one of: (True, False)

r2_sample_weight : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score, (default:None)

choose one of: []

ev_sample_weight : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score, (default:None)

choose one of: []

ev_multioutput : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score, (default:uniform_average)

choose one of: (‘raw_values’, ‘uniform_average’, ‘variance_weighted’)

mse_multioutput : http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error, (default:uniform_average)

choose one of: (‘raw_values’, ‘uniform_average’)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = evaluate_regression

<< mae_multioutput = uniform_average

<< r2_score = False

<< mean_absolute_error = False

<< multioutput = uniform_average

<< mse_sample_weight = None

<< rmse_multioutput = uniform_average

<< median_absolute_error = False

<< mae_sample_weight = None

<< rmse_sample_weight = None

<< track_header = True

<< mean_squared_error = False

<< root_mean_squared_error = False

<< explained_variance_score = False

<< r2_sample_weight = None

<< ev_sample_weight = None

<< ev_multioutput = uniform_average

<< mse_multioutput = uniform_average

>> id dfy

>> id dfy_predict

>> id evaluation_results_

>> id evaluator

Note

The documentation page for function parameters: http://scikit-learn.org/dev/modules/model_evaluation.html#regression-metrics

learning_curve¶

task: Search
subtask: grid
host: sklearn
function: learning_curve
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

estimator : instance of a machine learning class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”, “<class ‘sklearn.linear_model.ridge.Ridge’>”, “<class ‘sklearn.kernel_ridge.KernelRidge’>”, “<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”, “<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”, “<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”, “<class ‘sklearn.linear_model.least_angle.Lars’>”, “<class ‘sklearn.linear_model.least_angle.LassoLars’>”, “<class ‘sklearn.linear_model.bayes.BayesianRidge’>”, “<class ‘sklearn.linear_model.bayes.ARDRegression’>”, “<class ‘sklearn.linear_model.logistic.LogisticRegression’>”, “<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”, “<class ‘sklearn.svm.classes.SVR’>”, “<class ‘sklearn.svm.classes.NuSVR’>”, “<class ‘sklearn.svm.classes.LinearSVR’>”, “<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”, “<class ‘cheml.nn.keras.mlp.MLP_sklearn’>”)

scorer : instance of scikit-learn’s make_scorer class

types: (“<class ‘sklearn.metrics.scorer._PredictScorer’>”,)

cv : instance of scikit-learn’s cross validation generator or instance object

types: (“<type ‘generator’>”, “<class ‘sklearn.model_selection._split.KFold’>”, “<class ‘sklearn.model_selection._split.ShuffleSplit’>”, “<class ‘sklearn.model_selection._split.StratifiedShuffleSplit’>”, “<class ‘sklearn.model_selection._split.LeaveOneOut’>”)
output tokens (senders): train_sizes_abs : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

extended_result_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

test_scores : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

train_scores : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = learning_curve

<< track_header = True

<< scoring = None

<< n_jobs = 1

<< shuffle = False

<< groups = None

<< random_state = None

<< pre_dispatch = all

<< estimator = @estimator

<< exploit_incremental_learning = False

<< train_sizes = [0.1, 0.33, 0.55, 0.78, 1.0]

<< y = None

<< X = @dfx

<< cv = None

<< verbose = 0

>> id dfy

>> id dfx

>> id estimator

>> id scorer

>> id cv

>> id train_sizes_abs

>> id extended_result_

>> id test_scores

>> id train_scores

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve

scorer_regression¶

task: Search
subtask: evaluate
host: sklearn
function: scorer_regression
input tokens (receivers): this block doesn’t receive anything
output tokens (senders): scorer : Callable object that returns a scalar score

types: (“<class ‘sklearn.metrics.scorer._PredictScorer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

metric : string: ‘mae’, ‘mse’, ‘r2’, (default:mae)

http://scikit-learn.org/dev/modules/model_evaluation.html#regression-metrics

choose one of: (‘mae’, ‘mse’, ‘r2’)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = scorer_regression

<< track_header = True

<< metric = mae

<< greater_is_better = True

<< needs_threshold = False

<< needs_proba = False

<< kwargs = {}

>> id scorer

Note

The documentation page for function parameters: http://scikit-learn.org/0.15/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer

corr¶

task: Search
subtask: evaluate
host: pandas
function: corr
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: pandas, 0.20.3
config file view: ##

<< host = pandas << function = corr

<< min_periods = 1

<< method = pearson

>> id df

>> id df

Note

The documentation page for function parameters: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html

Mix¶

Visualize¶

decorator¶

task: Visualize
subtask: artist
host: cheml
function: decorator
input tokens (receivers): fig : a matplotlib object

types: (“<class ‘matplotlib.figure.Figure’>”, “<class ‘matplotlib.axes._subplots.AxesSubplot’>”)
output tokens (senders): fig : a matplotlib object

types: (“<class ‘matplotlib.figure.Figure’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3

matplotlib, 1.5.1
config file view: ##

<< host = cheml << function = decorator

<< weight = normal

<< family = normal

<< xlim = (None, None)

``<< title = ``

<< grid_color = k

<< variant = normal

<< style = normal

<< grid_linestyle = --

``<< xlabel = ``

<< grid_linewidth = 0.5

``<< ylabel = ``

<< grid = True

<< ylim = (None, None)

<< size = 18

>> id fig

>> id fig

Note

The documentation page for function parameters:

hist¶

task: Visualize
subtask: plot
host: cheml
function: hist
input tokens (receivers): dfx : a pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): fig : a matplotlib object

types: (“<class ‘matplotlib.figure.Figure’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3

matplotlib, 1.5.1
config file view: ##

<< host = cheml << function = hist

<< color = None

<< kwargs = {}

<< x = required_required

<< bins = None

>> id dfx

>> id fig

Note

The documentation page for function parameters:

scatter2D¶

task: Visualize
subtask: plot
host: cheml
function: scatter2D
input tokens (receivers): dfy : a pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : a pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): fig : a matplotlib.Figure object

types: (“<class ‘matplotlib.figure.Figure’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3

matplotlib, 1.5.1
config file view: ##

<< host = cheml << function = scatter2D

<< color = b

<< marker = .

<< y = required_required

<< x = required_required

<< linewidth = 2

``<< linestyle = ``

>> id dfy

>> id dfx

>> id fig

Note

The documentation page for function parameters:

plot¶

task: Visualize
subtask: plot
host: pandas
function: plot
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): fig : matplotlib figure or axes object

types: (“<class ‘matplotlib.axes._subplots.AxesSubplot’>”, “<class ‘matplotlib.figure.Figure’>”)
required packages: pandas, 0.20.3

matplotlib, 1.5.1
config file view: ##

<< host = pandas << function = plot

<< xlim = None

<< xerr = None

<< yerr = None

<< logx = False

<< logy = False

<< table = False

<< ax = None

<< rot = None

<< ylim = None

<< style = None

<< sharey = False

<< sharex = None

<< title = None

<< use_index = True

<< xticks = None

<< fontsize = None

<< sort_columns = False

<< loglog = False

<< colormap = None

<< grid = None

<< layout = None

<< legend = True

<< secondary_y = False

<< kind = line

<< subplots = False

<< figsize = None

<< yticks = None

<< y = None

<< x = None

>> id df

>> id fig

Note

The documentation page for function parameters: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html

Store¶

SaveFile¶

task: Store
subtask: file
host: cheml
function: SaveFile
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): filepath : pandas dataframe

types: (“<type ‘str’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = cheml << function = SaveFile

<< index = False

<< record_time = False

<< format = csv

<< output_directory = None

<< header = True

<< filename = required_required

>> id df

>> id filepath

Note

The documentation page for function parameters:

SavePlot¶

task: Store
subtask: figure
host: cheml
function: SavePlot
input tokens (receivers): fig : a matplotlib object

types: (“<class ‘matplotlib.figure.Figure’>”, “<class ‘matplotlib.axes._subplots.AxesSubplot’>”)
input tokens (receivers): this block doesn’t send anything
required packages: ChemML, 0.4.1

pandas, 0.20.3

matplotlib, 1.5.1
config file view: ##

<< host = cheml << function = SavePlot

<< format = png

<< output_directory = None

<< kwargs = {}

<< filename = required_required

>> id fig

Note

The documentation page for function parameters: https://matplotlib.org/users/index.html

Chem module¶

Magpie_Python module¶

Initialization module¶

Datasets module¶

Preprocessing module¶

Models module¶

Optimization module¶

Visualization module¶

License:¶

About us:¶

Maintainers

Johannes Hachmann, hachmann@buffalo.edu
Mojtaba Haghighatlari

University at Buffalo - The State University of New York (UB)

Contributors

Doaa Altarawy (MolSSI): scientific advice and software mentor
Gaurav Vishwakarma (UB): automated model optimization
Ramachandran Subramanian (UB): Magpie descriptor library port
Bhargava Urala Kota (UB): library database
Aditya Sonpal (UB): debugging
Srirangaraj Setlur (UB): scientific advice
Venugopal Govindaraju (UB): scientific advice
Krishna Rajan (UB): scientific advice
We encourage any contributions and feedback. Feel free to fork and make pull-request to the “development” branch.

Acknowledgements

ChemML is based upon work supported by the U.S. National Science Foundation under grant #OAC-1751161 and in part by #OAC-1640867.
ChemML was also supported by start-up funds provided by UB’s School of Engineering and Applied Science and UB’s Department of Chemical and Biological Engineering, the New York State Center of Excellence in Materials Informatics through seed grant #1140384-8-75163, and the U.S. Department of Energy under grant #DE-SC0017193.
Mojtaba Haghighatlari received 2018 Phase-I and 2019 Phase-II Software Fellowships by the Molecular Sciences Software Institute (MolSSI) for his work on ChemML.